The consequences of ignoring measurement invariance for path coefficients in structural equation models

PubMed Central

Guenole, Nigel; Brown, Anna

2014-01-01

We report a Monte Carlo study examining the effects of two strategies for handling measurement non-invariance – modeling and ignoring non-invariant items – on structural regression coefficients between latent variables measured with item response theory models for categorical indicators. These strategies were examined across four levels and three types of non-invariance – non-invariant loadings, non-invariant thresholds, and combined non-invariance on loadings and thresholds – in simple, partial, mediated and moderated regression models where the non-invariant latent variable occupied predictor, mediator, and criterion positions in the structural regression models. When non-invariance is ignored in the latent predictor, the focal group regression parameters are biased in the opposite direction to the difference in loadings and thresholds relative to the referent group (i.e., lower loadings and thresholds for the focal group lead to overestimated regression parameters). With criterion non-invariance, the focal group regression parameters are biased in the same direction as the difference in loadings and thresholds relative to the referent group. While unacceptable levels of parameter bias were confined to the focal group, bias occurred at considerably lower levels of ignored non-invariance than was previously recognized in referent and focal groups. PMID:25278911

Disgust proneness predicts obsessive-compulsive disorder symptom severity in a clinical sample of youth: Distinctions from negative affect.

PubMed

Olatunji, Bunmi O; Ebesutani, Chad; Kim, Jingu; Riemann, Bradley C; Jacobi, David M

2017-04-15

Although studies have linked disgust proneness to the etiology and maintenance of obsessive-compulsive disorder (OCD) in adults, there remains a paucity of research examining the specificity of this association among youth. The present study employed structural equation modeling to examine the association between disgust proneness, negative affect, and OCD symptom severity in a clinical sample of youth admitted to a residential treatment facility (N =471). Results indicate that disgust proneness and negative affect latent factors independently predicted an OCD symptom severity latent factor. However, when both variables were modeled as predictors simultaneously, latent disgust proneness remained significantly associated with OCD symptom severity, whereas the association between latent negative affect and OCD symptom severity became nonsignificant. Tests of mediation converged in support of disgust proneness as a significant intervening variable between negative affect and OCD symptom severity. Subsequent analysis showed that the path from disgust proneness to OCD symptom severity in the structural model was significantly stronger among those without a primary diagnosis of OCD compared to those with a primary diagnosis of OCD. Given the cross-sectional design, the causal inferences that can be made are limited. The present study is also limited by the exclusive reliance on self-report measures. Disgust proneness may play a uniquely important role in OCD among youth. Copyright © 2017 Elsevier B.V. All rights reserved.

On the explaining-away phenomenon in multivariate latent variable models.

PubMed

van Rijn, Peter; Rijmen, Frank

2015-02-01

Many probabilistic models for psychological and educational measurements contain latent variables. Well-known examples are factor analysis, item response theory, and latent class model families. We discuss what is referred to as the 'explaining-away' phenomenon in the context of such latent variable models. This phenomenon can occur when multiple latent variables are related to the same observed variable, and can elicit seemingly counterintuitive conditional dependencies between latent variables given observed variables. We illustrate the implications of explaining away for a number of well-known latent variable models by using both theoretical and real data examples. © 2014 The British Psychological Society.

Paths to tobacco abstinence: A repeated-measures latent class analysis.

PubMed

McCarthy, Danielle E; Ebssa, Lemma; Witkiewitz, Katie; Shiffman, Saul

2015-08-01

Knowledge of smoking change processes may be enhanced by identifying pathways to stable abstinence. We sought to identify latent classes of smokers based on their day-to-day smoking status in the first weeks of a cessation attempt. We examined treatment effects on class membership and compared classes on baseline individual differences and 6-month abstinence rates. In this secondary analysis of a double-blind randomized placebo-controlled clinical trial (N = 1,433) of 5 smoking cessation pharmacotherapies (nicotine patch, nicotine lozenge, bupropion SR, patch and lozenge, or bupropion SR and lozenge), we conducted repeated-measures latent class analysis of daily smoking status (any smoking vs. none) for the first 27 days of a quit attempt. Treatment and covariate relations with latent class membership were examined. Distal outcome analysis compared confirmed 6-month abstinence rates among the latent classes. A 5-class solution was selected. Three-quarters of smokers were in stable smoking or abstinent classes, but 25% were in classes with unstable abstinence probabilities over time. Active treatment (compared to placebo), and particularly the patch and lozenge combination, promoted early quitting. Latent classes differed in 6-month abstinence rates and on several baseline variables, including nicotine dependence, quitting history, self-efficacy, sleep disturbance, and minority status. Repeated-measures latent class analysis identified latent classes of smoking change patterns affected by treatment, related to known risk factors, and predictive of distal outcomes. Tracking behavior early in a change attempt may identify prognostic patterns of change and facilitate adaptive treatment planning. (c) 2015 APA, all rights reserved).

Consistent Partial Least Squares Path Modeling via Regularization

PubMed Central

Jung, Sunho; Park, JaeHong

2018-01-01

Partial least squares (PLS) path modeling is a component-based structural equation modeling that has been adopted in social and psychological research due to its data-analytic capability and flexibility. A recent methodological advance is consistent PLS (PLSc), designed to produce consistent estimates of path coefficients in structural models involving common factors. In practice, however, PLSc may frequently encounter multicollinearity in part because it takes a strategy of estimating path coefficients based on consistent correlations among independent latent variables. PLSc has yet no remedy for this multicollinearity problem, which can cause loss of statistical power and accuracy in parameter estimation. Thus, a ridge type of regularization is incorporated into PLSc, creating a new technique called regularized PLSc. A comprehensive simulation study is conducted to evaluate the performance of regularized PLSc as compared to its non-regularized counterpart in terms of power and accuracy. The results show that our regularized PLSc is recommended for use when serious multicollinearity is present. PMID:29515491

Intra-Sensor Variability Study of two BLS 900 Scintillometers

NASA Astrophysics Data System (ADS)

Thiem, Christina; Mauder, Matthias; Chwala, Christian; Bernhardt, Matthias; Kunstmann, Harald; Schulz, Karsten

2017-04-01

The latent heat flux is an important validation parameter for satellite measurements and a wide variety of hydrological and meteorological numerical models. Scintillometers can provide references for such validations due to their ability to spatially integrate turbulent fluxes. Large-aperture near-infrared scintillometers are capable of determining spatial averages of the structure parameter of temperature and the sensible heat flux over path lengths up to 5 km. One way to derive both sensible and latent heat flux is to use a combined optical and microwave scintillometer system. With only an optical scintillometer and additional measurements of ground heat flux and net radiation, the latent heat flux can be calculated from the residual of the energy balance. Studies have shown, however, that in certain cases measurements from the same types of scintillometers differ due to minute differences in construction. In order to prove the robustness of the measurements of two near-infrared scintillometers for future studies, we compared their observations and validated them by comparison to the sensible heat flux derived from an eddy covariance system. In this study two boundary layer scintillometers (BLS; BLS900, Scintec, Rottenburg, Germany) were installed in a central European valley as part of the TERENO preAlpine observatory during the years 2013 and 2015. An independent measurement of the sensible and latent heat flux was obtained from a permanent eddy covariance system installed in the vicinity of the scintillometer path. The structure parameter of the refractive index and average sensible heat fluxes of both BLS units were compared with each other. In general, the BLS structure parameters correlated very well and the high correlation between the BLS-derived sensible heat fluxes and the eddy covariance-derived sensible heat fluxes encouraged further application of these scintillometers in separate experiments.

