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.

Assessing School Work Culture: A Higher-Order Analysis and Strategy.

ERIC Educational Resources Information Center

Johnson, William L.; Johnson, Annabel M.; Zimmerman, Kurt J.

This paper reviews a work culture productivity model and reports the development of a work culture instrument based on the culture productivity model. Higher order principal components analysis was used to assess work culture, and a third-order factor analysis shows how the first-order factors group into higher-order factors. The school work…

A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis

ERIC Educational Resources Information Center

Edwards, Michael C.

2010-01-01

Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. The goal of this paper is to demonstrate a novel combination of various Markov chain Monte Carlo (MCMC) estimation routines to estimate parameters of a wide variety of confirmatory item factor analysis models. Further, I show…

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.

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.

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.

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…

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

Exploratory Bi-Factor Analysis: The Oblique Case

ERIC Educational Resources Information Center

Jennrich, Robert I.; Bentler, Peter M.

2012-01-01

Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford ("Psychometrika" 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler ("Psychometrika" 76:537-549, 2011) introduced an exploratory form of bi-factor…

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)

Factor Analysis via Components Analysis

ERIC Educational Resources Information Center

Bentler, Peter M.; de Leeuw, Jan

2011-01-01

When the factor analysis model holds, component loadings are linear combinations of factor loadings, and vice versa. This interrelation permits us to define new optimization criteria and estimation methods for exploratory factor analysis. Although this article is primarily conceptual in nature, an illustrative example and a small simulation show…

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.

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.

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.…

SEM-PLS Analysis of Inhibiting Factors of Cost Performance for Large Construction Projects in Malaysia: Perspective of Clients and Consultants

PubMed Central

Memon, Aftab Hameed; Rahman, Ismail Abdul

2014-01-01

This study uncovered inhibiting factors to cost performance in large construction projects of Malaysia. Questionnaire survey was conducted among clients and consultants involved in large construction projects. In the questionnaire, a total of 35 inhibiting factors grouped in 7 categories were presented to the respondents for rating significant level of each factor. A total of 300 questionnaire forms were distributed. Only 144 completed sets were received and analysed using advanced multivariate statistical software of Structural Equation Modelling (SmartPLS v2). The analysis involved three iteration processes where several of the factors were deleted in order to make the model acceptable. The result of the analysis found that R 2 value of the model is 0.422 which indicates that the developed model has a substantial impact on cost performance. Based on the final form of the model, contractor's site management category is the most prominent in exhibiting effect on cost performance of large construction projects. This finding is validated using advanced techniques of power analysis. This vigorous multivariate analysis has explicitly found the significant category which consists of several causative factors to poor cost performance in large construction projects. This will benefit all parties involved in construction projects for controlling cost overrun. PMID:24693227

SEM-PLS analysis of inhibiting factors of cost performance for large construction projects in Malaysia: perspective of clients and consultants.

PubMed

Memon, Aftab Hameed; Rahman, Ismail Abdul

2014-01-01

This study uncovered inhibiting factors to cost performance in large construction projects of Malaysia. Questionnaire survey was conducted among clients and consultants involved in large construction projects. In the questionnaire, a total of 35 inhibiting factors grouped in 7 categories were presented to the respondents for rating significant level of each factor. A total of 300 questionnaire forms were distributed. Only 144 completed sets were received and analysed using advanced multivariate statistical software of Structural Equation Modelling (SmartPLS v2). The analysis involved three iteration processes where several of the factors were deleted in order to make the model acceptable. The result of the analysis found that R(2) value of the model is 0.422 which indicates that the developed model has a substantial impact on cost performance. Based on the final form of the model, contractor's site management category is the most prominent in exhibiting effect on cost performance of large construction projects. This finding is validated using advanced techniques of power analysis. This vigorous multivariate analysis has explicitly found the significant category which consists of several causative factors to poor cost performance in large construction projects. This will benefit all parties involved in construction projects for controlling cost overrun.

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.

40 CFR 52.1490 - Original identification of plan.

Code of Federal Regulations, 2012 CFR

2012-07-01

... measures. (ii) A modeling analysis indicating 1982 attainment. (iii) Documentation of the modeling analysis... agencies, (ii) Additional supporting documentation for the 1982 attainment modeling analysis which included... factors for the model. (iii) A revised 1982 attainment modeling analysis and supporting documentation...

40 CFR 52.1490 - Original identification of plan.

Code of Federal Regulations, 2013 CFR

2013-07-01

... measures. (ii) A modeling analysis indicating 1982 attainment. (iii) Documentation of the modeling analysis... agencies, (ii) Additional supporting documentation for the 1982 attainment modeling analysis which included... factors for the model. (iii) A revised 1982 attainment modeling analysis and supporting documentation...

40 CFR 52.1490 - Original identification of plan.

Code of Federal Regulations, 2014 CFR

2014-07-01

... measures. (ii) A modeling analysis indicating 1982 attainment. (iii) Documentation of the modeling analysis... agencies, (ii) Additional supporting documentation for the 1982 attainment modeling analysis which included... factors for the model. (iii) A revised 1982 attainment modeling analysis and supporting documentation...

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.

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.

Bayesian Factor Analysis as a Variable Selection Problem: Alternative Priors and Consequences

PubMed Central

Lu, Zhao-Hua; Chow, Sy-Miin; Loken, Eric

2016-01-01

Factor analysis is a popular statistical technique for multivariate data analysis. Developments in the structural equation modeling framework have enabled the use of hybrid confirmatory/exploratory approaches in which factor loading structures can be explored relatively flexibly within a confirmatory factor analysis (CFA) framework. Recently, a Bayesian structural equation modeling (BSEM) approach (Muthén & Asparouhov, 2012) has been proposed as a way to explore the presence of cross-loadings in CFA models. We show that the issue of determining factor loading patterns may be formulated as a Bayesian variable selection problem in which Muthén and Asparouhov’s approach can be regarded as a BSEM approach with ridge regression prior (BSEM-RP). We propose another Bayesian approach, denoted herein as the Bayesian structural equation modeling with spike and slab prior (BSEM-SSP), which serves as a one-stage alternative to the BSEM-RP. We review the theoretical advantages and disadvantages of both approaches and compare their empirical performance relative to two modification indices-based approaches and exploratory factor analysis with target rotation. A teacher stress scale data set (Byrne, 2012; Pettegrew & Wolf, 1982) is used to demonstrate our approach. PMID:27314566

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.

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…

Detecting Outliers in Factor Analysis Using the Forward Search Algorithm

ERIC Educational Resources Information Center

Mavridis, Dimitris; Moustaki, Irini

2008-01-01

In this article we extend and implement the forward search algorithm for identifying atypical subjects/observations in factor analysis models. The forward search has been mainly developed for detecting aberrant observations in regression models (Atkinson, 1994) and in multivariate methods such as cluster and discriminant analysis (Atkinson, Riani,…

Dynamic Factor Analysis Models with Time-Varying Parameters

ERIC Educational Resources Information Center

Chow, Sy-Miin; Zu, Jiyun; Shifren, Kim; Zhang, Guangjian

2011-01-01

Dynamic factor analysis models with time-varying parameters offer a valuable tool for evaluating multivariate time series data with time-varying dynamics and/or measurement properties. We use the Dynamic Model of Activation proposed by Zautra and colleagues (Zautra, Potter, & Reich, 1997) as a motivating example to construct a dynamic factor…

Mixture Factor Analysis for Approximating a Nonnormally Distributed Continuous Latent Factor with Continuous and Dichotomous Observed Variables

ERIC Educational Resources Information Center

Wall, Melanie M.; Guo, Jia; Amemiya, Yasuo

2012-01-01

Mixture factor analysis is examined as a means of flexibly estimating nonnormally distributed continuous latent factors in the presence of both continuous and dichotomous observed variables. A simulation study compares mixture factor analysis with normal maximum likelihood (ML) latent factor modeling. Different results emerge for continuous versus…

Frequencies and Flutter Speed Estimation for Damaged Aircraft Wing Using Scaled Equivalent Plate Analysis

NASA Technical Reports Server (NTRS)

Krishnamurthy, Thiagarajan

2010-01-01

Equivalent plate analysis is often used to replace the computationally expensive finite element analysis in initial design stages or in conceptual design of aircraft wing structures. The equivalent plate model can also be used to design a wind tunnel model to match the stiffness characteristics of the wing box of a full-scale aircraft wing model while satisfying strength-based requirements An equivalent plate analysis technique is presented to predict the static and dynamic response of an aircraft wing with or without damage. First, a geometric scale factor and a dynamic pressure scale factor are defined to relate the stiffness, load and deformation of the equivalent plate to the aircraft wing. A procedure using an optimization technique is presented to create scaled equivalent plate models from the full scale aircraft wing using geometric and dynamic pressure scale factors. The scaled models are constructed by matching the stiffness of the scaled equivalent plate with the scaled aircraft wing stiffness. It is demonstrated that the scaled equivalent plate model can be used to predict the deformation of the aircraft wing accurately. Once the full equivalent plate geometry is obtained, any other scaled equivalent plate geometry can be obtained using the geometric scale factor. Next, an average frequency scale factor is defined as the average ratio of the frequencies of the aircraft wing to the frequencies of the full-scaled equivalent plate. The average frequency scale factor combined with the geometric scale factor is used to predict the frequency response of the aircraft wing from the scaled equivalent plate analysis. A procedure is outlined to estimate the frequency response and the flutter speed of an aircraft wing from the equivalent plate analysis using the frequency scale factor and geometric scale factor. The equivalent plate analysis is demonstrated using an aircraft wing without damage and another with damage. Both of the problems show that the scaled equivalent plate analysis can be successfully used to predict the frequencies and flutter speed of a typical aircraft wing.

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

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…

Exploring the Factor Structure of Neurocognitive Measures in Older Individuals

PubMed Central

Santos, Nadine Correia; Costa, Patrício Soares; Amorim, Liliana; Moreira, Pedro Silva; Cunha, Pedro; Cotter, Jorge; Sousa, Nuno

2015-01-01

Here we focus on factor analysis from a best practices point of view, by investigating the factor structure of neuropsychological tests and using the results obtained to illustrate on choosing a reasonable solution. The sample (n=1051 individuals) was randomly divided into two groups: one for exploratory factor analysis (EFA) and principal component analysis (PCA), to investigate the number of factors underlying the neurocognitive variables; the second to test the “best fit” model via confirmatory factor analysis (CFA). For the exploratory step, three extraction (maximum likelihood, principal axis factoring and principal components) and two rotation (orthogonal and oblique) methods were used. The analysis methodology allowed exploring how different cognitive/psychological tests correlated/discriminated between dimensions, indicating that to capture latent structures in similar sample sizes and measures, with approximately normal data distribution, reflective models with oblimin rotation might prove the most adequate. PMID:25880732

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.

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

How to Construct More Accurate Student Models: Comparing and Optimizing Knowledge Tracing and Performance Factor Analysis

ERIC Educational Resources Information Center

Gong, Yue; Beck, Joseph E.; Heffernan, Neil T.

2011-01-01

Student modeling is a fundamental concept applicable to a variety of intelligent tutoring systems (ITS). However, there is not a lot of practical guidance on how to construct and train such models. This paper compares two approaches for student modeling, Knowledge Tracing (KT) and Performance Factors Analysis (PFA), by evaluating their predictive…

The Recoverability of P-Technique Factor Analysis

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

2009-01-01

It seems that just when we are about to lay P-technique factor analysis finally to rest as obsolete because of newer, more sophisticated multivariate time-series models using latent variables--dynamic factor models--it rears its head to inform us that an obituary may be premature. We present the results of some simulations demonstrating that even…

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

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.

Human Modeling for Ground Processing Human Factors Engineering Analysis

NASA Technical Reports Server (NTRS)

Stambolian, Damon B.; Lawrence, Brad A.; Stelges, Katrine S.; Steady, Marie-Jeanne O.; Ridgwell, Lora C.; Mills, Robert E.; Henderson, Gena; Tran, Donald; Barth, Tim

2011-01-01

There have been many advancements and accomplishments over the last few years using human modeling for human factors engineering analysis for design of spacecraft. The key methods used for this are motion capture and computer generated human models. The focus of this paper is to explain the human modeling currently used at Kennedy Space Center (KSC), and to explain the future plans for human modeling for future spacecraft designs

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.

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

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.

A Time Series Analysis: Weather Factors, Human Migration and Malaria Cases in Endemic Area of Purworejo, Indonesia, 2005–2014

PubMed Central

REJEKI, Dwi Sarwani Sri; NURHAYATI, Nunung; AJI, Budi; MURHANDARWATI, E. Elsa Herdiana; KUSNANTO, Hari

2018-01-01

Background: Climatic and weather factors become important determinants of vector-borne diseases transmission like malaria. This study aimed to prove relationships between weather factors with considering human migration and previous case findings and malaria cases in endemic areas in Purworejo during 2005–2014. Methods: This study employed ecological time series analysis by using monthly data. The independent variables were the maximum temperature, minimum temperature, maximum humidity, minimum humidity, precipitation, human migration, and previous malaria cases, while the dependent variable was positive malaria cases. Three models of count data regression analysis i.e. Poisson model, quasi-Poisson model, and negative binomial model were applied to measure the relationship. The least Akaike Information Criteria (AIC) value was also performed to find the best model. Negative binomial regression analysis was considered as the best model. Results: The model showed that humidity (lag 2), precipitation (lag 3), precipitation (lag 12), migration (lag1) and previous malaria cases (lag 12) had a significant relationship with malaria cases. Conclusion: Weather, migration and previous malaria cases factors need to be considered as prominent indicators for the increase of malaria case projection. PMID:29900134

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

Kinetic Model Facilitates Analysis of Fibrin Generation and Its Modulation by Clotting Factors: Implications for Hemostasis-Enhancing Therapies

DTIC Science & Technology

2014-01-01

facilitates analysis of fibrin generation and its modulation by clotting factors : implications for hemostasis-enhancing therapies† Alexander Y...investigate the ability of fibrinogen and a recently proposed prothrombin complex concentrate composition, PCC-AT (a combination of the clotting factors II...kinetics. Moreover, the model qualitatively predicted the impact of tissue factor and tPA/tenecteplase level variations on the fibrin output. In the

A decision model for selecting sustainable drinking water supply and greywater reuse systems for developing communities with a case study in Cimahi, Indonesia.

PubMed

Henriques, Justin J; Louis, Garrick E

2011-01-01

Capacity Factor Analysis is a decision support system for selection of appropriate technologies for municipal sanitation services in developing communities. Developing communities are those that lack the capability to provide adequate access to one or more essential services, such as water and sanitation, to their residents. This research developed two elements of Capacity Factor Analysis: a capacity factor based classification for technologies using requirements analysis, and a matching policy for choosing technology options. First, requirements analysis is used to develop a ranking for drinking water supply and greywater reuse technologies. Second, using the Capacity Factor Analysis approach, a matching policy is developed to guide decision makers in selecting the appropriate drinking water supply or greywater reuse technology option for their community. Finally, a scenario-based informal hypothesis test is developed to assist in qualitative model validation through case study. Capacity Factor Analysis is then applied in Cimahi Indonesia as a form of validation. The completed Capacity Factor Analysis model will allow developing communities to select drinking water supply and greywater reuse systems that are safe, affordable, able to be built and managed by the community using local resources, and are amenable to expansion as the community's management capacity increases. Copyright © 2010 Elsevier Ltd. All rights reserved.

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.

Exploring the Latent Structure of the Luria Model for the KABC-II at School Age: Further Insights from Confirmatory Factor Analysis

ERIC Educational Resources Information Center

McGill, Ryan J.

2017-01-01

The present study examined the factor structure of the Luria interpretive model for the Kaufman Assessment Battery for Children-Second Edition (KABC-II) with normative sample participants aged 7-18 (N = 2,025) using confirmatory factor analysis with maximum-likelihood estimation. For the eight subtest Luria configuration, an alternative…

Modeling the effects of study abroad programs on college students

Treesearch

Alvin H. Yu; Garry E. Chick; Duarte B. Morais; Chung-Hsien Lin

2009-01-01

This study explored the possibility of modeling the effects of a study abroad program on students from a university in the northeastern United States. A program effect model was proposed after conducting an extensive literature review and empirically examining a sample of 265 participants in 2005. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA),...

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.

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…

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...

A Confirmatory Factor Analysis of the Structure of Statistics Anxiety Measure: An examination of four alternative models

PubMed Central

Vahedi, Shahram; Farrokhi, Farahman

2011-01-01

Objective The aim of this study is to explore the confirmatory factor analysis results of the Persian adaptation of Statistics Anxiety Measure (SAM), proposed by Earp. Method The validity and reliability assessments of the scale were performed on 298 college students chosen randomly from Tabriz University in Iran. Confirmatory factor analysis (CFA) was carried out to determine the factor structures of the Persian adaptation of SAM. Results As expected, the second order model provided a better fit to the data than the three alternative models. Conclusions Hence, SAM provides an equally valid measure for use among college students. The study both expands and adds support to the existing body of math anxiety literature. PMID:22952530

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.…

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.

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

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.

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

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.

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

Final Technical Report: Advanced Measurement and Analysis of PV Derate Factors.

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

King, Bruce Hardison; Burton, Patrick D.; Hansen, Clifford

2015-12-01

The Advanced Measurement and Analysis of PV Derate Factors project focuses on improving the accuracy and reducing the uncertainty of PV performance model predictions by addressing a common element of all PV performance models referred to as “derates”. Widespread use of “rules of thumb”, combined with significant uncertainty regarding appropriate values for these factors contribute to uncertainty in projected energy production.

Considering Horn's Parallel Analysis from a Random Matrix Theory Point of View.

PubMed

Saccenti, Edoardo; Timmerman, Marieke E

2017-03-01

Horn's parallel analysis is a widely used method for assessing the number of principal components and common factors. We discuss the theoretical foundations of parallel analysis for principal components based on a covariance matrix by making use of arguments from random matrix theory. In particular, we show that (i) for the first component, parallel analysis is an inferential method equivalent to the Tracy-Widom test, (ii) its use to test high-order eigenvalues is equivalent to the use of the joint distribution of the eigenvalues, and thus should be discouraged, and (iii) a formal test for higher-order components can be obtained based on a Tracy-Widom approximation. We illustrate the performance of the two testing procedures using simulated data generated under both a principal component model and a common factors model. For the principal component model, the Tracy-Widom test performs consistently in all conditions, while parallel analysis shows unpredictable behavior for higher-order components. For the common factor model, including major and minor factors, both procedures are heuristic approaches, with variable performance. We conclude that the Tracy-Widom procedure is preferred over parallel analysis for statistically testing the number of principal components based on a covariance matrix.

Deep Learning with Hierarchical Convolutional Factor Analysis

PubMed Central

Chen, Bo; Polatkan, Gungor; Sapiro, Guillermo; Blei, David; Dunson, David; Carin, Lawrence

2013-01-01

Unsupervised multi-layered (“deep”) models are considered for general data, with a particular focus on imagery. The model is represented using a hierarchical convolutional factor-analysis construction, with sparse factor loadings and scores. The computation of layer-dependent model parameters is implemented within a Bayesian setting, employing a Gibbs sampler and variational Bayesian (VB) analysis, that explicitly exploit the convolutional nature of the expansion. In order to address large-scale and streaming data, an online version of VB is also developed. The number of basis functions or dictionary elements at each layer is inferred from the data, based on a beta-Bernoulli implementation of the Indian buffet process. Example results are presented for several image-processing applications, with comparisons to related models in the literature. PMID:23787342

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.

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).

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.

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.

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.

78 FR 27883 - Approval and Promulgation of State Implementation Plans; State of Montana; Interstate Transport...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-13

... PM 2.5 NAAQS in other states. Modeling can be relied on when acceptable modeling technical analyses... on relevant data and factors. MDEQ's submission contains no technical analysis of potential... analysis relies on factors irrelevant to the 2006 PM 2.5 [[Page 27886

Mars approach for global sensitivity analysis of differential equation models with applications to dynamics of influenza infection.

PubMed

Lee, Yeonok; Wu, Hulin

2012-01-01

Differential equation models are widely used for the study of natural phenomena in many fields. The study usually involves unknown factors such as initial conditions and/or parameters. It is important to investigate the impact of unknown factors (parameters and initial conditions) on model outputs in order to better understand the system the model represents. Apportioning the uncertainty (variation) of output variables of a model according to the input factors is referred to as sensitivity analysis. In this paper, we focus on the global sensitivity analysis of ordinary differential equation (ODE) models over a time period using the multivariate adaptive regression spline (MARS) as a meta model based on the concept of the variance of conditional expectation (VCE). We suggest to evaluate the VCE analytically using the MARS model structure of univariate tensor-product functions which is more computationally efficient. Our simulation studies show that the MARS model approach performs very well and helps to significantly reduce the computational cost. We present an application example of sensitivity analysis of ODE models for influenza infection to further illustrate the usefulness of the proposed method.

Factor Analysis for Clustered Observations.

ERIC Educational Resources Information Center

Longford, N. T.; Muthen, B. O.

1992-01-01

A two-level model for factor analysis is defined, and formulas for a scoring algorithm for this model are derived. A simple noniterative method based on decomposition of total sums of the squares and cross-products is discussed and illustrated with simulated data and data from the Second International Mathematics Study. (SLD)

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.

Multicollinearity in prognostic factor analyses using the EORTC QLQ-C30: identification and impact on model selection.

PubMed

Van Steen, Kristel; Curran, Desmond; Kramer, Jocelyn; Molenberghs, Geert; Van Vreckem, Ann; Bottomley, Andrew; Sylvester, Richard

2002-12-30

Clinical and quality of life (QL) variables from an EORTC clinical trial of first line chemotherapy in advanced breast cancer were used in a prognostic factor analysis of survival and response to chemotherapy. For response, different final multivariate models were obtained from forward and backward selection methods, suggesting a disconcerting instability. Quality of life was measured using the EORTC QLQ-C30 questionnaire completed by patients. Subscales on the questionnaire are known to be highly correlated, and therefore it was hypothesized that multicollinearity contributed to model instability. A correlation matrix indicated that global QL was highly correlated with 7 out of 11 variables. In a first attempt to explore multicollinearity, we used global QL as dependent variable in a regression model with other QL subscales as predictors. Afterwards, standard diagnostic tests for multicollinearity were performed. An exploratory principal components analysis and factor analysis of the QL subscales identified at most three important components and indicated that inclusion of global QL made minimal difference to the loadings on each component, suggesting that it is redundant in the model. In a second approach, we advocate a bootstrap technique to assess the stability of the models. Based on these analyses and since global QL exacerbates problems of multicollinearity, we therefore recommend that global QL be excluded from prognostic factor analyses using the QLQ-C30. The prognostic factor analysis was rerun without global QL in the model, and selected the same significant prognostic factors as before. Copyright 2002 John Wiley & Sons, Ltd.

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.

Structure of the Wechsler Intelligence Scale for Children - Fourth Edition in a Group of Children with ADHD.

PubMed

Gomez, Rapson; Vance, Alasdair; Watson, Shaun D

2016-01-01

This study used confirmatory factor analysis to examine the factor structure for the 10 core WISC-IV subtests in a group of children (N = 812) with ADHD. The study examined oblique four- and five-factor models, higher order models with one general secondary factor and four and five primary factors, and a bifactor model with a general factor and four specific factors. The findings supported all models tested, with the bifactor model being the optimum model. For this model, only the general factor had high explained common variance and omega hierarchical value, and it predicted reading and arithmetic abilities. The findings favor the use of the FSIQ scores of the WISC-IV, but not the subscale index scores.

Examining evolving performance on the Force Concept Inventory using factor analysis

NASA Astrophysics Data System (ADS)

Semak, M. R.; Dietz, R. D.; Pearson, R. H.; Willis, C. W.

2017-06-01

The application of factor analysis to the Force Concept Inventory (FCI) has proven to be problematic. Some studies have suggested that factor analysis of test results serves as a helpful tool in assessing the recognition of Newtonian concepts by students. Other work has produced at best ambiguous results. For the FCI administered as a pre- and post-test, we see factor analysis as a tool by which the changes in conceptual associations made by our students may be gauged given the evolution of their response patterns. This analysis allows us to identify and track conceptual linkages, affording us insight as to how our students have matured due to instruction. We report on our analysis of 427 pre- and post-tests. The factor models for the pre- and post-tests are explored and compared along with the methodology by which these models were fit to the data. The post-test factor pattern is more aligned with an expert's interpretation of the questions' content, as it allows for a more readily identifiable relationship between factors and physical concepts. We discuss this evolution in the context of approaching the characteristics of an expert with force concepts. Also, we find that certain test items do not significantly contribute to the pre- or post-test factor models and attempt explanations as to why this is so. This may suggest that such questions may not be effective in probing the conceptual understanding of our students.

SENSITIVITY ANALYSIS AND EVALUATION OF MICROFACPM: A MICROSCALE MOTOR VEHICLE EMISSION FACTOR MODEL FOR PARTICULATE MATTER EMISSIONS

EPA Science Inventory

A microscale emission factor model (MicroFacPM) for predicting real-time site-specific motor vehicle particulate matter emissions was presented in the companion paper entitled "Development of a Microscale Emission Factor Model for Particulate Matter (MicroFacPM) for Predicting Re...

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…

Modeling of Individual and Organizational Factors Affecting Traumatic Occupational Injuries Based on the Structural Equation Modeling: A Case Study in Large Construction Industries.

PubMed

Mohammadfam, Iraj; Soltanzadeh, Ahmad; Moghimbeigi, Abbas; Akbarzadeh, Mehdi

2016-09-01

Individual and organizational factors are the factors influencing traumatic occupational injuries. The aim of the present study was the short path analysis of the severity of occupational injuries based on individual and organizational factors. The present cross-sectional analytical study was implemented on traumatic occupational injuries within a ten-year timeframe in 13 large Iranian construction industries. Modeling and data analysis were done using the structural equation modeling (SEM) approach and the IBM SPSS AMOS statistical software version 22.0, respectively. The mean age and working experience of the injured workers were 28.03 ± 5.33 and 4.53 ± 3.82 years, respectively. The portions of construction and installation activities of traumatic occupational injuries were 64.4% and 18.1%, respectively. The SEM findings showed that the individual, organizational and accident type factors significantly were considered as effective factors on occupational injuries' severity (P < 0.05). Path analysis of occupational injuries based on the SEM reveals that individual and organizational factors and their indicator variables are very influential on the severity of traumatic occupational injuries. So, these should be considered to reduce occupational accidents' severity in large construction industries.

On the Likelihood Ratio Test for the Number of Factors in Exploratory Factor Analysis

ERIC Educational Resources Information Center

Hayashi, Kentaro; Bentler, Peter M.; Yuan, Ke-Hai

2007-01-01

In the exploratory factor analysis, when the number of factors exceeds the true number of factors, the likelihood ratio test statistic no longer follows the chi-square distribution due to a problem of rank deficiency and nonidentifiability of model parameters. As a result, decisions regarding the number of factors may be incorrect. Several…

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.

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.…

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…

A confirmatory factor analysis of the Impact of Event Scale using a sample of World War II and Korean War veterans.

PubMed

Shevlin, M; Hunt, N; Robbins, I

2000-12-01

This study assessed the factor structure of the Impact of Event Scale (IES), a measure of intrusion and avoidance, using a sample of World War II and Korean War veterans who had experienced combat 40-50 years earlier. A series of 3 confirmatory factor analytic models were specified and estimated using LISREL 8.3. Model 1 specified a 1-factor model. Model 2 specified a correlated 2-factor model. Model 3 specified a 2-factor model with additional cross-factor loadings for Items 2 and 12. Model 3 was found to fit the data. In addition, this model was found to be a better explanation of the data than the other models. Also in addition, the correlations between the Intrusion and Avoidance factors and the 4 subscales of the 28-item General Health Questionnaire were examined to determine the distinctiveness of the two IES factors.

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.

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)

Affective Outcomes of Schooling: Full-Information Item Factor Analysis of a Student Questionnaire.

ERIC Educational Resources Information Center

Muraki, Eiji; Engelhard, George, Jr.

Recent developments in dichotomous factor analysis based on multidimensional item response models (Bock and Aitkin, 1981; Muthen, 1978) provide an effective method for exploring the dimensionality of questionnaire items. Implemented in the TESTFACT program, this "full information" item factor analysis accounts not only for the pairwise joint…

Item Factor Analysis: Current Approaches and Future Directions

ERIC Educational Resources Information Center

Wirth, R. J.; Edwards, Michael C.

2007-01-01

The rationale underlying factor analysis applies to continuous and categorical variables alike; however, the models and estimation methods for continuous (i.e., interval or ratio scale) data are not appropriate for item-level data that are categorical in nature. The authors provide a targeted review and synthesis of the item factor analysis (IFA)…

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

The Use of Multiple Regression Models to Determine if Conjoint Analysis Should Be Conducted on Aggregate Data.

ERIC Educational Resources Information Center

Fraas, John W.; Newman, Isadore

1996-01-01

In a conjoint-analysis consumer-preference study, researchers must determine whether the product factor estimates, which measure consumer preferences, should be calculated and interpreted for each respondent or collectively. Multiple regression models can determine whether to aggregate data by examining factor-respondent interaction effects. This…

Multilevel Factor Analysis by Model Segregation: New Applications for Robust Test Statistics

ERIC Educational Resources Information Center

Schweig, Jonathan

2014-01-01

Measures of classroom environments have become central to policy efforts that assess school and teacher quality. This has sparked a wide interest in using multilevel factor analysis to test measurement hypotheses about classroom-level variables. One approach partitions the total covariance matrix and tests models separately on the…

Modular Open-Source Software for Item Factor Analysis

ERIC Educational Resources Information Center

Pritikin, Joshua N.; Hunter, Micheal D.; Boker, Steven M.

2015-01-01

This article introduces an item factor analysis (IFA) module for "OpenMx," a free, open-source, and modular statistical modeling package that runs within the R programming environment on GNU/Linux, Mac OS X, and Microsoft Windows. The IFA module offers a novel model specification language that is well suited to programmatic generation…

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...

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.

Modelling runway incursion severity.

PubMed

Wilke, Sabine; Majumdar, Arnab; Ochieng, Washington Y

2015-06-01

Analysis of the causes underlying runway incursions is fundamental for the development of effective mitigation measures. However, there are significant weaknesses in the current methods to model these factors. This paper proposes a structured framework for modelling causal factors and their relationship to severity, which includes a description of the airport surface system architecture, establishment of terminological definitions, the determination and collection of appropriate data, the analysis of occurrences for severity and causes, and the execution of a statistical analysis framework. It is implemented in the context of U.S. airports, enabling the identification of a number of priority interventions, including the need for better investigation and causal factor capture, recommendations for airfield design, operating scenarios and technologies, and better training for human operators in the system. The framework is recommended for the analysis of runway incursions to support safety improvements and the methodology is transferable to other areas of aviation safety risk analysis. Copyright © 2015 Elsevier Ltd. All rights reserved.

Cure models for the analysis of time-to-event data in cancer studies.

PubMed

Jia, Xiaoyu; Sima, Camelia S; Brennan, Murray F; Panageas, Katherine S

2013-11-01

In settings when it is biologically plausible that some patients are cured after definitive treatment, cure models present an alternative to conventional survival analysis. Cure models can inform on the group of patients cured, by estimating the probability of cure, and identifying factors that influence it; while simultaneously focusing on time to recurrence and associated factors for the remaining patients. © 2013 Wiley Periodicals, Inc.

Structural analysis of a Petri net model of oxidative stress in atherosclerosis.

PubMed

Kozak, Adam; Formanowicz, Dorota; Formanowicz, Piotr

2018-06-01

Atherosclerosis is a complex process of gathering sub-endothelial plaques decreasing lumen of the blood vessels. This disorder affects people of all ages, but its progression is asymptomatic for many years. It is regulated by many typical and atypical factors including the immune system response, a chronic kidney disease, a diet rich in lipids, a local inflammatory process and a local oxidative stress that is here one of the key factors. In this study, a Petri net model of atherosclerosis regulation is presented. This model includes also some information about stoichiometric relationships between its components and covers all mentioned factors. For the model, a structural analysis based on invariants was made and biological conclusions are presented. Since the model contains inhibitor arcs, a heuristic method for analysis of such cases is presented. This method can be used to extend the concept of feasible t -invariants.

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...

An Evaluation on Factors Influencing Decision making for Malaysia Disaster Management: The Confirmatory Factor Analysis Approach

NASA Astrophysics Data System (ADS)

Zubir, S. N. A.; Thiruchelvam, S.; Mustapha, K. N. M.; Che Muda, Z.; Ghazali, A.; Hakimie, H.

2017-12-01

For the past few years, natural disaster has been the subject of debate in disaster management especially in flood disaster. Each year, natural disaster results in significant loss of life, destruction of homes and public infrastructure, and economic hardship. Hence, an effective and efficient flood disaster management would assure non-futile efforts for life saving. The aim of this article is to examine the relationship between approach, decision maker, influence factor, result, and ethic to decision making for flood disaster management in Malaysia. The key elements of decision making in the disaster management were studied based on the literature. Questionnaire surveys were administered among lead agencies at East Coast of Malaysia in the state of Kelantan and Pahang. A total of 307 valid responses had been obtained for further analysis. Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) were carried out to analyse the measurement model involved in the study. The CFA for second-order reflective and first-order reflective measurement model indicates that approach, decision maker, influence factor, result, and ethic have a significant and direct effect on decision making during disaster. The results from this study showed that decision- making during disaster is an important element for disaster management to necessitate a successful collaborative decision making. The measurement model is accepted to proceed with further analysis known as Structural Equation Modeling (SEM) and can be assessed for the future research.

Understanding clinician attitudes towards implementation of guided self-help cognitive behaviour therapy for those who hear distressing voices: using factor analysis to test normalisation process theory.

PubMed

Hazell, Cassie M; Strauss, Clara; Hayward, Mark; Cavanagh, Kate

2017-07-24

The Normalisation Process Theory (NPT) has been used to understand the implementation of physical health care interventions. The current study aims to apply the NPT model to a secondary mental health context, and test the model using exploratory factor analysis. This study will consider the implementation of a brief cognitive behaviour therapy for psychosis (CBTp) intervention. Mental health clinicians were asked to complete a NPT-based questionnaire on the implementation of a brief CBTp intervention. All clinicians had experience of either working with the target client group or were able to deliver psychological therapies. In total, 201 clinicians completed the questionnaire. The results of the exploratory factor analysis found partial support for the NPT model, as three of the NPT factors were extracted: (1) coherence, (2) cognitive participation, and (3) reflexive monitoring. We did not find support for the fourth NPT factor (collective action). All scales showed strong internal consistency. Secondary analysis of these factors showed clinicians to generally support the implementation of the brief CBTp intervention. This study provides strong evidence for the validity of the three NPT factors extracted. Further research is needed to determine whether participants' level of seniority moderates factor extraction, whether this factor structure can be generalised to other healthcare settings, and whether pre-implementation attitudes predict actual implementation outcomes.

A multilateral modelling of Youth Soccer Performance Index (YSPI)

NASA Astrophysics Data System (ADS)

Bisyri Husin Musawi Maliki, Ahmad; Razali Abdullah, Mohamad; Juahir, Hafizan; Abdullah, Farhana; Ain Shahirah Abdullah, Nurul; Muazu Musa, Rabiu; Musliha Mat-Rasid, Siti; Adnan, Aleesha; Azura Kosni, Norlaila; Muhamad, Wan Siti Amalina Wan; Afiqah Mohamad Nasir, Nur

2018-04-01

This study aims to identify the most dominant factors that influencing performance of soccer player and to predict group performance for soccer players. A total of 184 of youth soccer players from Malaysia sport school and six soccer academy encompasses as respondence of the study. Exploratory factor analysis (EFA) and Confirmatory factor analysis (CFA) were computed to identify the most dominant factors whereas reducing the initial 26 parameters with recommended >0.5 of factor loading. Meanwhile, prediction of the soccer performance was predicted by regression model. CFA revealed that sit and reach, vertical jump, VO2max, age, weight, height, sitting height, calf circumference (cc), medial upper arm circumference (muac), maturation, bicep, triceps, subscapular, suprailiac, 5M, 10M, and 20M speed were the most dominant factors. Further index analysis forming Youth Soccer Performance Index (YSPI) resulting by categorizing three groups namely, high, moderate, and low. The regression model for this study was significant set as p < 0.001 and R2 is 0.8222 which explained that the model contributed a total of 82% prediction ability to predict the whole set of the variables. The significant parameters in contributing prediction of YSPI are discussed. As a conclusion, the precision of the prediction models by integrating a multilateral factor reflecting for predicting potential soccer player and hopefully can create a competitive soccer games.

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

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.

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

Uncertainty analysis of hydrological modeling in a tropical area using different algorithms

NASA Astrophysics Data System (ADS)

Rafiei Emam, Ammar; Kappas, Martin; Fassnacht, Steven; Linh, Nguyen Hoang Khanh

2018-01-01

Hydrological modeling outputs are subject to uncertainty resulting from different sources of errors (e.g., error in input data, model structure, and model parameters), making quantification of uncertainty in hydrological modeling imperative and meant to improve reliability of modeling results. The uncertainty analysis must solve difficulties in calibration of hydrological models, which further increase in areas with data scarcity. The purpose of this study is to apply four uncertainty analysis algorithms to a semi-distributed hydrological model, quantifying different source of uncertainties (especially parameter uncertainty) and evaluate their performance. In this study, the Soil and Water Assessment Tools (SWAT) eco-hydrological model was implemented for the watershed in the center of Vietnam. The sensitivity of parameters was analyzed, and the model was calibrated. The uncertainty analysis for the hydrological model was conducted based on four algorithms: Generalized Likelihood Uncertainty Estimation (GLUE), Sequential Uncertainty Fitting (SUFI), Parameter Solution method (ParaSol) and Particle Swarm Optimization (PSO). The performance of the algorithms was compared using P-factor and Rfactor, coefficient of determination (R 2), the Nash Sutcliffe coefficient of efficiency (NSE) and Percent Bias (PBIAS). The results showed the high performance of SUFI and PSO with P-factor>0.83, R-factor <0.56 and R 2>0.91, NSE>0.89, and 0.18

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…

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.

Determination of apparent coupling factors for adhesive bonded acrylic plates using SEAL approach

NASA Astrophysics Data System (ADS)

Pankaj, Achuthan. C.; Shivaprasad, M. V.; Murigendrappa, S. M.

2018-04-01

Apparent coupling loss factors (CLF) and velocity responses has been computed for two lap joined adhesive bonded plates using finite element and experimental statistical energy analysis like approach. A finite element model of the plates has been created using ANSYS software. The statistical energy parameters have been computed using the velocity responses obtained from a harmonic forced excitation analysis. Experiments have been carried out for two different cases of adhesive bonded joints and the results have been compared with the apparent coupling factors and velocity responses obtained from finite element analysis. The results obtained from the studies signify the importance of modeling of adhesive bonded joints in computation of the apparent coupling factors and its further use in computation of energies and velocity responses using statistical energy analysis like approach.

A discriminant analysis prediction model of non-syndromic cleft lip with or without cleft palate based on risk factors.

PubMed

Li, Huixia; Luo, Miyang; Luo, Jiayou; Zheng, Jianfei; Zeng, Rong; Du, Qiyun; Fang, Junqun; Ouyang, Na

2016-11-23

A risk prediction model of non-syndromic cleft lip with or without cleft palate (NSCL/P) was established by a discriminant analysis to predict the individual risk of NSCL/P in pregnant women. A hospital-based case-control study was conducted with 113 cases of NSCL/P and 226 controls without NSCL/P. The cases and the controls were obtained from 52 birth defects' surveillance hospitals in Hunan Province, China. A questionnaire was administered in person to collect the variables relevant to NSCL/P by face to face interviews. Logistic regression models were used to analyze the influencing factors of NSCL/P, and a stepwise Fisher discriminant analysis was subsequently used to construct the prediction model. In the univariate analysis, 13 influencing factors were related to NSCL/P, of which the following 8 influencing factors as predictors determined the discriminant prediction model: family income, maternal occupational hazards exposure, premarital medical examination, housing renovation, milk/soymilk intake in the first trimester of pregnancy, paternal occupational hazards exposure, paternal strong tea drinking, and family history of NSCL/P. The model had statistical significance (lambda = 0.772, chi-square = 86.044, df = 8, P < 0.001). Self-verification showed that 83.8 % of the participants were correctly predicted to be NSCL/P cases or controls with a sensitivity of 74.3 % and a specificity of 88.5 %. The area under the receiver operating characteristic curve (AUC) was 0.846. The prediction model that was established using the risk factors of NSCL/P can be useful for predicting the risk of NSCL/P. Further research is needed to improve the model, and confirm the validity and reliability of the model.

Ground-Based Telescope Parametric Cost Model

NASA Technical Reports Server (NTRS)

Stahl, H. Philip; Rowell, Ginger Holmes

2004-01-01

A parametric cost model for ground-based telescopes is developed using multi-variable statistical analysis, The model includes both engineering and performance parameters. While diameter continues to be the dominant cost driver, other significant factors include primary mirror radius of curvature and diffraction limited wavelength. The model includes an explicit factor for primary mirror segmentation and/or duplication (i.e.. multi-telescope phased-array systems). Additionally, single variable models based on aperture diameter are derived. This analysis indicates that recent mirror technology advances have indeed reduced the historical telescope cost curve.

[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.

On the Extraction of Components and the Applicability of the Factor Model.

ERIC Educational Resources Information Center

Dziuban, Charles D.; Harris, Chester W.

A reanalysis of Shaycroft's matrix of intercorrelations of 10 test variables plus 4 random variables is discussed. Three different procedures were used in the reanalysis: (1) Image Component Analysis, (2) Uniqueness Rescaling Factor Analysis, and (3) Alpha Factor Analysis. The results of these analyses are presented in tables. It is concluded from…

Developing Multidimensional Likert Scales Using Item Factor Analysis: The Case of Four-Point Items

ERIC Educational Resources Information Center

Asún, Rodrigo A.; Rdz-Navarro, Karina; Alvarado, Jesús M.

2016-01-01

This study compares the performance of two approaches in analysing four-point Likert rating scales with a factorial model: the classical factor analysis (FA) and the item factor analysis (IFA). For FA, maximum likelihood and weighted least squares estimations using Pearson correlation matrices among items are compared. For IFA, diagonally weighted…

Modeling of Individual and Organizational Factors Affecting Traumatic Occupational Injuries Based on the Structural Equation Modeling: A Case Study in Large Construction Industries

PubMed Central

Mohammadfam, Iraj; Soltanzadeh, Ahmad; Moghimbeigi, Abbas; Akbarzadeh, Mehdi

2016-01-01

Background Individual and organizational factors are the factors influencing traumatic occupational injuries. Objectives The aim of the present study was the short path analysis of the severity of occupational injuries based on individual and organizational factors. Materials and Methods The present cross-sectional analytical study was implemented on traumatic occupational injuries within a ten-year timeframe in 13 large Iranian construction industries. Modeling and data analysis were done using the structural equation modeling (SEM) approach and the IBM SPSS AMOS statistical software version 22.0, respectively. Results The mean age and working experience of the injured workers were 28.03 ± 5.33 and 4.53 ± 3.82 years, respectively. The portions of construction and installation activities of traumatic occupational injuries were 64.4% and 18.1%, respectively. The SEM findings showed that the individual, organizational and accident type factors significantly were considered as effective factors on occupational injuries’ severity (P < 0.05). Conclusions Path analysis of occupational injuries based on the SEM reveals that individual and organizational factors and their indicator variables are very influential on the severity of traumatic occupational injuries. So, these should be considered to reduce occupational accidents’ severity in large construction industries. PMID:27800465

A Confirmatory Factor Analysis of the Academic Motivation Scale with Black College Students

ERIC Educational Resources Information Center

Cokley, Kevin

2015-01-01

The factor structure of the Academic Motivation Scale (AMS) was examined with a sample of 578 Black college students. A confirmatory factor analysis of the AMS was conducted. Results indicated that the hypothesized seven-factor model did not fit the data. Implications for future research with the AMS are discussed.

