Hierarchical and coupling model of factors influencing vessel traffic flow

PubMed Central

Liu, Jingxian; Li, Huanhuan; Li, Zongzhi; Tan, Zhirong; Liu, Ryan Wen; Liu, Yi

2017-01-01

Understanding the characteristics of vessel traffic flow is crucial in maintaining navigation safety, efficiency, and overall waterway transportation management. Factors influencing vessel traffic flow possess diverse features such as hierarchy, uncertainty, nonlinearity, complexity, and interdependency. To reveal the impact mechanism of the factors influencing vessel traffic flow, a hierarchical model and a coupling model are proposed in this study based on the interpretative structural modeling method. The hierarchical model explains the hierarchies and relationships of the factors using a graph. The coupling model provides a quantitative method that explores interaction effects of factors using a coupling coefficient. The coupling coefficient is obtained by determining the quantitative indicators of the factors and their weights. Thereafter, the data obtained from Port of Tianjin is used to verify the proposed coupling model. The results show that the hierarchical model of the factors influencing vessel traffic flow can explain the level, structure, and interaction effect of the factors; the coupling model is efficient in analyzing factors influencing traffic volumes. The proposed method can be used for analyzing increases in vessel traffic flow in waterway transportation system. PMID:28414747

Exploratory structural equation modeling of personality data.

PubMed

Booth, Tom; Hughes, David J

2014-06-01

The current article compares the use of exploratory structural equation modeling (ESEM) as an alternative to confirmatory factor analytic (CFA) models in personality research. We compare model fit, factor distinctiveness, and criterion associations of factors derived from ESEM and CFA models. In Sample 1 (n = 336) participants completed the NEO-FFI, the Trait Emotional Intelligence Questionnaire-Short Form, and the Creative Domains Questionnaire. In Sample 2 (n = 425) participants completed the Big Five Inventory and the depression and anxiety scales of the General Health Questionnaire. ESEM models provided better fit than CFA models, but ESEM solutions did not uniformly meet cutoff criteria for model fit. Factor scores derived from ESEM and CFA models correlated highly (.91 to .99), suggesting the additional factor loadings within the ESEM model add little in defining latent factor content. Lastly, criterion associations of each personality factor in CFA and ESEM models were near identical in both inventories. We provide an example of how ESEM and CFA might be used together in improving personality assessment. © The Author(s) 2014.

Latent Factor Structure of DSM-5 Posttraumatic Stress Disorder

PubMed Central

Gentes, Emily; Dennis, Paul A.; Kimbrel, Nathan A.; Kirby, Angela C.; Hair, Lauren P.; Beckham, Jean C.; Calhoun, Patrick S.

2015-01-01

The current study examined the latent factor structure of posttraumatic stress disorder (PTSD) based on DSM-5 criteria in a sample of participants (N = 374) recruited for studies on trauma and health. Confirmatory factor analyses (CFA) were used to compare the fit of the previous 3-factor DSM-IV model of PTSD to the 4-factor model specified in DSM-5 as well as to a competing 4-factor “dysphoria” model (Simms, Watson, & Doebbeling, 2002) and a 5-factor (Elhai et al., 2011) model of PTSD. Results indicated that the Elhai 5-factor model (re-experiencing, active avoidance, emotional numbing, dysphoric arousal, anxious arousal) provided the best fit to the data, although substantial support was demonstrated for the DSM-5 4-factor model. Low factor loadings were noted for two of the symptoms in the DSM-5 model (psychogenic amnesia and reckless/self-destructive behavior), which raises questions regarding the adequacy of fit of these symptoms with other core features of the disorder. Overall, the findings from the present research suggest the DSM-5 model of PTSD is a significant improvement over the previous DSM-IV model of PTSD. PMID:26366290

Confirmatory factor analysis of the female sexual function index.

PubMed

Opperman, Emily A; Benson, Lindsay E; Milhausen, Robin R

2013-01-01

The Female Sexual Functioning Index (Rosen et al., 2000 ) was designed to assess the key dimensions of female sexual functioning using six domains: desire, arousal, lubrication, orgasm, satisfaction, and pain. A full-scale score was proposed to represent women's overall sexual function. The fifth revision to the Diagnostic and Statistical Manual (DSM) is currently underway and includes a proposal to combine desire and arousal problems. The objective of this article was to evaluate and compare four models of the Female Sexual Functioning Index: (a) single-factor model, (b) six-factor model, (c) second-order factor model, and (4) five-factor model combining the desire and arousal subscales. Cross-sectional and observational data from 85 women were used to conduct a confirmatory factor analysis on the Female Sexual Functioning Index. Local and global goodness-of-fit measures, the chi-square test of differences, squared multiple correlations, and regression weights were used. The single-factor model fit was not acceptable. The original six-factor model was confirmed, and good model fit was found for the second-order and five-factor models. Delta chi-square tests of differences supported best fit for the six-factor model validating usage of the six domains. However, when revisions are made to the DSM-5, the Female Sexual Functioning Index can adapt to reflect these changes and remain a valid assessment tool for women's sexual functioning, as the five-factor structure was also supported.

Satisfiers and Dissatisfiers: A Two-Factor Model for Website Design and Evaluation.

ERIC Educational Resources Information Center

Zhang, Ping; von Dran, Gisela M.

2000-01-01

Investigates Web site design factors and their impact from a theoretical perspective. Presents a two-factor model that can guide Web site design and evaluation. According to the model, there are two types of design factors: hygiene and motivator. Results showed that the two-factor model provides a means for Web-user interface studies. Provides…

Factor Structure of the Wechsler Intelligence Scale for Children: Fourth Edition in Children with ADHD.

PubMed

Thaler, Nicholas S; Barchard, Kimberly A; Parke, Elyse; Jones, W Paul; Etcoff, Lewis M; Allen, Daniel N

2015-12-01

Recent evidence suggests that the factor structure of the Wechsler Intelligence Scale for Children-Fourth Edition (WISC-IV) is better explained by a five-factor model rather than the four-factor model in the standardization sample. The current study examined the WISC-IV's factor structure in a sample of children with ADHD. Participants included 314 children and adolescents who were diagnosed with ADHD. Confirmatory factor analysis was conducted on the 10 core subtests of the WISC-IV, and three models were examined including two based on Cattell-Horn-Carroll (CHC) theory. A five-factor model consisting of Gc, Gf, Gv, Gsm, and Gs factors provided the best fit for the data. The Perceptual Reasoning factor identified in the original four-factor model split into the two CHC factors, Gf and Gv, and cross-loaded the Symbol Search subtest onto the Gv factor. A five-factor model based on CHC theory provided superior fit for the WISC-IV in children with ADHD, as has been found with the standardization sample. © The Author(s) 2012.

Factor structure of a conceptual model of oral health tested among 65-year olds in Norway and Sweden.

PubMed

Astrøm, Anne Nordrehaug; Ekbäck, Gunnar; Ordell, Sven

2010-04-01

No studies have tested oral health-related quality of life models in dentate older adults across different populations. To test the factor structure of oral health outcomes within Gilbert's conceptual model among 65-year olds in Sweden and Norway. It was hypothesized that responses to 14 observed indicators could be explained by three correlated factors, symptom status, functional limitations and oral disadvantages, that each observed oral health indicator would associate more strongly with the factor it is supposed to measure than with competing factors and that the proposed 3-factor structure would possess satisfactory cross-national stability with 65-year olds in Norway and Sweden. In 2007, 6078 Swedish- and 4062 Norwegian adults borne in 1942 completed mailed questionnaires including oral symptoms, functional limitations and the eight item Oral Impacts on Daily Performances inventory. Model generation analysis was restricted to the Norwegian study group and the model achieved was tested without modifications in Swedish 65-year olds. A modified 3-factor solution with cross-loadings, improved the fit to the data compared with a 2-factor- and the initially proposed 3-factor model among the Norwegian [comparative fit index (CFI) = 0.97] and Swedish (CFI = 0.98) participants. All factor loadings for the modified 3-factor model were in the expected direction and were statistically significant at CR > 1. Multiple group confirmatory factor analyses, with Norwegian and Swedish data simultaneously revealed acceptable fit for the unconstrained model (CFI = 0.97), whereas unconstrained and constrained models were statistically significant different in nested model comparison. Within construct validity of Gilbert's model was supported with Norwegian and Swedish 65-year olds, indicating that the 14-item questionnaire reflected three constructs; symptom status, functional limitation and oral disadvantage. Measurement invariance was confirmed at the level of factor structure, suggesting that the 3-factor model is comparable to some extent across 65-year olds in Norway and Sweden.

A Two-Factor Model Better Explains Heterogeneity in Negative Symptoms: Evidence from the Positive and Negative Syndrome Scale.

PubMed

Jang, Seon-Kyeong; Choi, Hye-Im; Park, Soohyun; Jaekal, Eunju; Lee, Ga-Young; Cho, Young Il; Choi, Kee-Hong

2016-01-01

Acknowledging separable factors underlying negative symptoms may lead to better understanding and treatment of negative symptoms in individuals with schizophrenia. The current study aimed to test whether the negative symptoms factor (NSF) of the Positive and Negative Syndrome Scale (PANSS) would be better represented by expressive and experiential deficit factors, rather than by a single factor model, using confirmatory factor analysis (CFA). Two hundred and twenty individuals with schizophrenia spectrum disorders completed the PANSS; subsamples additionally completed the Brief Negative Symptom Scale (BNSS) and the Motivation and Pleasure Scale-Self-Report (MAP-SR). CFA results indicated that the two-factor model fit the data better than the one-factor model; however, latent variables were closely correlated. The two-factor model's fit was significantly improved by accounting for correlated residuals between N2 (emotional withdrawal) and N6 (lack of spontaneity and flow of conversation), and between N4 (passive social withdrawal) and G16 (active social avoidance), possibly reflecting common method variance. The two NSF factors exhibited differential patterns of correlation with subdomains of the BNSS and MAP-SR. These results suggest that the PANSS NSF would be better represented by a two-factor model than by a single-factor one, and support the two-factor model's adequate criterion-related validity. Common method variance among several items may be a potential source of measurement error under a two-factor model of the PANSS NSF.

System level analysis and control of manufacturing process variation

DOEpatents

Hamada, Michael S.; Martz, Harry F.; Eleswarpu, Jay K.; Preissler, Michael J.

2005-05-31

A computer-implemented method is implemented for determining the variability of a manufacturing system having a plurality of subsystems. Each subsystem of the plurality of subsystems is characterized by signal factors, noise factors, control factors, and an output response, all having mean and variance values. Response models are then fitted to each subsystem to determine unknown coefficients for use in the response models that characterize the relationship between the signal factors, noise factors, control factors, and the corresponding output response having mean and variance values that are related to the signal factors, noise factors, and control factors. The response models for each subsystem are coupled to model the output of the manufacturing system as a whole. The coefficients of the fitted response models are randomly varied to propagate variances through the plurality of subsystems and values of signal factors and control factors are found to optimize the output of the manufacturing system to meet a specified criterion.

Dimensionality of the 9-item Utrecht Work Engagement Scale revisited: A Bayesian structural equation modeling approach.

PubMed

Fong, Ted C T; Ho, Rainbow T H

2015-01-01

The aim of this study was to reexamine the dimensionality of the widely used 9-item Utrecht Work Engagement Scale using the maximum likelihood (ML) approach and Bayesian structural equation modeling (BSEM) approach. Three measurement models (1-factor, 3-factor, and bi-factor models) were evaluated in two split samples of 1,112 health-care workers using confirmatory factor analysis and BSEM, which specified small-variance informative priors for cross-loadings and residual covariances. Model fit and comparisons were evaluated by posterior predictive p-value (PPP), deviance information criterion, and Bayesian information criterion (BIC). None of the three ML-based models showed an adequate fit to the data. The use of informative priors for cross-loadings did not improve the PPP for the models. The 1-factor BSEM model with approximately zero residual covariances displayed a good fit (PPP>0.10) to both samples and a substantially lower BIC than its 3-factor and bi-factor counterparts. The BSEM results demonstrate empirical support for the 1-factor model as a parsimonious and reasonable representation of work engagement.

Structure of Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition Criteria for Obsessive–Compulsive Personality Disorder in Patients With Binge Eating Disorder

PubMed Central

Ansell, Emily B; Pinto, Anthony; Edelen, Maria Orlando; Grilo, Carlos M

2013-01-01

Objective To examine 1-, 2-, and 3-factor model structures through confirmatory analytic procedures for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) obsessive–compulsive personality disorder (OCPD) criteria in patients with binge eating disorder (BED). Method Participants were consecutive outpatients (n = 263) with binge eating disorder and were assessed with semi-structured interviews. The 8 OCPD criteria were submitted to confirmatory factor analyses in Mplus Version 4.2 (Los Angeles, CA) in which previously identified factor models of OCPD were compared for fit, theoretical relevance, and parsimony. Nested models were compared for significant improvements in model fit. Results Evaluation of indices of fit in combination with theoretical considerations suggest a multifactorial model is a significant improvement in fit over the current DSM-IV single-factor model of OCPD. Though the data support both 2- and 3-factor models, the 3-factor model is hindered by an underspecified third factor. Conclusion A multifactorial model of OCPD incorporating the factors perfectionism and rigidity represents the best compromise of fit and theory in modelling the structure of OCPD in patients with BED. A third factor representing miserliness may be relevant in BED populations but needs further development. The perfectionism and rigidity factors may represent distinct intrapersonal and interpersonal attempts at control and may have implications for the assessment of OCPD. PMID:19087485

Structure of diagnostic and statistical manual of mental disorders, fourth edition criteria for obsessive-compulsive personality disorder in patients with binge eating disorder.

PubMed

Ansell, Emily B; Pinto, Anthony; Edelen, Maria Orlando; Grilo, Carlos M

2008-12-01

To examine 1-, 2-, and 3-factor model structures through confirmatory analytic procedures for Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV) obsessive-compulsive personality disorder (OCPD) criteria in patients with binge eating disorder (BED). Participants were consecutive outpatients (n = 263) with binge eating disorder and were assessed with semi-structured interviews. The 8 OCPD criteria were submitted to confirmatory factor analyses in Mplus Version 4.2 (Los Angeles, CA) in which previously identified factor models of OCPD were compared for fit, theoretical relevance, and parsimony. Nested models were compared for significant improvements in model fit. Evaluation of indices of fit in combination with theoretical considerations suggest a multifactorial model is a significant improvement in fit over the current DSM-IV single- factor model of OCPD. Though the data support both 2- and 3-factor models, the 3-factor model is hindered by an underspecified third factor. A multifactorial model of OCPD incorporating the factors perfectionism and rigidity represents the best compromise of fit and theory in modelling the structure of OCPD in patients with BED. A third factor representing miserliness may be relevant in BED populations but needs further development. The perfectionism and rigidity factors may represent distinct intrapersonal and interpersonal attempts at control and may have implications for the assessment of OCPD.

Measuring the effects of socioeconomic factors on mental health among migrants in urban China: a multiple indicators multiple causes model.

PubMed

Guan, Ming

2017-01-01

Since 1978, rural-urban migrants mainly contribute Chinese urbanization. The purpose of this paper is to examine the effects of socioeconomic factors on mental health of them. Their mental health was measured by 12-item general health questionnaire (GHQ-12). The study sample comprised 5925 migrants obtained from the 2009 rural-to-urban migrants survey (RUMiC). The relationships among the instruments were assessed by the correlation analysis. The one-factor (overall items), two-factor (positive vs. negative items), and model conducted by principal component analysis were tested in the confirmatory factor analysis (CFA). On the basis of three CFA models, the three multiple indicators multiple causes (MIMIC) models with age, gender, marriage, ethnicity, and employment were constructed to investigate the concurrent associations between socioeconomic factors and GHQ-12. Of the sample, only 1.94% were of ethnic origin and mean age was 31.63 (SD = ±10.43) years. The one-factor, two-factor, and three-factor structure (i.e. semi-positive/negative/independent usefulness) had good model fits in the CFA analysis and gave order (i.e. 2 factor>3 factor>1 factor), which suggests that the three models can be used to assess psychological symptoms of migrants in urban China. All MIMIC models had acceptable fit and gave order (i.e. one-dimensional model>two-dimensional model>three-dimensional model). There were weak associations of socioeconomic factors with mental health among migrants in urban China. Policy discussion suggested that improvement of socioeconomic status of rural-urban migrants and mental health systems in urban China should be highlighted and strengthened.

Factor structure of PTSD, and relation with gender in trauma survivors from India.

PubMed

Charak, Ruby; Armour, Cherie; Elklit, Ask; Angmo, Disket; Elhai, Jon D; Koot, Hans M

2014-01-01

The factor structure of posttraumatic stress disorder (PTSD) has been extensively studied in Western countries. Some studies have assessed its factor structure in Asia (China, Sri Lanka, and Malaysia), but few have directly assessed the factor structure of PTSD in an Indian adult sample. Furthermore, in a largely patriarchal society in India with strong gender roles, it becomes imperative to assess the association between the factors of PTSD and gender. The purpose of the present study was to assess the factor structure of PTSD in an Indian sample of trauma survivors based on prevailing models of PTSD defined in the DSM-IV-TR (APA, 2000), and to assess the relation between PTSD factors and gender. The sample comprised of 313 participants (55.9% female) from Jammu and Kashmir, India, who had experienced a natural disaster (N=200) or displacement due to cross-border firing (N=113). Three existing PTSD models-two four-factor models (Emotional Numbing and Dysphoria), and a five-factor model (Dysphoric Arousal)-were tested using Confirmatory Factor Analysis with addition of gender as a covariate. The three competing models had similar fit indices although the Dysphoric Arousal model fit significantly better than Emotional Numbing and Dysphoria models. Gender differences were found across the factors of Re-experiencing and Anxious arousal. Findings indicate that the Dysphoric Arousal model of PTSD was the best model; albeit the fit indices of all models were fairly similar. Compared to males, females scored higher on factors of Re-experiencing and Anxious arousal. Gender differences found across two factors of PTSD are discussed in light of the social milieu in India.

Factor Structure of a Multidimensional Gender Identity Scale in a Sample of Chinese Elementary School Children

PubMed Central

Yu, Lu; Xie, Dong; Shek, Daniel T. L.

2012-01-01

This study examined the factor structure of a scale based on the four-dimensional gender identity model (Egan and Perry, 2001) in 726 Chinese elementary school students. Exploratory factor analyses suggested a three-factor model, two of which corresponded to “Felt Pressure” and “Intergroup Bias” in the original model. The third factor “Gender Compatibility” appeared to be a combination of “Gender Typicality” and “Gender Contentment” in the original model. Follow-up confirmatory factor analysis (CFA) indicated that, relative to the initial four-factor structure, the three-factor model fits the current Chinese sample better. These results are discussed in light of cross-cultural similarities and differences in development of gender identity. PMID:22701363

Factors accounting for youth suicide attempt in Hong Kong: a model building.

PubMed

Wan, Gloria W Y; Leung, Patrick W L

2010-10-01

This study aimed at proposing and testing a conceptual model of youth suicide attempt. We proposed a model that began with family factors such as a history of physical abuse and parental divorce/separation. Family relationship, presence of psychopathology, life stressors, and suicide ideation were postulated as mediators, leading to youth suicide attempt. The stepwise entry of the risk factors to a logistic regression model defined their proximity as related to suicide attempt. Path analysis further refined our proposed model of youth suicide attempt. Our originally proposed model was largely confirmed. The main revision was dropping parental divorce/separation as a risk factor in the model due to lack of significant contribution when examined alongside with other risk factors. This model was cross-validated by gender. This study moved research on youth suicide from identification of individual risk factors to model building, integrating separate findings of the past studies.

Sexual harassment: identifying risk factors.

PubMed

O'Hare, E A; O'Donohue, W

1998-12-01

A new model of the etiology of sexual harassment, the four-factor model, is presented and compared with several models of sexual harassment including the biological model, the organizational model, the sociocultural model, and the sex role spillover model. A number of risk factors associated with sexually harassing behavior are examined within the framework of the four-factor model of sexual harassment. These include characteristics of the work environment (e.g., sexist attitudes among co-workers, unprofessional work environment, skewed sex ratios in the workplace, knowledge of grievance procedures for sexual harassment incidents) as well as personal characteristics of the subject (e.g., physical attractiveness, job status, sex-role). Subjects were 266 university female faculty, staff, and students who completed the Sexual Experience Questionnaire to assess the experience of sexual harassment and a questionnaire designed to assess the risk factors stated above. Results indicated that the four-factor model is a better predictor of sexual harassment than the alternative models. The risk factors most strongly associated with sexual harassment were an unprofessional environment in the workplace, sexist atmosphere, and lack of knowledge about the organization's formal grievance procedures.

The structural invariance of the Temporal Experience of Pleasure Scale across time and culture.

PubMed

Li, Zhi; Shi, Hai-Song; Elis, Ori; Yang, Zhuo-Ya; Wang, Ya; Lui, Simon S Y; Cheung, Eric F C; Kring, Ann M; Chan, Raymond C K

2018-06-01

The Temporal Experience of Pleasure Scale (TEPS) is a self-report instrument that assesses pleasure experience. Initial scale development and validation in the United States yielded a two-factor solution comprising anticipatory and consummatory pleasure. However, a four-factor model that further parsed anticipatory and consummatory pleasure experience into abstract and contextual components was a better model fit in China. In this study, we tested both models using confirmatory factor analysis in an American and a Chinese sample and examined the configural measurement invariance of both models across culture. We also examined the temporal stability of the four-factor model in the Chinese sample. The results indicated that the four-factor model of the TEPS was a better fit than the two-factor model in the Chinese sample. In contrast, both models fit the American sample, which also included many Asian American participants. The four-factor model fit both the Asian American and Chinese samples equally well. Finally, the four-factor model demonstrated good measurement and structural invariance across culture and time, suggesting that this model may be applicable in both cross-cultural and longitudinal studies. © 2018 The Institute of Psychology, Chinese Academy of Sciences and John Wiley & Sons Australia, Ltd.

[Lake eutrophication modeling in considering climatic factors change: a review].

PubMed

Su, Jie-Qiong; Wang, Xuan; Yang, Zhi-Feng

2012-11-01

Climatic factors are considered as the key factors affecting the trophic status and its process in most lakes. Under the background of global climate change, to incorporate the variations of climatic factors into lake eutrophication models could provide solid technical support for the analysis of the trophic evolution trend of lake and the decision-making of lake environment management. This paper analyzed the effects of climatic factors such as air temperature, precipitation, sunlight, and atmosphere on lake eutrophication, and summarized the research results about the lake eutrophication modeling in considering in considering climatic factors change, including the modeling based on statistical analysis, ecological dynamic analysis, system analysis, and intelligent algorithm. The prospective approaches to improve the accuracy of lake eutrophication modeling with the consideration of climatic factors change were put forward, including 1) to strengthen the analysis of the mechanisms related to the effects of climatic factors change on lake trophic status, 2) to identify the appropriate simulation models to generate several scenarios under proper temporal and spatial scales and resolutions, and 3) to integrate the climatic factors change simulation, hydrodynamic model, ecological simulation, and intelligent algorithm into a general modeling system to achieve an accurate prediction of lake eutrophication under climatic change.

Factoring vs linear modeling in rate estimation: a simulation study of relative accuracy.

PubMed

Maldonado, G; Greenland, S

1998-07-01

A common strategy for modeling dose-response in epidemiology is to transform ordered exposures and covariates into sets of dichotomous indicator variables (that is, to factor the variables). Factoring tends to increase estimation variance, but it also tends to decrease bias and thus may increase or decrease total accuracy. We conducted a simulation study to examine the impact of factoring on the accuracy of rate estimation. Factored and unfactored Poisson regression models were fit to follow-up study datasets that were randomly generated from 37,500 population model forms that ranged from subadditive to supramultiplicative. In the situations we examined, factoring sometimes substantially improved accuracy relative to fitting the corresponding unfactored model, sometimes substantially decreased accuracy, and sometimes made little difference. The difference in accuracy between factored and unfactored models depended in a complicated fashion on the difference between the true and fitted model forms, the strength of exposure and covariate effects in the population, and the study size. It may be difficult in practice to predict when factoring is increasing or decreasing accuracy. We recommend, therefore, that the strategy of factoring variables be supplemented with other strategies for modeling dose-response.

Why College Students Cheat: A Conceptual Model of Five Factors

ERIC Educational Resources Information Center

Yu, Hongwei; Glanzer, Perry L.; Johnson, Byron R.; Sriram, Rishi; Moore, Brandon

2018-01-01

Though numerous studies have identified factors associated with academic misconduct, few have proposed conceptual models that could make sense of multiple factors. In this study, we used structural equation modeling (SEM) to test a conceptual model of five factors using data from a relatively large sample of 2,503 college students. The results…

Confirmatory factor analysis of the Oral Health Impact Profile.

PubMed

John, M T; Feuerstahler, L; Waller, N; Baba, K; Larsson, P; Celebić, A; Kende, D; Rener-Sitar, K; Reissmann, D R

2014-09-01

Previous exploratory analyses suggest that the Oral Health Impact Profile (OHIP) consists of four correlated dimensions and that individual differences in OHIP total scores reflect an underlying higher-order factor. The aim of this report is to corroborate these findings in the Dimensions of Oral Health-Related Quality of Life (DOQ) Project, an international study of general population subjects and prosthodontic patients. Using the project's Validation Sample (n = 5022), we conducted confirmatory factor analyses in a sample of 4993 subjects with sufficiently complete data. In particular, we compared the psychometric performance of three models: a unidimensional model, a four-factor model and a bifactor model that included one general factor and four group factors. Using model-fit criteria and factor interpretability as guides, the four-factor model was deemed best in terms of strong item loadings, model fit (RMSEA = 0·05, CFI = 0·99) and interpretability. These results corroborate our previous findings that four highly correlated factors - which we have named Oral Function, Oro-facial Pain, Oro-facial Appearance and Psychosocial Impact - can be reliably extracted from the OHIP item pool. However, the good fit of the unidimensional model and the high interfactor correlations in the four-factor solution suggest that OHRQoL can also be sufficiently described with one score. © 2014 John Wiley & Sons Ltd.

Dimensional structure of DSM-5 posttraumatic stress symptoms: support for a hybrid Anhedonia and Externalizing Behaviors model.

PubMed

Armour, Cherie; Tsai, Jack; Durham, Tory A; Charak, Ruby; Biehn, Tracey L; Elhai, Jon D; Pietrzak, Robert H

2015-02-01

Several revisions to the symptom clusters of posttraumatic stress disorder (PTSD) have been made in the 5th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-5). Central to the focus of this study was the revision of PTSD's tripartite structure in DSM-IV into four symptom clusters in DSM-5. Emerging confirmatory factor analytic (CFA) studies have suggested that DSM-5 PTSD symptoms may be best represented by one of two 6-factor models: (1) an Externalizing Behaviors model characterized by a factor which combines the irritability/anger and self-destructive/reckless behavior items; and (2) an Anhedonia model characterized by items of loss of interest, detachment, and restricted affect. The current study conducted CFAs of DSM-5 PTSD symptoms assessed using the PTSD Checklist for DSM-5 (PCL-5) in two independent and diverse trauma-exposed samples of a nationally representative sample of 1484 U.S. veterans and a sample of 497 Midwestern U.S. university undergraduate students. Relative fits of the DSM-5 model, the DSM-5 Dysphoria model, the DSM-5 Dysphoric Arousal model, the two 6-factor models, and a newly proposed 7-factor Hybrid model, which consolidates the two 6-factor models, were evaluated. Results revealed that, in both samples, both 6-factor models provided significantly better fit than the 4-factor DSM-5 model, the DSM-5 Dysphoria model and the DSM-5 Dysphoric Arousal model. Further, the 7-factor Hybrid model, which incorporates key features of both 6-factor models and is comprised of re-experiencing, avoidance, negative affect, anhedonia, externalizing behaviors, and anxious and dysphoric arousal symptom clusters, provided superior fit to the data in both samples. Results are discussed in light of theoretical and empirical support for the latent structure of DSM-5 PTSD symptoms. Copyright © 2014 Elsevier Ltd. All rights reserved.

Factor structure of PTSD, and relation with gender in trauma survivors from India

PubMed Central

Charak, Ruby; Armour, Cherie; Elklit, Ask; Angmo, Disket; Elhai, Jon D.; Koot, Hans M.

2014-01-01

Background The factor structure of posttraumatic stress disorder (PTSD) has been extensively studied in Western countries. Some studies have assessed its factor structure in Asia (China, Sri Lanka, and Malaysia), but few have directly assessed the factor structure of PTSD in an Indian adult sample. Furthermore, in a largely patriarchal society in India with strong gender roles, it becomes imperative to assess the association between the factors of PTSD and gender. Objective The purpose of the present study was to assess the factor structure of PTSD in an Indian sample of trauma survivors based on prevailing models of PTSD defined in the DSM-IV-TR (APA, 2000), and to assess the relation between PTSD factors and gender. Method The sample comprised of 313 participants (55.9% female) from Jammu and Kashmir, India, who had experienced a natural disaster (N=200) or displacement due to cross-border firing (N=113). Results Three existing PTSD models—two four-factor models (Emotional Numbing and Dysphoria), and a five-factor model (Dysphoric Arousal)—were tested using Confirmatory Factor Analysis with addition of gender as a covariate. The three competing models had similar fit indices although the Dysphoric Arousal model fit significantly better than Emotional Numbing and Dysphoria models. Gender differences were found across the factors of Re-experiencing and Anxious arousal. Conclusions Findings indicate that the Dysphoric Arousal model of PTSD was the best model; albeit the fit indices of all models were fairly similar. Compared to males, females scored higher on factors of Re-experiencing and Anxious arousal. Gender differences found across two factors of PTSD are discussed in light of the social milieu in India. PMID:25413575

Four- and five-factor models of the WAIS-IV in a clinical sample: Variations in indicator configuration and factor correlational structure.

PubMed

Staffaroni, Adam M; Eng, Megan E; Moses, James A; Zeiner, Harriet Katz; Wickham, Robert E

2018-05-01

A growing body of research supports the validity of 5-factor models for interpreting the Wechsler Adult Intelligence Scale-Fourth Edition (WAIS-IV). The majority of these studies have utilized the WAIS-IV normative or clinical sample, the latter of which differs in its diagnostic composition from the referrals seen at outpatient neuropsychology clinics. To address this concern, 2 related studies were conducted on a sample of 322 American military Veterans who were referred for outpatient neuropsychological assessment. In Study 1, 4 hierarchical models with varying indicator configurations were evaluated: 3 extant 5-factor models from the literature and the traditional 4-factor model. In Study 2, we evaluated 3 variations in correlation structure in the models from Study 1: indirect hierarchical (i.e., higher-order g), bifactor (direct hierarchical), and oblique models. The results from Study 1 suggested that both 4- and 5-factor models showed acceptable fit. The results from Study 2 showed that bifactor and oblique models offer improved fit over the typically specified indirect hierarchical model, and the oblique models outperformed the orthogonal bifactor models. An exploratory analysis found improved fit when bifactor models were specified with oblique rather than orthogonal latent factors. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Dimensional structure of DSM-5 posttraumatic stress symptoms in Spanish trauma victims

PubMed Central

Soberón, Carmen; Crespo, María; del Mar Gómez-Gutiérrez, María; Fernández-Lansac, Violeta; Armour, Cherie

2016-01-01

Background Confirmatory factor analytic studies have shown that posttraumatic stress disorder (PTSD) symptoms included in the fifth edition of the Diagnostic and Statistical Manual Disorders (DSM-5) may be better explained by two 6-factor models (the Externalizing Behaviours model and the Anhedonia model) and a 7-factor Hybrid model. The latter model comprises the symptom clusters of intrusion, avoidance, negative affect, anhedonia, externalizing behaviours, and anxious and dysphoric arousal. This model has received empirical support mainly in American samples. Of note, there have been a limited number of studies conducted on samples from other countries. Objective This study aimed to examine the underlying dimensionality of DSM-5 PTSD symptoms in a Spanish clinical sample exposed to a range of traumatic events. Method Participants included 165 adults (78.8% females) seeking treatment in trauma services in the Madrid area (Spain). PTSD was assessed using the Global Assessment of Posttraumatic Stress Scale 5, a Spanish self-report instrument assessing posttraumatic symptoms according to the DSM-5 criteria. Confirmatory factor analyses were conducted in Mplus. Results Both the 7-factor Hybrid model and the 6-factor Anhedonia model demonstrated good and equivalent fit to the data. Conclusions The findings of this study replicate and extend previous research by providing support for both the 7-factor Hybrid model and the 6-factor Anhedonia model in a clinical sample of Spanish trauma survivors. Given equivalent fit for these two models and the fewer number of latent factors in the Anhedonia model, it was selected as optimal in a traumatized Spanish sample. Implications and future research directions are discussed. Highlights of the article The 7-factor Hybrid model (which comprises the intrusion, avoidance, negative affect, anhedonia, externalizing behaviours, and anxious and dysphoric arousal symptoms clusters) and the 6-factor Anhedonia model (in which the externalizing behaviour symptoms are part of the dysphoric arousal symptom cluster) provided equivalent fit to the data. The Anhedonia model is the most parsimonious and thus the optimal-fitting model in the current sample. The findings support the distinctiveness between dysphoric arousal, anxious arousal, negative affect, and anhedonia factors. The separation of the externalizing behaviour symptoms from the dysphoric arousal symptoms does not improve the model fit in the current sample. PMID:27974133

The utility of the bifactor model in understanding unique components of anxiety sensitivity in a South Korean sample.

PubMed

Ebesutani, Chad; Kim, Mirihae; Park, Hee-Hoon

2016-08-01

The present study was the first to examine the applicability of the bifactor structure underlying the Anxiety Sensitivity Index-3 (ASI-3) in an East Asian (South Korean) sample and to determine which factors in the bifactor model were significantly associated with anxiety, depression, and negative affect. Using a sample of 289 South Korean university students, we compared (a) the original 3-factor AS model, (b) a 3-group bifactor AS model, and (c) a 2-group bifactor AS model (with only the physical and social concern group factors present). Results revealed that the 2-group bifactor AS model fit the ASI-3 data the best. Relatedly, although all ASI-3 items loaded on the general AS factor, the Cognitive Concern group factor was not defined in the bifactor model and may therefore need to be omitted in order to accurately model AS when conducting factor analysis and structural equation modeling (SEM) in cross cultural contexts. SEM results also revealed that the general AS factor was the only factor from the 2-group bifactor model that significantly predicted anxiety, depression, and negative affect. Implications and importance of this new bifactor structure of Anxiety Sensitivity in East Asian samples are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

The Effects of Autocorrelation on the Curve-of-Factors Growth Model

ERIC Educational Resources Information Center

Murphy, Daniel L.; Beretvas, S. Natasha; Pituch, Keenan A.

2011-01-01

This simulation study examined the performance of the curve-of-factors model (COFM) when autocorrelation and growth processes were present in the first-level factor structure. In addition to the standard curve-of factors growth model, 2 new models were examined: one COFM that included a first-order autoregressive autocorrelation parameter, and a…

Confirmatory Factor Analysis of the Behavior Rating Inventory of Executive Function-Adult Version in Healthy Adults and Application to Attention-Deficit/Hyperactivity Disorder

PubMed Central

Roth, Robert M.; Lance, Charles E.; Isquith, Peter K.; Fischer, Adina S.; Giancola, Peter R.

2013-01-01

The Behavior Rating Inventory of Executive Function-Adult Version (BRIEF-A) is a questionnaire measure designed to assess executive functioning in everyday life. Analysis of data from the BRIEF-A standardization sample yielded a two-factor solution (labeled Behavioral Regulation and Metacognition). The present investigation employed confirmatory factor analysis (CFA) to evaluate four alternative models of the factor structure of the BRIEF-A self-report form in a sample of 524 healthy young adults. Results indicated that a three-factor model best fits the data: a Metacognition factor, a Behavioral Regulation factor consisting of the Inhibit and Self-Monitor scales, and an Emotional Regulation factor composed of the Emotional Control and Shift scales. The three factors contributed 14%, 19%, and 24% of unique variance to the model, respectively, and a second-order general factor accounted for 41% of variance overall. This three-factor solution is consistent with recent CFAs of the Parent report form of the BRIEF. Furthermore, although the Behavioral Regulation factor score in the two-factor model did not differ between adults with attention-deficit/hyperactivity disorder and a matched healthy comparison group, greater impairment on the Behavioral Regulation factor but not the Emotional Regulation factor was found using the three-factor model. Together, these findings support the multidimensional nature of executive function and the clinical relevance of a three-factor model of the BRIEF-A. PMID:23676185

Dimensional structure of DSM-5 posttraumatic stress symptoms in Spanish trauma victims.

PubMed

Soberón, Carmen; Crespo, María; Del Mar Gómez-Gutiérrez, María; Fernández-Lansac, Violeta; Armour, Cherie

2016-01-01

Confirmatory factor analytic studies have shown that posttraumatic stress disorder (PTSD) symptoms included in the fifth edition of the Diagnostic and Statistical Manual Disorders (DSM-5) may be better explained by two 6-factor models (the Externalizing Behaviours model and the Anhedonia model) and a 7-factor Hybrid model. The latter model comprises the symptom clusters of intrusion, avoidance, negative affect, anhedonia, externalizing behaviours, and anxious and dysphoric arousal. This model has received empirical support mainly in American samples. Of note, there have been a limited number of studies conducted on samples from other countries. This study aimed to examine the underlying dimensionality of DSM-5 PTSD symptoms in a Spanish clinical sample exposed to a range of traumatic events. Participants included 165 adults (78.8% females) seeking treatment in trauma services in the Madrid area (Spain). PTSD was assessed using the Global Assessment of Posttraumatic Stress Scale 5, a Spanish self-report instrument assessing posttraumatic symptoms according to the DSM-5 criteria. Confirmatory factor analyses were conducted in Mplus. Both the 7-factor Hybrid model and the 6-factor Anhedonia model demonstrated good and equivalent fit to the data. The findings of this study replicate and extend previous research by providing support for both the 7-factor Hybrid model and the 6-factor Anhedonia model in a clinical sample of Spanish trauma survivors. Given equivalent fit for these two models and the fewer number of latent factors in the Anhedonia model, it was selected as optimal in a traumatized Spanish sample. Implications and future research directions are discussed.

Psychometric Properties of the “Sport Motivation Scale (SMS)” Adapted to Physical Education

PubMed Central

Granero-Gallegos, Antonio; Baena-Extremera, Antonio; Gómez-López, Manuel; Sánchez-Fuentes, José Antonio; Abraldes, J. Arturo

2014-01-01

The aim of this study was to investigate the factor structure of a Spanish version of the Sport Motivation Scale adapted to physical education. A second aim was to test which one of three hypothesized models (three, five and seven-factor) provided best model fit. 758 Spanish high school students completed the Sport Motivation Scale adapted for Physical Education and also completed the Learning and Performance Orientation in Physical Education Classes Questionnaire. We examined the factor structure of each model using confirmatory factor analysis and also assessed internal consistency and convergent validity. The results showed that all three models in Spanish produce good indicators of fitness, but we suggest using the seven-factor model (χ2/gl = 2.73; ECVI = 1.38) as it produces better values when adapted to physical education, that five-factor model (χ2/gl = 2.82; ECVI = 1.44) and three-factor model (χ2/gl = 3.02; ECVI = 1.53). Key Points Physical education research conducted in Spain has used the version of SMS designed to assess motivation in sport, but validity reliability and validity results in physical education have not been reported. Results of the present study lend support to the factorial validity and internal reliability of three alternative factor structures (3, 5, and 7 factors) of SMS adapted to Physical Education in Spanish. Although all three models in Spanish produce good indicators of fitness, but we suggest using the seven-factor model. PMID:25435772

Predicting Time to Hospital Discharge for Extremely Preterm Infants

PubMed Central

Hintz, Susan R.; Bann, Carla M.; Ambalavanan, Namasivayam; Cotten, C. Michael; Das, Abhik; Higgins, Rosemary D.

2010-01-01

As extremely preterm infant mortality rates have decreased, concerns regarding resource utilization have intensified. Accurate models to predict time to hospital discharge could aid in resource planning, family counseling, and perhaps stimulate quality improvement initiatives. Objectives For infants <27 weeks estimated gestational age (EGA), to develop, validate and compare several models to predict time to hospital discharge based on time-dependent covariates, and based on the presence of 5 key risk factors as predictors. Patients and Methods This was a retrospective analysis of infants <27 weeks EGA, born 7/2002-12/2005 and surviving to discharge from a NICHD Neonatal Research Network site. Time to discharge was modeled as continuous (postmenstrual age at discharge, PMAD), and categorical variables (“Early” and “Late” discharge). Three linear and logistic regression models with time-dependent covariate inclusion were developed (perinatal factors only, perinatal+early neonatal factors, perinatal+early+later factors). Models for Early and Late discharge using the cumulative presence of 5 key risk factors as predictors were also evaluated. Predictive capabilities were compared using coefficient of determination (R2) for linear models, and AUC of ROC curve for logistic models. Results Data from 2254 infants were included. Prediction of PMAD was poor, with only 38% of variation explained by linear models. However, models incorporating later clinical characteristics were more accurate in predicting “Early” or “Late” discharge (full models: AUC 0.76-0.83 vs. perinatal factor models: AUC 0.56-0.69). In simplified key risk factors models, predicted probabilities for Early and Late discharge compared favorably with observed rates. Furthermore, the AUC (0.75-0.77) were similar to those of models including the full factor set. Conclusions Prediction of Early or Late discharge is poor if only perinatal factors are considered, but improves substantially with knowledge of later-occurring morbidities. Prediction using a few key risk factors is comparable to full models, and may offer a clinically applicable strategy. PMID:20008430

Further insights on the French WISC-IV factor structure through Bayesian structural equation modeling.

PubMed

Golay, Philippe; Reverte, Isabelle; Rossier, Jérôme; Favez, Nicolas; Lecerf, Thierry

2013-06-01

The interpretation of the Wechsler Intelligence Scale for Children--Fourth Edition (WISC-IV) is based on a 4-factor model, which is only partially compatible with the mainstream Cattell-Horn-Carroll (CHC) model of intelligence measurement. The structure of cognitive batteries is frequently analyzed via exploratory factor analysis and/or confirmatory factor analysis. With classical confirmatory factor analysis, almost all cross-loadings between latent variables and measures are fixed to zero in order to allow the model to be identified. However, inappropriate zero cross-loadings can contribute to poor model fit, distorted factors, and biased factor correlations; most important, they do not necessarily faithfully reflect theory. To deal with these methodological and theoretical limitations, we used a new statistical approach, Bayesian structural equation modeling (BSEM), among a sample of 249 French-speaking Swiss children (8-12 years). With BSEM, zero-fixed cross-loadings between latent variables and measures are replaced by approximate zeros, based on informative, small-variance priors. Results indicated that a direct hierarchical CHC-based model with 5 factors plus a general intelligence factor better represented the structure of the WISC-IV than did the 4-factor structure and the higher order models. Because a direct hierarchical CHC model was more adequate, it was concluded that the general factor should be considered as a breadth rather than a superordinate factor. Because it was possible for us to estimate the influence of each of the latent variables on the 15 subtest scores, BSEM allowed improvement of the understanding of the structure of intelligence tests and the clinical interpretation of the subtest scores. PsycINFO Database Record (c) 2013 APA, all rights reserved.

Hospital survey on patient safety culture: psychometric analysis on a Scottish sample.

PubMed

Sarac, Cakil; Flin, Rhona; Mearns, Kathryn; Jackson, Jeanette

2011-10-01

To investigate the psychometric properties of the Hospital Survey on Patient Safety Culture on a Scottish NHS data set. The data were collected from 1969 clinical staff (estimated 22% response rate) from one acute hospital from each of seven Scottish Health boards. Using a split-half validation technique, the data were randomly split; an exploratory factor analysis was conducted on the calibration data set, and confirmatory factor analyses were conducted on the validation data set to investigate and check the original US model fit in a Scottish sample. Following the split-half validation technique, exploratory factor analysis results showed a 10-factor optimal measurement model. The confirmatory factor analyses were then performed to compare the model fit of two competing models (10-factor alternative model vs 12-factor original model). An S-B scaled χ(2) square difference test demonstrated that the original 12-factor model performed significantly better in a Scottish sample. Furthermore, reliability analyses of each component yielded satisfactory results. The mean scores on the climate dimensions in the Scottish sample were comparable with those found in other European countries. This study provided evidence that the original 12-factor structure of the Hospital Survey on Patient Safety Culture scale has been replicated in this Scottish sample. Therefore, no modifications are required to the original 12-factor model, which is suggested for use, since it would allow researchers the possibility of cross-national comparisons.

Assessing a five factor model of PTSD: is dysphoric arousal a unique PTSD construct showing differential relationships with anxiety and depression?

PubMed

Armour, Cherie; Elhai, Jon D; Richardson, Don; Ractliffe, Kendra; Wang, Li; Elklit, Ask

2012-03-01

Posttraumatic stress disorder's (PTSD) latent structure has been widely debated. To date, two four-factor models (Numbing and Dysphoria) have received the majority of factor analytic support. Recently, Elhai et al. (2011) proposed and supported a revised (five-factor) Dysphoric Arousal model. Data were gathered from two separate samples; War veterans and Primary Care medical patients. The three models were compared and the resultant factors of the Dysphoric Arousal model were validated against external constructs of depression and anxiety. The Dysphoric Arousal model provided significantly better fit than the Numbing and Dysphoria models across both samples. When differentiating between factors, the current results support the idea that Dysphoric Arousal can be differentiated from Anxious Arousal but not from Emotional Numbing when correlated with depression. In conclusion, the Dysphoria model may be a more parsimonious representation of PTSD's latent structure in these trauma populations despite superior fit of the Dysphoric Arousal model. Copyright © 2011 Elsevier Ltd. All rights reserved.

Recovery of Weak Factor Loadings When Adding the Mean Structure in Confirmatory Factor Analysis: A Simulation Study

PubMed Central

Ximénez, Carmen

2016-01-01

This article extends previous research on the recovery of weak factor loadings in confirmatory factor analysis (CFA) by exploring the effects of adding the mean structure. This issue has not been examined in previous research. This study is based on the framework of Yung and Bentler (1999) and aims to examine the conditions that affect the recovery of weak factor loadings when the model includes the mean structure, compared to analyzing the covariance structure alone. A simulation study was conducted in which several constraints were defined for one-, two-, and three-factor models. Results show that adding the mean structure improves the recovery of weak factor loadings and reduces the asymptotic variances for the factor loadings, particularly for the models with a smaller number of factors and a small sample size. Therefore, under certain circumstances, modeling the means should be seriously considered for covariance models containing weak factor loadings. PMID:26779071

ESTIMATING UNCERTAINITIES IN FACTOR ANALYTIC MODELS

EPA Science Inventory

When interpreting results from factor analytic models as used in receptor modeling, it is important to quantify the uncertainties in those results. For example, if the presence of a species on one of the factors is necessary to interpret the factor as originating from a certain ...

Forms of ethnic prejudice: assessing the dimensionality of a Spanish-language version of the Blatant and Subtle Prejudice Scale.

PubMed

Cárdenas Castro, Manuel

2010-02-01

The main purpose of this study was to investigate the dimensionality of a Spanish-language version of the Blatant and Subtle Prejudice Scale via exploratory (EFA) and confirmatory factor analysis (CFA). No research has confirmed the hypothesized factor structure in Latin American countries. Using data from a random and probability survey in population of the northern area of Chile (N= 896), four models were specified: single factor model (global prejudice factor), correlated two-factor model (subtle and blatant prejudice), correlated two-factor second-order model, and single-factor second-order model. The findings indicated that the two-factor second-order model had the best fit. The corresponding alpha coefficients were .82 (subtle prejudice) and .76 (blatant prejudice). Lastly, differences were examined between , , and regarding their feelings toward immigrants, their feelings about their beliefs concerning the state aid received by these out-groups, and their feelings about their beliefs regarding future policies for them.

Application of GA-SVM method with parameter optimization for landslide development prediction

NASA Astrophysics Data System (ADS)

Li, X. Z.; Kong, J. M.

2013-10-01

Prediction of landslide development process is always a hot issue in landslide research. So far, many methods for landslide displacement series prediction have been proposed. Support vector machine (SVM) has been proved to be a novel algorithm with good performance. However, the performance strongly depends on the right selection of the parameters (C and γ) of SVM model. In this study, we presented an application of GA-SVM method with parameter optimization in landslide displacement rate prediction. We selected a typical large-scale landslide in some hydro - electrical engineering area of Southwest China as a case. On the basis of analyzing the basic characteristics and monitoring data of the landslide, a single-factor GA-SVM model and a multi-factor GA-SVM model of the landslide were built. Moreover, the models were compared with single-factor and multi-factor SVM models of the landslide. The results show that, the four models have high prediction accuracies, but the accuracies of GA-SVM models are slightly higher than those of SVM models and the accuracies of multi-factor models are slightly higher than those of single-factor models for the landslide prediction. The accuracy of the multi-factor GA-SVM models is the highest, with the smallest RSME of 0.0009 and the biggest RI of 0.9992.

Using a latent variable model with non-constant factor loadings to examine PM2.5 constituents related to secondary inorganic aerosols.

PubMed

Zhang, Zhenzhen; O'Neill, Marie S; Sánchez, Brisa N

2016-04-01

Factor analysis is a commonly used method of modelling correlated multivariate exposure data. Typically, the measurement model is assumed to have constant factor loadings. However, from our preliminary analyses of the Environmental Protection Agency's (EPA's) PM 2.5 fine speciation data, we have observed that the factor loadings for four constituents change considerably in stratified analyses. Since invariance of factor loadings is a prerequisite for valid comparison of the underlying latent variables, we propose a factor model that includes non-constant factor loadings that change over time and space using P-spline penalized with the generalized cross-validation (GCV) criterion. The model is implemented using the Expectation-Maximization (EM) algorithm and we select the multiple spline smoothing parameters by minimizing the GCV criterion with Newton's method during each iteration of the EM algorithm. The algorithm is applied to a one-factor model that includes four constituents. Through bootstrap confidence bands, we find that the factor loading for total nitrate changes across seasons and geographic regions.

Equivalent Dynamic Models.

PubMed

Molenaar, Peter C M

2017-01-01

Equivalences of two classes of dynamic models for weakly stationary multivariate time series are discussed: dynamic factor models and autoregressive models. It is shown that exploratory dynamic factor models can be rotated, yielding an infinite set of equivalent solutions for any observed series. It also is shown that dynamic factor models with lagged factor loadings are not equivalent to the currently popular state-space models, and that restriction of attention to the latter type of models may yield invalid results. The known equivalent vector autoregressive model types, standard and structural, are given a new interpretation in which they are conceived of as the extremes of an innovating type of hybrid vector autoregressive models. It is shown that consideration of hybrid models solves many problems, in particular with Granger causality testing.

DSM-5 PTSD's symptom dimensions and relations with major depression's symptom dimensions in a primary care sample.

PubMed

Contractor, Ateka A; Durham, Tory A; Brennan, Julie A; Armour, Cherie; Wutrick, Hanna R; Frueh, B Christopher; Elhai, Jon D

2014-01-30

Existing literature indicates significant comorbidity between posttraumatic stress disorder (PTSD) and major depression. We examined whether PTSD's dysphoria and mood/cognitions factors, conceptualized by the empirically supported four-factor DSM-5 PTSD models, account for PTSD's inherent relationship with depression. We hypothesized that depression's somatic and non-somatic factors would be more related to PTSD's dysphoria and mood/cognitions factors than other PTSD model factors. Further, we hypothesized that PTSD's arousal would significantly mediate relations between PTSD's dysphoria and somatic/non-somatic depression. Using 181 trauma-exposed primary care patients, confirmatory factor analyses (CFA) indicated a well-fitting DSM-5 PTSD dysphoria model, DSM-5 numbing model and two-factor depression model. Both somatic and non-somatic depression factors were more related to PTSD's dysphoria and mood/cognitions factors than to re-experiencing and avoidance factors; non-somatic depression was more related to PTSD's dysphoria than PTSD's arousal factor. PTSD's arousal did not mediate the relationship between PTSD's dysphoria and somatic/non-somatic depression. Implications are discussed. © 2013 Published by Elsevier Ireland Ltd.

Comparisons of Four Methods for Estimating a Dynamic Factor Model

ERIC Educational Resources Information Center

Zhang, Zhiyong; Hamaker, Ellen L.; Nesselroade, John R.

2008-01-01

Four methods for estimating a dynamic factor model, the direct autoregressive factor score (DAFS) model, are evaluated and compared. The first method estimates the DAFS model using a Kalman filter algorithm based on its state space model representation. The second one employs the maximum likelihood estimation method based on the construction of a…

Temporal Stability, Correlates, and Longitudinal Outcomes of Career Indecision Factors

ERIC Educational Resources Information Center

Nauta, Margaret M.

2012-01-01

A confirmatory factor analysis (CFA) tested the fit of Kelly and Lee's six-factor model of career decision problems among 188 college students. The six-factor model did not fit the data well, but a five-factor (Lack of Information, Need for Information, Trait Indecision, Disagreement with Others, and Choice Anxiety) model did provide a good fit.…

[Psychosocial factors at work and cardiovascular diseases: contribution of the Effort-Reward Imbalance model].

PubMed

Niedhammer, I; Siegrist, J

1998-11-01

The effect of psychosocial factors at work on health, especially cardiovascular health, has given rise to growing concern in occupational epidemiology over the last few years. Two theoretical models, Karasek's model and the Effort-Reward Imbalance model, have been developed to evaluate psychosocial factors at work within specific conceptual frameworks in an attempt to take into account the serious methodological difficulties inherent in the evaluation of such factors. Karasek's model, the most widely used model, measures three factors: psychological demands, decision latitude and social support at work. Many studies have shown the predictive effects of these factors on cardiovascular diseases independently of well-known cardiovascular risk factors. More recently, the Effort-Reward Imbalance model takes into account the role of individual coping characteristics which was neglected in the Karasek model. The effort-reward imbalance model focuses on the reciprocity of exchange in occupational life where high-cost/low-gain conditions are considered particularly stressful. Three dimensions of rewards are distinguished: money, esteem and gratifications in terms of promotion prospects and job security. Some studies already support that high-effort/low reward-conditions are predictive of cardiovascular diseases.

A Twin Factor Mixture Modeling Approach to Childhood Temperament: Differential Heritability

ERIC Educational Resources Information Center

Scott, Brandon G.; Lemery-Chalfant, Kathryn; Clifford, Sierra; Tein, Jenn-Yun; Stoll, Ryan; Goldsmith, H.Hill

2016-01-01

Twin factor mixture modeling was used to identify temperament profiles while simultaneously estimating a latent factor model for each profile with a sample of 787 twin pairs (M[subscript age] = 7.4 years, SD = 0.84; 49% female; 88.3% Caucasian), using mother- and father-reported temperament. A four-profile, one-factor model fit the data well.…

Improved dual-porosity models for petrophysical analysis of vuggy reservoirs

NASA Astrophysics Data System (ADS)

Wang, Haitao

2017-08-01

A new vug interconnection, isolated vug (IVG), was investigated through resistivity modeling and the dual-porosity model for connected vug (CVG) vuggy reservoirs was tested. The vuggy models were built by pore-scale modeling, and their electrical resistivity was calculated by the finite difference method. For CVG vuggy reservoirs, the CVG reduced formation factors and increased the porosity exponents, and the existing dual-porosity model failed to match these results. Based on the existing dual-porosity model, a conceptual dual-porosity model for CVG was developed by introducing a decoupled term to reduce the resistivity of the model. For IVG vuggy reservoirs, IVG increased the formation factors and porosity exponents. The existing dual-porosity model succeeded due to accurate calculation of the formation factors of the deformed interparticle porous media caused by the insertion of the IVG. Based on the existing dual-porosity model, a new porosity model for IVG vuggy reservoirs was developed by simultaneously recalculating the formation factors of the altered interparticle pore-scale models. The formation factors and porosity exponents from the improved and extended dual-porosity models for CVG and IVG vuggy reservoirs well matched the simulated formation factors and porosity exponents. This work is helpful for understanding the influence of connected and disconnected vugs on resistivity factors—an issue of particular importance in carbonates.

Confirmatory factor analysis applied to the Force Concept Inventory

NASA Astrophysics Data System (ADS)

Eaton, Philip; Willoughby, Shannon D.

2018-06-01

In 1995, Huffman and Heller used exploratory factor analysis to draw into question the factors of the Force Concept Inventory (FCI). Since then several papers have been published examining the factors of the FCI on larger sets of student responses and understandable factors were extracted as a result. However, none of these proposed factor models have been verified to not be unique to their original sample through the use of independent sets of data. This paper seeks to confirm the factor models proposed by Scott et al. in 2012, and Hestenes et al. in 1992, as well as another expert model proposed within this study through the use of confirmatory factor analysis (CFA) and a sample of 20 822 postinstruction student responses to the FCI. Upon application of CFA using the full sample, all three models were found to fit the data with acceptable global fit statistics. However, when CFA was performed using these models on smaller sample sizes the models proposed by Scott et al. and Eaton and Willoughby were found to be far more stable than the model proposed by Hestenes et al. The goodness of fit of these models to the data suggests that the FCI can be scored on factors that are not unique to a single class. These scores could then be used to comment on how instruction methods effect the performance of students along a single factor and more in-depth analyses of curriculum changes may be possible as a result.

Exploratory Study of Factors Influencing Job-Related Stress in Japanese Psychiatric Nurses

PubMed Central

Yada, Hironori; Lu, Xi; Omori, Hisamitsu; Abe, Hiroshi; Matsuo, Hisae; Ishida, Yasushi; Katoh, Takahiko

2015-01-01

This study explored the factor structure of psychiatric nurses' job-related stress and examined the specificity of the related stressors using the job stressor scale of the Brief Job Stress Questionnaire (BJSQ). The stressor scale of the BJSQ was administered to 296 nurses and assistant nurses. Answers were examined statistically. Exploratory factor analysis was performed to identify factor structures; two factors (overload and job environment) were valid. Confirmatory factor analysis was conducted to examine the two-factor structure and found 11 items with factor loadings of >0.40 (model 1), 13 items with factor loadings from 0.30 to <0.40 (model 2), and 17 items with factor loadings from 0.20 to <0.30 (model 3) for one factor; model 1 demonstrated the highest goodness of fit. Then, we observed that the two-factor structure (model 1) showed a higher goodness of fit than the original six-factor structure. This differed from subscales based on general workers' job-related stressors, suggesting that the factor structure of psychiatric nurses' job-related stressors is specific. Further steps may be necessary to reduce job-related stress specifically related to overload including attention to many needs of patients and job environment including complex ethical dilemmas in psychiatric nursing. PMID:25922763

Exploratory study of factors influencing job-related stress in Japanese psychiatric nurses.

PubMed

Yada, Hironori; Lu, Xi; Omori, Hisamitsu; Abe, Hiroshi; Matsuo, Hisae; Ishida, Yasushi; Katoh, Takahiko

2015-01-01

This study explored the factor structure of psychiatric nurses' job-related stress and examined the specificity of the related stressors using the job stressor scale of the Brief Job Stress Questionnaire (BJSQ). The stressor scale of the BJSQ was administered to 296 nurses and assistant nurses. Answers were examined statistically. Exploratory factor analysis was performed to identify factor structures; two factors (overload and job environment) were valid. Confirmatory factor analysis was conducted to examine the two-factor structure and found 11 items with factor loadings of >0.40 (model 1), 13 items with factor loadings from 0.30 to <0.40 (model 2), and 17 items with factor loadings from 0.20 to <0.30 (model 3) for one factor; model 1 demonstrated the highest goodness of fit. Then, we observed that the two-factor structure (model 1) showed a higher goodness of fit than the original six-factor structure. This differed from subscales based on general workers' job-related stressors, suggesting that the factor structure of psychiatric nurses' job-related stressors is specific. Further steps may be necessary to reduce job-related stress specifically related to overload including attention to many needs of patients and job environment including complex ethical dilemmas in psychiatric nursing.

Components of Mathematics Anxiety: Factor Modeling of the MARS30-Brief

PubMed Central

Pletzer, Belinda; Wood, Guilherme; Scherndl, Thomas; Kerschbaum, Hubert H.; Nuerk, Hans-Christoph

2016-01-01

Mathematics anxiety involves feelings of tension, discomfort, high arousal, and physiological reactivity interfering with number manipulation and mathematical problem solving. Several factor analytic models indicate that mathematics anxiety is rather a multidimensional than unique construct. However, the factor structure of mathematics anxiety has not been fully clarified by now. This issue shall be addressed in the current study. The Mathematics Anxiety Rating Scale (MARS) is a reliable measure of mathematics anxiety (Richardson and Suinn, 1972), for which several reduced forms have been developed. Most recently, a shortened version of the MARS (MARS30-brief) with comparable reliability was published. Different studies suggest that mathematics anxiety involves up to seven different factors. Here we examined the factor structure of the MARS30-brief by means of confirmatory factor analysis. The best model fit was obtained by a six-factor model, dismembering the known two general factors “Mathematical Test Anxiety” (MTA) and “Numerical Anxiety” (NA) in three factors each. However, a more parsimonious 5-factor model with two sub-factors for MTA and three for NA fitted the data comparably well. Factors were differentially susceptible to sex differences and differences between majors. Measurement invariance for sex was established. PMID:26924996

Components of Mathematics Anxiety: Factor Modeling of the MARS30-Brief.

PubMed

Pletzer, Belinda; Wood, Guilherme; Scherndl, Thomas; Kerschbaum, Hubert H; Nuerk, Hans-Christoph

2016-01-01

Mathematics anxiety involves feelings of tension, discomfort, high arousal, and physiological reactivity interfering with number manipulation and mathematical problem solving. Several factor analytic models indicate that mathematics anxiety is rather a multidimensional than unique construct. However, the factor structure of mathematics anxiety has not been fully clarified by now. This issue shall be addressed in the current study. The Mathematics Anxiety Rating Scale (MARS) is a reliable measure of mathematics anxiety (Richardson and Suinn, 1972), for which several reduced forms have been developed. Most recently, a shortened version of the MARS (MARS30-brief) with comparable reliability was published. Different studies suggest that mathematics anxiety involves up to seven different factors. Here we examined the factor structure of the MARS30-brief by means of confirmatory factor analysis. The best model fit was obtained by a six-factor model, dismembering the known two general factors "Mathematical Test Anxiety" (MTA) and "Numerical Anxiety" (NA) in three factors each. However, a more parsimonious 5-factor model with two sub-factors for MTA and three for NA fitted the data comparably well. Factors were differentially susceptible to sex differences and differences between majors. Measurement invariance for sex was established.

[Emission Factors of Vehicle Exhaust in Beijing].

PubMed

Fan, Shou-bin; Tian, Ling-di; Zhang, Dong-xu; Qu, Song

2015-07-01

Based on the investigation of basic data such as vehicle type composition, driving conditions, ambient temperature and oil quality, etc., emission factors of vehicle exhaust pollutants including carbon monoxide (CO), nitrogen oxides (NOx), hydrocarbons (HC) and particulate matter(PM) were calculated using COPERT IV model. Emission factors of typical gasoline passenger cars and diesel trucks were measured using on-board measurement system on actual road. The measured and modeled emission factors were compared and the results showed that: the measured emission factors of CO, NOx and HC were 0. 96, 0. 64 and 4. 89 times of the modeled data for passenger cars conforming to the national IV emission standard. For the light, medium and heavy diesel trucks conforming to the national III emission standard, the measured data of CO emission factors were 1.61, 1. 07 and 1.76 times of the modeled data, respectively, the measured data of NOx emission factors were 1. 04, 1. 21 and 1. 18 times of the modeled data, and the measured data of HC emission factors were 3. 75, 1. 84 and 1. 47 times of the modeled data, while the model data of PM emission factors were 1. 31, 3. 42 and 6. 42 times of the measured data, respectively.

Assessing Cognitive and Affective Empathy Through the Interpersonal Reactivity Index: An Argument Against a Two-Factor Model.

PubMed

Chrysikou, Evangelia G; Thompson, W Jake

2016-12-01

One aspect of higher order social cognition is empathy, a psychological construct comprising a cognitive (recognizing emotions) and an affective (responding to emotions) component. The complex nature of empathy complicates the accurate measurement of these components. The most widely used measure of empathy is the Interpersonal Reactivity Index (IRI). However, the factor structure of the IRI as it is predominantly used in the psychological literature differs from Davis's original four-factor model in that it arbitrarily combines the subscales to form two factors: cognitive and affective empathy. This two-factor model of the IRI, although popular, has yet to be examined for psychometric support. In the current study, we examine, for the first time, the validity of this alternative model. A confirmatory factor analysis showed poor model fit for this two-factor structure. Additional analyses offered support for the original four-factor model, as well as a hierarchical model for the scale. In line with previous findings, females scored higher on the IRI than males. Our findings indicate that the IRI, as it is currently used in the literature, does not accurately measure cognitive and affective empathy and highlight the advantages of using the original four-factor structure of the scale for empathy assessments. © The Author(s) 2015.

An alternative method for centrifugal compressor loading factor modelling

NASA Astrophysics Data System (ADS)

Galerkin, Y.; Drozdov, A.; Rekstin, A.; Soldatova, K.

2017-08-01

The loading factor at design point is calculated by one or other empirical formula in classical design methods. Performance modelling as a whole is out of consideration. Test data of compressor stages demonstrates that loading factor versus flow coefficient at the impeller exit has a linear character independent of compressibility. Known Universal Modelling Method exploits this fact. Two points define the function - loading factor at design point and at zero flow rate. The proper formulae include empirical coefficients. A good modelling result is possible if the choice of coefficients is based on experience and close analogs. Earlier Y. Galerkin and K. Soldatova had proposed to define loading factor performance by the angle of its inclination to the ordinate axis and by the loading factor at zero flow rate. Simple and definite equations with four geometry parameters were proposed for loading factor performance calculated for inviscid flow. The authors of this publication have studied the test performance of thirteen stages of different types. The equations are proposed with universal empirical coefficients. The calculation error lies in the range of plus to minus 1,5%. The alternative model of a loading factor performance modelling is included in new versions of the Universal Modelling Method.

Extracting the Evaluations of Stereotypes: Bi-factor Model of the Stereotype Content Structure

PubMed Central

Sayans-Jiménez, Pablo; Cuadrado, Isabel; Rojas, Antonio J.; Barrada, Juan R.

2017-01-01

Stereotype dimensions—competence, morality and sociability—are fundamental to studying the perception of other groups. These dimensions have shown moderate/high positive correlations with each other that do not reflect the theoretical expectations. The explanation for this (e.g., halo effect) undervalues the utility of the shared variance identified. In contrast, in this work we propose that this common variance could represent the global evaluation of the perceived group. Bi-factor models are proposed to improve the internal structure and to take advantage of the information representing the shared variance among dimensions. Bi-factor models were compared with first order models and other alternative models in three large samples (300–309 participants). The relationships among the global and specific bi-factor dimensions with a global evaluation dimension (measured through a semantic differential) were estimated. The results support the use of bi-factor models rather than first order models (and other alternative models). Bi-factor models also show a greater utility to directly and more easily explore the stereotype content including its evaluative content. PMID:29085313

Testing Models of Psychopathology in Preschool-aged Children Using a Structured Interview-based Assessment

PubMed Central

Dougherty, Lea R.; Bufferd, Sara J.; Carlson, Gabrielle A.; Klein, Daniel N.

2014-01-01

A number of studies have found that broadband internalizing and externalizing factors provide a parsimonious framework for understanding the structure of psychopathology across childhood, adolescence, and adulthood. However, few of these studies have examined psychopathology in young children, and several recent studies have found support for alternative models, including a bi-factor model with common and specific factors. The present study used parents’ (typically mothers’) reports on a diagnostic interview in a community sample of 3-year old children (n=541; 53.9 % male) to compare the internalizing-externalizing latent factor model with a bi-factor model. The bi-factor model provided a better fit to the data. To test the concurrent validity of this solution, we examined associations between this model and paternal reports and laboratory observations of child temperament. The internalizing factor was associated with low levels of surgency and high levels of fear; the externalizing factor was associated with high levels of surgency and disinhibition and low levels of effortful control; and the common factor was associated with high levels of surgency and negative affect and low levels of effortful control. These results suggest that psychopathology in preschool-aged children may be explained by a single, common factor influencing nearly all disorders and unique internalizing and externalizing factors. These findings indicate that shared variance across internalizing and externalizing domains is substantial and are consistent with recent suggestions that emotion regulation difficulties may be a common vulnerability for a wide array of psychopathology. PMID:24652485

Applying the Technology Acceptance Model and flow theory to Cyworld user behavior: implication of the Web2.0 user acceptance.

PubMed

Shin, Dong-Hee; Kim, Won-Yong; Kim, Won-Young

2008-06-01

This study explores attitudinal and behavioral patterns when using Cyworld by adopting an expanded Technology Acceptance Model (TAM). A model for Cyworld acceptance is used to examine how various factors modified from the TAM influence acceptance and its antecedents. This model is examined through an empirical study involving Cyworld users using structural equation modeling techniques. The model shows reasonably good measurement properties and the constructs are validated. The results not only confirm the model but also reveal general factors applicable to Web2.0. A set of constructs in the model can be the Web2.0-specific factors, playing as enhancing factor to attitudes and intention.

The factor structure of posttraumatic stress disorder: a literature update, critique of methodology, and agenda for future research.