Why managers should care about fairness: the effects of aggregate justice perceptions on organizational outcomes.

PubMed

Simons, Tony; Roberson, Quinetta

2003-06-01

This work examines the aggregation of justice perceptions to the departmental level and the business-unit level, the impact of these aggregate perceptions on business-unit-level outcomes, and the usefulness of the distinction between procedural and interpersonal justice at different levels of analysis. Latent variables analyses of individual-level and department-level data from 4,539 employees in 783 departments at 97 hotel properties showed that the 2 justice types exercise unique paths of impact on employees' organizational commitment and thus on turnover intentions and discretionary service behavior. Business-unit-level analyses further demonstrate paths of association between aggregate justice perceptions, aggregate commitment levels, and the business-unit-level outcomes of employee turnover rates and customer satisfaction ratings.

Estimating and Interpreting Latent Variable Interactions: A Tutorial for Applying the Latent Moderated Structural Equations Method

ERIC Educational Resources Information Center

Maslowsky, Julie; Jager, Justin; Hemken, Douglas

2015-01-01

Latent variables are common in psychological research. Research questions involving the interaction of two variables are likewise quite common. Methods for estimating and interpreting interactions between latent variables within a structural equation modeling framework have recently become available. The latent moderated structural equations (LMS)…

Epstein-Barr Virus: The Path from Latent to Productive Infection.

PubMed

Chiu, Ya-Fang; Sugden, Bill

2016-09-29

The intrinsic properties of different viruses have driven their study. For example, the capacity for efficient productive infection of cultured cells by herpes simplex virus 1 has made it a paradigm for this mode of infection for herpesviruses in general. Epstein-Barr virus, another herpesvirus, has two properties that have driven its study: It causes human cancers, and it exhibits a tractable transition from its latent to its productive cycle in cell culture. Here, we review our understanding of the path Epstein-Barr virus follows to move from a latent infection to and through its productive cycle. We use information from human infections to provide a framework for describing studies in cell culture and, where possible, the molecular resolutions from these studies. We also pose questions whose answers we think are pivotal to understanding this path, and we provide answers where we can.

Psychological Processes Mediate the Impact of Familial Risk, Social Circumstances and Life Events on Mental Health

PubMed Central

Kinderman, Peter; Schwannauer, Matthias; Pontin, Eleanor; Tai, Sara

2013-01-01

Background Despite widespread acceptance of the ‘biopsychosocial model’, the aetiology of mental health problems has provoked debate amongst researchers and practitioners for decades. The role of psychological factors in the development of mental health problems remains particularly contentious, and to date there has not been a large enough dataset to conduct the necessary multivariate analysis of whether psychological factors influence, or are influenced by, mental health. This study reports on the first empirical, multivariate, test of the relationships between the key elements of the biospychosocial model of mental ill-health. Methods and Findings Participants were 32,827 (age 18–85 years) self-selected respondents from the general population who completed an open-access online battery of questionnaires hosted by the BBC. An initial confirmatory factor analysis was performed to assess the adequacy of the proposed factor structure and the relationships between latent and measured variables. The predictive path model was then tested whereby the latent variables of psychological processes were positioned as mediating between the causal latent variables (biological, social and circumstantial) and the outcome latent variables of mental health problems and well-being. This revealed an excellent fit to the data, S-B χ2 (3199, N = 23,397) = 126654·8, p<·001; RCFI = ·97; RMSEA = ·04 (·038–·039). As hypothesised, a family history of mental health difficulties, social deprivation, and traumatic or abusive life-experiences all strongly predicted higher levels of anxiety and depression. However, these relationships were strongly mediated by psychological processes; specifically lack of adaptive coping, rumination and self-blame. Conclusion These results support a significant revision of the biopsychosocial model, as psychological processes determine the causal impact of biological, social, and circumstantial risk factors on mental health. This has clear implications for policy, education and clinical practice as psychological processes such as rumination and self-blame are amenable to evidence-based psychological therapies. PMID:24146890

Structural identifiability of cyclic graphical models of biological networks with latent variables.

PubMed

Wang, Yulin; Lu, Na; Miao, Hongyu

2016-06-13

Graphical models have long been used to describe biological networks for a variety of important tasks such as the determination of key biological parameters, and the structure of graphical model ultimately determines whether such unknown parameters can be unambiguously obtained from experimental observations (i.e., the identifiability problem). Limited by resources or technical capacities, complex biological networks are usually partially observed in experiment, which thus introduces latent variables into the corresponding graphical models. A number of previous studies have tackled the parameter identifiability problem for graphical models such as linear structural equation models (SEMs) with or without latent variables. However, the limited resolution and efficiency of existing approaches necessarily calls for further development of novel structural identifiability analysis algorithms. An efficient structural identifiability analysis algorithm is developed in this study for a broad range of network structures. The proposed method adopts the Wright's path coefficient method to generate identifiability equations in forms of symbolic polynomials, and then converts these symbolic equations to binary matrices (called identifiability matrix). Several matrix operations are introduced for identifiability matrix reduction with system equivalency maintained. Based on the reduced identifiability matrices, the structural identifiability of each parameter is determined. A number of benchmark models are used to verify the validity of the proposed approach. Finally, the network module for influenza A virus replication is employed as a real example to illustrate the application of the proposed approach in practice. The proposed approach can deal with cyclic networks with latent variables. The key advantage is that it intentionally avoids symbolic computation and is thus highly efficient. Also, this method is capable of determining the identifiability of each single parameter and is thus of higher resolution in comparison with many existing approaches. Overall, this study provides a basis for systematic examination and refinement of graphical models of biological networks from the identifiability point of view, and it has a significant potential to be extended to more complex network structures or high-dimensional systems.

Psychological processes mediate the impact of familial risk, social circumstances and life events on mental health.