Psychometric analysis of the new ADHD DSM-V derived symptoms.

PubMed

Ghanizadeh, Ahmad

2012-03-20

Following the agreements on the reformulating and revising of ADHD diagnostic criteria, recently, the proposed revision for ADHD added 4 new symptoms to the hyperactivity and Impulsivity aspect in DSM-V. This study investigates the psychometric properties of the proposed ADHD diagnostic criteria. ADHD diagnosis was made according to DSM-IV. The parents completed the screening test of ADHD checklist of Child Symptom Inventory-4 and the 4 items describing the new proposed symptoms in DSM-V. The confirmatory factor analysis of the ADHD DSM-V derived items supports the loading of two factors including inattentiveness and hyperactivity/impulsivity. There is a sufficient reliability for the items. However, confirmatory factor analysis showed that the three-factor model is better fitted than the two-factor one. Moreover, the results of the exploratory analysis raised some concerns about the factor loading of the four new items. The current results support the two-factor model of the DSM-V ADHD diagnostic criteria including inattentiveness and hyperactivity/impulsivity. However, the four new items can be considered as a third factor.

The Computation of Orthogonal Independent Cluster Solutions and Their Oblique Analogs in Factor Analysis.

ERIC Educational Resources Information Center

Hofmann, Richard J.

A very general model for the computation of independent cluster solutions in factor analysis is presented. The model is discussed as being either orthogonal or oblique. Furthermore, it is demonstrated that for every orthogonal independent cluster solution there is an oblique analog. Using three illustrative examples, certain generalities are made…

Assessing Model Fit: Caveats and Recommendations for Confirmatory Factor Analysis and Exploratory Structural Equation Modeling

ERIC Educational Resources Information Center

Perry, John L.; Nicholls, Adam R.; Clough, Peter J.; Crust, Lee

2015-01-01

Despite the limitations of overgeneralizing cutoff values for confirmatory factor analysis (CFA; e.g., Marsh, Hau, & Wen, 2004), they are still often employed as golden rules for assessing factorial validity in sport and exercise psychology. The purpose of this study was to investigate the appropriateness of using the CFA approach with these…

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.

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 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

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.

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.

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.

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.

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…

Using structural equation modeling for network meta-analysis.

PubMed

Tu, Yu-Kang; Wu, Yun-Chun

2017-07-14

Network meta-analysis overcomes the limitations of traditional pair-wise meta-analysis by incorporating all available evidence into a general statistical framework for simultaneous comparisons of several treatments. Currently, network meta-analyses are undertaken either within the Bayesian hierarchical linear models or frequentist generalized linear mixed models. Structural equation modeling (SEM) is a statistical method originally developed for modeling causal relations among observed and latent variables. As random effect is explicitly modeled as a latent variable in SEM, it is very flexible for analysts to specify complex random effect structure and to make linear and nonlinear constraints on parameters. The aim of this article is to show how to undertake a network meta-analysis within the statistical framework of SEM. We used an example dataset to demonstrate the standard fixed and random effect network meta-analysis models can be easily implemented in SEM. It contains results of 26 studies that directly compared three treatment groups A, B and C for prevention of first bleeding in patients with liver cirrhosis. We also showed that a new approach to network meta-analysis based on the technique of unrestricted weighted least squares (UWLS) method can also be undertaken using SEM. For both the fixed and random effect network meta-analysis, SEM yielded similar coefficients and confidence intervals to those reported in the previous literature. The point estimates of two UWLS models were identical to those in the fixed effect model but the confidence intervals were greater. This is consistent with results from the traditional pairwise meta-analyses. Comparing to UWLS model with common variance adjusted factor, UWLS model with unique variance adjusted factor has greater confidence intervals when the heterogeneity was larger in the pairwise comparison. The UWLS model with unique variance adjusted factor reflects the difference in heterogeneity within each comparison. SEM provides a very flexible framework for univariate and multivariate meta-analysis, and its potential as a powerful tool for advanced meta-analysis is still to be explored.

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.

Individual risk factors for deep infection and compromised fracture healing after intramedullary nailing of tibial shaft fractures: a single centre experience of 480 patients.

PubMed

Metsemakers, W-J; Handojo, K; Reynders, P; Sermon, A; Vanderschot, P; Nijs, S

2015-04-01

Despite modern advances in the treatment of tibial shaft fractures, complications including nonunion, malunion, and infection remain relatively frequent. A better understanding of these injuries and its complications could lead to prevention rather than treatment strategies. A retrospective study was performed to identify risk factors for deep infection and compromised fracture healing after intramedullary nailing (IMN) of tibial shaft fractures. Between January 2000 and January 2012, 480 consecutive patients with 486 tibial shaft fractures were enrolled in the study. Statistical analysis was performed to determine predictors of deep infection and compromised fracture healing. Compromised fracture healing was subdivided in delayed union and nonunion. The following independent variables were selected for analysis: age, sex, smoking, obesity, diabetes, American Society of Anaesthesiologists (ASA) classification, polytrauma, fracture type, open fractures, Gustilo type, primary external fixation (EF), time to nailing (TTN) and reaming. As primary statistical evaluation we performed a univariate analysis, followed by a multiple logistic regression model. Univariate regression analysis revealed similar risk factors for delayed union and nonunion, including fracture type, open fractures and Gustilo type. Factors affecting the occurrence of deep infection in this model were primary EF, a prolonged TTN, open fractures and Gustilo type. Multiple logistic regression analysis revealed polytrauma as the single risk factor for nonunion. With respect to delayed union, no risk factors could be identified. In the same statistical model, deep infection was correlated with primary EF. The purpose of this study was to evaluate risk factors of poor outcome after IMN of tibial shaft fractures. The univariate regression analysis showed that the nature of complications after tibial shaft nailing could be multifactorial. This was not confirmed in a multiple logistic regression model, which only revealed polytrauma and primary EF as risk factors for nonunion and deep infection, respectively. Future strategies should focus on prevention in high-risk populations such as polytrauma patients treated with EF. Copyright © 2014 Elsevier Ltd. All rights reserved.

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.

INNOVATIVE INSTRUMENTATION AND ANALYSIS OF THE TEMPERATURE MEASUREMENT FOR HIGH TEMPERATURE GASIFICATION

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

Seong W. Lee

During this reporting period, the literature survey including the gasifier temperature measurement literature, the ultrasonic application and its background study in cleaning application, and spray coating process are completed. The gasifier simulator (cold model) testing has been successfully conducted. Four factors (blower voltage, ultrasonic application, injection time intervals, particle weight) were considered as significant factors that affect the temperature measurement. The Analysis of Variance (ANOVA) was applied to analyze the test data. The analysis shows that all four factors are significant to the temperature measurements in the gasifier simulator (cold model). The regression analysis for the case with the normalizedmore » room temperature shows that linear model fits the temperature data with 82% accuracy (18% error). The regression analysis for the case without the normalized room temperature shows 72.5% accuracy (27.5% error). The nonlinear regression analysis indicates a better fit than that of the linear regression. The nonlinear regression model's accuracy is 88.7% (11.3% error) for normalized room temperature case, which is better than the linear regression analysis. The hot model thermocouple sleeve design and fabrication are completed. The gasifier simulator (hot model) design and the fabrication are completed. The system tests of the gasifier simulator (hot model) have been conducted and some modifications have been made. Based on the system tests and results analysis, the gasifier simulator (hot model) has met the proposed design requirement and the ready for system test. The ultrasonic cleaning method is under evaluation and will be further studied for the gasifier simulator (hot model) application. The progress of this project has been on schedule.« less

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci

PubMed Central

Ju, Jin Hyun; Crystal, Ronald G.

2017-01-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL. PMID:28505156

An independent component analysis confounding factor correction framework for identifying broad impact expression quantitative trait loci.

PubMed

Ju, Jin Hyun; Shenoy, Sushila A; Crystal, Ronald G; Mezey, Jason G

2017-05-01

Genome-wide expression Quantitative Trait Loci (eQTL) studies in humans have provided numerous insights into the genetics of both gene expression and complex diseases. While the majority of eQTL identified in genome-wide analyses impact a single gene, eQTL that impact many genes are particularly valuable for network modeling and disease analysis. To enable the identification of such broad impact eQTL, we introduce CONFETI: Confounding Factor Estimation Through Independent component analysis. CONFETI is designed to address two conflicting issues when searching for broad impact eQTL: the need to account for non-genetic confounding factors that can lower the power of the analysis or produce broad impact eQTL false positives, and the tendency of methods that account for confounding factors to model broad impact eQTL as non-genetic variation. The key advance of the CONFETI framework is the use of Independent Component Analysis (ICA) to identify variation likely caused by broad impact eQTL when constructing the sample covariance matrix used for the random effect in a mixed model. We show that CONFETI has better performance than other mixed model confounding factor methods when considering broad impact eQTL recovery from synthetic data. We also used the CONFETI framework and these same confounding factor methods to identify eQTL that replicate between matched twin pair datasets in the Multiple Tissue Human Expression Resource (MuTHER), the Depression Genes Networks study (DGN), the Netherlands Study of Depression and Anxiety (NESDA), and multiple tissue types in the Genotype-Tissue Expression (GTEx) consortium. These analyses identified both cis-eQTL and trans-eQTL impacting individual genes, and CONFETI had better or comparable performance to other mixed model confounding factor analysis methods when identifying such eQTL. In these analyses, we were able to identify and replicate a few broad impact eQTL although the overall number was small even when applying CONFETI. In light of these results, we discuss the broad impact eQTL that have been previously reported from the analysis of human data and suggest that considerable caution should be exercised when making biological inferences based on these reported eQTL.

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.

Confirmatory factor analysis of the Malay version comprehensive feeding practices questionnaire tested among mothers of primary school children in Malaysia.

PubMed

Shohaimi, Shamarina; Wei, Wong Yoke; Shariff, Zalilah Mohd

2014-01-01

Comprehensive feeding practices questionnaire (CFPQ) is an instrument specifically developed to evaluate parental feeding practices. It has been confirmed among children in America and applied to populations in France, Norway, and New Zealand. In order to extend the application of CFPQ, we conducted a factor structure validation of the translated version of CFPQ (CFPQ-M) using confirmatory factor analysis among mothers of primary school children (N = 397) in Malaysia. Several items were modified for cultural adaptation. Of 49 items, 39 items with loading factors >0.40 were retained in the final model. The confirmatory factor analysis revealed that the final model (twelve-factor model with 39 items and 2 error covariances) displayed the best fit for our sample (Chi-square = 1147; df = 634; P < 0.05; CFI = 0.900; RMSEA = 0.045; SRMR = 0.0058). The instrument with some modifications was confirmed among mothers of school children in Malaysia. The present study extends the usability of the CFPQ and enables researchers and parents to better understand the relationships between parental feeding practices and related problems such as childhood obesity.

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.

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

The Counseling Competencies Scale: Validation and Refinement

ERIC Educational Resources Information Center

Lambie, Glenn W.; Mullen, Patrick R.; Swank, Jacqueline M.; Blount, Ashley

2018-01-01

Supervisors evaluated counselors-in-training at multiple points during their practicum experience using the Counseling Competencies Scale (CCS; N = 1,070). The CCS evaluations were randomly split to conduct exploratory factor analysis and confirmatory factor analysis, resulting in a 2-factor model (61.5% of the variance explained).

Bem Sex Role Inventory Validation in the International Mobility in Aging Study.

PubMed

Ahmed, Tamer; Vafaei, Afshin; Belanger, Emmanuelle; Phillips, Susan P; Zunzunegui, Maria-Victoria

2016-09-01

This study investigated the measurement structure of the Bem Sex Role Inventory (BSRI) with different factor analysis methods. Most previous studies on validity applied exploratory factor analysis (EFA) to examine the BSRI. We aimed to assess the psychometric properties and construct validity of the 12-item short-form BSRI in a sample administered to 1,995 older adults from wave 1 of the International Mobility in Aging Study (IMIAS). We used Cronbach's alpha to assess internal consistency reliability and confirmatory factor analysis (CFA) to assess psychometric properties. EFA revealed a three-factor model, further confirmed by CFA and compared with the original two-factor structure model. Results revealed that a two-factor solution (instrumentality-expressiveness) has satisfactory construct validity and superior fit to data compared to the three-factor solution. The two-factor solution confirms expected gender differences in older adults. The 12-item BSRI provides a brief, psychometrically sound, and reliable instrument in international samples of older adults.

FACTOR 9.2: A Comprehensive Program for Fitting Exploratory and Semiconfirmatory Factor Analysis and IRT Models

ERIC Educational Resources Information Center

Lorenzo-Seva, Urbano; Ferrando, Pere J.

2013-01-01

FACTOR 9.2 was developed for three reasons. First, exploratory factor analysis (FA) is still an active field of research although most recent developments have not been incorporated into available programs. Second, there is now renewed interest in semiconfirmatory (SC) solutions as suitable approaches to the complex structures are commonly found…

Sparse multivariate factor analysis regression models and its applications to integrative genomics analysis.

PubMed

Zhou, Yan; Wang, Pei; Wang, Xianlong; Zhu, Ji; Song, Peter X-K

2017-01-01

The multivariate regression model is a useful tool to explore complex associations between two kinds of molecular markers, which enables the understanding of the biological pathways underlying disease etiology. For a set of correlated response variables, accounting for such dependency can increase statistical power. Motivated by integrative genomic data analyses, we propose a new methodology-sparse multivariate factor analysis regression model (smFARM), in which correlations of response variables are assumed to follow a factor analysis model with latent factors. This proposed method not only allows us to address the challenge that the number of association parameters is larger than the sample size, but also to adjust for unobserved genetic and/or nongenetic factors that potentially conceal the underlying response-predictor associations. The proposed smFARM is implemented by the EM algorithm and the blockwise coordinate descent algorithm. The proposed methodology is evaluated and compared to the existing methods through extensive simulation studies. Our results show that accounting for latent factors through the proposed smFARM can improve sensitivity of signal detection and accuracy of sparse association map estimation. We illustrate smFARM by two integrative genomics analysis examples, a breast cancer dataset, and an ovarian cancer dataset, to assess the relationship between DNA copy numbers and gene expression arrays to understand genetic regulatory patterns relevant to the disease. We identify two trans-hub regions: one in cytoband 17q12 whose amplification influences the RNA expression levels of important breast cancer genes, and the other in cytoband 9q21.32-33, which is associated with chemoresistance in ovarian cancer. © 2016 WILEY PERIODICALS, INC.

Factor structure of the Japanese version of the Edinburgh Postnatal Depression Scale in the postpartum period.

PubMed

Kubota, Chika; Okada, Takashi; Aleksic, Branko; Nakamura, Yukako; Kunimoto, Shohko; Morikawa, Mako; Shiino, Tomoko; Tamaji, Ai; Ohoka, Harue; Banno, Naomi; Morita, Tokiko; Murase, Satomi; Goto, Setsuko; Kanai, Atsuko; Masuda, Tomoko; Ando, Masahiko; Ozaki, Norio

2014-01-01

The Edinburgh Postnatal Depression Scale (EPDS) is a widely used screening tool for postpartum depression (PPD). Although the reliability and validity of EPDS in Japanese has been confirmed and the prevalence of PPD is found to be about the same as Western countries, the factor structure of the Japanese version of EPDS has not been elucidated yet. 690 Japanese mothers completed all items of the EPDS at 1 month postpartum. We divided them randomly into two sample sets. The first sample set (n = 345) was used for exploratory factor analysis, and the second sample set was used (n = 345) for confirmatory factor analysis. The result of exploratory factor analysis indicated a three-factor model consisting of anxiety, depression and anhedonia. The results of confirmatory factor analysis suggested that the anxiety and anhedonia factors existed for EPDS in a sample of Japanese women at 1 month postpartum. The depression factor varies by the models of acceptable fit. We examined EPDS scores. As a result, "anxiety" and "anhedonia" exist for EPDS among postpartum women in Japan as already reported in Western countries. Cross-cultural research is needed for future research.

Speed estimation for air quality analysis.

DOT National Transportation Integrated Search

2005-05-01

Average speed is an essential input to the air quality analysis model MOBILE6 for emission factor calculation. Traditionally, speed is obtained from travel demand models. However, such models are not usually calibrated to speeds. Furthermore, for rur...

Analysis of mortality data from the former USSR: age-period-cohort analysis.

PubMed

Willekens, F; Scherbov, S

1992-01-01

The objective of this article is to review research on age-period-cohort (APC) analysis of mortality and to trace the effects of contemporary and historical factors on mortality change in the former USSR. Several events in USSR history have exerted a lasting influence on its people. These influences may be captured by an APC model in which the period effects measure the impact of contemporary factors and the cohort effects the past history of individuals which cannot be attributed to age or stage in the life cycle. APC models are extensively applied in the study of mortality. This article presents the statistical theory of the APC models and shows that they belong to the family of generalized linear models. The parameters of the APC model may therefore be estimated by any package of loglinear analysis that allows for hybrid loglinear models.

[Application of negative binomial regression and modified Poisson regression in the research of risk factors for injury frequency].

PubMed

Cao, Qingqing; Wu, Zhenqiang; Sun, Ying; Wang, Tiezhu; Han, Tengwei; Gu, Chaomei; Sun, Yehuan

2011-11-01

To Eexplore the application of negative binomial regression and modified Poisson regression analysis in analyzing the influential factors for injury frequency and the risk factors leading to the increase of injury frequency. 2917 primary and secondary school students were selected from Hefei by cluster random sampling method and surveyed by questionnaire. The data on the count event-based injuries used to fitted modified Poisson regression and negative binomial regression model. The risk factors incurring the increase of unintentional injury frequency for juvenile students was explored, so as to probe the efficiency of these two models in studying the influential factors for injury frequency. The Poisson model existed over-dispersion (P < 0.0001) based on testing by the Lagrangemultiplier. Therefore, the over-dispersion dispersed data using a modified Poisson regression and negative binomial regression model, was fitted better. respectively. Both showed that male gender, younger age, father working outside of the hometown, the level of the guardian being above junior high school and smoking might be the results of higher injury frequencies. On a tendency of clustered frequency data on injury event, both the modified Poisson regression analysis and negative binomial regression analysis can be used. However, based on our data, the modified Poisson regression fitted better and this model could give a more accurate interpretation of relevant factors affecting the frequency of injury.

The manifestation of depression in the context of urban poverty: a factor analysis of the Children's Depression Inventory in low-income urban youth.

PubMed

Taylor, Jeremy J; Grant, Kathryn E; Amrhein, Kelly; Carter, Jocelyn Smith; Farahmand, Farahnaz; Harrison, Aubrey; Thomas, Kina J; Carleton, Russell A; Lugo-Hernandez, Eduardo; Katz, Brian N

2014-12-01

The current study used confirmatory factor analysis (CFA) to compare the fit of 2 factor structures for the Children's Depression Inventory (CDI) in an urban community sample of low-income youth. Results suggest that the 6-factor model developed by Craighead and colleagues (1998) was a strong fit to the pattern of symptoms reported by low-income urban youth and was a superior fit with these data than the original 5-factor model of the CDI (Kovacs, 1992). Additionally, results indicated that all 6 factors from the Craighead model contributed to the measurement of depression, including School Problems and Externalizing Problems especially for older adolescents. This pattern of findings may reflect distinct contextual influences of urban poverty on the manifestation and measurement of depression in youth. (c) 2014 APA, all rights reserved.

[Development of the lung cancer diagnostic system].

PubMed

Lv, You-Jiang; Yu, Shou-Yi

2009-07-01

To develop a lung cancer diagnosis system. A retrospective analysis was conducted in 1883 patients with primary lung cancer or benign pulmonary diseases (pneumonia, tuberculosis, or pneumonia pseudotumor). SPSS11.5 software was used for data processing. For the relevant factors, a non-factor Logistic regression analysis was used followed by establishment of the regression model. Microsoft Visual Studio 2005 system development platform and VB.Net corresponding language were used to develop the lung cancer diagnosis system. The non-factor multi-factor regression model showed a goodness-of-fit (R2) of the model of 0.806, with a diagnostic accuracy for benign lung diseases of 92.8%, a diagnostic accuracy for lung cancer of 89.0%, and an overall accuracy of 90.8%. The model system for early clinical diagnosis of lung cancer has been established.

Factor Structure and Psychometric Properties of the Posttraumatic Stress Disorder (PTSD) Checklist and DSM-5 PTSD Symptom Set in a Long-Term Postearthquake Cohort in Armenia.

PubMed

Demirchyan, Anahit; Goenjian, Armen K; Khachadourian, Vahe

2015-10-01

Psychometric properties of the Armenian-language posttraumatic stress disorder (PTSD) Checklist-Civilian version (PCL-C) and the DSM-5 PTSD symptom set were examined in a long-term cohort of earthquake survivors. In 2012, 725 survivors completed the instruments. Item-/scale-level analysis and confirmatory factor analysis (CFA) were performed for both scales. In addition, exploratory factor analysis (EFA) was conducted for DSM-5 symptoms. Also, the differential internal versus external specificity of PTSD symptom clusters taken from the most supported PTSD structural models was examined. Both scales had Cronbach's alpha greater than .9. CFA of PCL-C structure demonstrated an excellent fit by a four-factor (reexperiencing, avoidance, numbing, and hyperarousal) model known as numbing model; however, a superior fit was achieved by a five-factor model (Elhai et al.). EFA yielded a five-factor structure for DSM-5 symptoms with the aforementioned four domains plus a negative state domain. This model achieved an acceptable fit during CFA, whereas the DSM-5 criteria-based model did not. The Armenian-language PCL-C was recommended as a valid PTSD screening tool. The study findings provided support to the proposed new classification of common mental disorders, where PTSD, depression, and generalized anxiety are grouped together as a subclass of distress disorders. Recommendations were made to further improve the PTSD diagnostic criteria. © The Author(s) 2014.

[Confirmative study of a French version of the Exercise Dependence Scale-revised with a French population].

PubMed

Allegre, B; Therme, P

2008-10-01

Since the first writings on excessive exercise, there has been an increased interest in exercise dependence. One of the major consequences of this increased interest has been the development of several definitions and measures of exercise dependence. The work of Veale [Does primary exercise dependence really exist? In: Annet J, Cripps B, Steinberg H, editors. Exercise addiction: Motivation for participation in sport and exercise.Leicester, UK: Br Psychol Soc; 1995. p. 1-5.] provides an advance for the definition and measure of exercise dependence. These studies have adapted the DSM-IV criteria for substance dependence to measure exercise dependence. The Exercise Dependence Scale-Revised is based on these diagnostic criteria, which are: tolerance; withdrawal effects; intention effect; lack of control; time; reductions in other activities; continuance. Confirmatory factor analyses of EDS-R provided support to present a measurement model (21 items loaded in seven factors) of EDS-R (Comparative Fit Index=0.97; Root mean Square Error of Approximation=0.05; Tucker-Lewis Index=0.96). The aim of this study was to examine the psychometric properties of a French version of the EDS-R [Factorial validity and psychometric examination of the exercise dependence scale-revised. Meas Phys Educ Exerc Sci 2004;8(4):183-201.] to test the stability of the seven-factor model of the original version with a French population. A total of 516 half-marathoners ranged in age from 17 to 74 years old (Mean age=39.02 years, ET=10.64), with 402 men (77.9%) and 114 women (22.1%) participated in the study. The principal component analysis results in a six-factor structure, which accounts for 68.60% of the total variance. Because principal component analysis presents a six-factor structure differing from the original seven-factor structure, two models were tested, using confirmatory factor analysis. The first model is the seven-factor model of the original version of the EDS-R and the second is the model produced by the principal component analysis. The results of confirmatory factor analysis presented the original model (with a seven-factor structure) as a good model and fit indices were good (X(2)/ddl=2.89, Root Mean Square Error of Approximation (RMSEA)=0.061, Expected Cross Validation Index (ECVI)=1.20, Goodness-of-Fit Index (GFI)=0.92, Comparative Fit Index (CFI)=0.94, Standardized Root Mean Square (SRMS)=0.048). These results showed that the French version of EDS-R has an identical factor structure to the original. Therefore, the French version of EDS-R was an acceptable scale to measure exercise dependence and can be used on a French population.

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.

[A competency model of rural general practitioners: theory construction and empirical study].

PubMed

Yang, Xiu-Mu; Qi, Yu-Long; Shne, Zheng-Fu; Han, Bu-Xin; Meng, Bei

2015-04-01

To perform theory construction and empirical study of the competency model of rural general practitioners. Through literature study, job analysis, interviews, and expert team discussion, the questionnaire of rural general practitioners competency was constructed. A total of 1458 rural general practitioners were surveyed by the questionnaire in 6 central provinces. The common factors were constructed using the principal component method of exploratory factor analysis and confirmatory factor analysis. The influence of the competency characteristics on the working performance was analyzed using regression equation analysis. The Cronbach 's alpha coefficient of the questionnaire was 0.974. The model consisted of 9 dimensions and 59 items. The 9 competency dimensions included basic public health service ability, basic clinical skills, system analysis capability, information management capability, communication and cooperation ability, occupational moral ability, non-medical professional knowledge, personal traits and psychological adaptability. The rate of explained cumulative total variance was 76.855%. The model fitting index were Χ(2)/df 1.88, GFI=0.94, NFI=0.96, NNFI=0.98, PNFI=0.91, RMSEA=0.068, CFI=0.97, IFI=0.97, RFI=0.96, suggesting good model fitting. Regression analysis showed that the competency characteristics had a significant effect on job performance. The rural general practitioners competency model provides reference for rural doctor training, rural order directional cultivation of medical students, and competency performance management of the rural general practitioners.

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,…

[A prediction model for internet game addiction in adolescents: using a decision tree analysis].

PubMed

Kim, Ki Sook; Kim, Kyung Hee

2010-06-01

This study was designed to build a theoretical frame to provide practical help to prevent and manage adolescent internet game addiction by developing a prediction model through a comprehensive analysis of related factors. The participants were 1,318 students studying in elementary, middle, and high schools in Seoul and Gyeonggi Province, Korea. Collected data were analyzed using the SPSS program. Decision Tree Analysis using the Clementine program was applied to build an optimum and significant prediction model to predict internet game addiction related to various factors, especially parent related factors. From the data analyses, the prediction model for factors related to internet game addiction presented with 5 pathways. Causative factors included gender, type of school, siblings, economic status, religion, time spent alone, gaming place, payment to Internet café, frequency, duration, parent's ability to use internet, occupation (mother), trust (father), expectations regarding adolescent's study (mother), supervising (both parents), rearing attitude (both parents). The results suggest preventive and managerial nursing programs for specific groups by path. Use of this predictive model can expand the role of school nurses, not only in counseling addicted adolescents but also, in developing and carrying out programs with parents and approaching adolescents individually through databases and computer programming.

A simple prognostic model for overall survival in metastatic renal cell carcinoma.

PubMed

Assi, Hazem I; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis.

A simple prognostic model for overall survival in metastatic renal cell carcinoma

PubMed Central

Assi, Hazem I.; Patenaude, Francois; Toumishey, Ethan; Ross, Laura; Abdelsalam, Mahmoud; Reiman, Tony

2016-01-01

Introduction: The primary purpose of this study was to develop a simpler prognostic model to predict overall survival for patients treated for metastatic renal cell carcinoma (mRCC) by examining variables shown in the literature to be associated with survival. Methods: We conducted a retrospective analysis of patients treated for mRCC at two Canadian centres. All patients who started first-line treatment were included in the analysis. A multivariate Cox proportional hazards regression model was constructed using a stepwise procedure. Patients were assigned to risk groups depending on how many of the three risk factors from the final multivariate model they had. Results: There were three risk factors in the final multivariate model: hemoglobin, prior nephrectomy, and time from diagnosis to treatment. Patients in the high-risk group (two or three risk factors) had a median survival of 5.9 months, while those in the intermediate-risk group (one risk factor) had a median survival of 16.2 months, and those in the low-risk group (no risk factors) had a median survival of 50.6 months. Conclusions: In multivariate analysis, shorter survival times were associated with hemoglobin below the lower limit of normal, absence of prior nephrectomy, and initiation of treatment within one year of diagnosis. PMID:27217858

Bayesian switching factor analysis for estimating time-varying functional connectivity in fMRI.

PubMed

Taghia, Jalil; Ryali, Srikanth; Chen, Tianwen; Supekar, Kaustubh; Cai, Weidong; Menon, Vinod

2017-07-15

There is growing interest in understanding the dynamical properties of functional interactions between distributed brain regions. However, robust estimation of temporal dynamics from functional magnetic resonance imaging (fMRI) data remains challenging due to limitations in extant multivariate methods for modeling time-varying functional interactions between multiple brain areas. Here, we develop a Bayesian generative model for fMRI time-series within the framework of hidden Markov models (HMMs). The model is a dynamic variant of the static factor analysis model (Ghahramani and Beal, 2000). We refer to this model as Bayesian switching factor analysis (BSFA) as it integrates factor analysis into a generative HMM in a unified Bayesian framework. In BSFA, brain dynamic functional networks are represented by latent states which are learnt from the data. Crucially, BSFA is a generative model which estimates the temporal evolution of brain states and transition probabilities between states as a function of time. An attractive feature of BSFA is the automatic determination of the number of latent states via Bayesian model selection arising from penalization of excessively complex models. Key features of BSFA are validated using extensive simulations on carefully designed synthetic data. We further validate BSFA using fingerprint analysis of multisession resting-state fMRI data from the Human Connectome Project (HCP). Our results show that modeling temporal dependencies in the generative model of BSFA results in improved fingerprinting of individual participants. Finally, we apply BSFA to elucidate the dynamic functional organization of the salience, central-executive, and default mode networks-three core neurocognitive systems with central role in cognitive and affective information processing (Menon, 2011). Across two HCP sessions, we demonstrate a high level of dynamic interactions between these networks and determine that the salience network has the highest temporal flexibility among the three networks. Our proposed methods provide a novel and powerful generative model for investigating dynamic brain connectivity. Copyright © 2017 Elsevier Inc. All rights reserved.

Application of discriminant analysis-based model for prediction of risk of low back disorders due to workplace design in industrial jobs.

PubMed

Ganga, G M D; Esposto, K F; Braatz, D

2012-01-01

The occupational exposure limits of different risk factors for development of low back disorders (LBDs) have not yet been established. One of the main problems in setting such guidelines is the limited understanding of how different risk factors for LBDs interact in causing injury, since the nature and mechanism of these disorders are relatively unknown phenomena. Industrial ergonomists' role becomes further complicated because the potential risk factors that may contribute towards the onset of LBDs interact in a complex manner, which makes it difficult to discriminate in detail among the jobs that place workers at high or low risk of LBDs. The purpose of this paper was to develop a comparative study between predictions based on the neural network-based model proposed by Zurada, Karwowski & Marras (1997) and a linear discriminant analysis model, for making predictions about industrial jobs according to their potential risk of low back disorders due to workplace design. The results obtained through applying the discriminant analysis-based model proved that it is as effective as the neural network-based model. Moreover, the discriminant analysis-based model proved to be more advantageous regarding cost and time savings for future data gathering.

The Influence of Accreditation on the Sustainability of Organizations with the Brazilian Accreditation Methodology

PubMed Central

de Paiva, Anderson Paulo

2018-01-01

This research evaluates the influence of the Brazilian accreditation methodology on the sustainability of the organizations. Critical factors for implementing accreditation were also examined, including measuring the relationships established between these factors in the organization sustainability. The present study was developed based on the survey methodology applied in the organizations accredited by ONA (National Accreditation Organization); 288 responses were received from the top level managers. The analysis of quantitative data of the measurement models was made with factorial analysis from principal components. The final model was evaluated from the confirmatory factorial analysis and structural equation modeling techniques. The results from the research are vital for the definition of factors that interfere in the accreditation processes, providing a better understanding for accredited organizations and for Brazilian accreditation. PMID:29599939

Confirmatory Factor Analysis of the Cancer Locus of Control Scale.

ERIC Educational Resources Information Center

Henderson, Jessica W.; Donatelle, Rebecca J.; Acock, Alan C.

2002-01-01

Conducted a confirmatory factor analysis of the Cancer Locus of Control scale (M. Watson and others, 1990), administered to 543 women with a history of breast cancer. Results support a three-factor model of the scale and support use of the scale to assess control dimensions. (SLD)

A Study of Item Bias for Attitudinal Measurement Using Maximum Likelihood Factor Analysis.

ERIC Educational Resources Information Center

Mayberry, Paul W.

A technique for detecting item bias that is responsive to attitudinal measurement considerations is a maximum likelihood factor analysis procedure comparing multivariate factor structures across various subpopulations, often referred to as SIFASP. The SIFASP technique allows for factorial model comparisons in the testing of various hypotheses…

Establishing Factor Validity Using Variable Reduction in Confirmatory Factor Analysis.

ERIC Educational Resources Information Center

Hofmann, Rich

1995-01-01

Using a 21-statement attitude-type instrument, an iterative procedure for improving confirmatory model fit is demonstrated within the context of the EQS program of P. M. Bentler and maximum likelihood factor analysis. Each iteration systematically eliminates the poorest fitting statement as identified by a variable fit index. (SLD)

Scale Development: Perceived Barriers to Public Use of School Recreational Facilities

ERIC Educational Resources Information Center

Spengler, John O.; Ko, Yong Jae; Connaughton, Daniel P.

2012-01-01

Objectives: To test an original scale assessing perceived barriers among school administrators to allowing community use of school recreational facilities outside of regular school hours. Methods: Exploratory factor analysis (EFA) and confirmatory factor analysis (CFA). Results: Using EFA and CFA, we found that a model including factors of…

Reporting Practices in Confirmatory Factor Analysis: An Overview and Some Recommendations

ERIC Educational Resources Information Center

Jackson, Dennis L.; Gillaspy, J. Arthur, Jr.; Purc-Stephenson, Rebecca

2009-01-01

Reporting practices in 194 confirmatory factor analysis studies (1,409 factor models) published in American Psychological Association journals from 1998 to 2006 were reviewed and compared with established reporting guidelines. Three research questions were addressed: (a) how do actual reporting practices compare with published guidelines? (b) how…

A Comparison of Rule-based Analysis with Regression Methods in Understanding the Risk Factors for Study Withdrawal in a Pediatric Study.

PubMed

Haghighi, Mona; Johnson, Suzanne Bennett; Qian, Xiaoning; Lynch, Kristian F; Vehik, Kendra; Huang, Shuai

2016-08-26

Regression models are extensively used in many epidemiological studies to understand the linkage between specific outcomes of interest and their risk factors. However, regression models in general examine the average effects of the risk factors and ignore subgroups with different risk profiles. As a result, interventions are often geared towards the average member of the population, without consideration of the special health needs of different subgroups within the population. This paper demonstrates the value of using rule-based analysis methods that can identify subgroups with heterogeneous risk profiles in a population without imposing assumptions on the subgroups or method. The rules define the risk pattern of subsets of individuals by not only considering the interactions between the risk factors but also their ranges. We compared the rule-based analysis results with the results from a logistic regression model in The Environmental Determinants of Diabetes in the Young (TEDDY) study. Both methods detected a similar suite of risk factors, but the rule-based analysis was superior at detecting multiple interactions between the risk factors that characterize the subgroups. A further investigation of the particular characteristics of each subgroup may detect the special health needs of the subgroup and lead to tailored interventions.

Exploratory and confirmatory factor analyses and demographic correlate models of the strategies for weight management measure for overweight or obese adults.

PubMed

Kolodziejczyk, Julia K; Norman, Gregory J; Roesch, Scott C; Rock, Cheryl L; Arredondo, Elva M; Madanat, Hala; Patrick, Kevin

2015-01-01

There is a need for a self-report measure that assesses use of recommended strategies related to weight management. Cross-sectional analysis. Universities, community. Exploratory factor analysis (EFA) involved data from 404 overweight/obese young adults (mean age = 22 years, 48% non-Hispanic white, 68% ethnic minority). Confirmatory factor analysis (CFA) involved data from 236 overweight/obese adults (mean age = 42 years, 63% non-Hispanic white, 84% ethnic minority). The Strategies for Weight Management (SWM) measure is a 35-item questionnaire that assesses use of recommended behavioral strategies for reducing energy intake and increasing energy expenditure in overweight/obese adults. EFA and CFA were conducted on the SWM. Correlate models assessed the associations between SWM factor/total scores and demographics by using linear regressions. EFA suggested a four-factor model: strategies categorized as targeting (1) energy intake, (2) energy expenditure, (3) self-monitoring, and (4) self-regulation. CFA indicated good model fit (χ(2)/df = 2.0, comparative fit index = .90, standardized root mean square residual = .06, and root mean square error of approximation = .07, confidence interval = .06-.08, R(2) = .11-.74). The fourth factor had the lowest loadings, possibly because the items cover a wide domain. The final model included 20 items. Correlate models revealed weak associations between the SWM scores and age, gender, Hispanic ethnicity, and relationship status in both samples, with the models explaining only 1% to 8% of the variance (betas = -.04 to .29, p < .05). The SWM has promising psychometric qualities in two diverse samples.

Measuring Filial Piety in the 21st Century: Development, Factor Structure, and Reliability of the 10-Item Contemporary Filial Piety Scale.

PubMed

Lum, Terry Y S; Yan, Elsie C W; Ho, Andy H Y; Shum, Michelle H Y; Wong, Gloria H Y; Lau, Mandy M Y; Wang, Junfang

2016-11-01

The experience and practice of filial piety have evolved in modern Chinese societies, and existing measures fail to capture these important changes. Based on a conceptual analysis on current literature, 42 items were initially compiled to form a Contemporary Filial Piety Scale (CFPS), and 1,080 individuals from a representative sample in Hong Kong were surveyed. Principal component analysis generated a 16-item three-factor model: Pragmatic Obligations (Factor 1; 10 items), Compassionate Reverence (Factor 2; 4 items), and Family Continuity (Factor 3; 2 items). Confirmatory factor analysis revealed strong factor loadings for Factors 1 and 2, while removing Factor 3 and conceptually duplicated items increased total variance explained from 58.02% to 60.09% and internal consistency from .84 to .88. A final 10-item two-factor structure model was adopted with a goodness of fit of 0.95. The CFPS-10 is a data-driven, simple, and efficient instrument with strong psychometric properties for assessing contemporary filial piety. © The Author(s) 2015.

Application of factor analysis of infrared spectra for quantitative determination of beta-tricalcium phosphate in calcium hydroxylapatite.

PubMed

Arsenyev, P A; Trezvov, V V; Saratovskaya, N V

1997-01-01

This work represents a method, which allows to determine phase composition of calcium hydroxylapatite basing on its infrared spectrum. The method uses factor analysis of the spectral data of calibration set of samples to determine minimal number of factors required to reproduce the spectra within experimental error. Multiple linear regression is applied to establish correlation between factor scores of calibration standards and their properties. The regression equations can be used to predict the property value of unknown sample. The regression model was built for determination of beta-tricalcium phosphate content in hydroxylapatite. Statistical estimation of quality of the model was carried out. Application of the factor analysis on spectral data allows to increase accuracy of beta-tricalcium phosphate determination and expand the range of determination towards its less concentration. Reproducibility of results is retained.

Population heterogeneity in the salience of multiple risk factors for adolescent delinquency.

PubMed

Lanza, Stephanie T; Cooper, Brittany R; Bray, Bethany C

2014-03-01

To present mixture regression analysis as an alternative to more standard regression analysis for predicting adolescent delinquency. We demonstrate how mixture regression analysis allows for the identification of population subgroups defined by the salience of multiple risk factors. We identified population subgroups (i.e., latent classes) of individuals based on their coefficients in a regression model predicting adolescent delinquency from eight previously established risk indices drawn from the community, school, family, peer, and individual levels. The study included N = 37,763 10th-grade adolescents who participated in the Communities That Care Youth Survey. Standard, zero-inflated, and mixture Poisson and negative binomial regression models were considered. Standard and mixture negative binomial regression models were selected as optimal. The five-class regression model was interpreted based on the class-specific regression coefficients, indicating that risk factors had varying salience across classes of adolescents. Standard regression showed that all risk factors were significantly associated with delinquency. Mixture regression provided more nuanced information, suggesting a unique set of risk factors that were salient for different subgroups of adolescents. Implications for the design of subgroup-specific interventions are discussed. Copyright © 2014 Society for Adolescent Health and Medicine. Published by Elsevier Inc. All rights reserved.

Analysis and compensation for the effect of the catheter position on image intensities in intravascular optical coherence tomography

NASA Astrophysics Data System (ADS)

Liu, Shengnan; Eggermont, Jeroen; Wolterbeek, Ron; Broersen, Alexander; Busk, Carol A. G. R.; Precht, Helle; Lelieveldt, Boudewijn P. F.; Dijkstra, Jouke

2016-12-01

Intravascular optical coherence tomography (IVOCT) is an imaging technique that is used to analyze the underlying cause of cardiovascular disease. Because a catheter is used during imaging, the intensities can be affected by the catheter position. This work aims to analyze the effect of the catheter position on IVOCT image intensities and to propose a compensation method to minimize this effect in order to improve the visualization and the automatic analysis of IVOCT images. The effect of catheter position is modeled with respect to the distance between the catheter and the arterial wall (distance-dependent factor) and the incident angle onto the arterial wall (angle-dependent factor). A light transmission model incorporating both factors is introduced. On the basis of this model, the interaction effect of both factors is estimated with a hierarchical multivariant linear regression model. Statistical analysis shows that IVOCT intensities are significantly affected by both factors with p<0.001, as either aspect increases the intensity decreases. This effect differs for different pullbacks. The regression results were used to compensate for this effect. Experiments show that the proposed compensation method can improve the performance of the automatic bioresorbable vascular scaffold strut detection.

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

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)

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.

A credit policy approach in a two-warehouse inventory model for deteriorating items with price- and stock-dependent demand under partial backlogging

NASA Astrophysics Data System (ADS)

Panda, Gobinda Chandra; Khan, Md. Al-Amin; Shaikh, Ali Akbar

2018-04-01

Advertisement of the product is an important factor in inventory analysis. Also, price and stock have an important role to attract more customers in the competitive business situations. Trade credit policy is another important role in inventory analysis. We have combined these three factors together in a two-warehouse inventory model and represented it mathematically. In addition, we have added deteriorating factor of our proposed problem with price- and stock-dependent demand under partial backlogged shortage and solved. The frequency of advertisement is considered constant for a year in this paper. The proposed model is highly nonlinear in nature. Due to highly nonlinearity, we could not find the closed form solution. In this paper, trade credit facility is taken in the perspective of retailer, and all the possible cases and subcases of the model are discussed and solved using lingo 10.0 software. The results of sensitivity analysis prove the effectiveness of the proposed model.

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.

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

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

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies.

PubMed

Chen, Bo; Chen, Minhua; Paisley, John; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S; Hero, Alfred; Lucas, Joseph; Dunson, David; Carin, Lawrence

2010-11-09

Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data.

Seepage-Based Factor of Safety Analysis Using 3D Groundwater Simulation Results

DTIC Science & Technology

2014-08-01

Edris, and D . Richards. 2006. A first-principle, physics- based watershed model: WASH123D. In Watershed models, ed. V. P. Singh and D . K . Frevert...should be cited as follows: Cheng, H.-P., K . D . Winters, S. M. England, and R. E. Pickett. 2014. Factor of safety analysis using 3D groundwater...Journal of Dam Safety 11(3): 33–42. Pickett, R. E., K . D . Winters, H.-P. Cheng, and S. M. England. 2013. Herbert Hoover Dike (HHD) flow model. Project

On the dimensionality of the stress-related growth scale: one, three, or seven factors?