PubMed

Elhai, Jon D; Palmieri, Patrick A

2011-08-01

We present an update of recent literature (since 2007) exploring the factor structure of posttraumatic stress disorder (PTSD) symptom measures. Research supporting a four-factor emotional numbing model and a four-factor dysphoria model is presented, with these models fitting better than all other models examined. Variables accounting for factor structure differences are reviewed, including PTSD query instructions, type of PTSD measure, extent of trauma exposure, ethnicity, and timing of administration. Methodological and statistical limitations with recent studies are presented. Finally, a research agenda and recommendations are offered to push this research area forward, including suggestions to validate PTSD’s factors against external measures of psychopathology, test moderators of factor structure, and examine heterogeneity of symptom presentations based on factor structure examination.

Using Multilevel Factor Analysis with Clustered Data: Investigating the Factor Structure of the Positive Values Scale

ERIC Educational Resources Information Center

Huang, Francis L.; Cornell, Dewey G.

2016-01-01

Advances in multilevel modeling techniques now make it possible to investigate the psychometric properties of instruments using clustered data. Factor models that overlook the clustering effect can lead to underestimated standard errors, incorrect parameter estimates, and model fit indices. In addition, factor structures may differ depending on…

Evidence for a General ADHD Factor from a Longitudinal General School Population Study

ERIC Educational Resources Information Center

Normand, Sebastien; Flora, David B.; Toplak, Maggie E.; Tannock, Rosemary

2012-01-01

Recent factor analytic studies in Attention-Deficit/Hyperactivity Disorder (ADHD) have shown that hierarchical models provide a better fit of ADHD symptoms than correlated models. A hierarchical model includes a general ADHD factor and specific factors for inattention, and hyperactivity/impulsivity. The aim of this 12-month longitudinal study was…

Examining Factor Score Distributions to Determine the Nature of Latent Spaces

ERIC Educational Resources Information Center

Steinley, Douglas; McDonald, Roderick P.

2007-01-01

Similarities between latent class models with K classes and linear factor models with K-1 factors are investigated. Specifically, the mathematical equivalence between the covariance structure of the two models is discussed, and a Monte Carlo simulation is performed using generated data that represents both latent factors and latent classes with…

Confirmatory Factor Analysis of the Combined Social Phobia Scale and Social Interaction Anxiety Scale: Support for a Bifactor Model.

PubMed

Gomez, Rapson; Watson, Shaun D

2017-01-01

For the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) together, this study examined support for a bifactor model, and also the internal consistency reliability and external validity of the factors in this model. Participants ( N = 526) were adults from the general community who completed the SPS and SIAS. Confirmatory factor analysis (CFA) of their ratings indicated good support for the bifactor model. For this model, the loadings for all but six items were higher on the general factor than the specific factors. The three positively worded items had negligible loadings on the general factor. The general factor explained most of the common variance in the SPS and SIAS, and demonstrated good model-based internal consistency reliability (omega hierarchical) and a strong association with fear of negative evaluation and extraversion. The practical implications of the findings for the utilization of the SPS and SIAS, and the theoretical and clinical implications for social anxiety are discussed.

Confirmatory Factor Analysis of the Combined Social Phobia Scale and Social Interaction Anxiety Scale: Support for a Bifactor Model

PubMed Central

Gomez, Rapson; Watson, Shaun D.

2017-01-01

For the Social Phobia Scale (SPS) and the Social Interaction Anxiety Scale (SIAS) together, this study examined support for a bifactor model, and also the internal consistency reliability and external validity of the factors in this model. Participants (N = 526) were adults from the general community who completed the SPS and SIAS. Confirmatory factor analysis (CFA) of their ratings indicated good support for the bifactor model. For this model, the loadings for all but six items were higher on the general factor than the specific factors. The three positively worded items had negligible loadings on the general factor. The general factor explained most of the common variance in the SPS and SIAS, and demonstrated good model-based internal consistency reliability (omega hierarchical) and a strong association with fear of negative evaluation and extraversion. The practical implications of the findings for the utilization of the SPS and SIAS, and the theoretical and clinical implications for social anxiety are discussed. PMID:28210232

Evaluating the MSCEIT V2.0 via CFA: comment on Mayer et al. (2003).

PubMed

Gignac, Gilles E

2005-06-01

This investigation uncovered several substantial errors in the confirmatory factor analysis results reported by J. D. Mayer, P. Salovey, D. R. Caruso, and G. Sitarenios (see record 2003-02341-015). Specifically, the values associated with the close-fit indices (normed fit index, Tucker-Lewis Index, and root-mean-square error of approximation) are inaccurate. A reanalysis of the Mayer et al. subscale intercorrelation matrix provided accurate values of the close-fit indices, which resulted in different evaluations of the models tested by J. D. Mayer et al. Contrary to J. D. Mayer et al., the 1-factor model and the 2-factor model did not provide good fit. Although the 4-factor model was still considered good fitting, the non-constrained 4-factor model yielded a non-positive definite matrix, which was interpreted to be due to the fact that two of the branch-level factors (Perceiving and Facilitating) were collinear, suggesting that a model with 4 factors was implausible.

Is there a reliable factorial structure in the 20-item Toronto Alexithymia Scale? A comparison of factor models in clinical and normal adult samples.

PubMed

Müller, Jochen; Bühner, Markus; Ellgring, Heiner

2003-12-01

The 20-item Toronto Alexithymia Scale (TAS-20) is the most widely used instrument for measuring alexithymia. However, different studies did not always yield identical factor structures of this scale. The present study aims at clarifying some discrepant results. Maximum likelihood confirmatory factor analyses of a German version of the TAS-20 were conducted on data from a clinical sample (N=204) and a sample of normal adults (N=224). Five different models with one to four factors were compared. A four-factor model with factors (F1) "Difficulty identifying feelings" (F2), "Difficulty describing feelings" (F3), "Low importance of emotion" and (F4) "Pragmatic thinking" and a three-factor model with the combined factor "Difficulties in identifying and describing feelings" described the data best. Factors related to "externally oriented thinking" provided no acceptable level of reliability. Results from the present and other studies indicate that the factorial structure of the TAS-20 may vary across samples. Whether factor structures different from the common three-factor structure are an exception in some mainly clinical populations or a common phenomenon outside student populations has still to be determined. For a further exploration of the factor structure of the TAS-20 in different populations, it would be important not only to test the fit of the common three-factor model, but also to consider other competing solutions like the models of the present study.

Global sensitivity analysis of a filtration model for submerged anaerobic membrane bioreactors (AnMBR).

PubMed

Robles, A; Ruano, M V; Ribes, J; Seco, A; Ferrer, J

2014-04-01

The results of a global sensitivity analysis of a filtration model for submerged anaerobic MBRs (AnMBRs) are assessed in this paper. This study aimed to (1) identify the less- (or non-) influential factors of the model in order to facilitate model calibration and (2) validate the modelling approach (i.e. to determine the need for each of the proposed factors to be included in the model). The sensitivity analysis was conducted using a revised version of the Morris screening method. The dynamic simulations were conducted using long-term data obtained from an AnMBR plant fitted with industrial-scale hollow-fibre membranes. Of the 14 factors in the model, six were identified as influential, i.e. those calibrated using off-line protocols. A dynamic calibration (based on optimisation algorithms) of these influential factors was conducted. The resulting estimated model factors accurately predicted membrane performance. Copyright © 2014 Elsevier Ltd. All rights reserved.

Testing the multidimensionality of the inventory of school motivation in a Dutch student sample.

PubMed

Korpershoek, Hanke; Xu, Kun; Mok, Magdalena Mo Ching; McInerney, Dennis M; van der Werf, Greetje

2015-01-01

A factor analytic and a Rasch measurement approach were applied to evaluate the multidimensional nature of the school motivation construct among more than 7,000 Dutch secondary school students. The Inventory of School Motivation (McInerney and Ali, 2006) was used, which intends to measure four motivation dimensions (mastery, performance, social, and extrinsic motivation), each comprising of two first-order factors. One unidimensional model and three multidimensional models (4-factor, 8-factor, higher order) were fit to the data. Results of both approaches showed that the multidimensional models validly represented the school motivation among Dutch secondary school pupils, whereas model fit of the unidimensional model was poor. The differences in model fit between the three multidimensional models were small, although a different model was favoured by the two approaches. The need for improvement of some of the items and the need to increase measurement precision of several first-order factors are discussed.

Bayes factors based on robust TDT-type tests for family trio design.

PubMed

Yuan, Min; Pan, Xiaoqing; Yang, Yaning

2015-06-01

Adaptive transmission disequilibrium test (aTDT) and MAX3 test are two robust-efficient association tests for case-parent family trio data. Both tests incorporate information of common genetic models including recessive, additive and dominant models and are efficient in power and robust to genetic model specifications. The aTDT uses information of departure from Hardy-Weinberg disequilibrium to identify the potential genetic model underlying the data and then applies the corresponding TDT-type test, and the MAX3 test is defined as the maximum of the absolute value of three TDT-type tests under the three common genetic models. In this article, we propose three robust Bayes procedures, the aTDT based Bayes factor, MAX3 based Bayes factor and Bayes model averaging (BMA), for association analysis with case-parent trio design. The asymptotic distributions of aTDT under the null and alternative hypothesis are derived in order to calculate its Bayes factor. Extensive simulations show that the Bayes factors and the p-values of the corresponding tests are generally consistent and these Bayes factors are robust to genetic model specifications, especially so when the priors on the genetic models are equal. When equal priors are used for the underlying genetic models, the Bayes factor method based on aTDT is more powerful than those based on MAX3 and Bayes model averaging. When the prior placed a small (large) probability on the true model, the Bayes factor based on aTDT (BMA) is more powerful. Analysis of a simulation data about RA from GAW15 is presented to illustrate applications of the proposed methods.

Bifactor Models Show a Superior Model Fit: Examination of the Factorial Validity of Parent-Reported and Self-Reported Symptoms of Attention-Deficit/Hyperactivity Disorders in Children and Adolescents.

PubMed

Rodenacker, Klaas; Hautmann, Christopher; Görtz-Dorten, Anja; Döpfner, Manfred

2016-01-01

Various studies have demonstrated that bifactor models yield better solutions than models with correlated factors. However, the kind of bifactor model that is most appropriate is yet to be examined. The current study is the first to test bifactor models across the full age range (11-18 years) of adolescents using self-reports, and the first to test bifactor models with German subjects and German questionnaires. The study sample included children and adolescents aged between 6 and 18 years recruited from a German clinical sample (n = 1,081) and a German community sample (n = 642). To examine the factorial validity, we compared unidimensional, correlated factors and higher-order and bifactor models and further tested a modified incomplete bifactor model for measurement invariance. Bifactor models displayed superior model fit statistics compared to correlated factor models or second-order models. However, a more parsimonious incomplete bifactor model with only 2 specific factors (inattention and impulsivity) showed a good model fit and a better factor structure than the other bifactor models. Scalar measurement invariance was given in most group comparisons. An incomplete bifactor model would suggest that the specific inattention and impulsivity factors represent entities separable from the general attention-deficit/hyperactivity disorder construct and might, therefore, give way to a new approach to subtyping of children beyond and above attention-deficit/hyperactivity disorder. © 2016 S. Karger AG, Basel.

Global sensitivity analysis for urban water quality modelling: Terminology, convergence and comparison of different methods

NASA Astrophysics Data System (ADS)

Vanrolleghem, Peter A.; Mannina, Giorgio; Cosenza, Alida; Neumann, Marc B.

2015-03-01

Sensitivity analysis represents an important step in improving the understanding and use of environmental models. Indeed, by means of global sensitivity analysis (GSA), modellers may identify both important (factor prioritisation) and non-influential (factor fixing) model factors. No general rule has yet been defined for verifying the convergence of the GSA methods. In order to fill this gap this paper presents a convergence analysis of three widely used GSA methods (SRC, Extended FAST and Morris screening) for an urban drainage stormwater quality-quantity model. After the convergence was achieved the results of each method were compared. In particular, a discussion on peculiarities, applicability, and reliability of the three methods is presented. Moreover, a graphical Venn diagram based classification scheme and a precise terminology for better identifying important, interacting and non-influential factors for each method is proposed. In terms of convergence, it was shown that sensitivity indices related to factors of the quantity model achieve convergence faster. Results for the Morris screening method deviated considerably from the other methods. Factors related to the quality model require a much higher number of simulations than the number suggested in literature for achieving convergence with this method. In fact, the results have shown that the term "screening" is improperly used as the method may exclude important factors from further analysis. Moreover, for the presented application the convergence analysis shows more stable sensitivity coefficients for the Extended-FAST method compared to SRC and Morris screening. Substantial agreement in terms of factor fixing was found between the Morris screening and Extended FAST methods. In general, the water quality related factors exhibited more important interactions than factors related to water quantity. Furthermore, in contrast to water quantity model outputs, water quality model outputs were found to be characterised by high non-linearity.

Does information available at admission for delivery improve prediction of vaginal birth after cesarean?

PubMed Central

Grobman, William A.; Lai, Yinglei; Landon, Mark B.; Spong, Catherine Y.; Leveno, Kenneth J.; Rouse, Dwight J.; Varner, Michael W.; Moawad, Atef H.; Simhan, Hyagriv N.; Harper, Margaret; Wapner, Ronald J.; Sorokin, Yoram; Miodovnik, Menachem; Carpenter, Marshall; O'sullivan, Mary J.; Sibai, Baha M.; Langer, Oded; Thorp, John M.; Ramin, Susan M.; Mercer, Brian M.

2010-01-01

Objective To construct a predictive model for vaginal birth after cesarean (VBAC) that combines factors that can be ascertained only as the pregnancy progresses with those known at initiation of prenatal care. Study design Using multivariable modeling, we constructed a predictive model for VBAC that included patient factors known at the initial prenatal visit as well as those that only became evident as the pregancy progressed to the admission for delivery. Results 9616 women were analyzed. The regression equation for VBAC success included multiple factors that could not be known at the first prenatal visit. The area under the curve for this model was significantly greater (P < .001) than that of a model that included only factors available at the first prenatal visit. Conclusion A prediction model for VBAC success that incorporates factors that can be ascertained only as the pregnancy progresses adds to the predictive accuracy of a model that uses only factors available at a first prenatal visit. PMID:19813165

An investigation into the psychometric properties of the Hospital Anxiety and Depression Scale in patients with breast cancer

PubMed Central

Rodgers, Jacqui; Martin, Colin R; Morse, Rachel C; Kendell, Kate; Verrill, Mark

2005-01-01

Background To determine the psychometric properties of the Hospital Anxiety and Depression Scale (HADS) in patients with breast cancer and determine the suitability of the instrument for use with this clinical group. Methods A cross-sectional design was used. The study used a pooled data set from three breast cancer clinical groups. The dependent variables were HADS anxiety and depression sub-scale scores. Exploratory and confirmatory factor analyses were conducted on the HADS to determine its psychometric properties in 110 patients with breast cancer. Seven models were tested to determine model fit to the data. Results Both factor analysis methods indicated that three-factor models provided a better fit to the data compared to two-factor (anxiety and depression) models for breast cancer patients. Clark and Watson's three factor tripartite and three factor hierarchical models provided the best fit. Conclusion The underlying factor structure of the HADS in breast cancer patients comprises three distinct, but correlated factors, negative affectivity, autonomic anxiety and anhedonic depression. The clinical utility of the HADS in screening for anxiety and depression in breast cancer patients may be enhanced by using a modified scoring procedure based on a three-factor model of psychological distress. This proposed alternate scoring method involving regressing autonomic anxiety and anhedonic depression factors onto the third factor (negative affectivity) requires further investigation in order to establish its efficacy. PMID:16018801

Bifactor latent structure of attention-deficit/hyperactivity disorder (ADHD)/oppositional defiant disorder (ODD) symptoms and first-order latent structure of sluggish cognitive tempo symptoms.

PubMed

Lee, SoYean; Burns, G Leonard; Beauchaine, Theodore P; Becker, Stephen P

2016-08-01

The objective was to determine if the latent structure of attention-deficit/hyperactivity disorder (ADHD) and oppositional defiant disorder (ODD) symptoms is best explained by a general disruptive behavior factor along with specific inattention (IN), hyperactivity/impulsivity (HI), and ODD factors (a bifactor model) whereas the latent structure of sluggish cognitive tempo (SCT) symptoms is best explained by a first-order factor independent of the bifactor model of ADHD/ODD. Parents' (n = 703) and teachers' (n = 366) ratings of SCT, ADHD-IN, ADHD-HI, and ODD symptoms on the Child and Adolescent Disruptive Behavior Inventory (CADBI) in a community sample of children (ages 5-13; 55% girls) were used to evaluate 4 models of symptom organization. Results indicated that a bifactor model of ADHD/ODD symptoms, in conjunction with a separate first-order SCT factor, was the best model for both parent and teacher ratings. The first-order SCT factor showed discriminant validity with the general disruptive behavior and specific IN factors in the bifactor model. In addition, higher scores on the SCT factor predicted greater academic and social impairment, even after controlling for the general disruptive behavior and 3 specific factors. Consistent with predictions from the trait-impulsivity etiological model of externalizing liability, a single, general disruptive behavior factor accounted for nearly all common variance in ADHD/ODD symptoms, whereas SCT symptoms represented a factor different from the general disruptive behavior and specific IN factor. These results provide additional support for distinguishing between SCT and ADHD-IN. The study also demonstrates how etiological models can be used to predict specific latent structures of symptom organization. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Cross-Cultural Evaluation of Antonovsky's Orientation to Life Questionnaire: Comparison Between Australian, Finnish, and Turkish Young Adults.

PubMed

Lajunen, Timo

2018-01-01

Antonovsky's concept "sense of coherence" (SOC) and the related measurement instrument "The Orientation to Life Questionnaire" (OLQ) has been widely applied in studies on health and well-being. The purpose of the present study is to investigate the cultural differences in factor structures and psychometric properties as well as mean scores of the 13-item form of Antonovsky's OLQ among Australian (n = 201), Finnish (n = 203), and Turkish (n = 152) students. Three models of factor structure were studied by using confirmatory factor analysis: single-factor model, first-order correlated-three-factor model, and the second-order three-factor model. Results obtained in all three countries suggest that the first- and second-order three-factor models fitted the data better that the single-factor model. Hence, the OLQ scoring based on comprehensibility, manageability, and meaningfulness scales was supported. Scale reliabilities and inter-correlations were in line with those reported in earlier studies. Two-way analyses of variance (gender × nationality) with age as a covariate showed no cultural differences in SOC scale scores. Women got higher scores on the meaningfulness scale than men, and age was positively related to all SOC scale scores indicating that SOC increases in early adulthood. The results support the three-factor model of OLQ which thus should be used in Australia, Finland, and Turkey instead of a single-factor model. Need for cross-cultural studies taking into account cultural correlates of SOC and its relation to health and well-being indicators as well as studies on gender differences in the OLQ are emphasized.

The Work Role Functioning Questionnaire v2.0 Showed Consistent Factor Structure Across Six Working Samples.

PubMed

Abma, Femke I; Bültmann, Ute; Amick Iii, Benjamin C; Arends, Iris; Dorland, Heleen F; Flach, Peter A; van der Klink, Jac J L; van de Ven, Hardy A; Bjørner, Jakob Bue

2017-09-09

Objective The Work Role Functioning Questionnaire v2.0 (WRFQ) is an outcome measure linking a persons' health to the ability to meet work demands in the twenty-first century. We aimed to examine the construct validity of the WRFQ in a heterogeneous set of working samples in the Netherlands with mixed clinical conditions and job types to evaluate the comparability of the scale structure. Methods Confirmatory factor and multi-group analyses were conducted in six cross-sectional working samples (total N = 2433) to evaluate and compare a five-factor model structure of the WRFQ (work scheduling demands, output demands, physical demands, mental and social demands, and flexibility demands). Model fit indices were calculated based on RMSEA ≤ 0.08 and CFI ≥ 0.95. After fitting the five-factor model, the multidimensional structure of the instrument was evaluated across samples using a second order factor model. Results The factor structure was robust across samples and a multi-group model had adequate fit (RMSEA = 0.63, CFI = 0.972). In sample specific analyses, minor modifications were necessary in three samples (final RMSEA 0.055-0.080, final CFI between 0.955 and 0.989). Applying the previous first order specifications, a second order factor model had adequate fit in all samples. Conclusion A five-factor model of the WRFQ showed consistent structural validity across samples. A second order factor model showed adequate fit, but the second order factor loadings varied across samples. Therefore subscale scores are recommended to compare across different clinical and working samples.

Quantitative analysis of factors that affect oil pipeline network accident based on Bayesian networks: A case study in China

NASA Astrophysics Data System (ADS)

Zhang, Chao; Qin, Ting Xin; Huang, Shuai; Wu, Jian Song; Meng, Xin Yan

2018-06-01

Some factors can affect the consequences of oil pipeline accident and their effects should be analyzed to improve emergency preparation and emergency response. Although there are some qualitative analysis models of risk factors' effects, the quantitative analysis model still should be researched. In this study, we introduce a Bayesian network (BN) model of risk factors' effects analysis in an oil pipeline accident case that happened in China. The incident evolution diagram is built to identify the risk factors. And the BN model is built based on the deployment rule for factor nodes in BN and the expert knowledge by Dempster-Shafer evidence theory. Then the probabilities of incident consequences and risk factors' effects can be calculated. The most likely consequences given by this model are consilient with the case. Meanwhile, the quantitative estimations of risk factors' effects may provide a theoretical basis to take optimal risk treatment measures for oil pipeline management, which can be used in emergency preparation and emergency response.

Taking the Error Term of the Factor Model into Account: The Factor Score Predictor Interval

ERIC Educational Resources Information Center

Beauducel, Andre

2013-01-01

The problem of factor score indeterminacy implies that the factor and the error scores cannot be completely disentangled in the factor model. It is therefore proposed to compute Harman's factor score predictor that contains an additive combination of factor and error variance. This additive combination is discussed in the framework of classical…

Longitudinal tests of competing factor structures for the Rosenberg Self-Esteem Scale: traits, ephemeral artifacts, and stable response styles.

PubMed

Marsh, Herbert W; Scalas, L Francesca; Nagengast, Benjamin

2010-06-01

Self-esteem, typically measured by the Rosenberg Self-Esteem Scale (RSE), is one of the most widely studied constructs in psychology. Nevertheless, there is broad agreement that a simple unidimensional factor model, consistent with the original design and typical application in applied research, does not provide an adequate explanation of RSE responses. However, there is no clear agreement about what alternative model is most appropriate-or even a clear rationale for how to test competing interpretations. Three alternative interpretations exist: (a) 2 substantively important trait factors (positive and negative self-esteem), (b) 1 trait factor and ephemeral method artifacts associated with positively or negatively worded items, or (c) 1 trait factor and stable response-style method factors associated with item wording. We have posited 8 alternative models and structural equation model tests based on longitudinal data (4 waves of data across 8 years with a large, representative sample of adolescents). Longitudinal models provide no support for the unidimensional model, undermine support for the 2-factor model, and clearly refute claims that wording effects are ephemeral, but they provide good support for models positing 1 substantive (self-esteem) factor and response-style method factors that are stable over time. This longitudinal methodological approach has not only resolved these long-standing issues in self-esteem research but also has broad applicability to most psychological assessments based on self-reports with a mix of positively and negatively worded items.

Parental Expression of Disappointment: Should It Be a Factor in Hoffman's Model of Parental Discipline?

ERIC Educational Resources Information Center

Patrick, Renee B.; Gibbs, John C.

2007-01-01

The authors addressed whether parental expression of disappointment should be included as a distinct factor in M. L. Hoffman's (2000) well-established typology of parenting styles (induction, love withdrawal, power assertion). Hoffman's 3-factor model, along with a more inclusive 4-factor model (induction, love withdrawal, power assertion, and…

A combined model of human erythropoiesis and granulopoiesis under growth factor and chemotherapy treatment

PubMed Central

2014-01-01

Background Haematotoxicity of conventional chemotherapies often results in delays of treatment or reduction of chemotherapy dose. To ameliorate these side-effects, patients are routinely treated with blood transfusions or haematopoietic growth factors such as erythropoietin (EPO) or granulocyte colony-stimulating factor (G-CSF). For the latter ones, pharmaceutical derivatives are available, which differ in absorption kinetics, pharmacokinetic and -dynamic properties. Due to the complex interaction of cytotoxic effects of chemotherapy and the stimulating effects of different growth factor derivatives, optimal treatment is a non-trivial task. In the past, we developed mathematical models of thrombopoiesis, granulopoiesis and erythropoiesis under chemotherapy and growth-factor applications which can be used to perform clinically relevant predictions regarding the feasibility of chemotherapy schedules and cytopenia prophylaxis with haematopoietic growth factors. However, interactions of lineages and growth-factors were ignored so far. Results To close this gap, we constructed a hybrid model of human granulopoiesis and erythropoiesis under conventional chemotherapy, G-CSF and EPO applications. This was achieved by combining our single lineage models of human erythropoiesis and granulopoiesis with a common stem cell model. G-CSF effects on erythropoiesis were also implemented. Pharmacodynamic models are based on ordinary differential equations describing proliferation and maturation of haematopoietic cells. The system is regulated by feedback loops partly mediated by endogenous and exogenous EPO and G-CSF. Chemotherapy is modelled by depletion of cells. Unknown model parameters were determined by fitting the model predictions to time series data of blood counts and cytokine profiles. Data were extracted from literature or received from cooperating clinical study groups. Our model explains dynamics of mature blood cells and cytokines after growth-factor applications in healthy volunteers. Moreover, we modelled 15 different chemotherapeutic drugs by estimating their bone marrow toxicity. Taking into account different growth-factor schedules, this adds up to 33 different chemotherapy regimens explained by the model. Conclusions We conclude that we established a comprehensive biomathematical model to explain the dynamics of granulopoiesis and erythropoiesis under combined chemotherapy, G-CSF, and EPO applications. We demonstrate how it can be used to make predictions regarding haematotoxicity of yet untested chemotherapy and growth-factor schedules. PMID:24886056

TED analysis of the Si(113) surface structure

NASA Astrophysics Data System (ADS)

Suzuki, T.; Minoda, H.; Tanishiro, Y.; Yagi, K.

1999-09-01

We carried out a TED (transmission electron diffraction) analysis of the Si(113) surface structure. The TED patterns taken at room temperature showed reflections due to the 3×2 reconstructed structure. The TED pattern indicated that a glide plane parallel to the <332> direction suggested in some models is excluded. We calculated the R-factors (reliability factors) for six surface structure models proposed previously. All structure models with energy-optimized atomic positions have large R-factors. After revision of the atomic positions, the R-factors of all the structure models decreased below 0.3, and the revised version of Dabrowski's 3×2 model has the smallest R-factor of 0.17.

Multi-Collinearity Based Model Selection for Landslide Susceptibility Mapping: A Case Study from Ulus District of Karabuk, Turkey

NASA Astrophysics Data System (ADS)

Sahin, E. K.; Colkesen, I., , Dr; Kavzoglu, T.

2017-12-01

Identification of localities prone to landslide areas plays an important role for emergency planning, disaster management and recovery planning. Due to its great importance for disaster management, producing accurate and up-to-date landslide susceptibility maps is essential for hazard mitigation purpose and regional planning. The main objective of the present study was to apply multi-collinearity based model selection approach for the production of a landslide susceptibility map of Ulus district of Karabuk, Turkey. It is a fact that data do not contain enough information to describe the problem under consideration when the factors are highly correlated with each other. In such cases, choosing a subset of the original features will often lead to better performance. This paper presents multi-collinearity based model selection approach to deal with the high correlation within the dataset. Two collinearity diagnostic factors (Tolerance (TOL) and the Variance Inflation Factor (VIF)) are commonly used to identify multi-collinearity. Values of VIF that exceed 10.0 and TOL values less than 1.0 are often regarded as indicating multi-collinearity. Five causative factors (slope length, curvature, plan curvature, profile curvature and topographical roughness index) were found highly correlated with each other among 15 factors available for the study area. As a result, the five correlated factors were removed from the model estimation, and performances of the models including the remaining 10 factors (aspect, drainage density, elevation, lithology, land use/land cover, NDVI, slope, sediment transport index, topographical position index and topographical wetness index) were evaluated using logistic regression. The performance of prediction model constructed with 10 factors was compared to that of 15-factor model. The prediction performance of two susceptibility maps was evaluated by overall accuracy and the area under the ROC curve (AUC) values. Results showed that overall accuracy and AUC was calculated as 77.15% and 96.62% respectively for the model with 10 selected factors whilst they were estimated as 73.45% and 89.45% respectively for the model with all factors. It is clear that the multi-collinearity based model outperformed the conventional model in the mapping of landslide susceptibility.

Interaction in Balanced Cross Nested Designs

NASA Astrophysics Data System (ADS)

Ramos, Paulo; Mexia, João T.; Carvalho, Francisco; Covas, Ricardo

2011-09-01

Commutative Jordan Algebras, CJA, are used in the study of mixed models obtained, through crossing and nesting, from simpler ones. In the study of cross nested models the interaction between nested factors have been systematically discarded. However this can constitutes an artificial simplification of the models. We point out that, when two crossed factors interact, such interaction is symmetric, both factors playing in it equivalent roles, while when two nested factors interact, the interaction is determined by the nesting factor. These interactions will be called interactions with nesting. In this work we present a coherent formulation of the algebraic structure of models enabling the choice of families of interactions between cross and nested factors using binary operations on CJA.

Comparing Factor, Class, and Mixture Models of Cannabis Initiation and DSM Cannabis Use Disorder Criteria, Including Craving, in the Brisbane Longitudinal Twin Study

PubMed Central

Kubarych, Thomas S.; Kendler, Kenneth S.; Aggen, Steven H.; Estabrook, Ryne; Edwards, Alexis C.; Clark, Shaunna L.; Martin, Nicholas G.; Hickie, Ian B.; Neale, Michael C.; Gillespie, Nathan A.

2014-01-01

Accumulating evidence suggests that the Diagnostic and Statistical Manual of Mental Disorders (DSM) diagnostic criteria for cannabis abuse and dependence are best represented by a single underlying factor. However, it remains possible that models with additional factors, or latent class models or hybrid models, may better explain the data. Using structured interviews, 626 adult male and female twins provided complete data on symptoms of cannabis abuse and dependence, plus a craving criterion. We compared latent factor analysis, latent class analysis, and factor mixture modeling using normal theory marginal maximum likelihood for ordinal data. Our aim was to derive a parsimonious, best-fitting cannabis use disorder (CUD) phenotype based on DSM-IV criteria and determine whether DSM-5 craving loads onto a general factor. When compared with latent class and mixture models, factor models provided a better fit to the data. When conditioned on initiation and cannabis use, the association between criteria for abuse, dependence, withdrawal, and craving were best explained by two correlated latent factors for males and females: a general risk factor to CUD and a factor capturing the symptoms of social and occupational impairment as a consequence of frequent use. Secondary analyses revealed a modest increase in the prevalence of DSM-5 CUD compared with DSM-IV cannabis abuse or dependence. It is concluded that, in addition to a general factor with loadings on cannabis use and symptoms of abuse, dependence, withdrawal, and craving, a second clinically relevant factor defined by features of social and occupational impairment was also found for frequent cannabis use. PMID:24588857

Interpretive Structural Model of Key Performance Indicators for Sustainable Maintenance Evaluatian in Rubber Industry

NASA Astrophysics Data System (ADS)

Amrina, E.; Yulianto, A.

2018-03-01

Sustainable maintenance is a new challenge for manufacturing companies to realize sustainable development. In this paper, an interpretive structural model is developed to evaluate sustainable maintenance in the rubber industry. The initial key performance indicators (KPIs) is identified and derived from literature and then validated by academic and industry experts. As a result, three factors of economic, social, and environmental dividing into a total of thirteen indicators are proposed as the KPIs for sustainable maintenance evaluation in rubber industry. Interpretive structural modeling (ISM) methodology is applied to develop a network structure model of the KPIs consisting of three levels. The results show the economic factor is regarded as the basic factor, the social factor as the intermediate factor, while the environmental factor indicated to be the leading factor. Two indicators of social factor i.e. labor relationship, and training and education have both high driver and dependence power, thus categorized as the unstable indicators which need further attention. All the indicators of environmental factor and one indicator of social factor are indicated as the most influencing indicator. The interpretive structural model hoped can aid the rubber companies in evaluating sustainable maintenance performance.

Mining geographic variations of Plasmodium vivax for active surveillance: a case study in China.

PubMed

Shi, Benyun; Tan, Qi; Zhou, Xiao-Nong; Liu, Jiming

2015-05-27

Geographic variations of an infectious disease characterize the spatial differentiation of disease incidences caused by various impact factors, such as environmental, demographic, and socioeconomic factors. Some factors may directly determine the force of infection of the disease (namely, explicit factors), while many other factors may indirectly affect the number of disease incidences via certain unmeasurable processes (namely, implicit factors). In this study, the impact of heterogeneous factors on geographic variations of Plasmodium vivax incidences is systematically investigate in Tengchong, Yunnan province, China. A space-time model that resembles a P. vivax transmission model and a hidden time-dependent process, is presented by taking into consideration both explicit and implicit factors. Specifically, the transmission model is built upon relevant demographic, environmental, and biophysical factors to describe the local infections of P. vivax. While the hidden time-dependent process is assessed by several socioeconomic factors to account for the imported cases of P. vivax. To quantitatively assess the impact of heterogeneous factors on geographic variations of P. vivax infections, a Markov chain Monte Carlo (MCMC) simulation method is developed to estimate the model parameters by fitting the space-time model to the reported spatial-temporal disease incidences. Since there is no ground-truth information available, the performance of the MCMC method is first evaluated against a synthetic dataset. The results show that the model parameters can be well estimated using the proposed MCMC method. Then, the proposed model is applied to investigate the geographic variations of P. vivax incidences among all 18 towns in Tengchong, Yunnan province, China. Based on the geographic variations, the 18 towns can be further classify into five groups with similar socioeconomic causality for P. vivax incidences. Although this study focuses mainly on the transmission of P. vivax, the proposed space-time model is general and can readily be extended to investigate geographic variations of other diseases. Practically, such a computational model will offer new insights into active surveillance and strategic planning for disease surveillance and control.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan.

PubMed

Dou, Jie; Tien Bui, Dieu; Yunus, Ali P; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

2015-01-01

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner.

Optimization of Causative Factors for Landslide Susceptibility Evaluation Using Remote Sensing and GIS Data in Parts of Niigata, Japan

PubMed Central

Dou, Jie; Tien Bui, Dieu; P. Yunus, Ali; Jia, Kun; Song, Xuan; Revhaug, Inge; Xia, Huan; Zhu, Zhongfan

2015-01-01

This paper assesses the potentiality of certainty factor models (CF) for the best suitable causative factors extraction for landslide susceptibility mapping in the Sado Island, Niigata Prefecture, Japan. To test the applicability of CF, a landslide inventory map provided by National Research Institute for Earth Science and Disaster Prevention (NIED) was split into two subsets: (i) 70% of the landslides in the inventory to be used for building the CF based model; (ii) 30% of the landslides to be used for the validation purpose. A spatial database with fifteen landslide causative factors was then constructed by processing ALOS satellite images, aerial photos, topographical and geological maps. CF model was then applied to select the best subset from the fifteen factors. Using all fifteen factors and the best subset factors, landslide susceptibility maps were produced using statistical index (SI) and logistic regression (LR) models. The susceptibility maps were validated and compared using landslide locations in the validation data. The prediction performance of two susceptibility maps was estimated using the Receiver Operating Characteristics (ROC). The result shows that the area under the ROC curve (AUC) for the LR model (AUC = 0.817) is slightly higher than those obtained from the SI model (AUC = 0.801). Further, it is noted that the SI and LR models using the best subset outperform the models using the fifteen original factors. Therefore, we conclude that the optimized factor model using CF is more accurate in predicting landslide susceptibility and obtaining a more homogeneous classification map. Our findings acknowledge that in the mountainous regions suffering from data scarcity, it is possible to select key factors related to landslide occurrence based on the CF models in a GIS platform. Hence, the development of a scenario for future planning of risk mitigation is achieved in an efficient manner. PMID:26214691

Validity of the Eating Attitude Test among Exercisers.

PubMed

Lane, Helen J; Lane, Andrew M; Matheson, Hilary

2004-12-01

Theory testing and construct measurement are inextricably linked. To date, no published research has looked at the factorial validity of an existing eating attitude inventory for use with exercisers. The Eating Attitude Test (EAT) is a 26-item measure that yields a single index of disordered eating attitudes. The original factor analysis showed three interrelated factors: Dieting behavior (13-items), oral control (7-items), and bulimia nervosa-food preoccupation (6-items). The primary purpose of the study was to examine the factorial validity of the EAT among a sample of exercisers. The second purpose was to investigate relationships between eating attitudes scores and selected psychological constructs. In stage one, 598 regular exercisers completed the EAT. Confirmatory factor analysis (CFA) was used to test the single-factor, a three-factor model, and a four-factor model, which distinguished bulimia from food pre-occupation. CFA of the single-factor model (RCFI = 0.66, RMSEA = 0.10), the three-factor-model (RCFI = 0.74; RMSEA = 0.09) showed poor model fit. There was marginal fit for the 4-factor model (RCFI = 0.91, RMSEA = 0.06). Results indicated five-items showed poor factor loadings. After these 5-items were discarded, the three models were re-analyzed. CFA results indicated that the single-factor model (RCFI = 0.76, RMSEA = 0.10) and three-factor model (RCFI = 0.82, RMSEA = 0.08) showed poor fit. CFA results for the four-factor model showed acceptable fit indices (RCFI = 0.98, RMSEA = 0.06). Stage two explored relationships between EAT scores, mood, self-esteem, and motivational indices toward exercise in terms of self-determination, enjoyment and competence. Correlation results indicated that depressed mood scores positively correlated with bulimia and dieting scores. Further, dieting was inversely related with self-determination toward exercising. Collectively, findings suggest that a 21-item four-factor model shows promising validity coefficients among exercise participants, and that future research is needed to investigate eating attitudes among samples of exercisers. Key PointsValidity of psychometric measures should be thoroughly investigated. Researchers should not assume that a scale validation on one sample will show the same validity coefficients in a different population.The Eating Attitude Test is a commonly used scale. The present study shows a revised 21-item scale was suitable for exercisers.Researchers using the Eating Attitude Test should use subscales of Dieting, Oral control, Food pre-occupation, and Bulimia.Future research should involve qualitative techniques and interview exercise participants to explore the nature of eating attitudes.

Beyond factor analysis: Multidimensionality and the Parkinson's Disease Sleep Scale-Revised.

PubMed

Pushpanathan, Maria E; Loftus, Andrea M; Gasson, Natalie; Thomas, Meghan G; Timms, Caitlin F; Olaithe, Michelle; Bucks, Romola S

2018-01-01

Many studies have sought to describe the relationship between sleep disturbance and cognition in Parkinson's disease (PD). The Parkinson's Disease Sleep Scale (PDSS) and its variants (the Parkinson's disease Sleep Scale-Revised; PDSS-R, and the Parkinson's Disease Sleep Scale-2; PDSS-2) quantify a range of symptoms impacting sleep in only 15 items. However, data from these scales may be problematic as included items have considerable conceptual breadth, and there may be overlap in the constructs assessed. Multidimensional measurement models, accounting for the tendency for items to measure multiple constructs, may be useful more accurately to model variance than traditional confirmatory factor analysis. In the present study, we tested the hypothesis that a multidimensional model (a bifactor model) is more appropriate than traditional factor analysis for data generated by these types of scales, using data collected using the PDSS-R as an exemplar. 166 participants diagnosed with idiopathic PD participated in this study. Using PDSS-R data, we compared three models: a unidimensional model; a 3-factor model consisting of sub-factors measuring insomnia, motor symptoms and obstructive sleep apnoea (OSA) and REM sleep behaviour disorder (RBD) symptoms; and, a confirmatory bifactor model with both a general factor and the same three sub-factors. Only the confirmatory bifactor model achieved satisfactory model fit, suggesting that PDSS-R data are multidimensional. There were differential associations between factor scores and patient characteristics, suggesting that some PDSS-R items, but not others, are influenced by mood and personality in addition to sleep symptoms. Multidimensional measurement models may also be a helpful tool in the PDSS and the PDSS-2 scales and may improve the sensitivity of these instruments.

Longitudinal Factor Structure of Posttraumatic Stress Symptoms Related to Intimate Partner Violence

ERIC Educational Resources Information Center

Krause, Elizabeth D.; Kaltman, Stacey; Goodman, Lisa A.; Dutton, Mary Ann

2007-01-01

Confirmatory factor analysis (CFA) studies have suggested that a model of posttraumatic stress disorder (PTSD) that is characterized by 4 factors is preferable to competing models. However, the composition of these 4 factors has varied across studies, with 1 model splitting avoidance and numbing symptoms (e.g., D. W. King, G. A. Leskin, L. A.…

Measurement Invariance of Second-Order Factor Model of the Multifactor Leadership Questionnaire (MLQ) across K-12 Principal Gender

ERIC Educational Resources Information Center

Xu, Lihua; Wubbena, Zane; Stewart, Trae

2016-01-01

Purpose: The purpose of this paper is to investigate the factor structure and the measurement invariance of the Multifactor Leadership Questionnaire (MLQ) across gender of K-12 school principals (n=6,317) in the USA. Design/methodology/approach: Nine first-order factor models and four second-order factor models were tested using confirmatory…

Simulation of tropical cyclone activity over the western North Pacific based on CMIP5 models

NASA Astrophysics Data System (ADS)

Shen, Haibo; Zhou, Weican; Zhao, Haikun

2017-09-01

Based on the Coupled Model Inter-comparison Project 5 (CMIP5) models, the tropical cyclone (TC) activity in the summers of 1965-2005 over the western North Pacific (WNP) is simulated by a TC dynamically downscaling system. In consideration of diversity among climate models, Bayesian model averaging (BMA) and equal-weighed model averaging (EMA) methods are applied to produce the ensemble large-scale environmental factors of the CMIP5 model outputs. The environmental factors generated by BMA and EMA methods are compared, as well as the corresponding TC simulations by the downscaling system. Results indicate that BMA method shows a significant advantage over the EMA. In addition, impacts of model selections on BMA method are examined. To each factor, ten models with better performance are selected from 30 CMIP5 models and then conduct BMA, respectively. As a consequence, the ensemble environmental factors and simulated TC activity are similar with the results from the 30 models' BMA, which verifies the BMA method can afford corresponding weight for each model in the ensemble based on the model's predictive skill. Thereby, the existence of poor performance models will not particularly affect the BMA effectiveness and the ensemble outcomes are improved. Finally, based upon the BMA method and downscaling system, we analyze the sensitivity of TC activity to three important environmental factors, i.e., sea surface temperature (SST), large-scale steering flow, and vertical wind shear. Among three factors, SST and large-scale steering flow greatly affect TC tracks, while average intensity distribution is sensitive to all three environmental factors. Moreover, SST and vertical wind shear jointly play a critical role in the inter-annual variability of TC lifetime maximum intensity and frequency of intense TCs.

Biotic and abiotic factors predicting the global distribution and population density of an invasive large mammal

PubMed Central

Lewis, Jesse S.; Farnsworth, Matthew L.; Burdett, Chris L.; Theobald, David M.; Gray, Miranda; Miller, Ryan S.

2017-01-01

Biotic and abiotic factors are increasingly acknowledged to synergistically shape broad-scale species distributions. However, the relative importance of biotic and abiotic factors in predicting species distributions is unclear. In particular, biotic factors, such as predation and vegetation, including those resulting from anthropogenic land-use change, are underrepresented in species distribution modeling, but could improve model predictions. Using generalized linear models and model selection techniques, we used 129 estimates of population density of wild pigs (Sus scrofa) from 5 continents to evaluate the relative importance, magnitude, and direction of biotic and abiotic factors in predicting population density of an invasive large mammal with a global distribution. Incorporating diverse biotic factors, including agriculture, vegetation cover, and large carnivore richness, into species distribution modeling substantially improved model fit and predictions. Abiotic factors, including precipitation and potential evapotranspiration, were also important predictors. The predictive map of population density revealed wide-ranging potential for an invasive large mammal to expand its distribution globally. This information can be used to proactively create conservation/management plans to control future invasions. Our study demonstrates that the ongoing paradigm shift, which recognizes that both biotic and abiotic factors shape species distributions across broad scales, can be advanced by incorporating diverse biotic factors. PMID:28276519

Estimating Escherichia coli loads in streams based on various physical, chemical, and biological factors

PubMed Central

Dwivedi, Dipankar; Mohanty, Binayak P.; Lesikar, Bruce J.

2013-01-01

Microbes have been identified as a major contaminant of water resources. Escherichia coli (E. coli) is a commonly used indicator organism. It is well recognized that the fate of E. coli in surface water systems is governed by multiple physical, chemical, and biological factors. The aim of this work is to provide insight into the physical, chemical, and biological factors along with their interactions that are critical in the estimation of E. coli loads in surface streams. There are various models to predict E. coli loads in streams, but they tend to be system or site specific or overly complex without enhancing our understanding of these factors. Hence, based on available data, a Bayesian Neural Network (BNN) is presented for estimating E. coli loads based on physical, chemical, and biological factors in streams. The BNN has the dual advantage of overcoming the absence of quality data (with regards to consistency in data) and determination of mechanistic model parameters by employing a probabilistic framework. This study evaluates whether the BNN model can be an effective alternative tool to mechanistic models for E. coli loads estimation in streams. For this purpose, a comparison with a traditional model (LOADEST, USGS) is conducted. The models are compared for estimated E. coli loads based on available water quality data in Plum Creek, Texas. All the model efficiency measures suggest that overall E. coli loads estimations by the BNN model are better than the E. coli loads estimations by the LOADEST model on all the three occasions (three-fold cross validation). Thirteen factors were used for estimating E. coli loads with the exhaustive feature selection technique, which indicated that six of thirteen factors are important for estimating E. coli loads. Physical factors included temperature and dissolved oxygen; chemical factors include phosphate and ammonia; biological factors include suspended solids and chlorophyll. The results highlight that the LOADEST model estimates E. coli loads better in the smaller ranges, whereas the BNN model estimates E. coli loads better in the higher ranges. Hence, the BNN model can be used to design targeted monitoring programs and implement regulatory standards through TMDL programs. PMID:24511166

78 FR 14533 - Official Release of EMFAC2011 Motor Vehicle Emission Factor Model for Use in the State of California

Federal Register 2010, 2011, 2012, 2013, 2014

2013-03-06

... testing of interim versions of the model with air districts and Metropolitan Planning Organizations (MPOs... Motor Vehicle Emission Factor Model for Use in the State of California AGENCY: Environmental Protection... of the latest version of the California EMFAC model (short for EMission FACtor) for use in state...

Model of white oak flower survival and maturation

Treesearch

David R. Larsen; Robert A. Cecich

1997-01-01

A stochastic model of oak flower dynamics is presented that integrates a number of factors which appear to affect the oak pistillate flower development process. The factors are modeled such that the distribution of the predicted flower populations could have come from the same distribution as the observed flower populations. Factors included in the model are; the range...

Alternative Factor Models and Heritability of the Short Leyton Obsessional Inventory--Children's Version

ERIC Educational Resources Information Center

Moore, Janette; Smith, Gillian W.; Shevlin, Mark; O'Neill, Francis A.

2010-01-01

An alternative models framework was used to test three confirmatory factor analytic models for the Short Leyton Obsessional Inventory-Children's Version (Short LOI-CV) in a general population sample of 517 young adolescent twins (11-16 years). A one-factor model as implicit in current classification systems of Obsessive-Compulsive Disorder (OCD),…

Maximum Likelihood Dynamic Factor Modeling for Arbitrary "N" and "T" Using SEM

ERIC Educational Resources Information Center

Voelkle, Manuel C.; Oud, Johan H. L.; von Oertzen, Timo; Lindenberger, Ulman

2012-01-01

This article has 3 objectives that build on each other. First, we demonstrate how to obtain maximum likelihood estimates for dynamic factor models (the direct autoregressive factor score model) with arbitrary "T" and "N" by means of structural equation modeling (SEM) and compare the approach to existing methods. Second, we go beyond standard time…

Alternative models of DSM-5 PTSD: Examining diagnostic implications.

PubMed

Murphy, Siobhan; Hansen, Maj; Elklit, Ask; Yong Chen, Yoke; Raudzah Ghazali, Siti; Shevlin, Mark

2018-04-01

The factor structure of DSM-5 posttraumatic stress disorder (PTSD) has been extensively debated with evidence supporting the recently proposed seven-factor Hybrid model. However, despite myriad studies examining PTSD symptom structure few have assessed the diagnostic implications of these proposed models. This study aimed to generate PTSD prevalence estimates derived from the 7 alternative factor models and assess whether pre-established risk factors associated with PTSD (e.g., transportation accidents and sexual victimisation) produce consistent risk estimates. Seven alternative models were estimated within a confirmatory factor analytic framework using the PTSD Checklist for DSM-5 (PCL-5). Data were analysed from a Malaysian adolescent community sample (n = 481) of which 61.7% were female, with a mean age of 17.03 years. The results indicated that all models provided satisfactory model fit with statistical superiority for the Externalising Behaviours and seven-factor Hybrid models. The PTSD prevalence estimates varied substantially ranging from 21.8% for the DSM-5 model to 10.0% for the Hybrid model. Estimates of risk associated with PTSD were inconsistent across the alternative models, with substantial variation emerging for sexual victimisation. These findings have important implications for research and practice and highlight that more research attention is needed to examine the diagnostic implications emerging from the alternative models of PTSD. Copyright © 2017 Elsevier B.V. All rights reserved.

FACTORS INFLUENCING TOTAL DIETARY EXPOSURES OF YOUNG CHILDREN

EPA Science Inventory

A deterministic model was developed to identify the critical input parameters needed to assess dietary intakes of young children. The model was used as a framework for understanding the important factors in data collection and data analysis. Factors incorporated into the model i...

Psychometric properties of the Intrinsic Motivation Inventory in a competitive sport setting: a confirmatory factor analysis.

PubMed

McAuley, E; Duncan, T; Tammen, V V

1989-03-01

The present study was designed to assess selected psychometric properties of the Intrinsic Motivation Inventory (IMI) (Ryan, 1982), a multidimensional measure of subjects' experience with regard to experimental tasks. Subjects (N = 116) competed in a basketball free-throw shooting game, following which they completed the IMI. The LISREL VI computer program was employed to conduct a confirmatory factor analysis to assess the tenability of a five factor hierarchical model representing four first-order factors or dimensions and a second-order general factor representing intrinsic motivation. Indices of model acceptability tentatively suggest that the sport data adequately fit the hypothesized five factor hierarchical model. Alternative models were tested but did not result in significant improvements in the goodness-of-fit indices, suggesting the proposed model to be the most accurate of the models tested. Coefficient alphas for the four dimensions and the overall scale indicated adequate reliability. The results are discussed with regard to the importance of accurate assessment of psychological constructs and the use of linear structural equations in confirming the factor structures of measures.

Quantifying the Strength of General Factors in Psychopathology: A Comparison of CFA with Maximum Likelihood Estimation, BSEM, and ESEM/EFA Bifactor Approaches.

PubMed

Murray, Aja Louise; Booth, Tom; Eisner, Manuel; Obsuth, Ingrid; Ribeaud, Denis

2018-05-22

Whether or not importance should be placed on an all-encompassing general factor of psychopathology (or p factor) in classifying, researching, diagnosing, and treating psychiatric disorders depends (among other issues) on the extent to which comorbidity is symptom-general rather than staying largely within the confines of narrower transdiagnostic factors such as internalizing and externalizing. In this study, we compared three methods of estimating p factor strength. We compared omega hierarchical and explained common variance calculated from confirmatory factor analysis (CFA) bifactor models with maximum likelihood (ML) estimation, from exploratory structural equation modeling/exploratory factor analysis models with a bifactor rotation, and from Bayesian structural equation modeling (BSEM) bifactor models. Our simulation results suggested that BSEM with small variance priors on secondary loadings might be the preferred option. However, CFA with ML also performed well provided secondary loadings were modeled. We provide two empirical examples of applying the three methodologies using a normative sample of youth (z-proso, n = 1,286) and a university counseling sample (n = 359).

[Factor structure of the German version of the BIS/BAS Scales in a population-based sample].

PubMed

Müller, A; Smits, D; Claes, L; de Zwaan, M

2013-02-01

The Behavioural Inhibition System/Behavioural Activation System Scale (BIS/BAS-Scales) developed by Carver and White 1 is a self-rating instrument to assess the dispositional sensitivity to punishment and reward. The present work aims to examine the factor structure of the German version of the BIS/BAS-Scales. In a large German population-based sample (n = 1881) the model fit of several factor models was tested by using confirmatory factor analyses. The best model fit was found for the 5-factor model with two BIS (anxiety, fear) and three BAS (drive, reward responsiveness, fun seeking) scales, whereas the BIS-fear, the BAS-reward responsiveness, and the BAS-fun seeking subscales showed low internal consistency. The BIS/BAS scales were negatively correlated with age, and women reported higher BIS subscale scores than men. Confirmatory factor analyses suggest a 5-factor model. However, due to the low internal reliability of some of the subscales the use of this model is questionable. © Georg Thieme Verlag KG Stuttgart · New York.

Confirmatory factor analysis of the Child Oral Health Impact Profile (Korean version).

PubMed

Cho, Young Il; Lee, Soonmook; Patton, Lauren L; Kim, Hae-Young

2016-04-01

Empirical support for the factor structure of the Child Oral Health Impact Profile (COHIP) has not been fully established. The purposes of this study were to evaluate the factor structure of the Korean version of the COHIP (COHIP-K) empirically using confirmatory factor analysis (CFA) based on the theoretical framework and then to assess whether any of the factors in the structure could be grouped into a simpler single second-order factor. Data were collected through self-reported COHIP-K responses from a representative community sample of 2,236 Korean children, 8-15 yr of age. Because a large inter-factor correlation of 0.92 was estimated in the original five-factor structure, the two strongly correlated factors were combined into one factor, resulting in a four-factor structure. The revised four-factor model showed a reasonable fit with appropriate inter-factor correlations. Additionally, the second-order model with four sub-factors was reasonable with sufficient fit and showed equal fit to the revised four-factor model. A cross-validation procedure confirmed the appropriateness of the findings. Our analysis empirically supported a four-factor structure of COHIP-K, a summarized second-order model, and the use of an integrated summary COHIP score. © 2016 Eur J Oral Sci.

Constructing the Japanese version of the Maslach Burnout Inventory-Student Survey: Confirmatory factor analysis.

PubMed

Tsubakita, Takashi; Shimazaki, Kazuyo

2016-01-01

To examine the factorial validity of the Maslach Burnout Inventory-Student Survey, using a sample of 2061 Japanese university students majoring in the medical and natural sciences (67.9% male, 31.8% female; Mage  = 19.6 years, standard deviation = 1.5). The back-translated scale used unreversed items to assess inefficacy. The inventory's descriptive properties and Cronbach's alphas were calculated using SPSS software. The present authors compared fit indices of the null, one factor, and default three factor models via confirmatory factor analysis with maximum-likelihood estimation using AMOS software, version 21.0. Intercorrelations between exhaustion, cynicism, and inefficacy were relatively higher than in prior studies. Cronbach's alphas were 0.76, 0.85, and 0.78, respectively. Although fit indices of the hypothesized three factor model did not meet the respective criteria, the model demonstrated better fit than did the null and one factor models. The present authors added four paths between error variables within items, but the modified model did not show satisfactory fit. Subsequent analysis revealed that a bi-factor model fit the data better than did the hypothesized or modified three factor models. The Japanese version of the Maslach Burnout Inventory-Student Survey needs minor changes to improve the fit of its three factor model, but the scale as a whole can be used to adequately assess overall academic burnout in Japanese university students. Although the scale was back-translated, two items measuring exhaustion whose expressions overlapped should be modified, and all items measuring inefficacy should be reversed in order to statistically clarify the factorial difference between the scale's three factors. © 2015 The Authors. Japan Journal of Nursing Science © 2015 Japan Academy of Nursing Science.

Bifactor Modeling of the Positive and Negative Syndrome Scale: Generalized Psychosis Spans Schizoaffective, Bipolar, and Schizophrenia Diagnoses.

PubMed

Anderson, Ariana E; Marder, Stephen; Reise, Steven P; Savitz, Adam; Salvadore, Giacomo; Fu, Dong Jing; Li, Qingqin; Turkoz, Ibrahim; Han, Carol; Bilder, Robert M

2018-02-06

Common genetic variation spans schizophrenia, schizoaffective and bipolar disorders, but historically, these syndromes have been distinguished categorically. A symptom dimension shared across these syndromes, if such a general factor exists, might provide a clearer target for understanding and treating mental illnesses that share core biological bases. We tested the hypothesis that a bifactor model of the Positive and Negative Syndrome Scale (PANSS), containing 1 general factor and 5 specific factors (positive, negative, disorganized, excited, anxiety), explains the cross-diagnostic structure of symptoms better than the traditional 5-factor model, and examined the extent to which a general factor reflects the overall severity of symptoms spanning diagnoses in 5094 total patients with a diagnosis of schizophrenia, schizoaffective, and bipolar disorder. The bifactor model provided superior fit across diagnoses, and was closer to the "true" model, compared to the traditional 5-factor model (Vuong test; P < .001). The general factor included high loadings on 28 of the 30 PANSS items, omitting symptoms associated with the excitement and anxiety/depression domains. The general factor had highest total loadings on symptoms that are often associated with the positive and disorganization syndromes, but there were also substantial loadings on the negative syndrome thus leading to the interpretation of this factor as reflecting generalized psychosis. A bifactor model derived from the PANSS can provide a stronger framework for measuring cross-diagnostic psychopathology than a 5-factor model, and includes a generalized psychosis dimension shared at least across schizophrenia, schizoaffective, and bipolar disorder. © The Author(s) 2018. Published by Oxford University Press on behalf of the Maryland Psychiatric Research Center. All rights reserved. For permissions, please email: journals.permissions@oup.com

Epistemic belief structures within introductory astronomy

NASA Astrophysics Data System (ADS)

Johnson, Keith; Willoughby, Shannon D.

2018-06-01

The reliability and validity of inventories should be verified in multiple ways. Although the epistemological beliefs about the physical science survey (EBAPS) has been deemed to be reliable and valid by the authors, the axes or factor structure proposed by the authors has not been independently checked. Using data from a study sample we discussed in previous publications, we performed exploratory factor analysis on 1,258 post-test EBAPS surveys. The students in the sample were from an introductory Astronomy course at a mid-sized western university. Inspection suggested the use of either a three-factor model or a five-factor model. Each of the factors is interpreted and discussed, and the factors are compared to the axes proposed by the authors of the EBAPS. We find that the five-factor model extrapolated from our data partially overlaps with the model put forth by the authors of the EBAPS, and that many of the questions did not load onto any factors.

Structural validity of the Wechsler Intelligence Scale for Children-Fifth Edition: Confirmatory factor analyses with the 16 primary and secondary subtests.

PubMed

Canivez, Gary L; Watkins, Marley W; Dombrowski, Stefan C

2017-04-01

The factor structure of the Wechsler Intelligence Scale for Children-Fifth Edition (WISC-V; Wechsler, 2014a) standardization sample (N = 2,200) was examined using confirmatory factor analyses (CFA) with maximum likelihood estimation for all reported models from the WISC-V Technical and Interpretation Manual (Wechsler, 2014b). Additionally, alternative bifactor models were examined and variance estimates and model-based reliability estimates (ω coefficients) were provided. Results from analyses of the 16 primary and secondary WISC-V subtests found that all higher-order CFA models with 5 group factors (VC, VS, FR, WM, and PS) produced model specification errors where the Fluid Reasoning factor produced negative variance and were thus judged inadequate. Of the 16 models tested, the bifactor model containing 4 group factors (VC, PR, WM, and PS) produced the best fit. Results from analyses of the 10 primary WISC-V subtests also found the bifactor model with 4 group factors (VC, PR, WM, and PS) produced the best fit. Variance estimates from both 16 and 10 subtest based bifactor models found dominance of general intelligence (g) in accounting for subtest variance (except for PS subtests) and large ω-hierarchical coefficients supporting general intelligence interpretation. The small portions of variance uniquely captured by the 4 group factors and low ω-hierarchical subscale coefficients likely render the group factors of questionable interpretive value independent of g (except perhaps for PS). Present CFA results confirm the EFA results reported by Canivez, Watkins, and Dombrowski (2015); Dombrowski, Canivez, Watkins, and Beaujean (2015); and Canivez, Dombrowski, and Watkins (2015). (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Theoretical implementation of prior knowledge in the design of a multi-scale prosthesis satisfaction questionnaire.

PubMed

Schürmann, Tim; Beckerle, Philipp; Preller, Julia; Vogt, Joachim; Christ, Oliver

2016-12-19

In product development for lower limb prosthetic devices, a set of special criteria needs to be met. Prosthetic devices have a direct impact on the rehabilitation process after an amputation with both perceived technological and psychological aspects playing an important role. However, available psychometric questionnaires fail to consider the important links between these two dimensions. In this article a probabilistic latent trait model is proposed with seven technical and psychological factors which measure satisfaction with the prosthesis. The results of a first study are used to determine the basic parameters of the statistical model. These distributions represent hypotheses about factor loadings between manifest items and latent factors of the proposed psychometric questionnaire. A study was conducted and analyzed to form hypotheses for the prior distributions of the questionnaire's measurement model. An expert agreement study conducted on 22 experts was used to determine the prior distribution of item-factor loadings in the model. Model parameters that had to be specified as part of the measurement model were informed prior distributions on the item-factor loadings. For the current 70 items in the questionnaire, each factor loading was set to represent the certainty with which experts had assigned the items to their respective factors. Considering only the measurement model and not the structural model of the questionnaire, 70 out of 217 informed prior distributions on parameters were set. The use of preliminary studies to set prior distributions in latent trait models, while being a relatively new approach in psychological research, provides helpful information towards the design of a seven factor questionnaire that means to identify relations between technical and psychological factors in prosthetic product design and rehabilitation medicine.

Technosocial Modeling of IED Threat Scenarios and Attacks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Whitney, Paul D.; Brothers, Alan J.; Coles, Garill A.

2009-03-23

This paper describes an approach for integrating sociological and technical models to develop more complete threat assessment. Current approaches to analyzing and addressing threats tend to focus on the technical factors. This paper addresses development of predictive models that encompass behavioral as well as these technical factors. Using improvised explosive device (IED) attacks as motivation, this model supports identification of intervention activities 'left of boom' as well as prioritizing attack modalities. We show how Bayes nets integrate social factors associated with IED attacks into general threat model containing technical and organizational steps from planning through obtaining the IED to initiationmore » of the attack. The social models are computationally-based representations of relevant social science literature that describes human decision making and physical factors. When combined with technical models, the resulting model provides improved knowledge integration into threat assessment for monitoring. This paper discusses the construction of IED threat scenarios, integration of diverse factors into an analytical framework for threat assessment, indicator identification for future threats, and future research directions.« less

Factor structure and psychometric properties of a Romanian translation of the drive for Muscularity Scale (DMS) in university men.

PubMed

Swami, Viren; Vintila, Mona; Tudorel, Otilia; Goian, Cosmin; Barron, David

2018-06-01

We examined the psychometric properties of a Romanian translation of the 15-item Drive for Muscularity Scale (DMS). Male university students from Romania (N = 343) completed the DMS, as well as measures of self-esteem, body appreciation, and muscle discrepancy. Exploratory factor analysis indicated that DMS scores reduced to two factors that related to muscularity-oriented attitudes and behaviours, with both first-order factors loading onto a higher-order factor. However, confirmatory factor analysis indicated that a model with two first-order factors and a higher-order factor had poor fit. A two-factor model without a higher-order construct achieved acceptable but mediocre fit. Scores on the two-factor DMS model had adequate internal consistency and demonstrated acceptable convergent validity (significant correlations with self-esteem, body appreciation, and muscle discrepancy). These results provide support for a two-factor model of DMS scores in a Romanian-speaking sample and extends the availability of the DMS to a rarely-examined linguistic group. Copyright © 2018 Elsevier Ltd. All rights reserved.

Peplau's Theory of Interpersonal Relations: An Alternate Factor Structure for Patient Experience Data?

PubMed

Hagerty, Thomas A; Samuels, William; Norcini-Pala, Andrea; Gigliotti, Eileen

2017-04-01

A confirmatory factor analysis of data from the responses of 12,436 patients to 16 items on the Consumer Assessment of Healthcare Providers and Systems-Hospital survey was used to test a latent factor structure based on Peplau's middle-range theory of interpersonal relations. A two-factor model based on Peplau's theory fit these data well, whereas a three-factor model also based on Peplau's theory fit them excellently and provided a suitable alternate factor structure for the data. Though neither the two- nor three-factor model fit as well as the original factor structure, these results support using Peplau's theory to demonstrate nursing's extensive contribution to the experiences of hospitalized patients.

Factor analysis and multiple regression between topography and precipitation on Jeju Island, Korea

NASA Astrophysics Data System (ADS)

Um, Myoung-Jin; Yun, Hyeseon; Jeong, Chang-Sam; Heo, Jun-Haeng

2011-11-01

SummaryIn this study, new factors that influence precipitation were extracted from geographic variables using factor analysis, which allow for an accurate estimation of orographic precipitation. Correlation analysis was also used to examine the relationship between nine topographic variables from digital elevation models (DEMs) and the precipitation in Jeju Island. In addition, a spatial analysis was performed in order to verify the validity of the regression model. From the results of the correlation analysis, it was found that all of the topographic variables had a positive correlation with the precipitation. The relations between the variables also changed in accordance with a change in the precipitation duration. However, upon examining the correlation matrix, no significant relationship between the latitude and the aspect was found. According to the factor analysis, eight topographic variables (latitude being the exception) were found to have a direct influence on the precipitation. Three factors were then extracted from the eight topographic variables. By directly comparing the multiple regression model with the factors (model 1) to the multiple regression model with the topographic variables (model 3), it was found that model 1 did not violate the limits of statistical significance and multicollinearity. As such, model 1 was considered to be appropriate for estimating the precipitation when taking into account the topography. In the study of model 1, the multiple regression model using factor analysis was found to be the best method for estimating the orographic precipitation on Jeju Island.

Gender differences in the factor structure of posttraumatic stress disorder symptoms in war-exposed adolescents.

PubMed

Armour, Cherie; Elhai, Jon D; Layne, Christopher M; Shevlin, Mark; Duraković-Belko, Elvira; Djapo, Nermin; Pynoos, Robert S

2011-05-01

DSM-IV's three-factor model of posttraumatic stress disorder (PTSD) is rarely empirically supported, whereas other four-factor models (King et al., 1998; Simms, Watson, & Doebbeling, 2002) have proven to be better representations of PTSD's latent structure. To date, a clear consensus as to which model provides the best representation of PTSD's underlying dimensions has yet to be reached. The current study investigated whether gender is associated with factor structure differences using the King et al. (1998) model of reexperiencing, avoidance, numbing, and hyperarousal PTSD symptoms. Participants were war-exposed Bosnian secondary/high school boys and girls (N=1572) assessed nearly two years after the 1992-1995 Bosnian conflict. Confirmatory factor analytic tests of measurement invariance across PTSD model parameters revealed many significant sex-linked differences. Implications regarding the potential role of gender as a moderator of the King et al. (1998) model's factor structure are discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

Model Fit and Item Factor Analysis: Overfactoring, Underfactoring, and a Program to Guide Interpretation.

PubMed

Clark, D Angus; Bowles, Ryan P

2018-04-23

In exploratory item factor analysis (IFA), researchers may use model fit statistics and commonly invoked fit thresholds to help determine the dimensionality of an assessment. However, these indices and thresholds may mislead as they were developed in a confirmatory framework for models with continuous, not categorical, indicators. The present study used Monte Carlo simulation methods to investigate the ability of popular model fit statistics (chi-square, root mean square error of approximation, the comparative fit index, and the Tucker-Lewis index) and their standard cutoff values to detect the optimal number of latent dimensions underlying sets of dichotomous items. Models were fit to data generated from three-factor population structures that varied in factor loading magnitude, factor intercorrelation magnitude, number of indicators, and whether cross loadings or minor factors were included. The effectiveness of the thresholds varied across fit statistics, and was conditional on many features of the underlying model. Together, results suggest that conventional fit thresholds offer questionable utility in the context of IFA.

General and specific attention-deficit/hyperactivity disorder factors of children 4 to 6 years of age: An exploratory structural equation modeling approach to assessing symptom multidimensionality.

PubMed

Arias, Víctor B; Ponce, Fernando P; Martínez-Molina, Agustín; Arias, Benito; Núñez, Daniel

2016-01-01

We tested first-order factor and bifactor models of attention-deficit/hyperactivity disorder (ADHD) using confirmatory factor analysis (CFA) and exploratory structural equation modeling (ESEM) to adequately summarize the Diagnostic and Statistical Manual of Mental Disorders, 4th Edition, (DSM-IV-TR) symptoms observed in a Spanish sample of preschoolers and kindergarteners. Six ESEM and CFA models were estimated based on teacher evaluations of the behavior of 638 children 4 to 6 years of age. An ESEM bifactor model with a central dimension plus 3 specific factors (inattention, hyperactivity, and impulsivity) showed the best fit and interpretability. Strict invariance between the sexes was observed. The bifactor model provided a solution to previously encountered inconsistencies in the factorial models of ADHD in young children. However, the low reliability of the specific factors casts doubt on the utility of the subscales for ADHD measurement. More research is necessary to clarify the nature of G and S factors of ADHD. (c) 2016 APA, all rights reserved.

Major psychological factors affecting acceptance of gene-recombination technology.