PubMed

Kinderman, Peter; Schwannauer, Matthias; Pontin, Eleanor; Tai, Sara

2013-01-01

Despite widespread acceptance of the 'biopsychosocial model', the aetiology of mental health problems has provoked debate amongst researchers and practitioners for decades. The role of psychological factors in the development of mental health problems remains particularly contentious, and to date there has not been a large enough dataset to conduct the necessary multivariate analysis of whether psychological factors influence, or are influenced by, mental health. This study reports on the first empirical, multivariate, test of the relationships between the key elements of the biospychosocial model of mental ill-health. Participants were 32,827 (age 18-85 years) self-selected respondents from the general population who completed an open-access online battery of questionnaires hosted by the BBC. An initial confirmatory factor analysis was performed to assess the adequacy of the proposed factor structure and the relationships between latent and measured variables. The predictive path model was then tested whereby the latent variables of psychological processes were positioned as mediating between the causal latent variables (biological, social and circumstantial) and the outcome latent variables of mental health problems and well-being. This revealed an excellent fit to the data, S-B χ(2) (3199, N = 23,397) = 126654.8, p<.001; RCFI = .97; RMSEA = .04 (.038-.039). As hypothesised, a family history of mental health difficulties, social deprivation, and traumatic or abusive life-experiences all strongly predicted higher levels of anxiety and depression. However, these relationships were strongly mediated by psychological processes; specifically lack of adaptive coping, rumination and self-blame. These results support a significant revision of the biopsychosocial model, as psychological processes determine the causal impact of biological, social, and circumstantial risk factors on mental health. This has clear implications for policy, education and clinical practice as psychological processes such as rumination and self-blame are amenable to evidence-based psychological therapies.

Bayesian Adaptive Lasso for Ordinal Regression with Latent Variables

ERIC Educational Resources Information Center

Feng, Xiang-Nan; Wu, Hao-Tian; Song, Xin-Yuan

2017-01-01

We consider an ordinal regression model with latent variables to investigate the effects of observable and latent explanatory variables on the ordinal responses of interest. Each latent variable is characterized by correlated observed variables through a confirmatory factor analysis model. We develop a Bayesian adaptive lasso procedure to conduct…

a Latent Variable Path Analysis Model of Secondary Physics Enrollments in New York State.

NASA Astrophysics Data System (ADS)

Sobolewski, Stanley John

The Percentage of Enrollment in Physics (PEP) at the secondary level nationally has been approximately 20% for the past few decades. For a more scientifically literate citizenry as well as specialists to continue scientific research and development, it is desirable that more students enroll in physics. Some of the predictor variables for physics enrollment and physics achievement that have been identified previously includes a community's socioeconomic status, the availability of physics, the sex of the student, the curriculum, as well as teacher and student data. This study isolated and identified predictor variables for PEP of secondary schools in New York. Data gathered by the State Education Department for the 1990-1991 school year was used. The source of this data included surveys completed by teachers and administrators on student characteristics and school facilities. A data analysis similar to that done by Bryant (1974) was conducted to determine if the relationships between a set of predictor variables related to physics enrollment had changed in the past 20 years. Variables which were isolated included: community, facilities, teacher experience, number of type of science courses, school size and school science facilities. When these variables were isolated, latent variable path diagrams were proposed and verified by the Linear Structural Relations computer modeling program (LISREL). These diagrams differed from those developed by Bryant in that there were more manifest variables used which included achievement scores in the form of Regents exam results. Two criterion variables were used, percentage of students enrolled in physics (PEP) and percent of students enrolled passing the Regents physics exam (PPP). The first model treated school and community level variables as exogenous while the second model treated only the community level variables as exogenous. The goodness of fit indices for the models was 0.77 for the first model and 0.83 for the second model. No dramatic differences were found between the relationship of predictor variables to physics enrollment in 1972 and 1991. New models indicated that smaller school size, enrollment in previous science and math courses and other school variables were more related to high enrollment rather than achievement. Exogenous variables such as community size were related to achievement. It was shown that achievement and enrollment were related to a different set of predictor variables.

Avoiding and Correcting Bias in Score-Based Latent Variable Regression with Discrete Manifest Items

ERIC Educational Resources Information Center

Lu, Irene R. R.; Thomas, D. Roland

2008-01-01

This article considers models involving a single structural equation with latent explanatory and/or latent dependent variables where discrete items are used to measure the latent variables. Our primary focus is the use of scores as proxies for the latent variables and carrying out ordinary least squares (OLS) regression on such scores to estimate…

Bayesian Semiparametric Structural Equation Models with Latent Variables

ERIC Educational Resources Information Center

Yang, Mingan; Dunson, David B.

2010-01-01

Structural equation models (SEMs) with latent variables are widely useful for sparse covariance structure modeling and for inferring relationships among latent variables. Bayesian SEMs are appealing in allowing for the incorporation of prior information and in providing exact posterior distributions of unknowns, including the latent variables. In…

The Role of Individual and Social Variables in Predicting Body Dissatisfaction and Eating Disorder Symptoms among Iranian Adolescent Girls: An Expanding of the Tripartite Influence Mode.

PubMed

Shahyad, Shima; Pakdaman, Shahla; Shokri, Omid; Saadat, Seyed Hassan

2018-01-12

The aim of the present study was to examine the causal relationships between psychological and social factors, being independent variables and body image dissatisfaction plus symptoms of eating disorders as dependent variables through the mediation of social comparison and thin-ideal internalization. To conduct the study, 477 high-school students from Tehran were recruited by method of cluster sampling. Next, they filled out Rosenberg Self-esteem Scale (RSES), Physical Appearance Comparison Scale (PACS), Self-Concept Clarity Scale (SCCS), Appearance Perfectionism Scale (APS), Eating Disorder Inventory (EDI), Multidimensional Body Self Relations Questionnaire (MBSRQ) and Sociocultural Attitudes towards Appearance Questionnaire (SATAQ-4). In the end, collected data were analyzed using structural equation modeling. Findings showed that the assumed model perfectly fitted the data after modification and as a result, all the path-coefficients of latent variables (except for the path between self-esteem and thin-ideal internalization) were statistically significant (p>0.05). Also, in this model, 75% of scores' distribution of body dissatisfaction was explained through psychological variables, socio-cultural variables, social comparison and internalization of the thin ideal. The results of the present study provid experimental basis for the confirmation of proposed causal model. The combination of psychological, social and cultural variables could efficiently predict body image dissatisfaction of young girls in Iran.

Defining a Family of Cognitive Diagnosis Models Using Log-Linear Models with Latent Variables

ERIC Educational Resources Information Center

Henson, Robert A.; Templin, Jonathan L.; Willse, John T.

2009-01-01

This paper uses log-linear models with latent variables (Hagenaars, in "Loglinear Models with Latent Variables," 1993) to define a family of cognitive diagnosis models. In doing so, the relationship between many common models is explicitly defined and discussed. In addition, because the log-linear model with latent variables is a general model for…

Unraveling interrelationships among psychopathology symptoms, cognitive domains and insight dimensions in chronic schizophrenia.