PubMed

Roesch, Scott C; Rowley, Anthony A; Vaughn, Allison A

2004-06-01

We examined the factorial validity and dimensionality of the Stress-Related Growth Scale (SRGS; Park, Cohen, & Murch, 1996) using a large multiethnic sample (n = 1,070). Exploratory and confirmatory factor analyses suggested that a multidimensional representation of the SRGS fit better than a unidimensional representation. Specifically, we cross-validated both a 3-factor model and a 7-factor model using confirmatory factor analysis and were shown to be invariant across gender and ethnic groups. The 3-factor model was represented by global dimensions of growth that included rational/mature thinking, affective/emotional growth, and religious/spiritual growth. We replicated the 7-factor model of Armeli, Gunthert, and Cohen (2001) and it represented more specific components of growth such as Self-Understanding and Treatment of Others. However, some factors of the 7-factor model had questionable internal consistency and were strongly intercorrelated, suggesting redundancy. The findings support the notion that the factor structure of both the original 1-factor and revised 7-factor models are unstable and that the 3-factor model developed in this research has more reliable psychometric properties and structure.

Psychometric properties of the college survey for students with brain injury: individuals with and without traumatic brain injury.

PubMed

Kennedy, Mary R T; Krause, Miriam O; O'Brien, Katy H

2014-01-01

The psychometric properties of the college challenges sub-set from The College Survey for Students with Brain Injury (CSS-BI) were investigated with adults with and without traumatic brain injury (TBI). Adults with and without TBI completed the CSS-BI. A sub-set of participants with TBI were interviewed, intentional and convergent validity were investigated, and the internal structure of the college challenges was analysed with exploratory factor analysis/principle component analysis. Respondents with TBI understood the items describing college challenges with evidence of intentional validity. More individuals with TBI than controls endorsed eight of the 13 college challenges. Those who reported more health issues endorsed more college challenges, demonstrating preliminary convergent validity. Cronbach's alphas of >0.85 demonstrated acceptable internal reliability. Factor analysis revealed a four-factor model for those with TBI: studying and learning (Factor 1), time management and organization (Factor 2), social (Factor 3) and nervousness/anxiety (Factor 4). This model explained 72% and 69% of the variance for those with and without TBI, respectively. The college challenges sub-set from the CSS-BI identifies challenges that individuals with TBI face when going to college. Some challenges were related to two factors in the model, demonstrating the inter-connections of these experiences.

Exploratory Bi-factor Analysis: The Oblique Case.

PubMed

Jennrich, Robert I; Bentler, Peter M

2012-07-01

Bi-factor analysis is a form of confirmatory factor analysis originally introduced by Holzinger and Swineford (Psychometrika 47:41-54, 1937). The bi-factor model has a general factor, a number of group factors, and an explicit bi-factor structure. Jennrich and Bentler (Psychometrika 76:537-549, 2011) introduced an exploratory form of bi-factor analysis that does not require one to provide an explicit bi-factor structure a priori. They use exploratory factor analysis and a bifactor rotation criterion designed to produce a rotated loading matrix that has an approximate bi-factor structure. Among other things this can be used as an aid in finding an explicit bi-factor structure for use in a confirmatory bi-factor analysis. They considered only orthogonal rotation. The purpose of this paper is to consider oblique rotation and to compare it to orthogonal rotation. Because there are many more oblique rotations of an initial loading matrix than orthogonal rotations, one expects the oblique results to approximate a bi-factor structure better than orthogonal rotations and this is indeed the case. A surprising result arises when oblique bi-factor rotation methods are applied to ideal data.

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…

Potential barriers to the application of multi-factor portfolio analysis in public hospitals: evidence from a pilot study in the Netherlands.

PubMed

Pavlova, Milena; Tsiachristas, Apostolos; Vermaeten, Gerhard; Groot, Wim

2009-01-01

Portfolio analysis is a business management tool that can assist health care managers to develop new organizational strategies. The application of portfolio analysis to US hospital settings has been frequently reported. In Europe however, the application of this technique has received little attention, especially concerning public hospitals. Therefore, this paper examines the peculiarities of portfolio analysis and its applicability to the strategic management of European public hospitals. The analysis is based on a pilot application of a multi-factor portfolio analysis in a Dutch university hospital. The nature of portfolio analysis and the steps in a multi-factor portfolio analysis are reviewed along with the characteristics of the research setting. Based on these data, a multi-factor portfolio model is developed and operationalized. The portfolio model is applied in a pilot investigation to analyze the market attractiveness and hospital strengths with regard to the provision of three orthopedic services: knee surgery, hip surgery, and arthroscopy. The pilot portfolio analysis is discussed to draw conclusions about potential barriers to the overall adoption of portfolio analysis in the management of a public hospital. Copyright (c) 2008 John Wiley & Sons, Ltd.

Cross-Cultural Adaptation and Validation of the MPAM-R to Brazilian Portuguese and Proposal of a New Method to Calculate Factor Scores

PubMed Central

Albuquerque, Maicon R.; Lopes, Mariana C.; de Paula, Jonas J.; Faria, Larissa O.; Pereira, Eveline T.; da Costa, Varley T.

2017-01-01

In order to understand the reasons that lead individuals to practice physical activity, researchers developed the Motives for Physical Activity Measure-Revised (MPAM-R) scale. In 2010, a translation of MPAM-R to Portuguese and its validation was performed. However, psychometric measures were not acceptable. In addition, factor scores in some sports psychology scales are calculated by the mean of scores by items of the factor. Nevertheless, it seems appropriate that items with higher factor loadings, extracted by Factor Analysis, have greater weight in the factor score, as items with lower factor loadings have less weight in the factor score. The aims of the present study are to translate, validate the MPAM-R for Portuguese versions, and investigate agreement between two methods used to calculate factor scores. Three hundred volunteers who were involved in physical activity programs for at least 6 months were collected. Confirmatory Factor Analysis of the 30 items indicated that the version did not fit the model. After excluding four items, the final model with 26 items showed acceptable model fit measures by Exploratory Factor Analysis, as well as it conceptually supports the five factors as the original proposal. When two methods are compared to calculate factors scores, our results showed that only “Enjoyment” and “Appearance” factors showed agreement between methods to calculate factor scores. So, the Portuguese version of the MPAM-R can be used in a Brazilian context, and a new proposal for the calculation of the factor score seems to be promising. PMID:28293203

Multilevel poisson regression modelling for determining factors of dengue fever cases in bandung

NASA Astrophysics Data System (ADS)

Arundina, Davila Rubianti; Tantular, Bertho; Pontoh, Resa Septiani

2017-03-01

Scralatina or Dengue Fever is a kind of fever caused by serotype virus which Flavivirus genus and be known as Dengue Virus. Dengue Fever caused by Aedes Aegipty Mosquito bites who infected by a dengue virus. The study was conducted in 151 villages in Bandung. Health Analysts believes that there are two factors that affect the dengue cases, Internal factor (individual) and external factor (environment). The data who used in this research is hierarchical data. The method is used for hierarchical data modelling is multilevel method. Which is, the level 1 is village and level 2 is sub-district. According exploration data analysis, the suitable Multilevel Method is Random Intercept Model. Penalized Quasi Likelihood (PQL) approach on multilevel Poisson is a proper analysis to determine factors that affecting dengue cases in the city of Bandung. Clean and Healthy Behavior factor from the village level have an effect on the number of cases of dengue fever in the city of Bandung. Factor from the sub-district level has no effect.

Assessing risk factors for periodontitis using regression

NASA Astrophysics Data System (ADS)

Lobo Pereira, J. A.; Ferreira, Maria Cristina; Oliveira, Teresa

2013-10-01

Multivariate statistical analysis is indispensable to assess the associations and interactions between different factors and the risk of periodontitis. Among others, regression analysis is a statistical technique widely used in healthcare to investigate and model the relationship between variables. In our work we study the impact of socio-demographic, medical and behavioral factors on periodontal health. Using regression, linear and logistic models, we can assess the relevance, as risk factors for periodontitis disease, of the following independent variables (IVs): Age, Gender, Diabetic Status, Education, Smoking status and Plaque Index. The multiple linear regression analysis model was built to evaluate the influence of IVs on mean Attachment Loss (AL). Thus, the regression coefficients along with respective p-values will be obtained as well as the respective p-values from the significance tests. The classification of a case (individual) adopted in the logistic model was the extent of the destruction of periodontal tissues defined by an Attachment Loss greater than or equal to 4 mm in 25% (AL≥4mm/≥25%) of sites surveyed. The association measures include the Odds Ratios together with the correspondent 95% confidence intervals.

Leadership: validation of a self-report scale.

PubMed

Dussault, Marc; Frenette, Eric; Fernet, Claude

2013-04-01

The aim of this paper was to propose and test the factor structure of a new self-report questionnaire on leadership. A sample of 373 school principals in the Province of Quebec, Canada completed the initial 46-item version of the questionnaire. In order to obtain a questionnaire of minimal length, a four-step procedure was retained. First, items analysis was performed using Classical Test Theory. Second, Rasch analysis was used to identify non-fitting or overlapping items. Third, a confirmatory factor analysis (CFA) using structural equation modelling was performed on the 21 remaining items to verify the factor structure of the scale. Results show that the model with a single third-order dimension (leadership), two second-order dimensions (transactional and transformational leadership), and one first-order dimension (laissez-faire leadership) provides a good fit to the data. Finally, invariance of factor structure was assessed with a second sample of 222 vice-principals in the Province of Quebec, Canada. This model is in agreement with the theoretical model developed by Bass (1985), upon which the questionnaire is based.

Analysis of Rainfall Infiltration Law in Unsaturated Soil Slope

PubMed Central

Zhang, Gui-rong; Qian, Ya-jun; Wang, Zhang-chun; Zhao, Bo

2014-01-01

In the study of unsaturated soil slope stability under rainfall infiltration, it is worth continuing to explore how much rainfall infiltrates into the slope in a rain process, and the amount of rainfall infiltrating into slope is the important factor influencing the stability. Therefore, rainfall infiltration capacity is an important issue of unsaturated seepage analysis for slope. On the basis of previous studies, rainfall infiltration law of unsaturated soil slope is analyzed. Considering the characteristics of slope and rainfall, the key factors affecting rainfall infiltration of slope, including hydraulic properties, water storage capacity (θ s - θ r), soil types, rainfall intensities, and antecedent and subsequent infiltration rates on unsaturated soil slope, are discussed by using theory analysis and numerical simulation technology. Based on critical factors changing, this paper presents three calculation models of rainfall infiltrability for unsaturated slope, including (1) infiltration model considering rainfall intensity; (2) effective rainfall model considering antecedent rainfall; (3) infiltration model considering comprehensive factors. Based on the technology of system response, the relationship of rainfall and infiltration is described, and the prototype of regression model of rainfall infiltration is given, in order to determine the amount of rain penetration during a rain process. PMID:24672332

Analysis of rainfall infiltration law in unsaturated soil slope.

PubMed

Zhang, Gui-rong; Qian, Ya-jun; Wang, Zhang-chun; Zhao, Bo

2014-01-01

In the study of unsaturated soil slope stability under rainfall infiltration, it is worth continuing to explore how much rainfall infiltrates into the slope in a rain process, and the amount of rainfall infiltrating into slope is the important factor influencing the stability. Therefore, rainfall infiltration capacity is an important issue of unsaturated seepage analysis for slope. On the basis of previous studies, rainfall infiltration law of unsaturated soil slope is analyzed. Considering the characteristics of slope and rainfall, the key factors affecting rainfall infiltration of slope, including hydraulic properties, water storage capacity (θs - θr), soil types, rainfall intensities, and antecedent and subsequent infiltration rates on unsaturated soil slope, are discussed by using theory analysis and numerical simulation technology. Based on critical factors changing, this paper presents three calculation models of rainfall infiltrability for unsaturated slope, including (1) infiltration model considering rainfall intensity; (2) effective rainfall model considering antecedent rainfall; (3) infiltration model considering comprehensive factors. Based on the technology of system response, the relationship of rainfall and infiltration is described, and the prototype of regression model of rainfall infiltration is given, in order to determine the amount of rain penetration during a rain process.

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.

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.

Spatial econometric analysis of factors influencing regional energy efficiency in China.

PubMed

Song, Malin; Chen, Yu; An, Qingxian

2018-05-01

Increased environmental pollution and energy consumption caused by the country's rapid development has raised considerable public concern, and has become the focus of the government and public. This study employs the super-efficiency slack-based model-data envelopment analysis (SBM-DEA) to measure the total factor energy efficiency of 30 provinces in China. The estimation model for the spatial interaction intensity of regional total factor energy efficiency is based on Wilson's maximum entropy model. The model is used to analyze the factors that affect the potential value of total factor energy efficiency using spatial dynamic panel data for 30 provinces during 2000-2014. The study found that there are differences and spatial correlations of energy efficiency among provinces and regions in China. The energy efficiency in the eastern, central, and western regions fluctuated significantly, and was mainly because of significant energy efficiency impacts on influences of industrial structure, energy intensity, and technological progress. This research is of great significance to China's energy efficiency and regional coordinated development.

Confirmatory factor analysis of the Early Arithmetic, Reading, and Learning Indicators (EARLI)☆

PubMed Central

Norwalk, Kate E.; DiPerna, James Clyde; Lei, Pui-Wa

2015-01-01

Despite growing interest in early intervention, there are few measures available to monitor the progress of early academic skills in preschoolers. The Early Arithmetic, Reading, and Learning Indicators (EARLI; DiPerna, Morgan, & Lei, 2007) were developed as brief assessments of critical early literacy and numeracy skills. The purpose of the current study was to examine the factor structure of the EARLI probes via confirmatory factor analysis (CFA) in a sample of Head Start preschoolers (N = 289). A two-factor model with correlated error terms and a bifactor model provided comparable fit to the data, although there were some structural problems with the latter model. The utility of the bifactor model for explaining the structure of early academic skills as well as the utility of the EARLI probes as measures of literacy and numeracy skills in preschool are discussed. PMID:24495496

Implementation of MCA Method for Identification of Factors for Conceptual Cost Estimation of Residential Buildings

NASA Astrophysics Data System (ADS)

Juszczyk, Michał; Leśniak, Agnieszka; Zima, Krzysztof

2013-06-01

Conceptual cost estimation is important for construction projects. Either underestimation or overestimation of building raising cost may lead to failure of a project. In the paper authors present application of a multicriteria comparative analysis (MCA) in order to select factors influencing residential building raising cost. The aim of the analysis is to indicate key factors useful in conceptual cost estimation in the early design stage. Key factors are being investigated on basis of the elementary information about the function, form and structure of the building, and primary assumptions of technological and organizational solutions applied in construction process. The mentioned factors are considered as variables of the model which aim is to make possible conceptual cost estimation fast and with satisfying accuracy. The whole analysis included three steps: preliminary research, choice of a set of potential variables and reduction of this set to select the final set of variables. Multicriteria comparative analysis is applied in problem solution. Performed analysis allowed to select group of factors, defined well enough at the conceptual stage of the design process, to be used as a describing variables of the model.

Measuring Adjustment to College: Construct Validity of the Student Adaptation to College Questionnaire

ERIC Educational Resources Information Center

Feldt, Ronald C.; Graham, Melody; Dew, Dennis

2011-01-01

This study employed confirmatory factor analysis to examine the quality of fit of two measurement models of the Student Adaptation to College Questionnaire (N = 305). Following the observation of poor fit, exploratory factor analysis was used. Results indicated six factors that account for the variance in Student Adaptation to College…

Exploratory factor analysis of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale in people newly diagnosed with advanced cancer.

PubMed

Bai, Mei; Dixon, Jane K

2014-01-01

The purpose of this study was to reexamine the factor pattern of the 12-item Functional Assessment of Chronic Illness Therapy-Spiritual Well-Being Scale (FACIT-Sp-12) using exploratory factor analysis in people newly diagnosed with advanced cancer. Principal components analysis (PCA) and 3 common factor analysis methods were used to explore the factor pattern of the FACIT-Sp-12. Factorial validity was assessed in association with quality of life (QOL). Principal factor analysis (PFA), iterative PFA, and maximum likelihood suggested retrieving 3 factors: Peace, Meaning, and Faith. Both Peace and Meaning positively related to QOL, whereas only Peace uniquely contributed to QOL. This study supported the 3-factor model of the FACIT-Sp-12. Suggestions for revision of items and further validation of the identified factor pattern were provided.

Assessment of family functioning in Caucasian and Hispanic Americans: reliability, validity, and factor structure of the Family Assessment Device.

PubMed

Aarons, Gregory A; McDonald, Elizabeth J; Connelly, Cynthia D; Newton, Rae R

2007-12-01

The purpose of this study was to examine the factor structure, reliability, and validity of the Family Assessment Device (FAD) among a national sample of Caucasian and Hispanic American families receiving public sector mental health services. A confirmatory factor analysis conducted to test model fit yielded equivocal findings. With few exceptions, indices of model fit, reliability, and validity were poorer for Hispanic Americans compared with Caucasian Americans. Contrary to our expectation, an exploratory factor analysis did not result in a better fitting model of family functioning. Without stronger evidence supporting a reformulation of the FAD, we recommend against such a course of action. Findings highlight the need for additional research on the role of culture in measurement of family functioning.

Quantification of source impact to PM using three-dimensional weighted factor model analysis on multi-site data

NASA Astrophysics Data System (ADS)

Shi, Guoliang; Peng, Xing; Huangfu, Yanqi; Wang, Wei; Xu, Jiao; Tian, Yingze; Feng, Yinchang; Ivey, Cesunica E.; Russell, Armistead G.

2017-07-01

Source apportionment technologies are used to understand the impacts of important sources of particulate matter (PM) air quality, and are widely used for both scientific studies and air quality management. Generally, receptor models apportion speciated PM data from a single sampling site. With the development of large scale monitoring networks, PM speciation are observed at multiple sites in an urban area. For these situations, the models should account for three factors, or dimensions, of the PM, including the chemical species concentrations, sampling periods and sampling site information, suggesting the potential power of a three-dimensional source apportionment approach. However, the principle of three-dimensional Parallel Factor Analysis (Ordinary PARAFAC) model does not always work well in real environmental situations for multi-site receptor datasets. In this work, a new three-way receptor model, called "multi-site three way factor analysis" model is proposed to deal with the multi-site receptor datasets. Synthetic datasets were developed and introduced into the new model to test its performance. Average absolute error (AAE, between estimated and true contributions) for extracted sources were all less than 50%. Additionally, three-dimensional ambient datasets from a Chinese mega-city, Chengdu, were analyzed using this new model to assess the application. Four factors are extracted by the multi-site WFA3 model: secondary source have the highest contributions (64.73 and 56.24 μg/m3), followed by vehicular exhaust (30.13 and 33.60 μg/m3), crustal dust (26.12 and 29.99 μg/m3) and coal combustion (10.73 and 14.83 μg/m3). The model was also compared to PMF, with general agreement, though PMF suggested a lower crustal contribution.

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…

Psychometric evaluation of the revised Illness Perception Questionnaire (IPQ-R) in cancer patients: confirmatory factor analysis and Rasch analysis.

PubMed

Ashley, Laura; Smith, Adam B; Keding, Ada; Jones, Helen; Velikova, Galina; Wright, Penny

2013-12-01

To provide new insights into the psychometrics of the revised Illness Perception Questionnaire (IPQ-R) in cancer patients. To undertake, for the first time using data from breast, colorectal and prostate cancer patients, a confirmatory factor analysis (CFA) to assess the validity of the IPQ-R's core seven-factor structure. Also, for the first time in any illness group, to undertake Rasch analysis to explore the extent to which the IPQ-R factors form unidimensional scales, with linear measurement properties and no Differential Item Functioning (DIF). Patients with potentially curable breast, colorectal or prostate cancer, within 6months post-diagnosis, completed the IPQ-R online (N=531). CFA was conducted, including multi-sample analysis, and for each IPQ-R factor fit to the Rasch model was assessed by examining, amongst other things, item fit, DIF and unidimensionality. The CFA showed a moderate fit of the data to the IPQ-R model, and stability across diagnosis, although fit was significantly improved following the removal of selected items. All seven factors achieved fit to the Rasch model, and exhibited unidimensionality and minimal DIF, although in most cases this was after some item rescoring and/or deletion. In both analyses, IPQ-R items 12, 18 and 24 were indicated as misfitting and removed. Given the rigorous standard of Rasch measurement, and the generic nature of the IPQ-R, it stood up well to the demands of the Rasch model in this study. Importantly, the results show that with some relatively minor, pragmatic modifications the IPQ-R could possess Rasch-standard measurement in cancer patients. © 2013.

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.

QSAR and 3D QSAR of inhibitors of the epidermal growth factor receptor

NASA Astrophysics Data System (ADS)

Pinto-Bazurco, Mariano; Tsakovska, Ivanka; Pajeva, Ilza

This article reports quantitative structure-activity relationships (QSAR) and 3D QSAR models of 134 structurally diverse inhibitors of the epidermal growth factor receptor (EGFR) tyrosine kinase. Free-Wilson analysis was used to derive the QSAR model. It identified the substituents in aniline, the polycyclic system, and the substituents at the 6- and 7-positions of the polycyclic system as the most important structural features. Comparative molecular field analysis (CoMFA) and comparative molecular similarity indices analysis (CoMSIA) were used in the 3D QSAR modeling. The steric and electrostatic interactions proved the most important for the inhibitory effect. Both QSAR and 3D QSAR models led to consistent results. On the basis of the statistically significant models, new structures were proposed and their inhibitory activities were predicted.

Model wall and recovery temperature effects on experimental heat transfer data analysis

NASA Technical Reports Server (NTRS)

Throckmorton, D. A.; Stone, D. R.

1974-01-01

Basic analytical procedures are used to illustrate, both qualitatively and quantitatively, the relative impact upon heat transfer data analysis of certain factors which may affect the accuracy of experimental heat transfer data. Inaccurate knowledge of adiabatic wall conditions results in a corresponding inaccuracy in the measured heat transfer coefficient. The magnitude of the resulting error is extreme for data obtained at wall temperatures approaching the adiabatic condition. High model wall temperatures and wall temperature gradients affect the level and distribution of heat transfer to an experimental model. The significance of each of these factors is examined and its impact upon heat transfer data analysis is assessed.

Using decision tree analysis to identify risk factors for relapse to smoking

PubMed Central

Piper, Megan E.; Loh, Wei-Yin; Smith, Stevens S.; Japuntich, Sandra J.; Baker, Timothy B.

2010-01-01

This research used classification tree analysis and logistic regression models to identify risk factors related to short- and long-term abstinence. Baseline and cessation outcome data from two smoking cessation trials, conducted from 2001 to 2002, in two Midwestern urban areas, were analyzed. There were 928 participants (53.1% women, 81.8% white) with complete data. Both analyses suggest that relapse risk is produced by interactions of risk factors and that early and late cessation outcomes reflect different vulnerability factors. The results illustrate the dynamic nature of relapse risk and suggest the importance of efficient modeling of interactions in relapse prediction. PMID:20397871

Exploring the Factor Structure of the Job Demands-Resources Measure With Patient Violence on Direct Care Workers in the Home Setting.

PubMed

Byon, Ha Do; Harrington, Donna; Storr, Carla L; Lipscomb, Jane

2017-08-01

Workplace violence research in health care settings using the Job Demands-Resources (JD-R) framework is hindered by the lack of comprehensive examination of the factor structure of the JD-R measure when it includes patient violence. Is patient violence a component of job demands or its own factor as an occupational outcome? Exploratory factor analysis and confirmatory factor analysis were conducted using a sample of direct care workers in the home setting (n = 961). The overall 2-construct JD-R structure persisted. Patient violence was not identified as a separate factor from job demands; rather, two demand factors emerged: violence/emotional and workload/physical demands. Although the three-factor model fits the data, the two-factor model with patient violence being a component of job demands is a parsimonious and effective measurement framework.

Confirmatory Factor Analysis of the Malay Version Comprehensive Feeding Practices Questionnaire Tested among Mothers of Primary School Children in Malaysia

PubMed Central

Shohaimi, Shamarina; Yoke Wei, Wong; Mohd Shariff, Zalilah

2014-01-01

Comprehensive feeding practices questionnaire (CFPQ) is an instrument specifically developed to evaluate parental feeding practices. It has been confirmed among children in America and applied to populations in France, Norway, and New Zealand. In order to extend the application of CFPQ, we conducted a factor structure validation of the translated version of CFPQ (CFPQ-M) using confirmatory factor analysis among mothers of primary school children (N = 397) in Malaysia. Several items were modified for cultural adaptation. Of 49 items, 39 items with loading factors >0.40 were retained in the final model. The confirmatory factor analysis revealed that the final model (twelve-factor model with 39 items and 2 error covariances) displayed the best fit for our sample (Chi-square = 1147; df = 634; P < 0.05; CFI = 0.900; RMSEA = 0.045; SRMR = 0.0058). The instrument with some modifications was confirmed among mothers of school children in Malaysia. The present study extends the usability of the CFPQ and enables researchers and parents to better understand the relationships between parental feeding practices and related problems such as childhood obesity. PMID:25538958

The self-transcendence scale: an investigation of the factor structure among nursing home patients.

PubMed

Haugan, Gørill; Rannestad, Toril; Garåsen, Helge; Hammervold, Randi; Espnes, Geir Arild

2012-09-01

Self-transcendence, the ability to expand personal boundaries in multiple ways, has been found to provide well-being. The purpose of this study was to examine the dimensionality of the Norwegian version of the Self-Transcendence Scale, which comprises 15 items. Reed's empirical nursing theory of self-transcendence provided the theoretical framework; self-transcendence includes an interpersonal, intrapersonal, transpersonal, and temporal dimension. Cross-sectional data were obtained from a sample of 202 cognitively intact elderly patients in 44 Norwegian nursing homes. Exploratory factor analysis revealed two and four internally consistent dimensions of self-transcendence, explaining 35.3% (two factors) and 50.7% (four factors) of the variance, respectively. Confirmatory factor analysis indicated that the hypothesized two- and four-factor models fitted better than the one-factor model (cx (2), root mean square error of approximation, standardized root mean square residual, normed fit index, nonnormed fit index, comparative fit index, goodness-of-fit index, and adjusted goodness-of-fit index). The findings indicate self-transcendence as a multifactorial construct; at present, we conclude that the two-factor model might be the most accurate and reasonable measure of self-transcendence. This research generates insights in the application of the widely used Self-Transcendence Scale by investigating its psychometric properties by applying a confirmatory factor analysis. It also generates new research-questions on the associations between self-transcendence and well-being.

The Effects of industrial workers' food choice attribute on sugar intake pattern and job satisfaction with Structural Equcation Model

PubMed Central

Park, Young Il

2016-01-01

BACKGROUND/OBJECTIVES This research analyzes the effects of the food choices of industrial workers according to their sugar intake pattern on their job satisfaction through the construction of a model on the relationship between sugar intake pattern and job satisfaction. SUBJECTS/METHODS Surveys were collected from May to July 2015. A statistical analysis of the 775 surveys from Kyungsangnam-do was conducted using SPSS13.0 for Windows and SEM was performed using the AMOS 5.0 statistics package. RESULTS The reliability of the data was confirmed by an exploratory factor analysis through a Cronbach's alpha coefficient, and the measurement model was proven to be appropriate by a confirmatory factor analysis in conjunction with AMOS. The results of factor analysis on food choice, sugar intake pattern and job satisfaction were categorized into five categories. The reliability of these findings was supported by a Cronbach's alpha coefficient of 0.6 and higher for all factors except confection (0.516) and dairy products (0.570). The multicollinearity results did not indicate a problem between the variables since the highest correlation coefficient was 0.494 (P < 0.01). In an attempt to study the sugar intake pattern in accordance with the food choices and job satisfaction of industrial workers, a structural equation model was constructed and analyzed. CONCLUSIONS All tests confirmed that the model satisfied the recommended levels for the goodness of fit index, and thus, the overall research model was proven to be appropriate. PMID:27478555

Confirmation of the three-factor model of problematic internet use on off-line adolescent and adult samples.

PubMed

Koronczai, Beatrix; Urbán, Róbert; Kökönyei, Gyöngyi; Paksi, Borbála; Papp, Krisztina; Kun, Bernadette; Arnold, Petra; Kállai, János; Demetrovics, Zsolt

2011-11-01

As the Internet became widely used, problems associated with its excessive use became increasingly apparent. Although for the assessment of these problems several models and related questionnaires have been elaborated, there has been little effort made to confirm them. The aim of the present study was to test the three-factor model of the previously created Problematic Internet Use Questionnaire (PIUQ) by data collection methods formerly not applied (off-line group and face-to-face settings), on the one hand, and by testing on different age groups (adolescent and adult representative samples), on the other hand. Data were collected from 438 high-school students (44.5 percent boys; mean age: 16.0 years; standard deviation=0.7 years) and also from 963 adults (49.9 percent males; mean age: 33.6 years; standard deviation=11.8 years). We applied confirmatory factor analysis to confirm the measurement model of problematic Internet use. The results of the analyses carried out inevitably support the original three-factor model over the possible one-factor solution. Using latent profile analysis, we identified 11 percent of adults and 18 percent of adolescent users characterized by problematic use. Based on exploratory factor analysis, we also suggest a short form of the PIUQ consisting of nine items. Both the original 18-item version of PIUQ and its short 9-item form have satisfactory reliability and validity characteristics, and thus, they are suitable for the assessment of problematic Internet use in future studies.

Testing Group Mean Differences of Latent Variables in Multilevel Data Using Multiple-Group Multilevel CFA and Multilevel MIMIC Modeling.

PubMed

Kim, Eun Sook; Cao, Chunhua

2015-01-01

Considering that group comparisons are common in social science, we examined two latent group mean testing methods when groups of interest were either at the between or within level of multilevel data: multiple-group multilevel confirmatory factor analysis (MG ML CFA) and multilevel multiple-indicators multiple-causes modeling (ML MIMIC). The performance of these methods were investigated through three Monte Carlo studies. In Studies 1 and 2, either factor variances or residual variances were manipulated to be heterogeneous between groups. In Study 3, which focused on within-level multiple-group analysis, six different model specifications were considered depending on how to model the intra-class group correlation (i.e., correlation between random effect factors for groups within cluster). The results of simulations generally supported the adequacy of MG ML CFA and ML MIMIC for multiple-group analysis with multilevel data. The two methods did not show any notable difference in the latent group mean testing across three studies. Finally, a demonstration with real data and guidelines in selecting an appropriate approach to multilevel multiple-group analysis are provided.

Assessing Stress in Cancer Patients: A Second-Order Factor Analysis Model for the Perceived Stress Scale

ERIC Educational Resources Information Center

Golden-Kreutz, Deanna M.; Browne, Michael W.; Frierson, Georita M.; Andersen, Barbara L.

2004-01-01

Using the Perceived Stress Scale (PSS), perceptions of global stress were assessed in 111women following breast cancer surgery and at 12 and 24 months later. This is the first study to factor analyze the PSS. The PSS data were factor analyzed each time using exploratory factor analysis with oblique direct quartimin rotation. Goodness-of-fit…

Sound transmission loss of composite sandwich panels

NASA Astrophysics Data System (ADS)

Zhou, Ran

Light composite sandwich panels are increasingly used in automobiles, ships and aircraft, because of the advantages they offer of high strength-to-weight ratios. However, the acoustical properties of these light and stiff structures can be less desirable than those of equivalent metal panels. These undesirable properties can lead to high interior noise levels. A number of researchers have studied the acoustical properties of honeycomb and foam sandwich panels. Not much work, however, has been carried out on foam-filled honeycomb sandwich panels. In this dissertation, governing equations for the forced vibration of asymmetric sandwich panels are developed. An analytical expression for modal densities of symmetric sandwich panels is derived from a sixth-order governing equation. A boundary element analysis model for the sound transmission loss of symmetric sandwich panels is proposed. Measurements of the modal density, total loss factor, radiation loss factor, and sound transmission loss of foam-filled honeycomb sandwich panels with different configurations and thicknesses are presented. Comparisons between the predicted sound transmission loss values obtained from wave impedance analysis, statistical energy analysis, boundary element analysis, and experimental values are presented. The wave impedance analysis model provides accurate predictions of sound transmission loss for the thin foam-filled honeycomb sandwich panels at frequencies above their first resonance frequencies. The predictions from the statistical energy analysis model are in better agreement with the experimental transmission loss values of the sandwich panels when the measured radiation loss factor values near coincidence are used instead of the theoretical values for single-layer panels. The proposed boundary element analysis model provides more accurate predictions of sound transmission loss for the thick foam-filled honeycomb sandwich panels than either the wave impedance analysis model or the statistical energy analysis model.

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…

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.

Determinants of job stress in chemical process industry: A factor analysis approach.

PubMed

Menon, Balagopal G; Praveensal, C J; Madhu, G

2015-01-01

Job stress is one of the active research domains in industrial safety research. The job stress can result in accidents and health related issues in workers in chemical process industries. Hence it is important to measure the level of job stress in workers so as to mitigate the same to avoid the worker's safety related problems in the industries. The objective of this study is to determine the job stress factors in the chemical process industry in Kerala state, India. This study also aims to propose a comprehensive model and an instrument framework for measuring job stress levels in the chemical process industries in Kerala, India. The data is collected through a questionnaire survey conducted in chemical process industries in Kerala. The collected data out of 1197 surveys is subjected to principal component and confirmatory factor analysis to develop the job stress factor structure. The factor analysis revealed 8 factors that influence the job stress in process industries. It is also found that the job stress in employees is most influenced by role ambiguity and the least by work environment. The study has developed an instrument framework towards measuring job stress utilizing exploratory factor analysis and structural equation modeling.

Psychometric Properties of the Serbian Version of the Maslach Burnout Inventory-Human Services Survey: A Validation Study among Anesthesiologists from Belgrade Teaching Hospitals

PubMed Central

Matejić, Bojana; Milenović, Miodrag; Kisić Tepavčević, Darija; Simić, Dušica; Pekmezović, Tatjana; Worley, Jody A.

2015-01-01

We report findings from a validation study of the translated and culturally adapted Serbian version of Maslach Burnout Inventory-Human Services Survey (MBI-HSS), for a sample of anesthesiologists working in the tertiary healthcare. The results showed the sufficient overall reliability (Cronbach's α = 0.72) of the scores (items 1–22). The results of Bartlett's test of sphericity (χ 2 = 1983.75, df = 231, p < 0.001) and Kaiser-Meyer-Olkin measure of sampling adequacy (0.866) provided solid justification for factor analysis. In order to increase sensitivity of this questionnaire, we performed unfitted factor analysis model (eigenvalue greater than 1) which enabled us to extract the most suitable factor structure for our study instrument. The exploratory factor analysis model revealed five factors with eigenvalues greater than 1.0, explaining 62.0% of cumulative variance. Velicer's MAP test has supported five-factor model with the smallest average squared correlation of 0,184. This study indicated that Serbian version of the MBI-HSS is a reliable and valid instrument to measure burnout among a population of anesthesiologists. Results confirmed strong psychometric characteristics of the study instrument, with recommendations for interpretation of two new factors that may be unique to the Serbian version of the MBI-HSS. PMID:26090517

Psychometric Properties of the Serbian Version of the Maslach Burnout Inventory-Human Services Survey: A Validation Study among Anesthesiologists from Belgrade Teaching Hospitals.

PubMed

Matejić, Bojana; Milenović, Miodrag; Kisić Tepavčević, Darija; Simić, Dušica; Pekmezović, Tatjana; Worley, Jody A

2015-01-01

We report findings from a validation study of the translated and culturally adapted Serbian version of Maslach Burnout Inventory-Human Services Survey (MBI-HSS), for a sample of anesthesiologists working in the tertiary healthcare. The results showed the sufficient overall reliability (Cronbach's α = 0.72) of the scores (items 1-22). The results of Bartlett's test of sphericity (χ(2) = 1983.75, df = 231, p < 0.001) and Kaiser-Meyer-Olkin measure of sampling adequacy (0.866) provided solid justification for factor analysis. In order to increase sensitivity of this questionnaire, we performed unfitted factor analysis model (eigenvalue greater than 1) which enabled us to extract the most suitable factor structure for our study instrument. The exploratory factor analysis model revealed five factors with eigenvalues greater than 1.0, explaining 62.0% of cumulative variance. Velicer's MAP test has supported five-factor model with the smallest average squared correlation of 0,184. This study indicated that Serbian version of the MBI-HSS is a reliable and valid instrument to measure burnout among a population of anesthesiologists. Results confirmed strong psychometric characteristics of the study instrument, with recommendations for interpretation of two new factors that may be unique to the Serbian version of the MBI-HSS.

A three-step approach for the derivation and validation of high-performing predictive models using an operational dataset: congestive heart failure readmission case study.

PubMed

AbdelRahman, Samir E; Zhang, Mingyuan; Bray, Bruce E; Kawamoto, Kensaku

2014-05-27

The aim of this study was to propose an analytical approach to develop high-performing predictive models for congestive heart failure (CHF) readmission using an operational dataset with incomplete records and changing data over time. Our analytical approach involves three steps: pre-processing, systematic model development, and risk factor analysis. For pre-processing, variables that were absent in >50% of records were removed. Moreover, the dataset was divided into a validation dataset and derivation datasets which were separated into three temporal subsets based on changes to the data over time. For systematic model development, using the different temporal datasets and the remaining explanatory variables, the models were developed by combining the use of various (i) statistical analyses to explore the relationships between the validation and the derivation datasets; (ii) adjustment methods for handling missing values; (iii) classifiers; (iv) feature selection methods; and (iv) discretization methods. We then selected the best derivation dataset and the models with the highest predictive performance. For risk factor analysis, factors in the highest-performing predictive models were analyzed and ranked using (i) statistical analyses of the best derivation dataset, (ii) feature rankers, and (iii) a newly developed algorithm to categorize risk factors as being strong, regular, or weak. The analysis dataset consisted of 2,787 CHF hospitalizations at University of Utah Health Care from January 2003 to June 2013. In this study, we used the complete-case analysis and mean-based imputation adjustment methods; the wrapper subset feature selection method; and four ranking strategies based on information gain, gain ratio, symmetrical uncertainty, and wrapper subset feature evaluators. The best-performing models resulted from the use of a complete-case analysis derivation dataset combined with the Class-Attribute Contingency Coefficient discretization method and a voting classifier which averaged the results of multi-nominal logistic regression and voting feature intervals classifiers. Of 42 final model risk factors, discharge disposition, discretized age, and indicators of anemia were the most significant. This model achieved a c-statistic of 86.8%. The proposed three-step analytical approach enhanced predictive model performance for CHF readmissions. It could potentially be leveraged to improve predictive model performance in other areas of clinical medicine.

Multifactor valuation models of energy futures and options on futures

NASA Astrophysics Data System (ADS)

Bertus, Mark J.

The intent of this dissertation is to investigate continuous time pricing models for commodity derivative contracts that consider mean reversion. The motivation for pricing commodity futures and option on futures contracts leads to improved practical risk management techniques in markets where uncertainty is increasing. In the dissertation closed-form solutions to mean reverting one-factor, two-factor, three-factor Brownian motions are developed for futures contracts. These solutions are obtained through risk neutral pricing methods that yield tractable expressions for futures prices, which are linear in the state variables, hence making them attractive for estimation. These functions, however, are expressed in terms of latent variables (i.e. spot prices, convenience yield) which complicate the estimation of the futures pricing equation. To address this complication a discussion on Dynamic factor analysis is given. This procedure documents latent variables using a Kalman filter and illustrations show how this technique may be used for the analysis. In addition, to the futures contracts closed form solutions for two option models are obtained. Solutions to the one- and two-factor models are tailored solutions of the Black-Scholes pricing model. Furthermore, since these contracts are written on the futures contracts, they too are influenced by the same underlying parameters of the state variables used to price the futures contracts. To conclude, the analysis finishes with an investigation of commodity futures options that incorporate random discrete jumps.

Comparing statistical and machine learning classifiers: alternatives for predictive modeling in human factors research.

PubMed

Carnahan, Brian; Meyer, Gérard; Kuntz, Lois-Ann

2003-01-01

Multivariate classification models play an increasingly important role in human factors research. In the past, these models have been based primarily on discriminant analysis and logistic regression. Models developed from machine learning research offer the human factors professional a viable alternative to these traditional statistical classification methods. To illustrate this point, two machine learning approaches--genetic programming and decision tree induction--were used to construct classification models designed to predict whether or not a student truck driver would pass his or her commercial driver license (CDL) examination. The models were developed and validated using the curriculum scores and CDL exam performances of 37 student truck drivers who had completed a 320-hr driver training course. Results indicated that the machine learning classification models were superior to discriminant analysis and logistic regression in terms of predictive accuracy. Actual or potential applications of this research include the creation of models that more accurately predict human performance outcomes.

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.

Application of Monte Carlo techniques to transient thermal modeling of cavity radiometers having diffuse-specular surfaces

NASA Technical Reports Server (NTRS)

Mahan, J. R.; Eskin, L. D.

1981-01-01

A viable alternative to the net exchange method of radiative analysis which is equally applicable to diffuse and diffuse-specular enclosures is presented. It is particularly more advantageous to use than the net exchange method in the case of a transient thermal analysis involving conduction and storage of energy as well as radiative exchange. A new quantity, called the distribution factor is defined which replaces the angle factor and the configuration factor. Once obtained, the array of distribution factors for an ensemble of surface elements which define an enclosure permits the instantaneous net radiative heat fluxes to all of the surfaces to be computed directly in terms of the known surface temperatures at that instant. The formulation of the thermal model is described, as is the determination of distribution factors by application of a Monte Carlo analysis. The results show that when fewer than 10,000 packets are emitted, an unsatisfactory approximation for the distribution factors is obtained, but that 10,000 packets is sufficient.

Regression Analysis of Physician Distribution to Identify Areas of Need: Some Preliminary Findings.

ERIC Educational Resources Information Center

Morgan, Bruce B.; And Others

A regression analysis was conducted of factors that help to explain the variance in physician distribution and which identify those factors that influence the maldistribution of physicians. Models were developed for different geographic areas to determine the most appropriate unit of analysis for the Western Missouri Area Health Education Center…

Critical Success Factors of Internet Shopping: The Case of Japan

NASA Astrophysics Data System (ADS)

Atchariyachanvanich, Kanokwan; Okada, Hitoshi; Sonehara, Noboru

This paper presents the results from a study conducted on the effect of differing factors on a customer's attitude towards using Internet shopping in Japan. The research model used was an extended version of the consumers' acceptance of virtual stores model with the addition of a new factor, need specificity, and a grouping of critical success factors based on their customer-centric and website-centric viewpoints sources. It examines how differences in the individual characteristics of customers affect the actual use of Internet shopping. According to an online questionnaire filled out by 1,215 online customers used to conduct a multiple regression analysis and a structural equation modeling analysis, the participant's gender, education level, innovativeness, net-orientation, and need specificity, which are the factors for the customer-centric viewpoints, have a positive impact on the actual use of Internet shopping. The implication also shows that Japanese online customers do not worry about the quality of service of Internet shopping, a factor in the website-centric viewpoint, as significantly as offline customers do.

[Analysis of dietary pattern and diabetes mellitus influencing factors identified by classification tree model in adults of Fujian].

PubMed

Yu, F L; Ye, Y; Yan, Y S

2017-05-10

Objective: To find out the dietary patterns and explore the relationship between environmental factors (especially dietary patterns) and diabetes mellitus in the adults of Fujian. Methods: Multi-stage sampling method were used to survey residents aged ≥18 years by questionnaire, physical examination and laboratory detection in 10 disease surveillance points in Fujian. Factor analysis was used to identify the dietary patterns, while logistic regression model was applied to analyze relationship between dietary patterns and diabetes mellitus, and classification tree model was adopted to identify the influencing factors for diabetes mellitus. Results: There were four dietary patterns in the population, including meat, plant, high-quality protein, and fried food and beverages patterns. The result of logistic analysis showed that plant pattern, which has higher factor loading of fresh fruit-vegetables and cereal-tubers, was a protective factor for non-diabetes mellitus. The risk of diabetes mellitus in the population at T2 and T3 levels of factor score were 0.727 (95 %CI: 0.561-0.943) times and 0.736 (95 %CI : 0.573-0.944) times higher, respectively, than those whose factor score was in lowest quartile. Thirteen influencing factors and eleven group at high-risk for diabetes mellitus were identified by classification tree model. The influencing factors were dyslipidemia, age, family history of diabetes, hypertension, physical activity, career, sex, sedentary time, abdominal adiposity, BMI, marital status, sleep time and high-quality protein pattern. Conclusion: There is a close association between dietary patterns and diabetes mellitus. It is necessary to promote healthy and reasonable diet, strengthen the monitoring and control of blood lipids, blood pressure and body weight, and have good lifestyle for the prevention and control of diabetes mellitus.