PubMed

Tanaka, Yutaka

2004-12-01

The purpose of this study was to verify the validity of a causal model that was made to predict the acceptance of gene-recombination technology. A structural equation model was used as a causal model. First of all, based on preceding studies, the factors of perceived risk, perceived benefit, and trust were set up as important psychological factors determining acceptance of gene-recombination technology in the structural equation model. An additional factor, "sense of bioethics," which I consider to be important for acceptance of biotechnology, was added to the model. Based on previous studies, trust was set up to have an indirect influence on the acceptance of gene-recombination technology through perceived risk and perceived benefit in the model. Participants were 231 undergraduate students in Japan who answered a questionnaire with a 5-point bipolar scale. The results indicated that the proposed model fits the data well, and showed that acceptance of gene-recombination technology is explained largely by four factors, that is, perceived risk, perceived benefit, trust, and sense of bioethics, whether the technology is applied to plants, animals, or human beings. However, the relative importance of the four factors was found to vary depending on whether the gene-recombination technology was applied to plants, animals, or human beings. Specifically, the factor of sense of bioethics is the most important factor in acceptance of plant gene-recombination technology and animal gene-recombination technology, and the factors of trust and perceived risk are the most important factors in acceptance of human being gene-recombination technology.

Behavioral Pediatrics Feeding Assessment Scale in Young Children With Autism Spectrum Disorder: Psychometrics and Associations With Child and Parent Variables

PubMed Central

Allen, Stephanie L.; Duku, Eric; Vaillancourt, Tracy; Szatmari, Peter; Bryson, Susan; Fombonne, Eric; Volden, Joanne; Waddell, Charlotte; Zwaigenbaum, Lonnie; Roberts, Wendy; Mirenda, Pat; Bennett, Teresa; Elsabbagh, Mayada; Georgiades, Stelios

2015-01-01

Objective The factor structure and validity of the Behavioral Pediatrics Feeding Assessment Scale (BPFAS; Crist & Napier-Phillips, 2001) were examined in preschoolers with autism spectrum disorder (ASD). Methods Confirmatory factor analysis was used to examine the original BPFAS five-factor model, the fit of each latent variable, and a rival one-factor model. None of the models was adequate, thus a categorical exploratory factor analysis (CEFA) was conducted. Correlations were used to examine relations between the BPFAS and concurrent variables of interest. Results The CEFA identified an acceptable three-factor model. Correlational analyses indicated that feeding problems were positively related to parent-reported autism symptoms, behavior problems, sleep problems, and parenting stress, but largely unrelated to performance-based indices of autism symptom severity, language, and cognitive abilities, as well as child age. Conclusion These results provide evidence supporting the use of the identified BPFAS three-factor model for samples of young children with ASD. PMID:25725217

Eating disorders and non-suicidal self-injury: Structural equation modelling of a conceptual model.

PubMed

Vieira, Ana Isabel; Machado, Bárbara C; Moreira, Célia S; Machado, Paulo P P; Brandão, Isabel; Roma-Torres, António; Gonçalves, Sónia

2018-06-14

Evidence suggests several risk factors for both eating disorders (ED) and nonsuicidal self-injury (NSSI), but the relationships between these factors are not well understood. Considering our previous work and a conceptual model, this cross-sectional study aimed to assess the relationships among distal and proximal factors for the presence of NSSI in ED. We assessed 245 ED patients with the Oxford Risk Factor Interview for ED. Structural equation modelling revealed that both distal and proximal factors were related to the presence of NSSI in ED, disclosing a mediating role of the proximal factors. Stressful life events mediated the relationship between childhood sexual abuse, peer aggression, and both ED and NSSI. Childhood physical abuse was related to ED and NSSI via substance use, negative self-evaluation, and suicide attempts. Findings provided support for the conceptual model and highlight the possible mechanisms by which psychosocial factors may lead to ED and NSSI. Copyright © 2018 John Wiley & Sons, Ltd and Eating Disorders Association.

A Model Incorporating Some of the Mechanical and Biochemical Factors Underlying Clot Formation and Dissolution in Flowing Blood

DOE PAGES

Anand, M.; Rajagopal, K.; Rajagopal, K. R.

2003-01-01

Multiple interacting mechanisms control the formation and dissolution of clots to maintain blood in a state of delicate balance. In addition to a myriad of biochemical reactions, rheological factors also play a crucial role in modulating the response of blood to external stimuli. To date, a comprehensive model for clot formation and dissolution, that takes into account the biochemical, medical and rheological factors, has not been put into place, the existing models emphasizing either one or the other of the factors. In this paper, after discussing the various biochemical, physiologic and rheological factors at some length, we develop a modelmore » for clot formation and dissolution that incorporates many of the relevant crucial factors that have a bearing on the problem. The model, though just a first step towards understanding a complex phenomenon, goes further than previous models in integrating the biochemical, physiologic and rheological factors that come into play.« less

[Does the GHQ-12 scoring system affect its factor structure? An exploratory study of Ibero American students].

PubMed

Urzúa, Alfonso; Caqueo-Urízar, Alejandra; Bargsted, Mariana; Irarrázaval, Matías

2015-06-01

This study aimed to evaluate whether the scoring system of the General Health Questionnaire (GHQ-12) alters the instrument's factor structure. The method considered 1,972 university students from nine Ibero American countries. Modeling was performed with structural equations for 1, 2, and 3 latent factors. The mechanism for scoring the questions was analyzed within each type of structure. The results indicate that models with 2 and 3 factors show better goodness-of-fit. In relation to scoring mechanisms, procedure 0-1-1-1 for models with 2 and 3 factors showed the best fit. In conclusion, there appears to be a relationship between the response format and the number of factors identified in the instrument's structure. The model with the best fit was 3-factor 0-1-1-1-formatted, but 0-1-2-3 has acceptable and more stable indicators and provides a better format for two- and three-dimensional models.

A dynamic factor model of the evaluation of the financial crisis in Turkey.

PubMed

Sezgin, F; Kinay, B

2010-01-01

Factor analysis has been widely used in economics and finance in situations where a relatively large number of variables are believed to be driven by few common causes of variation. Dynamic factor analysis (DFA) which is a combination of factor and time series analysis, involves autocorrelation matrices calculated from multivariate time series. Dynamic factor models were traditionally used to construct economic indicators, macroeconomic analysis, business cycles and forecasting. In recent years, dynamic factor models have become more popular in empirical macroeconomics. They have more advantages than other methods in various respects. Factor models can for instance cope with many variables without running into scarce degrees of freedom problems often faced in regression-based analysis. In this study, a model which determines the effect of the global crisis on Turkey is proposed. The main aim of the paper is to analyze how several macroeconomic quantities show an alteration before the evolution of the crisis and to decide if a crisis can be forecasted or not.

Reproductive Risk Factors and Coronary Heart Disease in the Women’s Health Initiative Observational Study

PubMed Central

Parikh, Nisha I.; Jeppson, Rebecca P.; Berger, Jeffrey S.; Eaton, Charles B.; Kroenke, Candyce H.; LeBlanc, Erin S.; Lewis, Cora E.; Loucks, Eric B.; Parker, Donna R.; Rillamas-Sun, Eileen; Ryckman, Kelli K; Waring, Molly E.; Schenken, Robert S.; Johnson, Karen C; Edstedt-Bonamy, Anna-Karin; Allison, Matthew A.; Howard, Barbara V.

2016-01-01

Background Reproductive factors provide an early window into a woman’s coronary heart disease (CHD) risk, however their contribution to CHD risk stratification is uncertain. Methods and Results In the Women’s Health Initiative Observational Study, we constructed Cox proportional hazards models for CHD including age, pregnancy status, number of live births, age at menarche, menstrual irregularity, age at first birth, stillbirths, miscarriages, infertility ≥ 1 year, infertility cause, and breastfeeding. We next added each candidate reproductive factor to an established CHD risk factor model. A final model was then constructed with significant reproductive factors added to established CHD risk factors. Improvement in C-statistic, net reclassification index (or NRI with risk categories of <5%, 5–<10%, and ≥10% 10-year risk of CHD) and integrated discriminatory index (IDI) were assessed. Among 72,982 women [n=4607 CHD events, median follow-up=12.0 (IQR=8.3–13.7) years, mean (SD) age 63.2 (7.2) years], an age-adjusted reproductive risk factor model had a C-statistic of 0.675 for CHD. In a model adjusted for established CHD risk factors, younger age at first birth, number of still births, number of miscarriages and lack of breastfeeding were positively associated with CHD. Reproductive factors modestly improved model discrimination (C-statistic increased from 0.726 to 0.730; IDI=0.0013, p-value < 0.0001). Net reclassification for women with events was not improved (NRI events=0.007, p-value=0.18); and for women without events was marginally improved (NRI non-events=0.002, p-value=0.04) Conclusions Key reproductive factors are associated with CHD independently of established CHD risk factors, very modestly improve model discrimination and do not materially improve net reclassification. PMID:27143682

Confirmatory factor analysis of the PTSD Checklist and the Clinician-Administered PTSD Scale in disaster workers exposed to the World Trade Center Ground Zero.

PubMed

Palmieri, Patrick A; Weathers, Frank W; Difede, JoAnn; King, Dainel W

2007-05-01

Although posttraumatic stress disorder (PTSD) factor analytic research has yielded little support for the DSM-IV 3-factor model of reexperiencing, avoidance, and hyperarousal symptoms, no clear consensus regarding alternative models has emerged. One possible explanation is differential instrumentation across studies. In the present study, the authors used confirmatory factor analysis to compare a self-report measure, the PTSD Checklist (PCL), and a structured clinical interview, the Clinician-Administered PTSD Scale (CAPS), in 2,960 utility workers exposed to the World Trade Center Ground Zero site. Although two 4-factor models fit adequately for each measure, the latent structure of the PCL was slightly better represented by correlated reexperiencing, avoidance, dysphoria, and hyperarousal factors, whereas that of the CAPS was slightly better represented by correlated reexperiencing, avoidance, emotional numbing, and hyperarousal factors. After accounting for method variance, the model specifying dysphoria as a distinct factor achieved slightly better fit. Patterns of correlations with external variables provided additional support for the dysphoria model. Implications regarding the underlying structure of PTSD are discussed.

Factor Structure of the Quality of Life Scale for Mental Disorders in Patients With Schizophrenia.

PubMed

Chiu, En-Chi; Lee, Shu-Chun

2018-06-01

The Quality of Life for Mental Disorders (QOLMD) scale was designed to measure health-related quality of life (HRQOL) in patients with mental illness, especially schizophrenia. The QOLMD contains 45 items, which are divided into eight domains. However, the factor structure of the QOLMD has not been evaluated, which restricts the interpretations of the results of this scale. The purpose of this study was to evaluate the factor structures (i.e., unidimensionality, eight-factor structure, and second-order model) of the QOLMD in patients with schizophrenia. Two hundred thirty-eight outpatients with schizophrenia participated. We first conducted confirmatory factor analysis to evaluate the unidimensionality of each domain. After the unidimensionality of the eight individual domains was supported, we examined the eight-factor structure and second-order model. The results of unidimensionality showed sufficient model fit in all of the domains with the exception of the autonomy domain. A good model fit was confirmed for the autonomy domain after deleting two of the original items. The eight-factor structure for the 43-item QOLMD showed an acceptable model fit, although the second-order model showed poor model fit. Our results supported the unidimensionality and eight-factor structure of the 43-item QOLMD. The sum score for each of the domains may be used to reflect its domain-specific function. We recommend using the 43-item QOLMD to capture the multiple domains of HRQOL. However, the second-order model showed an unsatisfactory model fit. Furthermore, caution is advised when interpreting overall HRQOL using the total score for the eight domains.

Separating Common from Unique Variance Within Emotional Distress: An Examination of Reliability and Relations to Worry.

PubMed

Marshall, Andrew J; Evanovich, Emma K; David, Sarah Jo; Mumma, Gregory H

2018-01-17

High comorbidity rates among emotional disorders have led researchers to examine transdiagnostic factors that may contribute to shared psychopathology. Bifactor models provide a unique method for examining transdiagnostic variables by modelling the common and unique factors within measures. Previous findings suggest that the bifactor model of the Depression Anxiety and Stress Scale (DASS) may provide a method for examining transdiagnostic factors within emotional disorders. This study aimed to replicate the bifactor model of the DASS, a multidimensional measure of psychological distress, within a US adult sample and provide initial estimates of the reliability of the general and domain-specific factors. Furthermore, this study hypothesized that Worry, a theorized transdiagnostic variable, would show stronger relations to general emotional distress than domain-specific subscales. Confirmatory factor analysis was used to evaluate the bifactor model structure of the DASS in 456 US adult participants (279 females and 177 males, mean age 35.9 years) recruited online. The DASS bifactor model fitted well (CFI = 0.98; RMSEA = 0.05). The General Emotional Distress factor accounted for most of the reliable variance in item scores. Domain-specific subscales accounted for modest portions of reliable variance in items after accounting for the general scale. Finally, structural equation modelling indicated that Worry was strongly predicted by the General Emotional Distress factor. The DASS bifactor model is generalizable to a US community sample and General Emotional Distress, but not domain-specific factors, strongly predict the transdiagnostic variable Worry.

Coopersmith Self-Esteem: Two Different Hypothesized Factor Models--Both Acceptable for the Same Data Structure.

ERIC Educational Resources Information Center

Hofmann, Rich; Sherman, Larry

Using data from 135 sixth-, seventh-, and eighth-graders between 11 and 15 years old attending a middle school in a suburban Southwest Ohio school district, two hypothesized models of the factor structures for the Coopersmith Self-Esteem Inventory were tested. One model represents the original Coopersmith factor structure, and the other model is…

Towards a Model for Research on the Effects of School Organizational Health Factors on Primary School Performance in Trinidad & Tobago

ERIC Educational Resources Information Center

Ramdass, Mala; Lewis, Theodore

2012-01-01

This article presents a model for research on the effects of school organizational heath factors on primary school academic achievement in Trinidad and Tobago. The model can be applicable for evaluating schools in other developing countries. As proposed, the model hypothesizes relationships between external factors (exogenous variables),…

Reliability and Validity Evidence for Achievement Goal Models in High School Physical Education Settings

ERIC Educational Resources Information Center

Guan, Jianmin; McBride, Ron; Xiang, Ping

2007-01-01

Although empirical research in academic areas provides support for both a 3-factor as well as a 4-factor achievement goal model, both models were proposed and tested with a collegiate sample. Little is known about the generalizability of either model with high school level samples. This study was designed to examine whether the 3-factor model…

Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution.

PubMed

Baele, Guy; Lemey, Philippe; Vansteelandt, Stijn

2013-03-06

Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each model's marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes. We here assess the original 'model-switch' path sampling approach for direct Bayes factor estimation in phylogenetics, as well as an extension that uses more samples, to construct a direct path between two competing models, thereby eliminating the need to calculate each model's marginal likelihood independently. Further, we provide a competing Bayes factor estimator using an adaptation of the recently introduced stepping-stone sampling algorithm and set out to determine appropriate settings for accurately calculating such Bayes factors, with context-dependent evolutionary models as an example. While we show that modest efforts are required to roughly identify the increase in model fit, only drastically increased computation times ensure the accuracy needed to detect more subtle details of the evolutionary process. We show that our adaptation of stepping-stone sampling for direct Bayes factor calculation outperforms the original path sampling approach as well as an extension that exploits more samples. Our proposed approach for Bayes factor estimation also has preferable statistical properties over the use of individual marginal likelihood estimates for both models under comparison. Assuming a sigmoid function to determine the path between two competing models, we provide evidence that a single well-chosen sigmoid shape value requires less computational efforts in order to approximate the true value of the (log) Bayes factor compared to the original approach. We show that the (log) Bayes factors calculated using path sampling and stepping-stone sampling differ drastically from those estimated using either of the harmonic mean estimators, supporting earlier claims that the latter systematically overestimate the performance of high-dimensional models, which we show can lead to erroneous conclusions. Based on our results, we argue that highly accurate estimation of differences in model fit for high-dimensional models requires much more computational effort than suggested in recent studies on marginal likelihood estimation.

Factor structure of DSM-5 PTSD symptoms in trauma-exposed adolescents: Examining stability across time.

PubMed

Wang, Li; Cao, Xing; Cao, Chengqi; Fang, Ruojiao; Yang, Haibo; Elhai, Jon D

2017-12-01

This study investigated the latent structure of DSM-5 PTSD symptoms using two-wave longitudinal data collected from a sample of adolescents exposed to an explosion accident. Two waves of surveys were conducted approximately 3 and 8 months after the accident, respectively. A total of 836 students completed the baseline survey, and 762 students completed the follow-up survey. The results of confirmatory factor analyses(CFA) indicated that a seven-factor hybrid model composed of intrusion, avoidance, negative affect, anhedonia, externalizing behaviors, anxious arousal and dysphoric arousal factors yielded significantly better data fit at both waves than the other models including the DSM-5 four-factor model, the six-factor anhedonia and externalizing behaviors models. Furthermore, the results of CFA invariance tests supported the longitudinal invariance of the model. Implications and limitations in terms of these results are discussed. Copyright © 2017 Elsevier Ltd. All rights reserved.

Emotional intelligence is a second-stratum factor of intelligence: evidence from hierarchical and bifactor models.

PubMed

MacCann, Carolyn; Joseph, Dana L; Newman, Daniel A; Roberts, Richard D

2014-04-01

This article examines the status of emotional intelligence (EI) within the structure of human cognitive abilities. To evaluate whether EI is a 2nd-stratum factor of intelligence, data were fit to a series of structural models involving 3 indicators each for fluid intelligence, crystallized intelligence, quantitative reasoning, visual processing, and broad retrieval ability, as well as 2 indicators each for emotion perception, emotion understanding, and emotion management. Unidimensional, multidimensional, hierarchical, and bifactor solutions were estimated in a sample of 688 college and community college students. Results suggest adequate fit for 2 models: (a) an oblique 8-factor model (with 5 traditional cognitive ability factors and 3 EI factors) and (b) a hierarchical solution (with cognitive g at the highest level and EI representing a 2nd-stratum factor that loads onto g at λ = .80). The acceptable relative fit of the hierarchical model confirms the notion that EI is a group factor of cognitive ability, marking the expression of intelligence in the emotion domain. The discussion proposes a possible expansion of Cattell-Horn-Carroll theory to include EI as a 2nd-stratum factor of similar standing to factors such as fluid intelligence and visual processing.

Using group model building to develop a culturally grounded model of breastfeeding for low-income African American women in the USA.

PubMed

Reno, Rebecca

2017-03-02

To identify barriers and supporting factors for breastfeeding, and the dynamic interactions between them, as identified by low-income African American women and lactation peer helpers. Stark breastfeeding disparities exist between African American mothers and their White counterparts in the USA. This pattern is often replicated across the globe, with marginalised populations demonstrating decreased breastfeeding rates. While breastfeeding research focused on sociocultural factors for different populations has been conducted, a more dynamic model of the factors impacting breastfeeding may help identify effective leverage points for change. Group model building was used as a grounded theoretical approach, to build and validate a model representing factors impacting breastfeeding and the relationships between them. Low-income African American women (n = 21) and lactation peer helpers (n = 3) were engaged in model building sessions to identify factors impacting breastfeeding. A two-cycle process was used for analysis, in vivo and axial coding. The final factors and model were validated with a subgroup of participants. The participants generated 82 factors that make breastfeeding easier, and 86 factors that make breastfeeding more challenging. These were grouped into 10 and 14 themes, respectively. A final model was constructed identifying three domains impacting breastfeeding: a mother's return to work or school, her knowledge, support and persistence, and the social acceptance of breastfeeding. This study documented the sociocultural context within which low-income African American women are situated by identifying factors impacting breastfeeding, and the dynamic interactions between them. The model also provided various leverage points from which breastfeeding women can be supported. Postpartum nurses are critical in supporting breastfeeding practices. To be most effective, they must be aware of the factors impacting breastfeeding, some of which may be unique to women based on their culture. © 2017 John Wiley & Sons Ltd.

Causal Indicators Can Help to Interpret Factors

ERIC Educational Resources Information Center

Bentler, Peter M.

2016-01-01

The latent factor in a causal indicator model is no more than the latent factor of the factor part of the model. However, if the causal indicator variables are well-understood and help to improve the prediction of individuals' factor scores, they can help to interpret the meaning of the latent factor. Aguirre-Urreta, Rönkkö, and Marakas (2016)…

A confirmatory factor analysis of the Beck Depression Inventory-II in end-stage renal disease patients.

PubMed

Chilcot, Joseph; Norton, Sam; Wellsted, David; Almond, Mike; Davenport, Andrew; Farrington, Ken

2011-09-01

We sought to examine several competing factor structures of the Beck Depression Inventory-II (BDI) in a sample of patients with End-Stage Renal Disease (ESRD), in which setting the factor structure is poorly defined, though depression symptoms are common. In addition, demographic and clinical correlates of the identified factors were examined. The BDI was administered to clinical sample of 460 ESRD patients attending 4 UK renal centres. Competing models of the factor structure of the BDI were evaluated using confirmatory factor analysis. The best fitting model consisted of general depression factor that accounted for 81% of the common variance between all items along with orthogonal cognitive and somatic factors (G-S-C model, CFI=.983, TLI=.979, RMSEA=.037), which explained 8% and 9% of the common variance, respectively. Age, diabetes, and ethnicity were significantly related to the cognitive factor, whereas albumin, dialysis adequacy, and ethnicity were related to the somatic factor. No demographic or clinical variable was associated with the general factor. The general-factor model provides the best fitting and conceptually most acceptable interpretation of the BDI. Furthermore, the cognitive and somatic factors appear to be related to specific demographic and clinical factors. Copyright © 2011 Elsevier Inc. All rights reserved.

The Meaning of Higher-Order Factors in Reflective-Measurement Models

ERIC Educational Resources Information Center

Eid, Michael; Koch, Tobias

2014-01-01

Higher-order factor analysis is a widely used approach for analyzing the structure of a multidimensional test. Whenever first-order factors are correlated researchers are tempted to apply a higher-order factor model. But is this reasonable? What do the higher-order factors measure? What is their meaning? Willoughby, Holochwost, Blanton, and Blair…

Confirmatory Factor Analysis of the WISC-III with Child Psychiatric Inpatients.

ERIC Educational Resources Information Center

Tupa, David J.; Wright, Margaret O'Dougherty; Fristad, Mary A.

1997-01-01

Factor models of the Wechsler Intelligence Scale for Children-Third Edition (WISC-III) for one, two, three, and four factors were tested using confirmatory factor analysis with a sample of 177 child psychiatric inpatients. The four-factor model proposed in the WISC-III manual provided the best fit to the data. (SLD)

Is the mental wellbeing of young Australians best represented by a single, multidimensional or bifactor model?

PubMed

Hides, Leanne; Quinn, Catherine; Stoyanov, Stoyan; Cockshaw, Wendell; Mitchell, Tegan; Kavanagh, David J

2016-07-30

Internationally there is a growing interest in the mental wellbeing of young people. However, it is unclear whether mental wellbeing is best conceptualized as a general wellbeing factor or a multidimensional construct. This paper investigated whether mental wellbeing, measured by the Mental Health Continuum-Short Form (MHC-SF), is best represented by: (1) a single-factor general model; (2) a three-factor multidimensional model or (3) a combination of both (bifactor model). 2220 young Australians aged between 16 and 25 years completed an online survey including the MHC-SF and a range of other wellbeing and mental ill-health measures. Exploratory factor analysis supported a bifactor solution, comprised of a general wellbeing factor, and specific group factors of psychological, social and emotional wellbeing. Confirmatory factor analysis indicated that the bifactor model had a better fit than competing single and three-factor models. The MHC-SF total score was more strongly associated with other wellbeing and mental ill-health measures than the social, emotional or psychological subscale scores. Findings indicate that the mental wellbeing of young people is best conceptualized as an overarching latent construct (general wellbeing) to which emotional, social and psychological domains contribute. The MHC-SF total score is a valid and reliable measure of this general wellbeing factor. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Examining the ethnoracial invariance of a bifactor model of anxiety sensitivity and the incremental validity of the physical domain-specific factor in a primary-care patient sample.

PubMed

Fergus, Thomas A; Kelley, Lance P; Griggs, Jackson O

2017-10-01

There is growing support for a bifactor conceptualization of the Anxiety Sensitivity Index-3 (ASI-3; Taylor et al., 2007), consisting of a General factor and 3 domain-specific factors (i.e., Physical, Cognitive, Social). Earlier studies supporting a bifactor model of the ASI-3 used samples that consisted of predominantly White respondents. In addition, extant research has yet to support the incremental validity of the Physical domain-specific factor while controlling for the General factor. The present study is an examination of a bifactor model of the ASI-3 and the measurement invariance of that model among an ethnoracially diverse sample of primary-care patients (N = 533). Results from multiple-group confirmatory factor analysis supported the configural and metric/scalar invariance of the bifactor model of the ASI-3 across self-identifying Black, Latino, and White respondents. The Physical domain-specific factor accounted for unique variance in an index of health anxiety beyond the General factor. These results provide support for the generalizability of a bifactor model of the ASI-3 across 3 ethnoracial groups, as well as indication of the incremental explanatory power of the Physical domain-specific factor. Study implications are discussed. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Associations between five-factor model of the Positive and Negative Syndrome Scale and plasma levels of monoamine metabolite in patients with schizophrenia.

PubMed

Watanabe, Kenya; Miura, Itaru; Kanno-Nozaki, Keiko; Horikoshi, Sho; Mashiko, Hirobumi; Niwa, Shin-Ichi; Yabe, Hirooki

2015-12-15

The five-factor model of the Positive and Negative Syndrome Scale (PANSS) for schizophrenia symptoms is the most common multiple-factor model used in analyses; its use may improve evaluation of symptoms in schizophrenia patients. Plasma monoamine metabolite levels are possible indicators of clinical symptoms or response to antipsychotics in schizophrenia. We investigated the association between five-factor model components and plasma monoamine metabolites levels to explore the model's biological basis. Plasma levels of homovanillic acid (HVA), 3-methoxy-4-hydroxyphenylglycol (MHPG), and 5-hydroxyindoleacetic acid (5-HIAA) were measured using high-performance liquid chromatography in 65 Japanese patients with schizophrenia. Significant negative correlation between plasma 5-HIAA levels and the depression/anxiety component was found. Furthermore, significant positive correlation was found between plasma MHPG levels and the excitement component. Plasma HVA levels were not correlated with any five-factor model component. These results suggest that the five-factor model of the PANSS may have a biological basis, and may be useful for elucidating the psychopathology of schizophrenia. Assessment using the five-factor model may enable understanding of monoaminergic dysfunction, possibly allowing more appropriate medication selection. Further studies of a larger number of first-episode schizophrenia patients are needed to confirm and extend these results. Copyright © 2015 Elsevier Ireland Ltd. All rights reserved.

Modeling the factors affecting unsafe behavior in the construction industry from safety supervisors' perspective.

PubMed

Khosravi, Yahya; Asilian-Mahabadi, Hassan; Hajizadeh, Ebrahim; Hassanzadeh-Rangi, Narmin; Bastani, Hamid; Khavanin, Ali; Mortazavi, Seyed Bagher

2014-01-01

There can be little doubt that the construction is the most hazardous industry in the worldwide. This study was designed to modeling the factors affecting unsafe behavior from the perspective of safety supervisors. The qualitative research was conducted to extract a conceptual model. A structural model was then developed based on a questionnaire survey (n=266) by two stage Structural Equation Model (SEM) approach. An excellent confirmed 12-factors structure explained about 62% of variances unsafe behavior in the construction industry. A good fit structural model indicated that safety climate factors were positively correlated with safety individual factors (P<0.001) and workplace safety condition (P<0.001). The workplace safety condition was found to play a strong mediating role in linking the safety climate and construction workers' engagement in safe or unsafe behavior. In order to improve construction safety performance, more focus on the workplace condition is required.

Measurement and Structural Model Class Separation in Mixture CFA: ML/EM versus MCMC

ERIC Educational Resources Information Center

Depaoli, Sarah

2012-01-01

Parameter recovery was assessed within mixture confirmatory factor analysis across multiple estimator conditions under different simulated levels of mixture class separation. Mixture class separation was defined in the measurement model (through factor loadings) and the structural model (through factor variances). Maximum likelihood (ML) via the…

Lessons from the Specific Factors Model of International Trade.

ERIC Educational Resources Information Center

Tohamy, Soumaya M.; Mixon, J. Wilson, Jr.

2003-01-01

Uses the Specific Factors model to illustrate the meaning of economic efficiency, how complex economies simultaneously determine prices and quantities, and how changes in demand conditions or technology can affect income distribution among owners of factors of production. Employs spreadsheets to help students see how the model works. (JEH)

Multiple robustness in factorized likelihood models.

PubMed

Molina, J; Rotnitzky, A; Sued, M; Robins, J M

2017-09-01

We consider inference under a nonparametric or semiparametric model with likelihood that factorizes as the product of two or more variation-independent factors. We are interested in a finite-dimensional parameter that depends on only one of the likelihood factors and whose estimation requires the auxiliary estimation of one or several nuisance functions. We investigate general structures conducive to the construction of so-called multiply robust estimating functions, whose computation requires postulating several dimension-reducing models but which have mean zero at the true parameter value provided one of these models is correct.

Multi-Group Covariance and Mean Structure Modeling of the Relationship between the WAIS-III Common Factors and Sex and Educational Attainment in Spain

ERIC Educational Resources Information Center

Dolan, Conor V.; Colom, Roberto; Abad, Francisco J.; Wicherts, Jelte M.; Hessen, David J.; van de Sluis, Sophie

2006-01-01

We investigated sex effects and the effects of educational attainment (EA) on the covariance structure of the WAIS-III in a subsample of the Spanish standardization data. We fitted both first order common factor models and second order common factor models. The latter include general intelligence ("g") as a second order common factor.…

An Investigation of the Factor Structure and Convergent and Discriminant Validity of the Five-Factor Model Rating Form

ERIC Educational Resources Information Center

Samuel, Douglas B.; Mullins-Sweatt, Stephanie N.; Widiger, Thomas A.

2013-01-01

The Five-Factor Model Rating Form (FFMRF) is a one-page measure designed to provide an efficient assessment of the higher order domains of the Five Factor Model (FFM) as well as the more specific, lower order facets proposed by McCrae and Costa. Although previous research has suggested that the FFMRF's assessment of the lower order facets converge…

Factor structure of overall autobiographical memory usage: the directive, self and social functions revisited.

PubMed

Rasmussen, Anne S; Habermas, Tilmann

2011-08-01

According to theory, autobiographical memory serves three broad functions of overall usage: directive, self, and social. However, there is evidence to suggest that the tripartite model may be better conceptualised in terms of a four-factor model with two social functions. In the present study we examined the two models in Danish and German samples, using the Thinking About Life Experiences Questionnaire (TALE; Bluck, Alea, Habermas, & Rubin, 2005), which measures the overall usage of the three functions generalised across concrete memories. Confirmatory factor analysis supported the four-factor model and rejected the theoretical three-factor model in both samples. The results are discussed in relation to cultural differences in overall autobiographical memory usage as well as sharing versus non-sharing aspects of social remembering.

Cross-cultural confirmation of bi-factor models of a symptom distress measure: Symptom Checklist-90-Revised in clinical samples.

PubMed

Urbán, Róbert; Arrindell, Willem A; Demetrovics, Zsolt; Unoka, Zsolt; Timman, Reinier

2016-05-30

Four decades have elapsed since the introduction for clinical and research purposes of the Symptom Checklist-90(-R). Yet, its underlying dimensional structure has not been clearly delineated. A shift has been observed in the methods utilized-from predominantly exploratory factor analytic in nature in the first two decades or so to different confirmatory methods in recent years. A need remains to search for a structure that remains invariant across samples and nations. In that context, the present study attempted to replicate and extend recent findings yielded in a Hungarian general population sample (N=2,874) with two psychiatric patient samples from Hungary (N=972) and The Netherlands (N=1,902). In doing so, four models were contrasted: the one-factor model, Derogatis' nine factor model, a second-ordered factor model, and a bi-factor model. The bi-factor model was shown to yield the closest fit to the data in both countries. Further studies are needed to determine the stable number and kind of subscale scores that reflect the specific (primary) symptoms best, that is, those subscales with minimal shared variance with the overall general psychological distress dimension. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Simultaneous tensor decomposition and completion using factor priors.

PubMed

Chen, Yi-Lei; Hsu, Chiou-Ting; Liao, Hong-Yuan Mark

2014-03-01

The success of research on matrix completion is evident in a variety of real-world applications. Tensor completion, which is a high-order extension of matrix completion, has also generated a great deal of research interest in recent years. Given a tensor with incomplete entries, existing methods use either factorization or completion schemes to recover the missing parts. However, as the number of missing entries increases, factorization schemes may overfit the model because of incorrectly predefined ranks, while completion schemes may fail to interpret the model factors. In this paper, we introduce a novel concept: complete the missing entries and simultaneously capture the underlying model structure. To this end, we propose a method called simultaneous tensor decomposition and completion (STDC) that combines a rank minimization technique with Tucker model decomposition. Moreover, as the model structure is implicitly included in the Tucker model, we use factor priors, which are usually known a priori in real-world tensor objects, to characterize the underlying joint-manifold drawn from the model factors. By exploiting this auxiliary information, our method leverages two classic schemes and accurately estimates the model factors and missing entries. We conducted experiments to empirically verify the convergence of our algorithm on synthetic data and evaluate its effectiveness on various kinds of real-world data. The results demonstrate the efficacy of the proposed method and its potential usage in tensor-based applications. It also outperforms state-of-the-art methods on multilinear model analysis and visual data completion tasks.

Higher-Order Factors of Personality: Do They Exist?

PubMed Central

Ashton, Michael C.; Lee, Kibeom; Goldberg, Lewis R.; de Vries, Reinout E.

2010-01-01

Scales that measure the Big Five personality factors are often substantially intercorrelated. These correlations are sometimes interpreted as implying the existence of two higher-order factors of personality. We show that correlations between measures of broad personality factors do not necessarily imply the existence of higher-order factors, and might instead be due to variables that represent same-signed blends of orthogonal factors. Therefore, the hypotheses of higher-order factors and blended variables can only be tested with data on lower-level personality variables that define the personality factors. We compared the higher-order factor model and the blended variable model in three participant samples using the Big Five Aspect Scales, and found better fit for the latter model. In other analyses using the HEXACO Personality Inventory, we identified mutually uncorrelated markers of six personality factors. We conclude that correlations between personality factor scales can be explained without postulating any higher-order dimensions of personality. PMID:19458345

Organizational Change in the U.S. Customs and Border Protection Agency

DTIC Science & Technology

2012-05-17

requirements. Models of organizational change, like the Burke- Litwin model, facilitate an assessment of CBP’s transformation because they identify factors...valuable insight. The Burke- Litwin model offers a framework for prioritizing the organizational factors of change. Others outline the importance of an...implementation plan first and then assesses CBP’s efforts against the Burke- Litwin model which stresses transformational factors: strategy, leadership, and

SCL-90-R emotional distress ratings in substance use and impulse control disorders: One-factor, oblique first-order, higher-order, and bi-factor models compared.

PubMed

Arrindell, Willem A; Urbán, Róbert; Carrozzino, Danilo; Bech, Per; Demetrovics, Zsolt; Roozen, Hendrik G

2017-09-01

To fully understand the dimensionality of an instrument in a certain population, rival bi-factor models should be routinely examined and tested against oblique first-order and higher-order structures. The present study is among the very few studies that have carried out such a comparison in relation to the Symptom Checklist-90-R. In doing so, it utilized a sample comprising 2593 patients with substance use and impulse control disorders. The study also included a test of a one-dimensional model of general psychological distress. Oblique first-order factors were based on the original a priori 9-dimensional model advanced by Derogatis (1977); and on an 8-dimensional model proposed by Arrindell and Ettema (2003)-Agoraphobia, Anxiety, Depression, Somatization, Cognitive-performance deficits, Interpersonal sensitivity and mistrust, Acting-out hostility, and Sleep difficulties. Taking individual symptoms as input, three higher-order models were tested with at the second-order levels either (1) General psychological distress; (2) 'Panic with agoraphobia', 'Depression' and 'Extra-punitive behavior'; or (3) 'Irritable-hostile depression' and 'Panic with agoraphobia'. In line with previous studies, no support was found for the one-factor model. Bi-factor models were found to fit the dataset best relative to the oblique first-order and higher-order models. However, oblique first-order and higher-order factor models also fit the data fairly well in absolute terms. Higher-order solution (2) provided support for R.F. Krueger's empirical model of psychopathology which distinguishes between fear, distress, and externalizing factors (Krueger, 1999). The higher-order model (3), which combines externalizing and distress factors (Irritable-hostile depression), fit the data numerically equally well. Overall, findings were interpreted as supporting the hypothesis that the prevalent forms of symptomatology addressed have both important common and unique features. Proposals were made to improve the Depression subscale as its scores represent more of a very common construct as is measured with the severity (total) scale than of a specific measure that purports to measure what it should assess-symptoms of depression. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

Evidence for a unique PTSD construct represented by PTSD's D1-D3 symptoms.

PubMed

Elhai, Jon D; Biehn, Tracey L; Armour, Cherie; Klopper, Jessica J; Frueh, B Christopher; Palmieri, Patrick A

2011-04-01

Two models of posttraumatic stress disorder (PTSD) have received the most empirical support in confirmatory factor analytic studies: King, Leskin, King, and Weathers' (1998) Emotional Numbing model of reexperiencing, avoidance, emotional numbing and hyperarousal; and Simms, Watson, and Doebbeling's (2002) Dysphoria model of reexperiencing, avoidance, dysphoria and hyperarousal. These models only differ in placement of three PTSD symptoms: sleep problems (D1), irritability (D2), and concentration problems (D3). In the present study, we recruited 252 women victims of domestic violence and tested whether there is empirical support to separate these three PTSD symptoms into a fifth factor, while retaining the Emotional Numbing and Dysphoria models' remaining four factors. Confirmatory factor analytic findings demonstrated that separating the three symptoms into a separate factor significantly enhanced model fit for the Emotional Numbing and Dysphoria models. These three symptoms may represent a unique latent construct. Implications are discussed. Copyright © 2010 Elsevier Ltd. All rights reserved.

Research on artistic gymnastics training guidance model

NASA Astrophysics Data System (ADS)

Luo, Lin; Sun, Xianzhong

2017-04-01

Rhythmic gymnastics training guidance model, taking into consideration the features of artistic gymnastics training, is put forward to help gymnasts identify their deficiencies and unskilled technical movements and improve their training effects. The model is built on the foundation of both physical quality indicator model and artistic gymnastics training indicator model. Physical quality indicator model composed of bodily factor, flexibility-strength factor and speed-dexterity factor delivers an objective evaluation with reference to basic sport testing data. Training indicator model, based on physical fitness indicator, helps analyze the technical movements, through which the impact from each bodily factor on technical movements is revealed. AG training guidance model, in further combination with actual training data and in comparison with the data shown in the training indicator model, helps identify the problems in trainings, and thus improve the training effect. These three models when in combined use and in comparison with historical model data can check and verify the improvement in training effect over a certain period of time.

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history

PubMed Central

Sparks, Jeffrey A.; Chen, Chia-Yen; Jiang, Xia; Askling, Johan; Hiraki, Linda T.; Malspeis, Susan; Klareskog, Lars; Alfredsson, Lars; Costenbader, Karen H.; Karlson, Elizabeth W.

2014-01-01

Objective To develop and validate rheumatoid arthritis (RA) risk models based on family history, epidemiologic factors, and known genetic risk factors. Methods We developed and validated models for RA based on known RA risk factors, among women in two cohorts: the Nurses’ Health Study (NHS, 381 RA cases and 410 controls) and the Epidemiological Investigation of RA (EIRA, 1244 RA cases and 971 controls). Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC) in logistic regression models for the study population and for those with positive family history. The joint effect of family history with genetics, smoking, and body mass index (BMI) was evaluated using logistic regression models to estimate odds ratios (OR) for RA. Results The complete model including family history, epidemiologic risk factors, and genetics demonstrated AUCs of 0.74 for seropositive RA in NHS and 0.77 for anti-citrullinated protein antibody (ACPA)-positive RA in EIRA. Among women with positive family history, discrimination was excellent for complete models for seropositive RA in NHS (AUC 0.82) and ACPA-positive RA in EIRA (AUC 0.83). Positive family history, high genetic susceptibility, smoking, and increased BMI had an OR of 21.73 for ACPA-positive RA. Conclusions We developed models for seropositive and seronegative RA phenotypes based on family history, epidemiologic and genetic factors. Among those with positive family history, models utilizing epidemiologic and genetic factors were highly discriminatory for seropositive and seronegative RA. Assessing epidemiological and genetic factors among those with positive family history may identify individuals suitable for RA prevention strategies. PMID:24685909

Dimensional Model for Estimating Factors influencing Childhood Obesity: Path Analysis Based Modeling

PubMed Central

Kheirollahpour, Maryam; Shohaimi, Shamarina

2014-01-01

The main objective of this study is to identify and develop a comprehensive model which estimates and evaluates the overall relations among the factors that lead to weight gain in children by using structural equation modeling. The proposed models in this study explore the connection among the socioeconomic status of the family, parental feeding practice, and physical activity. Six structural models were tested to identify the direct and indirect relationship between the socioeconomic status and parental feeding practice general level of physical activity, and weight status of children. Finally, a comprehensive model was devised to show how these factors relate to each other as well as to the body mass index (BMI) of the children simultaneously. Concerning the methodology of the current study, confirmatory factor analysis (CFA) was applied to reveal the hidden (secondary) effect of socioeconomic factors on feeding practice and ultimately on the weight status of the children and also to determine the degree of model fit. The comprehensive structural model tested in this study suggested that there are significant direct and indirect relationships among variables of interest. Moreover, the results suggest that parental feeding practice and physical activity are mediators in the structural model. PMID:25097878

Confirmatory Factor Analysis of the Minnesota Nicotine Withdrawal Scale

PubMed Central

Toll, Benjamin A.; O’Malley, Stephanie S.; McKee, Sherry A.; Salovey, Peter; Krishnan-Sarin, Suchitra

2008-01-01

The authors examined the factor structure of the Minnesota Nicotine Withdrawal Scale (MNWS) using confirmatory factor analysis in clinical research samples of smokers trying to quit (n = 723). Three confirmatory factor analytic models, based on previous research, were tested with each of the 3 study samples at multiple points in time. A unidimensional model including all 8 MNWS items was found to be the best explanation of the data. This model produced fair to good internal consistency estimates. Additionally, these data revealed that craving should be included in the total score of the MNWS. Factor scores derived from this single-factor, 8-item model showed that increases in withdrawal were associated with poor smoking outcome for 2 of the clinical studies. Confirmatory factor analyses of change scores showed that the MNWS symptoms cohere as a syndrome over time. Future investigators should report a total score using all of the items from the MNWS. PMID:17563141

Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models.

PubMed

Sae-Lim, Panya; Komen, Hans; Kause, Antti; Mulder, Han A

2014-02-26

Identifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes. Reaction norm and factor analytic models were used to identify which environmental variables (age at harvest, water temperature, oxygen, and photoperiod) may have caused the observed GxE interaction. Data on body weight at harvest was recorded on 8976 offspring reared in various locations: (1) a breeding environment in the USA (nucleus), (2) a recirculating aquaculture system in the Freshwater Institute in West Virginia, USA, (3) a high-altitude farm in Peru, and (4) a low-water temperature farm in Germany. Akaike and Bayesian information criteria were used to compare models. The combination of days to harvest multiplied with daily temperature (Day*Degree) and photoperiod were identified by the reaction norm model as the environmental variables responsible for the GxE interaction. The latent common factor that was identified by the factor analytic model showed the highest correlation with Day*Degree. Day*Degree and photoperiod were the environmental variables that differed most between Peru and other environments. Akaike and Bayesian information criteria indicated that the factor analytical model was more parsimonious than the reaction norm model. Day*Degree and photoperiod were identified as environmental variables responsible for the strong GxE interaction for body weight at harvest in rainbow trout across four environments. Both the reaction norm and the factor analytic models can help identify the environmental variables responsible for GxE interaction. A factor analytic model is preferred over a reaction norm model when limited information on differences in environmental variables between farms is available.

Identifying environmental variables explaining genotype-by-environment interaction for body weight of rainbow trout (Onchorynchus mykiss): reaction norm and factor analytic models

PubMed Central

2014-01-01

Background Identifying the relevant environmental variables that cause GxE interaction is often difficult when they cannot be experimentally manipulated. Two statistical approaches can be applied to address this question. When data on candidate environmental variables are available, GxE interaction can be quantified as a function of specific environmental variables using a reaction norm model. Alternatively, a factor analytic model can be used to identify the latent common factor that explains GxE interaction. This factor can be correlated with known environmental variables to identify those that are relevant. Previously, we reported a significant GxE interaction for body weight at harvest in rainbow trout reared on three continents. Here we explore their possible causes. Methods Reaction norm and factor analytic models were used to identify which environmental variables (age at harvest, water temperature, oxygen, and photoperiod) may have caused the observed GxE interaction. Data on body weight at harvest was recorded on 8976 offspring reared in various locations: (1) a breeding environment in the USA (nucleus), (2) a recirculating aquaculture system in the Freshwater Institute in West Virginia, USA, (3) a high-altitude farm in Peru, and (4) a low-water temperature farm in Germany. Akaike and Bayesian information criteria were used to compare models. Results The combination of days to harvest multiplied with daily temperature (Day*Degree) and photoperiod were identified by the reaction norm model as the environmental variables responsible for the GxE interaction. The latent common factor that was identified by the factor analytic model showed the highest correlation with Day*Degree. Day*Degree and photoperiod were the environmental variables that differed most between Peru and other environments. Akaike and Bayesian information criteria indicated that the factor analytical model was more parsimonious than the reaction norm model. Conclusions Day*Degree and photoperiod were identified as environmental variables responsible for the strong GxE interaction for body weight at harvest in rainbow trout across four environments. Both the reaction norm and the factor analytic models can help identify the environmental variables responsible for GxE interaction. A factor analytic model is preferred over a reaction norm model when limited information on differences in environmental variables between farms is available. PMID:24571451

Psychopathy, intelligence and conviction history.

PubMed

Heinzen, Hanna; Köhler, Denis; Godt, Nils; Geiger, Friedemann; Huchzermeier, Christian

2011-01-01

The current study examined the relationship between psychopathy, intelligence and two variables describing the conviction history (length of conviction and number of prior convictions). It was hypothesized that psychopathy factors (interpersonal and antisocial factors assuming a 2-factor model or interpersonal, affective, lifestyle and antisocial factors assuming a 4-factor model) would be related in different ways to IQ scores, length of conviction and number of prior convictions. Psychopathy and IQ were assessed using the PCL:SV and the CFT 20-R respectively. Results indicated no association between interpersonal psychopathy features (Factor 1, two-factor model), IQ and the number of prior convictions but a positive association between Factor 1 and the length of conviction. Antisocial features (Factor 2, two-factor model) were negatively related to IQ and the length of conviction and positively related to the number of prior convictions. Results were further differentiated for the four-factor model of psychopathy. The relationship between IQ and psychopathy features was further assessed by statistically isolating the effects of the two factors of psychopathy. It was found that individuals scoring high on interpersonal features of psychopathy are more intelligent than those scoring high on antisocial features, but less intelligent than those scoring low on both psychopathy features. The results underpin the importance of allocating psychopathic individuals to subgroups on the basis of personality characteristics and criminological features. These subgroups may identify different types of offenders and may be highly valuable for defining treatment needs and risk of future violence. Copyright © 2011 Elsevier Ltd. All rights reserved.

Spatial Resolution Effects of Digital Terrain Models on Landslide Susceptibility Analysis

NASA Astrophysics Data System (ADS)

Chang, K. T.; Dou, J.; Chang, Y.; Kuo, C. P.; Xu, K. M.; Liu, J. K.

2016-06-01

The purposes of this study are to identify the maximum number of correlated factors for landslide susceptibility mapping and to evaluate landslide susceptibility at Sihjhong river catchment in the southern Taiwan, integrating two techniques, namely certainty factor (CF) and artificial neural network (ANN). The landslide inventory data of the Central Geological Survey (CGS, MOEA) in 2004-2014 and two digital elevation model (DEM) datasets including a 5-meter LiDAR DEM and a 30-meter Aster DEM were prepared. We collected thirteen possible landslide-conditioning factors. Considering the multi-collinearity and factor redundancy, we applied the CF approach to optimize these thirteen conditioning factors. We hypothesize that if the CF values of the thematic factor layers are positive, it implies that these conditioning factors have a positive relationship with the landslide occurrence. Therefore, based on this assumption and positive CF values, seven conditioning factors including slope angle, slope aspect, elevation, terrain roughness index (TRI), terrain position index (TPI), total curvature, and lithology have been selected for further analysis. The results showed that the optimized-factors model provides a better accuracy for predicting landslide susceptibility in the study area. In conclusion, the optimized-factors model is suggested for selecting relative factors of landslide occurrence.

Bayes factors and multimodel inference

USGS Publications Warehouse

Link, W.A.; Barker, R.J.; Thomson, David L.; Cooch, Evan G.; Conroy, Michael J.

2009-01-01

Multimodel inference has two main themes: model selection, and model averaging. Model averaging is a means of making inference conditional on a model set, rather than on a selected model, allowing formal recognition of the uncertainty associated with model choice. The Bayesian paradigm provides a natural framework for model averaging, and provides a context for evaluation of the commonly used AIC weights. We review Bayesian multimodel inference, noting the importance of Bayes factors. Noting the sensitivity of Bayes factors to the choice of priors on parameters, we define and propose nonpreferential priors as offering a reasonable standard for objective multimodel inference.

Factor structure of posttraumatic stress disorder symptoms in OEF/OIF veterans presenting to a polytrauma clinic.

PubMed

Maestas, Kacey Little; Benge, Jared F; Pastorek, Nicholas J; Lemaire, Ashley; Darrow, Rachel

2011-11-01

A significant number of Operation Iraqi Freedom/Operation Enduring Freedom (OEF/OIF) veterans are returning from deployment and presenting to Veterans Health Administration (VHA) polytrauma clinics with elevated rates of posttraumatic stress disorder (PTSD) and mild traumatic brain injury (mTBI). Inherent to the accurate assessment and treatment of this diagnostically complex group of veterans is the assumption that the construct of PTSD is the same in this population as in other trauma groups. To our knowledge, no previous study has examined the structure of PTSD in this relevant and fast-growing population of treatment-seeking OEF/OIF veterans. Evidence suggests that the latent structure of PTSD symptoms is best represented by a four-factor model, rather than the three-factor model found in the current DSM-IV-TR. Thus, we examined the three and four-factor models using the PTSD Check List-Civilian (PCL-C) in a sample of treatment-seeking OEF/OIF veterans seen through a VHA polytrauma clinic. A chart review was conducted for OEF/OIF veterans (N = 361) seen through a VHA outpatient polytrauma clinic from September 2007 through August 2008. Participants completed the PCL-C as part of a comprehensive polytrauma evaluation. Confirmatory factor analyses showed that the DSM-IV-TR three-factor model did not fit the data well. A direct comparison showed that the four-factor model provided a superior fit relative to the three-factor model. Results extend the generalizability of the four-factor model to OEF/OIF veterans presenting to Veterans Health Administration (VHA) polytrauma clinics.

Development and application of a complex numerical model and software for the computation of dose conversion factors for radon progenies.

PubMed

Farkas, Árpád; Balásházy, Imre

2015-04-01

A more exact determination of dose conversion factors associated with radon progeny inhalation was possible due to the advancements in epidemiological health risk estimates in the last years. The enhancement of computational power and the development of numerical techniques allow computing dose conversion factors with increasing reliability. The objective of this study was to develop an integrated model and software based on a self-developed airway deposition code, an own bronchial dosimetry model and the computational methods accepted by International Commission on Radiological Protection (ICRP) to calculate dose conversion coefficients for different exposure conditions. The model was tested by its application for exposure and breathing conditions characteristic of mines and homes. The dose conversion factors were 8 and 16 mSv WLM(-1) for homes and mines when applying a stochastic deposition model combined with the ICRP dosimetry model (named PM-A model), and 9 and 17 mSv WLM(-1) when applying the same deposition model combined with authors' bronchial dosimetry model and the ICRP bronchiolar and alveolar-interstitial dosimetry model (called PM-B model). User friendly software for the computation of dose conversion factors has also been developed. The software allows one to compute conversion factors for a large range of exposure and breathing parameters and to perform sensitivity analyses. © The Author 2014. Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com.

Factor Analysis of Drawings: Application to college student models of the greenhouse effect

NASA Astrophysics Data System (ADS)

Libarkin, Julie C.; Thomas, Stephen R.; Ording, Gabriel

2015-09-01

Exploratory factor analysis was used to identify models underlying drawings of the greenhouse effect made by over 200 entering university freshmen. Initial content analysis allowed deconstruction of drawings into salient features, with grouping of these features via factor analysis. A resulting 4-factor solution explains 62% of the data variance, suggesting that 4 archetype models of the greenhouse effect dominate thinking within this population. Factor scores, indicating the extent to which each student's drawing aligned with representative models, were compared to performance on conceptual understanding and attitudes measures, demographics, and non-cognitive features of drawings. Student drawings were also compared to drawings made by scientists to ascertain the extent to which models reflect more sophisticated and accurate models. Results indicate that student and scientist drawings share some similarities, most notably the presence of some features of the most sophisticated non-scientific model held among the study population. Prior knowledge, prior attitudes, gender, and non-cognitive components are also predictive of an individual student's model. This work presents a new technique for analyzing drawings, with general implications for the use of drawings in investigating student conceptions.

Make the most of your samples: Bayes factor estimators for high-dimensional models of sequence evolution

PubMed Central

2013-01-01

Background Accurate model comparison requires extensive computation times, especially for parameter-rich models of sequence evolution. In the Bayesian framework, model selection is typically performed through the evaluation of a Bayes factor, the ratio of two marginal likelihoods (one for each model). Recently introduced techniques to estimate (log) marginal likelihoods, such as path sampling and stepping-stone sampling, offer increased accuracy over the traditional harmonic mean estimator at an increased computational cost. Most often, each model’s marginal likelihood will be estimated individually, which leads the resulting Bayes factor to suffer from errors associated with each of these independent estimation processes. Results We here assess the original ‘model-switch’ path sampling approach for direct Bayes factor estimation in phylogenetics, as well as an extension that uses more samples, to construct a direct path between two competing models, thereby eliminating the need to calculate each model’s marginal likelihood independently. Further, we provide a competing Bayes factor estimator using an adaptation of the recently introduced stepping-stone sampling algorithm and set out to determine appropriate settings for accurately calculating such Bayes factors, with context-dependent evolutionary models as an example. While we show that modest efforts are required to roughly identify the increase in model fit, only drastically increased computation times ensure the accuracy needed to detect more subtle details of the evolutionary process. Conclusions We show that our adaptation of stepping-stone sampling for direct Bayes factor calculation outperforms the original path sampling approach as well as an extension that exploits more samples. Our proposed approach for Bayes factor estimation also has preferable statistical properties over the use of individual marginal likelihood estimates for both models under comparison. Assuming a sigmoid function to determine the path between two competing models, we provide evidence that a single well-chosen sigmoid shape value requires less computational efforts in order to approximate the true value of the (log) Bayes factor compared to the original approach. We show that the (log) Bayes factors calculated using path sampling and stepping-stone sampling differ drastically from those estimated using either of the harmonic mean estimators, supporting earlier claims that the latter systematically overestimate the performance of high-dimensional models, which we show can lead to erroneous conclusions. Based on our results, we argue that highly accurate estimation of differences in model fit for high-dimensional models requires much more computational effort than suggested in recent studies on marginal likelihood estimation. PMID:23497171

Structural equation modeling in environmental risk assessment.

PubMed

Buncher, C R; Succop, P A; Dietrich, K N

1991-01-01

Environmental epidemiology requires effective models that take individual observations of environmental factors and connect them into meaningful patterns. Single-factor relationships have given way to multivariable analyses; simple additive models have been augmented by multiplicative (logistic) models. Each of these steps has produced greater enlightenment and understanding. Models that allow for factors causing outputs that can affect later outputs with putative causation working at several different time points (e.g., linkage) are not commonly used in the environmental literature. Structural equation models are a class of covariance structure models that have been used extensively in economics/business and social science but are still little used in the realm of biostatistics. Path analysis in genetic studies is one simplified form of this class of models. We have been using these models in a study of the health and development of infants who have been exposed to lead in utero and in the postnatal home environment. These models require as input the directionality of the relationship and then produce fitted models for multiple inputs causing each factor and the opportunity to have outputs serve as input variables into the next phase of the simultaneously fitted model. Some examples of these models from our research are presented to increase familiarity with this class of models. Use of these models can provide insight into the effect of changing an environmental factor when assessing risk. The usual cautions concerning believing a model, believing causation has been proven, and the assumptions that are required for each model are operative.

The School Counseling Program Implementation Survey: Initial Instrument Development and Exploratory Factor Analysis

ERIC Educational Resources Information Center

Clemens, Elysia V.; Carey, John C.; Harrington, Karen M.

2010-01-01

This article details the initial development of the School Counseling Program Implementation Survey and psychometric results including reliability and factor structure. An exploratory factor analysis revealed a three-factor model that accounted for 54% of the variance of the intercorrelation matrix and a two-factor model that accounted for 47% of…

Toward an Integrative Model of Creativity and Personality: Theoretical Suggestions and Preliminary Empirical Testing

ERIC Educational Resources Information Center

Fü rst, Guillaume; Ghisletta, Paolo; Lubart, Todd

2016-01-01

The present work proposes an integrative model of creativity that includes personality traits and cognitive processes. This model hypothesizes that three high-order personality factors predict two main process factors, which in turn predict intensity and achievement of creative activities. The personality factors are: "Plasticity" (high…

INCREASED PRODUCTION OF NERVE GROWTH FACTOR, NEUROTROPHIN-3, AND NEUROTROPHIN-4 IN A PENICILLIUM CHRYSOGENUM -INDUCED ALLERGIC ASTHMA MODEL IN MICE

EPA Science Inventory

Increased levels of neurotrophins (nerve growth factor [NGF], brain-derived neurotrophic factor [BDNF], neurotrophin [NT]-3, and/or NT-4) have been associated with asthmatics and in animal models of allergic asthma. In our mouse model for fungal allergic asthma, repeated pulmona...

Heterogeneity in the Latent Structure of PTSD Symptoms among Canadian Veterans

ERIC Educational Resources Information Center

Naifeh, James A.; Richardson, J. Don; Del Ben, Kevin S.; Elhai, Jon D.

2010-01-01

The current study used factor mixture modeling to identify heterogeneity (i.e., latent classes) in 2 well-supported models of posttraumatic stress disorder's (PTSD) factor structure. Data were analyzed from a clinical sample of 405 Canadian veterans evaluated for PTSD. Results were consistent with our hypotheses. Each PTSD factor model was best…

The Manifest Association Structure of the Single-Factor Model: Insights from Partial Correlations

ERIC Educational Resources Information Center

Salgueiro, Maria de Fatima; Smith, Peter W. F.; McDonald, John W.

2008-01-01

The association structure between manifest variables arising from the single-factor model is investigated using partial correlations. The additional insights to the practitioner provided by partial correlations for detecting a single-factor model are discussed. The parameter space for the partial correlations is presented, as are the patterns of…

Dimensions and categories: the "big five" factors and the DSM personality disorders.

PubMed

Morey, L C; Gunderson, J; Quigley, B D; Lyons, M

2000-09-01

The five-factor model of personality, which has been widely studied in personality psychology, has been hypothesized to have specific relevance for DSM-defined personality disorders. To evaluate hypothesized relationships of the five-factor model of personality to personality disorders, 144 patients with personality disorders (diagnosed via a structured interview) completed an inventory to assess the five-factor model. Results indicated that the majority of the personality disorders can be differentiated in theoretically predictable ways using the five-factor model of personality. However, while the personality disorders as a whole appear to be differentiable from normal personality functioning on the five factors, the patterns are quite similar across the disorders, a finding that may provide some insight into the general nature of personality pathology but may also suggest problems with discriminant validity. Third, it does not appear that considering disorders as special combinations of features (as might be expected in some categorical models) is more informative than considering them as the sum of certain features (as might be expected in a dimensional model).

Calculating the nutrient composition of recipes with computers.

PubMed

Powers, P M; Hoover, L W

1989-02-01

The objective of this research project was to compare the nutrient values computed by four commonly used computerized recipe calculation methods. The four methods compared were the yield factor, retention factor, summing, and simplified retention factor methods. Two versions of the summing method were modeled. Four pork entrée recipes were selected for analysis: roast pork, pork and noodle casserole, pan-broiled pork chops, and pork chops with vegetables. Assumptions were made about changes expected to occur in the ingredients during preparation and cooking. Models were designed to simulate the algorithms of the calculation methods using a microcomputer spreadsheet software package. Identical results were generated in the yield factor, retention factor, and summing-cooked models for roast pork. The retention factor and summing-cooked models also produced identical results for the recipe for pan-broiled pork chops. The summing-raw model gave the highest value for water in all four recipes and the lowest values for most of the other nutrients. A superior method or methods was not identified. However, on the basis of the capabilities provided with the yield factor and retention factor methods, more serious consideration of these two methods is recommended.

Quantitative Analysis of Intra Urban Growth Modeling using socio economic agents by combining cellular automata model with agent based model

NASA Astrophysics Data System (ADS)

Singh, V. K.; Jha, A. K.; Gupta, K.; Srivastav, S. K.

2017-12-01

Recent studies indicate that there is a significant improvement in the urban land use dynamics through modeling at finer spatial resolutions. Geo-computational models such as cellular automata and agent based model have given evident proof regarding the quantification of the urban growth pattern with urban boundary. In recent studies, socio- economic factors such as demography, education rate, household density, parcel price of the current year, distance to road, school, hospital, commercial centers and police station are considered to the major factors influencing the Land Use Land Cover (LULC) pattern of the city. These factors have unidirectional approach to land use pattern which makes it difficult to analyze the spatial aspects of model results both quantitatively and qualitatively. In this study, cellular automata model is combined with generic model known as Agent Based Model to evaluate the impact of socio economic factors on land use pattern. For this purpose, Dehradun an Indian city is selected as a case study. Socio economic factors were collected from field survey, Census of India, Directorate of economic census, Uttarakhand, India. A 3X3 simulating window is used to consider the impact on LULC. Cellular automata model results are examined for the identification of hot spot areas within the urban area and agent based model will be using logistic based regression approach where it will identify the correlation between each factor on LULC and classify the available area into low density, medium density, high density residential or commercial area. In the modeling phase, transition rule, neighborhood effect, cell change factors are used to improve the representation of built-up classes. Significant improvement is observed in the built-up classes from 84 % to 89 %. However after incorporating agent based model with cellular automata model the accuracy improved from 89 % to 94 % in 3 classes of urban i.e. low density, medium density and commercial classes. Sensitivity study of the model indicated that southern and south-west part of the city have shown improvement and small patches of growth are also observed in the north western part of the city.The study highlights the growing importance of socio economic factors and geo-computational modeling approach on changing LULC of newly growing cities of modern India.

Univariate and multivariate spatial models of health facility utilisation for childhood fevers in an area on the coast of Kenya.

PubMed

Ouma, Paul O; Agutu, Nathan O; Snow, Robert W; Noor, Abdisalan M

2017-09-18

Precise quantification of health service utilisation is important for the estimation of disease burden and allocation of health resources. Current approaches to mapping health facility utilisation rely on spatial accessibility alone as the predictor. However, other spatially varying social, demographic and economic factors may affect the use of health services. The exclusion of these factors can lead to the inaccurate estimation of health facility utilisation. Here, we compare the accuracy of a univariate spatial model, developed only from estimated travel time, to a multivariate model that also includes relevant social, demographic and economic factors. A theoretical surface of travel time to the nearest public health facility was developed. These were assigned to each child reported to have had fever in the Kenya demographic and health survey of 2014 (KDHS 2014). The relationship of child treatment seeking for fever with travel time, household and individual factors from the KDHS2014 were determined using multilevel mixed modelling. Bayesian information criterion (BIC) and likelihood ratio test (LRT) tests were carried out to measure how selected factors improve parsimony and goodness of fit of the time model. Using the mixed model, a univariate spatial model of health facility utilisation was fitted using travel time as the predictor. The mixed model was also used to compute a multivariate spatial model of utilisation, using travel time and modelled surfaces of selected household and individual factors as predictors. The univariate and multivariate spatial models were then compared using the receiver operating area under the curve (AUC) and a percent correct prediction (PCP) test. The best fitting multivariate model had travel time, household wealth index and number of children in household as the predictors. These factors reduced BIC of the time model from 4008 to 2959, a change which was confirmed by the LRT test. Although there was a high correlation of the two modelled probability surfaces (Adj R 2  = 88%), the multivariate model had better AUC compared to the univariate model; 0.83 versus 0.73 and PCP 0.61 versus 0.45 values. Our study shows that a model that uses travel time, as well as household and individual-level socio-demographic factors, results in a more accurate estimation of use of health facilities for the treatment of childhood fever, compared to one that relies on only travel time.

Simultaneous Tensor Decomposition and Completion Using Factor Priors.

PubMed

Chen, Yi-Lei; Hsu, Chiou-Ting Candy; Liao, Hong-Yuan Mark

2013-08-27

Tensor completion, which is a high-order extension of matrix completion, has generated a great deal of research interest in recent years. Given a tensor with incomplete entries, existing methods use either factorization or completion schemes to recover the missing parts. However, as the number of missing entries increases, factorization schemes may overfit the model because of incorrectly predefined ranks, while completion schemes may fail to interpret the model factors. In this paper, we introduce a novel concept: complete the missing entries and simultaneously capture the underlying model structure. To this end, we propose a method called Simultaneous Tensor Decomposition and Completion (STDC) that combines a rank minimization technique with Tucker model decomposition. Moreover, as the model structure is implicitly included in the Tucker model, we use factor priors, which are usually known a priori in real-world tensor objects, to characterize the underlying joint-manifold drawn from the model factors. We conducted experiments to empirically verify the convergence of our algorithm on synthetic data, and evaluate its effectiveness on various kinds of real-world data. The results demonstrate the efficacy of the proposed method and its potential usage in tensor-based applications. It also outperforms state-of-the-art methods on multilinear model analysis and visual data completion tasks.

In silico mining and PCR-based approaches to transcription factor discovery in non-model plants: gene discovery of the WRKY transcription factors in conifers.

PubMed

Liu, Jun-Jun; Xiang, Yu

2011-01-01

WRKY transcription factors are key regulators of numerous biological processes in plant growth and development, as well as plant responses to abiotic and biotic stresses. Research on biological functions of plant WRKY genes has focused in the past on model plant species or species with largely characterized transcriptomes. However, a variety of non-model plants, such as forest conifers, are essential as feed, biofuel, and wood or for sustainable ecosystems. Identification of WRKY genes in these non-model plants is equally important for understanding the evolutionary and function-adaptive processes of this transcription factor family. Because of limited genomic information, the rarity of regulatory gene mRNAs in transcriptomes, and the sequence divergence to model organism genes, identification of transcription factors in non-model plants using methods similar to those generally used for model plants is difficult. This chapter describes a gene family discovery strategy for identification of WRKY transcription factors in conifers by a combination of in silico-based prediction and PCR-based experimental approaches. Compared to traditional cDNA library screening or EST sequencing at transcriptome scales, this integrated gene discovery strategy provides fast, simple, reliable, and specific methods to unveil the WRKY gene family at both genome and transcriptome levels in non-model plants.

Empirical evidence for an invariant three-factor structure of the Parental Bonding Instrument in six European countries.

PubMed

Heider, Dirk; Matschinger, Herbert; Bernert, Sebastian; Vilagut, Gemma; Martínez-Alonso, Montserrat; Dietrich, Sandra; Angermeyer, Matthias C

2005-06-30

The objective of the present study was to test the Parental Bonding Instrument's (PBI) three-factor structure (care, overprotection, and authoritarianism) found by [Cox, B.J., Enns, M.W., Clara, I.P. 2000, The Parental Bonding Instrument: confirmatory evidence for a three-factor model in a psychiatric clinical sample and in the National Comorbidity Survey, Social Psychiatry and Psychiatric Epidemiology 35 (2000) 353-357.] on an eight-item short form of the scale. A total of 8813 respondents from the six European countries participating in the ESEMeD project (Belgium, France, Germany, Italy, The Netherlands, and Spain) completed either the PBI-paternal or the PBI-maternal scale. Maximum likelihood confirmatory factor analysis was used to compare the original factor model of Cox et al. with a three-factor solution that emerged from an exploration of the structure with principal component factor analysis. When gender and age subgroups, as well as different countries, were taken into account, the accuracy of the model was confirmed. The fit indices for the new model indicated a generally better model fit than the ones for the model originally developed by Cox et al. Further efforts should be directed to the modeling of the dimension authoritarianism. The results provide the opportunity to estimate the influence of the extracted factors on mental disorders in different countries. The application of the short form of the PBI seems suitable primarily for large epidemiological studies.