PubMed

Xavier, Rose Mary; Pan, Wei; Dungan, Jennifer R; Keefe, Richard S E; Vorderstrasse, Allison

2018-03-01

Insight in schizophrenia is long known to have a complex relationship with psychopathology symptoms and cognition. However, very few studies have examined models that explain these interrelationships. In a large sample derived from the NIMH Clinical Antipsychotic Trials of Intervention Effectiveness (CATIE) schizophrenia trial (N=1391), we interrogated these interrelationships for potential causal pathways using structural equation modeling. Using the NIMH consensus model, latent variables were constructed for psychopathology symptom dimensions, including positive, negative, disorganized, excited and depressed from the Positive and Negative Syndrome Scale (PANSS) items. Neurocognitive variables were created from five predefined domains of working memory, verbal memory, reasoning, vigilance and processing speed. Illness insight and treatment insight were tested using latent variables constructed from the Illness and Treatment Attitude Questionnaire (ITAQ). Disorganized symptoms had the strongest effect on insight. Illness insight mediated the relationship of positive, depressed, and disorganized symptoms with treatment insight. Neurocognition mediated the relationship between disorganized and treatment insight and depressed symptoms and treatment insight. There was no effect of negative symptoms on either illness insight or treatment insight. Taken together, our results indicate overlapping and unique relational paths for illness and treatment insight dimensions, which could suggest differences in causal mechanisms and potential interventions to improve insight. Copyright © 2017 Elsevier B.V. All rights reserved.

On the Interaction between Marine Boundary Layer Cellular Cloudiness and Surface Heat Fluxes

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

Kazil, J.; Feingold, G.; Wang, Hailong

2014-01-02

The interaction between marine boundary layer cellular cloudiness and surface uxes of sensible and latent heat is investigated. The investigation focuses on the non-precipitating closed-cell state and the precipitating open-cell state at low geostrophic wind speed. The Advanced Research WRF model is used to conduct cloud-system-resolving simulations with interactive surface fluxes of sensible heat, latent heat, and of sea salt aerosol, and with a detailed representation of the interaction between aerosol particles and clouds. The mechanisms responsible for the temporal evolution and spatial distribution of the surface heat fluxes in the closed- and open-cell state are investigated and explained. Itmore » is found that the horizontal spatial structure of the closed-cell state determines, by entrainment of dry free tropospheric air, the spatial distribution of surface air temperature and water vapor, and, to a lesser degree, of the surface sensible and latent heat flux. The synchronized dynamics of the the open-cell state drives oscillations in surface air temperature, water vapor, and in the surface fluxes of sensible and latent heat, and of sea salt aerosol. Open-cell cloud formation, cloud optical depth and liquid water path, and cloud and rain water path are identified as good predictors of the spatial distribution of surface air temperature and sensible heat flux, but not of surface water vapor and latent heat flux. It is shown that by enhancing the surface sensible heat flux, the open-cell state creates conditions by which it is maintained. While the open-cell state under consideration is not depleted in aerosol, and is insensitive to variations in sea-salt fluxes, it also enhances the sea-salt flux relative to the closed-cell state. In aerosol-depleted conditions, this enhancement may replenish the aerosol needed for cloud formation, and hence contribute to the perpetuation of the open-cell state as well. Spatial homogenization of the surface fluxes is found to have only a small effect on cloud properties in the investigated cases. This indicates that sub-grid scale spatial variability in the surface flux of sensible and latent heat and of sea salt aerosol may not be required in large scale and global models to describe marine boundary layer cellular cloudiness.« less

The Integration of Continuous and Discrete Latent Variable Models: Potential Problems and Promising Opportunities

ERIC Educational Resources Information Center

Bauer, Daniel J.; Curran, Patrick J.

2004-01-01

Structural equation mixture modeling (SEMM) integrates continuous and discrete latent variable models. Drawing on prior research on the relationships between continuous and discrete latent variable models, the authors identify 3 conditions that may lead to the estimation of spurious latent classes in SEMM: misspecification of the structural model,…

Latent mnemonic strengths are latent: a comment on Mickes, Wixted, and Wais (2007).

PubMed

Rouder, Jeffrey N; Pratte, Michael S; Morey, Richard D

2010-06-01

Mickes, Wixted, and Wais (2007) proposed a simple test of latent strength variability in recognition memory. They asked participants to rate their confidence using either a 20-point or a 99-point strength scale and plotted distributions of the resulting ratings. They found 25% more variability in ratings for studied than for new items, which they interpreted as providing evidence that latent mnemonic strength distributions are 25% more variable for studied than for new items. We show here that this conclusion is critically dependent on assumptions--so much so that these assumptions determine the conclusions. In fact, opposite conclusions, such that study does not affect the variability of latent strength, may be reached by making different but equally plausible assumptions. Because all measurements of mnemonic strength variability are critically dependent on untestable assumptions, all are arbitrary. Hence, there is no principled method for assessing the relative variability of latent mnemonic strength distributions.

Modeling Psychological Attributes in Psychology – An Epistemological Discussion: Network Analysis vs. Latent Variables

PubMed Central

Guyon, Hervé; Falissard, Bruno; Kop, Jean-Luc

2017-01-01

Network Analysis is considered as a new method that challenges Latent Variable models in inferring psychological attributes. With Network Analysis, psychological attributes are derived from a complex system of components without the need to call on any latent variables. But the ontological status of psychological attributes is not adequately defined with Network Analysis, because a psychological attribute is both a complex system and a property emerging from this complex system. The aim of this article is to reappraise the legitimacy of latent variable models by engaging in an ontological and epistemological discussion on psychological attributes. Psychological attributes relate to the mental equilibrium of individuals embedded in their social interactions, as robust attractors within complex dynamic processes with emergent properties, distinct from physical entities located in precise areas of the brain. Latent variables thus possess legitimacy, because the emergent properties can be conceptualized and analyzed on the sole basis of their manifestations, without exploring the upstream complex system. However, in opposition with the usual Latent Variable models, this article is in favor of the integration of a dynamic system of manifestations. Latent Variables models and Network Analysis thus appear as complementary approaches. New approaches combining Latent Network Models and Network Residuals are certainly a promising new way to infer psychological attributes, placing psychological attributes in an inter-subjective dynamic approach. Pragmatism-realism appears as the epistemological framework required if we are to use latent variables as representations of psychological attributes. PMID:28572780

A Latent Variable Approach to the Simple View of Reading

ERIC Educational Resources Information Center

Kershaw, Sarah; Schatschneider, Chris

2012-01-01

The present study utilized a latent variable modeling approach to examine the Simple View of Reading in a sample of students from 3rd, 7th, and 10th grades (N = 215, 188, and 180, respectively). Latent interaction modeling and other latent variable models were employed to investigate (a) the functional form of the relationship between decoding and…

Latent Transition Analysis with a Mixture Item Response Theory Measurement Model

ERIC Educational Resources Information Center

Cho, Sun-Joo; Cohen, Allan S.; Kim, Seock-Ho; Bottge, Brian

2010-01-01

A latent transition analysis (LTA) model was described with a mixture Rasch model (MRM) as the measurement model. Unlike the LTA, which was developed with a latent class measurement model, the LTA-MRM permits within-class variability on the latent variable, making it more useful for measuring treatment effects within latent classes. A simulation…

Person Re-Identification via Distance Metric Learning With Latent Variables.