Measurement Structure of the Trait Hope Scale in Persons with Spinal Cord Injury: A Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Smedema, Susan Miller; Pfaller, Joseph; Moser, Erin; Tu, Wei-Mo; Chan, Fong

2013-01-01

Objective: To evaluate the measurement structure of the Trait Hope Scale (THS) among individuals with spinal cord injury. Design: Confirmatory factor analysis and reliability and validity analyses were performed. Participants: 242 individuals with spinal cord injury. Results: Results support the two-factor measurement model for the THS with agency…

Factor Structure Invariance of the Kaufman Adolescent and Adult Intelligence Test across Male and Female Samples

ERIC Educational Resources Information Center

Immekus, Jason C.; Maller, Susan J.

2010-01-01

Multisample confirmatory factor analysis (MCFA) and latent mean structures analysis (LMS) were used to test measurement invariance and latent mean differences on the Kaufman Adolescent and Adult Intelligence Scale[TM] (KAIT) across males and females in the standardization sample. MCFA found that the parameters of the KAIT two-factor model were…

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.

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

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.

Procedures and models for estimating preconstruction costs of highway projects.

DOT National Transportation Integrated Search

2012-07-01

This study presents data driven and component based PE cost prediction models by utilizing critical factors retrieved from ten years of historical project data obtained from ODOT roadway division. The study used factor analysis of covariance and corr...

The correlation analysis of tumor necrosis factor-alpha-308G/A polymorphism and venous thromboembolism risk: A meta-analysis.

PubMed

Gao, Quangen; Zhang, Peijin; Wang, Wei; Ma, He; Tong, Yue; Zhang, Jing; Lu, Zhaojun

2016-10-01

Venous thromboembolism is a common complex disorder, being the resultant of gene-gene and gene-environment interactions. Tumor necrosis factor-alpha is a proinflammatory cytokine which has been implicated in venous thromboembolism risk. A promoter 308G/A polymorphism in the tumor necrosis factor-alpha gene has been suggested to modulate the risk for venous thromboembolism. However, the published findings remain inconsistent. In this study, we conducted a meta-analysis of all available data regarding this issue. Eligible studies were identified through search of Pubmed, EBSCO Medline, Web of Science, and China National Knowledge Infrastructure (CNKI, Chinese) databases up to June 2014. Pooled Odd ratios (ORs) with 95% confidence intervals were applied to estimating the strength of the genetic association in the random-effects model or fixed-effects model. A total of 10 studies involving 1999 venous thromboembolism cases and 2166 controls were included in this meta-analysis to evaluate the association between tumor necrosis factor-alpha-308G/A polymorphism and venous thromboembolism risk. Overall, no significantly increased risk venous thromboembolism was observed in all comparison models when all studies were pooled into the meta-analysis. However, in stratified analyses by ethnicity, there was a pronounced association with venous thromboembolism risk among West Asians in three genetic models (A vs. G: OR = 1.82, 95%CI = 1.13-2.94; GA vs. GG: OR = 1.82, 95%CI = 1.08-3.06; AA/GA vs. GG: OR = 1.88, 95%CI = 1.12-3.16). When stratifying by source of controls, no significant result was detected in all genetic models. This meta-analysis demonstrates that tumor necrosis factor-alpha 308G/A polymorphism may contribute to susceptibility to venous thromboembolism among West Asians. Studies are needed to ascertain these findings in larger samples and different racial groups. © The Author(s) 2015.

Assessing social isolation in motor neurone disease: a Rasch analysis of the MND Social Withdrawal Scale.

PubMed

Gibbons, Chris J; Thornton, Everard W; Ealing, John; Shaw, Pamela J; Talbot, Kevin; Tennant, Alan; Young, Carolyn A

2013-11-15

Social withdrawal is described as the condition in which an individual experiences a desire to make social contact, but is unable to satisfy that desire. It is an important issue for patients with motor neurone disease who are likely to experience severe physical impairment. This study aims to reassess the psychometric and scaling properties of the MND Social Withdrawal Scale (MND-SWS) domains and examine the feasibility of a summary scale, by applying scale data to the Rasch model. The MND Social Withdrawal Scale was administered to 298 patients with a diagnosis of MND, alongside the Hospital Anxiety and Depression Scale. The factor structure of the MND Social Withdrawal Scale was assessed using confirmatory factor analysis. Model fit, category threshold analysis, differential item functioning (DIF), dimensionality and local dependency were evaluated. Factor analysis confirmed the suitability of the four-factor solution suggested by the original authors. Mokken scale analysis suggested the removal of item five. Rasch analysis removed a further three items; from the Community (one item) and Emotional (two items) withdrawal subscales. Following item reduction, each scale exhibited excellent fit to the Rasch model. A 14-item Summary scale was shown to fit the Rasch model after subtesting the items into three subtests corresponding to the Community, Family and Emotional subscales, indicating that items from these three subscales could be summed together to create a total measure for social withdrawal. Removal of four items from the Social Withdrawal Scale led to a four factor solution with a 14-item hierarchical Summary scale that were all unidimensional, free for DIF and well fitted to the Rasch model. The scale is reliable and allows clinicians and researchers to measure social withdrawal in MND along a unidimensional construct. © 2013. Published by Elsevier B.V. All rights reserved.

Confirmatory factor analysis reveals a latent cognitive structure common to bipolar disorder, schizophrenia, and normal controls.

PubMed

Schretlen, David J; Peña, Javier; Aretouli, Eleni; Orue, Izaskun; Cascella, Nicola G; Pearlson, Godfrey D; Ojeda, Natalia

2013-06-01

We sought to determine whether a single hypothesized latent factor structure would characterize cognitive functioning in three distinct groups. We assessed 576 adults (340 community controls, 126 adults with bipolar disorder, and 110 adults with schizophrenia) using 15 measures derived from nine cognitive tests. Confirmatory factor analysis (CFA) was conducted to examine the fit of a hypothesized six-factor model. The hypothesized factors included attention, psychomotor speed, verbal memory, visual memory, ideational fluency, and executive functioning. The six-factor model provided an excellent fit for all three groups [for community controls, root mean square error of approximation (RMSEA) <0.048 and comparative fit index (CFI) = 0.99; for adults with bipolar disorder, RMSEA = 0.071 and CFI = 0.99; and for adults with schizophrenia, RMSEA = 0.06 and CFI = 0.98]. Alternate models that combined fluency with processing speed or verbal and visual memory reduced the goodness of fit. Multi-group CFA results supported factor invariance across the three groups. Confirmatory factor analysis supported a single six-factor structure of cognitive functioning among patients with schizophrenia or bipolar disorder and community controls. While the three groups clearly differ in level of performance, they share a common underlying architecture of information processing abilities. These cognitive factors could provide useful targets for clinical trials of treatments that aim to enhance information processing in persons with neurological and neuropsychiatric disorders. © 2013 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.

How Do Executive Functions Fit with the Cattell-Horn-Carroll Model? Some Evidence from a Joint Factor Analysis of the Delis-Kaplan Executive Function System and the Woodcock-Johnson III Tests of Cognitive Abilities

ERIC Educational Resources Information Center

Floyd, Randy G.; Bergeron, Renee; Hamilton, Gloria; Parra, Gilbert R.

2010-01-01

This study investigated the relations among executive functions and cognitive abilities through a joint exploratory factor analysis and joint confirmatory factor analysis of 25 test scores from the Delis-Kaplan Executive Function System and the Woodcock-Johnson III Tests of Cognitive Abilities. Participants were 100 children and adolescents…

Developing and validating a model to predict the success of an IHCS implementation: the Readiness for Implementation Model.

PubMed

Wen, Kuang-Yi; Gustafson, David H; Hawkins, Robert P; Brennan, Patricia F; Dinauer, Susan; Johnson, Pauley R; Siegler, Tracy

2010-01-01

To develop and validate the Readiness for Implementation Model (RIM). This model predicts a healthcare organization's potential for success in implementing an interactive health communication system (IHCS). The model consists of seven weighted factors, with each factor containing five to seven elements. Two decision-analytic approaches, self-explicated and conjoint analysis, were used to measure the weights of the RIM with a sample of 410 experts. The RIM model with weights was then validated in a prospective study of 25 IHCS implementation cases. Orthogonal main effects design was used to develop 700 conjoint-analysis profiles, which varied on seven factors. Each of the 410 experts rated the importance and desirability of the factors and their levels, as well as a set of 10 different profiles. For the prospective 25-case validation, three time-repeated measures of the RIM scores were collected for comparison with the implementation outcomes. Two of the seven factors, 'organizational motivation' and 'meeting user needs,' were found to be most important in predicting implementation readiness. No statistically significant difference was found in the predictive validity of the two approaches (self-explicated and conjoint analysis). The RIM was a better predictor for the 1-year implementation outcome than the half-year outcome. The expert sample, the order of the survey tasks, the additive model, and basing the RIM cut-off score on experience are possible limitations of the study. The RIM needs to be empirically evaluated in institutions adopting IHCS and sustaining the system in the long term.

The Internal Structure of Positive and Negative Affect: A Confirmatory Factor Analysis of the PANAS

ERIC Educational Resources Information Center

Tuccitto, Daniel E.; Giacobbi, Peter R., Jr.; Leite, Walter L.

2010-01-01

This study tested five confirmatory factor analytic (CFA) models of the Positive Affect Negative Affect Schedule (PANAS) to provide validity evidence based on its internal structure. A sample of 223 club sport athletes indicated their emotions during the past week. Results revealed that an orthogonal two-factor CFA model, specifying error…

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.

[Psychometric properties of the French version of the Effort-Reward Imbalance model].

PubMed

Niedhammer, I; Siegrist, J; Landre, M F; Goldberg, M; Leclerc, A

2000-10-01

Two main models are currently used to evaluate psychosocial factors at work: the Job Strain model developed by Karasek and the Effort-Reward Imbalance model. A French version of the first model has been validated for the dimensions of psychological demands and decision latitude. As regards the second one evaluating three dimensions (extrinsic effort, reward, and intrinsic effort), there are several versions in different languages, but until recently there was no validated French version. The objective of this study was to explore the psychometric properties of the French version of the Effort-Reward Imbalance model in terms of internal consistency, factorial validity, and discriminant validity. The present study was based on the GAZEL cohort and included the 10 174 subjects who were working at the French national electric and gas company (EDF-GDF) and answered the questionnaire in 1998. A French version of Effort-Reward Imbalance was included in this questionnaire. This version was obtained by a standard forward/backward translation procedure. Internal consistency was satisfactory for the three scales of extrinsic effort, reward, and intrinsic effort: Cronbach's Alpha coefficients higher than 0.7 were observed. A one-factor solution was retained for the factor analysis of the scale of extrinsic effort. A three-factor solution was retained for the factor analysis of reward, and these dimensions were interpreted as the factor analysis of intrinsic effort did not support the expected four-dimension structure. The analysis of discriminant validity displayed significant associations between measures of Effort-Reward Imbalance and the variables of sex, age, education level, and occupational grade. This study is the first one supporting satisfactory psychometric properties of the French version of the Effort-Reward Imbalance model. However, the factorial validity of intrinsic effort could be questioned. Furthermore, as most previous studies were based on male samples working in specific occupations, the present one is also one of the first to show strong associations between measures of this model and social class variables in a population of men and women employed in various occupations.

Factor analytical study of the short version of the World Health Organization Quality of Life Instrument.

PubMed

Ohaeri, Jude U; Olusina, Adewunmi K; Al-Abassi, Abdul-Hamid M

2004-01-01

The domains of the 26-item World Health Organization Quality of Life Instrument (WHOQOL-Bref) contain heterogeneous items and do not encompass the logical constructs of subjective quality of life (QOL). We compared the WHO 4-domain and 6-domain models of the WHOQOL-Bref with the 8-domain model that we obtained from factor analysis (FA). Data from 118 recently recovered Nigerian psychotic patients were used in confirmatory factor analysis (CFA) to assess goodness of fit and clarity of concept. Our FA model had superior goodness of fit for CFA and provided clarity of concept. Analysis of the WHOQOL-Bref should consider the domains from FA and include 'overall QOL' as an item and dependent variable. Subjective QOL is an aggregate of the following constructs: satisfaction with life circumstances; fulfillment of needs, and opportunity for experience in the milieu.

Delineation of geochemical anomalies based on stream sediment data utilizing fractal modeling and staged factor analysis

NASA Astrophysics Data System (ADS)

Afzal, Peyman; Mirzaei, Misagh; Yousefi, Mahyar; Adib, Ahmad; Khalajmasoumi, Masoumeh; Zarifi, Afshar Zia; Foster, Patrick; Yasrebi, Amir Bijan

2016-07-01

Recognition of significant geochemical signatures and separation of geochemical anomalies from background are critical issues in interpretation of stream sediment data to define exploration targets. In this paper, we used staged factor analysis in conjunction with the concentration-number (C-N) fractal model to generate exploration targets for prospecting Cr and Fe mineralization in Balvard area, SE Iran. The results show coexistence of derived multi-element geochemical signatures of the deposit-type sought and ultramafic-mafic rocks in the NE and northern parts of the study area indicating significant chromite and iron ore prospects. In this regard, application of staged factor analysis and fractal modeling resulted in recognition of significant multi-element signatures that have a high spatial association with host lithological units of the deposit-type sought, and therefore, the generated targets are reliable for further prospecting of the deposit in the study area.

ADAPTION OF NONSTANDARD PIPING COMPONENTS INTO PRESENT DAY SEISMIC CODES

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

D. T. Clark; M. J. Russell; R. E. Spears

2009-07-01

With spiraling energy demand and flat energy supply, there is a need to extend the life of older nuclear reactors. This sometimes requires that existing systems be evaluated to present day seismic codes. Older reactors built in the 1960s and early 1970s often used fabricated piping components that were code compliant during their initial construction time period, but are outside the standard parameters of present-day piping codes. There are several approaches available to the analyst in evaluating these non-standard components to modern codes. The simplest approach is to use the flexibility factors and stress indices for similar standard components withmore » the assumption that the non-standard component’s flexibility factors and stress indices will be very similar. This approach can require significant engineering judgment. A more rational approach available in Section III of the ASME Boiler and Pressure Vessel Code, which is the subject of this paper, involves calculation of flexibility factors using finite element analysis of the non-standard component. Such analysis allows modeling of geometric and material nonlinearities. Flexibility factors based on these analyses are sensitive to the load magnitudes used in their calculation, load magnitudes that need to be consistent with those produced by the linear system analyses where the flexibility factors are applied. This can lead to iteration, since the magnitude of the loads produced by the linear system analysis depend on the magnitude of the flexibility factors. After the loading applied to the nonstandard component finite element model has been matched to loads produced by the associated linear system model, the component finite element model can then be used to evaluate the performance of the component under the loads with the nonlinear analysis provisions of the Code, should the load levels lead to calculated stresses in excess of Allowable stresses. This paper details the application of component-level finite element modeling to account for geometric and material nonlinear component behavior in a linear elastic piping system model. Note that this technique can be applied to the analysis of B31 piping systems.« less

Evidence Regarding the Internal Structure: Confirmatory Factor Analysis

ERIC Educational Resources Information Center

Lewis, Todd F.

2017-01-01

American Educational Research Association (AERA) standards stipulate that researchers show evidence of the internal structure of instruments. Confirmatory factor analysis (CFA) is one structural equation modeling procedure designed to assess construct validity of assessments that has broad applicability for counselors interested in instrument…

Source apportionment of exposures to volatile organic compounds. I. Evaluation of receptor models using simulated exposure data

NASA Astrophysics Data System (ADS)

Miller, Shelly L.; Anderson, Melissa J.; Daly, Eileen P.; Milford, Jana B.

Four receptor-oriented source apportionment models were evaluated by applying them to simulated personal exposure data for select volatile organic compounds (VOCs) that were generated by Monte Carlo sampling from known source contributions and profiles. The exposure sources modeled are environmental tobacco smoke, paint emissions, cleaning and/or pesticide products, gasoline vapors, automobile exhaust, and wastewater treatment plant emissions. The receptor models analyzed are chemical mass balance, principal component analysis/absolute principal component scores, positive matrix factorization (PMF), and graphical ratio analysis for composition estimates/source apportionment by factors with explicit restriction, incorporated in the UNMIX model. All models identified only the major contributors to total exposure concentrations. PMF extracted factor profiles that most closely represented the major sources used to generate the simulated data. None of the models were able to distinguish between sources with similar chemical profiles. Sources that contributed <5% to the average total VOC exposure were not identified.

A structural model of the dimensions of teacher stress.

PubMed

Boyle, G J; Borg, M G; Falzon, J M; Baglioni, A J

1995-03-01

A comprehensive survey of teacher stress, job satisfaction and career commitment among 710 full-time primary school teachers was undertaken by Borg, Riding & Falzon (1991) in the Mediterranean islands of Malta and Gozo. A principal components analysis of a 20-item sources of teacher stress inventory had suggested four distinct dimensions which were labelled: Pupil Misbehaviour, Time/Resource Difficulties, Professional Recognition Needs, and Poor Relationships, respectively. To check on the validity of the Borg et al. factor solution, the group of 710 teachers was randomly split into two separate samples. Exploratory factor analysis was carried out on the data from Sample 1 (N = 335), while Sample 2 (N = 375) provided the cross-validational data for a LISREL confirmatory factor analysis. Results supported the proposed dimensionality of the sources of teacher stress (measurement model), along with evidence of an additional teacher stress factor (Workload). Consequently, structural modelling of the 'causal relationships' between the various latent variables and self-reported stress was undertaken on the combined samples (N = 710). Although both non-recursive and recursive models incorporating Poor Colleague Relations as a mediating variable were tested for their goodness-of-fit, a simple regression model provided the most parsimonious fit to the empirical data, wherein Workload and Student Misbehaviour accounted for most of the variance in predicting teaching stress.

Sensitivity analysis of a ground-water-flow model

USGS Publications Warehouse

Torak, Lynn J.; ,

1991-01-01

A sensitivity analysis was performed on 18 hydrological factors affecting steady-state groundwater flow in the Upper Floridan aquifer near Albany, southwestern Georgia. Computations were based on a calibrated, two-dimensional, finite-element digital model of the stream-aquifer system and the corresponding data inputs. Flow-system sensitivity was analyzed by computing water-level residuals obtained from simulations involving individual changes to each hydrological factor. Hydrological factors to which computed water levels were most sensitive were those that produced the largest change in the sum-of-squares of residuals for the smallest change in factor value. Plots of the sum-of-squares of residuals against multiplier or additive values that effect change in the hydrological factors are used to evaluate the influence of each factor on the simulated flow system. The shapes of these 'sensitivity curves' indicate the importance of each hydrological factor to the flow system. Because the sensitivity analysis can be performed during the preliminary phase of a water-resource investigation, it can be used to identify the types of hydrological data required to accurately characterize the flow system prior to collecting additional data or making management decisions.

Solar array electrical performance assessment for Space Station Freedom

NASA Technical Reports Server (NTRS)

Smith, Bryan K.; Brisco, Holly

1993-01-01

Electrical power for Space Station Freedom will be generated by large Photovoltaic arrays with a beginning of life power requirement of 30.8 kW per array. The solar arrays will operate in a Low Earth Orbit (LEO) over a design life of fifteen years. This paper provides an analysis of the predicted solar array electrical performance over the design life and presents a summary of supporting analysis and test data for the assigned model parameters and performance loss factors. Each model parameter and loss factor is assessed based upon program requirements, component analysis, and test data to date. A description of the LMSC performance model, future test plans, and predicted performance ranges are also given.

Solar array electrical performance assessment for Space Station Freedom

NASA Technical Reports Server (NTRS)

Smith, Bryan K.; Brisco, Holly

1993-01-01

Electrical power for Space Station Freedom will be generated by large photovoltaic arrays with a beginning of life power requirement of 30.8 kW per array. The solar arrays will operate in a Low Earth Orbit (LEO) over a design life of fifteen years. This paper provides an analysis of the predicted solar array electrical performance over the design life and presents a summary of supporting analysis and test data for the assigned model parameters and performance loss factors. Each model parameter and loss factor is assessed based upon program requirements, component analysis and test data to date. A description of the LMSC performance model future test plans and predicted performance ranges are also given.

Measuring striving for understanding and learning value of geometry: a validity study

NASA Astrophysics Data System (ADS)

Ubuz, Behiye; Aydınyer, Yurdagül

2017-11-01

The current study aimed to construct a questionnaire that measures students' personality traits related to striving for understanding and learning value of geometry and then examine its psychometric properties. Through the use of multiple methods on two independent samples of 402 and 521 middle school students, two studies were performed to address this issue to provide support for its validity. In Study 1, exploratory factor analysis indicated the two-factor model. In Study 2, confirmatory factor analysis indicated the better fit of two-factor model compared to one or three-factor model. Convergent and discriminant validity evidence provided insight into the distinctiveness of the two factors. Subgroup validity evidence revealed gender differences for striving for understanding geometry trait favouring girls and grade level differences for learning value of geometry trait favouring the sixth- and seventh-grade students. Predictive validity evidence demonstrated that the striving for understanding geometry trait but not learning value of geometry trait was significantly correlated with prior mathematics achievement. In both studies, each factor and the entire questionnaire showed satisfactory reliability. In conclusion, the questionnaire was psychometrically sound.

Measuring leader perceptions of school readiness for reforms: use of an iterative model combining classical and Rasch methods.

PubMed

Chatterji, Madhabi

2002-01-01

This study examines validity of data generated by the School Readiness for Reforms: Leader Questionnaire (SRR-LQ) using an iterative procedure that combines classical and Rasch rating scale analysis. Following content-validation and pilot-testing, principal axis factor extraction and promax rotation of factors yielded a five factor structure consistent with the content-validated subscales of the original instrument. Factors were identified based on inspection of pattern and structure coefficients. The rotated factor pattern, inter-factor correlations, convergent validity coefficients, and Cronbach's alpha reliability estimates supported the hypothesized construct properties. To further examine unidimensionality and efficacy of the rating scale structures, item-level data from each factor-defined subscale were subjected to analysis with the Rasch rating scale model. Data-to-model fit statistics and separation reliability for items and persons met acceptable criteria. Rating scale results suggested consistency of expected and observed step difficulties in rating categories, and correspondence of step calibrations with increases in the underlying variables. The combined approach yielded more comprehensive diagnostic information on the quality of the five SRR-LQ subscales; further research is continuing.

Man-machine analysis of translation and work tasks of Skylab films

NASA Technical Reports Server (NTRS)

Hosler, W. W.; Boelter, J. G.; Morrow, J. R., Jr.; Jackson, J. T.

1979-01-01

An objective approach to determine the concurrent validity of computer-graphic models is real time film analysis. This technique was illustrated through the procedures and results obtained in an evaluation of translation of Skylab mission astronauts. The quantitative analysis was facilitated by the use of an electronic film analyzer, minicomputer, and specifically supportive software. The uses of this technique for human factors research are: (1) validation of theoretical operator models; (2) biokinetic analysis; (3) objective data evaluation; (4) dynamic anthropometry; (5) empirical time-line analysis; and (6) consideration of human variability. Computer assisted techniques for interface design and evaluation have the potential for improving the capability for human factors engineering.

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China

PubMed Central

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-01-01

Background: In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. Methods: As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6–12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. Results: The prevalence of anemia was 12.60% with a range of 3.47%–40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. Conclusions: The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities. PMID:27174328

Chi-squared Automatic Interaction Detection Decision Tree Analysis of Risk Factors for Infant Anemia in Beijing, China.

PubMed

Ye, Fang; Chen, Zhi-Hua; Chen, Jie; Liu, Fang; Zhang, Yong; Fan, Qin-Ying; Wang, Lin

2016-05-20

In the past decades, studies on infant anemia have mainly focused on rural areas of China. With the increasing heterogeneity of population in recent years, available information on infant anemia is inconclusive in large cities of China, especially with comparison between native residents and floating population. This population-based cross-sectional study was implemented to determine the anemic status of infants as well as the risk factors in a representative downtown area of Beijing. As useful methods to build a predictive model, Chi-squared automatic interaction detection (CHAID) decision tree analysis and logistic regression analysis were introduced to explore risk factors of infant anemia. A total of 1091 infants aged 6-12 months together with their parents/caregivers living at Heping Avenue Subdistrict of Beijing were surveyed from January 1, 2013 to December 31, 2014. The prevalence of anemia was 12.60% with a range of 3.47%-40.00% in different subgroup characteristics. The CHAID decision tree model has demonstrated multilevel interaction among risk factors through stepwise pathways to detect anemia. Besides the three predictors identified by logistic regression model including maternal anemia during pregnancy, exclusive breastfeeding in the first 6 months, and floating population, CHAID decision tree analysis also identified the fourth risk factor, the maternal educational level, with higher overall classification accuracy and larger area below the receiver operating characteristic curve. The infant anemic status in metropolis is complex and should be carefully considered by the basic health care practitioners. CHAID decision tree analysis has demonstrated a better performance in hierarchical analysis of population with great heterogeneity. Risk factors identified by this study might be meaningful in the early detection and prompt treatment of infant anemia in large cities.

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.

[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.

Understanding the Support Needs of People with Intellectual and Related Developmental Disabilities through Cluster Analysis and Factor Analysis of Statewide Data

ERIC Educational Resources Information Center

Viriyangkura, Yuwadee

2014-01-01

Through a secondary analysis of statewide data from Colorado, people with intellectual and related developmental disabilities (ID/DD) were classified into five clusters based on their support needs characteristics using cluster analysis techniques. Prior latent factor models of support needs in the field of ID/DD were examined to investigate the…

Measuring motivation and volition of nursing students in nontraditional learning environments.

PubMed

Nagelsmith, Laurie; Bryer, Jason; Yan, Zheng

2012-01-01

The purpose of this study was to identify the best fitting model to represent interrelationships between motivation, volition, and academic success for adult nursing students learning in nontraditional environments. Participants (N=297) completed a survey that incorporated two measures: the Motivated Strategies for Learning Questionnaire (MSLQ) and the academic volitional strategies inventory (AVSI) as well as demographic information. Exploratory factor analysis (EFA), confirmatory factor analysis (CFA), and structural equation modeling (SEM) were used for data analysis. In phase 1, EFA resulted in factors that generally aligned with previous theoretical factors as defined by the psychometrics used. In Phase 2 of the analysis, CFA validated the use of predefined factor structures. In Phase 3, SEM analysis revealed that motivation has a larger effect on grade point average (GPA; beta = .28, p < .01) than volition (beta = .15, p < .05). The covariance between motivation and volition (r = .42, p < .01) was also found to be significant. These results suggest that there is a significant relationship among motivation, volition, and academic success for adult learners studying in nontraditional learning environments. These findings are consistent with and elaborate the relationship between motivation and volition with a population and setting underrepresented in the research.

The Mindful Attention Awareness Scale: Further Examination of Dimensionality, Reliability, and Concurrent Validity Estimates.

PubMed

Osman, Augustine; Lamis, Dorian A; Bagge, Courtney L; Freedenthal, Stacey; Barnes, Sean M

2016-01-01

We examined the factor structure and psychometric properties of the Mindful Attention Awareness Scale (MAAS) in a sample of 810 undergraduate students. Using common exploratory factor analysis (EFA), we obtained evidence for a 1-factor solution (41.84% common variance). To confirm unidimensionality of the 15-item MAAS, we conducted a 1-factor confirmatory factor analysis (CFA). Results of the EFA and CFA, respectively, provided support for a unidimensional model. Using differential item functioning analysis methods within item response theory modeling (IRT-based DIF), we found that individuals with high and low levels of nonattachment responded similarly to the MAAS items. Following a detailed item analysis, we proposed a 5-item short version of the instrument and present descriptive statistics and composite score reliability for the short and full versions of the MAAS. Finally, correlation analyses showed that scores on the full and short versions of the MAAS were associated with measures assessing related constructs. The 5-item MAAS is as useful as the original MAAS in enhancing our understanding of the mindfulness construct.

A Comparison of Measurement Equivalence Methods Based on Confirmatory Factor Analysis and Item Response Theory.

ERIC Educational Resources Information Center

Flowers, Claudia P.; Raju, Nambury S.; Oshima, T. C.

Current interest in the assessment of measurement equivalence emphasizes two methods of analysis, linear, and nonlinear procedures. This study simulated data using the graded response model to examine the performance of linear (confirmatory factor analysis or CFA) and nonlinear (item-response-theory-based differential item function or IRT-Based…

The transcription factor p53: Not a repressor, solely an activator

PubMed Central

Fischer, Martin; Steiner, Lydia; Engeland, Kurt

2014-01-01

The predominant function of the tumor suppressor p53 is transcriptional regulation. It is generally accepted that p53-dependent transcriptional activation occurs by binding to a specific recognition site in promoters of target genes. Additionally, several models for p53-dependent transcriptional repression have been postulated. Here, we evaluate these models based on a computational meta-analysis of genome-wide data. Surprisingly, several major models of p53-dependent gene regulation are implausible. Meta-analysis of large-scale data is unable to confirm reports on directly repressed p53 target genes and falsifies models of direct repression. This notion is supported by experimental re-analysis of representative genes reported as directly repressed by p53. Therefore, p53 is not a direct repressor of transcription, but solely activates its target genes. Moreover, models based on interference of p53 with activating transcription factors as well as models based on the function of ncRNAs are also not supported by the meta-analysis. As an alternative to models of direct repression, the meta-analysis leads to the conclusion that p53 represses transcription indirectly by activation of the p53-p21-DREAM/RB pathway. PMID:25486564

Variogram Analysis of Response surfaces (VARS): A New Framework for Global Sensitivity Analysis of Earth and Environmental Systems Models

NASA Astrophysics Data System (ADS)

Razavi, S.; Gupta, H. V.

2015-12-01

Earth and environmental systems models (EESMs) are continually growing in complexity and dimensionality with continuous advances in understanding and computing power. Complexity and dimensionality are manifested by introducing many different factors in EESMs (i.e., model parameters, forcings, boundary conditions, etc.) to be identified. Sensitivity Analysis (SA) provides an essential means for characterizing the role and importance of such factors in producing the model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to 'variogram analysis', that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are limiting cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. We also present a practical strategy for the application of VARS to real-world problems, called STAR-VARS, including a new sampling strategy, called "star-based sampling". Our results across several case studies show the STAR-VARS approach to provide reliable and stable assessments of "global" sensitivity across the full range of scales in the factor space, while being at least 1-2 orders of magnitude more efficient than the benchmark Morris and Sobol approaches.

Confirmation of the Three-Factor Model of Problematic Internet Use on Off-Line Adolescent and Adult Samples

PubMed Central

Koronczai, Beatrix; Urbán, Róbert; Kökönyei, Gyöngyi; Paksi, Borbála; Papp, Krisztina; Kun, Bernadette; Arnold, Petra; Kállai, János

2011-01-01

Abstract As the Internet became widely used, problems associated with its excessive use became increasingly apparent. Although for the assessment of these problems several models and related questionnaires have been elaborated, there has been little effort made to confirm them. The aim of the present study was to test the three-factor model of the previously created Problematic Internet Use Questionnaire (PIUQ) by data collection methods formerly not applied (off-line group and face-to-face settings), on the one hand, and by testing on different age groups (adolescent and adult representative samples), on the other hand. Data were collected from 438 high-school students (44.5 percent boys; mean age: 16.0 years; standard deviation=0.7 years) and also from 963 adults (49.9 percent males; mean age: 33.6 years; standard deviation=11.8 years). We applied confirmatory factor analysis to confirm the measurement model of problematic Internet use. The results of the analyses carried out inevitably support the original three-factor model over the possible one-factor solution. Using latent profile analysis, we identified 11 percent of adults and 18 percent of adolescent users characterized by problematic use. Based on exploratory factor analysis, we also suggest a short form of the PIUQ consisting of nine items. Both the original 18-item version of PIUQ and its short 9-item form have satisfactory reliability and validity characteristics, and thus, they are suitable for the assessment of problematic Internet use in future studies. PMID:21711129

The psychometric validation of the Social Problem-Solving Inventory--Revised with UK incarcerated sexual offenders.

PubMed

Wakeling, Helen C

2007-09-01

This study examined the reliability and validity of the Social Problem-Solving Inventory--Revised (SPSI-R; D'Zurilla, Nezu, & Maydeu-Olivares, 2002) with a population of incarcerated sexual offenders. An availability sample of 499 adult male sexual offenders was used. The SPSI-R had good reliability measured by internal consistency and test-retest reliability, and adequate validity. Construct validity was determined via factor analysis. An exploratory factor analysis extracted a two-factor model. This model was then tested against the theory-driven five-factor model using confirmatory factor analysis. The five-factor model was selected as the better fitting of the two, and confirmed the model according to social problem-solving theory (D'Zurilla & Nezu, 1982). The SPSI-R had good convergent validity; significant correlations were found between SPSI-R subscales and measures of self-esteem, impulsivity, and locus of control. SPSI-R subscales were however found to significantly correlate with a measure of socially desirable responding. This finding is discussed in relation to recent research suggesting that impression management may not invalidate self-report measures (e.g. Mills & Kroner, 2005). The SPSI-R was sensitive to sexual offender intervention, with problem-solving improving pre to post-treatment in both rapists and child molesters. The study concludes that the SPSI-R is a reasonably internally valid and appropriate tool to assess problem-solving in sexual offenders. However future research should cross-validate the SPSI-R with other behavioural outcomes to examine the external validity of the measure. Furthermore, future research should utilise a control group to determine treatment impact.

Mortality and economic instability: detailed analyses for Britain and comparative analyses for selected industrialized countries.

PubMed

Brenner, M H

1983-01-01

This paper discusses a first-stage analysis of the link of unemployment rates, as well as other economic, social and environmental health risk factors, to mortality rates in postwar Britain. The results presented represent part of an international study of the impact of economic change on mortality patterns in industrialized countries. The mortality patterns examined include total and infant mortality and (by cause) cardiovascular (total), cerebrovascular and heart disease, cirrhosis of the liver, and suicide, homicide and motor vehicle accidents. Among the most prominent factors that beneficially influence postwar mortality patterns in England/Wales and Scotland are economic growth and stability and health service availability. A principal detrimental factor to health is a high rate of unemployment. Additional factors that have an adverse influence on mortality rates are cigarette consumption and heavy alcohol use and unusually cold winter temperatures (especially in Scotland). The model of mortality that includes both economic changes and behavioral and environmental risk factors was successfully applied to infant mortality rates in the interwar period. In addition, the "simple" economic change model of mortality (using only economic indicators) was applied to other industrialized countries. In Canada, the United States, the United Kingdom, and Sweden, the simple version of the economic change model could be successfully applied only if the analysis was begun before World War II; for analysis beginning in the postwar era, the more sophisticated economic change model, including behavioral and environmental risk factors, was required. In France, West Germany, Italy, and Spain, by contrast, some success was achieved using the simple economic change model.

Pain and the defense response: structural equation modeling reveals a coordinated psychophysiological response to increasing painful stimulation.

PubMed

Donaldson, Gary W; Chapman, C Richard; Nakamura, Yoshi; Bradshaw, David H; Jacobson, Robert C; Chapman, Christopher N

2003-03-01

The defense response theory implies that individuals should respond to increasing levels of painful stimulation with correlated increases in affectively mediated psychophysiological responses. This paper employs structural equation modeling to infer the latent processes responsible for correlated growth in the pain report, evoked potential amplitudes, pupil dilation, and skin conductance of 92 normal volunteers who experienced 144 trials of three levels of increasingly painful electrical stimulation. The analysis assumed a two-level model of latent growth as a function of stimulus level. The first level of analysis formulated a nonlinear growth model for each response measure, and allowed intercorrelations among the parameters of these models across individuals. The second level of analysis posited latent process factors to account for these intercorrelations. The best-fitting parsimonious model suggests that two latent processes account for the correlations. One of these latent factors, the activation threshold, determines the initial threshold response, while the other, the response gradient, indicates the magnitude of the coherent increase in response with stimulus level. Collectively, these two second-order factors define the defense response, a broad construct comprising both subjective pain evaluation and physiological mechanisms.

The Construct Validity of Higher Order Structure-of-Intellect Abilities in a Battery of Tests Emphasizing the Product of Transformations: A Confirmatory Maximum Likelihood Factor Analysis.

ERIC Educational Resources Information Center

Khattab, Ali-Maher; And Others

1982-01-01

A causal modeling system, using confirmatory maximum likelihood factor analysis with the LISREL IV computer program, evaluated the construct validity underlying the higher order factor structure of a given correlation matrix of 46 structure-of-intellect tests emphasizing the product of transformations. (Author/PN)

Confirmatory Factor Analysis of U.S. Merit Systems Protection Board's Survey of Sexual Harassment: The Fit of a Three-Factor Model.

ERIC Educational Resources Information Center

Stockdale, Margaret S.; Hope, Kathryn G.

1997-01-01

Factor analysis of data from 1,070 federal employees, 575 undergraduates and 575 graduate students, faculty, and staff uncovered some weaknesses in the Merit Systems Protection Board's sexual harassment survey instrument. This type of survey does not adequately measure sexual coercion or quid pro quo forms of harassment. (SK)

Study of Factors Preventing Children from Enrolment in Primary School in the Republic of Honduras: Analysis Using Structural Equation Modelling

ERIC Educational Resources Information Center

Ashida, Akemi

2015-01-01

Studies have investigated factors that impede enrolment in Honduras. However, they have not analysed individual factors as a whole or identified the relationships among them. This study used longitudinal data for 1971 children who entered primary schools from 1986 to 2000, and employed structural equation modelling to examine the factors…

Socio-Economic Factors Affecting Adoption of Modern Information and Communication Technology by Farmers in India: Analysis Using Multivariate Probit Model

ERIC Educational Resources Information Center

Mittal, Surabhi; Mehar, Mamta

2016-01-01

Purpose: The paper analyzes factors that affect the likelihood of adoption of different agriculture-related information sources by farmers. Design/Methodology/Approach: The paper links the theoretical understanding of the existing multiple sources of information that farmers use, with the empirical model to analyze the factors that affect the…

Analysis of Korean Students' International Mobility by 2-D Model: Driving Force Factor and Directional Factor

ERIC Educational Resources Information Center

Park, Elisa L.

2009-01-01

The purpose of this study is to understand the dynamics of Korean students' international mobility to study abroad by using the 2-D Model. The first D, "the driving force factor," explains how and what components of the dissatisfaction with domestic higher education perceived by Korean students drives students' outward mobility to seek…

Factor Structure of the Torrance Tests of Creative Thinking Verbal Form B in a Spanish-Speaking Population

ERIC Educational Resources Information Center

Krumm, Gabriela; Aranguren, María; Arán Filippetti, Vanessa; Lemos, Viviana

2016-01-01

The objective of this study was to compare, through a Confirmatory Factor Analysis, two different theoretical models that explain the operationalized creativity construct with the Verbal Torrance Tests of Creative Thinking (TTCT), Form B. Model 1 is represented by six factors which correspond to each activity and its respective indicators while…

Impact of Retirement Choices of Early Career Marines: A Choice Analysis Model

DTIC Science & Technology

2013-03-01

CHOICES OF EARLY CAREER MARINES: A CHOICE ANALYSIS MODEL by André G. La Taste Aaron Masaitis March 2013 Thesis Advisor: Michael Dixon... ANALYSIS MODEL 5. FUNDING NUMBERS 6. AUTHOR(S) André G. La Taste, Aaron Masaitis 7. PERFORMING ORGANIZATION NAME(S) AND ADDRESS(ES) Naval Postgraduate...system. The research will be conducted using a discrete choice analysis methodology that is often used to differentiate factors that lead to

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.

Transformation Abilities: A Reanalysis and Confirmation of SOI Theory.

ERIC Educational Resources Information Center

Khattab, Ali-Maher; And Others

1987-01-01

Confirmatory factor analysis was used to reanalyze correlational data from selected variables in Guilford's Aptitudes Research Project. Results indicated Guilford's model reproduced the original correlation matrix more closely than other models. Most of Guilford's tests indicated high loadings on their hypothesized factors. (GDC)

Development of prognostic model for predicting survival after retrograde placement of ureteral stent in advanced gastrointestinal cancer patients and its evaluation by decision curve analysis.

PubMed

Kawano, Shingo; Komai, Yoshinobu; Ishioka, Junichiro; Sakai, Yasuyuki; Fuse, Nozomu; Ito, Masaaki; Kihara, Kazunori; Saito, Norio

2016-10-01

The aim of this study was to determine risk factors for survival after retrograde placement of ureteral stents and develop a prognostic model for advanced gastrointestinal tract (GIT: esophagus, stomach, colon and rectum) cancer patients. We examined the clinical records of 122 patients who underwent retrograde placement of a ureteral stent against malignant extrinsic ureteral obstruction. A prediction model for survival after stenting was developed. We compared its clinical usefulness with our previous model based on the results from nephrostomy cases by decision curve analysis. Median follow-up period was 201 days (8-1490) and 97 deaths occurred. The 1-year survival rate in this cohort was 29%. Based on multivariate analysis, primary site of colon origin, absence of retroperitoneal lymph node metastasis and serum albumin >3g/dL were significantly associated with a prolonged survival time. To develop a prognostic model, we divided the patients into 3 risk groups of favorable: 0-1 factors (N.=53), intermediate: 2 risk factors (N.=54), and poor: 3 risk factors (N.=15). There were significant differences in the survival profiles of these 3 risk groups (P<0.0001). Decision curve analyses revealed that the current model has a superior net benefit than our previous model for most of the examined probabilities. We have developed a novel prognostic model for GIT cancer patients who were treated with retrograde placement of a ureteral stent. The current model should help urologists and medical oncologists to predict survival in cases of malignant extrinsic ureteral obstruction.

Selection of asset investment models by hospitals: examination of influencing factors, using Switzerland as an example.

PubMed

Eicher, Bernhard

2016-10-01

Hospitals are responsible for a remarkable part of the annual increase in healthcare expenditure. This article examines one of the major cost drivers, the expenditure for investment in hospital assets. The study, conducted in Switzerland, identifies factors that influence hospitals' investment decisions. A suggestion on how to categorize asset investment models is presented based on the life cycle of an asset, and its influencing factors defined based on transaction cost economics. The influence of five factors (human asset specificity, physical asset specificity, uncertainty, bargaining power, and privacy of ownership) on the selection of an asset investment model is examined using a two-step fuzzy-set Qualitative Comparative Analysis. The research shows that outsourcing-oriented asset investment models are particularly favored in the presence of two combinations of influencing factors: First, if technological uncertainty is high and both human asset specificity and bargaining power of a hospital are low. Second, if assets are very specific, technological uncertainty is high and there is a private hospital with low bargaining power, outsourcing-oriented asset investment models are favored too. Using Qualitative Comparative Analysis, it can be demonstrated that investment decisions of hospitals do not depend on isolated influencing factors but on a combination of factors. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Bayesian inference of the number of factors in gene-expression analysis: application to human virus challenge studies

PubMed Central

2010-01-01

Background Nonparametric Bayesian techniques have been developed recently to extend the sophistication of factor models, allowing one to infer the number of appropriate factors from the observed data. We consider such techniques for sparse factor analysis, with application to gene-expression data from three virus challenge studies. Particular attention is placed on employing the Beta Process (BP), the Indian Buffet Process (IBP), and related sparseness-promoting techniques to infer a proper number of factors. The posterior density function on the model parameters is computed using Gibbs sampling and variational Bayesian (VB) analysis. Results Time-evolving gene-expression data are considered for respiratory syncytial virus (RSV), Rhino virus, and influenza, using blood samples from healthy human subjects. These data were acquired in three challenge studies, each executed after receiving institutional review board (IRB) approval from Duke University. Comparisons are made between several alternative means of per-forming nonparametric factor analysis on these data, with comparisons as well to sparse-PCA and Penalized Matrix Decomposition (PMD), closely related non-Bayesian approaches. Conclusions Applying the Beta Process to the factor scores, or to the singular values of a pseudo-SVD construction, the proposed algorithms infer the number of factors in gene-expression data. For real data the "true" number of factors is unknown; in our simulations we consider a range of noise variances, and the proposed Bayesian models inferred the number of factors accurately relative to other methods in the literature, such as sparse-PCA and PMD. We have also identified a "pan-viral" factor of importance for each of the three viruses considered in this study. We have identified a set of genes associated with this pan-viral factor, of interest for early detection of such viruses based upon the host response, as quantified via gene-expression data. PMID:21062443

Use of an Artificial Neural Network to Construct a Model of Predicting Deep Fungal Infection in Lung Cancer Patients.