Ranking and validation of spallation models for isotopic production cross sections of heavy residua

NASA Astrophysics Data System (ADS)

Sharma, Sushil K.; Kamys, Bogusław; Goldenbaum, Frank; Filges, Detlef

2017-07-01

The production cross sections of isotopically identified residual nuclei of spallation reactions induced by 136Xe projectiles at 500AMeV on hydrogen target were analyzed in a two-step model. The first stage of the reaction was described by the INCL4.6 model of an intranuclear cascade of nucleon-nucleon and pion-nucleon collisions whereas the second stage was analyzed by means of four different models; ABLA07, GEM2, GEMINI++ and SMM. The quality of the data description was judged quantitatively using two statistical deviation factors; the H-factor and the M-factor. It was found that the present analysis leads to a different ranking of models as compared to that obtained from the qualitative inspection of the data reproduction. The disagreement was caused by sensitivity of the deviation factors to large statistical errors present in some of the data. A new deviation factor, the A factor, was proposed, that is not sensitive to the statistical errors of the cross sections. The quantitative ranking of models performed using the A-factor agreed well with the qualitative analysis of the data. It was concluded that using the deviation factors weighted by statistical errors may lead to erroneous conclusions in the case when the data cover a large range of values. The quality of data reproduction by the theoretical models is discussed. Some systematic deviations of the theoretical predictions from the experimental results are observed.

A systematic literature review of PTSD's latent structure in the Diagnostic and Statistical Manual of Mental Disorders: DSM-IV to DSM-5.

PubMed

Armour, Cherie; Műllerová, Jana; Elhai, Jon D

2016-03-01

The factor structure of posttraumatic stress disorder (PTSD) has been widely researched, but consensus regarding the exact number and nature of factors is yet to be reached. The aim of the current study was to systematically review the extant literature on PTSD's latent structure in the Diagnostic and Statistical Manual of Mental Disorders (DSM) in order to identify the best-fitting model. One hundred and twelve research papers published after 1994 using confirmatory factor analysis and DSM-based measures of PTSD were included in the review. In the DSM-IV literature, four-factor models received substantial support, but the five-factor Dysphoric arousal model demonstrated the best fit, regardless of gender, measurement instrument or trauma type. The recently proposed DSM-5 PTSD model was found to be a good representation of PTSD's latent structure, but studies analysing the six- and seven-factor models suggest that the DSM-5 PTSD factor structure may need further alterations. Copyright © 2015 Elsevier Ltd. All rights reserved.

Behavioral Pediatrics Feeding Assessment Scale in Young Children With Autism Spectrum Disorder: Psychometrics and Associations With Child and Parent Variables.

PubMed

Allen, Stephanie L; Smith, Isabel M; Duku, Eric; Vaillancourt, Tracy; Szatmari, Peter; Bryson, Susan; Fombonne, Eric; Volden, Joanne; Waddell, Charlotte; Zwaigenbaum, Lonnie; Roberts, Wendy; Mirenda, Pat; Bennett, Teresa; Elsabbagh, Mayada; Georgiades, Stelios

2015-07-01

The factor structure and validity of the Behavioral Pediatrics Feeding Assessment Scale (BPFAS; Crist & Napier-Phillips, 2001) were examined in preschoolers with autism spectrum disorder (ASD). Confirmatory factor analysis was used to examine the original BPFAS five-factor model, the fit of each latent variable, and a rival one-factor model. None of the models was adequate, thus a categorical exploratory factor analysis (CEFA) was conducted. Correlations were used to examine relations between the BPFAS and concurrent variables of interest. The CEFA identified an acceptable three-factor model. Correlational analyses indicated that feeding problems were positively related to parent-reported autism symptoms, behavior problems, sleep problems, and parenting stress, but largely unrelated to performance-based indices of autism symptom severity, language, and cognitive abilities, as well as child age. These results provide evidence supporting the use of the identified BPFAS three-factor model for samples of young children with ASD. © The Author 2015. Published by Oxford University Press on behalf of the Society of Pediatric Psychology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Factors of collaborative working: a framework for a collaboration model.

PubMed

Patel, Harshada; Pettitt, Michael; Wilson, John R

2012-01-01

The ability of organisations to support collaborative working environments is of increasing importance as they move towards more distributed ways of working. Despite the attention collaboration has received from a number of disparate fields, there is a lack of a unified understanding of the component factors of collaboration. As part of our work on a European Integrated Project, CoSpaces, collaboration and collaborative working and the factors which define it were examined through the literature and new empirical work with a number of partner user companies in the aerospace, automotive and construction sectors. This was to support development of a descriptive human factors model of collaboration - the CoSpaces Collaborative Working Model (CCWM). We identified seven main categories of factors involved in collaboration: Context, Support, Tasks, Interaction Processes, Teams, Individuals, and Overarching Factors, and summarised these in a framework which forms a basis for the model. We discuss supporting evidence for the factors which emerged from our fieldwork with user partners, and use of the model in activities such as collaboration readiness profiling. Copyright © 2011 Elsevier Ltd and The Ergonomics Society. All rights reserved.

Overall Preference of Running Shoes Can Be Predicted by Suitable Perception Factors Using a Multiple Regression Model.

PubMed

Tay, Cheryl Sihui; Sterzing, Thorsten; Lim, Chen Yen; Ding, Rui; Kong, Pui Wah

2017-05-01

This study examined (a) the strength of four individual footwear perception factors to influence the overall preference of running shoes and (b) whether these perception factors satisfied the nonmulticollinear assumption in a regression model. Running footwear must fulfill multiple functional criteria to satisfy its potential users. Footwear perception factors, such as fit and cushioning, are commonly used to guide shoe design and development, but it is unclear whether running-footwear users are able to differentiate one factor from another. One hundred casual runners assessed four running shoes on a 15-cm visual analogue scale for four footwear perception factors (fit, cushioning, arch support, and stability) as well as for overall preference during a treadmill running protocol. Diagnostic tests showed an absence of multicollinearity between factors, where values for tolerance ranged from .36 to .72, corresponding to variance inflation factors of 2.8 to 1.4. The multiple regression model of these four footwear perception variables accounted for 77.7% to 81.6% of variance in overall preference, with each factor explaining a unique part of the total variance. Casual runners were able to rate each footwear perception factor separately, thus assigning each factor a true potential to improve overall preference for the users. The results also support the use of a multiple regression model of footwear perception factors to predict overall running shoe preference. Regression modeling is a useful tool for running-shoe manufacturers to more precisely evaluate how individual factors contribute to the subjective assessment of running footwear.

Dimensionality of organizational justice in a call center context.

PubMed

Flint, Douglas; Haley, Lynn M; McNally, Jeffrey J

2012-04-01

Summary.-Employees in three call centers were surveyed about their perceptions of organizational justice. Four factors were measured: distributive justice, procedural justice, interpersonal justice, and informational justice. Structural equation modeling was employed to test whether a two-, three-, or four-factor model best fit the call center data. A three-factor model of distributive, procedural, and informational justice provided the best fit to these data. The three-factor model that showed the best fit does not conform to any of the more traditional models identified in the organizational justice literature. This implies that the context in which organizational justice is measured may play a role in identifying which justice factors are relevant to employees. Findings add to the empirical evidence on the dimensionality of organizational justice and imply that dimensionality of organizational justice is more context-dependent than previously thought.

Dynamic Factor Analysis of Nonstationary Multivariate Time Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; And Others

1992-01-01

The dynamic factor model proposed by P. C. Molenaar (1985) is exhibited, and a dynamic nonstationary factor model (DNFM) is constructed with latent factor series that have time-varying mean functions. The use of a DNFM is illustrated using data from a television viewing habits study. (SLD)

Relating Factor Models for Longitudinal Data to Quasi-Simplex and NARMA Models

ERIC Educational Resources Information Center

Rovine, Michael J.; Molenaar, Peter C. M.

2005-01-01

In this article we show the one-factor model can be rewritten as a quasi-simplex model. Using this result along with addition theorems from time series analysis, we describe a common general model, the nonstationary autoregressive moving average (NARMA) model, that includes as a special case, any latent variable model with continuous indicators…

Confirmatory factor analysis and measurement invariance of the Child Feeding Questionnaire in low-income Hispanic and African-American mothers with preschool-age children.

PubMed

Kong, Angela; Vijayasiri, Ganga; Fitzgibbon, Marian L; Schiffer, Linda A; Campbell, Richard T

2015-07-01

Validation work of the Child Feeding Questionnaire (CFQ) in low-income minority samples suggests a need for further conceptual refinement of this instrument. Using confirmatory factor analysis, this study evaluated 5- and 6-factor models on a large sample of African-American and Hispanic mothers with preschool-age children (n = 962). The 5-factor model included: 'perceived responsibility', 'concern about child's weight', 'restriction', 'pressure to eat', and 'monitoring' and the 6-factor model also tested 'food as a reward'. Multi-group analysis assessed measurement invariance by race/ethnicity. In the 5-factor model, two low-loading items from 'restriction' and one low-variance item from 'perceived responsibility' were dropped to achieve fit. Only removal of the low-variance item was needed to achieve fit in the 6-factor model. Invariance analyses demonstrated differences in factor loadings. This finding suggests African-American and Hispanic mothers may vary in their interpretation of some CFQ items and use of cognitive interviews could enhance item interpretation. Our results also demonstrated that 'food as a reward' is a plausible construct among a low-income minority sample and adds to the evidence that this factor resonates conceptually with parents of preschoolers; however, further testing is needed to determine the validity of this factor with older age groups. Copyright © 2015 Elsevier Ltd. All rights reserved.

[Confirmatory factor analysis of the short French version of the Center for Epidemiological Studies of Depression Scale (CES-D10) in adolescents].

PubMed

Cartierre, N; Coulon, N; Demerval, R

2011-09-01

Screening depressivity among adolescents is a key public health priority. In order to measure the severity of depressive symptomatology, a four-dimensional 20 items scale called "Center for Epidemiological Studies-Depression Scale" (CES-D) was developed. A shorter 10-item version was developed and validated (Andresen et al.). For this brief version, several authors supported a two-factor structure - Negative and Positive affect - but the relationship between the two reversed-worded items of the Positive affect factor could be better accounted for by correlated errors. The aim of this study is triple: firstly to test a French version of the CES-D10 among adolescents; secondly to test the relevance of a one-dimensional structure by considering error correlation for Positive affect items; finally to examine the extent to which this structural model is invariant across gender. The sample was composed of 269 French middle school adolescents (139 girls and 130 boys, mean age: 13.8, SD=0.65). Confirmatory Factorial Analyses (CFA) using the LISREL 8.52 were conducted in order to assess the adjustment to the data of three factor models: a one-factor model, a two-factor model (Positive and Negative affect) and a one-factor model with specification of correlated errors between the two reverse-worded items. Then, multigroup analysis was conducted to test the scale invariance for girls and boys. Internal consistency of the CES-D10 was satisfying for the adolescent sample (α=0.75). The best fitting model is the one-factor model with correlated errors between the two items of the previous Positive affect factor (χ(2)/dl=2.50; GFI=0.939; CFI=0.894; RMSEA=0.076). This model presented a better statistical fit to the data than the one-factor model without error correlation: χ(2)(diff) (1)=22.14, p<0.001. Then, the one-factor model with correlated errors was analyzed across separate samples of girls and boys. The model explains the data somewhat better for boys than for girls. The model's overall χ(2)(68) without equality constraints from the multigroup analysis was 107.98. The χ(2)(89) statistic for the model with equality-constrained factor loadings was 121.31. The change in the overall Chi(2) is not statistically significant. This result implies that the model is, therefore, invariant across gender. The mean scores were higher for girls than boys: 9.69 versus 7.19; t(267)=4.13, p<0.001. To conclude, and waiting for further research using the French version of the CES-D10 for adolescents, it appears that this short scale is generally acceptable and can be a useful tool for both research and practice. The scale invariance across gender has been demonstrated but the invariance across age must be tested too. Copyright © 2011 L’Encéphale, Paris. Published by Elsevier Masson SAS. All rights reserved.

Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO 2 enrichment experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

De Kauwe, Martin G.; Medlyn, Belinda E.; Walker, Anthony P.

Multi-factor experiments are often advocated as important for advancing terrestrial biosphere models (TBMs), yet to date such models have only been tested against single-factor experiments. We applied 10 TBMs to the multi-factor Prairie Heating and CO 2 Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multi-factor experiments can be used to constrain models, and to identify a road map for model improvement. We found models performed poorly in ambient conditions; there was a wide spread in simulated above-ground net primary productivity (range: 31-390 g C m -2 yr -1). Comparison with data highlighted model failures particularlymore » in respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against single-factors was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the nitrogen cycle models, nitrogen availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they over-estimated the effect of warming on leaf onset and did not allow CO 2-induced water savings to extend growing season length. Observed interactive (CO 2 x warming) treatment effects were subtle and contingent on water stress, phenology and species composition. Since the models did not correctly represent these processes under ambient and single-factor conditions, little extra information was gained by comparing model predictions against interactive responses. Finally, we outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.« less

Challenging terrestrial biosphere models with data from the long-term multifactor Prairie Heating and CO 2 enrichment experiment

DOE PAGES

De Kauwe, Martin G.; Medlyn, Belinda E.; Walker, Anthony P.; ...

2017-02-01

Multi-factor experiments are often advocated as important for advancing terrestrial biosphere models (TBMs), yet to date such models have only been tested against single-factor experiments. We applied 10 TBMs to the multi-factor Prairie Heating and CO 2 Enrichment (PHACE) experiment in Wyoming, USA. Our goals were to investigate how multi-factor experiments can be used to constrain models, and to identify a road map for model improvement. We found models performed poorly in ambient conditions; there was a wide spread in simulated above-ground net primary productivity (range: 31-390 g C m -2 yr -1). Comparison with data highlighted model failures particularlymore » in respect to carbon allocation, phenology, and the impact of water stress on phenology. Performance against single-factors was also relatively poor. In addition, similar responses were predicted for different reasons across models: there were large differences among models in sensitivity to water stress and, among the nitrogen cycle models, nitrogen availability during the experiment. Models were also unable to capture observed treatment effects on phenology: they over-estimated the effect of warming on leaf onset and did not allow CO 2-induced water savings to extend growing season length. Observed interactive (CO 2 x warming) treatment effects were subtle and contingent on water stress, phenology and species composition. Since the models did not correctly represent these processes under ambient and single-factor conditions, little extra information was gained by comparing model predictions against interactive responses. Finally, we outline a series of key areas in which this and future experiments could be used to improve model predictions of grassland responses to global change.« less

Assessing the specificity of posttraumatic stress disorder's dysphoric items within the dysphoria model.

PubMed

Armour, Cherie; Shevlin, Mark

2013-10-01

The factor structure of posttraumatic stress disorder (PTSD) currently used by the Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition (DSM-IV), has received limited support. A four-factor dysphoria model is widely supported. However, the dysphoria factor of this model has been hailed as a nonspecific factor of PTSD. The present study investigated the specificity of the dysphoria factor within the dysphoria model by conducting a confirmatory factor analysis while statistically controlling for the variance attributable to depression. The sample consisted of 429 individuals who met the diagnostic criteria for PTSD in the National Comorbidity Survey. The results concluded that there was no significant attenuation in any of the PTSD items. This finding is pertinent given several proposals for the removal of dysphoric items from the diagnostic criteria set of PTSD in the upcoming DSM-5.

The use of the bi-factor model to test the uni-dimensionality of a battery of reasoning tests.

PubMed

Primi, Ricardo; Rocha da Silva, Marjorie Cristina; Rodrigues, Priscila; Muniz, Monalisa; Almeida, Leandro S

2013-02-01

The Battery of Reasoning Tests 5 (BPR-5) aims to assess the reasoning ability of individuals, using sub-tests with different formats and contents that require basic processes of inductive and deductive reasoning for their resolution. The BPR has three sequential forms: BPR-5i (for children from first to fifth grade), BPR-5 - Form A (for children from sixth to eighth grade) and BPR-5 - form B (for high school and undergraduate students). The present study analysed 412 questionnaires concerning BPR-5i, 603 questionnaires concerning BPR-5 - Form A and 1748 questionnaires concerning BPR-5 - Form B. The main goal was to test the uni-dimensionality of the battery and its tests in relation to items using the bi-factor model. Results suggest that the g factor loadings (extracted by the uni-dimensional model) do not change when the data is adjusted for a more flexible multi-factor model (bi-factor model). A general reasoning factor underlying different contents items is supported.

Beyond Negative Affectivity: A Hierarchical Model of Global and Transdiagnostic Vulnerabilities for Emotional Disorders.

PubMed

Paulus, Daniel J; Talkovsky, Alexander M; Heggeness, Luke F; Norton, Peter J

2015-01-01

Negative affectivity (NA) has been linked to anxiety and depression (DEP). Identifying the common factors between anxiety and DEP is important when explaining their overlap and comorbidity. However, general factors such as NA tend to have differential relationships with different disorders, suggesting the need to identify mediators in order to explicate these relationships. The current study tests a theoretically and empirically derived hierarchical model of emotional disorders including both a general factor (NA) and transdiagnostic risk factors [anxiety sensitivity (AS) and intolerance of uncertainty (IoU)] using structural equation modeling. AS was tested as a mid-level factor between NA and panic disorder/agoraphobia, while IoU was tested as a mid-level factor between NA and social phobia, generalized anxiety disorder, obsessive-compulsive disorder, and DEP. Data from 642 clinical outpatients with a heterogeneous presentation of emotional disorders were available for analysis. The hierarchical model fits the data adequately. Moreover, while a simplified model removing AS and IoU fits the data well, it resulted in a significant loss of information for all latent disorder constructs. Data were unavailable to estimate post-traumatic stress disorder or specific phobias. Future work will need to extend to other emotional disorders. This study demonstrates the importance of both general factors that link disorders together and semi-specific transdiagnostic factors partially explaining their heterogeneity. Including these mid-level factors in hierarchical models of psychopathology can help account for additional variance and help to clarify the relationship between disorder constructs and NA.

Can Optimism, Pessimism, Hope, Treatment Credibility and Treatment Expectancy Be Distinguished in Patients Undergoing Total Hip and Total Knee Arthroplasty?

PubMed Central

Haanstra, Tsjitske M.; Tilbury, Claire; Kamper, Steven J.; Tordoir, Rutger L.; Vliet Vlieland, Thea P. M.; Nelissen, Rob G. H. H.; Cuijpers, Pim; de Vet, Henrica C. W.; Dekker, Joost; Knol, Dirk L.; Ostelo, Raymond W.

2015-01-01

Objectives The constructs optimism, pessimism, hope, treatment credibility and treatment expectancy are associated with outcomes of medical treatment. While these constructs are grounded in different theoretical models, they nonetheless show some conceptual overlap. The purpose of this study was to examine whether currently available measurement instruments for these constructs capture the conceptual differences between these constructs within a treatment setting. Methods Patients undergoing Total Hip and Total Knee Arthroplasty (THA and TKA) (Total N = 361; 182 THA; 179 TKA), completed the Life Orientation Test-Revised for optimism and pessimism, the Hope Scale, the Credibility Expectancy Questionnaire for treatment credibility and treatment expectancy. Confirmatory factor analysis was used to examine whether the instruments measure distinct constructs. Four theory-driven models with one, two, four and five latent factors were evaluated using multiple fit indices and Δχ2 tests, followed by some posthoc models. Results The results of the theory driven confirmatory factor analysis showed that a five factor model in which all constructs loaded on separate factors yielded the most optimal and satisfactory fit. Posthoc, a bifactor model in which (besides the 5 separate factors) a general factor is hypothesized accounting for the commonality of the items showed a significantly better fit than the five factor model. All specific factors, except for the hope factor, showed to explain a substantial amount of variance beyond the general factor. Conclusion Based on our primary analyses we conclude that optimism, pessimism, hope, treatment credibility and treatment expectancy are distinguishable in THA and TKA patients. Postdoc, we determined that all constructs, except hope, showed substantial specific variance, while also sharing some general variance. PMID:26214176

Can Optimism, Pessimism, Hope, Treatment Credibility and Treatment Expectancy Be Distinguished in Patients Undergoing Total Hip and Total Knee Arthroplasty?

PubMed

Haanstra, Tsjitske M; Tilbury, Claire; Kamper, Steven J; Tordoir, Rutger L; Vliet Vlieland, Thea P M; Nelissen, Rob G H H; Cuijpers, Pim; de Vet, Henrica C W; Dekker, Joost; Knol, Dirk L; Ostelo, Raymond W

2015-01-01

The constructs optimism, pessimism, hope, treatment credibility and treatment expectancy are associated with outcomes of medical treatment. While these constructs are grounded in different theoretical models, they nonetheless show some conceptual overlap. The purpose of this study was to examine whether currently available measurement instruments for these constructs capture the conceptual differences between these constructs within a treatment setting. Patients undergoing Total Hip and Total Knee Arthroplasty (THA and TKA) (Total N = 361; 182 THA; 179 TKA), completed the Life Orientation Test-Revised for optimism and pessimism, the Hope Scale, the Credibility Expectancy Questionnaire for treatment credibility and treatment expectancy. Confirmatory factor analysis was used to examine whether the instruments measure distinct constructs. Four theory-driven models with one, two, four and five latent factors were evaluated using multiple fit indices and Δχ2 tests, followed by some posthoc models. The results of the theory driven confirmatory factor analysis showed that a five factor model in which all constructs loaded on separate factors yielded the most optimal and satisfactory fit. Posthoc, a bifactor model in which (besides the 5 separate factors) a general factor is hypothesized accounting for the commonality of the items showed a significantly better fit than the five factor model. All specific factors, except for the hope factor, showed to explain a substantial amount of variance beyond the general factor. Based on our primary analyses we conclude that optimism, pessimism, hope, treatment credibility and treatment expectancy are distinguishable in THA and TKA patients. Postdoc, we determined that all constructs, except hope, showed substantial specific variance, while also sharing some general variance.

Examining the dimensional structure models of secondary traumatic stress based on DSM-5 symptoms.

PubMed

Mordeno, Imelu G; Go, Geraldine P; Yangson-Serondo, April

2017-02-01

Latent factor structure of Secondary Traumatic Stress (STS) has been examined using Diagnostic Statistic Manual-IV (DSM-IV)'s Posttraumatic Stress Disorder (PTSD) nomenclature. With the advent of Diagnostic Statistic Manual-5 (DSM-5), there is an impending need to reexamine STS using DSM-5 symptoms in light of the most updated PTSD models in the literature. The study investigated and determined the best fitted PTSD models using DSM-5 PTSD criteria symptoms. Confirmatory factor analysis (CFA) was conducted to examine model fit using the Secondary Traumatic Stress Scale in 241 registered and practicing Filipino nurses (166 females and 75 males) who worked in the Philippines and gave direct nursing services to patients. Based on multiple fit indices, the results showed the 7-factor hybrid model, comprising of intrusion, avoidance, negative affect, anhedonia, externalizing behavior, anxious arousal, and dysphoric arousal factors has excellent fit to STS. This model asserts that: (1) hyperarousal criterion needs to be divided into anxious and dysphoric arousal factors; (2) symptoms characterizing negative and positive affect need to be separated to two separate factors, and; (3) a new factor would categorize externalized, self-initiated impulse and control-deficit behaviors. Comparison of nested and non-nested models showed Hybrid model to have superior fit over other models. The specificity of the symptom structure of STS based on DSM-5 PTSD criteria suggests having more specific interventions addressing the more elaborate symptom-groupings that would alleviate the condition of nurses exposed to STS on a daily basis. Copyright © 2016 Elsevier B.V. All rights reserved.

An empirical evaluation of the structure of DSM-IV personality disorders in a nationally representative sample: results of confirmatory factor analysis in the National Epidemiologic Survey on Alcohol and Related Conditions Waves 1 and 2.

PubMed

Cox, Brian J; Clara, Ian P; Worobec, Lydia M; Grant, Bridget F

2012-12-01

Individual personality disorders (PD) are grouped into three clusters in the DSM-IV (A, B, and C). There is very little empirical evidence available concerning the validity of this model in the general population. The current study included all 10 of the DSM-IV PD assessed in Wave 1 and Wave 2 of the National Epidemiologic Survey on Alcohol and Related Conditions (NESARC). Confirmatory factor analysis was used to evaluate three plausible models of the structure of Axis II personality disorders (the current hierarchical DSM-IV three-factor model in which individual PD are believed to load on their assigned clusters, which in turn load onto a single Axis II factor; a general single-factor model; and three independent factors). Each of these models was tested in both the total and also separately for gender. The higher order DSM-IV model demonstrated good fit to the data on a number of goodness-of-fit indices. The results for this model were very similar across genders. A model of PD based on the current DSM-IV hierarchical conceptualization of a higher order classification scheme received strong empirical support through confirmatory factor analysis using a number of goodness-of-fit indices in a nationally representative sample. Other models involving broad, higher order personality domains such as neuroticism in relation to personality disorders have yet to be tested in epidemiologic surveys and represent an important avenue for future research.

Evidence for the Trait-Impulsivity Etiological Model in a Clinical Sample: Bifactor Structure and Its Relation to Impairment and Environmental Risk.

PubMed

Rodenacker, Klaas; Hautmann, Christopher; Görtz-Dorten, Anja; Döpfner, Manfred

2018-05-01

The trait-impulsivity etiological model assumes that a general factor (trait-impulsivity) underlies attention-deficit/hyperactivity disorder (ADHD), oppositional defiant disorder (ODD), and other externalizing disorders. We investigated the plausibility of this assumption by testing the factor structure of ADHD and ODD in a bifactor framework for a clinical sample of 1420 children between 6 and 18 years of age (M = 9.99, SD = 3.34; 85% male). Further, the trait-impulsivity etiological model assumes that ODD emerges only if environmental risk factors are present. Our results support the validity of the trait-impulsivity etiological model, as they confirm that ADHD and ODD share a strong general factor of disruptive behavior (DB) in this clinical sample. Furthermore, unlike the subdimensions of ADHD, we found that the specific ODD factor explained as much true score variance as the general DB factor. This suggests that a common scale of ADHD and ODD may prove to be as important as a separate ODD subscale to assess externalizing problems in school-age children. However, all other subscales of ADHD may not explain sufficient true score variance once the impact of the general DB factor has been taken into consideration. In accordance with the trait-impulsivity model, we also showed that all factors, but predominantly the general factor and specific inattention factor, predicted parent-rated impairment, and that predominantly ODD and impulsivity are predicted by environmental risk factors.

Orthogonal higher order structure and confirmatory factor analysis of the French Wechsler Adult Intelligence Scale (WAIS-III).

PubMed

Golay, Philippe; Lecerf, Thierry

2011-03-01

According to the most widely accepted Cattell-Horn-Carroll (CHC) model of intelligence measurement, each subtest score of the Wechsler Intelligence Scale for Adults (3rd ed.; WAIS-III) should reflect both 1st- and 2nd-order factors (i.e., 4 or 5 broad abilities and 1 general factor). To disentangle the contribution of each factor, we applied a Schmid-Leiman orthogonalization transformation (SLT) to the standardization data published in the French technical manual for the WAIS-III. Results showed that the general factor accounted for 63% of the common variance and that the specific contributions of the 1st-order factors were weak (4.7%-15.9%). We also addressed this issue by using confirmatory factor analysis. Results indicated that the bifactor model (with 1st-order group and general factors) better fit the data than did the traditional higher order structure. Models based on the CHC framework were also tested. Results indicated that a higher order CHC model showed a better fit than did the classical 4-factor model; however, the WAIS bifactor structure was the most adequate. We recommend that users do not discount the Full Scale IQ when interpreting the index scores of the WAIS-III because the general factor accounts for the bulk of the common variance in the French WAIS-III. The 4 index scores cannot be considered to reflect only broad ability because they include a strong contribution of the general factor.

Confirmatory factor analysis of the Eating Disorder Examination-Questionnaire: A comparison of five factor solutions across vegan and omnivore participants.

PubMed

Heiss, Sydney; Boswell, James F; Hormes, Julia M

2018-05-01

The Eating Disorder Examination-Questionnaire (EDE-Q) is a valid and reliable measure of eating-related pathology, but its factor structure has proven difficult to replicate. Given differences in dietary patterns in vegans compared to omnivores, proper measurement of eating disorder symptoms is especially important in studies of animal product avoiders. This study compared goodness-of-fit of five alternative models of the EDE-Q in vegans (i.e., individuals refraining from all animal products, n = 318) and omnivores (i.e., individuals not restricting intake of animal products, n = 200). Confirmatory factor analyses were used to compare fit indices of the original four-factor model of the EDE-Q, along with alternative three-, two-, full one-, and brief one-factor models. No model provided adequate fit of the data in either sample of respondents. The fit of the brief one-factor model was the closest to acceptable in omnivores, but did not perform as well in vegans. Indicators of fit were comparable in vegans and omnivores across all other models. Our data confirm difficulties in replicating the proposed factor structure of the EDE-Q, including in vegans. More research is needed to determine the suitability of the EDE-Q for quantifying eating behaviors, including in those abstaining from animal products. © 2018 Wiley Periodicals, Inc.

A GRAPHICAL DIAGNOSTIC METHOD FOR ASSESSING THE ROTATION IN FACTOR ANALYTICAL MODELS OF ATMOSPHERIC POLLUTION. (R831078)

EPA Science Inventory

Factor analytic tools such as principal component analysis (PCA) and positive matrix factorization (PMF), suffer from rotational ambiguity in the results: different solutions (factors) provide equally good fits to the measured data. The PMF model imposes non-negativity of both...

A Dual-Driver Model of Retention and Turnover in the Direct Care Workforce

ERIC Educational Resources Information Center

Mittal, Vikas; Rosen, Jules; Leana, Carrie

2009-01-01

Purpose: The purpose of this study was to understand the factors associated with turnover and retention of direct care workers. We hypothesize that a dual-driver model that includes individual factors, on-the-job factors, off-the-job factors, and contextual factors can be used to distinguish between reasons for direct care workforces (DCWs)…

A Comparison of Factor Score Estimation Methods in the Presence of Missing Data: Reliability and an Application to Nicotine Dependence

ERIC Educational Resources Information Center

Estabrook, Ryne; Neale, Michael

2013-01-01

Factor score estimation is a controversial topic in psychometrics, and the estimation of factor scores from exploratory factor models has historically received a great deal of attention. However, both confirmatory factor models and the existence of missing data have generally been ignored in this debate. This article presents a simulation study…

Selecting the "Best" Factor Structure and Moving Measurement Validation Forward: An Illustration.

PubMed

Schmitt, Thomas A; Sass, Daniel A; Chappelle, Wayne; Thompson, William

2018-04-09

Despite the broad literature base on factor analysis best practices, research seeking to evaluate a measure's psychometric properties frequently fails to consider or follow these recommendations. This leads to incorrect factor structures, numerous and often overly complex competing factor models and, perhaps most harmful, biased model results. Our goal is to demonstrate a practical and actionable process for factor analysis through (a) an overview of six statistical and psychometric issues and approaches to be aware of, investigate, and report when engaging in factor structure validation, along with a flowchart for recommended procedures to understand latent factor structures; (b) demonstrating these issues to provide a summary of the updated Posttraumatic Stress Disorder Checklist (PCL-5) factor models and a rationale for validation; and (c) conducting a comprehensive statistical and psychometric validation of the PCL-5 factor structure to demonstrate all the issues we described earlier. Considering previous research, the PCL-5 was evaluated using a sample of 1,403 U.S. Air Force remotely piloted aircraft operators with high levels of battlefield exposure. Previously proposed PCL-5 factor structures were not supported by the data, but instead a bifactor model is arguably more statistically appropriate.

Finding of Correction Factor and Dimensional Error in Bio-AM Model by FDM Technique

NASA Astrophysics Data System (ADS)

Manmadhachary, Aiamunoori; Ravi Kumar, Yennam; Krishnanand, Lanka

2018-06-01

Additive Manufacturing (AM) is the swift manufacturing process, in which input data can be provided from various sources like 3-Dimensional (3D) Computer Aided Design (CAD), Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and 3D scanner data. From the CT/MRI data can be manufacture Biomedical Additive Manufacturing (Bio-AM) models. The Bio-AM model gives a better lead on preplanning of oral and maxillofacial surgery. However manufacturing of the accurate Bio-AM model is one of the unsolved problems. The current paper demonstrates error between the Standard Triangle Language (STL) model to Bio-AM model of dry mandible and found correction factor in Bio-AM model with Fused Deposition Modelling (FDM) technique. In the present work dry mandible CT images are acquired by CT scanner and supplied into a 3D CAD model in the form of STL model. Further the data is sent to FDM machine for fabrication of Bio-AM model. The difference between Bio-AM to STL model dimensions is considered as dimensional error and the ratio of STL to Bio-AM model dimensions considered as a correction factor. This correction factor helps to fabricate the AM model with accurate dimensions of the patient anatomy. These true dimensional Bio-AM models increasing the safety and accuracy in pre-planning of oral and maxillofacial surgery. The correction factor for Dimension SST 768 FDM AM machine is 1.003 and dimensional error is limited to 0.3 %.

Finding of Correction Factor and Dimensional Error in Bio-AM Model by FDM Technique

NASA Astrophysics Data System (ADS)

Manmadhachary, Aiamunoori; Ravi Kumar, Yennam; Krishnanand, Lanka

2016-06-01

Additive Manufacturing (AM) is the swift manufacturing process, in which input data can be provided from various sources like 3-Dimensional (3D) Computer Aided Design (CAD), Computed Tomography (CT), Magnetic Resonance Imaging (MRI) and 3D scanner data. From the CT/MRI data can be manufacture Biomedical Additive Manufacturing (Bio-AM) models. The Bio-AM model gives a better lead on preplanning of oral and maxillofacial surgery. However manufacturing of the accurate Bio-AM model is one of the unsolved problems. The current paper demonstrates error between the Standard Triangle Language (STL) model to Bio-AM model of dry mandible and found correction factor in Bio-AM model with Fused Deposition Modelling (FDM) technique. In the present work dry mandible CT images are acquired by CT scanner and supplied into a 3D CAD model in the form of STL model. Further the data is sent to FDM machine for fabrication of Bio-AM model. The difference between Bio-AM to STL model dimensions is considered as dimensional error and the ratio of STL to Bio-AM model dimensions considered as a correction factor. This correction factor helps to fabricate the AM model with accurate dimensions of the patient anatomy. These true dimensional Bio-AM models increasing the safety and accuracy in pre-planning of oral and maxillofacial surgery. The correction factor for Dimension SST 768 FDM AM machine is 1.003 and dimensional error is limited to 0.3 %.

Using Factor Mixture Models to Evaluate the Type A/B Classification of Alcohol Use Disorders in a Heterogeneous Treatment Sample

PubMed Central

Hildebrandt, Tom; Epstein, Elizabeth E.; Sysko, Robyn; Bux, Donald A.

2017-01-01

Background The type A/B classification model for alcohol use disorders (AUDs) has received considerable empirical support. However, few studies examine the underlying latent structure of this subtyping model, which has been challenged as a dichotomization of a single drinking severity dimension. Type B, relative to type A, alcoholics represent those with early age of onset, greater familial risk, and worse outcomes from alcohol use. Method We examined the latent structure of the type A/B model using categorical, dimensional, and factor mixture models in a mixed gender community treatment-seeking sample of adults with an AUD. Results Factor analytic models identified 2-factors (drinking severity/externalizing psychopathology and internalizing psychopathology) underlying the type A/B indicators. A factor mixture model with 2-dimensions and 3-classes emerged as the best overall fitting model. The classes reflected a type A class and two type B classes (B1 and B2) that differed on the respective level of drinking severity/externalizing pathology and internalizing pathology. Type B1 had a greater prevalence of women and more internalizing pathology and B2 had a greater prevalence of men and more drinking severity/externalizing pathology. The 2-factor, 3-class model also exhibited predictive validity by explaining significant variance in 12-month drinking and drug use outcomes. Conclusions The model identified in the current study may provide a basis for examining different sources of heterogeneity in the course and outcome of AUDs. PMID:28247423

Improved performance of epidemiologic and genetic risk models for rheumatoid arthritis serologic phenotypes using family history.

PubMed

Sparks, Jeffrey A; Chen, Chia-Yen; Jiang, Xia; Askling, Johan; Hiraki, Linda T; Malspeis, Susan; Klareskog, Lars; Alfredsson, Lars; Costenbader, Karen H; Karlson, Elizabeth W

2015-08-01

To develop and validate rheumatoid arthritis (RA) risk models based on family history, epidemiologic factors and known genetic risk factors. We developed and validated models for RA based on known RA risk factors, among women in two cohorts: the Nurses' Health Study (NHS, 381 RA cases and 410 controls) and the Epidemiological Investigation of RA (EIRA, 1244 RA cases and 971 controls). Model discrimination was evaluated using the area under the receiver operating characteristic curve (AUC) in logistic regression models for the study population and for those with positive family history. The joint effect of family history with genetics, smoking and body mass index (BMI) was evaluated using logistic regression models to estimate ORs for RA. The complete model including family history, epidemiologic risk factors and genetics demonstrated AUCs of 0.74 for seropositive RA in NHS and 0.77 for anti-citrullinated protein antibody (ACPA)-positive RA in EIRA. Among women with positive family history, discrimination was excellent for complete models for seropositive RA in NHS (AUC 0.82) and ACPA-positive RA in EIRA (AUC 0.83). Positive family history, high genetic susceptibility, smoking and increased BMI had an OR of 21.73 for ACPA-positive RA. We developed models for seropositive and seronegative RA phenotypes based on family history, epidemiological and genetic factors. Among those with positive family history, models using epidemiologic and genetic factors were highly discriminatory for seropositive and seronegative RA. Assessing epidemiological and genetic factors among those with positive family history may identify individuals suitable for RA prevention strategies. Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://group.bmj.com/group/rights-licensing/permissions.

Assessing a dysphoric arousal model of acute stress disorder symptoms in a clinical sample of rape and bank robbery victims

PubMed Central

Hansen, Maj; Armour, Cherie; Elklit, Ask

2012-01-01

Background Since the introduction of Acute Stress Disorder (ASD) into the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) research has focused on the ability of ASD to predict PTSD rather than focusing on addressing ASD's underlying latent structure. The few existing confirmatory factor analytic (CFA) studies of ASD have failed to reach a clear consensus regarding ASD's underlying dimensionality. Although, the discrepancy in the results may be due to varying ASD prevalence rates, it remains possible that the model capturing the latent structure of ASD has not yet been put forward. One such model may be a replication of a new five-factor model of PTSD, which separates the arousal symptom cluster into Dysphoric and Anxious Arousal. Given the pending DSM-5, uncovering ASD's latent structure is more pertinent than ever. Objective Using CFA, four different models of the latent structure of ASD were specified and tested: the proposed DSM-5 model, the DSM-IV model, a three factor model, and a five factor model separating the arousal symptom cluster. Method The analyses were based on a combined sample of rape and bank robbery victims, who all met the diagnostic criteria for ASD (N = 404) using the Acute Stress Disorder Scale. Results The results showed that the five factor model provided the best fit to the data. Conclusions The results of the present study suggest that the dimensionality of ASD may be best characterized as a five factor structure which separates dysphoric and anxious arousal items into two separate factors, akin to recent research on PTSD's latent structure. Thus, the current study adds to the debate about how ASD should be conceptualized in the pending DSM-5. PMID:22893845

Micropollutants throughout an integrated urban drainage model: Sensitivity and uncertainty analysis

NASA Astrophysics Data System (ADS)

Mannina, Giorgio; Cosenza, Alida; Viviani, Gaspare

2017-11-01

The paper presents the sensitivity and uncertainty analysis of an integrated urban drainage model which includes micropollutants. Specifically, a bespoke integrated model developed in previous studies has been modified in order to include the micropollutant assessment (namely, sulfamethoxazole - SMX). The model takes into account also the interactions between the three components of the system: sewer system (SS), wastewater treatment plant (WWTP) and receiving water body (RWB). The analysis has been applied to an experimental catchment nearby Palermo (Italy): the Nocella catchment. Overall, five scenarios, each characterized by different uncertainty combinations of sub-systems (i.e., SS, WWTP and RWB), have been considered applying, for the sensitivity analysis, the Extended-FAST method in order to select the key factors affecting the RWB quality and to design a reliable/useful experimental campaign. Results have demonstrated that sensitivity analysis is a powerful tool for increasing operator confidence in the modelling results. The approach adopted here can be used for blocking some non-identifiable factors, thus wisely modifying the structure of the model and reducing the related uncertainty. The model factors related to the SS have been found to be the most relevant factors affecting the SMX modeling in the RWB when all model factors (scenario 1) or model factors of SS (scenarios 2 and 3) are varied. If the only factors related to the WWTP are changed (scenarios 4 and 5), the SMX concentration in the RWB is mainly influenced (till to 95% influence of the total variance for SSMX,max) by the aerobic sorption coefficient. A progressive uncertainty reduction from the upstream to downstream was found for the soluble fraction of SMX in the RWB.

Assessing a dysphoric arousal model of acute stress disorder symptoms in a clinical sample of rape and bank robbery victims.

PubMed

Hansen, Maj; Armour, Cherie; Elklit, Ask

2012-01-01

Since the introduction of Acute Stress Disorder (ASD) into the 4th edition of the Diagnostic and Statistical Manual of Mental Disorders (DSM-IV) research has focused on the ability of ASD to predict PTSD rather than focusing on addressing ASD's underlying latent structure. The few existing confirmatory factor analytic (CFA) studies of ASD have failed to reach a clear consensus regarding ASD's underlying dimensionality. Although, the discrepancy in the results may be due to varying ASD prevalence rates, it remains possible that the model capturing the latent structure of ASD has not yet been put forward. One such model may be a replication of a new five-factor model of PTSD, which separates the arousal symptom cluster into Dysphoric and Anxious Arousal. Given the pending DSM-5, uncovering ASD's latent structure is more pertinent than ever. USING CFA, FOUR DIFFERENT MODELS OF THE LATENT STRUCTURE OF ASD WERE SPECIFIED AND TESTED: the proposed DSM-5 model, the DSM-IV model, a three factor model, and a five factor model separating the arousal symptom cluster. The analyses were based on a combined sample of rape and bank robbery victims, who all met the diagnostic criteria for ASD (N = 404) using the Acute Stress Disorder Scale. The results showed that the five factor model provided the best fit to the data. The results of the present study suggest that the dimensionality of ASD may be best characterized as a five factor structure which separates dysphoric and anxious arousal items into two separate factors, akin to recent research on PTSD's latent structure. Thus, the current study adds to the debate about how ASD should be conceptualized in the pending DSM-5.

What Do We Mean By Sensitivity Analysis? The Need For A Comprehensive Characterization Of Sensitivity In Earth System Models

NASA Astrophysics Data System (ADS)

Razavi, S.; Gupta, H. V.

2014-12-01

Sensitivity analysis (SA) is an important paradigm in the context of Earth System model development and application, and provides a powerful tool that serves several essential functions in modelling practice, including 1) Uncertainty Apportionment - attribution of total uncertainty to different uncertainty sources, 2) Assessment of Similarity - diagnostic testing and evaluation of similarities between the functioning of the model and the real system, 3) Factor and Model Reduction - identification of non-influential factors and/or insensitive components of model structure, and 4) Factor Interdependence - investigation of the nature and strength of interactions between the factors, and the degree to which factors intensify, cancel, or compensate for the effects of each other. A variety of sensitivity analysis approaches have been proposed, each of which formally characterizes a different "intuitive" understanding of what is meant by the "sensitivity" of one or more model responses to its dependent factors (such as model parameters or forcings). These approaches are based on different philosophies and theoretical definitions of sensitivity, and range from simple local derivatives and one-factor-at-a-time procedures to rigorous variance-based (Sobol-type) approaches. In general, each approach focuses on, and identifies, different features and properties of the model response and may therefore lead to different (even conflicting) conclusions about the underlying sensitivity. This presentation revisits the theoretical basis for sensitivity analysis, and critically evaluates existing approaches so as to demonstrate their flaws and shortcomings. With this background, we discuss several important properties of response surfaces that are associated with the understanding and interpretation of sensitivity. Finally, a new approach towards global sensitivity assessment is developed that is consistent with important properties of Earth System model response surfaces.

Latent structures of female sexual functioning.

PubMed

Carvalho, Joana; Vieira, Armando Luís; Nobre, Pedro

2012-08-01

For the last three decades, male and female sexual responses have been conceptualized as similar, based on separated and sequential phases as proposed by the models of Masters and Johnson (1966) and Kaplan (1979) model. However, there is a growing debate around the need to conceptualize female sexual response and the classification of sexual dysfunction in women, in view of the upcoming editions of the DSM and ICD. The aim of this study was to test, using structural equation modeling, five conceptual, alternative models of female sexual function, using a sample of women with sexual difficulties and a sample of women without sexual problems. A total of 1993 Portuguese women participated in the study and completed a modified version of the Female Sexual Function Index. Findings suggested a four-factor solution as the model that best fit the data regarding women presenting sexual difficulties: (1) desire/arousal; (2) lubrication; (3) orgasm; (4) pain/vaginismus. In relation to sexually healthy women, the best model was a five-factor solution comprising of (1) desire; (2) arousal; (3) lubrication; (4) orgasm; and (5) pain/vaginismus. Discriminant validity between factors was supported, suggesting that these dimensions measure distinct phenomena. Model fit to the data significantly decreased in both samples, as models began to successively consider greater levels of overlap among phases of sexual function, towards a single-factor solution. By suggesting the overlap between pain and vaginismus, results partially support the new classification that is currently being discussed regarding DSM-5. Additionally, results on the relationship between sexual desire and arousal were inconclusive as sexually healthy women were better characterized by a five-factor model that considered the structural independence among these factors, whereas women with sexual difficulties better fit with a four-factor model merging sexual desire and subjective sexual arousal.

Developmental and Individual Differences in Chinese Writing

PubMed Central

Guan, Connie Qun; Ye, Feifei; Wagner, Richard K.; Meng, Wanjin

2015-01-01

The goal of the present study was to examine the generalizability of a model of the underlying dimensions of written composition across writing systems (Chinese Mandarin vs. English) and level of writing skill. A five-factor model of writing originally developed from analyses of 1st and 4th grade English writing samples was applied to Chinese writing samples obtained from 4th and 7th grade students. Confirmatory factor analysis was used to compare the fits of alternative models of written composition. The results suggest that the five-factor model of written composition generalizes to Chinese writing samples and applies to both less skilled (Grade 4) and more skilled (Grade 7) writing, with differences in factor means between grades that vary in magnitude across factors. PMID:26038631

Factor structure of parent and teacher ratings of the ODD symptoms for Malaysian primary school children.

PubMed

Gomez, Rapson

2017-02-01

This present study used confirmatory factor analysis (CFA) to examine the applicability of one-, two- three- and second order Oppositional Defiant Disorder (ODD) factor models, proposed in previous studies, in a group of Malaysian primary school children. These models were primarily based on parent reports. In the current study, parent and teacher ratings of the ODD symptoms were obtained for 934 children. For both groups of respondents, the findings showing some support for all models examined, with most support for a second order model with Burke et al. (2010) three factors (oppositional, antagonistic, and negative affect) as the primary factors. The diagnostic implications of the findings are discussed. Copyright © 2016 Elsevier B.V. All rights reserved.

Systematics of g factors of 2{sub 1}{sup +} states in even-even nuclei from Gd to Pt: A microscopic description by the projected shell model

DOE Office of Scientific and Technical Information (OSTI.GOV)

Bian, Bao-An; Institute of Low Energy Nuclear Physics, Beijing Normal University, Beijing 100875; Di, Yao-Min

2007-01-15

The systematics of g factor of the first excited 2{sup +} state vs neutron number N is studied by the projected shell model. The study covers the even-even nuclei of all isotopic chains from Gd to Pt. g factors are calculated by using the many-body wave functions that well reproduce the energy levels and B(E2)s of the ground-state bands. For Gd to W isotopes the characteristic feature of the g factor data along an isotopic chain is described by the present model. Deficiency of the model in the g factor description for the heavier Os and Pt isotopes is discussed.

Parental expression of disappointment: should it be a factor in Hoffman's model of parental discipline?

PubMed

Patrick, Renee B; Gibbs, John C

2007-06-01

The authors addressed whether parental expression of disappointment should be included as a distinct factor in M. L. Hoffman's well-established typology of parenting styles (induction, love withdrawal, power assertion). Hoffman's 3-factor model, along with a more inclusive 4-factor model (induction, love withdrawal, power assertion, and expressions of disappointment), were respectively evaluated in exploratory factor analyses. The analysis utilized extant data comprised of responses by children (N = 73) and their mothers (N = 67) to an adaptation of M. L. Hoffman and H. D. Saltzstein's parental discipline measure. The findings supported Hoffman's original model. Disappointment may be reducible to love withdrawal or induction, although disappointment may be a more appropriate induction for adolescents.

DOSE-DEPENDENT INCREASE IN THE PRODUCTION OF NERVE GROWTH FACTOR, NEUROTROPHIN-3, AND NEUROTROPHIN-4 IN A PENICILLIUM CHRYSOGENUM-INDUCED ALLERGIC ASTHMA MODEL

EPA Science Inventory

Increased levels of neurotrophins (nerve growth factor [NGF], brain-derived neurotrophic factor [BDNF], neurotrophin [NT]-3, and/or NT-4) have been associated with asthma as well as in animal models of allergic asthma. In our mouse model for fungal allergic asthma, repeated ...

Default Bayes Factors for Model Selection in Regression

ERIC Educational Resources Information Center

Rouder, Jeffrey N.; Morey, Richard D.

2012-01-01

In this article, we present a Bayes factor solution for inference in multiple regression. Bayes factors are principled measures of the relative evidence from data for various models or positions, including models that embed null hypotheses. In this regard, they may be used to state positive evidence for a lack of an effect, which is not possible…

Modelling impulsive factors for electronics and restaurant coupons’ e-store display

NASA Astrophysics Data System (ADS)

Ariningsih, P. K.; Nainggolan, M.; Sandy, I. A.

2018-04-01

In many times, the increment of e-store visitors does not followed by sales increment. Most purchases through e-commerce are impulsive buying, however only small amount of study is available to understand impulsive factors of e-store display. This paper suggests a preliminary concept on understanding the impulsive factors in Electronics and Restaurant Coupons e-store display, which are two among few popular group products sold through e-commerce. By conducting literature study and survey, 31 attributes were identified as impulsive factors in electronics e-store display and 20 attributes were identified as impulsive factors for restaurant coupon e-store. The attributes were then grouped into comprehensive impulsive factors by factor analysis. Each group of impulsive attributes were generated into 3 factors. Accessibility Factors and Trust Factors appeared for each group products. The other factors are Internal Factors for electronics e-store and Marketing factors for restaurant coupons e-store. Structural Equation Model of the impulsive factors was developed for each type of e-store, which stated the covariance between Trust Factors and Accessibility Factors. Based on preliminary model, Internal Factor and Trust Factor are influencing impulsive buying in electronics store. Special factor for electronics e-store is Internal Factor, while for restaurant coupons e-store is Marketing Factor.

Modelling the factor structure of the Child Depression Inventory in a population of apparently healthy adolescents in Nigeria.

PubMed

Olorunju, Samson Bamidele; Akpa, Onoja Matthew; Afolabi, Rotimi Felix

2018-01-01

Childhood and adolescent depression is common and often persists into adulthood with negative implications for school performances, peer relationship and behavioural functioning. The Child Depression Inventory (CDI) has been used to assess depression among adolescents in many countries including Nigeria but it is uncertain if the theoretical structure of CDI appropriately fits the experiences of adolescents in Nigeria. This study assessed varying theoretical modelling structure of the CDI in a population of apparently healthy adolescents in Benue state, Nigeria. Data was extracted on CDI scale and demographic information from a total of 1, 963 adolescents (aged 10-19 years), who participated in a state wide study assessing adolescent psychosocial functioning. In addition to descriptive statistics and reliability tests, Exploratory Factor Analysis (EFA) and Confirmatory Factor analysis (CFA) were used to model the underlying factor structure and its adequacy. The suggested new model was compared with existing CDI models as well as the CDI's original theoretical model. A model is considered better, if it has minimum Root Mean Square Error of Approximation (RMSEA<0.05), Minimum value of Discrepancy (CMIN/DF<3.0) and Akaike information criteria. All analyses were performed at 95% confidence level, using the version 21 of AMOS and the R software. Participants were 14.7±2.1 years and mostly male (54.3%), from Monogamous homes (67.9%) and lived in urban areas (52.2%). The measure of the overall internal consistency of the 2-factor CDI was α = 0.84. The 2-factor model had the minimum RMSEA (0.044), CMIN/DF (2.87) and least AIC (1037.996) compared to the other five CDI models. The child depression inventory has a 2-factor structure in a non-clinical general population of adolescents in Nigeria. Future use of the CDI in related setting may consider the 2-factor model.

Replication and extension of a hierarchical model of social anxiety and depression: fear of positive evaluation as a key unique factor in social anxiety.

PubMed

Weeks, Justin W

2015-01-01

Wang, Hsu, Chiu, and Liang (2012, Journal of Anxiety Disorders, 26, 215-224) recently proposed a hierarchical model of social interaction anxiety and depression to account for both the commonalities and distinctions between these conditions. In the present paper, this model was extended to more broadly encompass the symptoms of social anxiety disorder, and replicated in a large unselected, undergraduate sample (n = 585). Structural equation modeling (SEM) and hierarchical regression analyses were employed. Negative affect and positive affect were conceptualized as general factors shared by social anxiety and depression; fear of negative evaluation (FNE) and disqualification of positive social outcomes were operationalized as specific factors, and fear of positive evaluation (FPE) was operationalized as a factor unique to social anxiety. This extended hierarchical model explicates structural relationships among these factors, in which the higher-level, general factors (i.e., high negative affect and low positive affect) represent vulnerability markers of both social anxiety and depression, and the lower-level factors (i.e., FNE, disqualification of positive social outcomes, and FPE) are the dimensions of specific cognitive features. Results from SEM and hierarchical regression analyses converged in support of the extended model. FPE is further supported as a key symptom that differentiates social anxiety from depression.

Formation factor in Bentheimer and Fontainebleau sandstones: Theory compared with pore-scale numerical simulations

NASA Astrophysics Data System (ADS)

Ghanbarian, Behzad; Berg, Carl F.

2017-09-01

Accurate quantification of formation resistivity factor F (also called formation factor) provides useful insight into connectivity and pore space topology in fully saturated porous media. In particular the formation factor has been extensively used to estimate permeability in reservoir rocks. One of the widely applied models to estimate F is Archie's law (F = ϕ- m in which ϕ is total porosity and m is cementation exponent) that is known to be valid in rocks with negligible clay content, such as clean sandstones. In this study we compare formation factors determined by percolation and effective-medium theories as well as Archie's law with numerical simulations of electrical resistivity on digital rock models. These digital models represent Bentheimer and Fontainebleau sandstones and are derived either by reconstruction or directly from micro-tomographic images. Results show that the universal quadratic power law from percolation theory accurately estimates the calculated formation factor values in network models over the entire range of porosity. However, it crosses over to the linear scaling from the effective-medium approximation at the porosity of 0.75 in grid models. We also show that the effect of critical porosity, disregarded in Archie's law, is nontrivial, and the Archie model inaccurately estimates the formation factor in low-porosity homogeneous sandstones.

Dimensional structure of DSM-5 posttraumatic stress disorder symptoms: results from the National Health and Resilience in Veterans Study.

PubMed

Tsai, Jack; Harpaz-Rotem, Ilan; Armour, Cherie; Southwick, Steven M; Krystal, John H; Pietrzak, Robert H

2015-05-01

To evaluate the prevalence of DSM-5 posttraumatic stress disorder (PTSD) and factor structure of PTSD symptomatology in a nationally representative sample of US veterans and examine how PTSD symptom clusters are related to depression, anxiety, suicidal ideation, hostility, physical and mental health-related functioning, and quality of life. Data were analyzed from the National Health and Resilience in Veterans Study, a nationally representative survey of 1,484 US veterans conducted from September through October 2013. Confirmatory factor analyses were conducted to evaluate the factor structure of PTSD symptoms, and structural equation models were constructed to examine the association between PTSD symptom clusters and external correlates. 12.0% of veterans screened positive for lifetime PTSD and 5.2% for past-month PTSD. A 5-factor dysphoric arousal model and a newly proposed 6-factor model both fit the data significantly better than the 4-factor model of DSM-5. The 6-factor model fit the data best in the full sample, as well as in subsamples of female veterans and veterans with lifetime PTSD. The emotional numbing symptom cluster was more strongly related to depression (P < .001) and worse mental health-related functioning (P < .001) than other symptom clusters, while the externalizing behavior symptom cluster was more strongly related to hostility (P < .001). A total of 5.2% of US veterans screened positive for past-month DSM-5 PTSD. A 6-factor model of DSM-5 PTSD symptoms, which builds on extant models and includes a sixth externalizing behavior factor, provides the best dimensional representation of DSM-5 PTSD symptom clusters and demonstrates validity in assessing health outcomes of interest in this population. © Copyright 2015 Physicians Postgraduate Press, Inc.

Regression-Based Norms for a Bi-factor Model for Scoring the Brief Test of Adult Cognition by Telephone (BTACT).

PubMed

Gurnani, Ashita S; John, Samantha E; Gavett, Brandon E

2015-05-01

The current study developed regression-based normative adjustments for a bi-factor model of the The Brief Test of Adult Cognition by Telephone (BTACT). Archival data from the Midlife Development in the United States-II Cognitive Project were used to develop eight separate linear regression models that predicted bi-factor BTACT scores, accounting for age, education, gender, and occupation-alone and in various combinations. All regression models provided statistically significant fit to the data. A three-predictor regression model fit best and accounted for 32.8% of the variance in the global bi-factor BTACT score. The fit of the regression models was not improved by gender. Eight different regression models are presented to allow the user flexibility in applying demographic corrections to the bi-factor BTACT scores. Occupation corrections, while not widely used, may provide useful demographic adjustments for adult populations or for those individuals who have attained an occupational status not commensurate with expected educational attainment. © The Author 2015. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The accuracy of climate models' simulated season lengths and the effectiveness of grid scale correction factors

DOE PAGES

Winterhalter, Wade E.

2011-09-01

Global climate change is expected to impact biological populations through a variety of mechanisms including increases in the length of their growing season. Climate models are useful tools for predicting how season length might change in the future. However, the accuracy of these models tends to be rather low at regional geographic scales. Here, I determined the ability of several atmosphere and ocean general circulating models (AOGCMs) to accurately simulate historical season lengths for a temperate ectotherm across the continental United States. I also evaluated the effectiveness of regional-scale correction factors to improve the accuracy of these models. I foundmore » that both the accuracy of simulated season lengths and the effectiveness of the correction factors to improve the model's accuracy varied geographically and across models. These results suggest that regional specific correction factors do not always adequately remove potential discrepancies between simulated and historically observed environmental parameters. As such, an explicit evaluation of the correction factors' effectiveness should be included in future studies of global climate change's impact on biological populations.« less

Perceived game realism: a test of three alternative models.

PubMed

Ribbens, Wannes

2013-01-01

Perceived realism is considered a key concept in explaining the mental processing of media messages and the societal impact of media. Despite its importance, little is known about its conceptualization and dimensional structure, especially with regard to digital games. The aim of this study was to test a six-factor model of perceived game realism comprised of simulational realism, freedom of choice, perceptual pervasiveness, social realism, authenticity, and character involvement and to assess it against an alternative single- and five-factor model. Data were collected from 380 male digital game users who judged the realism of the first-person shooter Half-Life 2 based upon their previous experience with the game. Confirmatory factor analysis was applied to investigate which model fits the data best. The results support the six-factor model over the single- and five-factor solutions. The study contributes to our knowledge of perceived game realism by further developing its conceptualization and measurement.

User acceptance of mobile commerce: an empirical study in Macau

NASA Astrophysics Data System (ADS)

Lai, Ivan K. W.; Lai, Donny C. F.

2014-06-01

This study aims to examine the positive and negative factors that can significantly explain user acceptance of mobile commerce (m-commerce) in Macau. A technology acceptance model for m-commerce with five factors is constructed. The proposed model is tested using data collected from 219 respondents. Confirmatory factor analysis is performed to examine the reliability and validity of the model, and structural equation modelling is performed to access the relationship between behaviour intention and each factor. The acceptance of m-commerce is influenced by factors including performance expectancy, social influence, facilitating conditions and privacy concern; while effort expectancy is insignificant in this case. The results of the study are useful for m-commerce service providers to adjust their strategies for promoting m-commerce services. This study contributes to the practice by providing a user technology acceptance model for m-commerce that can be used as a foundation for future research.

MECHANISMS OF HYPNOSIS:

PubMed Central

Jensen, Mark P.; Adachi, Tomonori; Tomé-Pires, Catarina; Lee, Jikwan; Osman, Zubaidah Jamil; Miró, Jordi

2014-01-01

Evidence supports the efficacy of hypnotic treatments, but there remain many unresolved questions regarding how hypnosis produces its beneficial effects. Most theoretical models focus more or less on biological, psychological, and social factors. This scoping review summarizes the empirical findings regarding the associations between specific factors in each of these domains and response to hypnosis. The findings indicate that: (1) no single factor appears primary; (2) different factors may contribute more or less to outcomes in different subsets of individuals or for different conditions; and (3) comprehensive models of hypnosis that incorporate factors from all 3 domains may ultimately prove to be more useful than more restrictive models that focus on 1 or a very few factors. PMID:25365127

Confirmatory factor analysis of posttraumatic stress symptoms in sexually harassed women.

PubMed

Palmieri, Patrick A; Fitzgerald, Louise F

2005-12-01

Posttraumatic stress disorder (PTSD) factor analytic research to date has not provided a clear consensus on the structure of posttraumatic stress symptoms. Seven hypothesized factor structures were evaluated using confirmatory factor analysis of the Posttraumatic Stress Disorder Checklist, a paper-and-pencil measure of posttraumatic stress symptom severity, in a sample of 1,218 women who experienced a broad range of workplace sexual harassment. The model specifying correlated re-experiencing, effortful avoidance, emotional numbing, and hyperarousal factors provided the best fit to the data. Virtually no support was obtained for the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV; American Psychiatric Association, 1994) three-factor model of re-experiencing, avoidance, and hyperarousal factors. Different patterns of correlations with external variables were found for the avoidance and emotional numbing factors, providing further validation of the supported model.

Moderating Factors of Video-Modeling with Other as Model: A Meta-Analysis of Single-Case Studies

ERIC Educational Resources Information Center

Mason, Rose A.; Ganz, Jennifer B.; Parker, Richard I.; Burke, Mack D.; Camargo, Siglia P.

2012-01-01

Video modeling with other as model (VMO) is a more practical method for implementing video-based modeling techniques, such as video self-modeling, which requires significantly more editing. Despite this, identification of contextual factors such as participant characteristics and targeted outcomes that moderate the effectiveness of VMO has not…

Tumor Secreted Autocrine Motility Factor (AMF): Causal Role in an Animal Model of Cachexia

DTIC Science & Technology

2005-08-01

AD Award Number: DAMD17-02-1-0586 TITLE: Tumor Secreted Autocrine Motility Factor ( AMF ): Causal Role in an Animal Model of Cachexia PRINCIPAL...5a. CONTRACT NUMBER Tumor Secreted Autocrine Motility Factor ( AMF ): Causal Role in an Animal Model of Cachexia 5b. GRANT NUMBER DAM D1 7-02-1-0586 5c...quality of life and postpone mortality. We proposed that autocrine motility factor ( AMF ) is released into the bloodstream from cancer sites and

Attitudes toward psychotropic medications among patients with chronic psychiatric disorders and their family caregivers

PubMed Central

Grover, Sandeep; Chakrabarti, Subho; Sharma, Aarti; Tyagi, Shikha

2014-01-01

Aim: To examine attitudes towards psychotropic medications among patients with chronic psychiatric disorders as well as their family caregivers by using factor analysis. Materials and Methods: The study included 200 patients and their family caregivers with chronic psychiatric disorders who are attending the psychiatry outpatient services. A self-designed 18-item self-rated questionnaire was used to evaluate the attitude toward psychotropics and factor analysis was done to study the different models of attitudes. Results: In general both patients and caregivers had positive attitude toward the psychotropic medications and there was no significant difference between the patients and caregivers on the various items of the questionnaire assessing the attitude. Factor analysis of the questionnaire indicated that either two-factor or four-factor models explained the attitude of the patients and caregivers. In the two-factor model there was one positive and one negative attitude factor, whereas the four-factor model comprised of two positive and two negative attitude factors. The four-factor model of attitudes provided a more comprehensive solution to how attitudes might be formed among patients and their family caregivers. Factors one and four in the four-factor solution still reflected positive attitudes, but appeared to portray a risk-benefit approach, in which benefits such as the efficacy of psychotropic medications in treating mental illnesses and preventing relapse, and medications being better than other options were being contrasted with the risks of side effects and permanent damage or harm. Conclusion: Attitudes of patients with chronic psychiatric disorders and their caregivers toward psychotropic medications appear to be shaped by factors such as perceived efficacy or benefit from medicines, the necessity for taking treatment and concerns such as side effects, harm or expense. PMID:25288840

Factor Structure and Psychometric Properties of English and Spanish Versions of the Edinburgh Postnatal Depression Scale Among Hispanic Women in a Primary Care Setting

PubMed Central

Hartley, Chelsey M.; Barroso, Nicole; Rey, Yasmin; Pettit, Jeremy W.; Bagner, Daniel M.

2015-01-01

Background Although a number of studies have examined the factor structure of the Edinburgh Postnatal Depression Scale (EPDS) in predominately White or African American samples, no published research has reported on the factor structure among Hispanic women who reside in the United States. Objective The current study examined the factor structure of the EPDS among Hispanic mothers in the United States. Method Among 220 Hispanic women, drawn from a pediatric primary care setting, with an infant aged 0 to 10 months, 6 structural models guided by the empirical literature were evaluated using confirmatory factor analysis. Results Results supported a 2-factor model of depression and anxiety as the best fitting model. Multigroup models supported the factorial invariance across women who completed the EDPS in English and Spanish. Conclusion These findings provide initial support for the 2-factor structure of the EPDS among Hispanic women in the United States. PMID:24807217

Factor structure and psychometric properties of english and spanish versions of the edinburgh postnatal depression scale among Hispanic women in a primary care setting.

PubMed

Hartley, Chelsey M; Barroso, Nicole; Rey, Yasmin; Pettit, Jeremy W; Bagner, Daniel M

2014-12-01

Although a number of studies have examined the factor structure of the Edinburgh Postnatal Depression Scale (EPDS) in predominately White or African American samples, no published research has reported on the factor structure among Hispanic women who reside in the United States. The current study examined the factor structure of the EPDS among Hispanic mothers in the United States. Among 220 Hispanic women, drawn from a pediatric primary care setting, with an infant aged 0 to 10 months, 6 structural models guided by the empirical literature were evaluated using confirmatory factor analysis. Results supported a 2-factor model of depression and anxiety as the best fitting model. Multigroup models supported the factorial invariance across women who completed the EDPS in English and Spanish. These findings provide initial support for the 2-factor structure of the EPDS among Hispanic women in the United States. © 2014 Wiley Periodicals, Inc.

Psychometric properties of a Norwegian adaption of the Barratt Impulsiveness Scale-11 in a sample of Parkinson patients, headache patients, and controls.

PubMed

Lindstrøm, Jonas C; Wyller, Nora G; Halvorsen, Marianne M; Hartberg, Silje; Lundqvist, Christofer

2017-01-01

To assess the psychometric properties of a Norwegian translation of the Barratt Impulsiveness Scale (BIS-11) for use in populations of headache, Parkinson's disease (PD), and healthy controls. The BIS-11 was forward and backward translated by native speakers of both Norwegian and English to give Norwegian BIS-11 (Nor-BIS-11). A convenience sample (110 subjects) of healthy controls (47), PD patients (43), and chronic headache patients (20) (the latter two recruited from a Neurology outpatient clinic), were asked to complete the scale (a subset twice for test-retest). Exploratory and confirmatory factor analyses were done for a single-factor model, the original three-factor model and a two-factor model. Test-retest results were analyzed using the Bland-Altman approach. The Nor-BIS-11 scale showed good utility and acceptability as well as good test-retest reliability in this sample. Cronbach's α was .68, test-retest bias was -0.73, Cohen's δ = -.134, and limits of agreement were -11.48 to 10.01. The factor structure was found to fit better with a two-factor model than with the original model with three factors. The model fit indices indicated a moderate fit. The Nor-BIS-11 scale is acceptable and reliable to use in Parkinson's disease patients, chronic headache patients, and healthy controls. The results should be interpreted in a two-factor model but with caution due to low construct validity. External validity needs to be further tested.

Cross-cultural generalizability of personality dimensions: relating indigenous and imported dimensions in two cultures.

PubMed

Katigbak, M S; Church, A T; Akamine, T X

1996-01-01

The cross-cultural generalizability of personality dimensions was investigated by (a) identifying indigenous Philippine dimensions, (b) testing the cross-cultural replicability of the NEO 5-factor model (P. T. Costa & R.R. McCrae, 1992), and (c) relating Philippine and Western dimensions in Philippine and U.S. samples of college students. Filipino self-ratings (N = 536) on indigenous items were factor analyzed, and 6 Philippine dimensions were obtained. Conclusions about the replicability of the 5-factor model in the Philippines (N = 432) depended on whether exploratory, Procrustes, or confirmatory factor methods were used. In regression and joint factor analyses, moderate to strong associations were found between the Philippine dimensions and (a) dimensions from the 5-factor model in both Philippine (N = 387) and U.S. (N = 610) samples, and (b) the Tellegen model (A. Tellegen, 1985; A. Tellegen & N.G. Waller, in press) in a U.S. sample (N = 603).