PubMed

Sun, Chong; Wang, Dong; Lu, Huchuan

2017-01-01

In this paper, we propose an effective person re-identification method with latent variables, which represents a pedestrian as the mixture of a holistic model and a number of flexible models. Three types of latent variables are introduced to model uncertain factors in the re-identification problem, including vertical misalignments, horizontal misalignments and leg posture variations. The distance between two pedestrians can be determined by minimizing a given distance function with respect to latent variables, and then be used to conduct the re-identification task. In addition, we develop a latent metric learning method for learning the effective metric matrix, which can be solved via an iterative manner: once latent information is specified, the metric matrix can be obtained based on some typical metric learning methods; with the computed metric matrix, the latent variables can be determined by searching the state space exhaustively. Finally, extensive experiments are conducted on seven databases to evaluate the proposed method. The experimental results demonstrate that our method achieves better performance than other competing algorithms.

Adolescent cigarette smoking: health-related behavior or normative transgression?

PubMed

Turbin, M S; Jessor, R; Costa, F M

2000-09-01

Relations among measures of adolescent behavior were examined to determine whether cigarette smoking fits into a structure of problem behaviors-behaviors that involve normative transgression-or a structure of health-related behaviors, or both. In an ethnically and socioeconomically diverse sample of 1782 male and female high school adolescents, four first-order problem behavior latent variables-sexual intercourse experience, alcohol abuse, illicit drug use, and delinquency-were established and together were shown to reflect a second-order latent variable of problem behavior. Four first-order latent variables of health-related behaviors-unhealthy dietary habits, sedentary behavior, unsafe behavior, and poor dental hygiene-were also established and together were shown to reflect a second-order latent variable of health-compromising behavior. The structure of relations among those latent variables was modeled. Cigarette smoking had a significant and substantial loading only on the problem-behavior latent variable; its loading on the health-compromising behavior latent variable was essentially zero. Adolescent cigarette smoking relates strongly and directly to problem behaviors and only indirectly, if at all, to health-compromising behaviors. Interventions to prevent or reduce adolescent smoking should attend more to factors that influence problem behaviors.

A Framework for Multifaceted Evaluation of Student Models

ERIC Educational Resources Information Center

Huang, Yun; González-Brenes, José P.; Kumar, Rohit; Brusilovsky, Peter

2015-01-01

Latent variable models, such as the popular Knowledge Tracing method, are often used to enable adaptive tutoring systems to personalize education. However, finding optimal model parameters is usually a difficult non-convex optimization problem when considering latent variable models. Prior work has reported that latent variable models obtained…

The Latent Variable Approach as Applied to Transitive Reasoning

ERIC Educational Resources Information Center

Bouwmeester, Samantha; Vermunt, Jeroen K.; Sijtsma, Klaas

2012-01-01

We discuss the limitations of hypothesis testing using (quasi-) experiments in the study of cognitive development and suggest latent variable modeling as a viable alternative to experimentation. Latent variable models allow testing a theory as a whole, incorporating individual differences with respect to developmental processes or abilities in the…

Much Ado about Nothing--Or at Best, Very Little

ERIC Educational Resources Information Center

Widaman, Keith F.

2014-01-01

Latent variable structural equation modeling has become the analytic method of choice in many domains of research in psychology and allied social sciences. One important aspect of a latent variable model concerns the relations hypothesized to hold between latent variables and their indicators. The most common specification of structural equation…

A Composite Likelihood Inference in Latent Variable Models for Ordinal Longitudinal Responses

ERIC Educational Resources Information Center

Vasdekis, Vassilis G. S.; Cagnone, Silvia; Moustaki, Irini

2012-01-01

The paper proposes a composite likelihood estimation approach that uses bivariate instead of multivariate marginal probabilities for ordinal longitudinal responses using a latent variable model. The model considers time-dependent latent variables and item-specific random effects to be accountable for the interdependencies of the multivariate…

A Bayesian Semiparametric Latent Variable Model for Mixed Responses

ERIC Educational Resources Information Center

Fahrmeir, Ludwig; Raach, Alexander

2007-01-01

In this paper we introduce a latent variable model (LVM) for mixed ordinal and continuous responses, where covariate effects on the continuous latent variables are modelled through a flexible semiparametric Gaussian regression model. We extend existing LVMs with the usual linear covariate effects by including nonparametric components for nonlinear…

Latent structure modeling underlying theophylline tablet formulations using a Bayesian network based on a self-organizing map clustering.

PubMed

Yasuda, Akihito; Onuki, Yoshinori; Obata, Yasuko; Takayama, Kozo

2015-01-01

The "quality by design" concept in pharmaceutical formulation development requires the establishment of a science-based rationale and design space. In this article, we integrate thin-plate spline (TPS) interpolation, Kohonen's self-organizing map (SOM) and a Bayesian network (BN) to visualize the latent structure underlying causal factors and pharmaceutical responses. As a model pharmaceutical product, theophylline tablets were prepared using a standard formulation. We measured the tensile strength and disintegration time as response variables and the compressibility, cohesion and dispersibility of the pretableting blend as latent variables. We predicted these variables quantitatively using nonlinear TPS, generated a large amount of data on pretableting blends and tablets and clustered these data into several clusters using a SOM. Our results show that we are able to predict the experimental values of the latent and response variables with a high degree of accuracy and are able to classify the tablet data into several distinct clusters. In addition, to visualize the latent structure between the causal and latent factors and the response variables, we applied a BN method to the SOM clustering results. We found that despite having inserted latent variables between the causal factors and response variables, their relation is equivalent to the results for the SOM clustering, and thus we are able to explain the underlying latent structure. Consequently, this technique provides a better understanding of the relationships between causal factors and pharmaceutical responses in theophylline tablet formulation.

Latent variable models are network models.

PubMed

Molenaar, Peter C M

2010-06-01

Cramer et al. present an original and interesting network perspective on comorbidity and contrast this perspective with a more traditional interpretation of comorbidity in terms of latent variable theory. My commentary focuses on the relationship between the two perspectives; that is, it aims to qualify the presumed contrast between interpretations in terms of networks and latent variables.

Examining Parallelism of Sets of Psychometric Measures Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko; Patelis, Thanos; Marcoulides, George A.

2011-01-01

A latent variable modeling approach that can be used to examine whether several psychometric tests are parallel is discussed. The method consists of sequentially testing the properties of parallel measures via a corresponding relaxation of parameter constraints in a saturated model or an appropriately constructed latent variable model. The…

Predictive Inference Using Latent Variables with Covariates*

PubMed Central

Schofield, Lynne Steuerle; Junker, Brian; Taylor, Lowell J.; Black, Dan A.

2014-01-01

Plausible Values (PVs) are a standard multiple imputation tool for analysis of large education survey data that measures latent proficiency variables. When latent proficiency is the dependent variable, we reconsider the standard institutionally-generated PV methodology and find it applies with greater generality than shown previously. When latent proficiency is an independent variable, we show that the standard institutional PV methodology produces biased inference because the institutional conditioning model places restrictions on the form of the secondary analysts’ model. We offer an alternative approach that avoids these biases based on the mixed effects structural equations (MESE) model of Schofield (2008). PMID:25231627

Detecting Mixtures from Structural Model Differences Using Latent Variable Mixture Modeling: A Comparison of Relative Model Fit Statistics

ERIC Educational Resources Information Center

Henson, James M.; Reise, Steven P.; Kim, Kevin H.