PubMed

Chen, Jian; Chen, Jie; Ding, Hong-Yan; Pan, Qin-Shi; Hong, Wan-Dong; Xu, Gang; Yu, Fang-You; Wang, Yu-Min

2015-01-01

The statistical methods to analyze and predict the related dangerous factors of deep fungal infection in lung cancer patients were several, such as logic regression analysis, meta-analysis, multivariate Cox proportional hazards model analysis, retrospective analysis, and so on, but the results are inconsistent. A total of 696 patients with lung cancer were enrolled. The factors were compared employing Student's t-test or the Mann-Whitney test or the Chi-square test and variables that were significantly related to the presence of deep fungal infection selected as candidates for input into the final artificial neural network analysis (ANN) model. The receiver operating characteristic (ROC) and area under curve (AUC) were used to evaluate the performance of the artificial neural network (ANN) model and logistic regression (LR) model. The prevalence of deep fungal infection from lung cancer in this entire study population was 32.04%(223/696), deep fungal infections occur in sputum specimens 44.05% (200/454). The ratio of candida albicans was 86.99% (194/223) in the total fungi. It was demonstrated that older (≥65 years), use of antibiotics, low serum albumin concentrations (≤37.18 g /L), radiotherapy, surgery, low hemoglobin hyperlipidemia (≤93.67 g /L), long time of hospitalization (≥14 days) were apt to deep fungal infection and the ANN model consisted of the seven factors. The AUC of ANN model (0.829±0.019) was higher than that of LR model (0.756±0.021). The artificial neural network model with variables consisting of age, use of antibiotics, serum albumin concentrations, received radiotherapy, received surgery, hemoglobin, time of hospitalization should be useful for predicting the deep fungal infection in lung cancer.

Structural validation of the Self-Compassion Scale with a German general population sample

PubMed Central

Kwakkenbos, Linda; Moran, Chelsea; Thombs, Brett; Albani, Cornelia; Bourkas, Sophia; Zenger, Markus; Brahler, Elmar; Körner, Annett

2018-01-01

Background Published validation studies have reported different factor structures for the Self-Compassion Scale (SCS). The objective of this study was to assess the factor structure of the SCS in a large general population sample representative of the German population. Methods A German population sample completed the SCS and other self-report measures. Confirmatory factor analysis (CFA) in MPlus was used to test six models previously found in factor analytic studies (unifactorial model, two-factor model, three-factor model, six-factor model, a hierarchical (second order) model with six first-order factors and two second-order factors, and a model with arbitrarily assigned items to six factors). In addition, three bifactor models were also tested: bifactor model #1 with two group factors (SCS positive items, called SCS positive) and SCS negative items, called SCS negative) and one general factor (overall SCS); bifactor model #2, which is a two-tier model with six group factors, three (SCS positive subscales) corresponding to one general dimension (SCS positive) and three (SCS negative subscales) corresponding to the second general dimension (SCS negative); bifactor model #3 with six group factors (six SCS subscales) and one general factor (overall SCS). Results The two-factor model, the six-factor model, and the hierarchical model showed less than ideal, but acceptable fit. The model fit indices for these models were comparable, with no apparent advantage of the six-factor model over the two-factor model. The one-factor model, the three-factor model, and bifactor model #3 showed poor fit. The other two bifactor models showed strong support for two factors: SCS positive and SCS negative. Conclusion The main results of this study are that, among the German general population, six SCS factors and two SCS factors fit the data reasonably well. While six factors can be modelled, the three negative factors and the three positive factors, respectively, did not reflect reliable or meaningful variance beyond the two summative positive and negative item factors. As such, we recommend the use of two subscale scores to capture a positive factor and a negative factor when administering the German SCS to general population samples and we strongly advise against the use of a total score across all SCS items. PMID:29408888

TheInternational Index of Erectile Function (IIEF-15): psychometric properties of the Portuguese version.

PubMed

Quinta Gomes, Ana Luísa; Nobre, Pedro

2012-01-01

The International Index of Erectile Function (IIEF) is a brief, reliable, and multidimensional scale for assessing sexual function in men in both research and clinical trials. The objective of the present study was to determine the psychometric properties of the Portuguese version of the IIEF. A total of 1,363 Portuguese men participated in this study (a clinical sample of 37 men and a community sample of 1,326 men). All participants completed a questionnaire regarding demographic information and the IIEF. Principal component analysis using varimax rotation indicated a two-factor structure explaining approximately 55% of the total variance (one factor encompassing erection and orgasmic function domains of the original IIEF, and a second factor corresponding to sexual desire, intercourse, and overall satisfaction). The differentiated factor structure with five separate domains of sexual function was not replicated in the Portuguese version. The two-factor model and the original five-factor model of male sexual function were assessed with confirmatory factor analysis (CFA), and overall acceptable fits were demonstrated for both models. However, despite a non-optimal performance, CFA provided a better support for the five-factor solution as the model that best fitted the data. An important lack of discriminant validity evidenced by high intercorrelations among dimensions was detected in both models, suggesting a substantial overlap among factors. Reliability studies showed good internal consistency for the five subscales, and test-retest reliability analysis supported the stability of the measure over time. Discriminant validity confirmed the ability of both subscales to differentiate men with erectile dysfunction from matched controls. Results suggested that the Portuguese version of the IIEF has adequate psychometric properties, and its use is recommended for clinical and research purposes. Further studies are needed in order to elucidate the association among dimensions of male sexual function and, ultimately, to offer a clearer conceptualization of male's sexual response. © 2011 International Society for Sexual Medicine.

Analysis of Traffic Crashes Involving Pedestrians Using Big Data: Investigation of Contributing Factors and Identification of Hotspots.

PubMed

Xie, Kun; Ozbay, Kaan; Kurkcu, Abdullah; Yang, Hong

2017-08-01

This study aims to explore the potential of using big data in advancing the pedestrian risk analysis including the investigation of contributing factors and the hotspot identification. Massive amounts of data of Manhattan from a variety of sources were collected, integrated, and processed, including taxi trips, subway turnstile counts, traffic volumes, road network, land use, sociodemographic, and social media data. The whole study area was uniformly split into grid cells as the basic geographical units of analysis. The cell-structured framework makes it easy to incorporate rich and diversified data into risk analysis. The cost of each crash, weighted by injury severity, was assigned to the cells based on the relative distance to the crash site using a kernel density function. A tobit model was developed to relate grid-cell-specific contributing factors to crash costs that are left-censored at zero. The potential for safety improvement (PSI) that could be obtained by using the actual crash cost minus the cost of "similar" sites estimated by the tobit model was used as a measure to identify and rank pedestrian crash hotspots. The proposed hotspot identification method takes into account two important factors that are generally ignored, i.e., injury severity and effects of exposure indicators. Big data, on the one hand, enable more precise estimation of the effects of risk factors by providing richer data for modeling, and on the other hand, enable large-scale hotspot identification with higher resolution than conventional methods based on census tracts or traffic analysis zones. © 2017 Society for Risk Analysis.

Estimating the actual subject-specific genetic correlations in behavior genetics.

PubMed

Molenaar, Peter C M

2012-10-01

Generalization of the standard behavior longitudinal genetic factor model for the analysis of interindividual phenotypic variation to a genetic state space model for the analysis of intraindividual variation enables the possibility to estimate subject-specific heritabilities.

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.

Analysis of Young Men: Chapter Two. Determinants of Adult Socioeconomic Attainment in Young Men: An Analysis of the Role of Risk and Social Capital Factors, and the Pathways through Which They Have Their Impacts.

ERIC Educational Resources Information Center

Brown, Brett V.

In this chapter, a series of nested regression models are estimated to analyze three measures of adult socioeconomic attainment measured at age 29: (1) educational attainment; (2) occupational attainment; and (3) earnings. The models seek to relate risk, social capital, social-psychological factors, and life course events in early adulthood, both…

A Factor Analytic Model of Drug-Related Behavior in Adolescence and Its Impact on Arrests at Multiple Stages of the Life Course

PubMed Central

2016-01-01

Objectives Recognizing the inherent variability of drug-related behaviors, this study develops an empirically-driven and holistic model of drug-related behavior during adolescence using factor analysis to simultaneously model multiple drug behaviors. Methods The factor analytic model uncovers latent dimensions of drug-related behaviors, rather than patterns of individuals. These latent dimensions are treated as empirical typologies which are then used to predict an individual’s number of arrests accrued at multiple phases of the life course. The data are robust enough to simultaneously capture drug behavior measures typically considered in isolation in the literature, and to allow for behavior to change and evolve over the period of adolescence. Results Results show that factor analysis is capable of developing highly descriptive patterns of drug offending, and that these patterns have great utility in predicting arrests. Results further demonstrate that while drug behavior patterns are predictive of arrests at the end of adolescence for both males and females, the impacts on arrests are longer lasting for females. Conclusions The various facets of drug behaviors have been a long-time concern of criminological research. However, the ability to model multiple behaviors simultaneously is often constrained by data that do not measure the constructs fully. Factor analysis is shown to be a useful technique for modeling adolescent drug involvement patterns in a way that accounts for the multitude and variability of possible behaviors, and in predicting future negative life outcomes, such as arrests. PMID:28435183

Spatial Bayesian Latent Factor Regression Modeling of Coordinate-based Meta-analysis Data

PubMed Central

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D.; Nichols, Thomas E.

2017-01-01

Summary Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the paper are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to 1) identify areas of consistent activation; and 2) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterised as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. PMID:28498564

Copula-based regression modeling of bivariate severity of temporary disability and permanent motor injuries.

PubMed

Ayuso, Mercedes; Bermúdez, Lluís; Santolino, Miguel

2016-04-01

The analysis of factors influencing the severity of the personal injuries suffered by victims of motor accidents is an issue of major interest. Yet, most of the extant literature has tended to address this question by focusing on either the severity of temporary disability or the severity of permanent injury. In this paper, a bivariate copula-based regression model for temporary disability and permanent injury severities is introduced for the joint analysis of the relationship with the set of factors that might influence both categories of injury. Using a motor insurance database with 21,361 observations, the copula-based regression model is shown to give a better performance than that of a model based on the assumption of independence. The inclusion of the dependence structure in the analysis has a higher impact on the variance estimates of the injury severities than it does on the point estimates. By taking into account the dependence between temporary and permanent severities a more extensive factor analysis can be conducted. We illustrate that the conditional distribution functions of injury severities may be estimated, thus, providing decision makers with valuable information. Copyright © 2016 Elsevier Ltd. All rights reserved.

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.

Continuity of care in mental health: understanding and measuring a complex phenomenon.

PubMed

Burns, T; Catty, J; White, S; Clement, S; Ellis, G; Jones, I R; Lissouba, P; McLaren, S; Rose, D; Wykes, T

2009-02-01

Continuity of care is considered by patients and clinicians an essential feature of good quality care in long-term disorders, yet there is general agreement that it is a complex concept. Most policies emphasize it and encourage systems to promote it. Despite this, there is no accepted definition or measure against which to test policies or interventions designed to improve continuity. We aimed to operationalize a multi-axial model of continuity of care and to use factor analysis to determine its validity for severe mental illness. A multi-axial model of continuity of care comprising eight facets was operationalized for quantitative data collection from mental health service users using 32 variables. Of these variables, 22 were subsequently entered into a factor analysis as independent components, using data from a clinical population considered to require long-term consistent care. Factor analysis produced seven independent continuity factors accounting for 62.5% of the total variance. These factors, Experience and Relationship, Regularity, Meeting Needs, Consolidation, Managed Transitions, Care Coordination and Supported Living, were close but not identical to the original theoretical model. We confirmed that continuity of care is multi-factorial. Our seven factors are intuitively meaningful and appear to work in mental health. These factors should be used as a starting-point in research into the determinants and outcomes of continuity of care in long-term disorders.

The Psychometric Evaluation of the Connor-Davidson Resilience Scale Using a Chinese Military Sample

PubMed Central

Xie, Yuanjun; Peng, Li; Zuo, Xin; Li, Min

2016-01-01

This study examined the psychometric properties of the Connor-Davidson Resilience Scale (CD-RISC) with a Chinese military population with the aim of finding a suitable instrument to quantify resilience in Chinese military service members. The confirmatory factor analysis results did not support the factorial structure of the original or the Chinese community version of the CD-RISC, but the exploratory factor analysis results revealed a three-factor model (composed of Competency, Toughness, and Adaptability) that seemed to fit. Moreover, the repeat confirmatory factory analysis replicated the three-factor model. Additionally, the CD-RISC with a Chinese military sample exhibited appropriate psychometric properties, including internal consistency, test-retest reliability, and structural and concurrent validity. The revised CD-RISC with a Chinese military sample provides insight into the resilience measurement framework and could be a reliable and valid measurement for evaluating resilience in a Chinese military population. PMID:26859484

Theoretical Assessment of the Impact of Climatic Factors in a Vibrio Cholerae Model.

PubMed

Kolaye, G; Damakoa, I; Bowong, S; Houe, R; Békollè, D

2018-05-04

A mathematical model for Vibrio Cholerae (V. Cholerae) in a closed environment is considered, with the aim of investigating the impact of climatic factors which exerts a direct influence on the bacterial metabolism and on the bacterial reservoir capacity. We first propose a V. Cholerae mathematical model in a closed environment. A sensitivity analysis using the eFast method was performed to show the most important parameters of the model. After, we extend this V. cholerae model by taking account climatic factors that influence the bacterial reservoir capacity. We present the theoretical analysis of the model. More precisely, we compute equilibria and study their stabilities. The stability of equilibria was investigated using the theory of periodic cooperative systems with a concave nonlinearity. Theoretical results are supported by numerical simulations which further suggest the necessity to implement sanitation campaigns of aquatic environments by using suitable products against the bacteria during the periods of growth of aquatic reservoirs.

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.

Multiple Statistical Models Based Analysis of Causative Factors and Loess Landslides in Tianshui City, China

NASA Astrophysics Data System (ADS)

Su, Xing; Meng, Xingmin; Ye, Weilin; Wu, Weijiang; Liu, Xingrong; Wei, Wanhong

2018-03-01

Tianshui City is one of the mountainous cities that are threatened by severe geo-hazards in Gansu Province, China. Statistical probability models have been widely used in analyzing and evaluating geo-hazards such as landslide. In this research, three approaches (Certainty Factor Method, Weight of Evidence Method and Information Quantity Method) were adopted to quantitively analyze the relationship between the causative factors and the landslides, respectively. The source data used in this study are including the SRTM DEM and local geological maps in the scale of 1:200,000. 12 causative factors (i.e., altitude, slope, aspect, curvature, plan curvature, profile curvature, roughness, relief amplitude, and distance to rivers, distance to faults, distance to roads, and the stratum lithology) were selected to do correlation analysis after thorough investigation of geological conditions and historical landslides. The results indicate that the outcomes of the three models are fairly consistent.

Rainfall or parameter uncertainty? The power of sensitivity analysis on grouped factors

NASA Astrophysics Data System (ADS)

Nossent, Jiri; Pereira, Fernando; Bauwens, Willy

2017-04-01

Hydrological models are typically used to study and represent (a part of) the hydrological cycle. In general, the output of these models mostly depends on their input rainfall and parameter values. Both model parameters and input precipitation however, are characterized by uncertainties and, therefore, lead to uncertainty on the model output. Sensitivity analysis (SA) allows to assess and compare the importance of the different factors for this output uncertainty. Hereto, the rainfall uncertainty can be incorporated in the SA by representing it as a probabilistic multiplier. Such multiplier can be defined for the entire time series, or several of these factors can be determined for every recorded rainfall pulse or for hydrological independent storm events. As a consequence, the number of parameters included in the SA related to the rainfall uncertainty can be (much) lower or (much) higher than the number of model parameters. Although such analyses can yield interesting results, it remains challenging to determine which type of uncertainty will affect the model output most due to the different weight both types will have within the SA. In this study, we apply the variance based Sobol' sensitivity analysis method to two different hydrological simulators (NAM and HyMod) for four diverse watersheds. Besides the different number of model parameters (NAM: 11 parameters; HyMod: 5 parameters), the setup of our sensitivity and uncertainty analysis-combination is also varied by defining a variety of scenarios including diverse numbers of rainfall multipliers. To overcome the issue of the different number of factors and, thus, the different weights of the two types of uncertainty, we build on one of the advantageous properties of the Sobol' SA, i.e. treating grouped parameters as a single parameter. The latter results in a setup with a single factor for each uncertainty type and allows for a straightforward comparison of their importance. In general, the results show a clear influence of the weights in the different SA scenarios. However, working with grouped factors resolves this issue and leads to clear importance results.

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.

Some aspects of the anemia of chronic disorders modeled and analyzed by petri net based approach.

PubMed

Formanowicz, Dorota; Sackmann, Andrea; Kozak, Adam; Błażewicz, Jacek; Formanowicz, Piotr

2011-06-01

Anemia of chronic disorders is a very important phenomenon and iron is a crucial factor of this complex process. To better understand this process and its influence on some other factors we have built a mathematical model of the human body iron homeostasis, which possibly most exactly would reflect the metabolism of iron in the case of anemia and inflammation. The model has been formulated in the language of Petri net theory, which allows for its simulation and precise analysis. The obtained results of the analysis of the model's behavior, concerning the influence of anemia and inflammation on the transferrin receptors, and hepcidin concentration changes are the valuable complements to the knowledge following from clinical research. This analysis is one of the first attempts to investigate properties and behavior of a not fully understood biological system on a basis of its Petri net based model.

[Logistic regression model of noninvasive prediction for portal hypertensive gastropathy in patients with hepatitis B associated cirrhosis].

PubMed

Wang, Qingliang; Li, Xiaojie; Hu, Kunpeng; Zhao, Kun; Yang, Peisheng; Liu, Bo

2015-05-12

To explore the risk factors of portal hypertensive gastropathy (PHG) in patients with hepatitis B associated cirrhosis and establish a Logistic regression model of noninvasive prediction. The clinical data of 234 hospitalized patients with hepatitis B associated cirrhosis from March 2012 to March 2014 were analyzed retrospectively. The dependent variable was the occurrence of PHG while the independent variables were screened by binary Logistic analysis. Multivariate Logistic regression was used for further analysis of significant noninvasive independent variables. Logistic regression model was established and odds ratio was calculated for each factor. The accuracy, sensitivity and specificity of model were evaluated by the curve of receiver operating characteristic (ROC). According to univariate Logistic regression, the risk factors included hepatic dysfunction, albumin (ALB), bilirubin (TB), prothrombin time (PT), platelet (PLT), white blood cell (WBC), portal vein diameter, spleen index, splenic vein diameter, diameter ratio, PLT to spleen volume ratio, esophageal varices (EV) and gastric varices (GV). Multivariate analysis showed that hepatic dysfunction (X1), TB (X2), PLT (X3) and splenic vein diameter (X4) were the major occurring factors for PHG. The established regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4. The accuracy of model for PHG was 79.1% with a sensitivity of 77.2% and a specificity of 80.8%. Hepatic dysfunction, TB, PLT and splenic vein diameter are risk factors for PHG and the noninvasive predicted Logistic regression model was Logit P=-2.667+2.186X1-2.167X2+0.725X3+0.976X4.

The Bilevel Structure of the Outcome Questionnaire-45

ERIC Educational Resources Information Center

Bludworth, Jamie L.; Tracey, Terence J. G.; Glidden-Tracey, Cynthia

2010-01-01

The structure of the Outcome Questionnaire-45 (Lambert et al., 2001) was examined in a sample of 1,100 university counseling center clients using confirmatory factor analysis. Specifically, the relative fit of 1-factor, 3-factor orthogonal, 3-factor oblique, 4-factor hierarchical, and 4-factor bilevel models were examined. Although the 3-factor…

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.

FACTOR ANALYTIC MODELS OF CLUSTERED MULTIVARIATE DATA WITH INFORMATIVE CENSORING

EPA Science Inventory

This paper describes a general class of factor analytic models for the analysis of clustered multivariate data in the presence of informative missingness. We assume that there are distinct sets of cluster-level latent variables related to the primary outcomes and to the censorin...

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.

Resolving the double tension: Toward a new approach to measurement modeling in cross-national research

NASA Astrophysics Data System (ADS)

Medina, Tait Runnfeldt

The increasing global reach of survey research provides sociologists with new opportunities to pursue theory building and refinement through comparative analysis. However, comparison across a broad array of diverse contexts introduces methodological complexities related to the development of constructs (i.e., measurement modeling) that if not adequately recognized and properly addressed undermine the quality of research findings and cast doubt on the validity of substantive conclusions. The motivation for this dissertation arises from a concern that the availability of cross-national survey data has outpaced sociologists' ability to appropriately analyze and draw meaningful conclusions from such data. I examine the implicit assumptions and detail the limitations of three commonly used measurement models in cross-national analysis---summative scale, pooled factor model, and multiple-group factor model with measurement invariance. Using the orienting lens of the double tension I argue that a new approach to measurement modeling that incorporates important cross-national differences into the measurement process is needed. Two such measurement models---multiple-group factor model with partial measurement invariance (Byrne, Shavelson and Muthen 1989) and the alignment method (Asparouhov and Muthen 2014; Muthen and Asparouhov 2014)---are discussed in detail and illustrated using a sociologically relevant substantive example. I demonstrate that the former approach is vulnerable to an identification problem that arbitrarily impacts substantive conclusions. I conclude that the alignment method is built on model assumptions that are consistent with theoretical understandings of cross-national comparability and provides an approach to measurement modeling and construct development that is uniquely suited for cross-national research. The dissertation makes three major contributions: First, it provides theoretical justification for a new cross-national measurement model and explicates a link between theoretical conceptions of cross-national comparability and a statistical method. Second, it provides a clear and detailed discussion of model identification in multiple-group confirmatory factor analysis that is missing from the literature. This discussion sets the stage for the introduction of the identification problem within multiple-group confirmatory factor analysis with partial measurement invariance and the alternative approach to model identification employed by the alignment method. Third, it offers the first pedagogical presentation of the alignment method using a sociologically relevant example.

The Big-Five factor structure as an integrative framework: an analysis of Clarke's AVA model.

PubMed

Goldberg, L R; Sweeney, D; Merenda, P F; Hughes, J E

1996-06-01

Using a large (N = 3,629) sample of participants selected to be representative of U.S. working adults in the year 2,000, we provide links between the constructs in 2 personality models that have been derived from quite different rationales. We demonstrate the use of a novel procedure for providing orthogonal Big-Five factor scores and use those scores to analyze the scales of the Activity Vector Analysis (AVA). We discuss the implications of our many findings both for the science of personality assessment and for future research using the AVA model.

High-order fuzzy time-series based on multi-period adaptation model for forecasting stock markets

NASA Astrophysics Data System (ADS)

Chen, Tai-Liang; Cheng, Ching-Hsue; Teoh, Hia-Jong

2008-02-01

Stock investors usually make their short-term investment decisions according to recent stock information such as the late market news, technical analysis reports, and price fluctuations. To reflect these short-term factors which impact stock price, this paper proposes a comprehensive fuzzy time-series, which factors linear relationships between recent periods of stock prices and fuzzy logical relationships (nonlinear relationships) mined from time-series into forecasting processes. In empirical analysis, the TAIEX (Taiwan Stock Exchange Capitalization Weighted Stock Index) and HSI (Heng Seng Index) are employed as experimental datasets, and four recent fuzzy time-series models, Chen’s (1996), Yu’s (2005), Cheng’s (2006) and Chen’s (2007), are used as comparison models. Besides, to compare with conventional statistic method, the method of least squares is utilized to estimate the auto-regressive models of the testing periods within the databases. From analysis results, the performance comparisons indicate that the multi-period adaptation model, proposed in this paper, can effectively improve the forecasting performance of conventional fuzzy time-series models which only factor fuzzy logical relationships in forecasting processes. From the empirical study, the traditional statistic method and the proposed model both reveal that stock price patterns in the Taiwan stock and Hong Kong stock markets are short-term.

Subconstructs of the Edinburgh Postnatal Depression Scale in a multi-ethnic inner-city population in the U.S.

PubMed

Chiu, Yueh-Hsiu Mathilda; Sheffield, Perry E; Hsu, Hsiao-Hsien Leon; Goldstein, Jonathan; Curtin, Paul C; Wright, Rosalind J

2017-12-01

The ten-item Edinburgh Postnatal Depression Scale (EPDS) is one of the most widely used self-report measures of postpartum depression. Although originally described as a one-dimensional measure, the recognition that depressive symptoms may be differentially experienced across cultural and racial/ethnic groups has led to studies examining structural equivalence of the EPDS in different populations. Variation of the factor structure remains understudied across racial/ethnic groups of US women. We examined the factor structure of the EPDS assessed 6 months postpartum in 515 women (29% black, 53% Hispanic, 18% white) enrolled in an urban Boston longitudinal birth cohort. Exploratory factor analysis (EFA) identified that a three-factor model, including depression, anxiety, and anhedonia subscales, was the most optimal fit in our sample as a whole and across race/ethnicity. Confirmatory factor analysis (CFA) was used to examine the fit of both the two- and three-factor models reported in prior research. CFA confirmed the best fit for a three-factor model, with minimal differences across race/ethnicity. "Things get on top of me" loaded on the anxiety factor among Hispanics, but loaded on the depression factor in whites and African Americans. These findings suggest that EPDS factor structure may need to be adjusted for diverse samples and warrants further study.

Developing a suitable model for supplier selection based on supply chain risks: an empirical study from Iranian pharmaceutical companies.

PubMed

Mehralian, Gholamhossein; Rajabzadeh Gatari, Ali; Morakabati, Mohadese; Vatanpour, Hossein

2012-01-01

The supply chain represents the critical link between the development of new product and the market in pharmaceutical industry. Over the years, improvements made in supply chain operations have focused largely on ways to reduce cost and gain efficiencies in scale. In addition, powerful regulatory and market forces have provided new incentives for pharmaceutical firms to basically rethink the way they produce and distribute products, and also to re-imagine the role of the supply chain in driving strategic growth, brand differentiation and economic value in the health continuum. The purpose of this paper is to formulate basic factors involved in risk analysis of pharmaceutical industry, and also determine the effective factors involved in suppliers selection and their priorities. This paper is based on the results of literature review, experts' opinion acquisition, statistical analysis and also using MADM models on data gathered from distributed questionnaires. The model consists of the following steps and components: first factors involved in to supply chain risks are determined. Based on them a framework is considered. According the result of statistical analysis and MADM models the risk factors are formulated. The paper determines the main components and influenceial factors involving in the supply chain risks. Results showed that delivery risk can make an important contribution to mitigate the risk of pharmaceutical industry.

Developing a Suitable Model for Supplier Selection Based on Supply Chain Risks: An Empirical Study from Iranian Pharmaceutical Companies

PubMed Central

Mehralian, Gholamhossein; Rajabzadeh Gatari, Ali; Morakabati, Mohadese; Vatanpour, Hossein

2012-01-01

The supply chain represents the critical link between the development of new product and the market in pharmaceutical industry. Over the years, improvements made in supply chain operations have focused largely on ways to reduce cost and gain efficiencies in scale. In addition, powerful regulatory and market forces have provided new incentives for pharmaceutical firms to basically rethink the way they produce and distribute products, and also to re-imagine the role of the supply chain in driving strategic growth, brand differentiation and economic value in the health continuum. The purpose of this paper is to formulate basic factors involved in risk analysis of pharmaceutical industry, and also determine the effective factors involved in suppliers selection and their priorities. This paper is based on the results of literature review, experts’ opinion acquisition, statistical analysis and also using MADM models on data gathered from distributed questionnaires. The model consists of the following steps and components: first factors involved in to supply chain risks are determined. Based on them a framework is considered. According the result of statistical analysis and MADM models the risk factors are formulated. The paper determines the main components and influenceial factors involving in the supply chain risks. Results showed that delivery risk can make an important contribution to mitigate the risk of pharmaceutical industry. PMID:24250442

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.

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.

[Factorial Structure of the Cardiff Anomalous Perceptions Scale (CAPS) in a Colombian Population Sample].

PubMed

Tamayo-Agudelo, William; Jaén-Moreno, María José; Luque-Luque, Rogelio

2015-01-01

The continuum hypothesis of psychosis assumes that hallucinations are not exclusive of psychotic disorders. A number of psychometric tests have been developed to assess psychosis using a dimensional model. To determine the factorial structure of the Cardiff Anomalous Perceptions Scale (CAPS) for the Colombian population, and to contrast the fit of two factor models previously reported in the literature by conducting a confirmatory factor analysis (CFA). This was a cross-sectional study in which 207 subjects from the general population were assessed using the Cardiff Anomalous Perceptions Scale. A two-factor structure with acceptable ordinal alpha coefficients (α=.88 and α=.87) was found. One factor gathered items related to multimodal perceptual alterations, and a second factor grouped items related with experiences linked to the temporal lobe. The analysis of the first factor indicated that it was dependent on cultural issues for the interpretation of sensations. The second factor appeared almost unchanged on diverse populations, suggesting its transcultural character. When comparing the models proposed by Bell et al. and Jaen-Moreno et al. using the data obtained from the sample, the confirmatory factor analysis conducted indicated inadequate goodness-of-fit indexes (χ(2)). However, some incremental goodness-of-fit indexes (normalized χ(2) [RMSEA]) were acceptable. The Jaén-Moreno et al. model showed the best fit to the data collected from the Colombian sample. The factorial structure of CAPS for the Colombian population appears to be sensitive to cultural issues, especially when describing anomalous sensorial experiences. Copyright © 2015 Asociación Colombiana de Psiquiatría. Publicado por Elsevier España. All rights reserved.

Bayesian Structural Equation Modeling: A More Flexible Representation of Substantive Theory

ERIC Educational Resources Information Center

Muthen, Bengt; Asparouhov, Tihomir

2012-01-01

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…

Comparison of Cox's Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000-2012.

PubMed

Adelian, R; Jamali, J; Zare, N; Ayatollahi, S M T; Pooladfar, G R; Roustaei, N

2015-01-01

Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. To compare Cox's regression model with parametric models for determining the independent factors for predicting adults' and pediatrics' survival after liver transplantation. This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Parametric regression model is a good alternative for the Cox's regression model.

Cost Efficiency in the University: A Departmental Evaluation Model

ERIC Educational Resources Information Center

Gimenez, Victor M.; Martinez, Jose Luis

2006-01-01

This article presents a model for the analysis of cost efficiency within the framework of data envelopment analysis models. It calculates the cost excess, separating a unit of production from its optimal or frontier levels, and, at the same time, breaks these excesses down into three explanatory factors: (a) technical inefficiency, which depends…

Human factors model concerning the man-machine interface of mining crewstations

NASA Technical Reports Server (NTRS)

Rider, James P.; Unger, Richard L.

1989-01-01

The U.S. Bureau of Mines is developing a computer model to analyze the human factors aspect of mining machine operator compartments. The model will be used as a research tool and as a design aid. It will have the capability to perform the following: simulated anthropometric or reach assessment, visibility analysis, illumination analysis, structural analysis of the protective canopy, operator fatigue analysis, and computation of an ingress-egress rating. The model will make extensive use of graphics to simplify data input and output. Two dimensional orthographic projections of the machine and its operator compartment are digitized and the data rebuilt into a three dimensional representation of the mining machine. Anthropometric data from either an individual or any size population may be used. The model is intended for use by equipment manufacturers and mining companies during initial design work on new machines. In addition to its use in machine design, the model should prove helpful as an accident investigation tool and for determining the effects of machine modifications made in the field on the critical areas of visibility and control reach ability.

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.

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.

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.

The SAM framework: modeling the effects of management factors on human behavior in risk analysis.

PubMed

Murphy, D M; Paté-Cornell, M E

1996-08-01

Complex engineered systems, such as nuclear reactors and chemical plants, have the potential for catastrophic failure with disastrous consequences. In recent years, human and management factors have been recognized as frequent root causes of major failures in such systems. However, classical probabilistic risk analysis (PRA) techniques do not account for the underlying causes of these errors because they focus on the physical system and do not explicitly address the link between components' performance and organizational factors. This paper describes a general approach for addressing the human and management causes of system failure, called the SAM (System-Action-Management) framework. Beginning with a quantitative risk model of the physical system, SAM expands the scope of analysis to incorporate first the decisions and actions of individuals that affect the physical system. SAM then links management factors (incentives, training, policies and procedures, selection criteria, etc.) to those decisions and actions. The focus of this paper is on four quantitative models of action that describe this last relationship. These models address the formation of intentions for action and their execution as a function of the organizational environment. Intention formation is described by three alternative models: a rational model, a bounded rationality model, and a rule-based model. The execution of intentions is then modeled separately. These four models are designed to assess the probabilities of individual actions from the perspective of management, thus reflecting the uncertainties inherent to human behavior. The SAM framework is illustrated for a hypothetical case of hazardous materials transportation. This framework can be used as a tool to increase the safety and reliability of complex technical systems by modifying the organization, rather than, or in addition to, re-designing the physical system.

State-Space Modeling of Dynamic Psychological Processes via the Kalman Smoother Algorithm: Rationale, Finite Sample Properties, and Applications

ERIC Educational Resources Information Center

Song, Hairong; Ferrer, Emilio

2009-01-01

This article presents a state-space modeling (SSM) technique for fitting process factor analysis models directly to raw data. The Kalman smoother via the expectation-maximization algorithm to obtain maximum likelihood parameter estimates is used. To examine the finite sample properties of the estimates in SSM when common factors are involved, a…

Examining the Factor Structure and Discriminant Validity of the 12-Item General Health Questionnaire (GHQ-12) Among Spanish Postpartum Women

ERIC Educational Resources Information Center

Aguado, Jaume; Campbell, Alistair; Ascaso, Carlos; Navarro, Purificacion; Garcia-Esteve, Lluisa; Luciano, Juan V.

2012-01-01

In this study, the authors tested alternative factor models of the 12-item General Health Questionnaire (GHQ-12) in a sample of Spanish postpartum women, using confirmatory factor analysis. The authors report the results of modeling three different methods for scoring the GHQ-12 using estimation methods recommended for categorical and binary data.…

An Integrated Analysis of the Physiological Effects of Space Flight: Executive Summary

NASA Technical Reports Server (NTRS)

Leonard, J. I.

1985-01-01

A large array of models were applied in a unified manner to solve problems in space flight physiology. Mathematical simulation was used as an alternative way of looking at physiological systems and maximizing the yield from previous space flight experiments. A medical data analysis system was created which consist of an automated data base, a computerized biostatistical and data analysis system, and a set of simulation models of physiological systems. Five basic models were employed: (1) a pulsatile cardiovascular model; (2) a respiratory model; (3) a thermoregulatory model; (4) a circulatory, fluid, and electrolyte balance model; and (5) an erythropoiesis regulatory model. Algorithms were provided to perform routine statistical tests, multivariate analysis, nonlinear regression analysis, and autocorrelation analysis. Special purpose programs were prepared for rank correlation, factor analysis, and the integration of the metabolic balance data.

Job Performance as Multivariate Dynamic Criteria: Experience Sampling and Multiway Component Analysis.

PubMed

Spain, Seth M; Miner, Andrew G; Kroonenberg, Pieter M; Drasgow, Fritz

2010-08-06

Questions about the dynamic processes that drive behavior at work have been the focus of increasing attention in recent years. Models describing behavior at work and research on momentary behavior indicate that substantial variation exists within individuals. This article examines the rationale behind this body of work and explores a method of analyzing momentary work behavior using experience sampling methods. The article also examines a previously unused set of methods for analyzing data produced by experience sampling. These methods are known collectively as multiway component analysis. Two archetypal techniques of multimode factor analysis, the Parallel factor analysis and the Tucker3 models, are used to analyze data from Miner, Glomb, and Hulin's (2010) experience sampling study of work behavior. The efficacy of these techniques for analyzing experience sampling data is discussed as are the substantive multimode component models obtained.

Measuring self-rated productivity: factor structure and variance component analysis of the Health and Work Questionnaire.

PubMed

von Thiele Schwarz, Ulrica; Sjöberg, Anders; Hasson, Henna; Tafvelin, Susanne

2014-12-01

To test the factor structure and variance components of the productivity subscales of the Health and Work Questionnaire (HWQ). A total of 272 individuals from one company answered the HWQ scale, including three dimensions (efficiency, quality, and quantity) that the respondent rated from three perspectives: their own, their supervisor's, and their coworkers'. A confirmatory factor analysis was performed, and common and unique variance components evaluated. A common factor explained 81% of the variance (reliability 0.95). All dimensions and rater perspectives contributed with unique variance. The final model provided a perfect fit to the data. Efficiency, quality, and quantity and three rater perspectives are valid parts of the self-rated productivity measurement model, but with a large common factor. Thus, the HWQ can be analyzed either as one factor or by extracting the unique variance for each subdimension.

[Construction of competency model of 'excellent doctor' in Chinese medicine].

PubMed

Jin, Aning; Tian, Yongquan; Zhao, Taiyang

2014-05-01

To evaluate outstanding and ordinary persons from personal characteristics using competency as the important criteria, which is the future direction of medical education reform. We carried on a behavior event interview about famous doctors of old traditional Chinese medicine, compiled competency dictionary, proceed control prediction test. SPSS and AMOS were used to be data analysis tools on statistics. We adopted the model of peer assessment and contrast to carry out empirical research. This project has carried on exploratory factor analysis and confirmatory factor analysis, established a "5A" competency model which include moral ability, thinking ability, communication ability, learning and practical ability. Competency model of "excellent doctor" in Chinese medicine has been validated, with good reliability and validity, and embodies the characteristics of traditional Chinese medicine personnel training, with theoretical and practical significance for excellence in medicine physician training.

Exploring Pattern of Socialisation Conditions and Human Development by Nonlinear Multivariate Analysis.

ERIC Educational Resources Information Center

Grundmann, Matthias

Following the assumptions of ecological socialization research, adequate analysis of socialization conditions must take into account the multilevel and multivariate structure of social factors that impact on human development. This statement implies that complex models of family configurations or of socialization factors are needed to explain the…

Factor Analysis by Generalized Least Squares.

ERIC Educational Resources Information Center

Joreskog, Karl G.; Goldberger, Arthur S.

Aitkin's generalized least squares (GLS) principle, with the inverse of the observed variance-covariance matrix as a weight matrix, is applied to estimate the factor analysis model in the exploratory (unrestricted) case. It is shown that the GLS estimates are scale free and asymptotically efficient. The estimates are computed by a rapidly…

The Student Perception of Faculty Scale: Development, Testing and Practical Application

ERIC Educational Resources Information Center

Mueller, Thomas S.

2017-01-01

This study involved a sample group of students residing in residential halls at a state university in a qualitative and quantitative analysis to measure their perceptions of the university's faculty. Exploratory, then confirmatory, factor analysis revealed a 3-factor model representing teaching faculty: a negative, emotionally challenging…

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.

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.

Verbal Neuropsychological Functions in Aphasia: An Integrative Model

ERIC Educational Resources Information Center

Vigliecca, Nora Silvana; Báez, Sandra

2015-01-01

A theoretical framework which considers the verbal functions of the brain under a multivariate and comprehensive cognitive model was statistically analyzed. A confirmatory factor analysis was performed to verify whether some recognized aphasia constructs can be hierarchically integrated as latent factors from a homogenously verbal test. The Brief…

Scale Development for Perceived School Climate for Girls' Physical Activity

ERIC Educational Resources Information Center

Birnbaum, Amanda S.; Evenson, Kelly R.; Motl, Robert W.; Dishman, Rod K.; Voorhees, Carolyn C.; Sallis, James F.; Elder, John P.; Dowda, Marsha

2005-01-01

Objectives: To test an original scale assessing perceived school climate for girls' physical activity in middle school girls. Methods: Confirmatory factor analysis (CFA) and structural equation modeling (SEM). Results: CFA retained 5 of 14 original items. A model with 2 correlated factors, perceptions about teachers' and boys' behaviors,…

Five-Factor Model of Personality and Career Exploration

ERIC Educational Resources Information Center

Reed, Mary Beth; Bruch, Monroe A.; Haase, Richard F.

2004-01-01

This study investigates whether the dimensions of the five-factor model (FFM) of personality are related to specific career exploration variables. Based on the FFM, predictions were made about the relevance of particular traits to career exploration variables. Results from a canonical correlation analysis showed that variable loadings on three…

Non-Linear Modeling of Growth Prerequisites in a Finnish Polytechnic Institution of Higher Education

ERIC Educational Resources Information Center

Nokelainen, Petri; Ruohotie, Pekka

2009-01-01

Purpose: This study aims to examine the factors of growth-oriented atmosphere in a Finnish polytechnic institution of higher education with categorical exploratory factor analysis, multidimensional scaling and Bayesian unsupervised model-based visualization. Design/methodology/approach: This study was designed to examine employee perceptions of…

A Dimensional Model of Psychopathology among Homeless Adolescents: Suicidality, Internalizing, and Externalizing Disorders

ERIC Educational Resources Information Center

Yoder, Kevin A.; Longley, Susan L.; Whitbeck, Les B.; Hoyt, Dan R.

2008-01-01

The present study examined associations among dimensions of suicidality and psychopathology in a sample of 428 homeless adolescents (56.3% female). Confirmatory factor analysis results provided support for a three-factor model in which suicidality (measured with lifetime suicidal ideation and suicide attempts), internalizing disorders (assessed…

[Dimensional structure of the Brazilian version of the Scale of Satisfaction with Interpersonal Processes of General Medical Care].

PubMed

Nascimento, Maria Isabel do; Reichenheim, Michael Eduardo; Monteiro, Gina Torres Rego

2011-12-01

The objective of this study was to reassess the dimensional structure of a Brazilian version of the Scale of Satisfaction with Interpersonal Processes of General Medical Care, proposed originally as a one-dimensional instrument. Strict confirmatory factor analysis (CFA) and exploratory factor analysis modeled within a CFA framework (E/CFA) were used to identify the best model. An initial CFA rejected the one-dimensional structure, while an E/CFA suggested a two-dimensional structure. The latter structure was followed by a new CFA, which showed that the model without cross-loading was the most parsimonious, with adequate fit indices (CFI = 0.982 and TLI = 0.988), except for RMSEA (0.062). Although the model achieved convergent validity, discriminant validity was questionable, with the square-root of the mean variance extracted from dimension 1 estimates falling below the respective factor correlation. According to these results, there is not sufficient evidence to recommend the immediate use of the instrument, and further studies are needed for a more in-depth analysis of the postulated structures.

The latent structure of post-traumatic stress disorder among Arabic-speaking refugees receiving psychiatric treatment in Denmark.