The Benefits of Including Clinical Factors in Rectal Normal Tissue Complication Probability Modeling After Radiotherapy for Prostate Cancer

DOE Office of Scientific and Technical Information (OSTI.GOV)

Defraene, Gilles, E-mail: gilles.defraene@uzleuven.be; Van den Bergh, Laura; Al-Mamgani, Abrahim

2012-03-01

Purpose: To study the impact of clinical predisposing factors on rectal normal tissue complication probability modeling using the updated results of the Dutch prostate dose-escalation trial. Methods and Materials: Toxicity data of 512 patients (conformally treated to 68 Gy [n = 284] and 78 Gy [n = 228]) with complete follow-up at 3 years after radiotherapy were studied. Scored end points were rectal bleeding, high stool frequency, and fecal incontinence. Two traditional dose-based models (Lyman-Kutcher-Burman (LKB) and Relative Seriality (RS) and a logistic model were fitted using a maximum likelihood approach. Furthermore, these model fits were improved by including themore » most significant clinical factors. The area under the receiver operating characteristic curve (AUC) was used to compare the discriminating ability of all fits. Results: Including clinical factors significantly increased the predictive power of the models for all end points. In the optimal LKB, RS, and logistic models for rectal bleeding and fecal incontinence, the first significant (p = 0.011-0.013) clinical factor was 'previous abdominal surgery.' As second significant (p = 0.012-0.016) factor, 'cardiac history' was included in all three rectal bleeding fits, whereas including 'diabetes' was significant (p = 0.039-0.048) in fecal incontinence modeling but only in the LKB and logistic models. High stool frequency fits only benefitted significantly (p = 0.003-0.006) from the inclusion of the baseline toxicity score. For all models rectal bleeding fits had the highest AUC (0.77) where it was 0.63 and 0.68 for high stool frequency and fecal incontinence, respectively. LKB and logistic model fits resulted in similar values for the volume parameter. The steepness parameter was somewhat higher in the logistic model, also resulting in a slightly lower D{sub 50}. Anal wall DVHs were used for fecal incontinence, whereas anorectal wall dose best described the other two endpoints. Conclusions: Comparable prediction models were obtained with LKB, RS, and logistic NTCP models. Including clinical factors improved the predictive power of all models significantly.« less

A Thermoelastic Damping Model for the Cone Microcantilever Resonator with Circular Cross-section

NASA Astrophysics Data System (ADS)

Li, Pu; Zhou, Hongyue

2017-07-01

Microbeams with variable cross-section have been applied in Microelectromechanical Systems (MEMS) resonators. Quality factor (Q-factor) is an important factor evaluating the performance of MEMS resonators, and high Q-factor stands for the excellent performance. Thermoelastic damping (TED), which has been verified as a fundamental energy lost mechanism for microresonators, determines the upper limit of Q-factor. TED can be calculated by the Zener’s model and Lifshits and Roukes (LR) model. However, for microbeam resonators with variable cross-sections, these two models become invalid in some cases. In this work, we derived the TED model for cone microcantilever with circular cross-section that is a representative non-uniform microbeam. The comparison of results obtained by the present model and Finite Element Method (FEM) model proves that the present model is valid for predicting TED value for cone microcantilever with circular cross-section. The results suggest that the first-order natural frequencies and TED values of cone microcantilever are larger than those of uniform microbeam for large aspect ratios (l/r 0). In addition, the Debye peak value of a uniform microcantilever is equal to 0.5ΔE, while that of cone microcantilever is about 0.438ΔE.

Indigenous Chinese Personality Constructs: Is the Five-Factor Model Complete?

ERIC Educational Resources Information Center

Cheung, Fanny M.; Leung, Kwok; Zhang, Jian-Xin; Sun, Hai-Fa; Gan, Yi-Qun; Song, Wei-Zhen; Xie, Dong

2001-01-01

Three studies involving Chinese respondents from China and Hong Kong and diverse respondents from Hawaii compared the Chinese Personality Assessment Inventory factor structure with the Revised NEO Personality Inventory (NEO-PI-R) and NEO-Five Factor Inventory. Results supported the universality of the five-factor model, the validity of NEO-PI-R,…

Posttraumatic stress disorder symptom structure in Chinese adolescents exposed to a deadly earthquake.

PubMed

Wang, Li; Long, Di; Li, Zhongquan; Armour, Cherie

2011-07-01

This present study examined the structure of posttraumatic stress disorder (PTSD) symptoms in a large sample of Chinese adolescents exposed to a deadly earthquake. A total of 2,800 middle school students aged 12 to 18 years participated in the study 6 months after the "Wenchuan Earthquake". Results of confirmatory factor analysis indicated that a five-factor intercorrelated model composed of intrusion, avoidance, numbing, dysphoric arousal, and anxious arousal, fit data significantly better than both the four-factor numbing model King et al. (Psychological Assessment 10:90-96, 1998) and the four-factor dysphoria model Simms et al. (Journal of Abnormal Psychology 111:637-647, 2002). Further examination of the external convergent and discriminant validity revealed that except for the dysphoric arousal factor, the remaining four PTSD factors yielded significantly different correlations with external measures of anxiety vs. depression. The findings add to the limited literature on the factor structure of PTSD in youths and on the five-factor PTSD model. In addition, they provide more detail into the latent psychopathological processes of PTSD, and inform the forthcoming DSM-5.

Evaluating the Factor Validity of the Children's Organizational Skills Scale in Youth with ADHD.

PubMed

Molitor, Stephen J; Langberg, Joshua M; Evans, Steven W; Dvorsky, Melissa R; Bourchtein, Elizaveta; Eddy, Laura D; Smith, Zoe R; Oddo, Lauren E

2017-06-01

Children and adolescents with ADHD often have difficulties with organization, time management, and planning (OTMP) skills, and these skills are a common target of intervention. A limited array of tools for measuring these abilities in youth is available, and one of the most prominent measures is the Children's Organizational Skills Scale (COSS). Although the COSS fills an important need, a replication of the COSS factor structure outside of initial measure development has not been conducted in any population. Given that the COSS is frequently used in ADHD research, the current study evaluated the factor structure of the parent-rated COSS in a sample ( N = 619) of adolescents with ADHD. Results indicated that the original factor structure could be replicated, although the use of item parcels appeared to affect model fit statistics. An alternative bi-factor model was also tested that did not require the use of parcels, with results suggesting similar model fit in comparison to the original factor structure. Exploratory validity tests indicated that the domain-general factor of the bi-factor model appears related to broad executive functioning abilities.

Analysis of psychological factors for quality assessment of interactive multimodal service

NASA Astrophysics Data System (ADS)

Yamagishi, Kazuhisa; Hayashi, Takanori

2005-03-01

We proposed a subjective quality assessment model for interactive multimodal services. First, psychological factors of an audiovisual communication service were extracted by using the semantic differential (SD) technique and factor analysis. Forty subjects participated in subjective tests and performed point-to-point conversational tasks on a PC-based TV phone that exhibits various network qualities. The subjects assessed those qualities on the basis of 25 pairs of adjectives. Two psychological factors, i.e., an aesthetic feeling and a feeling of activity, were extracted from the results. Then, quality impairment factors affecting these two psychological factors were analyzed. We found that the aesthetic feeling is mainly affected by IP packet loss and video coding bit rate, and the feeling of activity depends on delay time and video frame rate. We then proposed an opinion model derived from the relationships among quality impairment factors, psychological factors, and overall quality. The results indicated that the estimation error of the proposed model is almost equivalent to the statistical reliability of the subjective score. Finally, using the proposed model, we discuss guidelines for quality design of interactive audiovisual communication services.

Factor Structure of the Psychopathic Personality Inventory (PPI): Findings from a Large Incarcerated Sample

PubMed Central

Neumann, Craig S.; Malterer, Melanie B.; Newman, Joseph P.

2010-01-01

Recent exploratory factor analysis (EFA) of the Psychopathic Personality Inventory (PPI; Lilienfeld, 1990) with a community sample suggested that the PPI subscales may be comprised of two higher-order factors (Benning et al., 2003). However, little research has examined the PPI structure in offenders. The current study attempted to replicate the Benning et al. two-factor solution using a large (N=1224) incarcerated male sample. Confirmatory factor analysis (CFA) of this model with the full sample resulted in poor model fit. Next, to identify a factor solution that would summarize the offender data, EFA was conducted using a split-half of the total sample, followed by an attempt to replicate the EFA solution via CFA with the other split-half sample. Using the recommendations of Prooijen and van der Kloot (2001) for recovering EFA solutions, model fit results provided some evidence that the EFA solution could be recovered via CFA. However, this model involved extensive cross-loadings of the subscales across three factors, suggesting item overlap across PPI subscales. In sum, the two-factor solution reported by Benning et al. (2003) was not a viable model for the current sample of offenders, and additional research is needed to elucidate the latent structure of the PPI. PMID:18557694

Temperament factors and dimensional, latent bifactor models of child psychopathology: Transdiagnostic and specific associations in two youth samples.

PubMed

Hankin, Benjamin L; Davis, Elysia Poggi; Snyder, Hannah; Young, Jami F; Glynn, Laura M; Sandman, Curt A

2017-06-01

Common emotional and behavioral symptoms co-occur and are associated with core temperament factors. This study investigated links between temperament and dimensional, latent psychopathology factors, including a general common psychopathology factor (p factor) and specific latent internalizing and externalizing liabilities, as captured by a bifactor model, in two independent samples of youth. Specifically, we tested the hypothesis that temperament factors of negative affectivity (NA), positive affectivity (PA), and effortful control (EC) could serve as both transdiagnostic and specific risks in relation to recent bifactor models of child psychopathology. Sample 1 included 571 youth (average age 13.6, SD =2.37, range 9.3-17.5) with both youth and parent report. Sample 2 included 554 preadolescent children (average age 7.7, SD =1.35, range =5-11 years) with parent report. Structural equation modeling showed that the latent bifactor models fit in both samples. Replicated in both samples, the p factor was associated with lower EC and higher NA (transdiagnostic risks). Several specific risks replicated in both samples after controlling for co-occurring symptoms via the p factor: internalizing was associated with higher NA and lower PA, lower EC related to externalizing problems. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

PTSD's latent structure in Malaysian tsunami victims: assessing the newly proposed Dysphoric Arousal model.

PubMed

Armour, Cherie; Raudzah Ghazali, Siti; Elklit, Ask

2013-03-30

The underlying latent structure of Posttraumatic Stress Disorder (PTSD) is widely researched. However, despite a plethora of factor analytic studies, no single model has consistently been shown as superior to alternative models. The two most often supported models are the Emotional Numbing and the Dysphoria models. However, a recently proposed five-factor Dysphoric Arousal model has been gathering support over and above existing models. Data for the current study were gathered from Malaysian Tsunami survivors (N=250). Three competing models (Emotional Numbing/Dysphoria/Dysphoric Arousal) were specified and estimated using Confirmatory Factor Analysis (CFA). The Dysphoria model provided superior fit to the data compared to the Emotional Numbing model. However, using chi-square difference tests, the Dysphoric Arousal model showed a superior fit compared to both the Emotional Numbing and Dysphoria models. In conclusion, the current results suggest that the Dysphoric Arousal model better represents PTSD's latent structure and that items measuring sleeping difficulties, irritability/anger and concentration difficulties form a separate, unique PTSD factor. These results are discussed in relation to the role of Hyperarousal in PTSD's on-going symptom maintenance and in relation to the DSM-5. Copyright © 2012 Elsevier Ireland Ltd. All rights reserved.

Factor Structure, Reliability and Measurement Invariance of the Alberta Context Tool and the Conceptual Research Utilization Scale, for German Residential Long Term Care

PubMed Central

Hoben, Matthias; Estabrooks, Carole A.; Squires, Janet E.; Behrens, Johann

2016-01-01

We translated the Canadian residential long term care versions of the Alberta Context Tool (ACT) and the Conceptual Research Utilization (CRU) Scale into German, to study the association between organizational context factors and research utilization in German nursing homes. The rigorous translation process was based on best practice guidelines for tool translation, and we previously published methods and results of this process in two papers. Both instruments are self-report questionnaires used with care providers working in nursing homes. The aim of this study was to assess the factor structure, reliability, and measurement invariance (MI) between care provider groups responding to these instruments. In a stratified random sample of 38 nursing homes in one German region (Metropolregion Rhein-Neckar), we collected questionnaires from 273 care aides, 196 regulated nurses, 152 allied health providers, 6 quality improvement specialists, 129 clinical leaders, and 65 nursing students. The factor structure was assessed using confirmatory factor models. The first model included all 10 ACT concepts. We also decided a priori to run two separate models for the scale-based and the count-based ACT concepts as suggested by the instrument developers. The fourth model included the five CRU Scale items. Reliability scores were calculated based on the parameters of the best-fitting factor models. Multiple-group confirmatory factor models were used to assess MI between provider groups. Rather than the hypothesized ten-factor structure of the ACT, confirmatory factor models suggested 13 factors. The one-factor solution of the CRU Scale was confirmed. The reliability was acceptable (>0.7 in the entire sample and in all provider groups) for 10 of 13 ACT concepts, and high (0.90–0.96) for the CRU Scale. We could demonstrate partial strong MI for both ACT models and partial strict MI for the CRU Scale. Our results suggest that the scores of the German ACT and the CRU Scale for nursing homes are acceptably reliable and valid. However, as the ACT lacked strict MI, observed variables (or scale scores based on them) cannot be compared between provider groups. Rather, group comparisons should be based on latent variable models, which consider the different residual variances of each group. PMID:27656156

Recent development of risk-prediction models for incident hypertension: An updated systematic review

PubMed Central

Xiao, Lei; Liu, Ya; Wang, Zuoguang; Li, Chuang; Jin, Yongxin; Zhao, Qiong

2017-01-01

Background Hypertension is a leading global health threat and a major cardiovascular disease. Since clinical interventions are effective in delaying the disease progression from prehypertension to hypertension, diagnostic prediction models to identify patient populations at high risk for hypertension are imperative. Methods Both PubMed and Embase databases were searched for eligible reports of either prediction models or risk scores of hypertension. The study data were collected, including risk factors, statistic methods, characteristics of study design and participants, performance measurement, etc. Results From the searched literature, 26 studies reporting 48 prediction models were selected. Among them, 20 reports studied the established models using traditional risk factors, such as body mass index (BMI), age, smoking, blood pressure (BP) level, parental history of hypertension, and biochemical factors, whereas 6 reports used genetic risk score (GRS) as the prediction factor. AUC ranged from 0.64 to 0.97, and C-statistic ranged from 60% to 90%. Conclusions The traditional models are still the predominant risk prediction models for hypertension, but recently, more models have begun to incorporate genetic factors as part of their model predictors. However, these genetic predictors need to be well selected. The current reported models have acceptable to good discrimination and calibration ability, but whether the models can be applied in clinical practice still needs more validation and adjustment. PMID:29084293

Examining the Reliability and Validity of Clinician Ratings on the Five-Factor Model Score Sheet

ERIC Educational Resources Information Center

Few, Lauren R.; Miller, Joshua D.; Morse, Jennifer Q.; Yaggi, Kirsten E.; Reynolds, Sarah K.; Pilkonis, Paul A.

2010-01-01

Despite substantial research use, measures of the five-factor model (FFM) are infrequently used in clinical settings due, in part, to issues related to administration time and a reluctance to use self-report instruments. The current study examines the reliability and validity of the Five-Factor Model Score Sheet (FFMSS), which is a 30-item…

Maximizing the Information and Validity of a Linear Composite in the Factor Analysis Model for Continuous Item Responses

ERIC Educational Resources Information Center

Ferrando, Pere J.

2008-01-01

This paper develops results and procedures for obtaining linear composites of factor scores that maximize: (a) test information, and (b) validity with respect to external variables in the multiple factor analysis (FA) model. I treat FA as a multidimensional item response theory model, and use Ackerman's multidimensional information approach based…

A Twin Factor Mixture Modeling Approach to Childhood Temperament: Differential Heritability

PubMed Central

Scott, Brandon G.; Lemery-Chalfant, Kathryn; Clifford, Sierra; Tein, Jenn-Yun; Stoll, Ryan; Goldsmith, H. Hill

2016-01-01

Twin factor mixture modeling was used to identify temperament profiles, while simultaneously estimating a latent factor model for each profile with a sample of 787 twin pairs (Mage =7.4 years; SD = .84; 49% female; 88.3% Caucasian), using mother- and father-reported temperament. A 4-profile, 1-factor model fit the data well. Profiles included ‘Regulated, Typical Reactive’, ‘Well-regulated, Positive Reactive’, ‘Regulated, Surgent’, and ‘Dysregulated, Negative Reactive.’ All profiles were heritable, with heritability lower and shared environment also contributing to membership in the ‘Regulated, Typical Reactive’ and ‘Dysregulated, Negative Reactive’ profiles. PMID:27291568

Confirming the Multidimensionality of Psychologically Controlling Parenting among Chinese-American Mothers: Love Withdrawal, Guilt Induction, and Shaming.

PubMed

Cheah, Charissa; Yu, Jing; Hart, Craig; Sun, Shuyan; Olsen, Joseph

2015-05-01

Despite the theoretical conceptualization of parental psychological control as a multidimensional construct, the majority of previous studies have examined psychological control as a unidimensional scale. Moreover, the conceptualization of shaming and its associations with love withdrawal and guilt induction are unclear. The current study aimed to fill these gaps by evaluating the latent factor structure underlying 18 items from Olsen et al. (2002) that were conceptually relevant to love withdrawal, guilt induction, and shaming practices in a sample of 169 mothers of Chinese-American preschoolers. A multidimensional three-factor model and bi-factor model were specified based on our formulated operational definitions for the three dimensions of psychological control. Both models were found to be superior to the unidimensional model. In addition, results from the bi-factor model and an additional second-order factor model indicated that psychological control is essentially empirically isomorphic with guilt induction. Although love withdrawal and shaming factors were also fairly strong indicators of psychological control, each exhibited important additional unique variability and mutual distinctiveness. Implications for the conceptualization of love withdrawal, guilt induction, and shaming as well as directions for future studies are discussed.

Confirming the Multidimensionality of Psychologically Controlling Parenting among Chinese-American Mothers: Love Withdrawal, Guilt Induction, and Shaming

PubMed Central

Cheah, Charissa; Yu, Jing; Hart, Craig; Sun, Shuyan; Olsen, Joseph

2014-01-01

Despite the theoretical conceptualization of parental psychological control as a multidimensional construct, the majority of previous studies have examined psychological control as a unidimensional scale. Moreover, the conceptualization of shaming and its associations with love withdrawal and guilt induction are unclear. The current study aimed to fill these gaps by evaluating the latent factor structure underlying 18 items from Olsen et al. (2002) that were conceptually relevant to love withdrawal, guilt induction, and shaming practices in a sample of 169 mothers of Chinese-American preschoolers. A multidimensional three-factor model and bi-factor model were specified based on our formulated operational definitions for the three dimensions of psychological control. Both models were found to be superior to the unidimensional model. In addition, results from the bi-factor model and an additional second-order factor model indicated that psychological control is essentially empirically isomorphic with guilt induction. Although love withdrawal and shaming factors were also fairly strong indicators of psychological control, each exhibited important additional unique variability and mutual distinctiveness. Implications for the conceptualization of love withdrawal, guilt induction, and shaming as well as directions for future studies are discussed. PMID:26052168

A quantitative model of application slow-down in multi-resource shared systems

DOE PAGES

Lim, Seung-Hwan; Kim, Youngjae

2016-12-26

Scheduling multiple jobs onto a platform enhances system utilization by sharing resources. The benefits from higher resource utilization include reduced cost to construct, operate, and maintain a system, which often include energy consumption. Maximizing these benefits comes at a price-resource contention among jobs increases job completion time. In this study, we analyze slow-downs of jobs due to contention for multiple resources in a system; referred to as dilation factor. We observe that multiple-resource contention creates non-linear dilation factors of jobs. From this observation, we establish a general quantitative model for dilation factors of jobs in multi-resource systems. A job ismore » characterized by a vector-valued loading statistics and dilation factors of a job set are given by a quadratic function of their loading vectors. We demonstrate how to systematically characterize a job, maintain the data structure to calculate the dilation factor (loading matrix), and calculate the dilation factor of each job. We validate the accuracy of the model with multiple processes running on a native Linux server, virtualized servers, and with multiple MapReduce workloads co-scheduled in a cluster. Evaluation with measured data shows that the D-factor model has an error margin of less than 16%. We extended the D-factor model to capture the slow-down of applications when multiple identical resources exist such as multi-core environments and multi-disks environments. Finally, validation results of the extended D-factor model with HPC checkpoint applications on the parallel file systems show that D-factor accurately captures the slow down of concurrent applications in such environments.« less

A quantitative model of application slow-down in multi-resource shared systems

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lim, Seung-Hwan; Kim, Youngjae

Scheduling multiple jobs onto a platform enhances system utilization by sharing resources. The benefits from higher resource utilization include reduced cost to construct, operate, and maintain a system, which often include energy consumption. Maximizing these benefits comes at a price-resource contention among jobs increases job completion time. In this study, we analyze slow-downs of jobs due to contention for multiple resources in a system; referred to as dilation factor. We observe that multiple-resource contention creates non-linear dilation factors of jobs. From this observation, we establish a general quantitative model for dilation factors of jobs in multi-resource systems. A job ismore » characterized by a vector-valued loading statistics and dilation factors of a job set are given by a quadratic function of their loading vectors. We demonstrate how to systematically characterize a job, maintain the data structure to calculate the dilation factor (loading matrix), and calculate the dilation factor of each job. We validate the accuracy of the model with multiple processes running on a native Linux server, virtualized servers, and with multiple MapReduce workloads co-scheduled in a cluster. Evaluation with measured data shows that the D-factor model has an error margin of less than 16%. We extended the D-factor model to capture the slow-down of applications when multiple identical resources exist such as multi-core environments and multi-disks environments. Finally, validation results of the extended D-factor model with HPC checkpoint applications on the parallel file systems show that D-factor accurately captures the slow down of concurrent applications in such environments.« less

An investigation of the factor structure of the beck depression inventory-II in anorexia nervosa.

PubMed

Fuss, Samantha; Trottier, Kathryn; Carter, Jacqueline

2015-01-01

Symptoms of depression frequently co-occur with eating disorders and have been associated with negative outcomes. Self-report measures such as the Beck Depression Inventory-II (BDI-II) are commonly used to assess for the presence of depressive symptoms in eating disorders, but the instrument's factor structure in this population has not been examined. The purposes of this study were to explore the factor structure of the BDI-II in a sample of individuals (N = 437) with anorexia nervosa undergoing inpatient treatment and to examine changes in depressive symptoms on each of the identified factors following a course of treatment for anorexia nervosa in order to provide evidence supporting the construct validity of the measure. Exploratory factor analysis revealed that a three-factor model reflected the best fit for the data. Confirmatory factor analysis was used to validate this model against competing models and the three-factor model exhibited strong model fit characteristics. BDI-II scores were significantly reduced on all three factors following inpatient treatment, which supported the construct validity of the scale. The BDI-II appears to be reliable in this population, and the factor structure identified through this analysis may offer predictive utility for identifying individuals who may have more difficulty achieving weight restoration in the context of inpatient treatment. Copyright © 2014 John Wiley & Sons, Ltd and Eating Disorders Association. Copyright © 2014 John Wiley & Sons, Ltd and Eating Disorders Association.

Modelling the pre-assessment learning effects of assessment: evidence in the validity chain.

PubMed

Cilliers, Francois J; Schuwirth, Lambert W T; van der Vleuten, Cees P M

2012-11-01

We previously developed a model of the pre-assessment learning effects of consequential assessment and started to validate it. The model comprises assessment factors, mechanism factors and learning effects. The purpose of this study was to continue the validation process. For stringency, we focused on a subset of assessment factor-learning effect associations that featured least commonly in a baseline qualitative study. Our aims were to determine whether these uncommon associations were operational in a broader but similar population to that in which the model was initially derived. A cross-sectional survey of 361 senior medical students at one medical school was undertaken using a purpose-made questionnaire based on a grounded theory and comprising pairs of written situational tests. In each pair, the manifestation of an assessment factor was varied. The frequencies at which learning effects were selected were compared for each item pair, using an adjusted alpha to assign significance. The frequencies at which mechanism factors were selected were calculated. There were significant differences in the learning effect selected between the two scenarios of an item pair for 13 of this subset of 21 uncommon associations, even when a p-value of < 0.00625 was considered to indicate significance. Three mechanism factors were operational in most scenarios: agency; response efficacy, and response value. For a subset of uncommon associations in the model, the role of most assessment factor-learning effect associations and the mechanism factors involved were supported in a broader but similar population to that in which the model was derived. Although model validation is an ongoing process, these results move the model one step closer to the stage of usefully informing interventions. Results illustrate how factors not typically included in studies of the learning effects of assessment could confound the results of interventions aimed at using assessment to influence learning. © Blackwell Publishing Ltd 2012.

Reducing weapon-carrying among urban American Indian young people.

PubMed

Bearinger, Linda H; Pettingell, Sandra L; Resnick, Michael D; Potthoff, Sandra J

2010-07-01

To examine the likelihood of weapon-carrying among urban American Indian young people, given the presence of salient risk and protective factors. The study used data from a confidential, self-report Urban Indian Youth Health Survey with 200 forced-choice items examining risk and protective factors and social, contextual, and demographic information. Between 1995 and 1998, 569 American Indian youths, aged 9-15 years, completed surveys administered in public schools and an after-school program. Using logistic regression, probability profiles compared the likelihood of weapon-carrying, given the combinations of salient risk and protective factors. In the final models, weapon-carrying was associated significantly with one risk factor (substance use) and two protective factors (school connectedness, perceiving peers as having prosocial behavior attitudes/norms). With one risk factor and two protective factors, in various combinations in the models, the likelihood of weapon carrying ranged from 4% (with two protective factors and no risk factor in the model) to 80% of youth (with the risk factor and no protective factors in the model). Even in the presence of the risk factor, the two protective factors decreased the likelihood of weapon-carrying to 25%. This analysis highlights the importance of protective factors in comprehensive assessments and interventions for vulnerable youth. In that the risk factor and two protective factors significantly related to weapon-carrying are amenable to intervention at both individual and population-focused levels, study findings offer a guide for prioritizing strategies for decreasing weapon-carrying among urban American Indian young people. Copyright (c) 2010 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Method Effects on an Adaptation of the Rosenberg Self-Esteem Scale in Greek and the Role of Personality Traits.

PubMed

Michaelides, Michalis P; Koutsogiorgi, Chrystalla; Panayiotou, Georgia

2016-01-01

Rosenberg's Self-Esteem Scale is a balanced, 10-item scale designed to be unidimensional; however, research has repeatedly shown that its factorial structure is contaminated by method effects due to item wording. Beyond the substantive self-esteem factor, 2 additional factors linked to the positive and negative wording of items have been theoretically specified and empirically supported. Initial evidence has revealed systematic relations of the 2 method factors with variables expressing approach and avoidance motivation. This study assessed the fit of competing confirmatory factor analytic models for the Rosenberg Self-Esteem Scale using data from 2 samples of adult participants in Cyprus. Models that accounted for both positive and negative wording effects via 2 latent method factors had better fit compared to alternative models. Measures of experiential avoidance, social anxiety, and private self-consciousness were associated with the method factors in structural equation models. The findings highlight the need to specify models with wording effects for a more accurate representation of the scale's structure and support the hypothesis of method factors as response styles, which are associated with individual characteristics related to avoidance motivation, behavioral inhibition, and anxiety.

Testing alternative factor models of PTSD and the robustness of the dysphoria factor.

PubMed

Elklit, Ask; Armour, Cherie; Shevlin, Mark

2010-01-01

This study first aimed to examine the structure of self-reported posttraumatic stress disorder (PTSD) symptoms using three different samples. The second aim of the paper was to test the robustness of the factor analytic model when depression scores were controlled for. Based on previous factor analytic findings and the DSM-IV formulation, six confirmatory factor models were specified and estimated that reflected different symptom clusters. The best fitting model was subsequently re-fitted to the data after including a depression variable. The analyses were based on responses from 973 participants across three samples. Sample 1 consisted of 633 parents who were members of 'The National Association of Infant Death' and who had lost a child. Sample 2 consisted of 227 victims of rape, who completed a questionnaire within 4 weeks of the rape. Each respondent had been in contact with the Centre for Rape Victims (CRV) at the Aarhus University Hospital, Denmark. Sample 3 consisted of 113 refugees resident in Denmark. All participants had been referred to a treatment centre which focused on rehabilitating refugees through treatment for psychosocial integration problems (RRCF: Rehabliterings og Revliderings Centre for Flygtninge). In total 500 participants received a diagnosis of PTSD/sub-clinical PTSD (Sample 1, N=214; 2, N=176; 3, N=110). A correlated four-factor model with re-experiencing, avoidance, dysphoria, and arousal factors provided the best fit to the sample data. The average attenuation in the factor loadings was highest for the dysphoria factor (M=-.26, SD=.11) compared to the re-experiencing (M=-.14, SD=.18), avoidance (M=-.10, SD=.21), and arousal (M=-.09, SD=.13) factors. With regards to the best fitting factor model these results concur with previous research findings using different trauma populations but do not reflect the current DSM-IV symptom groupings. The attenuation of dysphoria factor loadings suggests that dysphoria is a non-specific component of PTSD.

Accuracy, calibration and clinical performance of the EuroSCORE: can we reduce the number of variables?

PubMed

Ranucci, Marco; Castelvecchio, Serenella; Menicanti, Lorenzo; Frigiola, Alessandro; Pelissero, Gabriele

2010-03-01

The European system for cardiac operative risk evaluation (EuroSCORE) is currently used in many institutions and is considered a reference tool in many countries. We hypothesised that too many variables were included in the EuroSCORE using limited patient series. We tested different models using a limited number of variables. A total of 11150 adult patients undergoing cardiac operations at our institution (2001-2007) were retrospectively analysed. The 17 risk factors composing the EuroSCORE were separately analysed and ranked for accuracy of prediction of hospital mortality. Seventeen models were created by progressively including one factor at a time. The models were compared for accuracy with a receiver operating characteristics (ROC) analysis and area under the curve (AUC) evaluation. Calibration was tested with Hosmer-Lemeshow statistics. Clinical performance was assessed by comparing the predicted with the observed mortality rates. The best accuracy (AUC 0.76) was obtained using a model including only age, left ventricular ejection fraction, serum creatinine, emergency operation and non-isolated coronary operation. The EuroSCORE AUC (0.75) was not significantly different. Calibration and clinical performance were better in the five-factor model than in the EuroSCORE. Only in high-risk patients were 12 factors needed to achieve a good performance. Including many factors in multivariable logistic models increases the risk for overfitting, multicollinearity and human error. A five-factor model offers the same level of accuracy but demonstrated better calibration and clinical performance. Models with a limited number of factors may work better than complex models when applied to a limited number of patients. Copyright (c) 2009 European Association for Cardio-Thoracic Surgery. Published by Elsevier B.V. All rights reserved.

Extracurricular activity participation moderates impact of family and school factors on adolescents' disruptive behavioural problems.

PubMed

Driessens, Corine M E F

2015-11-11

The prevalence of problem behaviours among British adolescents has increased in the past decades. Following Erikson's psychosocial developmental theory and Bronfenbrenner's developmental ecological model, it was hypothesized that youth problem behaviour is shaped in part by social environment. The aim of this project was to explore potential protective factors within the social environment of British youth's for the presentation of disruptive behavioural problems. This study used secondary data from the Longitudinal Study of Young People in England, a cohort study of secondary school students. These data were analysed with generalized estimation equations to take the correlation between the longitudinal observations into account. Three models were built. The first model determined the effect of family, school, and extracurricular setting on presentation of disruptive behavioural problems. The second model expanded the first model by assuming extracurricular activities as protective factors that moderated the interaction between family and school factors with disruptive behavioural problems. The third model described the effect of prior disruptive behaviour on current disruptive behaviour. Associations were found between school factors, family factors, involvement in extracurricular activities and presence of disruptive behavioural problems. Results from the second generalized estimating equation (GEE) logistic regression models indicated that extracurricular activities buffered the impact of school and family factors on the presence of disruptive behavioural problems. For instance, participation in sports activities decreased the effect of bullying on psychological distress. Results from the third model indicated that prior acts of disruptive behaviour reinforced current disruptive behaviour. This study supports Erikson's psychosocial developmental theory and Bronfenbrenner's developmental ecological model; social environment did influence the presence of disruptive behavioural problems for British adolescents. The potential of extracurricular activities to intervention strategies addressing disruptive behavioural problems of adolescents is discussed.

Factors affecting smartphone adoption for accessing information in medical settings.

PubMed

Tahamtan, Iman; Pajouhanfar, Sara; Sedghi, Shahram; Azad, Mohsen; Roudbari, Masoud

2017-06-01

This study aimed to acquire knowledge about the factors affecting smartphone adoption for accessing information in medical settings in Iranian Hospitals. A qualitative and quantitative approach was used to conduct this study. Semi-structured interviews were conducted with 21 medical residents and interns in 2013 to identify determinant factors for smartphone adoption. Afterwards, nine relationships were hypothesised. We developed a questionnaire to test these hypotheses and to evaluate the importance of each factor. Structural equation modelling was used to analyse the causal relations between model parameters and to accurately identify determinant factors. Eight factors were identified in the qualitative phase of the study, including perceived usefulness, perceived ease of use, training, internal environment, personal experience, social impacts, observability and job related characteristics. Among the studied factors, perceived usefulness, personal experience and job related characteristics were significantly associated with attitude to use a smartphone which accounted for 64% of the variance in attitude. Perceived usefulness had the strongest impact on attitude to use a smartphone. The factors that emerged from interviews were consistent with the Technology Acceptance Model (TAM) and some previous studies. TAM is a reliable model for understanding the factors of smartphone acceptance in medical settings. © 2017 Health Libraries Group.

The Common Factors Discrimination Model: An Integrated Approach to Counselor Supervision

ERIC Educational Resources Information Center

Crunk, A. Elizabeth; Barden, Sejal M.

2017-01-01

Numerous models of clinical supervision have been developed; however, there is little empirical support indicating that any one model is superior. Therefore, common factors approaches to supervision integrate essential components that are shared among counseling and supervision models. The purpose of this paper is to present an innovative model of…

On the Performance Characteristics of Latent-Factor and Knowledge Tracing Models

ERIC Educational Resources Information Center

Klingler, Severin; Käser, Tanja; Solenthaler, Barbara; Gross, Markus

2015-01-01

Modeling student knowledge is a fundamental task of an intelligent tutoring system. A popular approach for modeling the acquisition of knowledge is Bayesian Knowledge Tracing (BKT). Various extensions to the original BKT model have been proposed, among them two novel models that unify BKT and Item Response Theory (IRT). Latent Factor Knowledge…

On the Relation between the Linear Factor Model and the Latent Profile Model

ERIC Educational Resources Information Center

Halpin, Peter F.; Dolan, Conor V.; Grasman, Raoul P. P. P.; De Boeck, Paul

2011-01-01

The relationship between linear factor models and latent profile models is addressed within the context of maximum likelihood estimation based on the joint distribution of the manifest variables. Although the two models are well known to imply equivalent covariance decompositions, in general they do not yield equivalent estimates of the…

Molecular modelling of the Norrie disease protein predicts a cystine knot growth factor tertiary structure.

PubMed

Meitinger, T; Meindl, A; Bork, P; Rost, B; Sander, C; Haasemann, M; Murken, J

1993-12-01

The X-lined gene for Norrie disease, which is characterized by blindness, deafness and mental retardation has been cloned recently. This gene has been thought to code for a putative extracellular factor; its predicted amino acid sequence is homologous to the C-terminal domain of diverse extracellular proteins. Sequence pattern searches and three-dimensional modelling now suggest that the Norrie disease protein (NDP) has a tertiary structure similar to that of transforming growth factor beta (TGF beta). Our model identifies NDP as a member of an emerging family of growth factors containing a cystine knot motif, with direct implications for the physiological role of NDP. The model also sheds light on sequence related domains such as the C-terminal domain of mucins and of von Willebrand factor.

APPLICATION OF EPANET TO UNDERSTAND LEAD ...

EPA Pesticide Factsheets

This presentation describes the factors affecting lead concentration in tap water using an EPANET hydraulic model for a simplified home model, a realistic home model, and EPA's experimental home plumbing system. This presentation describes the factors affecting lead concentration in tap water using an EPANET hydraulic model.

Assessing the fit of the Dysphoric Arousal model across two nationally representative epidemiological surveys: The Australian NSMHWB and the United States NESARC.

PubMed

Armour, Cherie; Carragher, Natacha; Elhai, Jon D

2013-01-01

Since the initial inclusion of PTSD in the DSM nomenclature, PTSD symptomatology has been distributed across three symptom clusters. However, a wealth of empirical research has concluded that PTSD's latent structure is best represented by one of two four-factor models: Numbing or Dysphoria. Recently, a newly proposed five-factor Dysphoric Arousal model, which separates the DSM-IV's Arousal cluster into two factors of Anxious Arousal and Dysphoric Arousal, has gathered support across a variety of trauma samples. To date, the Dysphoric Arousal model has not been assessed using nationally representative epidemiological data. We employed confirmatory factor analysis to examine PTSD's latent structure in two independent population based surveys from American (NESARC) and Australia (NSWHWB). We specified and estimated the Numbing model, the Dysphoria model, and the Dysphoric Arousal model in both samples. Results revealed that the Dysphoric Arousal model provided superior fit to the data compared to the alternative models. In conclusion, these findings suggest that items D1-D3 (sleeping difficulties; irritability; concentration difficulties) represent a separate, fifth factor within PTSD's latent structure using nationally representative epidemiological data in addition to single trauma specific samples. Copyright © 2012 Elsevier Ltd. All rights reserved.

Determining factors influencing survival of breast cancer by fuzzy logistic regression model.

PubMed

Nikbakht, Roya; Bahrampour, Abbas

2017-01-01

Fuzzy logistic regression model can be used for determining influential factors of disease. This study explores the important factors of actual predictive survival factors of breast cancer's patients. We used breast cancer data which collected by cancer registry of Kerman University of Medical Sciences during the period of 2000-2007. The variables such as morphology, grade, age, and treatments (surgery, radiotherapy, and chemotherapy) were applied in the fuzzy logistic regression model. Performance of model was determined in terms of mean degree of membership (MDM). The study results showed that almost 41% of patients were in neoplasm and malignant group and more than two-third of them were still alive after 5-year follow-up. Based on the fuzzy logistic model, the most important factors influencing survival were chemotherapy, morphology, and radiotherapy, respectively. Furthermore, the MDM criteria show that the fuzzy logistic regression have a good fit on the data (MDM = 0.86). Fuzzy logistic regression model showed that chemotherapy is more important than radiotherapy in survival of patients with breast cancer. In addition, another ability of this model is calculating possibilistic odds of survival in cancer patients. The results of this study can be applied in clinical research. Furthermore, there are few studies which applied the fuzzy logistic models. Furthermore, we recommend using this model in various research areas.

Bayes factors for the linear ballistic accumulator model of decision-making.

PubMed

Evans, Nathan J; Brown, Scott D

2018-04-01

Evidence accumulation models of decision-making have led to advances in several different areas of psychology. These models provide a way to integrate response time and accuracy data, and to describe performance in terms of latent cognitive processes. Testing important psychological hypotheses using cognitive models requires a method to make inferences about different versions of the models which assume different parameters to cause observed effects. The task of model-based inference using noisy data is difficult, and has proven especially problematic with current model selection methods based on parameter estimation. We provide a method for computing Bayes factors through Monte-Carlo integration for the linear ballistic accumulator (LBA; Brown and Heathcote, 2008), a widely used evidence accumulation model. Bayes factors are used frequently for inference with simpler statistical models, and they do not require parameter estimation. In order to overcome the computational burden of estimating Bayes factors via brute force integration, we exploit general purpose graphical processing units; we provide free code for this. This approach allows estimation of Bayes factors via Monte-Carlo integration within a practical time frame. We demonstrate the method using both simulated and real data. We investigate the stability of the Monte-Carlo approximation, and the LBA's inferential properties, in simulation studies.

Factors influencing a problem-based learning implementation: A case study of IT courses

NASA Astrophysics Data System (ADS)

Darus, Norida Muhd; Mohd, Haslina; Baharom, Fauziah; Saip, Mohamed Ali; Puteh, Nurnasran; Marzuki @ Matt, Zaharin; Husain, Mohd Zabidin; Yasin, Azman

2016-08-01

IT students must be trained to work efficiently as teamwork. One of the techniques that can be used to train them is through Problem-Based Learning (PBL) approach. The PBL implementation can be influenced by various factors depending on the ultimate goal of the study. This study is focusing on the IT students' perception of the PBL implementation. The student's perception is important to ensure the successfulness of the PBL implementation. Therefore, it is important to identify the factors that might influence the implementation of PBL of IT courses. This study aims to identify some catalyst factors that may influence the PBL implementation of IT courses. The study involved three (3) main phases: identifying PBL implementation factors, constructing a PBL model, and PBL model validation using statistical analysis. Four main factors are identified: PBL Characteristics, PBL Course Assessment, PBL Practices, and PBL Perception. Based on these four factors, a PBL model is constructed. Then, based on the proposed PBL model, four hypotheses are formulated and analyzed to validate the model. All hypotheses are significantly acceptable. The result shows that the PBL Characteristics and PBL Course Assessment factors are significantly influenced the PBL Practices and indirectly influenced the Students' Perception of the PBL Implementation for IT courses. This PBL model can assist decision makers in enhancing the PBL teaching and learning strategy for IT courses. It is also can be tested to other courses in the future.

A re-evaluation of finite-element models and stress-intensity factors for surface cracks emanating from stress concentrations

NASA Technical Reports Server (NTRS)

Tan, P. W.; Raju, I. S.; Shivakumar, K. N.; Newman, J. C., Jr.

1988-01-01

A re-evaluation of the 3-D finite-element models and methods used to analyze surface crack at stress concentrations is presented. Previous finite-element models used by Raju and Newman for surface and corner cracks at holes were shown to have ill-shaped elements at the intersection of the hole and crack boundaries. These ill-shaped elements tended to make the model too stiff and, hence, gave lower stress-intensity factors near the hole-crack intersection than models without these elements. Improved models, without these ill-shaped elements, were developed for a surface crack at a circular hole and at a semi-circular edge notch. Stress-intensity factors were calculated by both the nodal-force and virtual-crack-closure methods. Both methods and different models gave essentially the same results. Comparisons made between the previously developed stress-intensity factor equations and the results from the improved models agreed well except for configurations with large notch-radii-to-plate-thickness ratios. Stress-intensity factors for a semi-elliptical surface crack located at the center of a semi-circular edge notch in a plate subjected to remote tensile loadings were calculated using the improved models. The ratio of crack depth to crack length ranged form 0.4 to 2; the ratio of crack depth to plate thickness ranged from 0.2 to 0.8; and the ratio of notch radius to the plate thickness ranged from 1 to 3. The models had about 15,000 degrees-of-freedom. Stress-intensity factors were calculated by using the nodal-force method.

The conceptualization and measurement of cognitive reserve using common proxy indicators: Testing some tenable reflective and formative models.

PubMed

Ikanga, Jean; Hill, Elizabeth M; MacDonald, Douglas A

2017-02-01

The examination of cognitive reserve (CR) literature reveals a lack of consensus regarding conceptualization and pervasive problems with its measurement. This study aimed at examining the conceptual nature of CR through the analysis of reflective and formative models using eight proxies commonly employed in the CR literature. We hypothesized that all CR proxies would significantly contribute to a one-factor reflective model and that educational and occupational attainment would produce the strongest loadings on a single CR factor. The sample consisted of 149 participants (82 male/67 female), with 18.1 average years of education and ages of 45-99 years. Participants were assessed for eight proxies of CR (parent socioeconomic status, intellectual functioning, level of education, health literacy, occupational prestige, life leisure activities, physical activities, and spiritual and religious activities). Primary statistical analyses consisted of confirmatory factor analysis (CFA) to test reflective models and structural equation modeling (SEM) to evaluate multiple indicators multiple causes (MIMIC) models. CFA did not produce compelling support for a unitary CR construct when using all eight of our CR proxy variables in a reflective model but fairly cogent evidence for a one-factor model with four variable proxies. A second three-factor reflective model based upon an exploratory principal components analysis of the eight proxies was tested using CFA. Though all eight indicators significantly loaded on their assigned factors, evidence in support of overall model fit was mixed. Based upon the results involving the three-factor reflective model, two alternative formative models were developed and evaluated. While some support was obtained for both, the model in which the formative influences were specified as latent variables appeared to best account for the contributions of all eight proxies to the CR construct. While the findings provide partial support for our hypothesis regarding CR as a one-dimensional reflective construct, the results strongly suggest that the construct is more complex than what can be captured in a reflective model alone. There is a need for theory to better identify and differentiate formative from reflective indicators and to articulate the mechanisms by which CR develops and operates.

The effect of choosing three different C factor formulae derived from NDVI on a fully raster-based erosion modelling

NASA Astrophysics Data System (ADS)

Sulistyo, Bambang

2016-11-01

The research was aimed at studying the efect of choosing three different C factor formulae derived from NDVI on a fully raster-based erosion modelling of The USLE using remote sensing data and GIS technique. Methods applied was by analysing all factors affecting erosion such that all data were in the form of raster. Those data were R, K, LS, C and P factors. Monthly R factor was evaluated based on formula developed by Abdurachman. K factor was determined using modified formula used by Ministry of Forestry based on soil samples taken in the field. LS factor was derived from Digital Elevation Model. Three C factors used were all derived from NDVI and developed by Suriyaprasit (non-linear) and by Sulistyo (linear and non-linear). P factor was derived from the combination between slope data and landcover classification interpreted from Landsat 7 ETM+. Another analysis was the creation of map of Bulk Density used to convert erosion unit. To know the model accuracy, model validation was done by applying statistical analysis and by comparing Emodel with Eactual. A threshold value of ≥ 0.80 or ≥ 80% was chosen to justify. The research result showed that all Emodel using three formulae of C factors have coeeficient of correlation value of > 0.8. The results of analysis of variance showed that there was significantly difference between Emodel and Eactual when using C factor formula developed by Suriyaprasit and Sulistyo (non-linear). Among the three formulae, only Emodel using C factor formula developed by Sulistyo (linear) reached the accuracy of 81.13% while the other only 56.02% as developed by Sulistyo (nonlinear) and 4.70% as developed by Suriyaprasit, respectively.

Factors influencing antibiotic prescribing in long-term care facilities: a qualitative in-depth study.

PubMed

van Buul, Laura W; van der Steen, Jenny T; Doncker, Sarah M M M; Achterberg, Wilco P; Schellevis, François G; Veenhuizen, Ruth B; Hertogh, Cees M P M

2014-12-16

Insight into factors that influence antibiotic prescribing is crucial when developing interventions aimed at a more rational use of antibiotics. We examined factors that influence antibiotic prescribing in long-term care facilities, and present a conceptual model that integrates these factors. Semi-structured qualitative interviews were conducted with physicians (n = 13) and nursing staff (n = 13) in five nursing homes and two residential care homes in the central-west region of the Netherlands. An iterative analysis was applied to interviews with physicians to identify and categorize factors that influence antibiotic prescribing, and to integrate these into a conceptual model. This conceptual model was triangulated with the perspectives of nursing staff. The analysis resulted in the identification of six categories of factors that can influence the antibiotic prescribing decision: the clinical situation, advance care plans, utilization of diagnostic resources, physicians' perceived risks, influence of others, and influence of the environment. Each category comprises several factors that may influence the decision to prescribe or not prescribe antibiotics directly (e.g. pressure of patients' family leading to antibiotic prescribing) or indirectly via influence on other factors (e.g. unfamiliarity with patients resulting in a higher physician perceived risk of non-treatment, in turn resulting in a higher tendency to prescribe antibiotics). Our interview study shows that several non-rational factors may affect antibiotic prescribing decision making in long-term care facilities, suggesting opportunities to reduce inappropriate antibiotic use. We developed a conceptual model that integrates the identified categories of influencing factors and shows the relationships between those categories. This model may be used as a practical tool in long-term care facilities to identify local factors potentially leading to inappropriate prescribing, and to subsequently intervene at the level of those factors to promote appropriate antibiotic prescribing.

Factor structure and psychometric properties of the trier inventory for chronic stress (TICS) in a representative german sample

PubMed Central

2012-01-01

Background Chronic stress results from an imbalance of personal traits, resources and the demands placed upon an individual by social and occupational situations. This chronic stress can be measured using the Trier Inventory for Chronic Stress (TICS). Aims of the present study are to test the factorial structure of the TICS, report its psychometric properties, and evaluate the influence of gender and age on chronic stress. Methods The TICS was answered by N = 2,339 healthy participants aged 14 to 99. The sample was selected by random-route sampling. Exploratory factor analyses with Oblimin-rotated Principal Axis extraction were calculated. Confirmatory factor analyses applying Robust Maximum Likelihood estimations (MLM) tested model fit and configural invariance as well as the measurement invariance for gender and age. Reliability estimations and effect sizes are reported. Results In the exploratory factor analyses, both a two-factor and a nine-factor model emerged. Confirmatory factor analyses resulted in acceptable model fit (RMSEA), with model comparison fit statistics corroborating the superiority of the nine-factor model. Most factors were moderately to highly intercorrelated. Reliabilities were good to very good. Measurement invariance tests gave evidence for differential effects of gender and age on the factor structure. Furthermore, women and younger individuals, especially those aged 35 to 44, tended to report more chronic stress than men and older individuals. Conclusions The proposed nine-factor structure could be factorially validated, results in good scale reliability, and heuristically can be grouped by two higher-order factors: "High Demands" and "Lack of Satisfaction". Age and gender represent differentiable and meaningful contributors to the perception of chronic stress. PMID:22463771

Spatial Data Mining for Estimating Cover Management Factor of Universal Soil Loss Equation

NASA Astrophysics Data System (ADS)

Tsai, F.; Lin, T. C.; Chiang, S. H.; Chen, W. W.

2016-12-01

Universal Soil Loss Equation (USLE) is a widely used mathematical model that describes long-term soil erosion processes. Among the six different soil erosion risk factors of USLE, the cover-management factor (C-factor) is related to land-cover/land-use. The value of C-factor ranges from 0.001 to 1, so it alone might cause a thousandfold difference in a soil erosion analysis using USLE. The traditional methods for the estimation of USLE C-factor include in situ experiments, soil physical parameter models, USLE look-up tables with land use maps, and regression models between vegetation indices and C-factors. However, these methods are either difficult or too expensive to implement in large areas. In addition, the values of C-factor obtained using these methods can not be updated frequently, either. To address this issue, this research developed a spatial data mining approach to estimate the values of C-factor with assorted spatial datasets for a multi-temporal (2004 to 2008) annual soil loss analysis of a reservoir watershed in northern Taiwan. The idea is to establish the relationship between the USLE C-factor and spatial data consisting of vegetation indices and texture features extracted from satellite images, soil and geology attributes, digital elevation model, road and river distribution etc. A decision tree classifier was used to rank influential conditional attributes in the preliminary data mining. Then, factor simplification and separation were considered to optimize the model and the random forest classifier was used to analyze 9 simplified factor groups. Experimental results indicate that the overall accuracy of the data mining model is about 79% with a kappa value of 0.76. The estimated soil erosion amounts in 2004-2008 according to the data mining results are about 50.39 - 74.57 ton/ha-year after applying the sediment delivery ratio and correction coefficient. Comparing with estimations calculated with C-factors from look-up tables, the soil erosion values estimated with C-factors generated from spatial data mining results are more in agreement with the values published by the watershed administration authority.

Examination of the factor structure of the Schizotypal Personality Questionnaire among British and Trinidadian adults.

PubMed

Barron, David; Swami, Viren; Towell, Tony; Hutchinson, Gerard; Morgan, Kevin D

2015-01-01

Much debate in schizotypal research has centred on the factor structure of the Schizotypal Personality Questionnaire (SPQ), with research variously showing higher-order dimensionality consisting of two to seven dimensions. In addition, cross-cultural support for the stability of those factors remains limited. Here, we examined the factor structure of the SPQ among British and Trinidadian adults. Participants from a White British subsample (n = 351) resident in the UK and from an African Caribbean subsample (n = 284) resident in Trinidad completed the SPQ. The higher-order factor structure of the SPQ was analysed through confirmatory factor analysis, followed by multiple-group analysis for the model of best fit. Between-group differences for sex and ethnicity were investigated using multivariate analysis of variance in relation to the higher-order domains. The model of best-fit was the four-factor structure, which demonstrated measurement invariance across groups. Additionally, these data had an adequate fit for two alternative models: (a) 3-factor and (b) modified 4-factor model. The British subsample had significantly higher scores across all domains than the Trinidadian group, and men scored significantly higher on the disorganised domain than women. The four-factor structure received confirmatory support and, importantly, support for use with populations varying in ethnicity and culture.

Common Cause Failure Modeling: Aerospace Versus Nuclear

NASA Technical Reports Server (NTRS)

Stott, James E.; Britton, Paul; Ring, Robert W.; Hark, Frank; Hatfield, G. Spencer

2010-01-01

Aggregate nuclear plant failure data is used to produce generic common-cause factors that are specifically for use in the common-cause failure models of NUREG/CR-5485. Furthermore, the models presented in NUREG/CR-5485 are specifically designed to incorporate two significantly distinct assumptions about the methods of surveillance testing from whence this aggregate failure data came. What are the implications of using these NUREG generic factors to model the common-cause failures of aerospace systems? Herein, the implications of using the NUREG generic factors in the modeling of aerospace systems are investigated in detail and strong recommendations for modeling the common-cause failures of aerospace systems are given.

Population-Level Prediction of Type 2 Diabetes From Claims Data and Analysis of Risk Factors.

PubMed

Razavian, Narges; Blecker, Saul; Schmidt, Ann Marie; Smith-McLallen, Aaron; Nigam, Somesh; Sontag, David

2015-12-01

We present a new approach to population health, in which data-driven predictive models are learned for outcomes such as type 2 diabetes. Our approach enables risk assessment from readily available electronic claims data on large populations, without additional screening cost. Proposed model uncovers early and late-stage risk factors. Using administrative claims, pharmacy records, healthcare utilization, and laboratory results of 4.1 million individuals between 2005 and 2009, an initial set of 42,000 variables were derived that together describe the full health status and history of every individual. Machine learning was then used to methodically enhance predictive variable set and fit models predicting onset of type 2 diabetes in 2009-2011, 2010-2012, and 2011-2013. We compared the enhanced model with a parsimonious model consisting of known diabetes risk factors in a real-world environment, where missing values are common and prevalent. Furthermore, we analyzed novel and known risk factors emerging from the model at different age groups at different stages before the onset. Parsimonious model using 21 classic diabetes risk factors resulted in area under ROC curve (AUC) of 0.75 for diabetes prediction within a 2-year window following the baseline. The enhanced model increased the AUC to 0.80, with about 900 variables selected as predictive (p < 0.0001 for differences between AUCs). Similar improvements were observed for models predicting diabetes onset 1-3 years and 2-4 years after baseline. The enhanced model improved positive predictive value by at least 50% and identified novel surrogate risk factors for type 2 diabetes, such as chronic liver disease (odds ratio [OR] 3.71), high alanine aminotransferase (OR 2.26), esophageal reflux (OR 1.85), and history of acute bronchitis (OR 1.45). Liver risk factors emerge later in the process of diabetes development compared with obesity-related factors such as hypertension and high hemoglobin A1c. In conclusion, population-level risk prediction for type 2 diabetes using readily available administrative data is feasible and has better prediction performance than classical diabetes risk prediction algorithms on very large populations with missing data. The new model enables intervention allocation at national scale quickly and accurately and recovers potentially novel risk factors at different stages before the disease onset.

Quality of life among people with multiple sclerosis: Replication of a three-factor prediction model.

PubMed

Bishop, Malachy; Rumrill, Phillip D; Roessler, Richard T

2015-01-01

This article presents a replication of Rumrill, Roessler, and Fitzgerald's 2004 analysis of a three-factor model of the impact of multiple sclerosis (MS) on quality of life (QOL). The three factors in the original model included illness-related, employment-related, and psychosocial adjustment factors. To test hypothesized relationships between QOL and illness-related, employment-related, and psychosocial variables using data from a survey of the employment concerns of Americans with MS (N = 1,839). An ex post facto, multiple correlational design was employed incorporating correlational and multiple regression analyses. QOL was positively related to educational level, employment status, job satisfaction, and job-match, and negatively related to number of symptoms, severity of symptoms, and perceived stress level. The three-factor model explained approximately 37 percent of the variance in QOL scores. The results of this replication confirm the continuing value of the three-factor model for predicting the QOL of adults with MS, and demonstrate the importance of medical, mental health, and vocational rehabilitation interventions and services in promoting QOL.

Impact of measurement invariance on construct correlations, mean differences, and relations with external correlates: an illustrative example using Big Five and RIASEC measures.

PubMed

Schmitt, Neal; Golubovich, Juliya; Leong, Frederick T L

2011-12-01

The impact of measurement invariance and the provision for partial invariance in confirmatory factor analytic models on factor intercorrelations, latent mean differences, and estimates of relations with external variables is investigated for measures of two sets of widely assessed constructs: Big Five personality and the six Holland interests (RIASEC). In comparing models that include provisions for partial invariance with models that do not, the results indicate quite small differences in parameter estimates involving the relations between factors, one relatively large standardized mean difference in factors between the subgroups compared and relatively small differences in the regression coefficients when the factors are used to predict external variables. The results provide support for the use of partially invariant models, but there does not seem to be a great deal of difference between structural coefficients when the measurement model does or does not include separate estimates of subgroup parameters that differ across subgroups. Future research should include simulations in which the impact of various factors related to invariance is estimated.

Early Life Stress and Sleep Restriction as Risk Factors in PTSD: An Integrative Pre-Clinical Approach

DTIC Science & Technology

2014-04-01

potential risk factors, with high relevance to soldiers. The primary aims of the project are thus. 1) To establish an effective animal model of PTSD that...develop the model as a platform for pharmacological testing of novel targets for drug development 5) As an additional aim – once an effective animal model...thus: 1) To establish an effective animal model of PTSD that would take into consideration the contribution of risk factors to the induction of the

Homology geoinformation modeling of the threat of avian influenza occuring in a region

NASA Astrophysics Data System (ADS)

Myasnikova, S. I.

2008-03-01

This paper addresses the problem of modeling the likely foci of Avian Influenza emergence and spread. The factors contributing to the emergence and spread of the virus are identified. The connection of the factors with invariant structure (landscape map) is determined, and the complex (homotopic) coefficient is calculated, which takes into account the geographical inhomogeneity of the factors, and of the model conditions. The computer-aided mapping and geoinformation modeling procedures are used to assess the situation.

Critical Factors Analysis for Offshore Software Development Success by Structural Equation Modeling

NASA Astrophysics Data System (ADS)

Wada, Yoshihisa; Tsuji, Hiroshi

In order to analyze the success/failure factors in offshore software development service by the structural equation modeling, this paper proposes to follow two approaches together; domain knowledge based heuristic analysis and factor analysis based rational analysis. The former works for generating and verifying of hypothesis to find factors and causalities. The latter works for verifying factors introduced by theory to build the model without heuristics. Following the proposed combined approaches for the responses from skilled project managers of the questionnaire, this paper found that the vendor property has high causality for the success compared to software property and project property.

Factor Analysis of Drawings: Application to College Student Models of the Greenhouse Effect

ERIC Educational Resources Information Center

Libarkin, Julie C.; Thomas, Stephen R.; Ording, Gabriel

2015-01-01

Exploratory factor analysis was used to identify models underlying drawings of the greenhouse effect made by over 200 entering university freshmen. Initial content analysis allowed deconstruction of drawings into salient features, with grouping of these features via factor analysis. A resulting 4-factor solution explains 62% of the data variance,…

A Multi-Domain Model of Risk Factors for ODD Symptoms in a Community Sample of 4-Year-Olds

ERIC Educational Resources Information Center

Lavigne, John V.; Gouze, Karen R.; Hopkins, Joyce; Bryant, Fred B.; LeBailly, Susan A.

2012-01-01

Few studies have been designed to assess the pathways by which risk factors are associated with symptoms of psychopathology across multiple domains, including contextual factors, parental depression, parenting, and child characteristics. The present study examines a cross-sectional model of risk factors for symptoms of Oppositional Defiant…

Confirmatory Analysis of Simultaneous, Sequential, and Achievement Factors on the K-ABC at 11 Age Levels Ranging from 2 1/2 to 12 1/2 years.

ERIC Educational Resources Information Center

Willson, Victor L.; And Others

1985-01-01

Presents results of confirmatory factor analysis of the Kaufman Assessment Battery for children which is based on the underlying theoretical model of sequential, simultaneous, and achievement factors. Found support for the two-factor, simultaneous and sequential processing model. (MCF)

Vulnerability and Resilience in Women with Arthritis: Test of a Two-Factor Model

ERIC Educational Resources Information Center

Smith, Bruce W.; Zautra, Alex J.

2008-01-01

The purpose of this study was to test a 2-factor model of affective health in women with rheumatoid arthritis (RA; n = 82) or osteoarthritis (OA; n = 88). Positive and negative social interactions and affect were assessed for 11 consecutive weeks. For each participant, Vulnerability and Resilience factors were created from factor analyses of…

Getting to the Heart of Performance.

ERIC Educational Resources Information Center

Stock, Byron

1996-01-01

Human performance technology (HPT) models are compared. One model groups performance factors by their relation to the performer (internal or external). A second model categorizes factors by which organizational level has the most control over them (executive, managerial, or individual). A third model considers rational and emotional intelligences;…

FACTORS INFLUENCING TOTAL DIETARY EXPOSURE OF YOUNG CHILDREN

EPA Science Inventory

A deterministic model was developed to identify critical input parameters to assess dietary intake of young children. The model was used as a framework for understanding important factors in data collection and analysis. Factors incorporated included transfer efficiencies of pest...

DOE Office of Scientific and Technical Information (OSTI.GOV)

Almus, F.E.; Rao, L.V.; Fleck, R.A.

An umbilical vein model was designed in which washed vein segments are filled with a reaction mixture containing factor VIIa, Ca(+)+, and a substrate, either 3H-factor IX or 3H-factor X. The vein wall provides the tissue factor (TF) for factor VIIa/TF complexes that activate the substrates as measured by activation peptide release. The model was developed to study TF induced on venous endothelium in situ. However, unlike previous studies with TF expressed on cultured umbilical vein endothelial cells, factors IX and X were activated without first having to expose the vein wall to a perturbing stimulus. Histologic studies revealed thatmore » washing the vein and mixing the reaction mixture before subsampling had disrupted the endothelium. Immunostaining with anti-TF antibodies revealed no staining of endothelium but intense staining in extensions of Wharton's jelly penetrating fenestrations of the muscularis media of the vein. Thus, the model provided data on factor VIIa/TF formed, not on endothelium, but within the mucoid connective tissue of Wharton's jelly. It is known that factor VIIa/TF formed with TF in suspension or with TF expressed on the surface of cultured cells activates factor X more rapidly than factor IX. In contrast, in the umbilical vein model, when each substrate was present in an 88 nmol/L concentration, factors IX and X were activated at equivalent rates (mean activation rate for factor IX, 18.8 +/- 3.6 nmol/L/h; for factor X, 17.8 +/- 2.9 nmol/L/h; n = 9 paired vein segments). These data strengthen the evidence that factor VIIa/TF activation of factor IX represents a key initial reaction of coagulation in tissues. These results also show that data obtained with factor VIIa/TF complexes formed on the surface of cultured cells need not hold for factor VIIa/TF complexes formed in extracellular matrix.« less

Interrelationship of Cytokines, Hypothalamic-Pituitary-Adrenal Axis Hormones, and Psychosocial Variables in the Prediction of Preterm Birth

PubMed Central

Pearce, B.D.; Grove, J.; Bonney, E.A.; Bliwise, N.; Dudley, D.J.; Schendel, D.E.; Thorsen, P.

2010-01-01

Background/Aims To examine the relationship of biological mediators (cytokines, stress hormones), psychosocial, obstetric history, and demographic factors in the early prediction of preterm birth (PTB) using a comprehensive logistic regression model incorporating diverse risk factors. Methods In this prospective case-control study, maternal serum biomarkers were quantified at 9–23 weeks’ gestation in 60 women delivering at <37 weeks compared to 123 women delivering at term. Biomarker data were combined with maternal sociodemographic factors and stress data into regression models encompassing 22 preterm risk factors and 1st-order interactions. Results Among individual biomarkers, we found that macrophage migration inhibitory factor (MIF), interleukin-10, C-reactive protein (CRP), and tumor necrosis factor-α were statistically significant predictors of PTB at all cutoff levels tested (75th, 85th, and 90th percentiles). We fit multifactor models for PTB prediction at each biomarker cutoff. Our best models revealed that MIF, CRP, risk-taking behavior, and low educational attainment were consistent predictors of PTB at all biomarker cutoffs. The 75th percentile cutoff yielded the best predicting model with an area under the ROC curve of 0.808 (95% CI 0.743–0.874). Conclusion Our comprehensive models highlight the prominence of behavioral risk factors for PTB and point to MIF as a possible psychobiological mediator. PMID:20160447

Interrelationship of cytokines, hypothalamic-pituitary-adrenal axis hormones, and psychosocial variables in the prediction of preterm birth.

PubMed

Pearce, B D; Grove, J; Bonney, E A; Bliwise, N; Dudley, D J; Schendel, D E; Thorsen, P

2010-01-01

To examine the relationship of biological mediators (cytokines, stress hormones), psychosocial, obstetric history, and demographic factors in the early prediction of preterm birth (PTB) using a comprehensive logistic regression model incorporating diverse risk factors. In this prospective case-control study, maternal serum biomarkers were quantified at 9-23 weeks' gestation in 60 women delivering at <37 weeks compared to 123 women delivering at term. Biomarker data were combined with maternal sociodemographic factors and stress data into regression models encompassing 22 preterm risk factors and 1st-order interactions. Among individual biomarkers, we found that macrophage migration inhibitory factor (MIF), interleukin-10, C-reactive protein (CRP), and tumor necrosis factor-alpha were statistically significant predictors of PTB at all cutoff levels tested (75th, 85th, and 90th percentiles). We fit multifactor models for PTB prediction at each biomarker cutoff. Our best models revealed that MIF, CRP, risk-taking behavior, and low educational attainment were consistent predictors of PTB at all biomarker cutoffs. The 75th percentile cutoff yielded the best predicting model with an area under the ROC curve of 0.808 (95% CI 0.743-0.874). Our comprehensive models highlight the prominence of behavioral risk factors for PTB and point to MIF as a possible psychobiological mediator. Copyright (c) 2010 S. Karger AG, Basel.

[Systematization and hygienic standardization of environmental factors on the basis of common graphic models].

PubMed

Galkin, A A

2012-01-01

On the basis of graphic models of the human response to environmental factors, two main types of complex quantitative influence as well as interrelation between determined effects at the level of an individual, and stochastic effects on population were revealed. Two main kinds of factors have been suggested to be distinguished. They are essential factors and accidental factors. The essential factors are common for environment. The accidental factors are foreign for environment. The above two kinds are different in approaches of hygienic standardization Accidental factors need a dot-like approach, whereas a two-level range approach is suitable for the essential factors.

Maturity of hospital information systems: Most important influencing factors.

PubMed

Vidal Carvalho, João; Rocha, Álvaro; Abreu, António

2017-07-01

Maturity models facilitate organizational management, including information systems management, with hospital organizations no exception. This article puts forth a study carried out with a group of experts in the field of hospital information systems management with a view to identifying the main influencing factors to be included in an encompassing maturity model for hospital information systems management. This study is based on the results of a literature review, which identified maturity models in the health field and relevant influencing factors. The development of this model is justified to the extent that the available maturity models for the hospital information systems management field reveal multiple limitations, including lack of detail, absence of tools to determine their maturity and lack of characterization for stages of maturity structured by different influencing factors.

A model for field toxicity tests

USGS Publications Warehouse

Kaiser, Mark S.; Finger, Susan E.

1996-01-01

Toxicity tests conducted under field conditions present an interesting challenge for statistical modelling. In contrast to laboratory tests, the concentrations of potential toxicants are not held constant over the test. In addition, the number and identity of toxicants that belong in a model as explanatory factors are not known and must be determined through a model selection process. We present one model to deal with these needs. This model takes the record of mortalities to form a multinomial distribution in which parameters are modelled as products of conditional daily survival probabilities. These conditional probabilities are in turn modelled as logistic functions of the explanatory factors. The model incorporates lagged values of the explanatory factors to deal with changes in the pattern of mortalities over time. The issue of model selection and assessment is approached through the use of generalized information criteria and power divergence goodness-of-fit tests. These model selection criteria are applied in a cross-validation scheme designed to assess the ability of a model to both fit data used in estimation and predict data deleted from the estimation data set. The example presented demonstrates the need for inclusion of lagged values of the explanatory factors and suggests that penalized likelihood criteria may not provide adequate protection against overparameterized models in model selection.

A Measurement Model for Likert Responses that Incorporates Response Time

ERIC Educational Resources Information Center

Ferrando, Pere J.; Lorenzo-Seva, Urbano

2007-01-01

This article describes a model for response times that is proposed as a supplement to the usual factor-analytic model for responses to graded or more continuous typical-response items. The use of the proposed model together with the factor model provides additional information about the respondent and can potentially increase the accuracy of the…

The Stability of Post Hoc Model Modifications in Covariance Structure Models.

ERIC Educational Resources Information Center

Hutchinson, Susan R.