2007-01-01

The accuracy of structural model parameter estimates in latent variable mixture modeling was explored with a 3 (sample size) [times] 3 (exogenous latent mean difference) [times] 3 (endogenous latent mean difference) [times] 3 (correlation between factors) [times] 3 (mixture proportions) factorial design. In addition, the efficacy of several…

Gene Variants Associated with Antisocial Behaviour: A Latent Variable Approach

ERIC Educational Resources Information Center

Bentley, Mary Jane; Lin, Haiqun; Fernandez, Thomas V.; Lee, Maria; Yrigollen, Carolyn M.; Pakstis, Andrew J.; Katsovich, Liliya; Olds, David L.; Grigorenko, Elena L.; Leckman, James F.

2013-01-01

Objective: The aim of this study was to determine if a latent variable approach might be useful in identifying shared variance across genetic risk alleles that is associated with antisocial behaviour at age 15 years. Methods: Using a conventional latent variable approach, we derived an antisocial phenotype in 328 adolescents utilizing data from a…

The Least-Squares Estimation of Latent Trait Variables.

ERIC Educational Resources Information Center

Tatsuoka, Kikumi

This paper presents a new method for estimating a given latent trait variable by the least-squares approach. The beta weights are obtained recursively with the help of Fourier series and expressed as functions of item parameters of response curves. The values of the latent trait variable estimated by this method and by maximum likelihood method…

Selection of latent variables for multiple mixed-outcome models

PubMed Central

ZHOU, LING; LIN, HUAZHEN; SONG, XINYUAN; LI, YI

2014-01-01

Latent variable models have been widely used for modeling the dependence structure of multiple outcomes data. However, the formulation of a latent variable model is often unknown a priori, the misspecification will distort the dependence structure and lead to unreliable model inference. Moreover, multiple outcomes with varying types present enormous analytical challenges. In this paper, we present a class of general latent variable models that can accommodate mixed types of outcomes. We propose a novel selection approach that simultaneously selects latent variables and estimates parameters. We show that the proposed estimator is consistent, asymptotically normal and has the oracle property. The practical utility of the methods is confirmed via simulations as well as an application to the analysis of the World Values Survey, a global research project that explores peoples’ values and beliefs and the social and personal characteristics that might influence them. PMID:27642219

A Bayesian Model for the Estimation of Latent Interaction and Quadratic Effects When Latent Variables Are Non-Normally Distributed

ERIC Educational Resources Information Center

Kelava, Augustin; Nagengast, Benjamin

2012-01-01

Structural equation models with interaction and quadratic effects have become a standard tool for testing nonlinear hypotheses in the social sciences. Most of the current approaches assume normally distributed latent predictor variables. In this article, we present a Bayesian model for the estimation of latent nonlinear effects when the latent…

Gene variants associated with antisocial behaviour: A latent variable approach

PubMed Central

Bentley, Mary Jane; Lin, Haiqun; Fernandez, Thomas V.; Lee, Maria; Yrigollen, Carolyn M.; Pakstis, Andrew J.; Katsovich, Liliya; Olds, David L.; Grigorenko, Elena L.; Leckman, James F.

2013-01-01

Objective The aim of this study was to determine if a latent variable approach might be useful in identifying shared variance across genetic risk alleles that is associated with antisocial behaviour at age 15 years. Methods Using a conventional latent variable approach, we derived an antisocial phenotype in 328 adolescents utilizing data from a 15-year follow-up of a randomized trial of a prenatal and infancy nurse-home visitation program in Elmira, New York. We then investigated, via a novel latent variable approach, 450 informative genetic polymorphisms in 71 genes previously associated with antisocial behaviour, drug use, affiliative behaviours, and stress response in 241 consenting individuals for whom DNA was available. Haplotype and Pathway analyses were also performed. Results Eight single-nucleotide polymorphisms (SNPs) from 8 genes contributed to the latent genetic variable that in turn accounted for 16.0% of the variance within the latent antisocial phenotype. The number of risk alleles was linearly related to the latent antisocial variable scores. Haplotypes that included the putative risk alleles for all 8 genes were also associated with higher latent antisocial variable scores. In addition, 33 SNPs from 63 of the remaining genes were also significant when added to the final model. Many of these genes interact on a molecular level, forming molecular networks. The results support a role for genes related to dopamine, norepinephrine, serotonin, glutamate, opioid, and cholinergic signaling as well as stress response pathways in mediating susceptibility to antisocial behaviour. Conclusions This preliminary study supports use of relevant behavioural indicators and latent variable approaches to study the potential “co-action” of gene variants associated with antisocial behaviour. It also underscores the cumulative relevance of common genetic variants for understanding the etiology of complex behaviour. If replicated in future studies, this approach may allow the identification of a ‘shared’ variance across genetic risk alleles associated with complex neuropsychiatric dimensional phenotypes using relatively small numbers of well-characterized research participants. PMID:23822756

Accuracy of latent-variable estimation in Bayesian semi-supervised learning.

PubMed

Yamazaki, Keisuke

2015-09-01

Hierarchical probabilistic models, such as Gaussian mixture models, are widely used for unsupervised learning tasks. These models consist of observable and latent variables, which represent the observable data and the underlying data-generation process, respectively. Unsupervised learning tasks, such as cluster analysis, are regarded as estimations of latent variables based on the observable ones. The estimation of latent variables in semi-supervised learning, where some labels are observed, will be more precise than that in unsupervised, and one of the concerns is to clarify the effect of the labeled data. However, there has not been sufficient theoretical analysis of the accuracy of the estimation of latent variables. In a previous study, a distribution-based error function was formulated, and its asymptotic form was calculated for unsupervised learning with generative models. It has been shown that, for the estimation of latent variables, the Bayes method is more accurate than the maximum-likelihood method. The present paper reveals the asymptotic forms of the error function in Bayesian semi-supervised learning for both discriminative and generative models. The results show that the generative model, which uses all of the given data, performs better when the model is well specified. Copyright © 2015 Elsevier Ltd. All rights reserved.

Estimating Latent Variable Interactions With Non-Normal Observed Data: A Comparison of Four Approaches

PubMed Central

Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S.

2012-01-01

A Monte Carlo simulation was conducted to investigate the robustness of four latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of non-normality of the observed exogenous variables. Results showed that the CPI and LMS approaches yielded biased estimates of the interaction effect when the exogenous variables were highly non-normal. When the violation of non-normality was not severe (normal; symmetric with excess kurtosis < 1), the LMS approach yielded the most efficient estimates of the latent interaction effect with the highest statistical power. In highly non-normal conditions, the GAPI and UPI approaches with ML estimation yielded unbiased latent interaction effect estimates, with acceptable actual Type-I error rates for both the Wald and likelihood ratio tests of interaction effect at N ≥ 500. An empirical example illustrated the use of the four approaches in testing a latent variable interaction between academic self-efficacy and positive family role models in the prediction of academic performance. PMID:23457417

Development and Application of Methods for Estimating Operating Characteristics of Discrete Test Item Responses without Assuming any Mathematical Form.