PubMed

Vindbjerg, Erik; Carlsson, Jessica; Mortensen, Erik Lykke; Elklit, Ask; Makransky, Guido

2016-09-05

Refugees are known to have high rates of post-traumatic stress disorder (PTSD). Although recent years have seen an increase in the number of refugees from Arabic speaking countries in the Middle East, no study so far has validated the construct of PTSD in an Arabic speaking sample of refugees. Responses to the Harvard Trauma Questionnaire (HTQ) were obtained from 409 Arabic-speaking refugees diagnosed with PTSD and undergoing treatment in Denmark. Confirmatory factor analysis was used to test and compare five alternative models. All four- and five-factor models provided sufficient fit indices. However, a combination of excessively small clusters, and a case of mistranslation in the official Arabic translation of the HTQ, rendered results two of the models inadmissible. A post hoc analysis revealed that a simpler factor structure is supported, once local dependence is addressed. Overall, the construct of PTSD is supported in this sample of Arabic-speaking refugees. Apart from pursuing maximum fit, future studies may wish to test simpler, potentially more stable models, which allow a more informative analysis of individual items.

[Approach to the Development of Mind and Persona].

PubMed

Sawaguchi, Toshiko

2018-01-01

To access medical specialists by health specialists working in the regional health field, the possibility of utilizing the voice approach for dissociative identity disorder (DID) patients as a health assessment for medical access (HAMA) was investigated. The first step is to investigate whether the plural personae in a single DID patient can be discriminated by voice analysis. Voices of DID patients including these with different personae were extracted from YouTube and were analysed using the software PRAAT with basic frequency, oral factors, chin factors and tongue factors. In addition, RAKUGO story teller voices made artificially and dramatically were analysed in the same manner. Quantitive and qualitative analysis method were carried out and nested logistic regression and a nested generalized linear model was developed. The voice from different personae in one DID patient could be visually and easily distinquished using basic frequency curve, cluster analysis and factor analysis. In the canonical analysis, only Roy's maximum root was <0.01. In the nested generalized linear model, the model using a standard deviation (SD) indicator fit best and some other possibilities are shown here. In DID patients, the short transition time among plural personae could guide to the risky situation such as suicide. So if the voice approach can show the time threshold of changes between the different personae, it would be useful as an Access Assessment in the form of a simple HAMA.

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.

Influence factors and forecast of carbon emission in China: structure adjustment for emission peak

NASA Astrophysics Data System (ADS)

Wang, B.; Cui, C. Q.; Li, Z. P.

2018-02-01

This paper introduced Principal Component Analysis and Multivariate Linear Regression Model to verify long-term balance relationships between Carbon Emissions and the impact factors. The integrated model of improved PCA and multivariate regression analysis model is attainable to figure out the pattern of carbon emission sources. Main empirical results indicate that among all selected variables, the role of energy consumption scale was largest. GDP and Population follow and also have significant impacts on carbon emission. Industrialization rate and fossil fuel proportion, which is the indicator of reflecting the economic structure and energy structure, have a higher importance than the factor of urbanization rate and the dweller consumption level of urban areas. In this way, some suggestions are put forward for government to achieve the peak of carbon emissions.

Effect of practical training on the learning motivation profile of Japanese pharmacy students using structural equation modeling.

PubMed

Yamamura, Shigeo; Takehira, Rieko

2017-01-01

To establish a model of Japanese pharmacy students' learning motivation profile and investigate the effects of pharmaceutical practical training programs on their learning motivation. The Science Motivation Questionnaire II was administered to pharmacy students in their 4th (before practical training), 5th (before practical training at clinical sites), and 6th (after all practical training) years of study at Josai International University in April, 2016. Factor analysis and multiple-group structural equation modeling were conducted for data analysis. A total of 165 students participated. The learning motivation profile was modeled with 4 factors (intrinsic, career, self-determination, and grade motivation), and the most effective learning motivation was grade motivation. In the multiple-group analysis, the fit of the model with the data was acceptable, and the estimated mean value of the factor of 'self-determination' in the learning motivation profile increased after the practical training programs (P= 0.048, Cohen's d = 0.43). Practical training programs in a 6-year course were effective for increasing learning motivation, based on 'self-determination' among Japanese pharmacy students. The results suggest that practical training programs are meaningful not only for providing clinical experience but also for raising learning motivation.

Characterizing and modeling the dynamics of online popularity.

PubMed

Ratkiewicz, Jacob; Fortunato, Santo; Flammini, Alessandro; Menczer, Filippo; Vespignani, Alessandro

2010-10-08

Online popularity has an enormous impact on opinions, culture, policy, and profits. We provide a quantitative, large scale, temporal analysis of the dynamics of online content popularity in two massive model systems: the Wikipedia and an entire country's Web space. We find that the dynamics of popularity are characterized by bursts, displaying characteristic features of critical systems such as fat-tailed distributions of magnitude and interevent time. We propose a minimal model combining the classic preferential popularity increase mechanism with the occurrence of random popularity shifts due to exogenous factors. The model recovers the critical features observed in the empirical analysis of the systems analyzed here, highlighting the key factors needed in the description of popularity dynamics.

Factors underlying the psychological and behavioral characteristics of Office of Strategic Services candidates: the assessment of men data revisited.

PubMed

Lenzenweger, Mark F

2015-01-01

During World War II, the Office of Strategic Services (OSS), the forerunner of the Central Intelligence Agency, sought the assistance of clinical psychologists and psychiatrists to establish an assessment program for evaluating candidates for the OSS. The assessment team developed a novel and rigorous program to evaluate OSS candidates. It is described in Assessment of Men: Selection of Personnel for the Office of Strategic Services (OSS Assessment Staff, 1948). This study examines the sole remaining multivariate data matrix that includes all final ratings for a group of candidates (n = 133) assessed near the end of the assessment program. It applies the modern statistical methods of both exploratory and confirmatory factor analysis to this rich and highly unique data set. An exploratory factor analysis solution suggested 3 factors underlie the OSS assessment staff ratings. Confirmatory factor analysis results of multiple plausible substantive models reveal that a 3-factor model provides the best fit to these data. The 3 factors are emotional/interpersonal factors (social relations, emotional stability, security), intelligence processing (effective IQ, propaganda skills, observing and reporting), and agency/surgency (motivation, energy and initiative, leadership, physical ability). These factors are discussed in terms of their potential utility for personnel selection within the intelligence community.

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).

Application of factor analysis to the water quality in reservoirs

NASA Astrophysics Data System (ADS)

Silva, Eliana Costa e.; Lopes, Isabel Cristina; Correia, Aldina; Gonçalves, A. Manuela

2017-06-01

In this work we present a Factor Analysis of chemical and environmental variables of the water column and hydro-morphological features of several Portuguese reservoirs. The objective is to reduce the initial number of variables, keeping their common characteristics. Using the Factor Analysis, the environmental variables measured in the epilimnion and in the hypolimnion, together with the hydromorphological characteristics of the dams were reduced from 63 variables to only 13 factors, which explained a total of 83.348% of the variance in the original data. After performing rotation using the Varimax method, the relations between the factors and the original variables got clearer and more explainable, which provided a Factor Analysis model for these environmental variables using 13 varifactors: Water quality and distance to the source, Hypolimnion chemical composition, Sulfite-reducing bacteria and nutrients, Coliforms and faecal streptococci, Reservoir depth, Temperature, Location, among other factors.

Evaluation of standardized and applied variables in predicting treatment outcomes of polytrauma patients.

PubMed

Aksamija, Goran; Mulabdic, Adi; Rasic, Ismar; Muhovic, Samir; Gavric, Igor

2011-01-01

Polytrauma is defined as an injury where they are affected by at least two different organ systems or body, with at least one life-threatening injuries. Given the multilevel model care of polytrauma patients within KCUS are inevitable weaknesses in the management of this category of patients. To determine the dynamics of existing procedures in treatment of polytrauma patients on admission to KCUS, and based on statistical analysis of variables applied to determine and define the factors that influence the final outcome of treatment, and determine their mutual relationship, which may result in eliminating the flaws in the approach to the problem. The study was based on 263 polytrauma patients. Parametric and non-parametric statistical methods were used. Basic statistics were calculated, based on the calculated parameters for the final achievement of research objectives, multicoleration analysis, image analysis, discriminant analysis and multifactorial analysis were used. From the universe of variables for this study we selected sample of n = 25 variables, of which the first two modular, others belong to the common measurement space (n = 23) and in this paper defined as a system variable methods, procedures and assessments of polytrauma patients. After the multicoleration analysis, since the image analysis gave a reliable measurement results, we started the analysis of eigenvalues, that is defining the factors upon which they obtain information about the system solve the problem of the existing model and its correlation with treatment outcome. The study singled out the essential factors that determine the current organizational model of care, which may affect the treatment and better outcome of polytrauma patients. This analysis has shown the maximum correlative relationships between these practices and contributed to development guidelines that are defined by isolated factors.

Psychosocial Modeling of Insider Threat Risk Based on Behavioral and Word Use Analysis

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

Greitzer, Frank L.; Kangas, Lars J.; Noonan, Christine F.

In many insider crimes, managers and other coworkers observed that the offenders had exhibited signs of stress, disgruntlement, or other issues, but no alarms were raised. Barriers to using such psychosocial indicators include the inability to recognize the signs and the failure to record the behaviors so that they can be assessed. A psychosocial model was developed to assess an employee’s behavior associated with an increased risk of insider abuse. The model is based on case studies and research literature on factors/correlates associated with precursor behavioral manifestations of individuals committing insider crimes. A complementary Personality Factor modeling approach was developedmore » based on analysis to derive relevant personality characteristics from word use. Several implementations of the psychosocial model were evaluated by comparing their agreement with judgments of human resources and management professionals; the personality factor modeling approach was examined using email samples. If implemented in an operational setting, these models should be part of a set of management tools for employee assessment to identify employees who pose a greater insider threat.« less

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.

What Constitutes a "Good" Sensitivity Analysis? Elements and Tools for a Robust Sensitivity Analysis with Reduced Computational Cost

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin; Haghnegahdar, Amin

2016-04-01

Global sensitivity analysis (GSA) is a systems theoretic approach to characterizing the overall (average) sensitivity of one or more model responses across the factor space, by attributing the variability of those responses to different controlling (but uncertain) factors (e.g., model parameters, forcings, and boundary and initial conditions). GSA can be very helpful to improve the credibility and utility of Earth and Environmental System Models (EESMs), as these models are continually growing in complexity and dimensionality with continuous advances in understanding and computing power. However, conventional approaches to GSA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we identify several important sensitivity-related characteristics of response surfaces that must be considered when investigating and interpreting the ''global sensitivity'' of a model response (e.g., a metric of model performance) to its parameters/factors. Accordingly, we present a new and general sensitivity and uncertainty analysis framework, Variogram Analysis of Response Surfaces (VARS), based on an analogy to 'variogram analysis', that characterizes a comprehensive spectrum of information on sensitivity. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices are contained within the VARS framework. We also present a practical strategy for the application of VARS to real-world problems, called STAR-VARS, including a new sampling strategy, called "star-based sampling". Our results across several case studies show the STAR-VARS approach to provide reliable and stable assessments of "global" sensitivity, while being at least 1-2 orders of magnitude more efficient than the benchmark Morris and Sobol approaches.

The influence of the interactions between anthropogenic activities and multiple ecological factors on land surface temperatures of urban forests

NASA Astrophysics Data System (ADS)

Ren, Y.

2017-12-01

Context Land surface temperatures (LSTs) spatio-temporal distribution pattern of urban forests are influenced by many ecological factors; the identification of interaction between these factors can improve simulations and predictions of spatial patterns of urban cold islands. This quantitative research requires an integrated method that combines multiple sources data with spatial statistical analysis. Objectives The purpose of this study was to clarify urban forest LST influence interaction between anthropogenic activities and multiple ecological factors using cluster analysis of hot and cold spots and Geogdetector model. We introduced the hypothesis that anthropogenic activity interacts with certain ecological factors, and their combination influences urban forests LST. We also assumed that spatio-temporal distributions of urban forest LST should be similar to those of ecological factors and can be represented quantitatively. Methods We used Jinjiang as a representative city in China as a case study. Population density was employed to represent anthropogenic activity. We built up a multi-source data (forest inventory, digital elevation models (DEM), population, and remote sensing imagery) on a unified urban scale to support urban forest LST influence interaction research. Through a combination of spatial statistical analysis results, multi-source spatial data, and Geogdetector model, the interaction mechanisms of urban forest LST were revealed. Results Although different ecological factors have different influences on forest LST, in two periods with different hot spots and cold spots, the patch area and dominant tree species were the main factors contributing to LST clustering in urban forests. The interaction between anthropogenic activity and multiple ecological factors increased LST in urban forest stands, linearly and nonlinearly. Strong interactions between elevation and dominant species were generally observed and were prevalent in either hot or cold spots areas in different years. Conclusions In conclusion, a combination of spatial statistics and GeogDetector models should be effective for quantitatively evaluating interactive relationships among ecological factors, anthropogenic activity and LST.

Development and validation of the ExPRESS instrument for primary health care providers' evaluation of external supervision.

PubMed

Schriver, Michael; Cubaka, Vincent Kalumire; Vedsted, Peter; Besigye, Innocent; Kallestrup, Per

2018-01-01

External supervision of primary health care facilities to monitor and improve services is common in low-income countries. Currently there are no tools to measure the quality of support in external supervision in these countries. To develop a provider-reported instrument to assess the support delivered through external supervision in Rwanda and other countries. "External supervision: Provider Evaluation of Supervisor Support" (ExPRESS) was developed in 18 steps, primarily in Rwanda. Content validity was optimised using systematic search for related instruments, interviews, translations, and relevance assessments by international supervision experts as well as local experts in Nigeria, Kenya, Uganda and Rwanda. Construct validity and reliability were examined in two separate field tests, the first using exploratory factor analysis and a test-retest design, the second for confirmatory factor analysis. We included 16 items in section A ('The most recent experience with an external supervisor'), and 13 items in section B ('The overall experience with external supervisors'). Item-content validity index was acceptable. In field test I, test-retest had acceptable kappa values and exploratory factor analysis suggested relevant factors in sections A and B used for model hypotheses. In field test II, models were tested by confirmatory factor analysis fitting a 4-factor model for section A, and a 3-factor model for section B. ExPRESS is a promising tool for evaluation of the quality of support of primary health care providers in external supervision of primary health care facilities in resource-constrained settings. ExPRESS may be used as specific feedback to external supervisors to help identify and address gaps in the supervision they provide. Further studies should determine optimal interpretation of scores and the number of respondents needed per supervisor to obtain precise results, as well as test the functionality of section B.

Factor Structure of the Exercise Self-Efficacy Scale

ERIC Educational Resources Information Center

Cornick, Jessica E.

2015-01-01

The current study utilized exercise self-efficacy ratings from undergraduate students to assess the factor structure of the Self-Efficacy to Regulate Exercise Scale (Bandura, 1997, 2006). An exploratory factor analysis (n = 759) indicated a two-factor model solution and three separate confirmatory factor analyses (n = 1,798) supported this…

Rapid fingerprinting of spilled petroleum products using fluorescence spectroscopy coupled with parallel factor and principal component analysis.

PubMed

Mirnaghi, Fatemeh S; Soucy, Nicholas; Hollebone, Bruce P; Brown, Carl E

2018-05-19

The characterization of spilled petroleum products in an oil spill is necessary for identifying the spill source, selection of clean-up strategies, and evaluating potential environmental and ecological impacts. Existing standard methods for the chemical characterization of spilled oils are time-consuming due to the lengthy sample preparation for analysis. The main objective of this study is the development of a rapid screening method for the fingerprinting of spilled petroleum products using excitation/emission matrix (EEM) fluorescence spectroscopy, thereby delivering a preliminary evaluation of the petroleum products within hours after a spill. In addition, the developed model can be used for monitoring the changes of aromatic compositions of known spilled oils over time. This study involves establishing a fingerprinting model based on the composition of polycyclic and heterocyclic aromatic hydrocarbons (PAH and HAHs, respectively) of 130 petroleum products at different states of evaporative weathering. The screening model was developed using parallel factor analysis (PARAFAC) of a large EEM dataset. The significant fluorescing components for each sample class were determined. After which, through principal component analysis (PCA), the variation of scores of their modeled factors was discriminated based on the different classes of petroleum products. This model was then validated using gas chromatography-mass spectrometry (GC-MS) analysis. The rapid fingerprinting and the identification of unknown and new spilled oils occurs through matching the spilled product with the products of the developed model. Finally, it was shown that HAH compounds in asphaltene and resins contribute to ≥4-ring PAHs compounds in petroleum products. Copyright © 2018. Published by Elsevier Ltd.

Students' proficiency scores within multitrait item response theory

NASA Astrophysics Data System (ADS)

Scott, Terry F.; Schumayer, Daniel

2015-12-01

In this paper we present a series of item response models of data collected using the Force Concept Inventory. The Force Concept Inventory (FCI) was designed to poll the Newtonian conception of force viewed as a multidimensional concept, that is, as a complex of distinguishable conceptual dimensions. Several previous studies have developed single-trait item response models of FCI data; however, we feel that multidimensional models are also appropriate given the explicitly multidimensional design of the inventory. The models employed in the research reported here vary in both the number of fitting parameters and the number of underlying latent traits assumed. We calculate several model information statistics to ensure adequate model fit and to determine which of the models provides the optimal balance of information and parsimony. Our analysis indicates that all item response models tested, from the single-trait Rasch model through to a model with ten latent traits, satisfy the standard requirements of fit. However, analysis of model information criteria indicates that the five-trait model is optimal. We note that an earlier factor analysis of the same FCI data also led to a five-factor model. Furthermore the factors in our previous study and the traits identified in the current work match each other well. The optimal five-trait model assigns proficiency scores to all respondents for each of the five traits. We construct a correlation matrix between the proficiencies in each of these traits. This correlation matrix shows strong correlations between some proficiencies, and strong anticorrelations between others. We present an interpretation of this correlation matrix.

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.

Return on Investment Analysis for the Almond Board of California

DTIC Science & Technology

2004-06-01

general approach for the analysis is first to identify relevant factors concerning consumer behavior using exploratory factor analysis (EFA) and...That completed the intermediate stage of the conceptual model below, referring to the latent drivers of consumer behavior that affect the almond... consumer behavior remains a challenge that will have to be continuously addressed by the ABC management. Finally, to improve the methodology for

Sufficient Forecasting Using Factor Models

PubMed Central

Fan, Jianqing; Xue, Lingzhou; Yao, Jiawei

2017-01-01

We consider forecasting a single time series when there is a large number of predictors and a possible nonlinear effect. The dimensionality was first reduced via a high-dimensional (approximate) factor model implemented by the principal component analysis. Using the extracted factors, we develop a novel forecasting method called the sufficient forecasting, which provides a set of sufficient predictive indices, inferred from high-dimensional predictors, to deliver additional predictive power. The projected principal component analysis will be employed to enhance the accuracy of inferred factors when a semi-parametric (approximate) factor model is assumed. Our method is also applicable to cross-sectional sufficient regression using extracted factors. The connection between the sufficient forecasting and the deep learning architecture is explicitly stated. The sufficient forecasting correctly estimates projection indices of the underlying factors even in the presence of a nonparametric forecasting function. The proposed method extends the sufficient dimension reduction to high-dimensional regimes by condensing the cross-sectional information through factor models. We derive asymptotic properties for the estimate of the central subspace spanned by these projection directions as well as the estimates of the sufficient predictive indices. We further show that the natural method of running multiple regression of target on estimated factors yields a linear estimate that actually falls into this central subspace. Our method and theory allow the number of predictors to be larger than the number of observations. We finally demonstrate that the sufficient forecasting improves upon the linear forecasting in both simulation studies and an empirical study of forecasting macroeconomic variables. PMID:29731537

A Meta-Analytic Review of the Relationships Between the Five-Factor Model and DSM-IV-TR Personality Disorders: A Facet Level Analysis

PubMed Central

Samuel, Douglas B.; Widiger, Thomas A.

2008-01-01

Theory and research have suggested that the personality disorders contained within the American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders (DSM-IV-TR) can be understood as maladaptive variants of the personality traits included within the five-factor model (FFM). The current meta-analysis of FFM personality disorder research both replicated and extended the 2004 work of Saulsman and Page (The five-factor model and personality disorder empirical literature: A meta-analytic review. Clinical Psychology Review, 23, 1055-1085) through a facet-level analysis that provides a more specific and nuanced description of each DSM-IV-TR personality disorder. The empirical FFM profiles generated for each personality disorder were generally congruent at the facet level with hypothesized FFM translations of the DSM-IV-TR personality disorders. However, notable exceptions to the hypotheses did occur and even some findings that were consistent with FFM theory could be said to be instrument specific. PMID:18708274

Factorial analysis of trihalomethanes formation in drinking water.

PubMed

Chowdhury, Shakhawat; Champagne, Pascale; McLellan, P James

2010-06-01

Disinfection of drinking water reduces pathogenic infection, but may pose risks to human health through the formation of disinfection byproducts. The effects of different factors on the formation of trihalomethanes were investigated using a statistically designed experimental program, and a predictive model for trihalomethanes formation was developed. Synthetic water samples with different factor levels were produced, and trihalomethanes concentrations were measured. A replicated fractional factorial design with center points was performed, and significant factors were identified through statistical analysis. A second-order trihalomethanes formation model was developed from 92 experiments, and the statistical adequacy was assessed through appropriate diagnostics. This model was validated using additional data from the Drinking Water Surveillance Program database and was applied to the Smiths Falls water supply system in Ontario, Canada. The model predictions were correlated strongly to the measured trihalomethanes, with correlations of 0.95 and 0.91, respectively. The resulting model can assist in analyzing risk-cost tradeoffs in the design and operation of water supply systems.

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.

Psychometric properties of the questionnaire of sociocultural influences on the aesthetic body shape model (CIMEC-26) in female Spanish adolescents.

PubMed

Jorquera, Mercedes; Baños, Rosa María; Cebolla, Ausiàs; Rasal, Paloma; Etchemendy, Ernestina

2012-05-01

The purpose of the present study was to analyse the psychometric properties of the 'Questionnaire of Sociocultural Influences on the Aesthetic Body Shape Model' (CIMEC-26) in a Spanish adolescent population. This questionnaire measures the influence of agents and situations that transmit the current aesthetic model, and assesses environmental influences favouring thinness. The CIMEC-26 was administered to a sample of 4031 female primary and secondary school students ranging in age from 10 to 17 years (M = 14, SD = 1.34). Results suggested that the CIMEC-26 has acceptable internal consistency (α = .93). The oldest group (15-17 years) had the highest scores on all factors and the highest total scores, suggesting greater influence of the aesthetic body shape model and higher vulnerability to social pressure to achieve it. Factor analysis suggested three moderately interrelated components of the scale. Confirmatory factor analysis showed that both the three-factor solution and the original five-factor structure had good fit indices, although the latter showed the best fit. The CIMEC-26 proved to be an effective instrument for research on the social influence on the aesthetic body model in female adolescents. Copyright © 2012 John Wiley & Sons, Ltd and Eating Disorders Association.

The relationship between cost estimates reliability and BIM adoption: SEM analysis

NASA Astrophysics Data System (ADS)

Ismail, N. A. A.; Idris, N. H.; Ramli, H.; Rooshdi, R. R. Raja Muhammad; Sahamir, S. R.

2018-02-01

This paper presents the usage of Structural Equation Modelling (SEM) approach in analysing the effects of Building Information Modelling (BIM) technology adoption in improving the reliability of cost estimates. Based on the questionnaire survey results, SEM analysis using SPSS-AMOS application examined the relationships between BIM-improved information and cost estimates reliability factors, leading to BIM technology adoption. Six hypotheses were established prior to SEM analysis employing two types of SEM models, namely the Confirmatory Factor Analysis (CFA) model and full structural model. The SEM models were then validated through the assessment on their uni-dimensionality, validity, reliability, and fitness index, in line with the hypotheses tested. The final SEM model fit measures are: P-value=0.000, RMSEA=0.079<0.08, GFI=0.824, CFI=0.962>0.90, TLI=0.956>0.90, NFI=0.935>0.90 and ChiSq/df=2.259; indicating that the overall index values achieved the required level of model fitness. The model supports all the hypotheses evaluated, confirming that all relationship exists amongst the constructs are positive and significant. Ultimately, the analysis verified that most of the respondents foresee better understanding of project input information through BIM visualization, its reliable database and coordinated data, in developing more reliable cost estimates. They also perceive to accelerate their cost estimating task through BIM adoption.

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.

[Geographical distribution of the Serum creatinine reference values of healthy adults].

PubMed

Wei, De-Zhi; Ge, Miao; Wang, Cong-Xia; Lin, Qian-Yi; Li, Meng-Jiao; Li, Peng

2016-11-20

To explore the relationship between serum creatinine (Scr) reference values in healthy adults and geographic factors and provide evidence for establishing Scr reference values in different regions. We collected 29 697 Scr reference values from healthy adults measured by 347 medical facilities from 23 provinces, 4 municipalities and 5 autonomous regions. We chose 23 geographical factors and analyzed their correlation with Scr reference values to identify the factors correlated significantly with Scr reference values. According to the Principal component analysis and Ridge regression analysis, two predictive models were constructed and the optimal model was chosen after comparison of the two model's fitting degree of predicted results and measured results. The distribution map of Scr reference values was drawn using the Kriging interpolation method. Seven geographic factors, including latitude, annual sunshine duration, annual average temperature, annual average relative humidity, annual precipitation, annual temperature range and topsoil (silt) cation exchange capacity were found to correlate significantly with Scr reference values. The overall distribution of Scr reference values featured a pattern that the values were high in the south and low in the north, varying consistently with the latitude change. The data of the geographic factors in a given region allows the prediction of the Scr values in healthy adults. Analysis of these geographical factors can facilitate the determination of the reference values specific to a region to improve the accuracy for clinical diagnoses.

Content Analysis in Computer-Mediated Communication: Analyzing Models for Assessing Critical Thinking through the Lens of Social Constructivism

ERIC Educational Resources Information Center

Buraphadeja, Vasa; Dawson, Kara

2008-01-01

This article reviews content analysis studies aimed to assess critical thinking in computer-mediated communication. It also discusses theories and content analysis models that encourage critical thinking skills in asynchronous learning environments and reviews theories and factors that may foster critical thinking skills and new knowledge…

Testing the psychometric properties of the Environmental Attitudes Inventory on undergraduate students in the Arab context: A test-retest approach.

PubMed

AlMenhali, Entesar Ali; Khalid, Khalizani; Iyanna, Shilpa

2018-01-01

The Environmental Attitudes Inventory (EAI) was developed to evaluate the multidimensional nature of environmental attitudes; however, it is based on a dataset from outside the Arab context. This study reinvestigated the construct validity of the EAI with a new dataset and confirmed the feasibility of applying it in the Arab context. One hundred and forty-eight subjects in Study 1 and 130 in Study 2 provided valid responses. An exploratory factor analysis (EFA) was used to extract a new factor structure in Study 1, and confirmatory factor analysis (CFA) was performed in Study 2. Both studies generated a seven-factor model, and the model fit was discussed for both the studies. Study 2 exhibited satisfactory model fit indices compared to Study 1. Factor loading values of a few items in Study 1 affected the reliability values and average variance extracted values, which demonstrated low discriminant validity. Based on the results of the EFA and CFA, this study showed sufficient model fit and suggested the feasibility of applying the EAI in the Arab context with a good construct validity and internal consistency.

Testing the psychometric properties of the Environmental Attitudes Inventory on undergraduate students in the Arab context: A test-retest approach

PubMed Central

2018-01-01

The Environmental Attitudes Inventory (EAI) was developed to evaluate the multidimensional nature of environmental attitudes; however, it is based on a dataset from outside the Arab context. This study reinvestigated the construct validity of the EAI with a new dataset and confirmed the feasibility of applying it in the Arab context. One hundred and forty-eight subjects in Study 1 and 130 in Study 2 provided valid responses. An exploratory factor analysis (EFA) was used to extract a new factor structure in Study 1, and confirmatory factor analysis (CFA) was performed in Study 2. Both studies generated a seven-factor model, and the model fit was discussed for both the studies. Study 2 exhibited satisfactory model fit indices compared to Study 1. Factor loading values of a few items in Study 1 affected the reliability values and average variance extracted values, which demonstrated low discriminant validity. Based on the results of the EFA and CFA, this study showed sufficient model fit and suggested the feasibility of applying the EAI in the Arab context with a good construct validity and internal consistency. PMID:29758021

Multi-level Bayesian safety analysis with unprocessed Automatic Vehicle Identification data for an urban expressway.

PubMed

Shi, Qi; Abdel-Aty, Mohamed; Yu, Rongjie

2016-03-01

In traffic safety studies, crash frequency modeling of total crashes is the cornerstone before proceeding to more detailed safety evaluation. The relationship between crash occurrence and factors such as traffic flow and roadway geometric characteristics has been extensively explored for a better understanding of crash mechanisms. In this study, a multi-level Bayesian framework has been developed in an effort to identify the crash contributing factors on an urban expressway in the Central Florida area. Two types of traffic data from the Automatic Vehicle Identification system, which are the processed data capped at speed limit and the unprocessed data retaining the original speed were incorporated in the analysis along with road geometric information. The model framework was proposed to account for the hierarchical data structure and the heterogeneity among the traffic and roadway geometric data. Multi-level and random parameters models were constructed and compared with the Negative Binomial model under the Bayesian inference framework. Results showed that the unprocessed traffic data was superior. Both multi-level models and random parameters models outperformed the Negative Binomial model and the models with random parameters achieved the best model fitting. The contributing factors identified imply that on the urban expressway lower speed and higher speed variation could significantly increase the crash likelihood. Other geometric factors were significant including auxiliary lanes and horizontal curvature. Copyright © 2015 Elsevier Ltd. All rights reserved.

Examining the factor structure of the Multiple Sclerosis Impact Scale.

PubMed

Fitzgerald, Shawn M; Li, Jian; Rumrill, Phillip D; Merchant, William; Bishop, Malachy

2014-01-01

The purpose of this study was to investigate the factor structure of the Multiple Sclerosis Impact Scale (MSIS-29) to assess its suitability for modeling the impact of MS on a nation-wide sample of individuals from the United States. Investigators completed a Confirmatory Factor Analysis (CFA) to examine the two-factor structure proposed by Hobart et al. [17]. Although the original MSIS-29 factor structure did not fit the data exactly, the hypothesized two-factor model was partially supported in the current data. Implications for future instrument development and rehabilitation practice are discussed.

Construct Validation of the Louisiana School Analysis Model (SAM) Instructional Staff Questionnaire

ERIC Educational Resources Information Center

Bray-Clark, Nikki; Bates, Reid

2005-01-01

The purpose of this study was to validate the Louisiana SAM Instructional Staff Questionnaire, a key component of the Louisiana School Analysis Model. The model was designed as a comprehensive evaluation tool for schools. Principle axis factoring with oblique rotation was used to uncover the underlying structure of the SISQ. (Contains 1 table.)

Academic Optimism and Collective Responsibility: An Organizational Model of the Dynamics of Student Achievement

ERIC Educational Resources Information Center

Wu, Jason H.

2013-01-01

This study was designed to examine the construct of academic optimism and its relationship with collective responsibility in a sample of Taiwan elementary schools. The construct of academic optimism was tested using confirmatory factor analysis, and the whole structural model was tested with a structural equation modeling analysis. The data were…

Applications of multivariate modeling to neuroimaging group analysis: A comprehensive alternative to univariate general linear model

PubMed Central

Chen, Gang; Adleman, Nancy E.; Saad, Ziad S.; Leibenluft, Ellen; Cox, RobertW.

2014-01-01

All neuroimaging packages can handle group analysis with t-tests or general linear modeling (GLM). However, they are quite hamstrung when there are multiple within-subject factors or when quantitative covariates are involved in the presence of a within-subject factor. In addition, sphericity is typically assumed for the variance–covariance structure when there are more than two levels in a within-subject factor. To overcome such limitations in the traditional AN(C)OVA and GLM, we adopt a multivariate modeling (MVM) approach to analyzing neuroimaging data at the group level with the following advantages: a) there is no limit on the number of factors as long as sample sizes are deemed appropriate; b) quantitative covariates can be analyzed together with within- subject factors; c) when a within-subject factor is involved, three testing methodologies are provided: traditional univariate testing (UVT)with sphericity assumption (UVT-UC) and with correction when the assumption is violated (UVT-SC), and within-subject multivariate testing (MVT-WS); d) to correct for sphericity violation at the voxel level, we propose a hybrid testing (HT) approach that achieves equal or higher power via combining traditional sphericity correction methods (Greenhouse–Geisser and Huynh–Feldt) with MVT-WS. PMID:24954281

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.

Multiscale weighted colored graphs for protein flexibility and rigidity analysis

NASA Astrophysics Data System (ADS)

Bramer, David; Wei, Guo-Wei

2018-02-01

Protein structural fluctuation, measured by Debye-Waller factors or B-factors, is known to correlate to protein flexibility and function. A variety of methods has been developed for protein Debye-Waller factor prediction and related applications to domain separation, docking pose ranking, entropy calculation, hinge detection, stability analysis, etc. Nevertheless, none of the current methodologies are able to deliver an accuracy of 0.7 in terms of the Pearson correlation coefficients averaged over a large set of proteins. In this work, we introduce a paradigm-shifting geometric graph model, multiscale weighted colored graph (MWCG), to provide a new generation of computational algorithms to significantly change the current status of protein structural fluctuation analysis. Our MWCG model divides a protein graph into multiple subgraphs based on interaction types between graph nodes and represents the protein rigidity by generalized centralities of subgraphs. MWCGs not only predict the B-factors of protein residues but also accurately analyze the flexibility of all atoms in a protein. The MWCG model is validated over a number of protein test sets and compared with many standard methods. An extensive numerical study indicates that the proposed MWCG offers an accuracy of over 0.8 and thus provides perhaps the first reliable method for estimating protein flexibility and B-factors. It also simultaneously predicts all-atom flexibility in a molecule.

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.

Integrated modeling and analysis of the multiple electromechanical couplings for the direct driven feed system in machine tools

NASA Astrophysics Data System (ADS)

Yang, Xiaojun; Lu, Dun; Liu, Hui; Zhao, Wanhua

2018-06-01

The complicated electromechanical coupling phenomena due to different kinds of causes have significant influences on the dynamic precision of the direct driven feed system in machine tools. In this paper, a novel integrated modeling and analysis method of the multiple electromechanical couplings for the direct driven feed system in machine tools is presented. At first, four different kinds of electromechanical coupling phenomena in the direct driven feed system are analyzed systematically. Then a novel integrated modeling and analysis method of the electromechanical coupling which is influenced by multiple factors is put forward. In addition, the effects of multiple electromechanical couplings on the dynamic precision of the feed system and their main influencing factors are compared and discussed, respectively. Finally, the results of modeling and analysis are verified by the experiments. It finds out that multiple electromechanical coupling loops, which are overlapped and influenced by each other, are the main reasons of the displacement fluctuations in the direct driven feed system.

Emotional and tangible social support in a German population-based sample: Development and validation of the Brief Social Support Scale (BS6).

PubMed

Beutel, Manfred E; Brähler, Elmar; Wiltink, Jörg; Michal, Matthias; Klein, Eva M; Jünger, Claus; Wild, Philipp S; Münzel, Thomas; Blettner, Maria; Lackner, Karl; Nickels, Stefan; Tibubos, Ana N

2017-01-01

Aim of the study was the development and validation of the psychometric properties of a six-item bi-factorial instrument for the assessment of social support (emotional and tangible support) with a population-based sample. A cross-sectional data set of N = 15,010 participants enrolled in the Gutenberg Health Study (GHS) in 2007-2012 was divided in two sub-samples. The GHS is a population-based, prospective, observational single-center cohort study in the Rhein-Main-Region in western Mid-Germany. The first sub-sample was used for scale development by performing an exploratory factor analysis. In order to test construct validity, confirmatory factor analyses were run to compare the extracted bi-factorial model with the one-factor solution. Reliability of the scales was indicated by calculating internal consistency. External validity was tested by investigating demographic characteristics health behavior, and distress using analysis of variance, Spearman and Pearson correlation analysis, and logistic regression analysis. Based on an exploratory factor analysis, a set of six items was extracted representing two independent factors. The two-factor structure of the Brief Social Support Scale (BS6) was confirmed by the results of the confirmatory factor analyses. Fit indices of the bi-factorial model were good and better compared to the one-factor solution. External validity was demonstrated for the BS6. The BS6 is a reliable and valid short scale that can be applied in social surveys due to its brevity to assess emotional and practical dimensions of social support.

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.

Simulation for Prediction of Entry Article Demise (SPEAD): an Analysis Tool for Spacecraft Safety Analysis and Ascent/Reentry Risk Assessment

NASA Technical Reports Server (NTRS)

Ling, Lisa

2014-01-01

For the purpose of performing safety analysis and risk assessment for a probable offnominal suborbital/orbital atmospheric reentry resulting in vehicle breakup, a synthesis of trajectory propagation coupled with thermal analysis and the evaluation of node failure is required to predict the sequence of events, the timeline, and the progressive demise of spacecraft components. To provide this capability, the Simulation for Prediction of Entry Article Demise (SPEAD) analysis tool was developed. This report discusses the capabilities, modeling, and validation of the SPEAD analysis tool. SPEAD is applicable for Earth or Mars, with the option for 3 or 6 degrees-of-freedom (DOF) trajectory propagation. The atmosphere and aerodynamics data are supplied in tables, for linear interpolation of up to 4 independent variables. The gravitation model can include up to 20 zonal harmonic coefficients. The modeling of a single motor is available and can be adapted to multiple motors. For thermal analysis, the aerodynamic radiative and free-molecular/continuum convective heating, black-body radiative cooling, conductive heat transfer between adjacent nodes, and node ablation are modeled. In a 6- DOF simulation, the local convective heating on a node is a function of Mach, angle-ofattack, and sideslip angle, and is dependent on 1) the location of the node in the spacecraft and its orientation to the flow modeled by an exposure factor, and 2) the geometries of the spacecraft and the node modeled by a heating factor and convective area. Node failure is evaluated using criteria based on melting temperature, reference heat load, g-load, or a combination of the above. The failure of a liquid propellant tank is evaluated based on burnout flux from nucleate boiling or excess internal pressure. Following a component failure, updates are made as needed to the spacecraft mass and aerodynamic properties, nodal exposure and heating factors, and nodal convective and conductive areas. This allows the trajectory to be propagated seamlessly in a single run, inclusive of the trajectories of components that have separated from the spacecraft. The node ablation simulates the decreasing mass and convective/reference areas, and variable heating factor. A built-in database provides the thermo-mechanical properties of For the purpose of performing safety analysis and risk assessment for a probable offnominal suborbital/orbital atmospheric reentry resulting in vehicle breakup, a synthesis of trajectory propagation coupled with thermal analysis and the evaluation of node failure is required to predict the sequence of events, the timeline, and the progressive demise of spacecraft components. To provide this capability, the Simulation for Prediction of Entry Article Demise (SPEAD) analysis tool was developed. This report discusses the capabilities, modeling, and validation of the SPEAD analysis tool. SPEAD is applicable for Earth or Mars, with the option for 3 or 6 degrees-of-freedom (DOF) trajectory propagation. The atmosphere and aerodynamics data are supplied in tables, for linear interpolation of up to 4 independent variables. The gravitation model can include up to 20 zonal harmonic coefficients. The modeling of a single motor is available and can be adapted to multiple motors. For thermal analysis, the aerodynamic radiative and free-molecular/continuum convective heating, black-body radiative cooling, conductive heat transfer between adjacent nodes, and node ablation are modeled. In a 6- DOF simulation, the local convective heating on a node is a function of Mach, angle-ofattack, and sideslip angle, and is dependent on 1) the location of the node in the spacecraft and its orientation to the flow modeled by an exposure factor, and 2) the geometries of the spacecraft and the node modeled by a heating factor and convective area. Node failure is evaluated using criteria based on melting temperature, reference heat load, g-load, or a combination of the above. The failure of a liquid propellant tank is evaluated based on burnout flux from nucleate boiling or excess internal pressure. Following a component failure, updates are made as needed to the spacecraft mass and aerodynamic properties, nodal exposure and heating factors, and nodal convective and conductive areas. This allows the trajectory to be propagated seamlessly in a single run, inclusive of the trajectories of components that have separated from the spacecraft. The node ablation simulates the decreasing mass and convective/reference areas, and variable heating factor. A built-in database provides the thermo-mechanical properties of

Predictive Factors of Regular Physical Activity among Middle-Aged Women in the West of Iran, Hamadan: Application of PRECEDE Model.

PubMed

Emdadi, Shohreh; Hazavehie, Seyed Mohammad Mehdi; Soltanian, Alireza; Bashirian, Saeed; Heidari Moghadam, Rashid

2015-01-01

Regular physical activity is important for midlife women. Models and theories help better understanding this behavior among middle-aged women and better planning for change behavior in target group. This study aimed to investigate predictive factors of regular physical activity among middle-aged women based on PRECEDE model as a theoretical framework. This descriptive-analytical study was performed on 866 middle-aged women of Hamadan City western Iran, recruited with a proportional stratified sampling method in 2015. The participants completed a self-administered questionnaire including questions on demographic characteristics and PRECEDE model constructs and IPAQ questionnaire. Data were then analyzed by SPSS-16 and AMOS-16 using the Pearson correlation test and the pathway analysis method. Overall, 57% of middle-aged women were inactive (light level) or not sufficiently active. With SEM (Structural Equation Modeling) analysis, knowledge b=0.84, P<0.001, attitude b=0.799, P<0.001, self-efficacy b=0.633, P<0.001 as predisposing factor and social support as reinforcing factor b=0.2, P<0.001 were the most important predictors for physical activity among middle-aged women in Hamadan. The framework of the PRECEDE model is useful in understanding regular physical activity among middle-aged women. Furthermore, results showed the importance of predisposing and reinforcing factors when planning educational interventions.

Impact of covariate models on the assessment of the air pollution-mortality association in a single- and multipollutant context.

PubMed

Sacks, Jason D; Ito, Kazuhiko; Wilson, William E; Neas, Lucas M

2012-10-01

With the advent of multicity studies, uniform statistical approaches have been developed to examine air pollution-mortality associations across cities. To assess the sensitivity of the air pollution-mortality association to different model specifications in a single and multipollutant context, the authors applied various regression models developed in previous multicity time-series studies of air pollution and mortality to data from Philadelphia, Pennsylvania (May 1992-September 1995). Single-pollutant analyses used daily cardiovascular mortality, fine particulate matter (particles with an aerodynamic diameter ≤2.5 µm; PM(2.5)), speciated PM(2.5), and gaseous pollutant data, while multipollutant analyses used source factors identified through principal component analysis. In single-pollutant analyses, risk estimates were relatively consistent across models for most PM(2.5) components and gaseous pollutants. However, risk estimates were inconsistent for ozone in all-year and warm-season analyses. Principal component analysis yielded factors with species associated with traffic, crustal material, residual oil, and coal. Risk estimates for these factors exhibited less sensitivity to alternative regression models compared with single-pollutant models. Factors associated with traffic and crustal material showed consistently positive associations in the warm season, while the coal combustion factor showed consistently positive associations in the cold season. Overall, mortality risk estimates examined using a source-oriented approach yielded more stable and precise risk estimates, compared with single-pollutant analyses.

The Development of an Empirical Model of Mental Health Stigma in Adolescents.

PubMed

Silke, Charlotte; Swords, Lorraine; Heary, Caroline

2016-08-30

Research on mental health stigma in adolescents is hampered by a lack of empirical investigation into the theoretical conceptualisation of stigma, as well as by the lack of validated stigma measures. This research aims to develop a model of public stigma toward depression in adolescents and to use this model to empirically examine whether stigma is composed of three separate dimensions (Stereotypes, Prejudice and Discrimination), as is theoretically proposed. Adolescents completed self-report measures assessing their stigmatising responses toward a fictional peer with depression. An exploratory factor analysis (EFA; N=332) was carried out on 58-items, which proposed to measure aspects of stigma. A confirmatory factor analysis (CFA; N=236) was then carried out to evaluate the validity of the observed stigma model. Finally, higher-order CFAs were conducted in order to assess whether the observed model supported the tripartite conceptualisation of stigma. The EFA returned a seven-factor model of stigma. These factors were designated as Dangerousness, Warmth & Competency, Responsibility, Negative Attributes, Prejudice, Classroom Discrimination and Friendship Discrimination. The CFA supported the goodness-of-fit of this seven-factor model. The higher-order CFAs indicated that these seven factors represented the latent constructs of, Stereotypes, Prejudice and Discrimination, which in turn represented Stigma. Overall, results support the tripartite conceptualisation of stigma and suggest that measurements of mental health stigma in adolescents should include assessments of all three dimensions. These results also highlight the importance of establishing valid and reliable measures for assessing stigma in adolescents. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Examination of the Beck Depression Inventory-II Factor Structure Among Bariatric Surgery Candidates.