The work of R. MacCallum et al. (1992) was extended by examining chance modifications through a Monte Carlo simulation. The stability of post hoc model modifications was examined under varying sample size, model complexity, and severity of misspecification using 2- and 4-factor oblique confirmatory factor analysis (CFA) models with four and eight…

Accounting for Slipping and Other False Negatives in Logistic Models of Student Learning

ERIC Educational Resources Information Center

MacLellan, Christopher J.; Liu, Ran; Koedinger, Kenneth R.

2015-01-01

Additive Factors Model (AFM) and Performance Factors Analysis (PFA) are two popular models of student learning that employ logistic regression to estimate parameters and predict performance. This is in contrast to Bayesian Knowledge Tracing (BKT) which uses a Hidden Markov Model formalism. While all three models tend to make similar predictions,…

msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding

PubMed Central

Gilad, Yoav; Pritchard, Jonathan K.; Stephens, Matthew

2015-01-01

Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede. PMID:26406244

msCentipede: Modeling Heterogeneity across Genomic Sites and Replicates Improves Accuracy in the Inference of Transcription Factor Binding.

PubMed

Raj, Anil; Shim, Heejung; Gilad, Yoav; Pritchard, Jonathan K; Stephens, Matthew

2015-01-01

Understanding global gene regulation depends critically on accurate annotation of regulatory elements that are functional in a given cell type. CENTIPEDE, a powerful, probabilistic framework for identifying transcription factor binding sites from tissue-specific DNase I cleavage patterns and genomic sequence content, leverages the hypersensitivity of factor-bound chromatin and the information in the DNase I spatial cleavage profile characteristic of each DNA binding protein to accurately infer functional factor binding sites. However, the model for the spatial profile in this framework fails to account for the substantial variation in the DNase I cleavage profiles across different binding sites. Neither does it account for variation in the profiles at the same binding site across multiple replicate DNase I experiments, which are increasingly available. In this work, we introduce new methods, based on multi-scale models for inhomogeneous Poisson processes, to account for such variation in DNase I cleavage patterns both within and across binding sites. These models account for the spatial structure in the heterogeneity in DNase I cleavage patterns for each factor. Using DNase-seq measurements assayed in a lymphoblastoid cell line, we demonstrate the improved performance of this model for several transcription factors by comparing against the Chip-seq peaks for those factors. Finally, we explore the effects of DNase I sequence bias on inference of factor binding using a simple extension to our framework that allows for a more flexible background model. The proposed model can also be easily applied to paired-end ATAC-seq and DNase-seq data. msCentipede, a Python implementation of our algorithm, is available at http://rajanil.github.io/msCentipede.

Using Structured Additive Regression Models to Estimate Risk Factors of Malaria: Analysis of 2010 Malawi Malaria Indicator Survey Data

PubMed Central

Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

2014-01-01

Background After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. Methods We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Results Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. Conclusions The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities. PMID:24991915

Anthropometric data reduction using confirmatory factor analysis.

PubMed

Rohani, Jafri Mohd; Olusegun, Akanbi Gabriel; Rani, Mat Rebi Abdul

2014-01-01

The unavailability of anthropometric data especially in developing countries has remained a limiting factor towards the design of learning facilities with sufficient ergonomic consideration. Attempts to use anthropometric data from developed countries have led to provision of school facilities unfit for the users. The purpose of this paper is to use factor analysis to investigate the suitability of the collected anthropometric data as a database for school design in Nigerian tertiary institutions. Anthropometric data were collected from 288 male students in a Federal Polytechnic in North-West of Nigeria. Their age is between 18-25 years. Nine vertical anthropometric dimensions related to heights were collected using the conventional traditional equipment. Exploratory factor analysis was used to categorize the variables into a model consisting of two factors. Thereafter, confirmatory factor analysis was used to investigate the fit of the data to the proposed model. A just identified model, made of two factors, each with three variables was developed. The variables within the model accounted for 81% of the total variation of the entire data. The model was found to demonstrate adequate validity and reliability. Various measuring indices were used to verify that the model fits the data properly. The final model reveals that stature height and eye height sitting were the most stable variables for designs that have to do with standing and sitting construct. The study has shown the application of factor analysis in anthropometric data analysis. The study highlighted the relevance of these statistical tools to investigate variability among anthropometric data involving diverse population, which has not been widely used for analyzing previous anthropometric data. The collected data is therefore suitable for use while designing for Nigerian students.

Soil Erosion map of Europe based on high resolution input datasets

NASA Astrophysics Data System (ADS)

Panagos, Panos; Borrelli, Pasquale; Meusburger, Katrin; Ballabio, Cristiano; Alewell, Christine

2015-04-01

Modelling soil erosion in European Union is of major importance for agro-environmental policies. Soil erosion estimates are important inputs for the Common Agricultural Policy (CAP) and the implementation of the Soil Thematic Strategy. Using the findings of a recent pan-European data collection through the EIONET network, it was concluded that most Member States are applying the empirical Revised Universal Soil Loss Equation (RUSLE) for the modelling soil erosion at National level. This model was chosen for the pan-European soil erosion risk assessment and it is based on 6 input factors. Compared to past approaches, each of the factors is modelled using the latest pan-European datasets, expertise and data from Member states and high resolution remote sensing data. The soil erodibility (K-factor) is modelled using the recently published LUCAS topsoil database with 20,000 point measurements and incorporating the surface stone cover which can reduce K-factor by 15%. The rainfall erosivity dataset (R-factor) has been implemented using high temporal resolution rainfall data from more than 1,500 precipitation stations well distributed in Europe. The cover-management (C-factor) incorporates crop statistics and management practices such as cover crops, tillage practices and plant residuals. The slope length and steepness (combined LS-factor) is based on the first ever 25m Digital Elevation Model (DEM) of Europe. Finally, the support practices (P-factor) is modelled for first time at this scale taking into account the 270,000 LUCAS earth observations and the Good Agricultural and Environmental Condition (GAEC) that farmers have to follow in Europe. The high resolution input layers produce the final soil erosion risk map at 100m resolution and allow policy makers to run future land use, management and climate change scenarios.

Using structured additive regression models to estimate risk factors of malaria: analysis of 2010 Malawi malaria indicator survey data.

PubMed

Chirombo, James; Lowe, Rachel; Kazembe, Lawrence

2014-01-01

After years of implementing Roll Back Malaria (RBM) interventions, the changing landscape of malaria in terms of risk factors and spatial pattern has not been fully investigated. This paper uses the 2010 malaria indicator survey data to investigate if known malaria risk factors remain relevant after many years of interventions. We adopted a structured additive logistic regression model that allowed for spatial correlation, to more realistically estimate malaria risk factors. Our model included child and household level covariates, as well as climatic and environmental factors. Continuous variables were modelled by assuming second order random walk priors, while spatial correlation was specified as a Markov random field prior, with fixed effects assigned diffuse priors. Inference was fully Bayesian resulting in an under five malaria risk map for Malawi. Malaria risk increased with increasing age of the child. With respect to socio-economic factors, the greater the household wealth, the lower the malaria prevalence. A general decline in malaria risk was observed as altitude increased. Minimum temperatures and average total rainfall in the three months preceding the survey did not show a strong association with disease risk. The structured additive regression model offered a flexible extension to standard regression models by enabling simultaneous modelling of possible nonlinear effects of continuous covariates, spatial correlation and heterogeneity, while estimating usual fixed effects of categorical and continuous observed variables. Our results confirmed that malaria epidemiology is a complex interaction of biotic and abiotic factors, both at the individual, household and community level and that risk factors are still relevant many years after extensive implementation of RBM activities.

Mammographic density, breast cancer risk and risk prediction

PubMed Central

Vachon, Celine M; van Gils, Carla H; Sellers, Thomas A; Ghosh, Karthik; Pruthi, Sandhya; Brandt, Kathleen R; Pankratz, V Shane

2007-01-01

In this review, we examine the evidence for mammographic density as an independent risk factor for breast cancer, describe the risk prediction models that have incorporated density, and discuss the current and future implications of using mammographic density in clinical practice. Mammographic density is a consistent and strong risk factor for breast cancer in several populations and across age at mammogram. Recently, this risk factor has been added to existing breast cancer risk prediction models, increasing the discriminatory accuracy with its inclusion, albeit slightly. With validation, these models may replace the existing Gail model for clinical risk assessment. However, absolute risk estimates resulting from these improved models are still limited in their ability to characterize an individual's probability of developing cancer. Promising new measures of mammographic density, including volumetric density, which can be standardized using full-field digital mammography, will likely result in a stronger risk factor and improve accuracy of risk prediction models. PMID:18190724

Bayesian structural equation modeling: a more flexible representation of substantive theory.

PubMed

Muthén, Bengt; Asparouhov, Tihomir

2012-09-01

This article proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, small-variance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximum-likelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling, such as with confirmatory factor analysis, and the measurement part of structural equation modeling. Two application areas are studied, cross-loadings and residual correlations in confirmatory factor analysis. An example using a full structural equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The real-data analyses use data from Holzinger and Swineford's (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.

NGA West 2 | Pacific Earthquake Engineering Research Center

Science.gov Websites

, multi-year research program to improve Next Generation Attenuation models for active tectonic regions earthquake engineering, including modeling of directivity and directionality; verification of NGA-West models epistemic uncertainty; and evaluation of soil amplification factors in NGA models versus NEHRP site factors

Analytic Couple Modeling Introducing Device Design Factor, Fin Factor, Thermal Diffusivity Factor, and Inductance Factor

NASA Technical Reports Server (NTRS)

Mackey, Jon; Sehirlioglu, Alp; Dynys, Fred

2014-01-01

A set of convenient thermoelectric device solutions have been derived in order to capture a number of factors which are previously only resolved with numerical techniques. The concise conversion efficiency equations derived from governing equations provide intuitive and straight-forward design guidelines. These guidelines allow for better device design without requiring detailed numerical modeling. The analytical modeling accounts for factors such as i) variable temperature boundary conditions, ii) lateral heat transfer, iii) temperature variable material properties, and iv) transient operation. New dimensionless parameters, similar to the figure of merit, are introduced including the device design factor, fin factor, thermal diffusivity factor, and inductance factor. These new device factors allow for the straight-forward description of phenomenon generally only captured with numerical work otherwise. As an example a device design factor of 0.38, which accounts for thermal resistance of the hot and cold shoes, can be used to calculate a conversion efficiency of 2.28 while the ideal conversion efficiency based on figure of merit alone would be 6.15. Likewise an ideal couple with efficiency of 6.15 will be reduced to 5.33 when lateral heat is accounted for with a fin factor of 1.0.

Development of an Advanced Respirator Fit-Test Headform

PubMed Central

Bergman, Michael S.; Zhuang, Ziqing; Hanson, David; Heimbuch, Brian K.; McDonald, Michael J.; Palmiero, Andrew J.; Shaffer, Ronald E.; Harnish, Delbert; Husband, Michael; Wander, Joseph D.

2015-01-01

Improved respirator test headforms are needed to measure the fit of N95 filtering facepiece respirators (FFRs) for protection studies against viable airborne particles. A Static (i.e., non-moving, non-speaking) Advanced Headform (StAH) was developed for evaluating the fit of N95 FFRs. The StAH was developed based on the anthropometric dimensions of a digital headform reported by the National Institute for Occupational Safety and Health (NIOSH) and has a silicone polymer skin with defined local tissue thicknesses. Quantitative fit factor evaluations were performed on seven N95 FFR models of various sizes and designs. Donnings were performed with and without a pre-test leak checking method. For each method, four replicate FFR samples of each of the seven models were tested with two donnings per replicate, resulting in a total of 56 tests per donning method. Each fit factor evaluation was comprised of three 86-sec exercises: “Normal Breathing” (NB, 11.2 liters per min (lpm)), “Deep Breathing” (DB, 20.4 lpm), then NB again. A fit factor for each exercise and an overall test fit factor were obtained. Analysis of variance methods were used to identify statistical differences among fit factors (analyzed as logarithms) for different FFR models, exercises, and testing methods. For each FFR model and for each testing method, the NB and DB fit factor data were not significantly different (P > 0.05). Significant differences were seen in the overall exercise fit factor data for the two donning methods among all FFR models (pooled data) and in the overall exercise fit factor data for the two testing methods within certain models. Utilization of the leak checking method improved the rate of obtaining overall exercise fit factors ≥100. The FFR models, which are expected to achieve overall fit factors ≥ 100 on human subjects, achieved overall exercise fit factors ≥ 100 on the StAH. Further research is needed to evaluate the correlation of FFRs fitted on the StAH to FFRs fitted on people. PMID:24369934

General Oral Health Assessment Index: A new evaluation proposal.

PubMed

Campos, Juliana A D B; Zucoloto, Miriane L; Bonafé, Fernanda S S; Maroco, João

2017-09-01

To validity the General Oral Health Assessment Index (GOHAI) among adults who sought dental care and to present a new proposal for calculating scores on self-perception of oral health. There is no study that presents a GOHAI scores using weight of the items. The one-factor model, the three-factor model (physical function, psychosocial/psychological function and pain/discomfort) and the second-order hierarchical model (SOHM) were evaluated from confirmatory factor analysis (λ, χ 2 /df, CFI,GFI and RMSEA). The reliability (CR,α) was estimated. Concurrent validity was assessed using the Oral Health Impact Profile (OHIP-14). The invariance of the models was estimated in independent samples. The calculation of an overall score using the factor scores was proposed to obtain the overall weighted scores. These overall weighted scores were compared to the scores estimated as the simple arithmetic mean (overall unweighted scores) using a repeated measures analysis of variance. A total of 1000 individuals participated (74.1% female; age: 40.7 (SD=14.3) years). Three items of the GOHAI were excluded (λ<0.40). The one-factor model (λ=0.40-0.77; χ 2 /df=6.291; CFI=0.947; GFI=0.960; RMSEA=0.073) and the three-factor model (λ=0.40-0.78; χ 2 /df=8.321; CFI=0.932; GFI=0.954; RMSEA=0.086) each presented an adequate fit. Reliability was adequate (one-factor: CR=0.83/α=0.83; three-factor: CR=0.53-0.76/α=0.53-0.73), with the exception of the pain/discomfort factor. The GOHAI was invariant in independent samples, and the concurrent validity was adequate. The overall unweighted scores overestimated self-perceptions of oral health when compared with the weighted scores. Both the one-factor and three-factor models of the GOHAI were found to be valid, reliable and invariant for the sample after the exclusion of three items. The use of overall weighted scores is recommended for calculating the score of self-perception of oral health. © 2017 John Wiley & Sons A/S and The Gerodontology Association. Published by John Wiley & Sons Ltd.

Modeling wind adjustment factor and midflame wind speed for Rothermel's surface fire spread model

Treesearch

Patricia L. Andrews

2012-01-01

Rothermel's surface fire spread model was developed to use a value for the wind speed that affects surface fire, called midflame wind speed. Models have been developed to adjust 20-ft wind speed to midflame wind speed for sheltered and unsheltered surface fuel. In this report, Wind Adjustment Factor (WAF) model equations are given, and the BehavePlus fire modeling...

Implicit theories of a desire for fame.

PubMed

Maltby, John; Day, Liz; Giles, David; Gillett, Raphael; Quick, Marianne; Langcaster-James, Honey; Linley, P Alex

2008-05-01

The aim of the present studies was to generate implicit theories of a desire for fame among the general population. In Study 1, we were able to develop a nine-factor analytic model of conceptions of the desire to be famous that initially comprised nine separate factors; ambition, meaning derived through comparison with others, psychologically vulnerable, attention seeking, conceitedness, social access, altruistic, positive affect, and glamour. Analysis that sought to examine replicability among these factors suggested that three factors (altruistic, positive affect, and glamour) neither display factor congruence nor display adequate internal reliability. A second study examined the validity of these factors in predicting profiles of individuals who may desire fame. The findings from this study suggested that two of the nine factors (positive affect and altruism) could not be considered strong factors within the model. Overall, the findings suggest that implicit theories of a desire for fame comprise six factors. The discussion focuses on how an implicit model of a desire for fame might progress into formal theories of a desire for fame.

State Effect of Traumatic Experience on Personality Structure

PubMed Central

Lee, Hong-seock; Lee, Sang-Kyu; Lee, Heung-Pyo

2012-01-01

Objective Personality is defined as the trait-like qualities of a person. However, it has been recently suggested that the state effect of a situation leads to changes in scores on personality assessments. We predicted that traumatic experiences would induce changes not only in personality scores but also in the factor structures of personality assessments. Methods MethodsaaWe conducted a cross-sectional, case-controlled study using two data sets: a traumatized adolescent sample (n=71) and a non-traumatized adolescent sample (n=296). Personality factor structures were compared between the two samples using exploratory factor analyses for 25 lower-ordered subscales of the Temperament and Character Inventory (TCI). In the non-traumatized sample, evaluation of the scree plot suggested a five-factor solution supporting TCI's original seven-factor model. Results The traumatized sample showed a three-factor structure representing a biological factor, a social factor and an existential factor. This decrease in number of personality factors was caused by strengthened correlations among personality subscales related to coping with traumatic situations. Cloninger's psychobiological model of personality (i.e., temperament-character) was adequate in capturing personality traits of non-traumatized adolescents, but the tripartite view of existential psychology (i.e., body-mind-spirit) clearly corresponded to the factor structure of the traumatized adolescents. Conclusion The three-factor solution of the present traumatized group is consistent with the tripartite model of personality (i.e., body-mind-spirit), while the five-factor solution of the non-traumatized group corresponds to Cloninger's seven-factor model. This is the first study to describe the state effects of traumatic experiences on personality structure. PMID:23251200

The application of Global Sensitivity Analysis to quantify the dominant input factors for hydraulic model simulations

NASA Astrophysics Data System (ADS)

Savage, James; Pianosi, Francesca; Bates, Paul; Freer, Jim; Wagener, Thorsten

2015-04-01

Predicting flood inundation extents using hydraulic models is subject to a number of critical uncertainties. For a specific event, these uncertainties are known to have a large influence on model outputs and any subsequent analyses made by risk managers. Hydraulic modellers often approach such problems by applying uncertainty analysis techniques such as the Generalised Likelihood Uncertainty Estimation (GLUE) methodology. However, these methods do not allow one to attribute which source of uncertainty has the most influence on the various model outputs that inform flood risk decision making. Another issue facing modellers is the amount of computational resource that is available to spend on modelling flood inundations that are 'fit for purpose' to the modelling objectives. Therefore a balance needs to be struck between computation time, realism and spatial resolution, and effectively characterising the uncertainty spread of predictions (for example from boundary conditions and model parameterisations). However, it is not fully understood how much of an impact each factor has on model performance, for example how much influence changing the spatial resolution of a model has on inundation predictions in comparison to other uncertainties inherent in the modelling process. Furthermore, when resampling fine scale topographic data in the form of a Digital Elevation Model (DEM) to coarser resolutions, there are a number of possible coarser DEMs that can be produced. Deciding which DEM is then chosen to represent the surface elevations in the model could also influence model performance. In this study we model a flood event using the hydraulic model LISFLOOD-FP and apply Sobol' Sensitivity Analysis to estimate which input factor, among the uncertainty in model boundary conditions, uncertain model parameters, the spatial resolution of the DEM and the choice of resampled DEM, have the most influence on a range of model outputs. These outputs include whole domain maximum inundation indicators and flood wave travel time in addition to temporally and spatially variable indicators. This enables us to assess whether the sensitivity of the model to various input factors is stationary in both time and space. Furthermore, competing models are assessed against observations of water depths from a historical flood event. Consequently we are able to determine which of the input factors has the most influence on model performance. Initial findings suggest the sensitivity of the model to different input factors varies depending on the type of model output assessed and at what stage during the flood hydrograph the model output is assessed. We have also found that initial decisions regarding the characterisation of the input factors, for example defining the upper and lower bounds of the parameter sample space, can be significant in influencing the implied sensitivities.

Sex similarities and differences in risk factors for recurrence of major depression.

PubMed

van Loo, Hanna M; Aggen, Steven H; Gardner, Charles O; Kendler, Kenneth S

2017-11-27

Major depression (MD) occurs about twice as often in women as in men, but it is unclear whether sex differences subsist after disease onset. This study aims to elucidate potential sex differences in rates and risk factors for MD recurrence, in order to improve prediction of course of illness and understanding of its underlying mechanisms. We used prospective data from a general population sample (n = 653) that experienced a recent episode of MD. A diverse set of potential risk factors for recurrence of MD was analyzed using Cox models subject to elastic net regularization for males and females separately. Accuracy of the prediction models was tested in same-sex and opposite-sex test data. Additionally, interactions between sex and each of the risk factors were investigated to identify potential sex differences. Recurrence rates and the impact of most risk factors were similar for men and women. For both sexes, prediction models were highly multifactorial including risk factors such as comorbid anxiety, early traumas, and family history. Some subtle sex differences were detected: for men, prediction models included more risk factors concerning characteristics of the depressive episode and family history of MD and generalized anxiety, whereas for women, models included more risk factors concerning early and recent adverse life events and socioeconomic problems. No prominent sex differences in risk factors for recurrence of MD were found, potentially indicating similar disease maintaining mechanisms for both sexes. Course of MD is a multifactorial phenomenon for both males and females.

Development of database of real-world diesel vehicle emission factors for China.

PubMed

Shen, Xianbao; Yao, Zhiliang; Zhang, Qiang; Wagner, David Vance; Huo, Hong; Zhang, Yingzhi; Zheng, Bo; He, Kebin

2015-05-01

A database of real-world diesel vehicle emission factors, based on type and technology, has been developed following tests on more than 300 diesel vehicles in China using a portable emission measurement system. The database provides better understanding of diesel vehicle emissions under actual driving conditions. We found that although new regulations have reduced real-world emission levels of diesel trucks and buses significantly for most pollutants in China, NOx emissions have been inadequately controlled by the current standards, especially for diesel buses, because of bad driving conditions in the real world. We also compared the emission factors in the database with those calculated by emission factor models and used in inventory studies. The emission factors derived from COPERT (Computer Programmer to calculate Emissions from Road Transport) and MOBILE may both underestimate real emission factors, whereas the updated COPERT and PART5 (Highway Vehicle Particulate Emission Modeling Software) models may overestimate emission factors in China. Real-world measurement results and emission factors used in recent emission inventory studies are inconsistent, which has led to inaccurate estimates of emissions from diesel trucks and buses over recent years. This suggests that emission factors derived from European or US-based models will not truly represent real-world emissions in China. Therefore, it is useful and necessary to conduct systematic real-world measurements of vehicle emissions in China in order to obtain the optimum inputs for emission inventory models. Copyright © 2015. Published by Elsevier B.V.

Modelling and analysis of FMS productivity variables by ISM, SEM and GTMA approach

NASA Astrophysics Data System (ADS)

Jain, Vineet; Raj, Tilak

2014-09-01

Productivity has often been cited as a key factor in a flexible manufacturing system (FMS) performance, and actions to increase it are said to improve profitability and the wage earning capacity of employees. Improving productivity is seen as a key issue for survival and success in the long term of a manufacturing system. The purpose of this paper is to make a model and analysis of the productivity variables of FMS. This study was performed by different approaches viz. interpretive structural modelling (ISM), structural equation modelling (SEM), graph theory and matrix approach (GTMA) and a cross-sectional survey within manufacturing firms in India. ISM has been used to develop a model of productivity variables, and then it has been analyzed. Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA) are powerful statistical techniques. CFA is carried by SEM. EFA is applied to extract the factors in FMS by the statistical package for social sciences (SPSS 20) software and confirming these factors by CFA through analysis of moment structures (AMOS 20) software. The twenty productivity variables are identified through literature and four factors extracted, which involves the productivity of FMS. The four factors are people, quality, machine and flexibility. SEM using AMOS 20 was used to perform the first order four-factor structures. GTMA is a multiple attribute decision making (MADM) methodology used to find intensity/quantification of productivity variables in an organization. The FMS productivity index has purposed to intensify the factors which affect FMS.

Methodology to develop crash modification functions for road safety treatments with fully specified and hierarchical models.

PubMed

Chen, Yongsheng; Persaud, Bhagwant

2014-09-01

Crash modification factors (CMFs) for road safety treatments are developed as multiplicative factors that are used to reflect the expected changes in safety performance associated with changes in highway design and/or the traffic control features. However, current CMFs have methodological drawbacks. For example, variability with application circumstance is not well understood, and, as important, correlation is not addressed when several CMFs are applied multiplicatively. These issues can be addressed by developing safety performance functions (SPFs) with components of crash modification functions (CM-Functions), an approach that includes all CMF related variables, along with others, while capturing quantitative and other effects of factors and accounting for cross-factor correlations. CM-Functions can capture the safety impact of factors through a continuous and quantitative approach, avoiding the problematic categorical analysis that is often used to capture CMF variability. There are two formulations to develop such SPFs with CM-Function components - fully specified models and hierarchical models. Based on sample datasets from two Canadian cities, both approaches are investigated in this paper. While both model formulations yielded promising results and reasonable CM-Functions, the hierarchical model was found to be more suitable in retaining homogeneity of first-level SPFs, while addressing CM-Functions in sub-level modeling. In addition, hierarchical models better capture the correlations between different impact factors. Copyright © 2014 Elsevier Ltd. All rights reserved.

Confirmatory factorial analysis of the children´s attraction to physical activity scale (capa).

PubMed

Seabra, A C; Maia, J A; Parker, M; Seabra, A; Brustad, R; Fonseca, A M

2015-03-27

Attraction to physical activity (PA) is an important contributor to children´s intrinsic motivation to engage in games, and sports. Previous studies have supported the utility of the children´s attraction to PA scale (CAPA) (Brustad, 1996) but the validity of this measure for use in Portugal has not been established. The purpose of this study was to cross-validate the shorter version of the CAPA scale in the Portuguese cultural context. A sample of 342 children (8--10 years of age) was used. Confirmatory factor analyses using EQS software ( version 6.1) tested t hree competing measurement models: a single--factor model, a five factor model, and a second order factor model. The single--factor model and the second order model showed a poor fit to the data. It was found that a five-factor model similar to the original one revealed good fit to the data (S--B χ 2 (67) =94.27,p=0.02; NNFI=0.93; CFI=0.95; RMSEA=0.04; 90%CI=0.02;0.05). The results indicated that the CAPA scale is valid and appropriate for use in the Portuguese cultural context. The availability of a valid scale to evaluate attraction to PA at schools should provide improved opportunities for better assessment and understanding of children´s involvement in PA.

A Developmental Analysis of the Factorial Validity of the Parent-Report Version of the Adult Responses to Children’s Symptoms in Children Versus Adolescents With Chronic Pain or Pain-Related Chronic Illness

PubMed Central

Noel, Melanie; Palermo, Tonya M.; Essner, Bonnie; Zhou, Chuan; Levy, Rona L.; Langer, Shelby L.; Sherman, Amanda L.; Walker, Lynn S.

2015-01-01

The widely used Adult Responses to Children’s Symptoms measures parental responses to child symptom complaints among youth aged 7 to 18 years with recurrent/chronic pain. Given developmental differences between children and adolescents and the impact of developmental stage on parenting, the factorial validity of the parent-report version of the Adult Responses to Children’s Symptoms with a pain-specific stem was examined separately in 743 parents of 281 children (7–11 years) and 462 adolescents (12–18 years) with chronic pain or pain-related chronic illness. Factor structures of the Adult Responses to Children’s Symptoms beyond the original 3-factor model were also examined. Exploratory factor analysis with oblique rotation was conducted on a randomly chosen half of the sample of children and adolescents as well as the 2 groups combined to assess underlying factor structure. Confirmatory factor analysis was conducted on the other randomly chosen half of the sample to cross-validate factor structure revealed by exploratory factor analyses and compare it to other model variants. Poor loading and high cross loading items were removed. A 4-factor model (Protect, Minimize, Monitor, and Distract) for children and the combined (child and adolescent) sample and a 5-factor model (Protect, Minimize, Monitor, Distract, and Solicitousness) for adolescents was superior to the 3-factor model proposed in previous literature. Future research should examine the validity of derived subscales and developmental differences in their relationships with parent and child functioning. PMID:25451623

Measuring chronic condition self-management in an Australian community: factor structure of the revised Partners in Health (PIH) scale.

PubMed

Smith, David; Harvey, Peter; Lawn, Sharon; Harris, Melanie; Battersby, Malcolm

2017-01-01

To evaluate the factor structure of the revised Partners in Health (PIH) scale for measuring chronic condition self-management in a representative sample from the Australian community. A series of consultations between clinical groups underpinned the revision of the PIH. The factors in the revised instrument were proposed to be: knowledge of illness and treatment, patient-health professional partnership, recognition and management of symptoms and coping with chronic illness. Participants (N = 904) reporting having a chronic illness completed the revised 12-item scale. Two a priori models, the 4-factor and bi-factor models were then evaluated using Bayesian confirmatory factor analysis (BCFA). Final model selection was established on model complexity, posterior predictive p values and deviance information criterion. Both 4-factor and bi-factor BCFA models with small informative priors for cross-loadings provided an acceptable fit with the data. The 4-factor model was shown to provide a better and more parsimonious fit with the observed data in terms of substantive theory. McDonald's omega coefficients indicated that the reliability of subscale raw scores was mostly in the acceptable range. The findings showed that the PIH scale is a relevant and structurally valid instrument for measuring chronic condition self-management in an Australian community. The PIH scale may help health professionals to introduce the concept of self-management to their patients and provide assessment of areas of self-management. A limitation is the narrow range of validated PIH measurement properties to date. Further research is needed to evaluate other important properties such as test-retest reliability, responsiveness over time and content validity.

An assessment of the construct distinctiveness of stress arousal and burnout.

PubMed

Smith, Kenneth J; Davy, Jeanette A; Everly, George S

2006-10-01

This study examined the construct and discriminant validity of stress arousal and burnout as measured on the Stress Arousal Scale and the multidimensional role-specific version of the Maslach Burnout Inventory, respectively. The analyses utilized data from 148 individuals randomly selected from a database of 563 respondents to a larger study. The sample responded to a survey sent to members of the American Institute of Certified Public Accountants (AICPA). Sample size used in this study fell within Loehlin's 1992 prescription that for confirmatory factor analysis with two to four factors, a minimum of 100 to 200 cases should be collected. Forty-six respondents indicated that they were partners, principals, or sole practitioners in accounting firms, and 103 indicated that they were staff members (juniors, seniors, or managers). Latent variables were first constructed for the stress arousal and burnout factors. Confirmatory factor analysis was then conducted on the scale data to assess whether the factors would load on their respective underlying theoretical constructs. Finally, a nested model constraining stress arousal and burnout to load on one underlying construct was tested against the hypothesized two-factor model. The results indicated good model fit for the two-factor model and a significant loss of fit for the one-factor model, thus providing strong support for the conceptualization of stress arousal and burnout as distinct constructs.

The Analysis of the Contribution of Human Factors to the In-Flight Loss of Control Accidents

NASA Technical Reports Server (NTRS)

Ancel, Ersin; Shih, Ann T.

2012-01-01

In-flight loss of control (LOC) is currently the leading cause of fatal accidents based on various commercial aircraft accident statistics. As the Next Generation Air Transportation System (NextGen) emerges, new contributing factors leading to LOC are anticipated. The NASA Aviation Safety Program (AvSP), along with other aviation agencies and communities are actively developing safety products to mitigate the LOC risk. This paper discusses the approach used to construct a generic integrated LOC accident framework (LOCAF) model based on a detailed review of LOC accidents over the past two decades. The LOCAF model is comprised of causal factors from the domain of human factors, aircraft system component failures, and atmospheric environment. The multiple interdependent causal factors are expressed in an Object-Oriented Bayesian belief network. In addition to predicting the likelihood of LOC accident occurrence, the system-level integrated LOCAF model is able to evaluate the impact of new safety technology products developed in AvSP. This provides valuable information to decision makers in strategizing NASA's aviation safety technology portfolio. The focus of this paper is on the analysis of human causal factors in the model, including the contributions from flight crew and maintenance workers. The Human Factors Analysis and Classification System (HFACS) taxonomy was used to develop human related causal factors. The preliminary results from the baseline LOCAF model are also presented.

An investigation of PTSD's core dimensions and relations with anxiety and depression.

PubMed

Byllesby, Brianna M; Durham, Tory A; Forbes, David; Armour, Cherie; Elhai, Jon D

2016-03-01

Posttraumatic stress disorder (PTSD) is highly comorbid with anxiety and depressive disorders, which is suggestive of shared variance or common underlying dimensions. The purpose of the present study was to examine the relationship between the latent factors of PTSD with the constructs of anxiety and depression in order to increase understanding of the co-occurrence of these disorders. Data were collected from a nonclinical sample of 186 trauma-exposed participants using the PTSD Checklist and Hospital Anxiety and Depression Scale. Confirmatory factor analyses were conducted to determine model fit comparing 3 PTSD factor structure models, followed by Wald tests comparing the relationships between PTSD factors and the core dimensions of anxiety and depression. In model comparisons, the 5-factor dysphoric arousal model of PTSD provided the best fit for the data, compared to the emotional numbing and dysphoria models of PTSD. Compared to anxious arousal, the dysphoric arousal and numbing factors of PTSD were more related to depression severity. Numbing, anxious arousal, and dysphoric arousal were not differentially related to the latent anxiety factor. The underlying factors of PTSD contain aspects of the core dimensions of both anxiety and depression. The heterogeneity of PTSD's associations with anxiety and depressive constructs requires additional empirical exploration because clarification regarding these relationships will impact diagnostic classification as well as clinical practice. (c) 2016 APA, all rights reserved).

The Structure of Personality Disorders in Individuals with Posttraumatic Stress Disorder

PubMed Central

Wolf, Erika J.; Miller, Mark W.; Brown, Timothy A.

2015-01-01

Research on the structure of personality disorders (PDs) has relied primarily on exploratory analyses to evaluate trait-based models of the factors underlying the covariation of these disorders. This study used confirmatory factor analysis to evaluate whether a model that included both PD traits and a general personality dysfunction factor would account for the comorbidity of the PDs better than a trait-only model. It also examined if the internalizing/externalizing model of psychopathology, developed previously through research on the structure of Axis I disorders, might similarly account for the covariation of the Axis II disorders in a sample of 245 veterans and non-veterans with posttraumatic stress disorder. Results indicated that the best fitting model was a modified bifactor structure composed of nine lower-order common factors. These factors indexed pathology ranging from aggression to dependency, with the correlations among them accounted for by higher-order Internalizing and Externalizing factors. Further, a general factor, reflecting a construct that we termed boundary disturbance, accounted for additional variance and covariance across nearly all the indicators. The Internalizing, Externalizing, and Boundary Disturbance factors evidenced differential associations with trauma-related covariates. These findings suggest continuity in the underlying structure of psychopathology across DSM-IV Axes I & II and provide empirical evidence of a pervasive, core disturbance in the boundary between self and other across the PDs. PMID:22448802

Factor structure and longitudinal measurement invariance of the demand control support model: an evidence from the Swedish Longitudinal Occupational Survey of Health (SLOSH).

PubMed

Chungkham, Holendro Singh; Ingre, Michael; Karasek, Robert; Westerlund, Hugo; Theorell, Töres

2013-01-01

To examine the factor structure and to evaluate the longitudinal measurement invariance of the demand-control-support questionnaire (DCSQ), using the Swedish Longitudinal Occupational Survey of Health (SLOSH). A confirmatory factor analysis (CFA) and multi-group confirmatory factor analysis (MGCFA) models within the framework of structural equation modeling (SEM) have been used to examine the factor structure and invariance across time. Four factors: psychological demand, skill discretion, decision authority and social support, were confirmed by CFA at baseline, with the best fit obtained by removing the item repetitive work of skill discretion. A measurement error correlation (0.42) between work fast and work intensively for psychological demands was also detected. Acceptable composite reliability measures were obtained except for skill discretion (0.68). The invariance of the same factor structure was established, but caution in comparing mean levels of factors over time is warranted as lack of intercept invariance was evident. However, partial intercept invariance was established for work intensively. Our findings indicate that skill discretion and decision authority represent two distinct constructs in the retained model. However removing the item repetitive work along with either work fast or work intensively would improve model fit. Care should also be taken while making comparisons in the constructs across time. Further research should investigate invariance across occupations or socio-economic classes.

Factor Structure and Longitudinal Measurement Invariance of the Demand Control Support Model: An Evidence from the Swedish Longitudinal Occupational Survey of Health (SLOSH)

PubMed Central

Chungkham, Holendro Singh; Ingre, Michael; Karasek, Robert; Westerlund, Hugo; Theorell, Töres

2013-01-01

Objectives To examine the factor structure and to evaluate the longitudinal measurement invariance of the demand-control-support questionnaire (DCSQ), using the Swedish Longitudinal Occupational Survey of Health (SLOSH). Methods A confirmatory factor analysis (CFA) and multi-group confirmatory factor analysis (MGCFA) models within the framework of structural equation modeling (SEM) have been used to examine the factor structure and invariance across time. Results Four factors: psychological demand, skill discretion, decision authority and social support, were confirmed by CFA at baseline, with the best fit obtained by removing the item repetitive work of skill discretion. A measurement error correlation (0.42) between work fast and work intensively for psychological demands was also detected. Acceptable composite reliability measures were obtained except for skill discretion (0.68). The invariance of the same factor structure was established, but caution in comparing mean levels of factors over time is warranted as lack of intercept invariance was evident. However, partial intercept invariance was established for work intensively. Conclusion Our findings indicate that skill discretion and decision authority represent two distinct constructs in the retained model. However removing the item repetitive work along with either work fast or work intensively would improve model fit. Care should also be taken while making comparisons in the constructs across time. Further research should investigate invariance across occupations or socio-economic classes. PMID:23950957

Analysis of Food Hub Commerce and Participation Using Agent-Based Modeling: Integrating Financial and Social Drivers.

PubMed

Krejci, Caroline C; Stone, Richard T; Dorneich, Michael C; Gilbert, Stephen B

2016-02-01

Factors influencing long-term viability of an intermediated regional food supply network (food hub) were modeled using agent-based modeling techniques informed by interview data gathered from food hub participants. Previous analyses of food hub dynamics focused primarily on financial drivers rather than social factors and have not used mathematical models. Based on qualitative and quantitative data gathered from 22 customers and 11 vendors at a midwestern food hub, an agent-based model (ABM) was created with distinct consumer personas characterizing the range of consumer priorities. A comparison study determined if the ABM behaved differently than a model based on traditional economic assumptions. Further simulation studies assessed the effect of changes in parameters, such as producer reliability and the consumer profiles, on long-term food hub sustainability. The persona-based ABM model produced different and more resilient results than the more traditional way of modeling consumers. Reduced producer reliability significantly reduced trade; in some instances, a modest reduction in reliability threatened the sustainability of the system. Finally, a modest increase in price-driven consumers at the outset of the simulation quickly resulted in those consumers becoming a majority of the overall customer base. Results suggest that social factors, such as desire to support the community, can be more important than financial factors. An ABM of food hub dynamics, based on human factors data gathered from the field, can be a useful tool for policy decisions. Similar approaches can be used for modeling customer dynamics with other sustainable organizations. © 2015, Human Factors and Ergonomics Society.

Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer.

PubMed

Gagnon, B; Abrahamowicz, M; Xiao, Y; Beauchamp, M-E; MacDonald, N; Kasymjanova, G; Kreisman, H; Small, D

2010-03-30

C-reactive protein (CRP) is gaining credibility as a prognostic factor in different cancers. Cox's proportional hazard (PH) model is usually used to assess prognostic factors. However, this model imposes a priori assumptions, which are rarely tested, that (1) the hazard ratio associated with each prognostic factor remains constant across the follow-up (PH assumption) and (2) the relationship between a continuous predictor and the logarithm of the mortality hazard is linear (linearity assumption). We tested these two assumptions of the Cox's PH model for CRP, using a flexible statistical model, while adjusting for other known prognostic factors, in a cohort of 269 patients newly diagnosed with non-small cell lung cancer (NSCLC). In the Cox's PH model, high CRP increased the risk of death (HR=1.11 per each doubling of CRP value, 95% CI: 1.03-1.20, P=0.008). However, both the PH assumption (P=0.033) and the linearity assumption (P=0.015) were rejected for CRP, measured at the initiation of chemotherapy, which kept its prognostic value for approximately 18 months. Our analysis shows that flexible modeling provides new insights regarding the value of CRP as a prognostic factor in NSCLC and that Cox's PH model underestimates early risks associated with high CRP.

A multiphase model for chemically- and mechanically- induced cell differentiation in a hollow fibre membrane bioreactor: minimising growth factor consumption.

PubMed

Pearson, Natalie C; Oliver, James M; Shipley, Rebecca J; Waters, Sarah L

2016-06-01

We present a simplified two-dimensional model of fluid flow, solute transport, and cell distribution in a hollow fibre membrane bioreactor. We consider two cell populations, one undifferentiated and one differentiated, with differentiation stimulated either by growth factor alone, or by both growth factor and fluid shear stress. Two experimental configurations are considered, a 3-layer model in which the cells are seeded in a scaffold throughout the extracapillary space (ECS), and a 4-layer model in which the cell-scaffold construct occupies a layer surrounding the outside of the hollow fibre, only partially filling the ECS. Above this is a region of free-flowing fluid, referred to as the upper fluid layer. Following previous models by the authors (Pearson et al. in Math Med Biol, 2013, Biomech Model Mechanbiol 1-16, 2014a, we employ porous mixture theory to model the dynamics of, and interactions between, the cells, scaffold, and fluid in the cell-scaffold construct. We use this model to determine operating conditions (experiment end time, growth factor inlet concentration, and inlet fluid fluxes) which result in a required percentage of differentiated cells, as well as maximising the differentiated cell yield and minimising the consumption of expensive growth factor.

Adolescent mental health and academic functioning: empirical support for contrasting models of risk and vulnerability.

PubMed

Lucier-Greer, Mallory; O'Neal, Catherine W; Arnold, A Laura; Mancini, Jay A; Wickrama, Kandauda K A S

2014-11-01

Adolescents in military families contend with normative stressors that are universal and exist across social contexts (minority status, family disruptions, and social isolation) as well as stressors reflective of their military life context (e.g., parental deployment, school transitions, and living outside the United States). This study utilizes a social ecological perspective and a stress process lens to examine the relationship between multiple risk factors and relevant indicators of youth well-being, namely depressive symptoms and academic performance, as well as the mediating role of self-efficacy (N = 1,036). Three risk models were tested: an additive effects model (each risk factor uniquely influences outcomes), a full cumulative effects model (the collection of risk factors influences outcomes), a comparative model (a cumulative effects model exploring the differential effects of normative and military-related risks). This design allowed for the simultaneous examination of multiple risk factors and a comparison of alternative perspectives on measuring risk. Each model was predictive of depressive symptoms and academic performance through persistence; however, each model provides unique findings about the relationship between risk factors and youth outcomes. Discussion is provided pertinent to service providers and researchers on how risk is conceptualized and suggestions for identifying at-risk youth. Reprint & Copyright © 2014 Association of Military Surgeons of the U.S.

A New Look at Genetic and Environmental Architecture on Lipids Using Non-Normal Structural Equation Modeling in Male Twins: The NHLBI Twin Study.

PubMed

Wu, Sheng-Hui; Ozaki, Koken; Reed, Terry; Krasnow, Ruth E; Dai, Jun

2017-07-01

This study examined genetic and environmental influences on the lipid concentrations of 1028 male twins using the novel univariate non-normal structural equation modeling (nnSEM) ADCE and ACE models. In the best fitting nnSEM ADCE model that was also better than the nnSEM ACE model, additive genetic factors (A) explained 4%, dominant genetic factors (D) explained 17%, and common (C) and unique (E) environmental factors explained 47% and 33% of the total variance of high-density lipoprotein cholesterol (HDL-C). The percentage of variation explained for other lipids was 0% (A), 30% (D), 34% (C) and 37% (E) for low-density lipoprotein cholesterol (LDL-C); 30, 0, 31 and 39% for total cholesterol; and 0, 31, 12 and 57% for triglycerides. It was concluded that additive and dominant genetic factors simultaneously affected HDL-C concentrations but not other lipids. Common and unique environmental factors influenced concentrations of all lipids.

Multigroup confirmatory factor analysis and structural invariance with age of the Behavior Rating Inventory of Executive Function (BRIEF)--French version.

PubMed

Fournet, Nathalie; Roulin, Jean-Luc; Monnier, Catherine; Atzeni, Thierry; Cosnefroy, Olivier; Le Gall, Didier; Roy, Arnaud

2015-01-01

The parent and teacher forms of the French version of the Behavioral Rating Inventory of Executive Function (BRIEF) were used to evaluate executive function in everyday life in a large sample of healthy children (N = 951) aged between 5 and 18. Several psychometric methods were applied, with a view to providing clinicians with tools for score interpretation. The parent and teacher forms of the BRIEF were acceptably reliable. Demographic variables (such as age and gender) were found to influence the BRIEF scores. Confirmatory factor analysis was then used to test five competing models of the BRIEF's latent structure. Two of these models (a three-factor model and a two-factor model, both based on a nine-scale structure) had a good fit. However, structural invariance with age was only obtained with the two-factor model. The French version of the BRIEF provides a useful measure of everyday executive function and can be recommended for use in clinical research and practice.

Bayes Factor Covariance Testing in Item Response Models.

PubMed

Fox, Jean-Paul; Mulder, Joris; Sinharay, Sandip

2017-12-01

Two marginal one-parameter item response theory models are introduced, by integrating out the latent variable or random item parameter. It is shown that both marginal response models are multivariate (probit) models with a compound symmetry covariance structure. Several common hypotheses concerning the underlying covariance structure are evaluated using (fractional) Bayes factor tests. The support for a unidimensional factor (i.e., assumption of local independence) and differential item functioning are evaluated by testing the covariance components. The posterior distribution of common covariance components is obtained in closed form by transforming latent responses with an orthogonal (Helmert) matrix. This posterior distribution is defined as a shifted-inverse-gamma, thereby introducing a default prior and a balanced prior distribution. Based on that, an MCMC algorithm is described to estimate all model parameters and to compute (fractional) Bayes factor tests. Simulation studies are used to show that the (fractional) Bayes factor tests have good properties for testing the underlying covariance structure of binary response data. The method is illustrated with two real data studies.

Modeling and projection of dengue fever cases in Guangzhou based on variation of weather factors.

PubMed

Li, Chenlu; Wang, Xiaofeng; Wu, Xiaoxu; Liu, Jianing; Ji, Duoying; Du, Juan

2017-12-15

Dengue fever is one of the most serious vector-borne infectious diseases, especially in Guangzhou, China. Dengue viruses and their vectors Aedes albopictus are sensitive to climate change primarily in relation to weather factors. Previous research has mainly focused on identifying the relationship between climate factors and dengue cases, or developing dengue case models with some non-climate factors. However, there has been little research addressing the modeling and projection of dengue cases only from the perspective of climate change. This study considered this topic using long time series data (1998-2014). First, sensitive weather factors were identified through meta-analysis that included literature review screening, lagged analysis, and collinear analysis. Then, key factors that included monthly average temperature at a lag of two months, and monthly average relative humidity and monthly average precipitation at lags of three months were determined. Second, time series Poisson analysis was used with the generalized additive model approach to develop a dengue model based on key weather factors for January 1998 to December 2012. Data from January 2013 to July 2014 were used to validate that the model was reliable and reasonable. Finally, future weather data (January 2020 to December 2070) were input into the model to project the occurrence of dengue cases under different climate scenarios (RCP 2.6 and RCP 8.5). Longer time series analysis and scientifically selected weather variables were used to develop a dengue model to ensure reliability. The projections suggested that seasonal disease control (especially in summer and fall) and mitigation of greenhouse gas emissions could help reduce the incidence of dengue fever. The results of this study hope to provide a scientifically theoretical basis for the prevention and control of dengue fever in Guangzhou. Copyright © 2017 Elsevier B.V. All rights reserved.

Factor structure and internal reliability of an exercise health belief model scale in a Mexican population.

PubMed

Villar, Oscar Armando Esparza-Del; Montañez-Alvarado, Priscila; Gutiérrez-Vega, Marisela; Carrillo-Saucedo, Irene Concepción; Gurrola-Peña, Gloria Margarita; Ruvalcaba-Romero, Norma Alicia; García-Sánchez, María Dolores; Ochoa-Alcaraz, Sergio Gabriel

2017-03-01

Mexico is one of the countries with the highest rates of overweight and obesity around the world, with 68.8% of men and 73% of women reporting both. This is a public health problem since there are several health related consequences of not exercising, like having cardiovascular diseases or some types of cancers. All of these problems can be prevented by promoting exercise, so it is important to evaluate models of health behaviors to achieve this goal. Among several models the Health Belief Model is one of the most studied models to promote health related behaviors. This study validates the first exercise scale based on the Health Belief Model (HBM) in Mexicans with the objective of studying and analyzing this model in Mexico. Items for the scale called the Exercise Health Belief Model Scale (EHBMS) were developed by a health research team, then the items were applied to a sample of 746 participants, male and female, from five cities in Mexico. The factor structure of the items was analyzed with an exploratory factor analysis and the internal reliability with Cronbach's alpha. The exploratory factor analysis reported the expected factor structure based in the HBM. The KMO index (0.92) and the Barlett's sphericity test (p < 0.01) indicated an adequate and normally distributed sample. Items had adequate factor loadings, ranging from 0.31 to 0.92, and the internal consistencies of the factors were also acceptable, with alpha values ranging from 0.67 to 0.91. The EHBMS is a validated scale that can be used to measure exercise based on the HBM in Mexican populations.

Examining the Latent Structure of the Delis-Kaplan Executive Function System.

PubMed

Karr, Justin E; Hofer, Scott M; Iverson, Grant L; Garcia-Barrera, Mauricio A

2018-05-04

The current study aimed to determine whether the Delis-Kaplan Executive Function System (D-KEFS) taps into three executive function factors (inhibition, shifting, fluency) and to assess the relationship between these factors and tests of executive-related constructs less often measured in latent variable research: reasoning, abstraction, and problem solving. Participants included 425 adults from the D-KEFS standardization sample (20-49 years old; 50.1% female; 70.1% White). Eight alternative measurement models were compared based on model fit, with test scores assigned a priori to three factors: inhibition (Color-Word Interference, Tower), shifting (Trail Making, Sorting, Design Fluency), and fluency (Verbal/Design Fluency). The Twenty Questions, Word Context, and Proverb Tests were predicted in separate structural models. The three-factor model fit the data well (CFI = 0.938; RMSEA = 0.047), although a two-factor model, with shifting and fluency merged, fit similarly well (CFI = 0.929; RMSEA = 0.048). A bifactor model fit best (CFI = 0.977; RMSEA = 0.032) and explained the most variance in shifting indicators, but rarely converged among 5,000 bootstrapped samples. When the three first-order factors simultaneously predicted the criterion variables, only shifting was uniquely predictive (p < .05; R2 = 0.246-0.408). The bifactor significantly predicted all three criterion variables (p < .001; R2 = 0.141-242). Results supported a three-factor D-KEFS model (i.e., inhibition, shifting, and fluency), although shifting and fluency were highly related (r = 0.696). The bifactor showed superior fit, but converged less often than other models. Shifting best predicted tests of reasoning, abstraction, and problem solving. These findings support the validity of D-KEFS scores for measuring executive-related constructs and provide a framework through which clinicians can interpret D-KEFS results.

Polarizable molecular interactions in condensed phase and their equivalent nonpolarizable models.

PubMed

Leontyev, Igor V; Stuchebrukhov, Alexei A

2014-07-07

Earlier, using phenomenological approach, we showed that in some cases polarizable models of condensed phase systems can be reduced to nonpolarizable equivalent models with scaled charges. Examples of such systems include ionic liquids, TIPnP-type models of water, protein force fields, and others, where interactions and dynamics of inherently polarizable species can be accurately described by nonpolarizable models. To describe electrostatic interactions, the effective charges of simple ionic liquids are obtained by scaling the actual charges of ions by a factor of 1/√(ε(el)), which is due to electronic polarization screening effect; the scaling factor of neutral species is more complicated. Here, using several theoretical models, we examine how exactly the scaling factors appear in theory, and how, and under what conditions, polarizable Hamiltonians are reduced to nonpolarizable ones. These models allow one to trace the origin of the scaling factors, determine their values, and obtain important insights on the nature of polarizable interactions in condensed matter systems.

Depression in young adolescents: investigations using 2 and 3 factor versions of the Parental Bonding Instrument.

PubMed

Martin, Graham; Bergen, Helen A; Roeger, Leigh; Allison, Stephen

2004-10-01

Associations between parenting style and depressive symptomatology in a community sample of young adolescents (N = 2596) were investigated using self-report measures including the Parental Bonding Instrument and the Center for Epidemiologic Studies Depression Scale. Specifically, the 25-item 2-factor and 3-factor models by Parker et al. (1979), Kendler's (1996) 16-item 3-factor model, and Parker's (1983) quadrant model for the Parental Bonding Instrument were compared. Data analysis included analysis of variance and logistic regression. Reanalysis of Parker's original scale indicates that overprotection is composed of separate factors: intrusiveness (at the individual level) and restrictiveness (in the social context). All models reveal significant independent contributions from paternal care, maternal care, and maternal overprotection (2-factor) or intrusiveness (3-factor) to moderate and serious depressive symptomatology, controlling for sex and family living arrangement. Additive rather than multiplicative interactions between care and overprotection were found. Regardless of the level of parental care and affection, clinicians should note that maternal intrusiveness is strongly associated with adverse psychosocial health in young adolescents.

Hardware-Based Non-Optimum Factors for Launch Vehicle Structural Design

NASA Technical Reports Server (NTRS)

Wu, K. Chauncey; Cerro, Jeffrey A.

2010-01-01

During aerospace vehicle conceptual and preliminary design, empirical non-optimum factors are typically applied to predicted structural component weights to account for undefined manufacturing and design details. Non-optimum factors are developed here for 32 aluminum-lithium 2195 orthogrid panels comprising the liquid hydrogen tank barrel of the Space Shuttle External Tank using measured panel weights and manufacturing drawings. Minimum values for skin thickness, axial and circumferential blade stiffener thickness and spacing, and overall panel thickness are used to estimate individual panel weights. Panel non-optimum factors computed using a coarse weights model range from 1.21 to 1.77, and a refined weights model (including weld lands and skin and stiffener transition details) yields non-optimum factors of between 1.02 and 1.54. Acreage panels have an average 1.24 non-optimum factor using the coarse model, and 1.03 with the refined version. The observed consistency of these acreage non-optimum factors suggests that relatively simple models can be used to accurately predict large structural component weights for future launch vehicles.

A Bayesian procedure for evaluating the frequency of calibration factor updates in highway safety manual (HSM) applications.

PubMed

Saha, Dibakar; Alluri, Priyanka; Gan, Albert

2017-01-01

The Highway Safety Manual (HSM) presents statistical models to quantitatively estimate an agency's safety performance. The models were developed using data from only a few U.S. states. To account for the effects of the local attributes and temporal factors on crash occurrence, agencies are required to calibrate the HSM-default models for crash predictions. The manual suggests updating calibration factors every two to three years, or preferably on an annual basis. Given that the calibration process involves substantial time, effort, and resources, a comprehensive analysis of the required calibration factor update frequency is valuable to the agencies. Accordingly, the objective of this study is to evaluate the HSM's recommendation and determine the required frequency of calibration factor updates. A robust Bayesian estimation procedure is used to assess the variation between calibration factors computed annually, biennially, and triennially using data collected from over 2400 miles of segments and over 700 intersections on urban and suburban facilities in Florida. Bayesian model yields a posterior distribution of the model parameters that give credible information to infer whether the difference between calibration factors computed at specified intervals is credibly different from the null value which represents unaltered calibration factors between the comparison years or in other words, zero difference. The concept of the null value is extended to include the range of values that are practically equivalent to zero. Bayesian inference shows that calibration factors based on total crash frequency are required to be updated every two years in cases where the variations between calibration factors are not greater than 0.01. When the variations are between 0.01 and 0.05, calibration factors based on total crash frequency could be updated every three years. Copyright © 2016 Elsevier Ltd. All rights reserved.

A New Lease of Life for Thomson's Bonds Model of Intelligence

ERIC Educational Resources Information Center

Bartholomew, David J.; Deary, Ian J.; Lawn, Martin

2009-01-01

Modern factor analysis is the outgrowth of Spearman's original "2-factor" model of intelligence, according to which a mental test score is regarded as the sum of a general factor and a specific factor. As early as 1914, Godfrey Thomson realized that the data did not require this interpretation and he demonstrated this by proposing what became…

Examining the Factor Structure and Hierarchical Nature of the Quality of Life Construct

ERIC Educational Resources Information Center

Wang, Mian; Schalock, Robert L.; Verdugo, Miguel A.; Jenaro, Christina

2010-01-01

There is considerable debate in the area of individual quality of life research regarding the factor structure and hierarchical nature of the quality of life construct. Our purpose in this study was to test via structural equation modeling an a priori quality of life model consisting of eight first-order factors and one second-order factor. Data…

Family Risk and Resiliency Factors, Substance Use, and the Drug Resistance Process in Adolescence.

ERIC Educational Resources Information Center

Moon, Dreama G.; Jackson, Kristina M.; Hecht, Michael L.

2000-01-01

Study tests two models to compare the effects of risk and resiliency across gender and ethnicity. Results support the model in which risk and resiliency are discrete sets of factors and demonstrate that overall resiliency factors play a larger role than risk factors in substance use and drug resistance processes. Gender proved to be an important…

Structure of four executive functioning tests in healthy older adults.

PubMed

de Frias, Cindy M; Dixon, Roger A; Strauss, Esther

2006-03-01

The authors examined the factor structure of 4 indicators of executive functioning derived from 2 new (i.e., Hayling and Brixton) and 2 traditional (i.e., Stroop and Color Trails) tests. Data were from a cross-sectional sample of 55- to 85-year-old healthy adults (N=427) from the Victoria Longitudinal Study. Confirmatory factor analysis (LISREL 8.52) tested both a 2-factor model of Inhibition (Hayling, Stroop) and Shifting (Brixton, Color Trails) and a single-factor model. The 2-factor model did not fit the data because the covariance matrix of the factors was not positive definite. The single-factor model fit the data well, chi(2)(2, N=427)=0.32, p=.85, root-mean-square error of approximation (RMSEA)=.00, comparative fit index (CFI)=1.00, goodness-of-fit index (GFI)=1.00. Moreover, the single-factor structure of executive functioning was invariant (configural and metric) across gender, and invariant (configural with limited metric) across age. Structural relations showed that poorer executive functioning performance was related to older age and lower fluid intelligence, chi(2)(11, N=418)=23.04, p=.02, RMSEA=.05, CFI=.97, GFI=.98.

Model invariance across genders of the Broad Autism Phenotype Questionnaire.

PubMed

Broderick, Neill; Wade, Jordan L; Meyer, J Patrick; Hull, Michael; Reeve, Ronald E

2015-10-01

ASD is one of the most heritable neuropsychiatric disorders, though comprehensive genetic liability remains elusive. To facilitate genetic research, researchers employ the concept of the broad autism phenotype (BAP), a milder presentation of traits in undiagnosed relatives. Research suggests that the BAP Questionnaire (BAPQ) demonstrates psychometric properties superior to other self-report measures. To examine evidence regarding validity of the BAPQ, the current study used confirmatory factor analysis to test the assumption of model invariance across genders. Results of the current study upheld model invariance at each level of parameter constraint; however, model fit indices suggested limited goodness-of-fit between the proposed model and the sample. Exploratory analyses investigated alternate factor structure models but ultimately supported the proposed three-factor structure model.

Prediction of beef carcass and meat traits from rearing factors in young bulls and cull cows.

PubMed

Soulat, J; Picard, B; Léger, S; Monteils, V

2016-04-01

The aim of this study was to predict the beef carcass and LM (thoracis part) characteristics and the sensory properties of the LM from rearing factors applied during the fattening period. Individual data from 995 animals (688 young bulls and 307 cull cows) in 15 experiments were used to establish prediction models. The data concerned rearing factors (13 variables), carcass characteristics (5 variables), LM characteristics (2 variables), and LM sensory properties (3 variables). In this study, 8 prediction models were established: dressing percentage and the proportions of fat tissue and muscle in the carcass to characterize the beef carcass; cross-sectional area of fibers (mean fiber area) and isocitrate dehydrogenase activity to characterize the LM; and, finally, overall tenderness, juiciness, and flavor intensity scores to characterize the LM sensory properties. A random effect was considered in each model: the breed for the prediction models for the carcass and LM characteristics and the trained taste panel for the prediction of the meat sensory properties. To evaluate the quality of prediction models, 3 criteria were measured: robustness, accuracy, and precision. The model was robust when the root mean square errors of prediction of calibration and validation sub-data sets were near to one another. Except for the mean fiber area model, the obtained predicted models were robust. The prediction models were considered to have a high accuracy when the mean prediction error (MPE) was ≤0.10 and to have a high precision when the was the closest to 1. The prediction of the characteristics of the carcass from the rearing factors had a high precision ( > 0.70) and a high prediction accuracy (MPE < 0.10), except for the fat percentage model ( = 0.67, MPE = 0.16). However, the predictions of the LM characteristics and LM sensory properties from the rearing factors were not sufficiently precise ( < 0.50) and accurate (MPE > 0.10). Only the flavor intensity of the beef score could be satisfactorily predicted from the rearing factors with high precision ( = 0.72) and accuracy (MPE = 0.10). All the prediction models displayed different effects of the rearing factors according to animal categories (young bulls or cull cows). In consequence, these prediction models display the necessary adaption of rearing factors during the fattening period according to animal categories to optimize the carcass traits according to animal categories.

On the measurement of stability in over-time data.

PubMed

Kenny, D A; Campbell, D T

1989-06-01

In this article, autoregressive models and growth curve models are compared. Autoregressive models are useful because they allow for random change, permit scores to increase or decrease, and do not require strong assumptions about the level of measurement. Three previously presented designs for estimating stability are described: (a) time-series, (b) simplex, and (c) two-wave, one-factor methods. A two-wave, multiple-factor model also is presented, in which the variables are assumed to be caused by a set of latent variables. The factor structure does not change over time and so the synchronous relationships are temporally invariant. The factors do not cause each other and have the same stability. The parameters of the model are the factor loading structure, each variable's reliability, and the stability of the factors. We apply the model to two data sets. For eight cognitive skill variables measured at four times, the 2-year stability is estimated to be .92 and the 6-year stability is .83. For nine personality variables, the 3-year stability is .68. We speculate that for many variables there are two components: one component that changes very slowly (the trait component) and another that changes very rapidly (the state component); thus each variable is a mixture of trait and state. Circumstantial evidence supporting this view is presented.

Children, computer exposure and musculoskeletal outcomes: the development of pathway models for school and home computer-related musculoskeletal outcomes.

PubMed

Harris, Courtenay; Straker, Leon; Pollock, Clare; Smith, Anne

2015-01-01

Children's computer use is rapidly growing, together with reports of related musculoskeletal outcomes. Models and theories of adult-related risk factors demonstrate multivariate risk factors associated with computer use. Children's use of computers is different from adult's computer use at work. This study developed and tested a child-specific model demonstrating multivariate relationships between musculoskeletal outcomes, computer exposure and child factors. Using pathway modelling, factors such as gender, age, television exposure, computer anxiety, sustained attention (flow), socio-economic status and somatic complaints (headache and stomach pain) were found to have effects on children's reports of musculoskeletal symptoms. The potential for children's computer exposure to follow a dose-response relationship was also evident. Developing a child-related model can assist in understanding risk factors for children's computer use and support the development of recommendations to encourage children to use this valuable resource in educational, recreational and communication environments in a safe and productive manner. Computer use is an important part of children's school and home life. Application of this developed model, that encapsulates related risk factors, enables practitioners, researchers, teachers and parents to develop strategies that assist young people to use information technology for school, home and leisure in a safe and productive manner.

SEPARABLE FACTOR ANALYSIS WITH APPLICATIONS TO MORTALITY DATA

PubMed Central

Fosdick, Bailey K.; Hoff, Peter D.

2014-01-01

Human mortality data sets can be expressed as multiway data arrays, the dimensions of which correspond to categories by which mortality rates are reported, such as age, sex, country and year. Regression models for such data typically assume an independent error distribution or an error model that allows for dependence along at most one or two dimensions of the data array. However, failing to account for other dependencies can lead to inefficient estimates of regression parameters, inaccurate standard errors and poor predictions. An alternative to assuming independent errors is to allow for dependence along each dimension of the array using a separable covariance model. However, the number of parameters in this model increases rapidly with the dimensions of the array and, for many arrays, maximum likelihood estimates of the covariance parameters do not exist. In this paper, we propose a submodel of the separable covariance model that estimates the covariance matrix for each dimension as having factor analytic structure. This model can be viewed as an extension of factor analysis to array-valued data, as it uses a factor model to estimate the covariance along each dimension of the array. We discuss properties of this model as they relate to ordinary factor analysis, describe maximum likelihood and Bayesian estimation methods, and provide a likelihood ratio testing procedure for selecting the factor model ranks. We apply this methodology to the analysis of data from the Human Mortality Database, and show in a cross-validation experiment how it outperforms simpler methods. Additionally, we use this model to impute mortality rates for countries that have no mortality data for several years. Unlike other approaches, our methodology is able to estimate similarities between the mortality rates of countries, time periods and sexes, and use this information to assist with the imputations. PMID:25489353

Selection of higher order regression models in the analysis of multi-factorial transcription data.

PubMed

Prazeres da Costa, Olivia; Hoffman, Arthur; Rey, Johannes W; Mansmann, Ulrich; Buch, Thorsten; Tresch, Achim

2014-01-01

Many studies examine gene expression data that has been obtained under the influence of multiple factors, such as genetic background, environmental conditions, or exposure to diseases. The interplay of multiple factors may lead to effect modification and confounding. Higher order linear regression models can account for these effects. We present a new methodology for linear model selection and apply it to microarray data of bone marrow-derived macrophages. This experiment investigates the influence of three variable factors: the genetic background of the mice from which the macrophages were obtained, Yersinia enterocolitica infection (two strains, and a mock control), and treatment/non-treatment with interferon-γ. We set up four different linear regression models in a hierarchical order. We introduce the eruption plot as a new practical tool for model selection complementary to global testing. It visually compares the size and significance of effect estimates between two nested models. Using this methodology we were able to select the most appropriate model by keeping only relevant factors showing additional explanatory power. Application to experimental data allowed us to qualify the interaction of factors as either neutral (no interaction), alleviating (co-occurring effects are weaker than expected from the single effects), or aggravating (stronger than expected). We find a biologically meaningful gene cluster of putative C2TA target genes that appear to be co-regulated with MHC class II genes. We introduced the eruption plot as a tool for visual model comparison to identify relevant higher order interactions in the analysis of expression data obtained under the influence of multiple factors. We conclude that model selection in higher order linear regression models should generally be performed for the analysis of multi-factorial microarray data.

Data Sources for the Model-based Small Area Estimates of Cancer Risk Factors and Screening Behaviors - Small Area Estimates

Cancer.gov

The model-based estimates of important cancer risk factors and screening behaviors are obtained by combining the responses to the Behavioral Risk Factor Surveillance System (BRFSS) and the National Health Interview Survey (NHIS).

Construct validity of the Beck Hopelessness Scale (BHS) among university students: A multitrait-multimethod approach.

PubMed

Boduszek, Daniel; Dhingra, Katie

2016-10-01

There is considerable debate about the underlying factor structure of the Beck Hopelessness Scale (BHS) in the literature. An established view is that it reflects a unitary or bidimensional construct in nonclinical samples. There are, however, reasons to reconsider this conceptualization. Based on previous factor analytic findings from both clinical and nonclinical studies, the aim of the present study was to compare 16 competing models of the BHS in a large university student sample (N = 1, 733). Sixteen distinct factor models were specified and tested using conventional confirmatory factor analytic techniques, along with confirmatory bifactor modeling. A 3-factor solution with 2 method effects (i.e., a multitrait-multimethod model) provided the best fit to the data. The reliability of this conceptualization was supported by McDonald's coefficient omega and the differential relationships exhibited between the 3 hopelessness factors ("feelings about the future," "loss of motivation," and "future expectations") and measures of goal disengagement, brooding rumination, suicide ideation, and suicide attempt history. The results provide statistical support for a 3-trait and 2-method factor model, and hence the 3 dimensions of hopelessness theorized by Beck. The theoretical and methodological implications of these findings are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Development of the Internet addiction scale based on the Internet Gaming Disorder criteria suggested in DSM-5.

PubMed

Cho, Hyun; Kwon, Min; Choi, Ji-Hye; Lee, Sang-Kyu; Choi, Jung Seok; Choi, Sam-Wook; Kim, Dai-Jin

2014-09-01

This study was conducted to develop and validate a standardized self-diagnostic Internet addiction (IA) scale based on the diagnosis criteria for Internet Gaming Disorder (IGD) in the Diagnostic and Statistical Manual of Mental Disorder, 5th edition (DSM-5). Items based on the IGD diagnosis criteria were developed using items of the previous Internet addiction scales. Data were collected from a community sample. The data were divided into two sets, and confirmatory factor analysis (CFA) was performed repeatedly. The model was modified after discussion with professionals based on the first CFA results, after which the second CFA was performed. The internal consistency reliability was generally good. The items that showed significantly low correlation values based on the item-total correlation of each factor were excluded. After the first CFA was performed, some factors and items were excluded. Seven factors and 26 items were prepared for the final model. The second CFA results showed good general factor loading, Squared Multiple Correlation (SMC) and model fit. The model fit of the final model was good, but some factors were very highly correlated. It is recommended that some of the factors be refined through further studies. Copyright © 2014. Published by Elsevier Ltd.