ERIC Educational Resources Information Center

Samejima, Fumiko

In latent trait theory the latent space, or space of the hypothetical construct, is usually represented by some unidimensional or multi-dimensional continuum of real numbers. Like the latent space, the item response can either be treated as a discrete variable or as a continuous variable. Latent trait theory relates the item response to the latent…

Longitudinal change in physical activity and its correlates in relapsing-remitting multiple sclerosis.

PubMed

Motl, Robert W; McAuley, Edward; Sandroff, Brian M

2013-08-01

Physical activity is beneficial for people with multiple sclerosis (MS), but this population is largely inactive. There is minimal information on change in physical activity and its correlates for informing the development of behavioral interventions. This study examined change in physical activity and its symptomatic, social-cognitive, and ambulatory or disability correlates over a 2.5-year period of time in people with relapsing-remitting multiple sclerosis. On 6 occasions, each separated by 6 months, people (N=269) with relapsing-remitting multiple sclerosis completed assessments of symptoms, self-efficacy, walking impairment, disability, and physical activity. The participants wore an accelerometer for 7 days. The change in study variables over 6 time points was examined with unconditional latent growth curve modeling. The association among changes in study variables over time was examined using conditional latent growth curve modeling, and the associations were expressed as standardized path coefficients (β). There were significant linear changes in self-reported and objectively measured physical activity, self-efficacy, walking impairment, and disability over the 2.5-year period; there were no changes in fatigue, depression, and pain. The changes in self-reported and objective physical activity were associated with change in self-efficacy (β=.49 and β=.61, respectively), after controlling for other variables and confounders. The primary limitations of the study were the generalizability of results among those with progressive multiple sclerosis and inclusion of a single variable from social-cognitive theory. Researchers should consider designing interventions that target self-efficacy for the promotion and maintenance of physical activity in this population.

Structural Equation Modelling with Three Schemes Estimation of Score Factors on Partial Least Square (Case Study: The Quality Of Education Level SMA/MA in Sumenep Regency)

NASA Astrophysics Data System (ADS)

Anekawati, Anik; Widjanarko Otok, Bambang; Purhadi; Sutikno

2017-06-01

Research in education often involves a latent variable. Statistical analysis technique that has the ability to analyze the pattern of relationship among latent variables as well as between latent variables and their indicators is Structural Equation Modeling (SEM). SEM partial least square (PLS) was developed as an alternative if these conditions are met: the theory that underlying the design of the model is weak, does not assume a certain scale measurement, the sample size should not be large and the data does not have the multivariate normal distribution. The purpose of this paper is to compare the results of modeling of the educational quality in high school level (SMA/MA) in Sumenep Regency with structural equation modeling approach partial least square with three schemes estimation of score factors. This paper is a result of explanatory research using secondary data from Sumenep Education Department and Badan Pusat Statistik (BPS) Sumenep which was data of Sumenep in the Figures and the District of Sumenep in the Figures for the year 2015. The unit of observation in this study were districts in Sumenep that consists of 18 districts on the mainland and 9 districts in the islands. There were two endogenous variables and one exogenous variable. Endogenous variables are the quality of education level of SMA/MA (Y1) and school infrastructure (Y2), whereas exogenous variable is socio-economic condition (X1). In this study, There is one improved model which represented by model from path scheme because this model is a consistent, all of its indicators are valid and its the value of R-square increased which is: Y1=0.651Y2. In this model, the quality of education influenced only by the school infrastructure (0.651). The socio-economic condition did not affect neither the school infrastructure nor the quality of education. If the school infrastructure increased 1 point, then the quality of education increased 0.651 point. The quality of education had an R2 of 0.418, which indicates that 41.8 percent of variance in the quality of education is explained by the school infrastructure, the remaining 58.2% is explained by the other factors which were not investigated in this work.

Replicates in high dimensions, with applications to latent variable graphical models.

PubMed

Tan, Kean Ming; Ning, Yang; Witten, Daniela M; Liu, Han

2016-12-01

In classical statistics, much thought has been put into experimental design and data collection. In the high-dimensional setting, however, experimental design has been less of a focus. In this paper, we stress the importance of collecting multiple replicates for each subject in this setting. We consider learning the structure of a graphical model with latent variables, under the assumption that these variables take a constant value across replicates within each subject. By collecting multiple replicates for each subject, we are able to estimate the conditional dependence relationships among the observed variables given the latent variables. To test the null hypothesis of conditional independence between two observed variables, we propose a pairwise decorrelated score test. Theoretical guarantees are established for parameter estimation and for this test. We show that our proposal is able to estimate latent variable graphical models more accurately than some existing proposals, and apply the proposed method to a brain imaging dataset.

Clinical Insight Into Latent Variables of Psychiatric Questionnaires for Mood Symptom Self-Assessment

PubMed Central

Saunders, Kate; Bilderbeck, Amy; Palmius, Niclas; Goodwin, Guy; De Vos, Maarten

2017-01-01

Background We recently described a new questionnaire to monitor mood called mood zoom (MZ). MZ comprises 6 items assessing mood symptoms on a 7-point Likert scale; we had previously used standard principal component analysis (PCA) to tentatively understand its properties, but the presence of multiple nonzero loadings obstructed the interpretation of its latent variables. Objective The aim of this study was to rigorously investigate the internal properties and latent variables of MZ using an algorithmic approach which may lead to more interpretable results than PCA. Additionally, we explored three other widely used psychiatric questionnaires to investigate latent variable structure similarities with MZ: (1) Altman self-rating mania scale (ASRM), assessing mania; (2) quick inventory of depressive symptomatology (QIDS) self-report, assessing depression; and (3) generalized anxiety disorder (7-item) (GAD-7), assessing anxiety. Methods We elicited responses from 131 participants: 48 bipolar disorder (BD), 32 borderline personality disorder (BPD), and 51 healthy controls (HC), collected longitudinally (median [interquartile range, IQR]: 363 [276] days). Participants were requested to complete ASRM, QIDS, and GAD-7 weekly (all 3 questionnaires were completed on the Web) and MZ daily (using a custom-based smartphone app). We applied sparse PCA (SPCA) to determine the latent variables for the four questionnaires, where a small subset of the original items contributes toward each latent variable. Results We found that MZ had great consistency across the three cohorts studied. Three main principal components were derived using SPCA, which can be tentatively interpreted as (1) anxiety and sadness, (2) positive affect, and (3) irritability. The MZ principal component comprising anxiety and sadness explains most of the variance in BD and BPD, whereas the positive affect of MZ explains most of the variance in HC. The latent variables in ASRM were identical for the patient groups but different for HC; nevertheless, the latent variables shared common items across both the patient group and HC. On the contrary, QIDS had overall very different principal components across groups; sleep was a key element in HC and BD but was absent in BPD. In GAD-7, nervousness was the principal component explaining most of the variance in BD and HC. Conclusions This study has important implications for understanding self-reported mood. MZ has a consistent, intuitively interpretable latent variable structure and hence may be a good instrument for generic mood assessment. Irritability appears to be the key distinguishing latent variable between BD and BPD and might be useful for differential diagnosis. Anxiety and sadness are closely interlinked, a finding that might inform treatment effects to jointly address these covarying symptoms. Anxiety and nervousness appear to be amongst the cardinal latent variable symptoms in BD and merit close attention in clinical practice. PMID:28546141

Confidence Intervals for a Semiparametric Approach to Modeling Nonlinear Relations among Latent Variables

ERIC Educational Resources Information Center

Pek, Jolynn; Losardo, Diane; Bauer, Daniel J.