PubMed

Hayes, Sharon; Stoeckel, Nina; Napolitano, Melissa A; Collins, Charlotte; Wood, G Craig; Seiler, Jamie; Grunwald, Heidi E; Foster, Gary D; Still, Christopher D

2015-07-01

The Beck Depression Inventory-II (BDI-II) is frequently used to evaluate bariatric patients in clinical and research settings; yet, there are limited data regarding the factor structure of the BDI-II with a bariatric surgery population. Exploratory factor analysis (EFA) using principal axis factoring with oblimin rotation was employed with data from 1228 consecutive presurgical bariatric candidates. Independent t tests were used to examine potential differences between sexes. Confirmatory factor analysis (CFA) was conducted with the next 383 consecutive presurgical patients to evaluate the proposed model based on EFA results. EFA revealed three factors: negative perceptions, diminished vigor, and cognitive dysregulation, each with adequate internal consistency. Six BDI-II items did not load significantly on any of the three factors. CFA results largely supported the proposed model. Results suggest that dimensions of depression for presurgical bariatric candidates vary from other populations and raise important caveats regarding the utility of the BDI-II in bariatric research.

Perceived experiences of atheist discrimination: Instrument development and evaluation.

PubMed

Brewster, Melanie E; Hammer, Joseph; Sawyer, Jacob S; Eklund, Austin; Palamar, Joseph

2016-10-01

The present 2 studies describe the development and initial psychometric evaluation of a new instrument, the Measure of Atheist Discrimination Experiences (MADE), which may be used to examine the minority stress experiences of atheist people. Items were created from prior literature, revised by a panel of expert researchers, and assessed psychometrically. In Study 1 (N = 1,341 atheist-identified people), an exploratory factor analysis with 665 participants suggested the presence of 5 related dimensions of perceived discrimination. However, bifactor modeling via confirmatory factor analysis and model-based reliability estimates with data from the remaining 676 participants affirmed the presence of a strong "general" factor of discrimination and mixed to poor support for substantive subdimensions. In Study 2 (N = 1,057 atheist-identified people), another confirmatory factor analysis and model-based reliability estimates strongly supported the bifactor model from Study 1 (i.e., 1 strong "general" discrimination factor) and poor support for subdimensions. Across both studies, the MADE general factor score demonstrated evidence of good reliability (i.e., Cronbach's alphas of .94 and .95; omega hierarchical coefficients of .90 and .92), convergent validity (i.e., with stigma consciousness, β = .56; with awareness of public devaluation, β = .37), and preliminary evidence for concurrent validity (i.e., with loneliness β = .18; with psychological distress β = .27). Reliability and validity evidence for the MADE subscale scores was not sufficient to warrant future use of the subscales. Limitations and implications for future research and clinical work with atheist individuals are discussed. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Structural Equation Analysis of the Wechsler Adult Intelligence Scale-Revised in a Normal Elderly Sample.

ERIC Educational Resources Information Center

Burton, D. Bradley; And Others

1994-01-01

A maximum-likelihood confirmatory factor analysis was performed by applying LISREL VII to the Wechsler Adult Intelligence Scale-Revised results of a normal elderly sample of 225 adults. Results indicate that a three-factor model fits best across all sample combinations. A mild gender effect is discussed. (SLD)

A Comparison of Imputation Methods for Bayesian Factor Analysis Models

ERIC Educational Resources Information Center

Merkle, Edgar C.

2011-01-01

Imputation methods are popular for the handling of missing data in psychology. The methods generally consist of predicting missing data based on observed data, yielding a complete data set that is amiable to standard statistical analyses. In the context of Bayesian factor analysis, this article compares imputation under an unrestricted…

Analysis and optimization of dynamic model of eccentric shaft grinder

NASA Astrophysics Data System (ADS)

Gao, Yangjie; Han, Qiushi; Li, Qiguang; Peng, Baoying

2018-04-01

Eccentric shaft servo grinder is the core equipment in the process chain of machining eccentric shaft. The establishment of the movement model and the determination of the kinematic relation of the-axis in the grinding process directly affect the quality of the grinding process, and there are many error factors in grinding, and it is very important to analyze the influence of these factors on the work piece quality. The three-dimensional model of eccentric shaft grinder is drawn by Pro/E three-dimensional drawing software, the model is imported into ANSYS Workbench Finite element analysis software, and the finite element analysis is carried out, and then the variation and parameters of each component of the bed are obtained by the modal analysis result. The natural frequencies and formations of the first six steps of the eccentric shaft grinder are obtained by modal analysis, and the weak links of the parts of the grinder are found out, and a reference improvement method is proposed for the design of the eccentric shaft grinder in the future.

Analysis of algae growth mechanism and water bloom prediction under the effect of multi-affecting factor.

PubMed

Wang, Li; Wang, Xiaoyi; Jin, Xuebo; Xu, Jiping; Zhang, Huiyan; Yu, Jiabin; Sun, Qian; Gao, Chong; Wang, Lingbin

2017-03-01

The formation process of algae is described inaccurately and water blooms are predicted with a low precision by current methods. In this paper, chemical mechanism of algae growth is analyzed, and a correlation analysis of chlorophyll-a and algal density is conducted by chemical measurement. Taking into account the influence of multi-factors on algae growth and water blooms, the comprehensive prediction method combined with multivariate time series and intelligent model is put forward in this paper. Firstly, through the process of photosynthesis, the main factors that affect the reproduction of the algae are analyzed. A compensation prediction method of multivariate time series analysis based on neural network and Support Vector Machine has been put forward which is combined with Kernel Principal Component Analysis to deal with dimension reduction of the influence factors of blooms. Then, Genetic Algorithm is applied to improve the generalization ability of the BP network and Least Squares Support Vector Machine. Experimental results show that this method could better compensate the prediction model of multivariate time series analysis which is an effective way to improve the description accuracy of algae growth and prediction precision of water blooms.

Structural Biology of Tumor Necrosis Factor Demonstrated for Undergraduates Instruction by Computer Simulation

ERIC Educational Resources Information Center

Roy, Urmi

2016-01-01

This work presents a three-dimensional (3D) modeling exercise for undergraduate students in chemistry and health sciences disciplines, focusing on a protein-group linked to immune system regulation. Specifically, the exercise involves molecular modeling and structural analysis of tumor necrosis factor (TNF) proteins, both wild type and mutant. The…

Employability Skills Assessment: Measuring Work Ethic for Research and Learning

ERIC Educational Resources Information Center

Park, HwaChoon; Hill, Roger B.

2016-01-01

The Employability Skills Assessment (ESA) was developed by Hill (1995) to provide an alternative measure of work ethic needed for success in employment. This study tested goodness-of-fit for a model used to interpret ESA results. The model had three factors: interpersonal skills, initiative, and dependability. Confirmatory factor analysis results…

The Five-Factor Model of Personality Traits and Organizational Citizenship Behaviors: A Meta-Analysis

ERIC Educational Resources Information Center

Chiaburu, Dan S.; Oh, In-Sue; Berry, Christopher M.; Li, Ning; Gardner, Richard G.

2011-01-01

Using meta-analytic tests based on 87 statistically independent samples, we investigated the relationships between the five-factor model (FFM) of personality traits and organizational citizenship behaviors in both the aggregate and specific forms, including individual-directed, organization-directed, and change-oriented citizenship. We found that…

Explaining Relative Incomes of Low-Income Families in U.S. Cities.

ERIC Educational Resources Information Center

Blackley, Paul R.

1988-01-01

Uses an interurban analysis model to assess the position of lower income families. Identifies factors determining the relative incomes of the poor through use of a supply and demand model of aggregate income inequality. Found high school graduation a significant factor in income inequality. Implications for policy are discussed. (KO)

The Employee Satisfaction Inventory (ESI): Development of a Scale to Measure Satisfaction of Greek Employees.

ERIC Educational Resources Information Center

Koustelios, Athanasios D.; Bagiatis, Konstantinos

1997-01-01

An instrument to measure employee job satisfaction in Greece was developed and tested with 212 and 516 employees. Exploratory factor analysis indicated a six-factor solution with high internal consistency. Structural equation modeling showed a fairly good fit to the model, with need for slight improvement. (SLD)

Reliability and Validity of the Sexual Pressure Scale for Women-Revised

PubMed Central

Jones, Rachel; Gulick, Elsie

2008-01-01

Sexual pressure among young urban women represents adherence to gender stereotypical expectations to engage in sex. Revision of the original 5-factor Sexual Pressure Scale was undertaken in two studies to improve reliabilities in two of the five factors. In Study 1 the reliability of the Sexual Pressure Scale for Women-Revised (SPSW-R) was tested, and principal components analysis was performed in a sample of 325 young, urban women. A parsimonious 18-item, 4-factor model explained 61% of the variance. In Study 2 the theory underlying sexual pressure was supported by confirmatory factor analysis using structural equation modeling in a sample of 181 women. Reliabilities of the SPSW-R total and subscales were very satisfactory, suggesting it may be used in intervention research. PMID:18666222

Personality in sanctuary-housed chimpanzees: A comparative approach of psychobiological and penta-factorial human models.

PubMed

Úbeda, Yulán; Llorente, Miquel

2015-02-18

We evaluate a sanctuary chimpanzee sample (N = 11) using two adapted human assessment instruments: the Five-Factor Model (FFM) and Eysenck's Psychoticism-Extraversion-Neuroticism (PEN) model. The former has been widely used in studies of animal personality, whereas the latter has never been used to assess chimpanzees. We asked familiar keepers and scientists (N = 28) to rate 38 (FFM) and 12 (PEN) personality items. The personality surveys showed reliability in all of the items for both instruments. These were then analyzed in a principal component analysis and a regularized exploratory factor analysis, which revealed four and three components, respectively. The results indicate that both questionnaires show a clear factor structure, with characteristic factors not just for the species, but also for the sample type. However, due to its brevity, the PEN may be more suitable for assessing personality in a sanctuary, where employees do not have much time to devote to the evaluation process. In summary, both models are sensitive enough to evaluate the personality of a group of chimpanzees housed in a sanctuary.

Can pain and function be distinguished in the Oxford Hip Score in a meaningful way? : an exploratory and confirmatory factor analysis.

PubMed

Harris, K K; Price, A J; Beard, D J; Fitzpatrick, R; Jenkinson, C; Dawson, J

2014-11-01

The objective of this study was to explore dimensionality of the Oxford Hip Score (OHS) and examine whether self-reported pain and functioning can be distinguished in the form of subscales. This was a secondary data analysis of the UK NHS hospital episode statistics/patient-reported outcome measures dataset containing pre-operative OHS scores on 97 487 patients who were undergoing hip replacement surgery. The proposed number of factors to extract depended on the method of extraction employed. Velicer's Minimum Average Partial test and the Parallel Analysis suggested one factor, the Cattell's scree test and Kaiser-over-1 rule suggested two factors. Exploratory factor analysis demonstrated that the two-factor OHS had most of the items saliently loading either of the two factors. These factors were named 'Pain' and 'Function' and their respective subscales were created. There was some cross-loading of items: 8 (pain on standing up from a chair) and 11 (pain during work). These items were assigned to the 'Pain' subscale. The final 'Pain' subscale consisted of items 1, 8, 9, 10, 11 and 12. The 'Function' subscale consisted of items 2, 3, 4, 5, 6 and 7, with the recommended scoring of the subscales being from 0 (worst) to 100 (best). Cronbach's alpha was 0.855 for the 'Pain' subscale and 0.861 for the 'Function' subscale. A confirmatory factor analysis demonstrated that the two-factor model of the OHS had a better fit. However, none of the one-factor or two-factor models was rejected. Factor analyses demonstrated that, in addition to current usage as a single summary scale, separate information on pain and self-reported function can be extracted from the OHS in a meaningful way in the form of subscales. Cite this article: Bone Joint Res 2014;3:305-9. ©2014 The British Editorial Society of Bone & Joint Surgery.

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

Kuypers, Marshall A.; Lambert, Gregory Joseph; Moore, Thomas W.

Chronic infection with Hepatitis C virus (HCV) results in cirrhosis, liver cancer and death. As the nations largest provider of care for HCV, US Veterans Health Administration (VHA) invests extensive resources in the diagnosis and treatment of the disease. This report documents modeling and analysis of HCV treatment dynamics performed for the VHA aimed at improving service delivery efficiency. System dynamics modeling of disease treatment demonstrated the benefits of early detection and the role of comorbidities in disease progress and patient mortality. Preliminary modeling showed that adherence to rigorous treatment protocols is a primary determinant of treatment success. In depthmore » meta-analysis revealed correlations of adherence and various psycho-social factors. This initial meta-analysis indicates areas where substantial improvement in patient outcomes can potentially result from VA programs which incorporate these factors into their design.« less

Spatial Bayesian latent factor regression modeling of coordinate-based meta-analysis data.

PubMed

Montagna, Silvia; Wager, Tor; Barrett, Lisa Feldman; Johnson, Timothy D; Nichols, Thomas E

2018-03-01

Now over 20 years old, functional MRI (fMRI) has a large and growing literature that is best synthesised with meta-analytic tools. As most authors do not share image data, only the peak activation coordinates (foci) reported in the article are available for Coordinate-Based Meta-Analysis (CBMA). Neuroimaging meta-analysis is used to (i) identify areas of consistent activation; and (ii) build a predictive model of task type or cognitive process for new studies (reverse inference). To simultaneously address these aims, we propose a Bayesian point process hierarchical model for CBMA. We model the foci from each study as a doubly stochastic Poisson process, where the study-specific log intensity function is characterized as a linear combination of a high-dimensional basis set. A sparse representation of the intensities is guaranteed through latent factor modeling of the basis coefficients. Within our framework, it is also possible to account for the effect of study-level covariates (meta-regression), significantly expanding the capabilities of the current neuroimaging meta-analysis methods available. We apply our methodology to synthetic data and neuroimaging meta-analysis datasets. © 2017, The International Biometric Society.

Validation of the Malay Version of the Parental Bonding Instrument among Malaysian Youths Using Exploratory Factor Analysis.

PubMed

Muhammad, Noor Azimah; Shamsuddin, Khadijah; Omar, Khairani; Shah, Shamsul Azhar; Mohd Amin, Rahmah

2014-01-01

Parenting behaviour is culturally sensitive. The aims of this study were (1) to translate the Parental Bonding Instrument into Malay (PBI-M) and (2) to determine its factorial structure and validity among the Malaysian population. The PBI-M was generated from a standard translation process and comprehension testing. The validation study of the PBI-M was administered to 248 college students aged 18 to 22 years. Participants in the comprehension testing had difficulty understanding negative items. Five translated double negative items were replaced with five positive items with similar meanings. Exploratory factor analysis showed a three-factor model for the PBI-M with acceptable reliability. Four negative items (items 3, 4, 8, and 16) and item 19 were omitted from the final PBI-M list because of incorrect placement or low factor loading (< 0.32). Out of the final 20 items of the PBI-M, there were 10 items for the care factor, five items for the autonomy factor and five items for the overprotection factor. All the items loaded positively on their respective factors. The Malaysian population favoured positive items in answering questions. The PBI-M confirmed the three-factor model that consisted of care, autonomy and overprotection. The PBI-M is a valid and reliable instrument to assess the Malaysian parenting style. Confirmatory factor analysis may further support this finding. Malaysia, parenting, questionnaire, validity.

Applying parallel factor analysis and Tucker-3 methods on sensory and instrumental data to establish preference maps: case study on sweet corn varieties.

PubMed

Gere, Attila; Losó, Viktor; Györey, Annamária; Kovács, Sándor; Huzsvai, László; Nábrádi, András; Kókai, Zoltán; Sipos, László

2014-12-01

Traditional internal and external preference mapping methods are based on principal component analysis (PCA). However, parallel factor analysis (PARAFAC) and Tucker-3 methods could be a better choice. To evaluate the methods, preference maps of sweet corn varieties will be introduced. A preference map of eight sweet corn varieties was established using PARAFAC and Tucker-3 methods. Instrumental data were also integrated into the maps. The triplot created by the PARAFAC model explains better how odour is separated from texture or appearance, and how some varieties are separated from others. Internal and external preference maps were created using parallel factor analysis (PARAFAC) and Tucker-3 models employing both sensory (trained panel and consumers) and instrumental parameters simultaneously. Triplots of the applied three-way models have a competitive advantage compared to the traditional biplots of the PCA-based external preference maps. The solution of PARAFAC and Tucker-3 is very similar regarding the interpretation of the first and third factors. The main difference is due to the second factor as it differentiated the attributes better. Consumers who prefer 'super sweet' varieties (they place great emphasis especially on taste) are much younger and have significantly higher incomes, and buy sweet corn products rarely (once a month). Consumers who consume sweet corn products mainly because of their texture and appearance are significantly older and include a higher ratio of men. © 2014 Society of Chemical Industry.

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.

Analysis of uncertainties in the estimates of nitrous oxide and methane emissions in the UK's greenhouse gas inventory for agriculture

NASA Astrophysics Data System (ADS)

Milne, Alice E.; Glendining, Margaret J.; Bellamy, Pat; Misselbrook, Tom; Gilhespy, Sarah; Rivas Casado, Monica; Hulin, Adele; van Oijen, Marcel; Whitmore, Andrew P.

2014-01-01

The UK's greenhouse gas inventory for agriculture uses a model based on the IPCC Tier 1 and Tier 2 methods to estimate the emissions of methane and nitrous oxide from agriculture. The inventory calculations are disaggregated at country level (England, Wales, Scotland and Northern Ireland). Before now, no detailed assessment of the uncertainties in the estimates of emissions had been done. We used Monte Carlo simulation to do such an analysis. We collated information on the uncertainties of each of the model inputs. The uncertainties propagate through the model and result in uncertainties in the estimated emissions. Using a sensitivity analysis, we found that in England and Scotland the uncertainty in the emission factor for emissions from N inputs (EF1) affected uncertainty the most, but that in Wales and Northern Ireland, the emission factor for N leaching and runoff (EF5) had greater influence. We showed that if the uncertainty in any one of these emission factors is reduced by 50%, the uncertainty in emissions of nitrous oxide reduces by 10%. The uncertainty in the estimate for the emissions of methane emission factors for enteric fermentation in cows and sheep most affected the uncertainty in methane emissions. When inventories are disaggregated (as that for the UK is) correlation between separate instances of each emission factor will affect the uncertainty in emissions. As more countries move towards inventory models with disaggregation, it is important that the IPCC give firm guidance on this topic.

STAMP-Based HRA Considering Causality Within a Sociotechnical System: A Case of Minuteman III Missile Accident.

PubMed

Rong, Hao; Tian, Jin

2015-05-01

The study contributes to human reliability analysis (HRA) by proposing a method that focuses more on human error causality within a sociotechnical system, illustrating its rationality and feasibility by using a case of the Minuteman (MM) III missile accident. Due to the complexity and dynamics within a sociotechnical system, previous analyses of accidents involving human and organizational factors clearly demonstrated that the methods using a sequential accident model are inadequate to analyze human error within a sociotechnical system. System-theoretic accident model and processes (STAMP) was used to develop a universal framework of human error causal analysis. To elaborate the causal relationships and demonstrate the dynamics of human error, system dynamics (SD) modeling was conducted based on the framework. A total of 41 contributing factors, categorized into four types of human error, were identified through the STAMP-based analysis. All factors are related to a broad view of sociotechnical systems, and more comprehensive than the causation presented in the accident investigation report issued officially. Recommendations regarding both technical and managerial improvement for a lower risk of the accident are proposed. The interests of an interdisciplinary approach provide complementary support between system safety and human factors. The integrated method based on STAMP and SD model contributes to HRA effectively. The proposed method will be beneficial to HRA, risk assessment, and control of the MM III operating process, as well as other sociotechnical systems. © 2014, Human Factors and Ergonomics Society.

Bayesian Data-Model Fit Assessment for Structural Equation Modeling

ERIC Educational Resources Information Center

Levy, Roy

2011-01-01

Bayesian approaches to modeling are receiving an increasing amount of attention in the areas of model construction and estimation in factor analysis, structural equation modeling (SEM), and related latent variable models. However, model diagnostics and model criticism remain relatively understudied aspects of Bayesian SEM. This article describes…

Confirmatory factor analysis of teaching and learning guiding principles instrument among teacher educators in higher education institutions

NASA Astrophysics Data System (ADS)

Masuwai, Azwani; Tajudin, Nor'ain Mohd; Saad, Noor Shah

2017-05-01

The purpose of this study is to develop and establish the validity and reliability of an instrument to generate teaching and learning guiding principles using Teaching and Learning Guiding Principles Instrument (TLGPI). Participants consisted of 171 Malaysian teacher educators. It is an essential instrument to reflect in generating the teaching and learning guiding principles in higher education level in Malaysia. Confirmatory Factor Analysis has validated all 19 items of TLGPI whereby all items indicated high reliability and internal consistency. A Confirmatory Factor Analysis also confirmed that a single factor model was used to generate teaching and learning guiding principles.

Validation of the Polish version of the Multidimensional Body-Self Relations Questionnaire among women.

PubMed

Brytek-Matera, Anna; Rogoza, Radosław

2015-03-01

In Poland, appropriate means to assess body image are relatively limited. The aim of the study was to evaluate the psychometric properties of the Polish version of the Multidimensional Body-Self Relations Questionnaire (MBSRQ). To do so, a sample of 341 females ranging in age from 18 to 35 years (M = 23.09; SD = 3.14) participated in the present study. Owing to the fact that the confirmatory factor analysis of the original nine-factor model was not well fitted to the data (RMSEA = 0.06; CFI = 0.75) the exploratory approach was employed. Based on parallel analysis and minimum average partial an eight-factor structure of the Polish version of the MBSRQ was distinguished. Exploratory factor analysis revealed a factorial structure similar to the original version. The proposed model was tested using an exploratory structural equation modelling approach which resulted in good fit (RMSEA = 0.04; CFI = 0.91). In the present study, the internal reliability assessed by McDonald's ω coefficient amounts from 0.66 to 0.91. In conclusion, the Polish version of the MBSRQ is a useful measure for the attitudinal component of body image assessment.

Is the Factor Observed in Investigations on the Item-Position Effect Actually the Difficulty Factor?

ERIC Educational Resources Information Center

Schweizer, Karl; Troche, Stefan

2018-01-01

In confirmatory factor analysis quite similar models of measurement serve the detection of the difficulty factor and the factor due to the item-position effect. The item-position effect refers to the increasing dependency among the responses to successively presented items of a test whereas the difficulty factor is ascribed to the wide range of…

Comparison of Cox’s Regression Model and Parametric Models in Evaluating the Prognostic Factors for Survival after Liver Transplantation in Shiraz during 2000–2012

PubMed Central

Adelian, R.; Jamali, J.; Zare, N.; Ayatollahi, S. M. T.; Pooladfar, G. R.; Roustaei, N.

2015-01-01

Background: Identification of the prognostic factors for survival in patients with liver transplantation is challengeable. Various methods of survival analysis have provided different, sometimes contradictory, results from the same data. Objective: To compare Cox’s regression model with parametric models for determining the independent factors for predicting adults’ and pediatrics’ survival after liver transplantation. Method: This study was conducted on 183 pediatric patients and 346 adults underwent liver transplantation in Namazi Hospital, Shiraz, southern Iran. The study population included all patients undergoing liver transplantation from 2000 to 2012. The prognostic factors sex, age, Child class, initial diagnosis of the liver disease, PELD/MELD score, and pre-operative laboratory markers were selected for survival analysis. Result: Among 529 patients, 346 (64.5%) were adult and 183 (34.6%) were pediatric cases. Overall, the lognormal distribution was the best-fitting model for adult and pediatric patients. Age in adults (HR=1.16, p<0.05) and weight (HR=2.68, p<0.01) and Child class B (HR=2.12, p<0.05) in pediatric patients were the most important factors for prediction of survival after liver transplantation. Adult patients younger than the mean age and pediatric patients weighing above the mean and Child class A (compared to those with classes B or C) had better survival. Conclusion: Parametric regression model is a good alternative for the Cox’s regression model. PMID:26306158

Time Factor in the Theory of Anthropogenic Risk Prediction in Complex Dynamic Systems

NASA Astrophysics Data System (ADS)

Ostreikovsky, V. A.; Shevchenko, Ye N.; Yurkov, N. K.; Kochegarov, I. I.; Grishko, A. K.

2018-01-01

The article overviews the anthropogenic risk models that take into consideration the development of different factors in time that influence the complex system. Three classes of mathematical models have been analyzed for the use in assessing the anthropogenic risk of complex dynamic systems. These models take into consideration time factor in determining the prospect of safety change of critical systems. The originality of the study is in the analysis of five time postulates in the theory of anthropogenic risk and the safety of highly important objects. It has to be stressed that the given postulates are still rarely used in practical assessment of equipment service life of critically important systems. That is why, the results of study presented in the article can be used in safety engineering and analysis of critically important complex technical systems.

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.

Item-level and subscale-level factoring of Biggs' Learning Process Questionnaire (LPQ) in a mainland Chinese sample.

PubMed

Sachs, J; Gao, L

2000-09-01

The learning process questionnaire (LPQ) has been the source of intensive cross-cultural study. However, an item-level factor analysis of all the LPQ items simultaneously has never been reported. Rather, items within each subscale have been factor analysed to establish subscale unidimensionality and justify the use of composite subscale scores. It was of major interest to see if the six logically constructed items groups of the LPQ would be supported by empirical evidence. Additionally, it was of interest to compare the consistency of the reliability and correlational structure of the LPQ subscales in our study with those of previous cross-cultural studies. Confirmatory factor analysis was used to fit the six-factor item level model and to fit five representative subscale level factor models. A total of 1070 students between the ages of 15 to 18 years was drawn from a representative selection of 29 classes from within 15 secondary schools in Guangzhou, China. Males and females were almost equally represented. The six-factor item level model of the LPQ seemed to fit reasonably well, thus supporting the six dimensional structure of the LPQ and justifying the use of composite subscale scores for each LPQ dimension. However, the reliability of many of these subscales was low. Furthermore, only two subscale-level factor models showed marginally acceptable fit. Substantive considerations supported an oblique three-factor model. Because the LPQ subscales often show low internal consistency reliability, experimental and correlational studies that have used these subscales as dependent measures have been disappointing. It is suggested that some LPQ items should be revised and other items added to improve the inventory's overall psychometric properties.

The latent structure of the functional dyspepsia symptom complex: a taxometric analysis.

PubMed

Van Oudenhove, L; Jasper, F; Walentynowicz, M; Witthöft, M; Van den Bergh, O; Tack, J

2016-07-01

Rome III introduced a subdivision of functional dyspepsia (FD) into postprandial distress syndrome and epigastric pain syndrome, characterized by early satiation/postprandial fullness, and epigastric pain/burning, respectively. However, evidence on their degree of overlap is mixed. We aimed to investigate the latent structure of FD to test whether distinguishable symptom-based subgroups exist. Consecutive tertiary care Rome II FD patients completed the dyspepsia symptom severity scale. Confirmatory factor analysis (CFA) was used to compare the fit of a single factor model, a correlated three-factor model based on Rome III subgroups and a bifactor model consisting of a general FD factor and orthogonal subgroup factors. Taxometric analyses were subsequently used to investigate the latent structure of FD. Nine hundred and fifty-seven FD patients (71.1% women, age 41 ± 14.8) participated. In CFA, the bifactor model yielded a significantly better fit than the two other models (χ² difference tests both p < 0.001). All symptoms had significant loadings on both the general and the subgroup-specific factors (all p < 0.05). Somatization was associated with the general (r = 0.72, p < 0.01), but not the subgroup-specific factors (all r < 0.13, p > 0.05). Taxometric analyses supported a dimensional structure of FD (all CCFI<0.38). We found a dimensional rather than categorical latent structure of the FD symptom complex in tertiary care. A combination of a general dyspepsia symptom reporting factor, which was associated with somatization, and symptom-specific factors reflecting the Rome III subdivision fitted the data best. This has implications for classification, pathophysiology, and treatment of FD. © 2016 John Wiley & Sons Ltd.

DeltaSA tool for source apportionment benchmarking, description and sensitivity analysis

NASA Astrophysics Data System (ADS)

Pernigotti, D.; Belis, C. A.

2018-05-01

DeltaSA is an R-package and a Java on-line tool developed at the EC-Joint Research Centre to assist and benchmark source apportionment applications. Its key functionalities support two critical tasks in this kind of studies: the assignment of a factor to a source in factor analytical models (source identification) and the model performance evaluation. The source identification is based on the similarity between a given factor and source chemical profiles from public databases. The model performance evaluation is based on statistical indicators used to compare model output with reference values generated in intercomparison exercises. The references values are calculated as the ensemble average of the results reported by participants that have passed a set of testing criteria based on chemical profiles and time series similarity. In this study, a sensitivity analysis of the model performance criteria is accomplished using the results of a synthetic dataset where "a priori" references are available. The consensus modulated standard deviation punc gives the best choice for the model performance evaluation when a conservative approach is adopted.

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.

Directional variance adjustment: bias reduction in covariance matrices based on factor analysis with an application to portfolio optimization.

PubMed

Bartz, Daniel; Hatrick, Kerr; Hesse, Christian W; Müller, Klaus-Robert; Lemm, Steven

2013-01-01

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation.

Directional Variance Adjustment: Bias Reduction in Covariance Matrices Based on Factor Analysis with an Application to Portfolio Optimization

PubMed Central

Bartz, Daniel; Hatrick, Kerr; Hesse, Christian W.; Müller, Klaus-Robert; Lemm, Steven

2013-01-01

Robust and reliable covariance estimates play a decisive role in financial and many other applications. An important class of estimators is based on factor models. Here, we show by extensive Monte Carlo simulations that covariance matrices derived from the statistical Factor Analysis model exhibit a systematic error, which is similar to the well-known systematic error of the spectrum of the sample covariance matrix. Moreover, we introduce the Directional Variance Adjustment (DVA) algorithm, which diminishes the systematic error. In a thorough empirical study for the US, European, and Hong Kong stock market we show that our proposed method leads to improved portfolio allocation. PMID:23844016

Selecting risk factors: a comparison of discriminant analysis, logistic regression and Cox's regression model using data from the Tromsø Heart Study.

PubMed

Brenn, T; Arnesen, E

1985-01-01

For comparative evaluation, discriminant analysis, logistic regression and Cox's model were used to select risk factors for total and coronary deaths among 6595 men aged 20-49 followed for 9 years. Groups with mortality between 5 and 93 per 1000 were considered. Discriminant analysis selected variable sets only marginally different from the logistic and Cox methods which always selected the same sets. A time-saving option, offered for both the logistic and Cox selection, showed no advantage compared with discriminant analysis. Analysing more than 3800 subjects, the logistic and Cox methods consumed, respectively, 80 and 10 times more computer time than discriminant analysis. When including the same set of variables in non-stepwise analyses, all methods estimated coefficients that in most cases were almost identical. In conclusion, discriminant analysis is advocated for preliminary or stepwise analysis, otherwise Cox's method should be used.

Prognostic factors in multiple myeloma: selection using Cox's proportional hazard model.

PubMed

Pasqualetti, P; Collacciani, A; Maccarone, C; Casale, R

1996-01-01

The pretreatment characteristics of 210 patients with multiple myeloma, observed between 1980 and 1994, were evaluated as potential prognostic factors for survival. Multivariate analysis according to Cox's proportional hazard model identified in the 160 dead patients with myeloma, among 26 different single prognostic variables, the following factors in order of importance: beta 2-microglobulin; bone marrow plasma cell percentage, hemoglobinemia, degree of lytic bone lesions, serum creatinine, and serum albumin. By analysis of these variables a prognostic index (PI), that considers the regression coefficients derived by Cox's model of all significant factors, was obtained. Using this it was possible to separate the whole patient group into three stages: stage I (PI < 1.485, 67 patients), stage II (PI: 1.485-2.090, 76 patients), and stage III (PI > 2.090, 67 patients), with a median survivals of 68, 36 and 13 months (P < 0.0001), respectively. Also the responses to therapy (P < 0.0001) and the survival curves (P < 0.00001) presented significant differences among the three subgroups. Knowledge of these factors could be of value in predicting prognosis and in planning therapy in patients with multiple myeloma.

Addressing the Hard Factors for Command File Errors by Probabilistic Reasoning

NASA Technical Reports Server (NTRS)

Meshkat, Leila; Bryant, Larry

2014-01-01

Command File Errors (CFE) are managed using standard risk management approaches at the Jet Propulsion Laboratory. Over the last few years, more emphasis has been made on the collection, organization, and analysis of these errors for the purpose of reducing the CFE rates. More recently, probabilistic modeling techniques have been used for more in depth analysis of the perceived error rates of the DAWN mission and for managing the soft factors in the upcoming phases of the mission. We broadly classify the factors that can lead to CFE's as soft factors, which relate to the cognition of the operators and hard factors which relate to the Mission System which is composed of the hardware, software and procedures used for the generation, verification & validation and execution of commands. The focus of this paper is to use probabilistic models that represent multiple missions at JPL to determine the root cause and sensitivities of the various components of the mission system and develop recommendations and techniques for addressing them. The customization of these multi-mission models to a sample interplanetary spacecraft is done for this purpose.

Uncertainty Analysis of Power Grid Investment Capacity Based on Monte Carlo

NASA Astrophysics Data System (ADS)

Qin, Junsong; Liu, Bingyi; Niu, Dongxiao

By analyzing the influence factors of the investment capacity of power grid, to depreciation cost, sales price and sales quantity, net profit, financing and GDP of the second industry as the dependent variable to build the investment capacity analysis model. After carrying out Kolmogorov-Smirnov test, get the probability distribution of each influence factor. Finally, obtained the grid investment capacity uncertainty of analysis results by Monte Carlo simulation.

Electroacoustic analysis, design, and implementation of a small balanced armature speaker.

PubMed

Bai, Mingsian R; You, Bo-Cheng; Lo, Yi-Yang

2014-11-01

This paper presents a new design and implementation of a balanced armature speaker (BAS), which is composed of permanent magnetic circuits, a moving armature, and a coil. The armature rocks about a pivot with the coil at one end and the permanent magnet on another. A magnetic circuit analysis is conducted for the designed BAS to formulate the force factor, which is required for modeling the coupling between the electrical and mechanical systems. In addition, an electromechanoacoustical analogous circuit is established for the BAS, which bears the same structure as the moving coil loudspeaker, except that the force factor is different. A hybrid model, which combines the lumped parameter model in the electrical and acoustical domains with a finite element model in the mechanical domain, is developed to model the high-frequency response because of the high-order modes of the membrane, the drive rod, and the armature. The electroacoustic analysis is experimentally verified. The results indicate that the sound pressure response that is simulated using the hybrid model is in superior agreement with the measured response to that simulated using the lumped parameter model.

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.

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.

Data mining-based coefficient of influence factors optimization of test paper reliability

NASA Astrophysics Data System (ADS)

Xu, Peiyao; Jiang, Huiping; Wei, Jieyao

2018-05-01

Test is a significant part of the teaching process. It demonstrates the final outcome of school teaching through teachers' teaching level and students' scores. The analysis of test paper is a complex operation that has the characteristics of non-linear relation in the length of the paper, time duration and the degree of difficulty. It is therefore difficult to optimize the coefficient of influence factors under different conditions in order to get text papers with clearly higher reliability with general methods [1]. With data mining techniques like Support Vector Regression (SVR) and Genetic Algorithm (GA), we can model the test paper analysis and optimize the coefficient of impact factors for higher reliability. It's easy to find that the combination of SVR and GA can get an effective advance in reliability from the test results. The optimal coefficient of influence factors optimization has a practicability in actual application, and the whole optimizing operation can offer model basis for test paper analysis.

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.

Air Force Research Laboratory Warfighter Readiness Research Division Participation in the 2008 IITSEC

DTIC Science & Technology

2008-12-15

of the underlying behaviors that led to each element being cited. The AFSC Human Factors Database listed all human factors cited in the Life...situations of increased pressure. Through an understanding of the causal factors of human behavior , and by analysis of one’s own behavioral patterns...JTAC training and overall lessons learned from modeling and simulation of the JTAC environment to include behavior scripting, artillery models

Identifying key sources of uncertainty in the modelling of greenhouse gas emissions from wastewater treatment.

PubMed

Sweetapple, Christine; Fu, Guangtao; Butler, David

2013-09-01

This study investigates sources of uncertainty in the modelling of greenhouse gas emissions from wastewater treatment, through the use of local and global sensitivity analysis tools, and contributes to an in-depth understanding of wastewater treatment modelling by revealing critical parameters and parameter interactions. One-factor-at-a-time sensitivity analysis is used to screen model parameters and identify those with significant individual effects on three performance indicators: total greenhouse gas emissions, effluent quality and operational cost. Sobol's method enables identification of parameters with significant higher order effects and of particular parameter pairs to which model outputs are sensitive. Use of a variance-based global sensitivity analysis tool to investigate parameter interactions enables identification of important parameters not revealed in one-factor-at-a-time sensitivity analysis. These interaction effects have not been considered in previous studies and thus provide a better understanding wastewater treatment plant model characterisation. It was found that uncertainty in modelled nitrous oxide emissions is the primary contributor to uncertainty in total greenhouse gas emissions, due largely to the interaction effects of three nitrogen conversion modelling parameters. The higher order effects of these parameters are also shown to be a key source of uncertainty in effluent quality. Copyright © 2013 Elsevier Ltd. All rights reserved.

Population Biology of Schistosoma Mating, Aggregation, and Transmission Breakpoints: More Reliable Model Analysis for the End-Game in Communities at Risk

PubMed Central

Gurarie, David; King, Charles H.

2014-01-01

Mathematical modeling is widely used for predictive analysis of control options for infectious agents. Challenging problems arise for modeling host-parasite systems having complex life-cycles and transmission environments. Macroparasites, like Schistosoma, inhabit highly fragmented habitats that shape their reproductive success and distribution. Overdispersion and mating success are important factors to consider in modeling control options for such systems. Simpler models based on mean worm burden (MWB) formulations do not take these into account and overestimate transmission. Proposed MWB revisions have employed prescribed distributions and mating factor corrections to derive modified MWB models that have qualitatively different equilibria, including ‘breakpoints’ below which the parasite goes to extinction, suggesting the possibility of elimination via long-term mass-treatment control. Despite common use, no one has attempted to validate the scope and hypotheses underlying such MWB approaches. We conducted a systematic analysis of both the classical MWB and more recent “stratified worm burden” (SWB) modeling that accounts for mating and reproductive hurdles (Allee effect). Our analysis reveals some similarities, including breakpoints, between MWB and SWB, but also significant differences between the two types of model. We show the classic MWB has inherent inconsistencies, and propose SWB as a reliable alternative for projection of long-term control outcomes. PMID:25549362

Impact of Competing Values and Choices on Democratic Support in Hong Kong.

PubMed

Lam, Wai-Man

2013-08-01

This paper examines the reasons for the relatively low democratic support (DS) in Hong Kong in the context of competing values and choices based on the previous Asian Barometer Surveys. In so doing, it establishes a three-factor theoretical model that includes survey attitudinal statements related to authoritarianism (AU), nationalism (NA) and economic evaluations (EC) on DS. Using confirmatory factor analysis (CFA), the analysis shows that the hypothesized model is a very good fit. The Hong Kong people's relatively low DS, in terms of their unconditional support for democracy and the degree of democracy they want for Hong Kong, can be well explained by the three factors in combination. The factors have various extent of impact on DS, with AU being the strongest, followed by EC, and then NA. The paper contributes by illustrating the usefulness of CFA in political values research, unraveling the comparative importance of the values and choices in affecting DS, and establishing a model for further testing.

Emergent Writing in Preschoolers: Preliminary Evidence for a Theoretical Framework

PubMed Central

Puranik, Cynthia S.; Lonigan, Christopher J.

2014-01-01

Researchers and educators use the term emergent literacy to refer to a broad set of skills and attitudes that serve as foundational skills for acquiring success in later reading and writing; however, models of emergent literacy have generally focused on reading and reading-related behaviors. Hence, the primary aim of this study was to articulate and evaluate a theoretical model of the components of emergent writing. Alternative models of the structure of individual and developmental differences of emergent writing and writing-related skills were examined in 372 preschool children who ranged in age from 3- to 5-years using confirmatory factor analysis. Results from a confirmatory factor analysis provide evidence that these emergent writing skills are best described by three correlated but distinct factors, (a) Conceptual Knowledge, (b) Procedural Knowledge, and (c) Generative Knowledge. Evidence that these three emergent writing factors show different patterns of relations to emergent literacy constructs is presented. Implications for understanding the development of writing and assessment of early writing skills are discussed. PMID:25316955

A Comparison of Pseudo-Maximum Likelihood and Asymptotically Distribution-Free Dynamic Factor Analysis Parameter Estimation in Fitting Covariance Structure Models to Block-Toeplitz Matrices Representing Single-Subject Multivariate Time-Series.

ERIC Educational Resources Information Center

Molenaar, Peter C. M.; Nesselroade, John R.

1998-01-01

Pseudo-Maximum Likelihood (p-ML) and Asymptotically Distribution Free (ADF) estimation methods for estimating dynamic factor model parameters within a covariance structure framework were compared through a Monte Carlo simulation. Both methods appear to give consistent model parameter estimates, but only ADF gives standard errors and chi-square…

Radiomics-based Prognosis Analysis for Non-Small Cell Lung Cancer

NASA Astrophysics Data System (ADS)

Zhang, Yucheng; Oikonomou, Anastasia; Wong, Alexander; Haider, Masoom A.; Khalvati, Farzad

2017-04-01

Radiomics characterizes tumor phenotypes by extracting large numbers of quantitative features from radiological images. Radiomic features have been shown to provide prognostic value in predicting clinical outcomes in several studies. However, several challenges including feature redundancy, unbalanced data, and small sample sizes have led to relatively low predictive accuracy. In this study, we explore different strategies for overcoming these challenges and improving predictive performance of radiomics-based prognosis for non-small cell lung cancer (NSCLC). CT images of 112 patients (mean age 75 years) with NSCLC who underwent stereotactic body radiotherapy were used to predict recurrence, death, and recurrence-free survival using a comprehensive radiomics analysis. Different feature selection and predictive modeling techniques were used to determine the optimal configuration of prognosis analysis. To address feature redundancy, comprehensive analysis indicated that Random Forest models and Principal Component Analysis were optimum predictive modeling and feature selection methods, respectively, for achieving high prognosis performance. To address unbalanced data, Synthetic Minority Over-sampling technique was found to significantly increase predictive accuracy. A full analysis of variance showed that data endpoints, feature selection techniques, and classifiers were significant factors in affecting predictive accuracy, suggesting that these factors must be investigated when building radiomics-based predictive models for cancer prognosis.

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.

SWAT model uncertainty analysis, calibration and validation for runoff simulation in the Luvuvhu River catchment, South Africa

NASA Astrophysics Data System (ADS)

Thavhana, M. P.; Savage, M. J.; Moeletsi, M. E.