Factor Structure of the PTSD Checklist for DSM-5: Relationships Among Symptom Clusters, Anger, and Impulsivity.

PubMed

Armour, Cherie; Contractor, Ateka; Shea, Tracie; Elhai, Jon D; Pietrzak, Robert H

2016-02-01

Scarce data are available regarding the dimensional structure of Diagnostic and Statistical Manual of Mental Disorders, 5th Edition (DSM-5) posttraumatic stress disorder (PTSD) symptoms and how factors relate to external constructs. We evaluated six competing models of DSM-5 PTSD symptoms, including Anhedonia, Externalizing Behaviors, and Hybrid models, using confirmatory factor analyses in a sample of 412 trauma-exposed college students. We then examined whether PTSD symptom clusters were differentially related to measures of anger and impulsivity using Wald chi-square tests. The seven-factor Hybrid model was deemed optimal compared with the alternatives. All symptom clusters were associated with anger; the strongest association was between externalizing behaviors and anger (r = 0.54). All symptom clusters, except re-experiencing and avoidance, were associated with impulsivity, with the strongest association between externalizing behaviors and impulsivity (r = 0.49). A seven-factor Hybrid model provides superior fit to DSM-5 PTSD symptom data, with the externalizing behaviors factor being most strongly related to anger and impulsivity.

Depression Anxiety Stress Scales (DASS-21): Factor Structure in Traumatic Brain Injury Rehabilitation.

PubMed

Randall, Diane; Thomas, Matt; Whiting, Diane; McGrath, Andrew

To confirm the construct validity of the Depression Anxiety Stress Scales-21 (DASS-21) by investigating the fit of published factor structures in a sample of adults with moderate to severe traumatic brain injury (posttraumatic amnesia > 24 hours). Archival data from 504 patient records at the Brain Injury Rehabilitation Unit at Liverpool Hospital, Australia. Participants were aged between 16 and 71 years and were engaged in a specialist rehabilitation program. The DASS-21. Two of the 6 models had adequate fit using structural equation modeling. The data best fit Henry and Crawford's quadripartite model, which comprised a Depression, Anxiety and Stress factor, as well as a General Distress factor. The data also adequately fit Lovibond and Lovibond's original 3-factor model, and the internal consistencies of each factor were very good (α = 0.82-0.90). This study confirms the structure and construct validity of the DASS-21 and provides support for its use as a screening tool in traumatic brain injury rehabilitation.

Monitoring hand, foot and mouth disease by combining search engine query data and meteorological factors.

PubMed

Huang, Da-Cang; Wang, Jin-Feng

2018-01-15

Hand, foot and mouth disease (HFMD) has been recognized as a significant public health threat and poses a tremendous challenge to disease control departments. To date, the relationship between meteorological factors and HFMD has been documented, and public interest of disease has been proven to be trackable from the Internet. However, no study has explored the combination of these two factors in the monitoring of HFMD. Therefore, the main aim of this study was to develop an effective monitoring model of HFMD in Guangzhou, China by utilizing historical HFMD cases, Internet-based search engine query data and meteorological factors. To this end, a case study was conducted in Guangzhou, using a network-based generalized additive model (GAM) including all factors related to HFMD. Three other models were also constructed using some of the variables for comparison. The results suggested that the model showed the best estimating ability when considering all of the related factors. Copyright © 2017 Elsevier B.V. All rights reserved.

The structure of post-traumatic stress symptoms in survivors of war: confirmatory factor analyses of the Impact of Event Scale--revised.

PubMed

Morina, Nexhmedin; Böhme, Hendryk F; Ajdukovic, Dean; Bogic, Marija; Franciskovic, Tanja; Galeazzi, Gian M; Kucukalic, Abdulah; Lecic-Tosevski, Dusica; Popovski, Mihajlo; Schützwohl, Matthias; Stangier, Ulrich; Priebe, Stefan

2010-08-01

The study aimed at establishing the factor structure of the Impact of Event Scale-Revised (IES-R) in survivors of war. A total sample of 4167 participants with potentially traumatic experiences during the war in Ex-Yugoslavia was split into three samples: two independent samples of people who stayed in the area of conflict and one sample of refugees to Western European countries. Alternative models with three, four, and five factors of post-traumatic symptoms were tested in one sample. The other samples were used for cross-validation. Results indicated that the model of best fit had five factors, i.e., intrusion, avoidance, hyperarousal, numbing, and sleep disturbance. Model superiority was cross-validated in the two other samples. These findings suggest a five-factor model of post-traumatic stress symptoms in war survivors with numbing and sleep disturbance as separate factors in addition to intrusion, avoidance and hyperarousal. (c) 2010 Elsevier Ltd. All rights reserved.

Exploratory and Confirmatory Factor Analysis of the Career Decision-Making Difficulties Questionnaire

PubMed Central

Farrokhi, Farahman; Mahdavi, Ali; Moradi, Samad

2012-01-01

Objective The present study aimed at validating the structure of Career Decision-making Difficulties Questionnaire (CDDQ). Methods Five hundred and eleven undergraduate students took part in this research; from these participants, 63 males and 200 females took part in the first study, and 63 males and 185 females completed the survey for the second study. Results The results of exploratory factor analysis (EFA) indicated strong support for the three-factor structure, consisting of lack of information about the self, inconsistent information, lack of information and lack of readiness factors. A confirmatory factor analysis was run with the second sample using structural equation modeling. As expected, the three-factor solution provided a better fit to the data than the alternative models. Conclusion CDDQ was recommended to be used for college students in this study due to the fact that this instrument measures all three aspects of the model. Future research is needed to learn whether this model would fit other different samples. PMID:22952549

The factor structure of the 12-item general health questionnaire (GHQ-12) in young Chinese civil servants.

PubMed

Liang, Ying; Wang, Lei; Yin, Xican

2016-09-26

The 12-item General Health Questionnaire (GHQ-12) is a commonly used screening instrument for measuring mental disorders. However, few studies have measured the mental health of Chinese professionals or explored the factor structure of the GHQ-12 through investigations of young Chinese civil servants. This study analyses the factor structure of the GHQ-12 on young Chinese civil servants. Respondents include 1051 participants from six cities in eastern China. Exploratory Factor Analysis (EFA) is used to identify the potential factor structure of the GHQ-12. Confirmatory Factor Analysis (CFA) models of previous studies are referred to for model fitting. The results indicate the GHQ-12 has very good reliability and validity. All ten CFA models are well fitted with the actual data. All the ten models are feasible and fit the data equally well. The Chinese version of the GHQ-12 is suitable for professional groups and can serve as a screening tool to detect anxiety and psychiatric disorders.

Validating the cross-cultural factor structure and invariance property of the Insomnia Severity Index: evidence based on ordinal EFA and CFA.

PubMed

Chen, Po-Yi; Yang, Chien-Ming; Morin, Charles M

2015-05-01

The purpose of this study is to examine the factor structure of the Insomnia Severity Index (ISI) across samples recruited from different countries. We tried to identify the most appropriate factor model for the ISI and further examined the measurement invariance property of the ISI across samples from different countries. Our analyses included one data set collected from a Taiwanese sample and two data sets obtained from samples in Hong Kong and Canada. The data set collected in Taiwan was analyzed with ordinal exploratory factor analysis (EFA) to obtain the appropriate factor model for the ISI. After that, we conducted a series of confirmatory factor analyses (CFAs), which is a special case of the structural equation model (SEM) that concerns the parameters in the measurement model, to the statistics collected in Canada and Hong Kong. The purposes of these CFA were to cross-validate the result obtained from EFA and further examine the cross-cultural measurement invariance of the ISI. The three-factor model outperforms other models in terms of global fit indices in Taiwan's population. Its external validity is also supported by confirmatory factor analyses. Furthermore, the measurement invariance analyses show that the strong invariance property between the samples from different cultures holds, providing evidence that the ISI results obtained in different cultures are comparable. The factorial validity of the ISI is stable in different populations. More importantly, its invariance property across cultures suggests that the ISI is a valid measure of the insomnia severity construct across countries. Copyright © 2014 Elsevier B.V. All rights reserved.

Relationship between oral health-related knowledge, attitudes and behavior among 15-16-year-old adolescents: a structural equation modeling approach.

PubMed

Tolvanen, Mimmi; Lahti, Satu; Miettunen, Jouko; Hausen, Hannu

2012-03-01

The aim of this study was to confirm the previously observed attitudinal factor structure related to behavioral change and the knowledge-attitude-behavior model on dental health and hygiene among adolescents. The study population consisted of all 8(th) and 9(th) graders (15-16 years) who started the 2004-2005 school year in Rauma, Finland (n = 827). Data on knowledge, attitudes, toothbrushing and using fluoride toothpaste were gathered by questionnaires. Hypothesized structure included four attitudinal factors related to dental health and hygiene: 'importance of toothbrushing when participating in social situations' (F1), 'importance of toothbrushing for health-related reasons and better appearance' (F2), 'being concerned about developing caries lesions' (F3) and 'importance of toothbrushing for feeling accepted' (F4). Structural equation modeling (SEM) was used to test the hypothesized model: pathways lead from knowledge to behavior both directly and via attitudes. The hypothesized model was also modified by removing non-significant pathways and studying the inter-relationships between attitudes. A confirmatory factor analysis revealed that factor F4 had to be removed. In the final model, knowledge influenced behavior directly and via two attitude factors, F1 and F2, which were inter-related. 'Concern about developing caries lesions' was a background factor influencing only knowledge. The final factor structure and SEM model were acceptable-to-good fit. Knowledge had a smaller effect on behavior than on attitudes. Our results support theories about the causal knowledge-attitudes-behavior chain, also for adolescents' oral health-related behaviors.

The two populations’ cellular automata model with predation based on the Penna model

NASA Astrophysics Data System (ADS)

He, Mingfeng; Lin, Jing; Jiang, Heng; Liu, Xin

2002-09-01

In Penna's single-species asexual bit-string model of biological ageing, the Verhulst factor has too strong a restraining effect on the development of the population. Danuta Makowiec gave an improved model based on the lattice, where the restraining factor of the four neighbours take the place of the Verhulst factor. Here, we discuss the two populations’ Penna model with predation on the planar lattice of two dimensions. A cellular automata model containing movable wolves and sheep has been built. The results show that both the quantity of the wolves and the sheep fluctuate in accordance with the law that one quantity increases while the other one decreases.

Unidimensional factor models imply weaker partial correlations than zero-order correlations.

PubMed

van Bork, Riet; Grasman, Raoul P P P; Waldorp, Lourens J

2018-06-01

In this paper we present a new implication of the unidimensional factor model. We prove that the partial correlation between two observed variables that load on one factor given any subset of other observed variables that load on this factor lies between zero and the zero-order correlation between these two observed variables. We implement this result in an empirical bootstrap test that rejects the unidimensional factor model when partial correlations are identified that are either stronger than the zero-order correlation or have a different sign than the zero-order correlation. We demonstrate the use of the test in an empirical data example with data consisting of fourteen items that measure extraversion.

Application of zero-inflated poisson mixed models in prognostic factors of hepatitis C.

PubMed

Akbarzadeh Baghban, Alireza; Pourhoseingholi, Asma; Zayeri, Farid; Jafari, Ali Akbar; Alavian, Seyed Moayed

2013-01-01

In recent years, hepatitis C virus (HCV) infection represents a major public health problem. Evaluation of risk factors is one of the solutions which help protect people from the infection. This study aims to employ zero-inflated Poisson mixed models to evaluate prognostic factors of hepatitis C. The data was collected from a longitudinal study during 2005-2010. First, mixed Poisson regression (PR) model was fitted to the data. Then, a mixed zero-inflated Poisson model was fitted with compound Poisson random effects. For evaluating the performance of the proposed mixed model, standard errors of estimators were compared. The results obtained from mixed PR showed that genotype 3 and treatment protocol were statistically significant. Results of zero-inflated Poisson mixed model showed that age, sex, genotypes 2 and 3, the treatment protocol, and having risk factors had significant effects on viral load of HCV patients. Of these two models, the estimators of zero-inflated Poisson mixed model had the minimum standard errors. The results showed that a mixed zero-inflated Poisson model was the almost best fit. The proposed model can capture serial dependence, additional overdispersion, and excess zeros in the longitudinal count data.

The contributions of human factors on human error in Malaysia aviation maintenance industries

NASA Astrophysics Data System (ADS)

Padil, H.; Said, M. N.; Azizan, A.

2018-05-01

Aviation maintenance is a multitasking activity in which individuals perform varied tasks under constant pressure to meet deadlines as well as challenging work conditions. These situational characteristics combined with human factors can lead to various types of human related errors. The primary objective of this research is to develop a structural relationship model that incorporates human factors, organizational factors, and their impact on human errors in aviation maintenance. Towards that end, a questionnaire was developed which was administered to Malaysian aviation maintenance professionals. Structural Equation Modelling (SEM) approach was used in this study utilizing AMOS software. Results showed that there were a significant relationship of human factors on human errors and were tested in the model. Human factors had a partial effect on organizational factors while organizational factors had a direct and positive impact on human errors. It was also revealed that organizational factors contributed to human errors when coupled with human factors construct. This study has contributed to the advancement of knowledge on human factors effecting safety and has provided guidelines for improving human factors performance relating to aviation maintenance activities and could be used as a reference for improving safety performance in the Malaysian aviation maintenance companies.

Object detection in natural backgrounds predicted by discrimination performance and models

NASA Technical Reports Server (NTRS)

Rohaly, A. M.; Ahumada, A. J. Jr; Watson, A. B.

1997-01-01

Many models of visual performance predict image discriminability, the visibility of the difference between a pair of images. We compared the ability of three image discrimination models to predict the detectability of objects embedded in natural backgrounds. The three models were: a multiple channel Cortex transform model with within-channel masking; a single channel contrast sensitivity filter model; and a digital image difference metric. Each model used a Minkowski distance metric (generalized vector magnitude) to summate absolute differences between the background and object plus background images. For each model, this summation was implemented with three different exponents: 2, 4 and infinity. In addition, each combination of model and summation exponent was implemented with and without a simple contrast gain factor. The model outputs were compared to measures of object detectability obtained from 19 observers. Among the models without the contrast gain factor, the multiple channel model with a summation exponent of 4 performed best, predicting the pattern of observer d's with an RMS error of 2.3 dB. The contrast gain factor improved the predictions of all three models for all three exponents. With the factor, the best exponent was 4 for all three models, and their prediction errors were near 1 dB. These results demonstrate that image discrimination models can predict the relative detectability of objects in natural scenes.

Impact of Lead Time and Safety Factor in Mixed Inventory Models with Backorder Discounts

NASA Astrophysics Data System (ADS)

Lo, Ming-Cheng; Chao-Hsien Pan, Jason; Lin, Kai-Cing; Hsu, Jia-Wei

This study investigates the impact of safety factor on the continuous review inventory model involving controllable lead time with mixture of backorder discount and partial lost sales. The objective is to minimize the expected total annual cost with respect to order quantity, backorder price discount, safety factor and lead time. A model with normal demand is also discussed. Numerical examples are presented to illustrate the procedures of the algorithms and the effects of parameters on the result of the proposed models are analyzed.

A comprehensive probabilistic analysis model of oil pipelines network based on Bayesian network

NASA Astrophysics Data System (ADS)

Zhang, C.; Qin, T. X.; Jiang, B.; Huang, C.

2018-02-01

Oil pipelines network is one of the most important facilities of energy transportation. But oil pipelines network accident may result in serious disasters. Some analysis models for these accidents have been established mainly based on three methods, including event-tree, accident simulation and Bayesian network. Among these methods, Bayesian network is suitable for probabilistic analysis. But not all the important influencing factors are considered and the deployment rule of the factors has not been established. This paper proposed a probabilistic analysis model of oil pipelines network based on Bayesian network. Most of the important influencing factors, including the key environment condition and emergency response are considered in this model. Moreover, the paper also introduces a deployment rule for these factors. The model can be used in probabilistic analysis and sensitive analysis of oil pipelines network accident.

Drug development costs when financial risk is measured using the Fama-French three-factor model.

PubMed

Vernon, John A; Golec, Joseph H; Dimasi, Joseph A

2010-08-01

In a widely cited article, DiMasi, Hansen, and Grabowski (2003) estimate the average pre-tax cost of bringing a new molecular entity to market. Their base case estimate, excluding post-marketing studies, was $802 million (in $US 2000). Strikingly, almost half of this cost (or $399 million) is the cost of capital (COC) used to fund clinical development expenses to the point of FDA marketing approval. The authors used an 11% real COC computed using the capital asset pricing model (CAPM). But the CAPM is a single factor risk model, and multi-factor risk models are the current state of the art in finance. Using the Fama-French three factor model we find that the cost of drug development to be higher than the earlier estimate. Copyright (c) 2009 John Wiley & Sons, Ltd.

Relationship between stress-related psychosocial work factors and suboptimal health among Chinese medical staff: a cross-sectional study.

PubMed

Liang, Ying-Zhi; Chu, Xi; Meng, Shi-Jiao; Zhang, Jie; Wu, Li-Juan; Yan, Yu-Xiang

2018-03-06

The study aimed to develop and validate a model to measure psychosocial factors at work among medical staff in China based on confirmatory factor analysis (CFA). The second aim of the current study was to clarify the association between stress-related psychosocial work factors and suboptimal health status. The cross-sectional study was conducted using clustered sampling method. Xuanwu Hospital, a 3A grade hospital in Beijing. Nine hundred and fourteen medical staff aged over 40 years were sampled. Seven hundred and ninety-seven valid questionnaires were collected and used for further analyses. The sample included 94% of the Han population. The Copenhagen Psychosocial Questionnaire (COPSOQ) and the Suboptimal Health Status Questionnaires-25 were used to assess the psychosocial factors at work and suboptimal health status, respectively. CFA was conducted to establish the evaluating method of COPSOQ. A multivariate logistic regression model was used to estimate the relationship between suboptimal health status and stress-related psychosocial work factors among Chinese medical staff. There was a strong correlation among the five dimensions of COPSOQ based on the first-order factor model. Then, we established two second-order factors including negative and positive psychosocial work stress factors to evaluate psychosocial factors at work, and the second-order factor model fit well. The high score in negative (OR (95% CI)=1.47 (1.34 to 1.62), P<0.001) and positive (OR (95% CI)=0.96 (0.94 to 0.98), P<0.001) psychosocial work factors increased and decreased the risk of suboptimal health, respectively. This relationship remained statistically significant after adjusting for confounders and when using different cut-offs of suboptimal health status. Among medical staff, the second-order factor model was a suitable method to evaluate the COPSOQ. The negative and positive psychosocial work stress factors might be the risk and protective factors of suboptimal health, respectively. Moreover, negative psychosocial work stress was the most associated factor to predict suboptimal health. © Article author(s) (or their employer(s) unless otherwise stated in the text of the article) 2018. All rights reserved. No commercial use is permitted unless otherwise expressly granted.

Relationship between stress-related psychosocial work factors and suboptimal health among Chinese medical staff: a cross-sectional study

PubMed Central

Meng, Shi-Jiao; Zhang, Jie; Wu, Li-Juan; Yan, Yu-Xiang

2018-01-01

Objectives The study aimed to develop and validate a model to measure psychosocial factors at work among medical staff in China based on confirmatory factor analysis (CFA). The second aim of the current study was to clarify the association between stress-related psychosocial work factors and suboptimal health status. Design The cross-sectional study was conducted using clustered sampling method. Setting Xuanwu Hospital, a 3A grade hospital in Beijing. Participants Nine hundred and fourteen medical staff aged over 40 years were sampled. Seven hundred and ninety-seven valid questionnaires were collected and used for further analyses. The sample included 94% of the Han population. Main outcome measures The Copenhagen Psychosocial Questionnaire (COPSOQ) and the Suboptimal Health Status Questionnaires-25 were used to assess the psychosocial factors at work and suboptimal health status, respectively. CFA was conducted to establish the evaluating method of COPSOQ. A multivariate logistic regression model was used to estimate the relationship between suboptimal health status and stress-related psychosocial work factors among Chinese medical staff. Results There was a strong correlation among the five dimensions of COPSOQ based on the first-order factor model. Then, we established two second-order factors including negative and positive psychosocial work stress factors to evaluate psychosocial factors at work, and the second-order factor model fit well. The high score in negative (OR (95% CI)=1.47 (1.34 to 1.62), P<0.001) and positive (OR (95% CI)=0.96 (0.94 to 0.98), P<0.001) psychosocial work factors increased and decreased the risk of suboptimal health, respectively. This relationship remained statistically significant after adjusting for confounders and when using different cut-offs of suboptimal health status. Conclusions Among medical staff, the second-order factor model was a suitable method to evaluate the COPSOQ. The negative and positive psychosocial work stress factors might be the risk and protective factors of suboptimal health, respectively. Moreover, negative psychosocial work stress was the most associated factor to predict suboptimal health. PMID:29511008

The Analysis of Three-Way Contingency Tables by Three-Mode Association Models.

ERIC Educational Resources Information Center

Anderson, Carolyn J.

1996-01-01

Generalizations of L. A. Goodman's RC(M) association model (1991 and earlier) are presented for three-way tables. These three-mode association models use L. R. Tucker's three-mode components model (1964, 1966) to represent the three-factor interaction or the combined effects of two- and three-factor interactions. (SLD)

How do various maize crop models vary in their responses to climate change factors?

USDA-ARS?s Scientific Manuscript database

Potential consequences of climate change on crop production can be studied using mechanistic crop simulation models. While a broad variety of maize simulation models exist, it is not known whether different models give similar grain yield responses to changes in climatic factors, or whether they agr...

The HEXACO and Five-Factor Models of Personality in Relation to RIASEC Vocational Interests

ERIC Educational Resources Information Center

McKay, Derek A.; Tokar, David M.

2012-01-01

The current study extended the empirical research on the overlap of vocational interests and personality by (a) testing hypothesized relations between RIASEC interests and the personality dimensions of the HEXACO model, and (b) exploring the HEXACO personality model's predictive advantage over the five-factor model (FFM) in capturing RIASEC…

42 CFR § 512.315 - Composite quality scores for determining reconciliation payment eligibility and effective and...

Code of Federal Regulations, 2010 CFR

2017-10-01

... (CONTINUED) HEALTH CARE INFRASTRUCTURE AND MODEL PROGRAMS EPISODE PAYMENT MODEL Pricing and Payment § 512.315... and suppresses the measure value. (5) Establishing SHFFT model reconciliation payment eligibility and... factor for reconciliation payments. (A) A 3.0 percentage point effective discount factor for SHFFT model...

[Factor structure validity of the social capital scale used at baseline in the ELSA-Brasil study].

PubMed

Souto, Ester Paiva; Vasconcelos, Ana Glória Godoi; Chor, Dora; Reichenheim, Michael E; Griep, Rosane Härter

2016-07-21

This study aims to analyze the factor structure of the Brazilian version of the Resource Generator (RG) scale, using baseline data from the Brazilian Longitudinal Health Study in Adults (ELSA-Brasil). Cross-validation was performed in three random subsamples. Exploratory factor analysis using exploratory structural equation models was conducted in the first two subsamples to diagnose the factor structure, and confirmatory factor analysis was used in the third to corroborate the model defined by the exploratory analyses. Based on the 31 initial items, the model with the best fit included 25 items distributed across three dimensions. They all presented satisfactory convergent validity (values greater than 0.50 for the extracted variance) and precision (values greater than 0.70 for compound reliability). All factor correlations were below 0.85, indicating full discriminative factor validity. The RG scale presents acceptable psychometric properties and can be used in populations with similar characteristics.

Confirmatory factor analysis of the Center for Epidemiologic Studies – Depression Scale in Black and White dementia caregivers

PubMed Central

Flynn Longmire, Crystal V.; Knight, Bob G.

2012-01-01

Objectives In order to better understand if measurement problems underlie the inconsistent findings that exist regarding differences in depression levels between Black and White caregivers, this study examined the factor structure and invariance of the Center for Epidemiologic Studies-Depression scale (CES-D). Method A confirmatory factor analysis of the 20-item CES-D was performed on a sample of 167 Black and 214 White family caregivers of older adults with dementia from Los Angeles County. Results The relationships between the 20 items and the four factors, as well as the relationships among each of the factors, were equivalent across both caregiver groups, indicating that the four-factor model fit the data for both racial groups. Conclusion These findings offer further evidence that the standard four-factor model is the best fitting model for the CES-D and is invariant across racial groups. PMID:21069602

Confirmatory factor analysis of the Center for Epidemiologic Studies-Depression Scale in black and white dementia caregivers.

PubMed

Flynn Longmire, Crystal V; Knight, Bob G

2010-11-01

In order to better understand if measurement problems underlie the inconsistent findings that exist regarding differences in depression levels between Black and White caregivers, this study examined the factor structure and invariance of the Center for Epidemiologic Studies-Depression (CES-D) Scale. A confirmatory factor analysis of the 20-item CES-D was performed on a sample of 167 Black and 214 White family caregivers of older adults with dementia from Los Angeles County. The relationships between the 20 items and the four factors, as well as the relationships among each of the factors, were equivalent across both caregiver groups, indicating that the four-factor model fit the data for both the racial groups. These findings offer further evidence that the standard four-factor model is the best fitting model for the CES-D and is invariant across racial groups.

Recurrent personality dimensions in inclusive lexical studies: indications for a big six structure.

PubMed

Saucier, Gerard

2009-10-01

Previous evidence for both the Big Five and the alternative six-factor model has been drawn from lexical studies with relatively narrow selections of attributes. This study examined factors from previous lexical studies using a wider selection of attributes in 7 languages (Chinese, English, Filipino, Greek, Hebrew, Spanish, and Turkish) and found 6 recurrent factors, each with common conceptual content across most of the studies. The previous narrow-selection-based six-factor model outperformed the Big Five in capturing the content of the 6 recurrent wideband factors. Adjective markers of the 6 recurrent wideband factors showed substantial incremental prediction of important criterion variables over and above the Big Five. Correspondence between wideband 6 and narrowband 6 factors indicate they are variants of a "Big Six" model that is more general across variable-selection procedures and may be more general across languages and populations.

Loss Factor Estimation Using the Impulse Response Decay Method on a Stiffened Structure

NASA Technical Reports Server (NTRS)

Cabell, Randolph; Schiller, Noah; Allen, Albert; Moeller, Mark

2009-01-01

High-frequency vibroacoustic modeling is typically performed using energy-based techniques such as Statistical Energy Analysis (SEA). Energy models require an estimate of the internal damping loss factor. Unfortunately, the loss factor is difficult to estimate analytically, and experimental methods such as the power injection method can require extensive measurements over the structure of interest. This paper discusses the implications of estimating damping loss factors using the impulse response decay method (IRDM) from a limited set of response measurements. An automated procedure for implementing IRDM is described and then evaluated using data from a finite element model of a stiffened, curved panel. Estimated loss factors are compared with loss factors computed using a power injection method and a manual curve fit. The paper discusses the sensitivity of the IRDM loss factor estimates to damping of connected subsystems and the number and location of points in the measurement ensemble.

Evaluating Individual Students' Perceptions of Instructional Quality: An Investigation of their Factor Structure, Measurement Invariance, and Relations to Educational Outcomes.

PubMed

Scherer, Ronny; Nilsen, Trude; Jansen, Malte

2016-01-01

Students' perceptions of instructional quality are among the most important criteria for evaluating teaching effectiveness. The present study evaluates different latent variable modeling approaches (confirmatory factor analysis, exploratory structural equation modeling, and bifactor modeling), which are used to describe these individual perceptions with respect to their factor structure, measurement invariance, and the relations to selected educational outcomes (achievement, self-concept, and motivation in mathematics). On the basis of the Programme for International Student Assessment (PISA) 2012 large-scale data sets of Australia, Canada, and the USA (N = 26,746 students), we find support for the distinction between three factors of individual students' perceptions and full measurement invariance across countries for all modeling approaches. In this regard, bifactor exploratory structural equation modeling outperformed alternative approaches with respect to model fit. Our findings reveal significant relations to the educational outcomes. This study synthesizes different modeling approaches of individual students' perceptions of instructional quality and provides insights into the nature of these perceptions from an individual differences perspective. Implications for the measurement and modeling of individually perceived instructional quality are discussed.

A comparative study of mixture cure models with covariate

NASA Astrophysics Data System (ADS)

Leng, Oh Yit; Khalid, Zarina Mohd

2017-05-01

In survival analysis, the survival time is assumed to follow a non-negative distribution, such as the exponential, Weibull, and log-normal distributions. In some cases, the survival time is influenced by some observed factors. The absence of these observed factors may cause an inaccurate estimation in the survival function. Therefore, a survival model which incorporates the influences of observed factors is more appropriate to be used in such cases. These observed factors are included in the survival model as covariates. Besides that, there are cases where a group of individuals who are cured, that is, not experiencing the event of interest. Ignoring the cure fraction may lead to overestimate in estimating the survival function. Thus, a mixture cure model is more suitable to be employed in modelling survival data with the presence of a cure fraction. In this study, three mixture cure survival models are used to analyse survival data with a covariate and a cure fraction. The first model includes covariate in the parameterization of the susceptible individuals survival function, the second model allows the cure fraction to depend on covariate, and the third model incorporates covariate in both cure fraction and survival function of susceptible individuals. This study aims to compare the performance of these models via a simulation approach. Therefore, in this study, survival data with varying sample sizes and cure fractions are simulated and the survival time is assumed to follow the Weibull distribution. The simulated data are then modelled using the three mixture cure survival models. The results show that the three mixture cure models are more appropriate to be used in modelling survival data with the presence of cure fraction and an observed factor.

Bayesian Exploratory Factor Analysis

PubMed Central

Conti, Gabriella; Frühwirth-Schnatter, Sylvia; Heckman, James J.; Piatek, Rémi

2014-01-01

This paper develops and applies a Bayesian approach to Exploratory Factor Analysis that improves on ad hoc classical approaches. Our framework relies on dedicated factor models and simultaneously determines the number of factors, the allocation of each measurement to a unique factor, and the corresponding factor loadings. Classical identification criteria are applied and integrated into our Bayesian procedure to generate models that are stable and clearly interpretable. A Monte Carlo study confirms the validity of the approach. The method is used to produce interpretable low dimensional aggregates from a high dimensional set of psychological measurements. PMID:25431517

Development of Evaluation Indicators for Hospice and Palliative Care Professionals Training Programs in Korea.

PubMed

Kang, Jina; Park, Kyoung-Ok

2017-01-01

The importance of training for Hospice and Palliative Care (HPC) professionals has been increasing with the systemization of HPC in Korea. Hence, the need and importance of training quality for HPC professionals are growing. This study evaluated the construct validity and reliability of the Evaluation Indicators for standard Hospice and Palliative Care Training (EIHPCT) program. As a framework to develop evaluation indicators, an invented theoretical model combining Stufflebeam's CIPP (Context-Input-Process-Product) evaluation model with PRECEDE-PROCEED model was used. To verify the construct validity of the EIHPCT program, a structured survey was performed with 169 professionals who were the HPC training program administrators, trainers, and trainees. To examine the validity of the areas of the EIHPCT program, exploratory factor analysis and confirmatory factor analysis were conducted. First, in the exploratory factor analysis, the indicators with factor loadings above 0.4 were chosen as desirable items, and some cross-loaded items that loaded at 0.4 or higher on two or more factors were adjusted as the higher factor. Second, the model fit of the modified EIHPCT program was quite good in the confirmatory factor analysis (Goodness-of-Fit Index > 0.70, Comparative Fit Index > 0.80, Normed Fit Index > 0.80, Root Mean square of Residuals < 0.05). The modified model of the EIHPCT comprised 4 areas, 13 subdomains, and 61 indicators. The evaluation indicators of the modified model will be valuable references for improving the HPC professional training program.

Reading component skills of learners in adult basic education.

PubMed

MacArthur, Charles A; Konold, Timothy R; Glutting, Joseph J; Alamprese, Judith A

2010-01-01

The purposes of this study were to investigate the reliability and construct validity of measures of reading component skills with a sample of adult basic education (ABE) learners, including both native and nonnative English speakers, and to describe the performance of those learners on the measures. Investigation of measures of reading components is needed because available measures were neither developed for nor normed on ABE populations or with nonnative speakers of English. The study included 486 students, 334 born or educated in the United States (native) and 152 not born or educated in the United States (nonnative) but who spoke English well enough to participate in English reading classes. All students had scores on 11 measures covering five constructs: decoding, word recognition, spelling, fluency, and comprehension. Confirmatory factor analysis (CFA) was used to test three models: a two-factor model with print and meaning factors; a three-factor model that separated out a fluency factor; and a five-factor model based on the hypothesized constructs. The five-factor model fit best. In addition, the CFA model fit both native and nonnative populations equally well without modification, showing that the tests measure the same constructs with the same accuracy for both groups. Group comparisons found no difference between the native and nonnative samples on word recognition, but the native sample scored higher on fluency and comprehension and lower on decoding than did the nonnative sample. Students with self-reported learning disabilities scored lower on all reading components. Differences by age and gender were also analyzed.

Recovering hidden diagonal structures via non-negative matrix factorization with multiple constraints.

PubMed

Yang, Xi; Han, Guoqiang; Cai, Hongmin; Song, Yan

2017-03-31

Revealing data with intrinsically diagonal block structures is particularly useful for analyzing groups of highly correlated variables. Earlier researches based on non-negative matrix factorization (NMF) have been shown to be effective in representing such data by decomposing the observed data into two factors, where one factor is considered to be the feature and the other the expansion loading from a linear algebra perspective. If the data are sampled from multiple independent subspaces, the loading factor would possess a diagonal structure under an ideal matrix decomposition. However, the standard NMF method and its variants have not been reported to exploit this type of data via direct estimation. To address this issue, a non-negative matrix factorization with multiple constraints model is proposed in this paper. The constraints include an sparsity norm on the feature matrix and a total variational norm on each column of the loading matrix. The proposed model is shown to be capable of efficiently recovering diagonal block structures hidden in observed samples. An efficient numerical algorithm using the alternating direction method of multipliers model is proposed for optimizing the new model. Compared with several benchmark models, the proposed method performs robustly and effectively for simulated and real biological data.

Inference of sigma factor controlled networks by using numerical modeling applied to microarray time series data of the germinating prokaryote.

PubMed

Strakova, Eva; Zikova, Alice; Vohradsky, Jiri

2014-01-01

A computational model of gene expression was applied to a novel test set of microarray time series measurements to reveal regulatory interactions between transcriptional regulators represented by 45 sigma factors and the genes expressed during germination of a prokaryote Streptomyces coelicolor. Using microarrays, the first 5.5 h of the process was recorded in 13 time points, which provided a database of gene expression time series on genome-wide scale. The computational modeling of the kinetic relations between the sigma factors, individual genes and genes clustered according to the similarity of their expression kinetics identified kinetically plausible sigma factor-controlled networks. Using genome sequence annotations, functional groups of genes that were predominantly controlled by specific sigma factors were identified. Using external binding data complementing the modeling approach, specific genes involved in the control of the studied process were identified and their function suggested.

The structure of DSM-IV-TR personality disorder diagnoses in NESARC: a reanalysis.

PubMed

Trull, Timothy J; Vergés, Alvaro; Wood, Phillip K; Sher, Kenneth J

2013-12-01

Cox, Clara, Worobec, and Grant (2012) recently presented results from a series of analyses aimed at identifying the factor structure underlying the DSM-IV-TR (APA, 2000) personality diagnoses assessed in the large NESARC study. Cox et al. (2012) concluded that the best fitting model was one that modeled three lower-order factors (the three clusters of PDs as outlined by DSM-IV-TR), which in turn loaded on a single PD higher-order factor. Our reanalyses of the NESARC Wave 1 and Wave 2 data for personality disorder diagnoses revealed that the best fitting model was that of a general PD factor that spans each of the ten DSM-IV PD diagnoses, and our reanalyses do not support the three-cluster hierarchical structure outlined by Cox et al. (2012) and DSM-IV-TR. Finally, we note the importance of modeling the Wave 2 assessment method factor in analyses of NESARC PD data.

Generalized ghost pilgrim dark energy in F(T,TG) cosmology

NASA Astrophysics Data System (ADS)

Sharif, M.; Nazir, Kanwal

2016-07-01

This paper is devoted to study the generalized ghost pilgrim dark energy (PDE) model in F(T,TG) gravity with flat Friedmann-Robertson-Walker (FRW) universe. In this scenario, we reconstruct F(T,TG) models and evaluate the corresponding equation of state (EoS) parameter for different choices of the scale factors. We assume power-law scale factor, scale factor for unification of two phases, intermediate and bouncing scale factor. We study the behavior of reconstructed models and EoS parameters graphically. It is found that all the reconstructed models show decreasing behavior for PDE parameter u = -2. On the other hand, the EoS parameter indicates transition from dust-like matter to phantom era for all choices of the scale factor except intermediate for which this is less than - 1. We conclude that all the results are in agreement with PDE phenomenon.

The Hull Method for Selecting the Number of Common Factors

ERIC Educational Resources Information Center

Lorenzo-Seva, Urbano; Timmerman, Marieke E.; Kiers, Henk A. L.

2011-01-01

A common problem in exploratory factor analysis is how many factors need to be extracted from a particular data set. We propose a new method for selecting the number of major common factors: the Hull method, which aims to find a model with an optimal balance between model fit and number of parameters. We examine the performance of the method in an…

Scale-model charge-transfer technique for measuring enhancement factors

NASA Technical Reports Server (NTRS)

Kositsky, J.; Nanevicz, J. E.

1991-01-01

Determination of aircraft electric field enhancement factors is crucial when using airborne field mill (ABFM) systems to accurately measure electric fields aloft. SRI used the scale model charge transfer technique to determine enhancement factors of several canonical shapes and a scale model Learjet 36A. The measured values for the canonical shapes agreed with known analytic solutions within about 6 percent. The laboratory determined enhancement factors for the aircraft were compared with those derived from in-flight data gathered by a Learjet 36A outfitted with eight field mills. The values agreed to within experimental error (approx. 15 percent).

Validating the 11-Item Revised University of California Los Angeles Scale to Assess Loneliness Among Older Adults: An Evaluation of Factor Structure and Other Measurement Properties.

PubMed

Lee, Joonyup; Cagle, John G

2017-11-01

To examine the measurement properties and factor structure of the short version of the Revised University of California Los Angeles (R-UCLA) loneliness scale from the Health and Retirement Study (HRS). Based on data from 3,706 HRS participants aged 65 + who completed the 2012 wave of the HRS and its Psychosocial Supplement, the measurement properties and factorability of the R-UCLA were examined by conducting an exploratory factor analysis (EFA) and the confirmatory factor analysis (CFA) on randomly split halves. The average score for the 11-item loneliness scale was 16.4 (standard deviation: 4.5). An evaluation of the internal consistency produced a Cronbach's α of 0.87. Results from the EFA showed that two- and three-factor models were appropriate. However, based on the results of the CFA, only a two-factor model was determined to be suitable because there was a very high correlation between two factors identified in the three-factor model, available social connections and sense of belonging. This study provides important data on the properties of the 11-item R-UCLA scale by identifying a two-factor model of loneliness: feeling isolated and available social connections. Our findings suggest the 11-item R-UCLA has good factorability and internal reliability. Copyright © 2017 American Association for Geriatric Psychiatry. Published by Elsevier Inc. All rights reserved.

Complementary exploratory and confirmatory factor analyses of the French WISC-V: Analyses based on the standardization sample.

PubMed

Lecerf, Thierry; Canivez, Gary L

2018-06-01

Interpretation of the French Wechsler Intelligence Scale for Children-Fifth Edition (French WISC-V; Wechsler, 2016a) is based on a 5-factor model including Verbal Comprehension (VC), Visual Spatial (VS), Fluid Reasoning (FR), Working Memory (WM), and Processing Speed (PS). Evidence for the French WISC-V factorial structure was established exclusively through confirmatory factor analyses (CFAs). However, as recommended by Carroll (1995); Reise (2012), and Brown (2015), factorial structure should derive from both exploratory factor analysis (EFA) and CFA. The first goal of this study was to examine the factorial structure of the French WISC-V using EFA. The 15 French WISC-V primary and secondary subtest scaled scores intercorrelation matrix was used and factor extraction criteria suggested from 1 to 4 factors. To disentangle the contribution of first- and second-order factors, the Schmid and Leiman (1957) orthogonalization transformation (SLT) was applied. Overall, no EFA evidence for 5 factors was found. Results indicated that the g factor accounted for about 67% of the common variance and that the contributions of the first-order factors were weak (3.6 to 11.9%). CFA was used to test numerous alternative models. Results indicated that bifactor models produced better fit to these data than higher-order models. Consistent with previous studies, findings suggested dominance of the general intelligence factor and that users should thus emphasize the Full Scale IQ (FSIQ) when interpreting the French WISC-V. (PsycINFO Database Record (c) 2018 APA, all rights reserved).

Investigation on Constrained Matrix Factorization for Hyperspectral Image Analysis

DTIC Science & Technology

2005-07-25

analysis. Keywords: matrix factorization; nonnegative matrix factorization; linear mixture model ; unsupervised linear unmixing; hyperspectral imagery...spatial resolution permits different materials present in the area covered by a single pixel. The linear mixture model says that a pixel reflectance in...in r. In the linear mixture model , r is considered as the linear mixture of m1, m2, …, mP as nMαr += (1) where n is included to account for

A Linear Regression Model Identifying the Primary Factors Contributing to Maintenance Man Hours for the C-17 Globemaster III in the Air National Guard

DTIC Science & Technology

2012-06-15

Maintenance AFSCs ................................................................................................. 14 2. Variation Inflation Factors...total variability in the data. It is an indication of how much of the   20    variation in the data can be accounted for in the regression model. In... Variation Inflation Factors for each independent variable (predictor) as regressed against all of the other independent variables in the model. The

Factorial invariance of pediatric patient self-reported fatigue across age and gender: a multigroup confirmatory factor analysis approach utilizing the PedsQL™ Multidimensional Fatigue Scale.

PubMed

Varni, James W; Beaujean, A Alexander; Limbers, Christine A

2013-11-01

In order to compare multidimensional fatigue research findings across age and gender subpopulations, it is important to demonstrate measurement invariance, that is, that the items from an instrument have equivalent meaning across the groups studied. This study examined the factorial invariance of the 18-item PedsQL™ Multidimensional Fatigue Scale items across age and gender and tested a bifactor model. Multigroup confirmatory factor analysis (MG-CFA) was performed specifying a three-factor model across three age groups (5-7, 8-12, and 13-18 years) and gender. MG-CFA models were proposed in order to compare the factor structure, metric, scalar, and error variance across age groups and gender. The analyses were based on 837 children and adolescents recruited from general pediatric clinics, subspecialty clinics, and hospitals in which children were being seen for well-child checks, mild acute illness, or chronic illness care. A bifactor model of the items with one general factor influencing all the items and three domain-specific factors representing the General, Sleep/Rest, and Cognitive Fatigue domains fit the data better than oblique factor models. Based on the multiple measures of model fit, configural, metric, and scalar invariance were found for almost all items across the age and gender groups, as was invariance in the factor covariances. The PedsQL™ Multidimensional Fatigue Scale demonstrated strict factorial invariance for child and adolescent self-report across gender and strong factorial invariance across age subpopulations. The findings support an equivalent three-factor structure across the age and gender groups studied. Based on these data, it can be concluded that pediatric patients across the groups interpreted the items in a similar manner regardless of their age or gender, supporting the multidimensional factor structure interpretation of the PedsQL™ Multidimensional Fatigue Scale.

Development of Non-Optimum Factors for Launch Vehicle Propellant Tank Bulkhead Weight Estimation

NASA Technical Reports Server (NTRS)

Wu, K. Chauncey; Wallace, Matthew L.; Cerro, Jeffrey A.

2012-01-01

Non-optimum factors are used during aerospace conceptual and preliminary design to account for the increased weights of as-built structures due to future manufacturing and design details. Use of higher-fidelity non-optimum factors in these early stages of vehicle design can result in more accurate predictions of a concept s actual weights and performance. To help achieve this objective, non-optimum factors are calculated for the aluminum-alloy gores that compose the ogive and ellipsoidal bulkheads of the Space Shuttle Super-Lightweight Tank propellant tanks. Minimum values for actual gore skin thicknesses and weld land dimensions are extracted from selected production drawings, and are used to predict reference gore weights. These actual skin thicknesses are also compared to skin thicknesses predicted using classical structural mechanics and tank proof-test pressures. Both coarse and refined weights models are developed for the gores. The coarse model is based on the proof pressure-sized skin thicknesses, and the refined model uses the actual gore skin thicknesses and design detail dimensions. To determine the gore non-optimum factors, these reference weights are then compared to flight hardware weights reported in a mass properties database. When manufacturing tolerance weight estimates are taken into account, the gore non-optimum factors computed using the coarse weights model range from 1.28 to 2.76, with an average non-optimum factor of 1.90. Application of the refined weights model yields non-optimum factors between 1.00 and 1.50, with an average non-optimum factor of 1.14. To demonstrate their use, these calculated non-optimum factors are used to predict heavier, more realistic gore weights for a proposed heavy-lift launch vehicle s propellant tank bulkheads. These results indicate that relatively simple models can be developed to better estimate the actual weights of large structures for future launch vehicles.

Effects of leukemia inhibitory factor and basic fibroblast growth factor on free radicals and endogenous stem cell proliferation in a mouse model of cerebral infarction.

PubMed

Huang, Weihui; Li, Yadan; Lin, Yufeng; Ye, Xue; Zang, Dawei

2012-07-05

The present study established a mouse model of cerebral infarction by middle cerebral artery occlusion, and monitored the effect of 25 μg/kg leukemia inhibitory factor and (or) basic fibroblast growth factor administration 2 hours after model establishment. Results showed that following administration, the number of endogenous neural stem cells in the infarct area significantly increased, malondialdehyde content in brain tissue homogenates significantly decreased, nitric oxide content, glutathione peroxidase and superoxide dismutase activity significantly elevated, and mouse motor function significantly improved as confirmed by the rotarod and bar grab tests. In particular, the effect of leukemia inhibitory factor in combination with basic fibroblast growth factor was the most significant. Results indicate that leukemia inhibitory factor and basic fibroblast growth factor can improve the microenvironment after cerebral infarction by altering free radical levels, improving the quantity of endogenous neural stem cells, and promoting neurological function of mice with cerebral infarction.

Computational Thermochemistry: Scale Factor Databases and Scale Factors for Vibrational Frequencies Obtained from Electronic Model Chemistries.

PubMed

Alecu, I M; Zheng, Jingjing; Zhao, Yan; Truhlar, Donald G

2010-09-14

Optimized scale factors for calculating vibrational harmonic and fundamental frequencies and zero-point energies have been determined for 145 electronic model chemistries, including 119 based on approximate functionals depending on occupied orbitals, 19 based on single-level wave function theory, three based on the neglect-of-diatomic-differential-overlap, two based on doubly hybrid density functional theory, and two based on multicoefficient correlation methods. Forty of the scale factors are obtained from large databases, which are also used to derive two universal scale factor ratios that can be used to interconvert between scale factors optimized for various properties, enabling the derivation of three key scale factors at the effort of optimizing only one of them. A reduced scale factor optimization model is formulated in order to further reduce the cost of optimizing scale factors, and the reduced model is illustrated by using it to obtain 105 additional scale factors. Using root-mean-square errors from the values in the large databases, we find that scaling reduces errors in zero-point energies by a factor of 2.3 and errors in fundamental vibrational frequencies by a factor of 3.0, but it reduces errors in harmonic vibrational frequencies by only a factor of 1.3. It is shown that, upon scaling, the balanced multicoefficient correlation method based on coupled cluster theory with single and double excitations (BMC-CCSD) can lead to very accurate predictions of vibrational frequencies. With a polarized, minimally augmented basis set, the density functionals with zero-point energy scale factors closest to unity are MPWLYP1M (1.009), τHCTHhyb (0.989), BB95 (1.012), BLYP (1.013), BP86 (1.014), B3LYP (0.986), MPW3LYP (0.986), and VSXC (0.986).

Influence of Psychosocial Factors and Habitual Behavior in Temporomandibular Disorder–Related Symptoms in a Working Population in Japan

PubMed Central

Nishiyama, Akira; Kino, Koji; Sugisaki, Masashi; Tsukagoshi, Kaori

2012-01-01

Background: The symptoms of temporomandibular disorders (TMD) are directly influenced by numerous factors, and it is thought that additional factors exert indirect influences. However, the relationships between TMD-related symptoms (TRS) and these contributing factors are largely unknown. Thus, the goal of the present study was to investigate influences on TRS in a working population by determining the prevalence of TRS, analyzing contributing factors, and determining their relative influences on TRS. Materials and Methods: The study subjects were 2203 adults who worked for a single company. Subjects completed a questionnaire assessing TRS, psychosocial factors (stress, anxiety, depressed mood, and chronic fatigue), tooth-contacting habit, and sleep bruxism-related morning symptoms, using a 5-point numeric rating scale. Our analysis proceeded in 2 phases. First, all variables of the descriptor were divided into parts by using an exploratory factor analysis. Second, this factorial structure was verified by using a confirmatory factor analysis with structural equation modeling. Results: Of 2203 employees, 362 reported experiencing TRS (16.4%). Structural equation modeling generated a final model with a goodness of fit index of 0.991, an adjusted goodness of fit index of 0.984, and a root mean square error of approximately 0.021. These indices indicate a strong structural model. The standardized path coefficients for “habitual behavioral factors and TRS,” “psychosocial factors and habitual behavioral factors,” “psychosocial factors and TRS,” and “gender and habitual behavior factors” were 0.48, 0.38, 0.14, and 0.18, respectively. Conclusions: Habitual behavioral factors exert a stronger effect on TRS than do psychosocial factors. PMID:23346261

Individual and Situational Factors Associated with Social Barriers for Persons with Mobility Impairment

ERIC Educational Resources Information Center

McCaughey, Tiffany

2009-01-01

Decades of research have examined factors involved in complex, and sometimes stressful, interpersonal interactions between individuals with and without disabilities. The present study applies structural equation modeling to test an integrative model of individual and situational factors affecting encounters between able-bodied college students and…

Classroom Factors Affecting Students: Self-Evaluation: An Interactional Model.

ERIC Educational Resources Information Center

Marshall, Hermine H.; Weinstein, Rhona S.

1984-01-01

A complex interactional model of classroom factors that contribute to the development of students' self-evaluations is presented. Factors described are: (1) task structure; (2) grouping practices; (3) feedback and evaluation procedures and information about ability; (4) motivational strategies; (5) locus of responsibility for learning; and (6) the…

Model Effectiveness as a Function of Personnel (ME = f(PER))

DTIC Science & Technology

1986-10-01

Human Factor in Military Modernization, The RAND Corporation, R- 2460-NA, 1979 AD-A072955 D-7. SUPPRESSION Mueller, M. P., K. H. Pietsch , Human Factors in...H. Pietsch , Human Factors in Field Experimentation, Design and Analysis of an Analytical Suppression Model, 1978 A061417 Office of Naval Research

A Model of Factors Contributing to STEM Learning and Career Orientation

ERIC Educational Resources Information Center

Nugent, Gwen; Barker, Bradley; Welch, Greg; Grandgenett, Neal; Wu, ChaoRong; Nelson, Carl

2015-01-01

The purpose of this research was to develop and test a model of factors contributing to science, technology, engineering, and mathematics (STEM) learning and career orientation, examining the complex paths and relationships among social, motivational, and instructional factors underlying these outcomes for middle school youth. Social cognitive…

Determinants of Standard Errors of MLEs in Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Cheng, Ying; Zhang, Wei

2010-01-01

This paper studies changes of standard errors (SE) of the normal-distribution-based maximum likelihood estimates (MLE) for confirmatory factor models as model parameters vary. Using logical analysis, simplified formulas and numerical verification, monotonic relationships between SEs and factor loadings as well as unique variances are found.…

Comparing the Fit of Item Response Theory and Factor Analysis Models

ERIC Educational Resources Information Center

Maydeu-Olivares, Alberto; Cai, Li; Hernandez, Adolfo

2011-01-01

Linear factor analysis (FA) models can be reliably tested using test statistics based on residual covariances. We show that the same statistics can be used to reliably test the fit of item response theory (IRT) models for ordinal data (under some conditions). Hence, the fit of an FA model and of an IRT model to the same data set can now be…

An evaluation of evaluative personality terms: a comparison of the big seven and five-factor model in predicting psychopathology.

PubMed

Durrett, Christine; Trull, Timothy J

2005-09-01

Two personality models are compared regarding their relationship with personality disorder (PD) symptom counts and with lifetime Axis I diagnoses. These models share 5 similar domains, and the Big 7 model also includes 2 domains assessing self-evaluation: positive and negative valence. The Big 7 model accounted for more variance in PDs than the 5-factor model, primarily because of the association of negative valence with most PDs. Although low-positive valence was associated with most Axis I diagnoses, the 5-factor model generally accounted for more variance in Axis I diagnoses than the Big 7 model. Some predicted associations between self-evaluation and psychopathology were not found, and unanticipated associations emerged. These findings are discussed regarding the utility of evaluative terms in clinical assessment.

The Two Stage Model of Preeclampsia: Variations on the Theme

PubMed Central

Roberts, James M; Hubel, Carl A.

2009-01-01

The Two Stage Model of preeclampsia proposes that a poorly perfused placenta (Stage 1) produces factor(s) leading to the clinical manifestations of preeclampsia (Stage2). Stage 1 is not sufficient to cause the maternal syndrome but interacts with maternal constitutional factors (genetic, behavioral or environmental) to result in Stage 2. Recent information indicates the necessity for modifications of this model. It is apparent that changes relevant to preeclampsia and other implantation disorders can be detected in the first trimester, long before the failed vascular remodeling necessary to reduce placental perfusion. In addition, although the factor(s) released from the placenta has usually been considered a toxin, we suggest that what is released may also be an appropriate signal from the fetal/placental unit to overcome reduced nutrient availability that cannot by tolerated by some women who develop preeclampsia. Further, it is evident that linkage is not likely to be by one factor but several, different for different women. Also although the initial model limited the role of maternal constitutional factors to the genesis of Stage 2, this does not appear to be the case. It is evident that the factors increasing risk for preeclampsia are also associated with abnormal implantation. These several modifications have important implications. An earlier origin for Stage 1, which appears to be recognizable by altered concentrations of placental products, could allow earlier intervention. The possibility of a fetal placental factor increasing nutrient availability could provide novel therapeutic options. Different linkages and preeclampsia subtypes could direct specific preventive treatments for different women while the role of maternal constitutional factors to affect placentation provides targets for prepregnancy therapy. The modified Two Stage Model provides a useful guide towards investigating pathophysiology and guiding therapy. PMID:19070896

The classification of body dysmorphic disorder symptoms in male and female adolescents.

PubMed

Schneider, Sophie C; Baillie, Andrew J; Mond, Jonathan; Turner, Cynthia M; Hudson, Jennifer L

2018-01-01

Body dysmorphic disorder (BDD) was categorised in DSM-5 within the newly created 'obsessive-compulsive and related disorders' chapter, however this classification remains subject to debate. Confirmatory factor analysis was used to test competing models of the co-occurrence of symptoms of BDD, obsessive-compulsive disorder, unipolar depression, anxiety, and eating disorders in a community sample of adolescents, and to explore potential sex differences in these models. Self-report questionnaires assessing disorder symptoms were completed by 3149 Australian adolescents. The fit of correlated factor models was calculated separately in males and females, and measurement invariance testing compared parameters of the best-fitting model between males and females. All theoretical models of the classification of BDD had poor fit to the data. Good fit was found for a novel model where BDD symptoms formed a distinct latent factor, correlated with affective disorder and eating disorder latent factors. Metric non-invariance was found between males and females, and the majority of factor loadings differed between males and females. Correlations between some latent factors also differed by sex. Only cross-sectional data were collected, and the study did not assess a broad range of DSM-5 defined eating disorder symptoms or other disorders in the DSM-5 obsessive-compulsive and related disorders chapter. This study is the first to statistically evaluate competing models of BDD classification. The findings highlight the unique features of BDD and its associations with affective and eating disorders. Future studies examining the classification of BDD should consider developmental and sex differences in their models. Copyright © 2017. Published by Elsevier B.V.

Suicidal ideation in transgender people: Gender minority stress and interpersonal theory factors.

PubMed

Testa, Rylan J; Michaels, Matthew S; Bliss, Whitney; Rogers, Megan L; Balsam, Kimberly F; Joiner, Thomas

2017-01-01

Research has revealed alarmingly high rates of suicidal ideation (SI) and suicide attempts among transgender and gender nonconforming (TGNC) people. This study aims to analyze the role of factors from the gender minority stress and resilience (GMSR) model (Testa, Habarth, Peta, Balsam, & Bockting, 2015), the interpersonal-psychological theory of suicide (IPTS; Joiner, 2005; Van Orden et al., 2010), and the potential integration of these factors, in explaining SI in this population. A convenience sample of 816 TGNC adults responded to measures of current SI, gender minority stressors, and IPTS factors. Path analysis was utilized to test 2 models. Model 1 evaluated the associations between external minority stressors and SI through internal minority stressors. Model 2 examined the relationships between internal minority stressors and SI through IPTS variables (perceived burdensomeness and thwarted belongingness). All GMSR external stressors (rejection, nonaffirmation, victimization, and discrimination), internal stressors (internalized transphobia, negative expectations, and nondisclosure), and IPTS factors (thwarted belongingness and perceived burdensomeness) were related to SI. Both models demonstrated good fit. Model 1 revealed that rejection, nonaffirmation, and victimization were related to SI through experiences of internalized transphobia and negative expectations. Model 2 indicated that internalized transphobia and negative expectations were associated with SI through IPTS factors. The models demonstrate pathways through which GMSR and IPTS constructs relate to one another and confer risk for SI among TGNC individuals. These pathways and several recently proposed constructs examined here provide promising directions for future research and clinical interventions in this area. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

DOE Office of Scientific and Technical Information (OSTI.GOV)

Matthias C. M. Troffaes; Gero Walter; Dana Kelly

In a standard Bayesian approach to the alpha-factor model for common-cause failure, a precise Dirichlet prior distribution models epistemic uncertainty in the alpha-factors. This Dirichlet prior is then updated with observed data to obtain a posterior distribution, which forms the basis for further inferences. In this paper, we adapt the imprecise Dirichlet model of Walley to represent epistemic uncertainty in the alpha-factors. In this approach, epistemic uncertainty is expressed more cautiously via lower and upper expectations for each alpha-factor, along with a learning parameter which determines how quickly the model learns from observed data. For this application, we focus onmore » elicitation of the learning parameter, and find that values in the range of 1 to 10 seem reasonable. The approach is compared with Kelly and Atwood's minimally informative Dirichlet prior for the alpha-factor model, which incorporated precise mean values for the alpha-factors, but which was otherwise quite diffuse. Next, we explore the use of a set of Gamma priors to model epistemic uncertainty in the marginal failure rate, expressed via a lower and upper expectation for this rate, again along with a learning parameter. As zero counts are generally less of an issue here, we find that the choice of this learning parameter is less crucial. Finally, we demonstrate how both epistemic uncertainty models can be combined to arrive at lower and upper expectations for all common-cause failure rates. Thereby, we effectively provide a full sensitivity analysis of common-cause failure rates, properly reflecting epistemic uncertainty of the analyst on all levels of the common-cause failure model.« less

An ensemble model of competitive multi-factor binding of the genome

PubMed Central

Wasson, Todd; Hartemink, Alexander J.

2009-01-01

Hundreds of different factors adorn the eukaryotic genome, binding to it in large number. These DNA binding factors (DBFs) include nucleosomes, transcription factors (TFs), and other proteins and protein complexes, such as the origin recognition complex (ORC). DBFs compete with one another for binding along the genome, yet many current models of genome binding do not consider different types of DBFs together simultaneously. Additionally, binding is a stochastic process that results in a continuum of binding probabilities at any position along the genome, but many current models tend to consider positions as being either binding sites or not. Here, we present a model that allows a multitude of DBFs, each at different concentrations, to compete with one another for binding sites along the genome. The result is an “occupancy profile,” a probabilistic description of the DNA occupancy of each factor at each position. We implement our model efficiently as the software package COMPETE. We demonstrate genome-wide and at specific loci how modeling nucleosome binding alters TF binding, and vice versa, and illustrate how factor concentration influences binding occupancy. Binding cooperativity between nearby TFs arises implicitly via mutual competition with nucleosomes. Our method applies not only to TFs, but also recapitulates known occupancy profiles of a well-studied replication origin with and without ORC binding. Importantly, the sequence preferences our model takes as input are derived from in vitro experiments. This ensures that the calculated occupancy profiles are the result of the forces of competition represented explicitly in our model and the inherent sequence affinities of the constituent DBFs. PMID:19720867

The WRKY transcription factor family in Brachypodium distachyon.

PubMed

Tripathi, Prateek; Rabara, Roel C; Langum, Tanner J; Boken, Ashley K; Rushton, Deena L; Boomsma, Darius D; Rinerson, Charles I; Rabara, Jennifer; Reese, R Neil; Chen, Xianfeng; Rohila, Jai S; Rushton, Paul J

2012-06-22

A complete assembled genome sequence of wheat is not yet available. Therefore, model plant systems for wheat are very valuable. Brachypodium distachyon (Brachypodium) is such a system. The WRKY family of transcription factors is one of the most important families of plant transcriptional regulators with members regulating important agronomic traits. Studies of WRKY transcription factors in Brachypodium and wheat therefore promise to lead to new strategies for wheat improvement. We have identified and manually curated the WRKY transcription factor family from Brachypodium using a pipeline designed to identify all potential WRKY genes. 86 WRKY transcription factors were found, a total higher than all other current databases. We therefore propose that our numbering system (BdWRKY1-BdWRKY86) becomes the standard nomenclature. In the JGI v1.0 assembly of Brachypodium with the MIPS/JGI v1.0 annotation, nine of the transcription factors have no gene model and eleven gene models are probably incorrectly predicted. In total, twenty WRKY transcription factors (23.3%) do not appear to have accurate gene models. To facilitate use of our data, we have produced The Database of Brachypodium distachyon WRKY Transcription Factors. Each WRKY transcription factor has a gene page that includes predicted protein domains from MEME analyses. These conserved protein domains reflect possible input and output domains in signaling. The database also contains a BLAST search function where a large dataset of WRKY transcription factors, published genes, and an extensive set of wheat ESTs can be searched. We also produced a phylogram containing the WRKY transcription factor families from Brachypodium, rice, Arabidopsis, soybean, and Physcomitrella patens, together with published WRKY transcription factors from wheat. This phylogenetic tree provides evidence for orthologues, co-orthologues, and paralogues of Brachypodium WRKY transcription factors. The description of the WRKY transcription factor family in Brachypodium that we report here provides a framework for functional genomics studies in an important model system. Our database is a resource for both Brachypodium and wheat studies and ultimately projects aimed at improving wheat through manipulation of WRKY transcription factors.

The WRKY transcription factor family in Brachypodium distachyon

PubMed Central

2012-01-01

Background A complete assembled genome sequence of wheat is not yet available. Therefore, model plant systems for wheat are very valuable. Brachypodium distachyon (Brachypodium) is such a system. The WRKY family of transcription factors is one of the most important families of plant transcriptional regulators with members regulating important agronomic traits. Studies of WRKY transcription factors in Brachypodium and wheat therefore promise to lead to new strategies for wheat improvement. Results We have identified and manually curated the WRKY transcription factor family from Brachypodium using a pipeline designed to identify all potential WRKY genes. 86 WRKY transcription factors were found, a total higher than all other current databases. We therefore propose that our numbering system (BdWRKY1-BdWRKY86) becomes the standard nomenclature. In the JGI v1.0 assembly of Brachypodium with the MIPS/JGI v1.0 annotation, nine of the transcription factors have no gene model and eleven gene models are probably incorrectly predicted. In total, twenty WRKY transcription factors (23.3%) do not appear to have accurate gene models. To facilitate use of our data, we have produced The Database of Brachypodium distachyon WRKY Transcription Factors. Each WRKY transcription factor has a gene page that includes predicted protein domains from MEME analyses. These conserved protein domains reflect possible input and output domains in signaling. The database also contains a BLAST search function where a large dataset of WRKY transcription factors, published genes, and an extensive set of wheat ESTs can be searched. We also produced a phylogram containing the WRKY transcription factor families from Brachypodium, rice, Arabidopsis, soybean, and Physcomitrella patens, together with published WRKY transcription factors from wheat. This phylogenetic tree provides evidence for orthologues, co-orthologues, and paralogues of Brachypodium WRKY transcription factors. Conclusions The description of the WRKY transcription factor family in Brachypodium that we report here provides a framework for functional genomics studies in an important model system. Our database is a resource for both Brachypodium and wheat studies and ultimately projects aimed at improving wheat through manipulation of WRKY transcription factors. PMID:22726208

Structural Model of psychological risk and protective factors affecting on quality of life in patients with coronary heart disease: A psychocardiology model

PubMed Central

Nekouei, Zohreh Khayyam; Yousefy, Alireza; Doost, Hamid Taher Neshat; Manshaee, Gholamreza; Sadeghei, Masoumeh

2014-01-01

Background: Conducted researches show that psychological factors may have a very important role in the etiology, continuity and consequences of coronary heart diseases. This study has drawn the psychological risk and protective factors and their effects in patients with coronary heart diseases (CHD) in a structural model. It aims to determine the structural relations between psychological risk and protective factors with quality of life in patients with coronary heart disease. Materials and Methods: The present cross-sectional and correlational studies were conducted using structural equation modeling. The study sample included 398 patients of coronary heart disease in the university referral Hospital, as well as other city health care centers in Isfahan city. They were selected based on random sampling method. Then, in case, they were executed the following questionnaires: Coping with stressful situations (CISS- 21), life orientation (LOT-10), general self-efficacy (GSE-10), depression, anxiety and stress (DASS-21), perceived stress (PSS-14), multidimensional social support (MSPSS-12), alexithymia (TAS-20), spiritual intelligence (SQ-23) and quality of life (WHOQOL-26). Results: The results showed that protective and risk factors could affect the quality of life in patients with CHD with factor loadings of 0.35 and −0.60, respectively. Moreover, based on the values of the framework of the model such as relative chi-square (CMIN/DF = 3.25), the Comparative Fit Index (CFI = 0.93), the Parsimony Comparative Fit Index (PCFI = 0.68), the Root Mean Square Error of Approximation (RMSEA = 0.07) and details of the model (significance of the relationships) it has been confirmed that the psychocardiological structural model of the study is the good fitting model. Conclusion: This study was among the first to research the different psychological risk and protective factors of coronary heart diseases in the form of a structural model. The results of this study have emphasized the necessity of noticing the psychological factors in primary prevention by preventive programs and in secondary prevention by rehabilitation centers to improve the quality of life of the people with heart diseases. PMID:24778660

Longitudinal factorial invariance of the PedsQL 4.0 Generic Core Scales child self-report Version: one year prospective evidence from the California State Children's Health Insurance Program (SCHIP).

PubMed

Varni, James W; Limbers, Christine A; Newman, Daniel A; Seid, Michael

2008-11-01

The measurement of health-related quality of life (HRQOL) in pediatric medicine and health services research has grown significantly over the past decade. The paradigm shift toward patient-reported outcomes (PROs) has provided the opportunity to emphasize the value and critical need for pediatric patient self-report. In order for changes in HRQOL/PRO outcomes to be meaningful over time, it is essential to demonstrate longitudinal factorial invariance. This study examined the longitudinal factor structure of the PedsQL 4.0 Generic Core Scales over a one-year period for child self-report ages 5-17 in 2,887 children from a statewide evaluation of the California State Children's Health Insurance Program (SCHIP) utilizing a structural equation modeling framework. Specifying four- and five-factor measurement models, longitudinal structural equation modeling was used to compare factor structures over a one-year interval on the PedsQL 4.0 Generic Core Scales. While the four-factor conceptually-derived measurement model for the PedsQL 4.0 Generic Core Scales produced an acceptable fit, the five-factor empirically-derived measurement model from the initial field test of the PedsQL 4.0 Generic Core Scales produced a marginally superior fit in comparison to the four-factor model. For the five-factor measurement model, the best fitting model, strict factorial invariance of the PedsQL 4.0 Generic Core Scales across the two measurement occasions was supported by the stability of the comparative fit index between the unconstrained and constrained models, and several additional indices of practical fit including the root mean squared error of approximation, the non-normed fit index, and the parsimony normed fit index. The findings support an equivalent factor structure on the PedsQL 4.0 Generic Core Scales over time. Based on these data, it can be concluded that over a one-year period children in our study interpreted items on the PedsQL 4.0 Generic Core Scales in a similar manner.

Differences in within- and between-person factor structure of positive and negative affect: analysis of two intensive measurement studies using multilevel structural equation modeling.

PubMed

Rush, Jonathan; Hofer, Scott M

2014-06-01

The Positive and Negative Affect Schedule (PANAS) is a widely used measure of emotional experience. The factor structure of the PANAS has been examined predominantly with cross-sectional designs, which fails to disaggregate within-person variation from between-person differences. There is still uncertainty as to the factor structure of positive and negative affect and whether they constitute 2 distinct independent factors. The present study examined the within-person and between-person factor structure of the PANAS in 2 independent samples that reported daily affect over 7 and 14 occasions, respectively. Results from multilevel confirmatory factor analyses revealed that a 2-factor structure at both the within-person and between-person levels, with correlated specific factors for overlapping items, provided good model fit. The best-fitting solution was one where within-person factors of positive and negative affect were inversely correlated, but between-person factors were independent. The structure was further validated through multilevel structural equation modeling examining the effects of cognitive interference, daily stress, physical symptoms, and physical activity on positive and negative affect factors.