2018-06-01

The soil and water assessment tool (SWAT) was calibrated for the Luvuvhu River catchment, South Africa in order to simulate runoff. The model was executed through QSWAT which is an interface between SWAT and QGIS. Data from four weather stations and four weir stations evenly distributed over the catchment were used. The model was run for a 33-year period of 1983-2015. Sensitivity analysis, calibration and validation were conducted using the sequential uncertainty fitting (SUFI-2) algorithm through its interface with SWAT calibration and uncertainty procedure (SWAT-CUP). The calibration process was conducted for the period 1986 to 2005 while the validation process was from 2006 to 2015. Six model efficiency measures were used, namely: coefficient of determination (R2), Nash-Sutcliffe efficiency (NSE) index, root mean square error (RMSE)-observations standard deviation ratio (RSR), percent bias (PBIAS), probability (P)-factor and correlation coefficient (R)-factor were used. Initial results indicated an over-estimation of low flows with regression slope of less than 0.7. Twelve model parameters were applied for sensitivity analysis with four (ALPHA_BF, CN2, GW_DELAY and SOL_K) found to be more distinguishable and sensitive to streamflow (p < 0.05). The SUFI-2 algorithm through the interface with the SWAT-CUP was capable of capturing the model's behaviour, with calibration results showing an R2 of 0.63, NSE index of 0.66, RSR of 0.56 and a positive PBIAS of 16.3 while validation results revealed an R2 of 0.52, NSE of 0.48, RSR of 0.72 and PBIAS of 19.90. The model produced P-factor of 0.67 and R-factor of 0.68 during calibration and during validation, 0.69 and 0.53 respectively. Although performance indicators yielded fair and acceptable results, the P-factor was still below the recommended model performance of 70%. Apart from the unacceptable P-factor values, the results obtained in this study demonstrate acceptable model performance during calibration while validation results were still inconclusive. It can be concluded that calibration of the SWAT model yielded acceptable results with exception to validation results. Having said this, the model can be a useful tool for general water resources assessment and not for analysing hydrological extremes in the Luvuvhu River catchment.

Factor Analysis of the Brazilian Version of UPPS Impulsive Behavior Scale.

PubMed

Sediyama, Cristina Y N; Moura, Ricardo; Garcia, Marina S; da Silva, Antonio G; Soraggi, Carolina; Neves, Fernando S; Albuquerque, Maicon R; Whiteside, Setephen P; Malloy-Diniz, Leandro F

2017-01-01

Objective: To examine the internal consistency and factor structure of the Brazilian adaptation of the UPPS Impulsive Behavior Scale. Methods: UPPS is a self-report scale composed by 40 items assessing four factors of impulsivity: (a) urgency, (b) lack of premeditation; (c) lack of perseverance; (d) sensation seeking. In the present study 384 participants (278 women and 106 men), who were recruited from schools, universities, leisure centers and workplaces fulfilled the UPPS scale. An exploratory factor analysis was performed by using Varimax factor rotation and Kaiser Normalization, and we also conducted two confirmatory analyses to test the independency of the UPPS components found in previous analysis. Results: Results showed a decrease in mean UPPS total scores with age and this analysis showed that the youngest participants (below 30 years) scored significantly higher than the other groups over 30 years. No difference in gender was found. Cronbach's alpha, results indicated satisfactory values for all subscales, with similar high values for the subscales and confirmatory factor analysis indexes also indicated a poor model fit. The results of two exploratory factor analysis were satisfactory. Conclusion: Our results showed that the Portuguese version has the same four-factor structure of the original and previous translations of the UPPS.

The Edinburgh Postnatal Depression Scale: Screening Tool for Postpartum Anxiety as Well? Findings from a Confirmatory Factor Analysis of the Hebrew Version.

PubMed

Bina, Rena; Harrington, Donna

2016-04-01

The Edinburgh Postnatal Depression Scale (EPDS) was originally created as a uni-dimensional scale to screen for postpartum depression (PPD); however, evidence from various studies suggests that it is a multi-dimensional scale measuring mainly anxiety in addition to depression. The factor structure of the EPDS seems to differ across various language translations, raising questions regarding its stability. This study examined the factor structure of the Hebrew version of the EPDS to assess whether it is uni- or multi-dimensional. Seven hundred and fifteen (n = 715) women were screened at 6 weeks postpartum using the Hebrew version of the EPDS. Confirmatory factor analysis (CFA) was used to test four models derived from the literature. Of the four CFA models tested, a 9-item two factor model fit the data best, with one factor representing an underlying depression construct and the other representing an underlying anxiety construct. for Practice The Hebrew version of the EPDS appears to consist of depression and anxiety sub-scales. Given the widespread PPD screening initiatives, anxiety symptoms should be addressed in addition to depressive symptoms, and a short scale, such as the EPDS, assessing both may be efficient.

A Second-Order Confirmatory Factor Analysis of the Moral Distress Scale-Revised for Nurses.

PubMed

Sharif Nia, Hamid; Shafipour, Vida; Allen, Kelly-Ann; Heidari, Mohammad Reza; Yazdani-Charati, Jamshid; Zareiyan, Armin

2017-01-01

Moral distress is a growing problem for healthcare professionals that may lead to dissatisfaction, resignation, or occupational burnout if left unattended, and nurses experience different levels of this phenomenon. This study aims to investigate the factor structure of the Persian version of the Moral Distress Scale-Revised in intensive care and general nurses. This methodological research was conducted with 771 nurses from eight hospitals in the Mazandaran Province of Iran in 2017. Participants completed the Moral Distress Scale-Revised, data collected, and factor structure assessed using the construct, convergent, and divergent validity methods. The reliability of the scale was assessed using internal consistency (Cronbach's alpha, Theta, and McDonald's omega coefficients) and construct reliability. Ethical considerations: This study was approved by the Ethics Committee of Mazandaran University of Medical Sciences. The exploratory factor analysis ( N = 380) showed that the Moral Distress Scale-Revised has five factors: lack of professional competence at work, ignoring ethical issues and patient conditions, futile care, carrying out the physician's orders without question and unsafe care, and providing care under personal and organizational pressures, which explained 56.62% of the overall variance. The confirmatory factor analysis ( N = 391) supported the five-factor solution and the second-order latent factor model. The first-order model did not show a favorable convergent and divergent validity. Ultimately, the Moral Distress Scale-Revised was found to have a favorable internal consistency and construct reliability. The Moral Distress Scale-Revised was found to be a multidimensional construct. The data obtained confirmed the hypothesis of the factor structure model with a latent second-order variable. Since the convergent and divergent validity of the scale were not confirmed in this study, further assessment is necessary in future studies.

Validity and reliability of Chinese version of Adult Carer Quality of Life questionnaire (AC-QoL) in family caregivers of stroke survivors

PubMed Central

Li, Yingshuang; Ding, Chunge

2017-01-01

The Adult Carer Quality of Life questionnaire (AC-QoL) is a reliable and valid instrument used to assess the quality of life (QoL) of adult family caregivers. We explored the psychometric properties and tested the reliability and validity of a Chinese version of the AC-QoL with reliability and validity testing in 409 Chinese stroke caregivers. We used item-total correlation and extreme group comparison to do item analysis. To evaluate its reliability, we used a test-retest reliability approach, intraclass correlation coefficient (ICC), together with Cronbach’s alpha and model-based internal consistency index; to evaluate its validity, we used scale content validity, confirmatory factor analysis (CFA) and exploratory factor analysis (EFA) via principal component analysis with varimax rotation. We found that the CFA did not in fact confirm the original factor model and our EFA yielded a 31-item measure with a five-factor model. In conclusions, although some items performed differently in our analysis of the original English language version and our Chinese language version, our translated AC-QoL is a reliable and valid tool which can be used to assess the quality of life of stroke caregivers in mainland China. Chinese version AC-QoL is a comprehensive and good measurement to understand caregivers and has the potential to be a screening tool to assess QoL of caregiver. PMID:29131845

Dental Students' Perceptions of Risk Factors for Musculoskeletal Disorders: Adapting the Job Factors Questionnaire for Dentistry.

PubMed

Presoto, Cristina D; Wajngarten, Danielle; Domingos, Patrícia A S; Campos, Juliana A D B; Garcia, Patrícia P N S

2018-01-01

The aims of this study were to adapt the Job Factors Questionnaire to the field of dentistry, evaluate its psychometric properties, evaluate dental students' perceptions of work/study risk factors for musculoskeletal disorders, and determine the influence of gender and academic level on those perceptions. All 580 students enrolled in two Brazilian dental schools in 2015 were invited to participate in the study. A three-factor structure (Repetitiveness, Work Posture, and External Factors) was tested through confirmatory factor analysis. Convergent validity was estimated using the average variance extracted (AVE), discriminant validity was based on the correlational analysis of the factors, and reliability was assessed. A causal model was created using structural equation modeling to evaluate the influence of gender and academic level on students' perceptions. A total of 480 students completed the questionnaire for an 83% response rate. The responding students' average age was 21.6 years (SD=2.98), and 74.8% were women. Higher scores were observed on the Work Posture factor items. The refined model presented proper fit to the studied sample. Convergent validity was compromised only for External Factors (AVE=0.47), and discriminant validity was compromised for Work Posture and External Factors (r 2 =0.69). Reliability was adequate. Academic level did not have a significant impact on the factors, but the women students exhibited greater perception. Overall, the adaptation resulted in a useful instrument for assessing perceptions of risk factors for musculoskeletal disorders. Gender was found to significantly influence all three factors, with women showing greater perception of the risk factors.

Factors predicting early postpartum glucose intolerance in Japanese women with gestational diabetes mellitus: decision-curve analysis.

PubMed

Kondo, M; Nagao, Y; Mahbub, M H; Tanabe, T; Tanizawa, Y

2018-04-29

To identify factors predicting early postpartum glucose intolerance in Japanese women with gestational diabetes mellitus, using decision-curve analysis. A retrospective cohort study was performed. The participants were 123 Japanese women with gestational diabetes who underwent 75-g oral glucose tolerance tests at 8-12 weeks after delivery. They were divided into a glucose intolerance and a normal glucose tolerance group based on postpartum oral glucose tolerance test results. Analysis of the pregnancy oral glucose tolerance test results showed predictive factors for postpartum glucose intolerance. We also evaluated the clinical usefulness of the prediction model based on decision-curve analysis. Of 123 women, 78 (63.4%) had normoglycaemia and 45 (36.6%) had glucose intolerance. Multivariable logistic regression analysis showed insulinogenic index/fasting immunoreactive insulin and summation of glucose levels, assessed during pregnancy oral glucose tolerance tests (total glucose), to be independent risk factors for postpartum glucose intolerance. Evaluating the regression models, the best discrimination (area under the curve 0.725) was obtained using the basic model (i.e. age, family history of diabetes, BMI ≥25 kg/m 2 and use of insulin during pregnancy) plus insulinogenic index/fasting immunoreactive insulin <1.1. Decision-curve analysis showed that combining insulinogenic index/fasting immunoreactive insulin <1.1 with basic clinical information resulted in superior net benefits for prediction of postpartum glucose intolerance. Insulinogenic index/fasting immunoreactive insulin calculated using oral glucose tolerance test results during pregnancy is potentially useful for predicting early postpartum glucose intolerance in Japanese women with gestational diabetes. © 2018 Diabetes UK.

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.

Overcoming redundancies in bedside nursing assessments by validating a parsimonious meta-tool: findings from a methodological exercise study.

PubMed

Palese, Alvisa; Marini, Eva; Guarnier, Annamaria; Barelli, Paolo; Zambiasi, Paola; Allegrini, Elisabetta; Bazoli, Letizia; Casson, Paola; Marin, Meri; Padovan, Marisa; Picogna, Michele; Taddia, Patrizia; Chiari, Paolo; Salmaso, Daniele; Marognolli, Oliva; Canzan, Federica; Ambrosi, Elisa; Saiani, Luisa; Grassetti, Luca

2016-10-01

There is growing interest in validating tools aimed at supporting the clinical decision-making process and research. However, an increased bureaucratization of clinical practice and redundancies in the measures collected have been reported by clinicians. Redundancies in clinical assessments affect negatively both patients and nurses. To validate a meta-tool measuring the risks/problems currently estimated by multiple tools used in daily practice. A secondary analysis of a database was performed, using a cross-validation and a longitudinal study designs. In total, 1464 patients admitted to 12 medical units in 2012 were assessed at admission with the Brass, Barthel, Conley and Braden tools. Pertinent outcomes such as the occurrence of post-discharge need for resources and functional decline at discharge, as well as falls and pressure sores, were measured. Explorative factor analysis of each tool, inter-tool correlations and a conceptual evaluation of the redundant/similar items across tools were performed. Therefore, the validation of the meta-tool was performed through explorative factor analysis, confirmatory factor analysis and the structural equation model to establish the ability of the meta-tool to predict the outcomes estimated by the original tools. High correlations between the tools have emerged (from r 0.428 to 0.867) with a common variance from 18.3% to 75.1%. Through a conceptual evaluation and explorative factor analysis, the items were reduced from 42 to 20, and the three factors that emerged were confirmed by confirmatory factor analysis. According to the structural equation model results, two out of three emerged factors predicted the outcomes. From the initial 42 items, the meta-tool is composed of 20 items capable of predicting the outcomes as with the original tools. © 2016 John Wiley & Sons, Ltd.

Modeling the Trajectory of Analgesic Demand Over Time After Total Knee Arthroplasty Using the Latent Curve Analysis.

PubMed

Lo, Po-Han; Tsou, Mei-Yung; Chang, Kuang-Yi

2015-09-01

Patient-controlled epidural analgesia (PCEA) is commonly used for pain relief after total knee arthroplasty (TKA). This study aimed to model the trajectory of analgesic demand over time after TKA and explore its influential factors using latent curve analysis. Data were retrospectively collected from 916 patients receiving unilateral or bilateral TKA and postoperative PCEA. PCEA demands during 12-hour intervals for 48 hours were directly retrieved from infusion pumps. Potentially influential factors of PCEA demand, including age, height, weight, body mass index, sex, and infusion pump settings, were also collected. A latent curve analysis with 2 latent variables, the intercept (baseline) and slope (trend), was applied to model the changes in PCEA demand over time. The effects of influential factors on these 2 latent variables were estimated to examine how these factors interacted with time to alter the trajectory of PCEA demand over time. On average, the difference in analgesic demand between the first and second 12-hour intervals was only 15% of that between the first and third 12-hour intervals. No significant difference in PCEA demand was noted between the third and fourth 12-hour intervals. Aging tended to decrease the baseline PCEA demand but body mass index and infusion rate were positively correlated with the baseline. Only sex significantly affected the trend parameter and male individuals tended to have a smoother decreasing trend of analgesic demands over time. Patients receiving bilateral procedures did not consume more analgesics than their unilateral counterparts. Goodness of fit analysis indicated acceptable model fit to the observed data. Latent curve analysis provided valuable information about how analgesic demand after TKA changed over time and how patient characteristics affected its trajectory.

Multivariate logistic regression analysis of postoperative complications and risk model establishment of gastrectomy for gastric cancer: A single-center cohort report.

PubMed

Zhou, Jinzhe; Zhou, Yanbing; Cao, Shougen; Li, Shikuan; Wang, Hao; Niu, Zhaojian; Chen, Dong; Wang, Dongsheng; Lv, Liang; Zhang, Jian; Li, Yu; Jiao, Xuelong; Tan, Xiaojie; Zhang, Jianli; Wang, Haibo; Zhang, Bingyuan; Lu, Yun; Sun, Zhenqing

2016-01-01

Reporting of surgical complications is common, but few provide information about the severity and estimate risk factors of complications. If have, but lack of specificity. We retrospectively analyzed data on 2795 gastric cancer patients underwent surgical procedure at the Affiliated Hospital of Qingdao University between June 2007 and June 2012, established multivariate logistic regression model to predictive risk factors related to the postoperative complications according to the Clavien-Dindo classification system. Twenty-four out of 86 variables were identified statistically significant in univariate logistic regression analysis, 11 significant variables entered multivariate analysis were employed to produce the risk model. Liver cirrhosis, diabetes mellitus, Child classification, invasion of neighboring organs, combined resection, introperative transfusion, Billroth II anastomosis of reconstruction, malnutrition, surgical volume of surgeons, operating time and age were independent risk factors for postoperative complications after gastrectomy. Based on logistic regression equation, p=Exp∑BiXi / (1+Exp∑BiXi), multivariate logistic regression predictive model that calculated the risk of postoperative morbidity was developed, p = 1/(1 + e((4.810-1.287X1-0.504X2-0.500X3-0.474X4-0.405X5-0.318X6-0.316X7-0.305X8-0.278X9-0.255X10-0.138X11))). The accuracy, sensitivity and specificity of the model to predict the postoperative complications were 86.7%, 76.2% and 88.6%, respectively. This risk model based on Clavien-Dindo grading severity of complications system and logistic regression analysis can predict severe morbidity specific to an individual patient's risk factors, estimate patients' risks and benefits of gastric surgery as an accurate decision-making tool and may serve as a template for the development of risk models for other surgical groups.

Confirmatory factors analysis of science teacher leadership in the Thailand world-class standard schools

NASA Astrophysics Data System (ADS)

Thawinkarn, Dawruwan

2018-01-01

This research aims to analyze factors of science teacher leadership in the Thailand World-Class Standard Schools. The research instrument was a five scale rating questionnaire with reliability 0.986. The sample group included 500 science teachers from World-Class Standard Schools who had been selected by using the stratified random sampling technique. Factor analysis of science teacher leadership in the Thailand World-Class Standard Schools was conducted by using M plus for Windows. The results are as follows: The results of confirmatory factor analysis on science teacher leadership in the Thailand World-Class Standard Schools revealed that the model significantly correlated with the empirical data. The consistency index value was x2 = 105.655, df = 88, P-Value = 0.086, TLI = 0.997, CFI = 0.999, RMSEA = 0.022, and SRMR = 0.019. The value of factor loading of science teacher leadership was positive, with statistical significance at the level of 0.01. The value of six factors was between 0.880-0.996. The highest factor loading was the professional learning community, followed by child-centered instruction, participation in development, the role model in teaching, transformational leaders, and self-development with factor loading at 0.996, 0.928, 0.911, 0.907, 0.901, and 0.871, respectively. The reliability of each factor was 99.1%, 86.0%, 83.0%, 82.2%, 81.0%, and 75.8%, respectively.

Psychometrican analysis and dimensional structure of the Brazilian version of melasma quality of life scale (MELASQoL-BP)*

PubMed Central

Maranzatto, Camila Fernandes Pollo; Miot, Hélio Amante; Miot, Luciane Donida Bartoli; Meneguin, Silmara

2016-01-01

Background Although asymptomatic, melasma inflicts significant impact on quality of life. MELASQoL is the main instrument used to assess quality of life associated with melasma, it has been validated in several languages, but its latent dimensional structure and psychometric properties haven´t been fully explored. Objectives To evaluate psychometric characteristics, information and dimensional structure of the Brazilian version of MELASQoL. Methods Survey with patients with facial melasma through socio-demographic questionnaire, DLQI-BRA, MASI and MELASQoL-BP, exploratory and confirmatory factor analysis, internal consistency of MELASQoL and latent dimensions (Cronbach's alpha). The informativeness of the model and items were investigated by the Rasch model (ordinal data). Results We evaluated 154 patients, 134 (87%) were female, mean age (± SD) of 39 (± 8) years, the onset of melasma at 27 (± 8) years, median (p25-p75) of MASI scores , DLQI and MELASQoL 8 (5-15) 2 (1-6) and 30 (17-44). The correlation (rho) of MELASQoL with DLQI and MASI were: 0.70 and 0.36. Exploratory factor analysis identified two latent dimensions: Q1-Q3 and Q4-Q10, which had significantly more adjusted factor structure than the one-dimensional model: Χ2 / gl = 2.03, CFI = 0.95, AGFI = 0.94, RMSEA = 0.08. Cronbach's coefficient for the one-dimensional model and the factors were: 0.95, 0.92 and 0.93. Rasch analysis demonstrated that the use of seven alternatives per item resulted in no increase in the model informativeness. Conclusions MELASQoL-BP showed good psychometric performance and a latent structure of two dimensions. We also identified an oversizing of item alternatives to characterize the aggregate information to each dimension. PMID:27579735

Quantifying the importance of spatial resolution and other factors through global sensitivity analysis of a flood inundation model

NASA Astrophysics Data System (ADS)

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

2016-11-01

Where high-resolution topographic data are available, modelers are faced with the decision of whether it is better to spend computational resource on resolving topography at finer resolutions or on running more simulations to account for various uncertain input factors (e.g., model parameters). In this paper we apply global sensitivity analysis to explore how influential the choice of spatial resolution is when compared to uncertainties in the Manning's friction coefficient parameters, the inflow hydrograph, and those stemming from the coarsening of topographic data used to produce Digital Elevation Models (DEMs). We apply the hydraulic model LISFLOOD-FP to produce several temporally and spatially variable model outputs that represent different aspects of flood inundation processes, including flood extent, water depth, and time of inundation. We find that the most influential input factor for flood extent predictions changes during the flood event, starting with the inflow hydrograph during the rising limb before switching to the channel friction parameter during peak flood inundation, and finally to the floodplain friction parameter during the drying phase of the flood event. Spatial resolution and uncertainty introduced by resampling topographic data to coarser resolutions are much more important for water depth predictions, which are also sensitive to different input factors spatially and temporally. Our findings indicate that the sensitivity of LISFLOOD-FP predictions is more complex than previously thought. Consequently, the input factors that modelers should prioritize will differ depending on the model output assessed, and the location and time of when and where this output is most relevant.

Confirmatory Factor Analysis of the Kaufman Assessment Battery for Children: A Reevaluation.

ERIC Educational Resources Information Center

Strommen, Erik

1988-01-01

Performed confirmatory factor analyses of Kaufman Assessment Battery for Children (K-ABC) using subtest correlations for standardization samples provided in manuals to test hypothesis that factors underlying K-ABC are substantially intercorrelated at all age levels for two- and three-factor models. Findings suggest K-ABC cannot distinguish between…

Rotational Uniqueness Conditions under Oblique Factor Correlation Metric

ERIC Educational Resources Information Center

Peeters, Carel F. W.

2012-01-01

In an addendum to his seminal 1969 article Joreskog stated two sets of conditions for rotational identification of the oblique factor solution under utilization of fixed zero elements in the factor loadings matrix (Joreskog in "Advances in factor analysis and structural equation models," pp. 40-43, 1979). These condition sets, formulated under…

Local Spatial Analysis and Dynamic Simulation of Childhood Obesity and Neighbourhood Walkability in a Major Canadian City.

PubMed

Shahid, Rizwan; Bertazzon, Stefania

2015-01-01

Body weight is an important indicator of current and future health and it is even more critical in children, who are tomorrow's adults. This paper analyzes the relationship between childhood obesity and neighbourhood walkability in Calgary, Canada. A multivariate analytical framework recognizes that childhood obesity is also associated with many factors, including socioeconomic status, foodscapes, and environmental factors, as well as less measurable factors, such as individual preferences, that could not be included in this analysis. In contrast with more conventional global analysis, this research employs localized analysis and assesses need-based interventions. The one-size-fit-all strategy may not effectively control obesity rates, since each neighbourhood has unique characteristics that need to be addressed individually. This paper presents an innovative framework combining local analysis with simulation modeling to analyze childhood obesity. Spatial models generally do not deal with simulation over time, making it cumbersome for health planners and policy makers to effectively design and implement interventions and to quantify their impact over time. This research fills this gap by integrating geographically weighted regression (GWR), which identifies vulnerable neighbourhoods and critical factors for childhood obesity, with simulation modeling, which evaluates the impact of the suggested interventions on the targeted neighbourhoods. Neighbourhood walkability was chosen as a potential target for localized interventions, owing to the crucial role of walking in developing a healthy lifestyle, as well as because increasing walkability is relatively more feasible and less expensive then modifying other factors, such as income. Simulation results suggest that local walkability interventions can achieve measurable declines in childhood obesity rates. The results are encouraging, as improvements are likely to compound over time. The results demonstrate that the integration of GWR and simulation modeling is effective, and the proposed framework can assist in designing local interventions to control and prevent childhood obesity.

Promoting motivation through mode of instruction: The relationship between use of affective teaching techniques and motivation to learn science

NASA Astrophysics Data System (ADS)

Sanchez Rivera, Yamil

The purpose of this study is to add to what we know about the affective domain and to create a valid instrument for future studies. The Motivation to Learn Science (MLS) Inventory is based on Krathwohl's Taxonomy of Affective Behaviors (Krathwohl et al., 1964). The results of the Exploratory Factor Analysis (EFA) and Confirmatory Factor Analysis (CFA) demonstrated that the MLS Inventory is a valid and reliable instrument. Therefore, the MLS Inventory is a uni-dimensional instrument composed of 9 items with convergent validity (no divergence). The instrument had a high Chronbach Alpha value of .898 during the EFA analysis and .919 with the CFA analysis. Factor loadings on the 9 items ranged from .617 to .800. Standardized regression weights ranged from .639 to .835 in the CFA analysis. Various indices (RMSEA = .033; NFI = .987; GFI = .985; CFI = 1.000) demonstrated a good fitness of the proposed model. Hierarchical linear modeling was used to statistical analyze data where students' motivation to learn science scores (level-1) were nested within teachers (level-2). The analysis was geared toward identifying if teachers' use of affective behavior (a level-2 classroom variable) was significantly related with students' MLS scores (level-1 criterion variable). Model testing proceeded in three phases: intercept-only model, means-as-outcome model, and a random-regression coefficient model. The intercept-only model revealed an intra-class correlation coefficient of .224 with an estimated reliability of .726. Therefore, data suggested that only 22.4% of the variance in MLS scores is between-classes and the remaining 77.6% is at the student-level. Due to the significant variance in MLS scores, X2(62.756, p<.0001), teachers' TAB scores were added as a level-2 predictor. The regression coefficient was non-significant (p>.05). Therefore, the teachers' self-reported use of affective behaviors was not a significant predictor of students' motivation to learn science.

The structure of the Hospital Anxiety and Depression Scale in four cohorts of community-based, healthy older people: the HALCyon program.

PubMed

Gale, Catharine R; Allerhand, Michael; Sayer, Avan Aihie; Cooper, Cyrus; Dennison, Elaine M; Starr, John M; Ben-Shlomo, Yoav; Gallacher, John E; Kuh, Diana; Deary, Ian J

2010-06-01

The Hospital Anxiety and Depression Scale (HADS) is widely used but evaluation of its psychometric properties has produced equivocal results. Little is known about its structure in non-clinical samples of older people. We used data from four cohorts in the HALCyon collaborative research program into healthy aging: the Caerphilly Prospective Study, the Hertfordshire Ageing Study, the Hertfordshire Cohort Study, and the Lothian Birth Cohort 1921. We used exploratory factor analysis and confirmatory factor analysis with multi-group comparisons to establish the structure of the HADS and test for factorial invariance between samples. Exploratory factor analysis showed a bi-dimensional structure (anxiety and depression) of the scale in men and women in each cohort. We tested a hypothesized three-factor model but high correlations between two of the factors made a two-factor model more psychologically plausible. Multi-group confirmatory factor analysis revealed that the sizes of the respective item loadings on the two factors were effectively identical in men and women from the same cohort. There was more variation between cohorts, particularly those from different parts of the U.K. and in whom the HADS was administered differently. Differences in social-class distribution accounted for part of this variation. Scoring the HADS as two subscales of anxiety and depression is appropriate in non-clinical populations of older men and women. However, there were differences between cohorts in the way that individual items were linked with the constructs of anxiety and depression, perhaps due to differences in sociocultural factors and/or in the administration of the scale.

Parallel Analysis with Unidimensional Binary Data

ERIC Educational Resources Information Center

Weng, Li-Jen; Cheng, Chung-Ping

2005-01-01

The present simulation investigated the performance of parallel analysis for unidimensional binary data. Single-factor models with 8 and 20 indicators were examined, and sample size (50, 100, 200, 500, and 1,000), factor loading (.45, .70, and .90), response ratio on two categories (50/50, 60/40, 70/30, 80/20, and 90/10), and types of correlation…

Comparing of Cox model and parametric models in analysis of effective factors on event time of neuropathy in patients with type 2 diabetes.

PubMed

Kargarian-Marvasti, Sadegh; Rimaz, Shahnaz; Abolghasemi, Jamileh; Heydari, Iraj

2017-01-01

Cox proportional hazard model is the most common method for analyzing the effects of several variables on survival time. However, under certain circumstances, parametric models give more precise estimates to analyze survival data than Cox. The purpose of this study was to investigate the comparative performance of Cox and parametric models in a survival analysis of factors affecting the event time of neuropathy in patients with type 2 diabetes. This study included 371 patients with type 2 diabetes without neuropathy who were registered at Fereydunshahr diabetes clinic. Subjects were followed up for the development of neuropathy between 2006 to March 2016. To investigate the factors influencing the event time of neuropathy, significant variables in univariate model ( P < 0.20) were entered into the multivariate Cox and parametric models ( P < 0.05). In addition, Akaike information criterion (AIC) and area under ROC curves were used to evaluate the relative goodness of fitted model and the efficiency of each procedure, respectively. Statistical computing was performed using R software version 3.2.3 (UNIX platforms, Windows and MacOS). Using Kaplan-Meier, survival time of neuropathy was computed 76.6 ± 5 months after initial diagnosis of diabetes. After multivariate analysis of Cox and parametric models, ethnicity, high-density lipoprotein and family history of diabetes were identified as predictors of event time of neuropathy ( P < 0.05). According to AIC, "log-normal" model with the lowest Akaike's was the best-fitted model among Cox and parametric models. According to the results of comparison of survival receiver operating characteristics curves, log-normal model was considered as the most efficient and fitted model.

Measuring engagement in nurses: the psychometric properties of the Persian version of Utrecht Work Engagement Scale

PubMed Central

Torabinia, Mansour; Mahmoudi, Sara; Dolatshahi, Mojtaba; Abyaz, Mohamad Reza

2017-01-01

Background: Considering the overall tendency in psychology, researchers in the field of work and organizational psychology have become progressively interested in employees’ effective and optimistic experiments at work such as work engagement. This study was conducted to investigate 2 main purposes: assessing the psychometric properties of the Utrecht Work Engagement Scale, and finding any association between work engagement and burnout in nurses. Methods: The present methodological study was conducted in 2015 and included 248 females and 34 males with 6 months to 30 years of job experience. After the translation process, face and content validity were calculated by qualitative and quantitative methods. Moreover, content validation ratio, scale-level content validity index and item-level content validity index were measured for this scale. Construct validity was determined by factor analysis. Moreover, internal consistency and stability reliability were assessed. Factor analysis, test-retest, Cronbach’s alpha, and association analysis were used as statistical methods. Results: Face and content validity were acceptable. Exploratory factor analysis suggested a new 3- factor model. In this new model, some items from the construct model of the original version were dislocated with the same 17 items. The new model was confirmed by divergent Copenhagen Burnout Inventory as the Persian version of UWES. Internal consistency reliability for the total scale and the subscales was 0.76 to 0.89. Results from Pearson correlation test indicated a high degree of test-retest reliability (r = 0. 89). ICC was also 0.91. Engagement was negatively related to burnout and overtime per month, whereas it was positively related with age and job experiment. Conclusion: The Persian 3– factor model of Utrecht Work Engagement Scale is a valid and reliable instrument to measure work engagement in Iranian nurses as well as in other medical professionals. PMID:28955665

Soccer Players Cultural Capital and Its Impact on Migration

PubMed Central

Leskošek, Bojan; Vodičar, Janez; Topič, Mojca Doupona

2016-01-01

Abstract The purpose of this study was to identify factors that constituted the cultural capital among soccer players. We assumed that in the increasingly globalized world of professional soccer, a player’s success would often depend on migrating and adjusting to life in other countries. Willingness to migrate and successful adjustment are tied to player’s previous attitudes and/or behaviours (habitus), significant support from others, including family members, and previous experiences and success in sports and education. Our hypothesised model of the cultural capital was based on the Pierre Bourdieu’s theoretical framework. It consisted of 26 variables related to three sets of factors: soccer experiences, a family context and support, and educational achievements of the players and their parents. The model was tested using a sample of 79 current soccer coaches who also had been players at the elite level. A factor analysis was used to empirically verify the content of the hypothetical model of the soccer players’ cultural capital. Nine latent factors were extracted and together, they accounted for 55.01% of the total model variance. Individual factors obtained showed a sufficient level of substantial connection. The Cronbach’s alpha value of 0.77 confirmed the internal consistency of the operationalised variables in the hypothetical model. In addition, the impact of these aforementioned life dimensions on the migration of soccer players was studied. The results of the binary logistic regression analysis showed that the first factor of the hypothetical model (F1) had 2.2 times and the second factor (F8) had 3.9 times higher odds for migration abroad. Sociocultural findings using this new assessment approach could help create better “success conditions” in the talent development of young players. PMID:28031770

Measuring Global Physical Health in Children with Cerebral Palsy: Illustration of a Multidimensional Bi-factor Model and Computerized Adaptive Testing

PubMed Central

Haley, Stephen M.; Ni, Pengsheng; Dumas, Helene M.; Fragala-Pinkham, Maria A.; Hambleton, Ronald K.; Montpetit, Kathleen; Bilodeau, Nathalie; Gorton, George E.; Watson, Kyle; Tucker, Carole A

2009-01-01

Purpose The purpose of this study was to apply a bi-factor model for the determination of test dimensionality and a multidimensional CAT using computer simulations of real data for the assessment of a new global physical health measure for children with cerebral palsy (CP). Methods Parent respondents of 306 children with cerebral palsy were recruited from four pediatric rehabilitation hospitals and outpatient clinics. We compared confirmatory factor analysis results across four models: (1) one-factor unidimensional; (2) two-factor multidimensional (MIRT); (3) bi-factor MIRT with fixed slopes; and (4) bi-factor MIRT with varied slopes. We tested whether the general and content (fatigue and pain) person score estimates could discriminate across severity and types of CP, and whether score estimates from a simulated CAT were similar to estimates based on the total item bank, and whether they correlated as expected with external measures. Results Confirmatory factor analysis suggested separate pain and fatigue sub-factors; all 37 items were retained in the analyses. From the bi-factor MIRT model with fixed slopes, the full item bank scores discriminated across levels of severity and types of CP, and compared favorably to external instruments. CAT scores based on 10- and 15-item versions accurately captured the global physical health scores. Conclusions The bi-factor MIRT CAT application, especially the 10- and 15-item version, yielded accurate global physical health scores that discriminated across known severity groups and types of CP, and correlated as expected with concurrent measures. The CATs have potential for collecting complex data on the physical health of children with CP in an efficient manner. PMID:19221892

Rapid Genetic Analysis of Epithelial-Mesenchymal Signaling During Hair Regeneration

PubMed Central

Zhen, Hanson H.; Oro, Anthony E.

2013-01-01

Hair follicle morphogenesis, a complex process requiring interaction between epithelia-derived keratinocytes and the underlying mesenchyme, is an attractive model system to study organ development and tissue-specific signaling. Although hair follicle development is genetically tractable, fast and reproducible analysis of factors essential for this process remains a challenge. Here we describe a procedure to generate targeted overexpression or shRNA-mediated knockdown of factors using lentivirus in a tissue-specific manner. Using a modified version of a hair regeneration model 5, 6, 11, we can achieve robust gain- or loss-of-function analysis in primary mouse keratinocytes or dermal cells to facilitate study of epithelial-mesenchymal signaling pathways that lead to hair follicle morphogenesis. We describe how to isolate fresh primary mouse keratinocytes and dermal cells, which contain dermal papilla cells and their precursors, deliver lentivirus containing either shRNA or cDNA to one of the cell populations, and combine the cells to generate fully formed hair follicles on the backs of nude mice. This approach allows analysis of tissue-specific factors required to generate hair follicles within three weeks and provides a fast and convenient companion to existing genetic models. PMID:23486463

A new framework for comprehensive, robust, and efficient global sensitivity analysis: 1. Theory

NASA Astrophysics Data System (ADS)

Razavi, Saman; Gupta, Hoshin V.

2016-01-01

Computer simulation models are continually growing in complexity with increasingly more factors to be identified. Sensitivity Analysis (SA) provides an essential means for understanding the role and importance of these factors in producing model responses. However, conventional approaches to SA suffer from (1) an ambiguous characterization of sensitivity, and (2) poor computational efficiency, particularly as the problem dimension grows. Here, we present a new and general sensitivity analysis framework (called VARS), based on an analogy to "variogram analysis," that provides an intuitive and comprehensive characterization of sensitivity across the full spectrum of scales in the factor space. We prove, theoretically, that Morris (derivative-based) and Sobol (variance-based) methods and their extensions are special cases of VARS, and that their SA indices can be computed as by-products of the VARS framework. Synthetic functions that resemble actual model response surfaces are used to illustrate the concepts, and show VARS to be as much as two orders of magnitude more computationally efficient than the state-of-the-art Sobol approach. In a companion paper, we propose a practical implementation strategy, and demonstrate the effectiveness, efficiency, and reliability (robustness) of the VARS framework on real-data case studies.

Effect of practical training on the learning motivation profile of Japanese pharmacy students using structural equation modeling

PubMed Central

2017-01-01

Purpose To establish a model of Japanese pharmacy students’ learning motivation profile and investigate the effects of pharmaceutical practical training programs on their learning motivation. Methods The Science Motivation Questionnaire II was administered to pharmacy students in their 4th (before practical training), 5th (before practical training at clinical sites), and 6th (after all practical training) years of study at Josai International University in April, 2016. Factor analysis and multiple-group structural equation modeling were conducted for data analysis. Results A total of 165 students participated. The learning motivation profile was modeled with 4 factors (intrinsic, career, self-determination, and grade motivation), and the most effective learning motivation was grade motivation. In the multiple-group analysis, the fit of the model with the data was acceptable, and the estimated mean value of the factor of ‘self-determination’ in the learning motivation profile increased after the practical training programs (P= 0.048, Cohen’s d= 0.43). Conclusion Practical training programs in a 6-year course were effective for increasing learning motivation, based on ‘self-determination’ among Japanese pharmacy students. The results suggest that practical training programs are meaningful not only for providing clinical experience but also for raising learning motivation. PMID:28167812

[Methods and Applications to estimate the conversion factor of Resource-Based Relative Value Scale for nurse-midwife's delivery service in the national health insurance].

PubMed

Kim, Jinhyun; Jung, Yoomi

2009-08-01

This paper analyzed alternative methods of calculating the conversion factor for nurse-midwife's delivery services in the national health insurance and estimated the optimal reimbursement level for the services. A cost accounting model and Sustainable Growth Rate (SGR) model were developed to estimate the conversion factor of Resource-Based Relative Value Scale (RBRVS) for nurse-midwife's services, depending on the scope of revenue considered in financial analysis. The data and sources from the government and the financial statements from nurse-midwife clinics were used in analysis. The cost accounting model and SGR model showed a 17.6-37.9% increase and 19.0-23.6% increase, respectively, in nurse-midwife fee for delivery services in the national health insurance. The SGR model measured an overall trend of medical expenditures rather than an individual financial status of nurse-midwife clinics, and the cost analysis properly estimated the level of reimbursement for nurse-midwife's services. Normal vaginal delivery in nurse-midwife clinics is considered cost-effective in terms of insurance financing. Upon a declining share of health expenditures on midwife clinics, designing a reimbursement strategy for midwife's services could be an opportunity as well as a challenge when it comes to efficient resource allocation.

Factorial Validity of the Decisional Involvement Scale as a Measure of Content and Context of Nursing Practice.

PubMed

Yurek, Leo A; Havens, Donna S; Hays, Spencer; Hughes, Linda C

2015-10-01

Decisional involvement is widely recognized as an essential component of a professional nursing practice environment. In recent years, researchers have added to the conceptualization of nurses' role in decision-making to differentiate between the content and context of nursing practice. Yet, instruments that clearly distinguish between these two dimensions of practice are lacking. The purpose of this study was to examine the factorial validity of the Decisional Involvement Scale (DIS) as a measure of both the content and context of nursing practice. This secondary analysis was conducted using data from a longitudinal action research project to improve the quality of nursing practice and patient care in six hospitals (N = 1,034) in medically underserved counties of Pennsylvania. A cross-sectional analysis of baseline data from the parent study was used to compare the factor structure of two models (one nested within the other) using confirmatory factor analysis. Although a comparison of the two models indicated that the addition of second-order factors for the content and context of nursing practice improved model fit, neither model provided optimal fit to the data. Additional model-generating research is needed to develop the DIS as a valid measure of decisional involvement for both the content and context of nursing practice. © 2015 Wiley Periodicals, Inc.

Numerical Modeling of Unsteady Thermofluid Dynamics in Cryogenic Systems

NASA Technical Reports Server (NTRS)

Majumdar, Alok

2003-01-01

A finite volume based network analysis procedure has been applied to model unsteady flow without and with heat transfer. Liquid has been modeled as compressible fluid where the compressibility factor is computed from the equation of state for a real fluid. The modeling approach recognizes that the pressure oscillation is linked with the variation of the compressibility factor; therefore, the speed of sound does not explicitly appear in the governing equations. The numerical results of chilldown process also suggest that the flow and heat transfer are strongly coupled. This is evident by observing that the mass flow rate during 90-second chilldown process increases by factor of ten.

A structural equation modelling approach examining the pathways between safety climate, behaviour performance and workplace slipping

PubMed Central

Swedler, David I; Verma, Santosh K; Huang, Yueng-Hsiang; Lombardi, David A; Chang, Wen-Ruey; Brennan, Melayne; Courtney, Theodore K

2015-01-01

Objective Safety climate has previously been associated with increasing safe workplace behaviours and decreasing occupational injuries. This study seeks to understand the structural relationship between employees’ perceptions of safety climate, performing a safety behaviour (ie, wearing slip-resistant shoes) and risk of slipping in the setting of limited-service restaurants. Methods At baseline, we surveyed 349 employees at 30 restaurants for their perceptions of their safety training and management commitment to safety as well as demographic data. Safety performance was identified as wearing slip-resistant shoes, as measured by direct observation by the study team. We then prospectively collected participants’ hours worked and number of slips weekly for the next 12 weeks. Using a confirmatory factor analysis, we modelled safety climate as a higher order factor composed of previously identified training and management commitment factors. Results The 349 study participants experienced 1075 slips during the 12-week follow-up. Confirmatory factor analysis supported modelling safety climate as a higher order factor composed of safety training and management commitment. In a structural equation model, safety climate indirectly affected prospective risk of slipping through safety performance, but no direct relationship between safety climate and slips was evident. Conclusions Results suggest that safety climate can reduce workplace slips through performance of a safety behaviour as well as suggesting a potential causal mechanism through which safety climate can reduce workplace injuries. Safety climate can be modelled as a higher order factor composed of safety training and management commitment. PMID:25710968

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.

Personal Attitudes or Structural Factors? A Contextual Analysis of Breastfeeding Duration

ERIC Educational Resources Information Center

McKinley, Nita Mary; Hyde, Janet Shibley

2004-01-01

A personal attitudes model (i.e., infant feeding choices are based on personal attitudes primarily) and a structural factors model (i.e., feeding choices are shaped by the structural contexts of women's lives, as much as personal attitudes) of women's breastfeeding behavior were tested by surveying a longitudinal sample of 548 mostly European…

Modeling Differentiation of Cognitive Abilities within the Higher-Order Factor Model Using Moderated Factor Analysis

ERIC Educational Resources Information Center

Molenaar, Dylan; Dolan, Conor V.; Wicherts, Jelte M.; van der Maas, Han L. J.

2010-01-01

The general differentiation hypothesis states that the strength of the correlations among a set of IQ subtests varies with a given variable. Instances of the general differentiation hypothesis that have been considered in the literature include age and ability differentiation. Traditionally, the differentiation effect is attributed to the varying…

Using Structural Equation Modeling to Validate Online Game Players' Motivations Relative to Self-Concept and Life Adaptation

ERIC Educational Resources Information Center

Yang, Shu Ching; Huang, Chiao Ling

2013-01-01

This study aimed to validate a systematic instrument to measure online players' motivations for playing online games (MPOG) and examine how the interplay of differential motivations impacts young gamers' self-concept and life adaptation. Confirmatory factor analysis determined that a hierarchical model with a two-factor structure of…

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