Attention-deficit/hyperactivity disorder dimensionality: the reliable 'g' and the elusive 's' dimensions.

PubMed

Wagner, Flávia; Martel, Michelle M; Cogo-Moreira, Hugo; Maia, Carlos Renato Moreira; Pan, Pedro Mario; Rohde, Luis Augusto; Salum, Giovanni Abrahão

2016-01-01

The best structural model for attention-deficit/hyperactivity disorder (ADHD) symptoms remains a matter of debate. The objective of this study is to test the fit and factor reliability of competing models of the dimensional structure of ADHD symptoms in a sample of randomly selected and high-risk children and pre-adolescents from Brazil. Our sample comprised 2512 children aged 6-12 years from 57 schools in Brazil. The ADHD symptoms were assessed using parent report on the development and well-being assessment (DAWBA). Fit indexes from confirmatory factor analysis were used to test unidimensional, correlated, and bifactor models of ADHD, the latter including "g" ADHD and "s" symptom domain factors. Reliability of all models was measured with omega coefficients. A bifactor model with one general factor and three specific factors (inattention, hyperactivity, impulsivity) exhibited the best fit to the data, according to fit indices, as well as the most consistent factor loadings. However, based on omega reliability statistics, the specific inattention, hyperactivity, and impulsivity dimensions provided very little reliable information after accounting for the reliable general ADHD factor. Our study presents some psychometric evidence that ADHD specific ("s") factors might be unreliable after taking common ("g" factor) variance into account. These results are in accordance with the lack of longitudinal stability among subtypes, the absence of dimension-specific molecular genetic findings and non-specific effects of treatment strategies. Therefore, researchers and clinicians might most effectively rely on the "g" ADHD to characterize ADHD dimensional phenotype, based on currently available symptom items.

Factor structure of the Childhood Autism Rating Scale as per DSM-5.

PubMed

Park, Eun-Young; Kim, Joungmin

2016-02-01

The DSM-5 recently proposed new diagnostic criteria for autism spectrum disorder (ASD). Although many new or updated tools have been developed since the DSM-IV was published in 1994, the Childhood Autism Rating Scale (CARS) has been used consistently in ASD diagnosis and research due to its technical adequacy, cost-effectiveness, and practicality. Additionally, items in the CARS did not alter following the release of the revised DSM-IV because the CARS factor structure was found to be consistent with the revised criteria after factor analysis. For that reason, in this study confirmatory factor analysis was used to identify the factor structure of the CARS. Participants (n = 150) consisted of children with an ASD diagnosis or who met the criteria for broader autism or emotional/behavior disorder with comorbid disorders such as attention-deficit hyperactivity disorder, bipolar disorder, intellectual or developmental disabilities. Previous studies used one-, two-, and four-factor models, all of which we examined to confirm the best-fit model on confirmatory factor analysis. Appropriate comparative fit indices and root mean square errors were obtained for all four models. The two-factor model, based on DSM-5 criteria, was the most valid and reliable. The inter-item consistency of the CARS was 0.926 and demonstrated adequate reliability, thereby supporting the validity and reliability of the two-factor model of CARS. Although CARS was developed prior to the introduction of DSM-5, its psychometric properties, conceptual relevance, and flexible administration procedures support its continued role as a screening device in the diagnostic decision-making process. © 2015 Japan Pediatric Society.

Developing a dimensional model for successful cognitive and emotional aging.

PubMed

Vahia, Ipsit V; Thompson, Wesley K; Depp, Colin A; Allison, Matthew; Jeste, Dilip V

2012-04-01

There is currently a lack of consensus on the definition of successful aging (SA) and existing implementations have omitted constructs associated with SA. We used empirical methods to develop a dimensional model of SA that incorporates a wider range of associated variables, and we examined the relationship among these components using factor analysis and Bayesian Belief Nets. We administered a successful aging questionnaire comprising several standardized measures related to SA to a sample of 1948 older women enrolled in the San Diego site of the Women's Health Initiative study. The SA-related variables we included in the model were self-rated successful aging, depression severity, physical and emotional functioning, optimism, resilience, attitude towards own aging, self-efficacy, and cognitive ability. After adjusting for age, education and income, we fitted an exploratory factor analysis model to the SA-related variables and then, in order to address relationships among these factors, we computed a Bayesian Belief Net (BBN) using rotated factor scores. The SA-related variables loaded onto five factors. Based on the loading, we labeled the factors as follows: self-rated successful aging, cognition, psychosocial protective factors, physical functioning, and emotional functioning. In the BBN, self-rated successful aging emerged as the primary downstream factor and exhibited significant partial correlations with psychosocial protective factors, physical/general status and mental/emotional status but not with cognitive ability. Our study represents a step forward in developing a dimensional model of SA. Our findings also point to a potential role for psychiatry in improving successful aging by managing depressive symptoms and developing psychosocial interventions to improve self-efficacy, resilience, and optimism.

Examining General and Specific Factors in the Dimensionality of Oral Language and Reading in 4th–10th Grades

PubMed Central

Foorman, Barbara R.; Koon, Sharon; Petscher, Yaacov; Mitchell, Alison; Truckenmiller, Adrea

2015-01-01

The objective of this study was to explore dimensions of oral language and reading and their influence on reading comprehension in a relatively understudied population—adolescent readers in 4th through 10th grades. The current study employed latent variable modeling of decoding fluency, vocabulary, syntax, and reading comprehension so as to represent these constructs with minimal error and to examine whether residual variance unaccounted for by oral language can be captured by specific factors of syntax and vocabulary. A 1-, 3-, 4-, and bifactor model were tested with 1,792 students in 18 schools in 2 large urban districts in the Southeast. Students were individually administered measures of expressive and receptive vocabulary, syntax, and decoding fluency in mid-year. At the end of the year students took the state reading test as well as a group-administered, norm-referenced test of reading comprehension. The bifactor model fit the data best in all 7 grades and explained 72% to 99% of the variance in reading comprehension. The specific factors of syntax and vocabulary explained significant unique variance in reading comprehension in 1 grade each. The decoding fluency factor was significantly correlated with the reading comprehension and oral language factors in all grades, but, in the presence of the oral language factor, was not significantly associated with the reading comprehension factor. Results support a bifactor model of lexical knowledge rather than the 3-factor model of the Simple View of Reading, with the vast amount of variance in reading comprehension explained by a general oral language factor. PMID:26346839

Bifactor structure of the Wechsler Preschool and Primary Scale of Intelligence--Fourth Edition.

PubMed

Watkins, Marley W; Beaujean, A Alexander

2014-03-01

The Wechsler Preschool and Primary Scale of Intelligence--Fourth Edition (WPPSI-IV; Wechsler, 2012) represents a substantial departure from its predecessor, including omission of 4 subtests, addition of 5 new subtests, and modification of the contents of the 5 retained subtests. Wechsler (2012) explicitly assumed a higher-order structure with general intelligence (g) as the second-order factor that explained all the covariation of several first-order factors but failed to consider a bifactor model. The WPPSI-IV normative sample contains 1,700 children aged 2 years and 6 months through 7 years and 7 months, bifurcated into 2 age groups: 2:6-3:11 year olds (n = 600) and 4:0-7:7 year olds (n = 1,100). This study applied confirmatory factor analysis to the WPPSI-IV normative sample data to test the fit of a bifactor model and to determine the reliability of the resulting factors. The bifactor model fit the WPPSI-IV normative sample data as well as or better than the higher-order models favored by Wechsler (2012). In the bifactor model, the general factor accounted for more variance in every subtest than did its corresponding domain-specific factor and the general factor accounted for more total and common variance than all domain-specific factors combined. Further, the domain-specific factors exhibited poor reliability independent of g (i.e., ωh coefficients of .05 to .33). These results suggest that only the general intelligence dimension was sufficiently robust and precise for clinical use. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Investigation of the factor structure of spirituality and religiosity in Iranian Shiite university students.

PubMed

Joshanloo, Mohsen

2012-01-01

One of the important challenges facing psychologists of religion pertains to the definition of religiosity and spirituality. One way of understanding the connection between these two concepts is to suppose that one of them is a subset of the other. Another useful and sensitive way, however, is to view spirituality and religiosity as overlapping constructs, sharing some characteristics but also retaining nonshared features. Empirical studies examining the factor structure of spirituality and religiosity are scant and almost all of them come from Western culture. These factor analytic studies generally confirm that religiosity and spirituality can best be described in terms of two distinct yet correlated factors. To date, no study has investigated the relationship between these two constructs in Islamic cultures. To redress this imbalance, confirmatory factor analysis was used to examine the factor structure of religiosity and spirituality in two Iranian Shiite samples using an extensive set of scales (including Santa Clara Strength of Religious Faith, Spiritual Involvement and Beliefs Scale - Revised, Spiritual Meaning Scale, and Spiritual Transcendence Scale). Two hypothetical models were tested: a model that viewed spirituality and religiosity as correlated but separate constructs and a model that combined the indicators of religiosity and spirituality into a single construct. In keeping with the results obtained in Western cultures, results of confirmatory factor analyses, conducted in Study 1 (N=225) and Study 2 (N=288), revealed that a two-factor model fitted the data better than a single-factor model. Implications of the results are discussed, as are study limitations and directions for further research.

Joint genome-wide prediction in several populations accounting for randomness of genotypes: A hierarchical Bayes approach. II: Multivariate spike and slab priors for marker effects and derivation of approximate Bayes and fractional Bayes factors for the complete family of models.

PubMed

Martínez, Carlos Alberto; Khare, Kshitij; Banerjee, Arunava; Elzo, Mauricio A

2017-03-21

This study corresponds to the second part of a companion paper devoted to the development of Bayesian multiple regression models accounting for randomness of genotypes in across population genome-wide prediction. This family of models considers heterogeneous and correlated marker effects and allelic frequencies across populations, and has the ability of considering records from non-genotyped individuals and individuals with missing genotypes in any subset of loci without the need for previous imputation, taking into account uncertainty about imputed genotypes. This paper extends this family of models by considering multivariate spike and slab conditional priors for marker allele substitution effects and contains derivations of approximate Bayes factors and fractional Bayes factors to compare models from part I and those developed here with their null versions. These null versions correspond to simpler models ignoring heterogeneity of populations, but still accounting for randomness of genotypes. For each marker loci, the spike component of priors corresponded to point mass at 0 in R S , where S is the number of populations, and the slab component was a S-variate Gaussian distribution, independent conditional priors were assumed. For the Gaussian components, covariance matrices were assumed to be either the same for all markers or different for each marker. For null models, the priors were simply univariate versions of these finite mixture distributions. Approximate algebraic expressions for Bayes factors and fractional Bayes factors were found using the Laplace approximation. Using the simulated datasets described in part I, these models were implemented and compared with models derived in part I using measures of predictive performance based on squared Pearson correlations, Deviance Information Criterion, Bayes factors, and fractional Bayes factors. The extensions presented here enlarge our family of genome-wide prediction models making it more flexible in the sense that it now offers more modeling options. Copyright © 2017 Elsevier Ltd. All rights reserved.

Analysis of significant factors for dengue fever incidence prediction.

PubMed

Siriyasatien, Padet; Phumee, Atchara; Ongruk, Phatsavee; Jampachaisri, Katechan; Kesorn, Kraisak

2016-04-16

Many popular dengue forecasting techniques have been used by several researchers to extrapolate dengue incidence rates, including the K-H model, support vector machines (SVM), and artificial neural networks (ANN). The time series analysis methodology, particularly ARIMA and SARIMA, has been increasingly applied to the field of epidemiological research for dengue fever, dengue hemorrhagic fever, and other infectious diseases. The main drawback of these methods is that they do not consider other variables that are associated with the dependent variable. Additionally, new factors correlated to the disease are needed to enhance the prediction accuracy of the model when it is applied to areas of similar climates, where weather factors such as temperature, total rainfall, and humidity are not substantially different. Such drawbacks may consequently lower the predictive power for the outbreak. The predictive power of the forecasting model-assessed by Akaike's information criterion (AIC), Bayesian information criterion (BIC), and the mean absolute percentage error (MAPE)-is improved by including the new parameters for dengue outbreak prediction. This study's selected model outperforms all three other competing models with the lowest AIC, the lowest BIC, and a small MAPE value. The exclusive use of climate factors from similar locations decreases a model's prediction power. The multivariate Poisson regression, however, effectively forecasts even when climate variables are slightly different. Female mosquitoes and seasons were strongly correlated with dengue cases. Therefore, the dengue incidence trends provided by this model will assist the optimization of dengue prevention. The present work demonstrates the important roles of female mosquito infection rates from the previous season and climate factors (represented as seasons) in dengue outbreaks. Incorporating these two factors in the model significantly improves the predictive power of dengue hemorrhagic fever forecasting models, as confirmed by AIC, BIC, and MAPE.

Using Ryff's scales of psychological well-being in adolescents in mainland China.

PubMed

Gao, Jie; McLellan, Ros

2018-04-20

Psychological well-being in adolescence has always been a focus of public attention and academic research. Ryff's six-factor model of psychological well-being potentially provides a comprehensive theoretical framework for investigating positive functioning of adolescents. However, previous studies reported inconsistent findings of the reliability and validity of Ryff's Scales of Psychological Well-being (SPWB). The present study aimed to explore whether Ryff's six-factor model of psychological well-being could be applied in Chinese adolescents. The Scales of Psychological Well-being (SPWB) were adapted for assessing the psychological well-being of adolescents in mainland China. 772 adolescents (365 boys to 401 girls, 6 missing gender data, mean age = 13.65) completed the adapted 33-item SPWB. The data was used to examine the reliability and construct validity of the adapted SPWB. Results showed that five of the six sub-scales had acceptable internal consistency of items, except the sub-scale of autonomy. The factorial structure of the SPWB was not as clear-cut as the theoretical framework suggested. Among the models under examination, the six-factor model had better model fit than the hierarchical model and the one-factor model. However, the goodness-of-fit of the six-factor model was hardly acceptable. High factor correlations were identified between the sub-scales of environmental mastery, purpose in life and personal growth. Findings of the present study echoed a number of previous studies which reported inadequate reliability and validity of Ryff's scales. Given the evidence, it was suggested that future adolescent studies should seek to develop more age-specific and context-appropriate items for a better operationalisation of Ryff's theoretical model of psychological well-being.

A new predictive model for the bioconcentration factors of polychlorinated biphenyls (PCBs) based on the molecular electronegativity distance vector (MEDV).

PubMed

Qin, Li-Tang; Liu, Shu-Shen; Liu, Hai-Ling; Ge, Hui-Lin

2008-02-01

Polychlorinated biphenyls (PCBs) are some of the most prevalent pollutants in the total environment and receive more and more concerns as a group of ubiquitous potential persistent organic pollutants. Using the variable selection and modeling based on prediction (VSMP), the molecular electronegativity distance vector (MEDV) derived directly from the molecular topological structures was employed to develop a linear model (MI) between the bioconcentration factors (BCF) and two MEDV descriptors of 58 PCBs. The MI model showed a good estimation ability with a correlation coefficient (r) of 0.9605 and a high stability with a leave-one-out cross-validation correlation coefficient (q) of 0.9564. The MEDV-base model (MI) is easier to use than the splinoid poset method reported by Ivanciuc et al. [Ivanciuc, T., Ivanciuc, O., Klein, D.J., 2006. Modeling the bioconcentration factors and bioaccumulation factors of polychlorinated biphenyls with posetic quatitative super-structure/activity relationships (QSSAR). Mol. Divers. 10, 133-145] and gives a better statistics than molecular connectivity index (MCI)-base model developed by Hu et al. [Hu, H.Y., Xu, F.L., Li, B.G., Cao, J., Dawson, R., Tao, S., 2005. Prediction of the bioconcentration factor of PCBs in fish using the molecular connectivity index and fragment constant models. Water Environ. Res. 77, 87-97]. Main structural factors influencing the BCF of PCBs are the substructures expressed by two atomic groups >C= and -CH=. 58 PCBs were divided into an "odd set" and "even set" in order to ensure the predicted potential of the MI for the external samples. It was shown that three models, MI, MO for "odd set", and ME for "even set", can be used to predict the BCF of remaining 152 PCBs in which the experimental BCFs are not available.

East meets West: the influence of racial, ethnic and cultural risk factors on cardiac surgical risk model performance.

PubMed

Soo-Hoo, Sarah; Nemeth, Samantha; Baser, Onur; Argenziano, Michael; Kurlansky, Paul

2018-01-01

To explore the impact of racial and ethnic diversity on the performance of cardiac surgical risk models, the Chinese SinoSCORE was compared with the Society of Thoracic Surgeons (STS) risk model in a diverse American population. The SinoSCORE risk model was applied to 13 969 consecutive coronary artery bypass surgery patients from twelve American institutions. SinoSCORE risk factors were entered into a logistic regression to create a 'derived' SinoSCORE whose performance was compared with that of the STS risk model. Observed mortality was 1.51% (66% of that predicted by STS model). The SinoSCORE 'low-risk' group had a mortality of 0.15%±0.04%, while the medium-risk and high-risk groups had mortalities of 0.35%±0.06% and 2.13%±0.14%, respectively. The derived SinoSCORE model had a relatively good discrimination (area under of the curve (AUC)=0.785) compared with that of the STS risk score (AUC=0.811; P=0.18 comparing the two). However, specific factors that were significant in the original SinoSCORE but that lacked significance in our derived model included body mass index, preoperative atrial fibrillation and chronic obstructive pulmonary disease. SinoSCORE demonstrated limited discrimination when applied to an American population. The derived SinoSCORE had a discrimination comparable with that of the STS, suggesting underlying similarities of physiological substrate undergoing surgery. However, differential influence of various risk factors suggests that there may be varying degrees of importance and interactions between risk factors. Clinicians should exercise caution when applying risk models across varying populations due to potential differences that racial, ethnic and geographic factors may play in cardiac disease and surgical outcomes.

Financial Distress Prediction Using Discrete-time Hazard Model and Rating Transition Matrix Approach

NASA Astrophysics Data System (ADS)

Tsai, Bi-Huei; Chang, Chih-Huei

2009-08-01

Previous studies used constant cut-off indicator to distinguish distressed firms from non-distressed ones in the one-stage prediction models. However, distressed cut-off indicator must shift according to economic prosperity, rather than remains fixed all the time. This study focuses on Taiwanese listed firms and develops financial distress prediction models based upon the two-stage method. First, this study employs the firm-specific financial ratio and market factors to measure the probability of financial distress based on the discrete-time hazard models. Second, this paper further focuses on macroeconomic factors and applies rating transition matrix approach to determine the distressed cut-off indicator. The prediction models are developed by using the training sample from 1987 to 2004, and their levels of accuracy are compared with the test sample from 2005 to 2007. As for the one-stage prediction model, the model in incorporation with macroeconomic factors does not perform better than that without macroeconomic factors. This suggests that the accuracy is not improved for one-stage models which pool the firm-specific and macroeconomic factors together. In regards to the two stage models, the negative credit cycle index implies the worse economic status during the test period, so the distressed cut-off point is adjusted to increase based on such negative credit cycle index. After the two-stage models employ such adjusted cut-off point to discriminate the distressed firms from non-distressed ones, their error of misclassification becomes lower than that of one-stage ones. The two-stage models presented in this paper have incremental usefulness in predicting financial distress.

Analysis on trust influencing factors and trust model from multiple perspectives of online Auction

NASA Astrophysics Data System (ADS)

Yu, Wang

2017-10-01

Current reputation models lack the research on online auction trading completely so they cannot entirely reflect the reputation status of users and may cause problems on operability. To evaluate the user trust in online auction correctly, a trust computing model based on multiple influencing factors is established. It aims at overcoming the efficiency of current trust computing methods and the limitations of traditional theoretical trust models. The improved model comprehensively considers the trust degree evaluation factors of three types of participants according to different participation modes of online auctioneers, to improve the accuracy, effectiveness and robustness of the trust degree. The experiments test the efficiency and the performance of our model under different scale of malicious user, under environment like eBay and Sporas model. The experimental results analysis show the model proposed in this paper makes up the deficiency of existing model and it also has better feasibility.

Method for assessing coal-floor water-inrush risk based on the variable-weight model and unascertained measure theory

NASA Astrophysics Data System (ADS)

Wu, Qiang; Zhao, Dekang; Wang, Yang; Shen, Jianjun; Mu, Wenping; Liu, Honglei

2017-11-01

Water inrush from coal-seam floors greatly threatens mining safety in North China and is a complex process controlled by multiple factors. This study presents a mathematical assessment system for coal-floor water-inrush risk based on the variable-weight model (VWM) and unascertained measure theory (UMT). In contrast to the traditional constant-weight model (CWM), which assigns a fixed weight to each factor, the VWM varies with the factor-state value. The UMT employs the confidence principle, which is more effective in ordered partition problems than the maximum membership principle adopted in the former mathematical theory. The method is applied to the Datang Tashan Coal Mine in North China. First, eight main controlling factors are selected to construct the comprehensive evaluation index system. Subsequently, an incentive-penalty variable-weight model is built to calculate the variable weights of each factor. Then, the VWM-UMT model is established using the quantitative risk-grade divide of each factor according to the UMT. On this basis, the risk of coal-floor water inrush in Tashan Mine No. 8 is divided into five grades. For comparison, the CWM is also adopted for the risk assessment, and a differences distribution map is obtained between the two methods. Finally, the verification of water-inrush points indicates that the VWM-UMT model is powerful and more feasible and reasonable. The model has great potential and practical significance in future engineering applications.

Validation of the Sexual Orientation Microaggression Inventory In Two Diverse Samples of LGBTQ Youth

PubMed Central

Swann, Gregory; Minshew, Reese; Newcomb, Michael E.; Mustanski, Brian

2016-01-01

Critical race theory asserts that microaggressions, or low-level, covert acts of aggression, are commonplace in the lives of people of color. These theorists also assert a taxonomy of microaggressions, which includes “microassaults,” “microinsults,” and “microinvalidations.” The theory of microaggressions has been adopted by researchers of LGBTQ communities. This study investigated the three-factor taxonomy as it relates to a diverse sample of LGBTQ youth using the newly developed Sexual Orientation Microaggression Inventory (SOMI). Exploratory factor analysis was used to determine the number of factors that exist in SOMI in a sample of 206 LGBTQ-identifying youth. Follow up confirmatory factor analyses (CFAs) were conducted in order to compare single factor, unrestricted four factor, second order, and bi-factor models in a separate sample of 363 young men who have sex with men. The best fitting model was used to predict victimization, depressive symptoms, and depression diagnosis in order to test validity. The best fitting model was a bi-factor model utilizing 19 of the original 26 items with a general factor and four specific factors representing anti-gay attitudes (“microinsults”), denial of homosexuality, heterosexism (“microinvalidations”), and societal disapproval (“microassaults”). Reliability analyses found that the majority of reliable variance was accounted for by the general factor. The general factor was a significant predictor of victimization and depressive symptoms, as well as unrelated to social desirability, suggesting convergent, criterion-related, and discriminant validity. SOMI emerged as a scale with evidence of validity for assessing exposure to microaggressions in a diverse sample of LGBTQ youth. PMID:27067241

Validation of the Sexual Orientation Microaggression Inventory in Two Diverse Samples of LGBTQ Youth.

PubMed

Swann, Gregory; Minshew, Reese; Newcomb, Michael E; Mustanski, Brian

2016-08-01

Critical race theory asserts that microaggressions, or low-level, covert acts of aggression, are commonplace in the lives of people of color. These theorists also assert a taxonomy of microaggressions, which includes "microassaults," "microinsults," and "microinvalidations". The theory of microaggressions has been adopted by researchers of LGBTQ communities. This study investigated the three-factor taxonomy as it relates to a diverse sample of LGBTQ youth using the newly developed Sexual Orientation Microaggression Inventory (SOMI). Exploratory factor analysis was used to determine the number of factors that exist in SOMI in a sample of 206 LGBTQ-identifying youth. Follow up confirmatory factor analyses were conducted in order to compare single-factor, unrestricted four-factor, second-order, and bi-factor models in a separate sample of 363 young men who have sex with men. The best fitting model was used to predict victimization, depressive symptoms, and depression diagnosis in order to test validity. The best fitting model was a bi-factor model utilizing 19 of the original 26 items with a general factor and four specific factors representing anti-gay attitudes ("microinsults"), denial of homosexuality, heterosexism ("microinvalidations"), and societal disapproval ("microassaults"). Reliability analyses found that the majority of reliable variance was accounted for by the general factor. The general factor was a significant predictor of victimization and depressive symptoms, as well as unrelated to social desirability, suggesting convergent, criterion-related, and discriminant validity. SOMI emerged as a scale with evidence of validity for assessing exposure to microaggressions in a diverse sample of LGBTQ youth.

Use of Cox's Cure Model to Establish Clinical Determinants of Long-Term Disease-Free Survival in Neoadjuvant-Chemotherapy-Treated Breast Cancer Patients without Pathologic Complete Response.

PubMed

Asano, Junichi; Hirakawa, Akihiro; Hamada, Chikuma; Yonemori, Kan; Hirata, Taizo; Shimizu, Chikako; Tamura, Kenji; Fujiwara, Yasuhiro

2013-01-01

In prognostic studies for breast cancer patients treated with neoadjuvant chemotherapy (NAC), the ordinary Cox proportional-hazards (PH) model has been often used to identify prognostic factors for disease-free survival (DFS). This model assumes that all patients eventually experience relapse or death. However, a subset of NAC-treated breast cancer patients never experience these events during long-term follow-up (>10 years) and may be considered clinically "cured." Clinical factors associated with cure have not been studied adequately. Because the ordinary Cox PH model cannot be used to identify such clinical factors, we used the Cox PH cure model, a recently developed statistical method. This model includes both a logistic regression component for the cure rate and a Cox regression component for the hazard for uncured patients. The purpose of this study was to identify the clinical factors associated with cure and the variables associated with the time to recurrence or death in NAC-treated breast cancer patients without a pathologic complete response, by using the Cox PH cure model. We found that hormone receptor status, clinical response, human epidermal growth factor receptor 2 status, histological grade, and the number of lymph node metastases were associated with cure.

New prognostic model for extranodal natural killer/T cell lymphoma, nasal type.

PubMed

Cai, Qingqing; Luo, Xiaolin; Zhang, Guanrong; Huang, Huiqiang; Huang, Hui; Lin, Tongyu; Jiang, Wenqi; Xia, Zhongjun; Young, Ken H

2014-09-01

Extranodal natural killer/T cell lymphoma, nasal type (ENKTL) is an aggressive disease with a poor prognosis, requiring risk stratification in affected patients. We designed a new prognostic model specifically for ENKTL to identify high-risk patients who need more aggressive therapy. We retrospectively reviewed 158 patients who were newly diagnosed with ENKTL. The estimated 5-year overall survival rate was 39.4 %. Independent prognostic factors included total protein (TP) <60 g/L, fasting blood glucose (FBG) >100 mg/dL, and Korean Prognostic Index (KPI) score ≥2. We constructed a new prognostic model by combining these prognostic factors: group 1 (64 cases (41.0 %)), no adverse factors; group 2 (58 cases (37.2 %)), one adverse factor; and group 3 (34 cases (21.8 %)), two or three adverse factors. The 5-year overall survival (OS) rates of these groups were 66.7, 23.0, and 5.9 %, respectively (p < 0.001). Our new prognostic model had a better prognostic value than did the KPI model alone (p < 0.001). Our proposed prognostic model for ENKTL, including the newly identified prognostic indicators, TP and FBG, demonstrated a balanced distribution of patients into different risk groups with better prognostic discrimination compared with the KPI model alone.

Determination of effective loss factors in reduced SEA models

NASA Astrophysics Data System (ADS)

Chimeno Manguán, M.; Fernández de las Heras, M. J.; Roibás Millán, E.; Simón Hidalgo, F.

2017-01-01

The definition of Statistical Energy Analysis (SEA) models for large complex structures is highly conditioned by the classification of the structure elements into a set of coupled subsystems and the subsequent determination of the loss factors representing both the internal damping and the coupling between subsystems. The accurate definition of the complete system can lead to excessively large models as the size and complexity increases. This fact can also rise practical issues for the experimental determination of the loss factors. This work presents a formulation of reduced SEA models for incomplete systems defined by a set of effective loss factors. This reduced SEA model provides a feasible number of subsystems for the application of the Power Injection Method (PIM). For structures of high complexity, their components accessibility can be restricted, for instance internal equipments or panels. For these cases the use of PIM to carry out an experimental SEA analysis is not possible. New methods are presented for this case in combination with the reduced SEA models. These methods allow defining some of the model loss factors that could not be obtained through PIM. The methods are validated with a numerical analysis case and they are also applied to an actual spacecraft structure with accessibility restrictions: a solar wing in folded configuration.

Analyzing musculoskeletal neck pain, measured as present pain and periods of pain, with three different regression models: a cohort study.

PubMed

Grimby-Ekman, Anna; Andersson, Eva M; Hagberg, Mats

2009-06-19

In the literature there are discussions on the choice of outcome and the need for more longitudinal studies of musculoskeletal disorders. The general aim of this longitudinal study was to analyze musculoskeletal neck pain, in a group of young adults. Specific aims were to determine whether psychosocial factors, computer use, high work/study demands, and lifestyle are long-term or short-term factors for musculoskeletal neck pain, and whether these factors are important for developing or ongoing musculoskeletal neck pain. Three regression models were used to analyze the different outcomes. Pain at present was analyzed with a marginal logistic model, for number of years with pain a Poisson regression model was used and for developing and ongoing pain a logistic model was used. Presented results are odds ratios and proportion ratios (logistic models) and rate ratios (Poisson model). The material consisted of web-based questionnaires answered by 1204 Swedish university students from a prospective cohort recruited in 2002. Perceived stress was a risk factor for pain at present (PR = 1.6), for developing pain (PR = 1.7) and for number of years with pain (RR = 1.3). High work/study demands was associated with pain at present (PR = 1.6); and with number of years with pain when the demands negatively affect home life (RR = 1.3). Computer use pattern (number of times/week with a computer session > or = 4 h, without break) was a risk factor for developing pain (PR = 1.7), but also associated with pain at present (PR = 1.4) and number of years with pain (RR = 1.2). Among life style factors smoking (PR = 1.8) was found to be associated to pain at present. The difference between men and women in prevalence of musculoskeletal pain was confirmed in this study. It was smallest for the outcome ongoing pain (PR = 1.4) compared to pain at present (PR = 2.4) and developing pain (PR = 2.5). By using different regression models different aspects of neck pain pattern could be addressed and the risk factors impact on pain pattern was identified. Short-term risk factors were perceived stress, high work/study demands and computer use pattern (break pattern). Those were also long-term risk factors. For developing pain perceived stress and computer use pattern were risk factors.

Analyzing musculoskeletal neck pain, measured as present pain and periods of pain, with three different regression models: a cohort study

PubMed Central

Grimby-Ekman, Anna; Andersson, Eva M; Hagberg, Mats

2009-01-01

Background In the literature there are discussions on the choice of outcome and the need for more longitudinal studies of musculoskeletal disorders. The general aim of this longitudinal study was to analyze musculoskeletal neck pain, in a group of young adults. Specific aims were to determine whether psychosocial factors, computer use, high work/study demands, and lifestyle are long-term or short-term factors for musculoskeletal neck pain, and whether these factors are important for developing or ongoing musculoskeletal neck pain. Methods Three regression models were used to analyze the different outcomes. Pain at present was analyzed with a marginal logistic model, for number of years with pain a Poisson regression model was used and for developing and ongoing pain a logistic model was used. Presented results are odds ratios and proportion ratios (logistic models) and rate ratios (Poisson model). The material consisted of web-based questionnaires answered by 1204 Swedish university students from a prospective cohort recruited in 2002. Results Perceived stress was a risk factor for pain at present (PR = 1.6), for developing pain (PR = 1.7) and for number of years with pain (RR = 1.3). High work/study demands was associated with pain at present (PR = 1.6); and with number of years with pain when the demands negatively affect home life (RR = 1.3). Computer use pattern (number of times/week with a computer session ≥ 4 h, without break) was a risk factor for developing pain (PR = 1.7), but also associated with pain at present (PR = 1.4) and number of years with pain (RR = 1.2). Among life style factors smoking (PR = 1.8) was found to be associated to pain at present. The difference between men and women in prevalence of musculoskeletal pain was confirmed in this study. It was smallest for the outcome ongoing pain (PR = 1.4) compared to pain at present (PR = 2.4) and developing pain (PR = 2.5). Conclusion By using different regression models different aspects of neck pain pattern could be addressed and the risk factors impact on pain pattern was identified. Short-term risk factors were perceived stress, high work/study demands and computer use pattern (break pattern). Those were also long-term risk factors. For developing pain perceived stress and computer use pattern were risk factors. PMID:19545386

A conceptual model of psychosocial risk and protective factors for excessive gestational weight gain.

PubMed

Hill, Briony; Skouteris, Helen; McCabe, Marita; Milgrom, Jeannette; Kent, Bridie; Herring, Sharon J; Hartley-Clark, Linda; Gale, Janette

2013-02-01

nearly half of all women exceed the guideline recommended pregnancy weight gain for their Body Mass Index (BMI) category. Excessive gestational weight gain (GWG) is correlated positively with postpartum weight retention and is a predictor of long-term, higher BMI in mothers and their children. Psychosocial factors are generally not targeted in GWG behaviour change interventions, however, multifactorial, conceptual models that include these factors, may be useful in determining the pathways that contribute to excessive GWG. We propose a conceptual model, underpinned by health behaviour change theory, which outlines the psychosocial determinants of GWG, including the role of motivation and self-efficacy towards healthy behaviours. This model is based on a review of the existing literature in this area. there is increasing evidence to show that psychosocial factors, such as increased depressive symptoms, anxiety, lower self-esteem and body image dissatisfaction, are associated with excessive GWG. What is less known is how these factors might lead to excessive GWG. Our conceptual model proposes a pathway of factors that affect GWG, and may be useful for understanding the mechanisms by which interventions impact on weight management during pregnancy. This involves tracking the relationships among maternal psychosocial factors, including body image concerns, motivation to adopt healthy lifestyle behaviours, confidence in adopting healthy lifestyle behaviours for the purposes of weight management, and actual behaviour changes. health-care providers may improve weight gain outcomes in pregnancy if they assess and address psychosocial factors in pregnancy. Copyright © 2011 Elsevier Ltd. All rights reserved.

Prediction and Informative Risk Factor Selection of Bone Diseases.

PubMed

Li, Hui; Li, Xiaoyi; Ramanathan, Murali; Zhang, Aidong

2015-01-01

With the booming of healthcare industry and the overwhelming amount of electronic health records (EHRs) shared by healthcare institutions and practitioners, we take advantage of EHR data to develop an effective disease risk management model that not only models the progression of the disease, but also predicts the risk of the disease for early disease control or prevention. Existing models for answering these questions usually fall into two categories: the expert knowledge based model or the handcrafted feature set based model. To fully utilize the whole EHR data, we will build a framework to construct an integrated representation of features from all available risk factors in the EHR data and use these integrated features to effectively predict osteoporosis and bone fractures. We will also develop a framework for informative risk factor selection of bone diseases. A pair of models for two contrast cohorts (e.g., diseased patients versus non-diseased patients) will be established to discriminate their characteristics and find the most informative risk factors. Several empirical results on a real bone disease data set show that the proposed framework can successfully predict bone diseases and select informative risk factors that are beneficial and useful to guide clinical decisions.

Can theory predict the process of suicide on entry to prison? Predicting dynamic risk factors for suicide ideation in a high-risk prison population.

PubMed

Slade, Karen; Edelman, Robert

2014-01-01

Each year approximately 110,000 people are imprisoned in England and Wales and new prisoners remain one of the highest risk groups for suicide across the world. The reduction of suicide in prisoners remains difficult as assessments and interventions tend to rely on static risk factors with few theoretical or integrated models yet evaluated. To identify the dynamic factors that contribute to suicide ideation in this population based on Williams and Pollock's (2001) Cry of Pain (CoP) model. New arrivals (N = 198) into prison were asked to complete measures derived from the CoP model plus clinical and prison-specific factors. It was hypothesized that the factors of the CoP model would be predictive of suicide ideation. Support was provided for the defeat and entrapment aspects of the CoP model with previous self-harm, repeated times in prison, and suicide-permissive cognitions also key in predicting suicide ideation for prisoners on entry to prison. An integrated and dynamic model was developed that has utility in predicting suicide in early-stage prisoners. Implications for both theory and practice are discussed along with recommendations for future research.

Item Parameter Estimation for the MIRT Model: Bias and Precision of Confirmatory Factor Analysis-Based Models

ERIC Educational Resources Information Center

Finch, Holmes

2010-01-01

The accuracy of item parameter estimates in the multidimensional item response theory (MIRT) model context is one that has not been researched in great detail. This study examines the ability of two confirmatory factor analysis models specifically for dichotomous data to properly estimate item parameters using common formulae for converting factor…

An Introduction to Human Factors and Combat Models

DTIC Science & Technology

1989-03-01

Combat Models by Timothy F. Schroth Captain, United States Army B . A., Temple University, 1982 Submitted in partial fulfillment of the requirements for...INTRODUCTION .......... ................. 4 B . DEFINING HUMAN FACTORS - AN HISTORICAL APPROACH 4 C. BEFORE/AFTER THE BATTLE ...... ........... 8 1. Culture...16 III. COMBAT MODELS ....... .................. 18 A. INTRODUCTION ....... ................. 18 B . PURPOSE OF COMBAT MODELS ... ........... 20 1

Constrained Maximum Likelihood Estimation for Two-Level Mean and Covariance Structure Models

ERIC Educational Resources Information Center

Bentler, Peter M.; Liang, Jiajuan; Tang, Man-Lai; Yuan, Ke-Hai

2011-01-01

Maximum likelihood is commonly used for the estimation of model parameters in the analysis of two-level structural equation models. Constraints on model parameters could be encountered in some situations such as equal factor loadings for different factors. Linear constraints are the most common ones and they are relatively easy to handle in…

A Model of E-Learning Uptake and Continued Use in Higher Education Institutions

ERIC Educational Resources Information Center

Pinpathomrat, Nakarin; Gilbert, Lester; Wills, Gary B.

2013-01-01

This research investigates the factors that affect a students' take-up and continued use of E-learning. A mathematical model was constructed by applying three grounded theories; Unified Theory of Acceptance and Use of Technology, Keller's ARCS model, and Expectancy Disconfirm Theory. The learning preference factor was included in the model.…

Attachment change processes in the early years of marriage.

PubMed

Davila, J; Karney, B R; Bradbury, T N

1999-05-01

The authors examined 4 models of attachment change: a contextual model, a social-cognitive model, an individual-difference model, and a diathesis-stress model. Models were examined in a sample of newlyweds over the first 2 years of marriage, using growth curve analyses. Reciprocal processes, whereby attachment representations and interpersonal life circumstances affect one another over time, also were studied. On average, newlyweds became more secure over time. However, there was significant within-subject variability on attachment change that was predicted by intra- and interpersonal factors. Attachment representations changed in response to contextual, social-cognitive, and individual-difference factors. Reciprocal processes between attachment representations and marital variables emerged, suggesting that these factors influence one another in an ongoing way.

Asymptotic behaviour of two-point functions in multi-species models

NASA Astrophysics Data System (ADS)

Kozlowski, Karol K.; Ragoucy, Eric

2016-05-01

We extract the long-distance asymptotic behaviour of two-point correlation functions in massless quantum integrable models containing multi-species excitations. For such a purpose, we extend to these models the method of a large-distance regime re-summation of the form factor expansion of correlation functions. The key feature of our analysis is a technical hypothesis on the large-volume behaviour of the form factors of local operators in such models. We check the validity of this hypothesis on the example of the SU (3)-invariant XXX magnet by means of the determinant representations for the form factors of local operators in this model. Our approach confirms the structure of the critical exponents obtained previously for numerous models solvable by the nested Bethe Ansatz.

Moving in Parallel Toward a Modern Modeling Epistemology: Bayes Factors and Frequentist Modeling Methods.

PubMed

Rodgers, Joseph Lee

2016-01-01

The Bayesian-frequentist debate typically portrays these statistical perspectives as opposing views. However, both Bayesian and frequentist statisticians have expanded their epistemological basis away from a singular focus on the null hypothesis, to a broader perspective involving the development and comparison of competing statistical/mathematical models. For frequentists, statistical developments such as structural equation modeling and multilevel modeling have facilitated this transition. For Bayesians, the Bayes factor has facilitated this transition. The Bayes factor is treated in articles within this issue of Multivariate Behavioral Research. The current presentation provides brief commentary on those articles and more extended discussion of the transition toward a modern modeling epistemology. In certain respects, Bayesians and frequentists share common goals.

Flexible modeling improves assessment of prognostic value of C-reactive protein in advanced non-small cell lung cancer

PubMed Central

Gagnon, B; Abrahamowicz, M; Xiao, Y; Beauchamp, M-E; MacDonald, N; Kasymjanova, G; Kreisman, H; Small, D

2010-01-01

Background: C-reactive protein (CRP) is gaining credibility as a prognostic factor in different cancers. Cox's proportional hazard (PH) model is usually used to assess prognostic factors. However, this model imposes a priori assumptions, which are rarely tested, that (1) the hazard ratio associated with each prognostic factor remains constant across the follow-up (PH assumption) and (2) the relationship between a continuous predictor and the logarithm of the mortality hazard is linear (linearity assumption). Methods: We tested these two assumptions of the Cox's PH model for CRP, using a flexible statistical model, while adjusting for other known prognostic factors, in a cohort of 269 patients newly diagnosed with non-small cell lung cancer (NSCLC). Results: In the Cox's PH model, high CRP increased the risk of death (HR=1.11 per each doubling of CRP value, 95% CI: 1.03–1.20, P=0.008). However, both the PH assumption (P=0.033) and the linearity assumption (P=0.015) were rejected for CRP, measured at the initiation of chemotherapy, which kept its prognostic value for approximately 18 months. Conclusion: Our analysis shows that flexible modeling provides new insights regarding the value of CRP as a prognostic factor in NSCLC and that Cox's PH model underestimates early risks associated with high CRP. PMID:20234363

Effects of source shape on the numerical aperture factor with a geometrical-optics model.

PubMed

Wan, Der-Shen; Schmit, Joanna; Novak, Erik

2004-04-01

We study the effects of an extended light source on the calibration of an interference microscope, also referred to as an optical profiler. Theoretical and experimental numerical aperture (NA) factors for circular and linear light sources along with collimated laser illumination demonstrate that the shape of the light source or effective aperture cone is critical for a correct NA factor calculation. In practice, more-accurate results for the NA factor are obtained when a linear approximation to the filament light source shape is used in a geometric model. We show that previously measured and derived NA factors show some discrepancies because a circular rather than linear approximation to the filament source was used in the modeling.

A general psychopathology factor in early adolescence.

PubMed

Patalay, Praveetha; Fonagy, Peter; Deighton, Jessica; Belsky, Jay; Vostanis, Panos; Wolpert, Miranda

2015-07-01

Recently, a general psychopathology dimension reflecting common aspects among disorders has been identified in adults. This has not yet been considered in children and adolescents, where the focus has been on externalising and internalising dimensions. To examine the existence, correlates and predictive value of a general psychopathology dimension in young people. Alternative factor models were estimated using self-reports of symptoms in a large community-based sample aged 11-13.5 years (N = 23 477), and resulting dimensions were assessed in terms of associations with external correlates and future functioning. Both a traditional two-factor model and a bi-factor model with a general psychopathology bi-factor fitted the data well. The general psychopathology bi-factor best predicted future psychopathology and academic attainment. Associations with correlates and factor loadings are discussed. A general psychopathology factor, which is equal across genders, can be identified in young people. Its associations with correlates and future functioning indicate that investigating this factor can increase our understanding of the aetiology, risk and correlates of psychopathology. © The Royal College of Psychiatrists 2015.

Probabilistic simulation of the human factor in structural reliability

NASA Technical Reports Server (NTRS)

Shah, Ashwin R.; Chamis, Christos C.

1991-01-01

Many structural failures have occasionally been attributed to human factors in engineering design, analyses maintenance, and fabrication processes. Every facet of the engineering process is heavily governed by human factors and the degree of uncertainty associated with them. Factors such as societal, physical, professional, psychological, and many others introduce uncertainties that significantly influence the reliability of human performance. Quantifying human factors and associated uncertainties in structural reliability require: (1) identification of the fundamental factors that influence human performance, and (2) models to describe the interaction of these factors. An approach is being developed to quantify the uncertainties associated with the human performance. This approach consists of a multi factor model in conjunction with direct Monte-Carlo simulation.

The Role of Light in the Emergence of Weeds: Using Camelina microcarpa as an Example.

PubMed

Royo-Esnal, Aritz; Gesch, Russell W; Forcella, Frank; Torra, Joel; Recasens, Jordi; Necajeva, Jevgenija

2015-01-01

When modelling the emergence of weeds, two main factors are considered that condition this process: temperature and soil moisture. Optimum temperature is necessary for metabolic processes that generate energy for growth, while turgor pressure is necessary for root and shoot elongation which eventually leads to seedling emergence from the soil. Most emergence models do not usually consider light as a residual factor, but it could have an important role as it can alter directly or indirectly the dormancy and germination of seeds. In this paper, inclusion of light as an additional factor to photoperiod and radiation in emergence models is explored and compared with the classical hydrothermal time (HTT) model using Camelina microcarpa as an example. HTT based on hourly estimates is also compared with that based on daily estimates. Results suggest that, although HTT based models are accurate enough for local applications, the precision of these models is improved when HTT is estimated hourly and solar radiation is included as a factor.

The Rosenberg Self-Esteem Scale: a bifactor answer to a two-factor question?

PubMed

McKay, Michael T; Boduszek, Daniel; Harvey, Séamus A

2014-01-01

Despite its long-standing and widespread use, disagreement remains regarding the structure of the Rosenberg Self-Esteem Scale (RSES). In particular, concern remains regarding the degree to which the scale assesses self-esteem as a unidimensional or multidimensional (positive and negative self-esteem) construct. Using a sample of 3,862 high school students in the United Kingdom, 4 models were tested: (a) a unidimensional model, (b) a correlated 2-factor model in which the 2 latent variables are represented by positive and negative self-esteem, (c) a hierarchical model, and (d) a bifactor model. The totality of results including item loadings, goodness-of-fit indexes, reliability estimates, and correlations with self-efficacy measures all supported the bifactor model, suggesting that the 2 hypothesized factors are better understood as "grouping" factors rather than as representative of latent constructs. Accordingly, this study supports the unidimensionality of the RSES and the scoring of all 10 items to produce a global self-esteem score.

Evaluation of finite-element models and stress-intensity factors for surface cracks emanating from stress concentrations

NASA Technical Reports Server (NTRS)

Tan, P. W.; Raju, I. S.; Shivakumar, K. N.; Newman, J. C., Jr.

1990-01-01

A re-evaluation of the 3-D finite-element models and methods used to analyze surface crack at stress concentrations is presented. Previous finite-element models used by Raju and Newman for surface and corner cracks at holes were shown to have ill-shaped elements at the intersection of the hole and crack boundaries. Improved models, without these ill-shaped elements, were developed for a surface crack at a circular hole and at a semi-circular edge notch. Stress-intensity factors were calculated by both the nodal-force and virtual-crack-closure methods. Comparisons made between the previously developed stress-intensity factor equations and the results from the improved models agreed well except for configurations with large notch-radii-to-plate-thickness ratios. Stress-intensity factors for a semi-elliptical surface crack located at the center of a semi-circular edge notch in a plate subjected to remote tensile loadings were calculated using the improved models.

Entrance and exit region friction factor models for annular seal analysis. Ph.D. Thesis

NASA Technical Reports Server (NTRS)

Elrod, David Alan

1988-01-01

The Mach number definition and boundary conditions in Nelson's nominally-centered, annular gas seal analysis are revised. A method is described for determining the wall shear stress characteristics of an annular gas seal experimentally. Two friction factor models are developed for annular seal analysis; one model is based on flat-plate flow theory; the other uses empirical entrance and exit region friction factors. The friction factor predictions of the models are compared to experimental results. Each friction model is used in an annular gas seal analysis. The seal characteristics predicted by the two seal analyses are compared to experimental results and to the predictions of Nelson's analysis. The comparisons are for smooth-rotor seals with smooth and honeycomb stators. The comparisons show that the analysis which uses empirical entrance and exit region shear stress models predicts the static and stability characteristics of annular gas seals better than the other analyses. The analyses predict direct stiffness poorly.

Sensitivity Analysis of earth and environmental models: a systematic review to guide scientific advancement

NASA Astrophysics Data System (ADS)

Wagener, Thorsten; Pianosi, Francesca

2016-04-01

Sensitivity Analysis (SA) investigates how the variation in the output of a numerical model can be attributed to variations of its input factors. SA is increasingly being used in earth and environmental modelling for a variety of purposes, including uncertainty assessment, model calibration and diagnostic evaluation, dominant control analysis and robust decision-making. Here we provide some practical advice regarding best practice in SA and discuss important open questions based on a detailed recent review of the existing body of work in SA. Open questions relate to the consideration of input factor interactions, methods for factor mapping and the formal inclusion of discrete factors in SA (for example for model structure comparison). We will analyse these questions using relevant examples and discuss possible ways forward. We aim at stimulating the discussion within the community of SA developers and users regarding the setting of good practices and on defining priorities for future research.

Assessing the latent structure of DSM-5 PTSD among Chinese adolescents after the Ya'an earthquake.

PubMed

Zhou, Xiao; Wu, Xinchun; Zhen, Rui

2017-08-01

To examine the underlying substructure of DSM-5 PTSD in an adolescent sample, this study used a confirmatory factor analysis alternative model approach to assess 813 adolescents two and a half years after the Ya'an earthquake. Participants completed the PTSD Checklist for DSM-5, the Center for Epidemiologic Studies Depression Scale for Children, and the Screen for Child Anxiety Related Emotional Disorders. The results found that the seven-factor hybrid PTSD model entailing intrusion, avoidance, negative affect, anhedonia, externalizing behaviors, anxious arousal, and dysphoric arousal had significantly better fit indices than other alternative models. Depression and anxiety displayed high correlations with the seven-factor model. The findings suggested that the seven-factor model was more applicable to adolescents following the earthquake, and may carry important implications for further clinical practice and research on posttraumatic stress symptomatology. Copyright © 2017 Elsevier Ireland Ltd. All rights reserved.

[Mathematic concept model of accumulation of functional disorders associated with environmental factors].

PubMed

Zaĭtseva, N V; Trusov, P V; Kir'ianov, D A

2012-01-01

The mathematic concept model presented describes accumulation of functional disorders associated with environmental factors, plays predictive role and is designed for assessments of possible effects caused by heterogenous factors with variable exposures. Considering exposure changes with self-restoration process opens prospects of using the model to evaluate, analyse and manage occupational risks. To develop current theoretic approaches, the authors suggested a model considering age-related body peculiarities, systemic interactions of organs, including neuro-humoral regulation, accumulation of functional disorders due to external factors, rehabilitation of functions during treatment. General objective setting covers defining over a hundred unknow coefficients that characterize speed of various processes within the body. To solve this problem, the authors used iteration approach, successive identification, that starts from the certain primary approximation of the model parameters and processes subsequent updating on the basis of new theoretic and empirical knowledge.

Two-vehicle injury severity models based on integration of pavement management and traffic engineering factors.

PubMed

Jiang, Ximiao; Huang, Baoshan; Yan, Xuedong; Zaretzki, Russell L; Richards, Stephen

2013-01-01

The severity of traffic-related injuries has been studied by many researchers in recent decades. However, the evaluation of many factors is still in dispute and, until this point, few studies have taken into account pavement management factors as points of interest. The objective of this article is to evaluate the combined influences of pavement management factors and traditional traffic engineering factors on the injury severity of 2-vehicle crashes. This study examines 2-vehicle rear-end, sideswipe, and angle collisions that occurred on Tennessee state routes from 2004 to 2008. Both the traditional ordered probit (OP) model and Bayesian ordered probit (BOP) model with weak informative prior were fitted for each collision type. The performances of these models were evaluated based on the parameter estimates and deviances. The results indicated that pavement management factors played identical roles in all 3 collision types. Pavement serviceability produces significant positive effects on the severity of injuries. The pavement distress index (PDI), rutting depth (RD), and rutting depth difference between right and left wheels (RD_df) were not significant in any of these 3 collision types. The effects of traffic engineering factors varied across collision types, except that a few were consistently significant in all 3 collision types, such as annual average daily traffic (AADT), rural-urban location, speed limit, peaking hour, and light condition. The findings of this study indicated that improved pavement quality does not necessarily lessen the severity of injuries when a 2-vehicle crash occurs. The effects of traffic engineering factors are not universal but vary by the type of crash. The study also found that the BOP model with a weak informative prior can be used as an alternative but was not superior to the traditional OP model in terms of overall performance.

Pragmatics fragmented: the factor structure of the Dutch children's communication checklist (CCC).

PubMed

Geurts, Hilde M; Hartman, Catharina; Verté, Sylvie; Oosterlaan, Jaap; Roeyers, Herbert; Sergeant, Joseph A

2009-01-01

A number of disorders are associated with pragmatic difficulties. Instruments that can make subdivisions within the larger construct of pragmatics could be important tools for disentangling profiles of pragmatic difficulty in different disorders. The deficits underlying the observed pragmatic difficulties may be different for different disorders. To study the construct validity of a pragmatic language questionnaire. The construct of pragmatics is studied by applying exploratory factor analysis (EFA) and confirmatory factor analysis to the parent version of the Dutch Children's Communication Checklist (CCC; Bishop 1998 ). Parent ratings of 1589 typically developing children and 481 children with a clinical diagnosis were collected. Four different factor models derived from the original CCC scales and five different factor models based on EFA were compared with each other. The models were cross-validated. The EFA-derived models were substantively different from the originally proposed CCC factor structure. EFA models gave a slightly better fit than the models based on the original CCC scales, though neither provided a good fit to the parent data. Coherence seemed to be part of language form and not of pragmatics, which is in line with the adaptation of the CCC, the CCC-2 (Bishop 2003 ). Most pragmatic items clustered together in one factor and these pragmatic items also clustered with items related to social relationships and specific interests. The nine scales of the original CCC do not reflect the underlying factor structure. Therefore, scale composition may be improved on and scores on subscale level need to be interpreted cautiously. Therefore, in interpreting the CCC profiles, the overall measure might be more informative than the postulated subscales as more information is needed to determine which constructs the suggested subscales are actually measuring.

Examining the integrity of measurement of cognitive abilities in the prediction of achievement: Comparisons and contrasts across variables from higher-order and bifactor models.

PubMed

Benson, Nicholas F; Kranzler, John H; Floyd, Randy G

2016-10-01

Prior research examining cognitive ability and academic achievement relations have been based on different theoretical models, have employed both latent variables as well as observed variables, and have used a variety of analytic methods. Not surprisingly, results have been inconsistent across studies. The aims of this study were to (a) examine how relations between psychometric g, Cattell-Horn-Carroll (CHC) broad abilities, and academic achievement differ across higher-order and bifactor models; (b) examine how well various types of observed scores corresponded with latent variables; and (c) compare two types of observed scores (i.e., refined and non-refined factor scores) as predictors of academic achievement. Results suggest that cognitive-achievement relations vary across theoretical models and that both types of factor scores tend to correspond well with the models on which they are based. However, orthogonal refined factor scores (derived from a bifactor model) have the advantage of controlling for multicollinearity arising from the measurement of psychometric g across all measures of cognitive abilities. Results indicate that the refined factor scores provide more precise representations of their targeted constructs than non-refined factor scores and maintain close correspondence with the cognitive-achievement relations observed for latent variables. Thus, we argue that orthogonal refined factor scores provide more accurate representations of the relations between CHC broad abilities and achievement outcomes than non-refined scores do. Further, the use of refined factor scores addresses calls for the application of scores based on latent variable models. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Testing a cognitive model to predict posttraumatic stress disorder following childbirth.

PubMed

King, Lydia; McKenzie-McHarg, Kirstie; Horsch, Antje

2017-01-14

One third of women describes their childbirth as traumatic and between 0.8 and 6.9% goes on to develop posttraumatic stress disorder (PTSD). The cognitive model of PTSD has been shown to be applicable to a range of trauma samples. However, childbirth is qualitatively different to other trauma types and special consideration needs to be taken when applying it to this population. Previous studies have investigated some cognitive variables in isolation but no study has so far looked at all the key processes described in the cognitive model. This study therefore aimed to investigate whether theoretically-derived variables of the cognitive model explain unique variance in postnatal PTSD symptoms when key demographic, obstetric and clinical risk factors are controlled for. One-hundred and fifty-seven women who were between 1 and 12 months post-partum (M = 6.5 months) completed validated questionnaires assessing PTSD and depressive symptoms, childbirth experience, postnatal social support, trauma memory, peritraumatic processing, negative appraisals, dysfunctional cognitive and behavioural strategies and obstetric as well as demographic risk factors in an online survey. A PTSD screening questionnaire suggested that 5.7% of the sample might fulfil diagnostic criteria for PTSD. Overall, risk factors alone predicted 43% of variance in PTSD symptoms and cognitive behavioural factors alone predicted 72.7%. A final model including both risk factors and cognitive behavioural factors explained 73.7% of the variance in PTSD symptoms, 37.1% of which was unique variance predicted by cognitive factors. All variables derived from Ehlers and Clark's cognitive model significantly explained variance in PTSD symptoms following childbirth, even when clinical, demographic and obstetric were controlled for. Our findings suggest that the CBT model is applicable and useful as a way of understanding and informing the treatment of PTSD following childbirth.

MOVES (MOTOR VEHICLE EMISSION SIMULATOR) MODEL ...

EPA Pesticide Factsheets

A computer model, intended to eventually replace the MOBILE model and to incorporate the NONROAD model, that will provide the ability to estimate criteria and toxic air pollutant emission factors and emission inventories that are specific to the areas and time periods of interest, at scales ranging from local to national. Development of a new emission factor and inventory model for mobile source emissions. The model will be used by air pollution modelers within EPA, and at the State and local levels.

Proof of factorization using background field method of QCD

NASA Astrophysics Data System (ADS)

Nayak, Gouranga C.

2010-02-01

Factorization theorem plays the central role at high energy colliders to study standard model and beyond standard model physics. The proof of factorization theorem is given by Collins, Soper and Sterman to all orders in perturbation theory by using diagrammatic approach. One might wonder if one can obtain the proof of factorization theorem through symmetry considerations at the lagrangian level. In this paper we provide such a proof.

Proof of factorization using background field method of QCD

DOE Office of Scientific and Technical Information (OSTI.GOV)

Nayak, Gouranga C.

Factorization theorem plays the central role at high energy colliders to study standard model and beyond standard model physics. The proof of factorization theorem is given by Collins, Soper and Sterman to all orders in perturbation theory by using diagrammatic approach. One might wonder if one can obtain the proof of factorization theorem through symmetry considerations at the lagrangian level. In this paper we provide such a proof.

The asset pricing model of musharakah factors

NASA Astrophysics Data System (ADS)

Simon, Shahril; Omar, Mohd; Lazam, Norazliani Md

2015-02-01

The existing three-factor model developed by Fama and French for conventional investment was formulated based on risk-free rates element in which contradict with Shariah principles. We note that the underlying principles that govern Shariah investment were mutual risk and profit sharing between parties, the assurance of fairness for all and that transactions were based on an underlying asset. In addition, the three-factor model did not exclude stock that was not permissible by Shariah such as financial services based on riba (interest), gambling operator, manufacture or sale of non-halal products or related products and other activities deemed non-permissible according to Shariah. Our approach to construct the factor model for Shariah investment was based on the basic tenets of musharakah in tabulating the factors. We start by noting that Islamic stocks with similar characteristics should have similar returns and risks. This similarity between Islamic stocks was defined by the similarity of musharakah attributes such as business, management, profitability and capital. These attributes define factor exposures (or betas) to factors. The main takeaways were that musharakah attributes we chose had explain stock returns well in cross section and were significant in different market environments. The management factor seemed to be responsible for the general dynamics of the explanatory power.

Personality Correlates of Midlife Cardiometabolic Risk: The Explanatory Role of Higher-Order Factors of the Five Factor Model

PubMed Central

Dermody, Sarah S.; Wright, Aidan G.C.; Cheong, JeeWon; Miller, Karissa G.; Muldoon, Matthew F.; Flory, Janine D.; Gianaros, Peter J.; Marsland, Anna L.; Manuck, Stephen B.

2015-01-01

Objective Varying associations are reported between Five Factor Model (FFM) personality traits and cardiovascular diseaabolic risk within a hierarchical model of personality that posits higherse risk. Here, we further examine dispositional correlates of cardiomet -order traits of Stability (shared variance of Agreeableness, Conscientiousness, inverse Neuroticism) and Plasticity (Extraversion, Openness), and test hypothesized mediation via biological and behavioral factors. Method In an observational study of 856 community volunteers aged 30–54 years (46% male, 86% Caucasian), latent variable FFM traits (using multiple-informant reports) and aggregated cardiometabolic risk (indicators: insulin resistance, dyslipidemia, blood pressure, adiposity) were estimated using confirmatory factor analysis (CFA). The cardiometabolic factor was regressed on each personality factor or higher-order trait. Cross-sectional indirect effects via systemic inflammation, cardiac autonomic control, and physical activity were tested. Results CFA models confirmed the Stability “meta-trait,” but not Plasticity. Lower Stability was associated with heightened cardiometabolic risk. This association was accounted for by inflammation, autonomic function, and physical activity. Among FFM traits, only Openness was associated with risk over and above Stability and, unlike Stablity, this relationship was unexplained by the intervening variables. Conclusions A Stability meta-trait covaries with midlife cardiometabolic risk, and this association is accounted for by three candidate biological and behavioral factors. PMID:26249259

Predictive models and prognostic factors for upper tract urothelial carcinoma: a comprehensive review of the literature.

PubMed

Mbeutcha, Aurélie; Mathieu, Romain; Rouprêt, Morgan; Gust, Kilian M; Briganti, Alberto; Karakiewicz, Pierre I; Shariat, Shahrokh F

2016-10-01

In the context of customized patient care for upper tract urothelial carcinoma (UTUC), decision-making could be facilitated by risk assessment and prediction tools. The aim of this study was to provide a critical overview of existing predictive models and to review emerging promising prognostic factors for UTUC. A literature search of articles published in English from January 2000 to June 2016 was performed using PubMed. Studies on risk group stratification models and predictive tools in UTUC were selected, together with studies on predictive factors and biomarkers associated with advanced-stage UTUC and oncological outcomes after surgery. Various predictive tools have been described for advanced-stage UTUC assessment, disease recurrence and cancer-specific survival (CSS). Most of these models are based on well-established prognostic factors such as tumor stage, grade and lymph node (LN) metastasis, but some also integrate newly described prognostic factors and biomarkers. These new prediction tools seem to reach a high level of accuracy, but they lack external validation and decision-making analysis. The combinations of patient-, pathology- and surgery-related factors together with novel biomarkers have led to promising predictive tools for oncological outcomes in UTUC. However, external validation of these predictive models is a prerequisite before their introduction into daily practice. New models predicting response to therapy are urgently needed to allow accurate and safe individualized management in this heterogeneous disease.

Psychometric properties and Confirmatory structure of the Strengths and difficulties questionnaire in a sample of adolescents in Nigeria.

PubMed

Akpa, Onoja M; Afolabi, Rotimi F; Fowobaje, Kayode R

Though the SDQ has been used in selected studies in Nigeria, its theoretical structure has not been fully and appropriately investigated in the setting. The present study employs Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) to investigate the theoretical structure of the self-reported version of the SDQ in a sample of adolescents in Benue state, Nigeria. A total of 1,244 adolescents from different categories of secondary schools in Makurdi and Vandekya Local government areas of Benue state participated in the study. Preliminary data analyses were performed using descriptive statistics while the theoretical structure of the SDQ was assessed using EFA and CFA. Model fits were assessed using Chi-square test and other fit indices at 5% significance level. Participants were 14.19±2.45 (Vandekya) and 14.19±2.45 (Makurdi) years old. Results of the EFA and CFA revealed a 3-factor oblique model as the best model for the sample of adolescents studied ( χ 2 / df =2.20, p<0.001) with all fit indices yielding better results. A correlated 3-factor model fits the present data better than the 5-factor theoretical model of the SDQ. The use of the original 5-factor model of the SDQ in the present setting should be interpreted with caution.

The role of Patient Health Engagement Model (PHE-model) in affecting patient activation and medication adherence: A structural equation model

PubMed Central

Graffigna, Guendalina; Bonanomi, Andrea

2017-01-01

Background Increasing bodies of scientific research today examines the factors and interventions affecting patients’ ability to self-manage and adhere to treatment. Patient activation is considered the most reliable indicator of patients’ ability to manage health autonomously. Only a few studies have tried to assess the role of psychosocial factors in promoting patient activation. A more systematic modeling of the psychosocial factors explaining the variance of patient activation is needed. Objective To test the hypothesized effect of patient activation on medication adherence; to test the the hypothesized effects of positive emotions and of the quality of the patient/doctor relationship on patient activation; and to test the hypothesized mediating effect of Patient Health Engagement (PHE-model) in this pathway. Material and methods This cross-sectional study involved 352 Italian-speaking adult chronic patients. The survey included measures of i) patient activation (Patient Activation Measure 13 –short form); ii) Patient Health Engagement model (Patient Health Engagement Scale); iii) patient adherence (4 item-Morinsky Medication Adherence Scale); iv) the quality of the patients’ emotional feelings (Manikin Self Assessment Scale); v) the quality of the patient/doctor relationship (Health Care Climate Questionnaire). Structural equation modeling was used to test the hypotheses proposed. Results According to the theoretical model we hypothesized, research results confirmed that patients’ activation significantly affects their reported medication adherence. Moreover, psychosocial factors, such as the patients’ quality of the emotional feelings and the quality of the patient/doctor relationship were demonstrated to be factors affecting the level of patient activation. Finally, the mediation effect of the Patient Health Engagement model was confirmed by the analysis. Conclusions Consistently with the results of previous studies, these findings demonstrate that the Patient Health Engagement Model is a critical factor in enhancing the quality of care. The Patient Health Engagement Model might acts as a mechanism to increase patient activation and adherence. PMID:28654686

The role of Patient Health Engagement Model (PHE-model) in affecting patient activation and medication adherence: A structural equation model.

PubMed

Graffigna, Guendalina; Barello, Serena; Bonanomi, Andrea

2017-01-01

Increasing bodies of scientific research today examines the factors and interventions affecting patients' ability to self-manage and adhere to treatment. Patient activation is considered the most reliable indicator of patients' ability to manage health autonomously. Only a few studies have tried to assess the role of psychosocial factors in promoting patient activation. A more systematic modeling of the psychosocial factors explaining the variance of patient activation is needed. To test the hypothesized effect of patient activation on medication adherence; to test the the hypothesized effects of positive emotions and of the quality of the patient/doctor relationship on patient activation; and to test the hypothesized mediating effect of Patient Health Engagement (PHE-model) in this pathway. This cross-sectional study involved 352 Italian-speaking adult chronic patients. The survey included measures of i) patient activation (Patient Activation Measure 13 -short form); ii) Patient Health Engagement model (Patient Health Engagement Scale); iii) patient adherence (4 item-Morinsky Medication Adherence Scale); iv) the quality of the patients' emotional feelings (Manikin Self Assessment Scale); v) the quality of the patient/doctor relationship (Health Care Climate Questionnaire). Structural equation modeling was used to test the hypotheses proposed. According to the theoretical model we hypothesized, research results confirmed that patients' activation significantly affects their reported medication adherence. Moreover, psychosocial factors, such as the patients' quality of the emotional feelings and the quality of the patient/doctor relationship were demonstrated to be factors affecting the level of patient activation. Finally, the mediation effect of the Patient Health Engagement model was confirmed by the analysis. Consistently with the results of previous studies, these findings demonstrate that the Patient Health Engagement Model is a critical factor in enhancing the quality of care. The Patient Health Engagement Model might acts as a mechanism to increase patient activation and adherence.

Characterizing executive functioning in older special populations: from cognitively elite to cognitively impaired.

PubMed

de Frias, Cindy M; Dixon, Roger A; Strauss, Esther

2009-11-01

The authors examined the structure and invariance of executive functions (EF) across (a) a continuum of cognitive status in 3 groups of older adults (cognitively elite [CE], cognitively normal [CN], and cognitively impaired [CI]) and (b) a 3-year longitudinal interval. Using latent variable analyses (LISREL 8.80), the authors tested 3-factor models ("Inhibition": Hayling [Burgess & Shallice, 1997], Stroop [Regard, 1981]; "Shifting": Brixton [Burgess & Shallice, 1997], Color Trails [D'Elia et al., 1996]; and "Updating": Reading and Computational Span [Salthouse & Babcock, 1991]) and 1-factor models within each group. Participants (initial N = 570; 53-90 years) were from the Victoria Longitudinal Study (Sample 3, Waves 1 and 2). Cross-sectionally, the authors observed a 3-factor EF structure especially for the CE group and 1-factor solutions for all 3 groups. Longitudinally, temporal invariance was supported for the 3-factor model (CE and CN groups) and the 1-factor model (CI and CN groups). Subgroups with higher cognitive status and greater 3-year stability performed better on EF factors than corresponding groups with lower cognitive status and less stability. Studies of EF structure, performance, dedifferentiation, and dysfunction will benefit from considering initial cognitive status and longitudinal stability.