2011-01-01

Compared to parametric models, nonparametric and semiparametric approaches to modeling nonlinearity between latent variables have the advantage of recovering global relationships of unknown functional form. Bauer (2005) proposed an indirect application of finite mixtures of structural equation models where latent components are estimated in the…

Estimating Latent Variable Interactions with Nonnormal Observed Data: A Comparison of Four Approaches

ERIC Educational Resources Information Center

Cham, Heining; West, Stephen G.; Ma, Yue; Aiken, Leona S.

2012-01-01

A Monte Carlo simulation was conducted to investigate the robustness of 4 latent variable interaction modeling approaches (Constrained Product Indicator [CPI], Generalized Appended Product Indicator [GAPI], Unconstrained Product Indicator [UPI], and Latent Moderated Structural Equations [LMS]) under high degrees of nonnormality of the observed…

Refinement of the tripartite influence model for men: dual body image pathways to body change behaviors.

PubMed

Tylka, Tracy L

2011-06-01

Although muscularity and body fat concerns are central to conceptualizing men's body image, they have not been examined together within existing structural models. This study refined the tripartite influence model (Thompson, Heinberg, Altabe, & Tantleff-Dunn, 1999) by including dual body image pathways (muscularity and body fat dissatisfaction) to engagement in muscular enhancement and disordered eating behaviors, respectively, and added dating partners as a source of social influence. Latent variable structural equation modeling analyses supported this quadripartite model in 473 undergraduate men. Nonsignificant paths were trimmed and two unanticipated paths were added. Muscularity dissatisfaction and body fat dissatisfaction represented dual body image pathways to men's engagement in muscularity enhancement behaviors and disordered eating behaviors, respectively. Pressures to be mesomorphic from friends, family, media, and dating partners made unique contributions to the model. Internalization of the mesomorphic ideal, muscularity dissatisfaction, and body fat dissatisfaction played key meditational roles within the model. Copyright © 2011 Elsevier Ltd. All rights reserved.

Dynamic Latent Trait Models with Mixed Hidden Markov Structure for Mixed Longitudinal Outcomes.

PubMed

Zhang, Yue; Berhane, Kiros

2016-01-01

We propose a general Bayesian joint modeling approach to model mixed longitudinal outcomes from the exponential family for taking into account any differential misclassification that may exist among categorical outcomes. Under this framework, outcomes observed without measurement error are related to latent trait variables through generalized linear mixed effect models. The misclassified outcomes are related to the latent class variables, which represent unobserved real states, using mixed hidden Markov models (MHMM). In addition to enabling the estimation of parameters in prevalence, transition and misclassification probabilities, MHMMs capture cluster level heterogeneity. A transition modeling structure allows the latent trait and latent class variables to depend on observed predictors at the same time period and also on latent trait and latent class variables at previous time periods for each individual. Simulation studies are conducted to make comparisons with traditional models in order to illustrate the gains from the proposed approach. The new approach is applied to data from the Southern California Children Health Study (CHS) to jointly model questionnaire based asthma state and multiple lung function measurements in order to gain better insight about the underlying biological mechanism that governs the inter-relationship between asthma state and lung function development.

The spatial pattern of suicide in the US in relation to deprivation, fragmentation and rurality.

PubMed

Congdon, Peter

2011-01-01

Analysis of geographical patterns of suicide and psychiatric morbidity has demonstrated the impact of latent ecological variables (such as deprivation, rurality). Such latent variables may be derived by conventional multivariate techniques from sets of observed indices (for example, by principal components), by composite variable methods or by methods which explicitly consider the spatial framework of areas and, in particular, the spatial clustering of latent risks and outcomes. This article considers a latent random variable approach to explaining geographical contrasts in suicide in the US; and it develops a spatial structural equation model incorporating deprivation, social fragmentation and rurality. The approach allows for such latent spatial constructs to be correlated both within and between areas. Potential effects of area ethnic mix are also included. The model is applied to male and female suicide deaths over 2002–06 in 3142 US counties.

Evaluating aggregate effects of rare and common variants in the 1000 Genomes Project exon sequencing data using latent variable structural equation modeling.

PubMed

Nock, Nl; Zhang, Lx

2011-11-29

Methods that can evaluate aggregate effects of rare and common variants are limited. Therefore, we applied a two-stage approach to evaluate aggregate gene effects in the 1000 Genomes Project data, which contain 24,487 single-nucleotide polymorphisms (SNPs) in 697 unrelated individuals from 7 populations. In stage 1, we identified potentially interesting genes (PIGs) as those having at least one SNP meeting Bonferroni correction using univariate, multiple regression models. In stage 2, we evaluate aggregate PIG effects on trait, Q1, by modeling each gene as a latent construct, which is defined by multiple common and rare variants, using the multivariate statistical framework of structural equation modeling (SEM). In stage 1, we found that PIGs varied markedly between a randomly selected replicate (replicate 137) and 100 other replicates, with the exception of FLT1. In stage 1, collapsing rare variants decreased false positives but increased false negatives. In stage 2, we developed a good-fitting SEM model that included all nine genes simulated to affect Q1 (FLT1, KDR, ARNT, ELAV4, FLT4, HIF1A, HIF3A, VEGFA, VEGFC) and found that FLT1 had the largest effect on Q1 (βstd = 0.33 ± 0.05). Using replicate 137 estimates as population values, we found that the mean relative bias in the parameters (loadings, paths, residuals) and their standard errors across 100 replicates was on average, less than 5%. Our latent variable SEM approach provides a viable framework for modeling aggregate effects of rare and common variants in multiple genes, but more elegant methods are needed in stage 1 to minimize type I and type II error.

Internalized HIV Stigma and Disclosure Concerns: Development and Validation of Two Scales in Spanish-Speaking Populations.

PubMed

Hernansaiz-Garrido, Helena; Alonso-Tapia, Jesús

2017-01-01

Internalized stigma and disclosure concerns are key elements for the study of mental health in people living with HIV. Since no measures of these constructs were available for Spanish population, this study sought to develop such instruments, to analyze their reliability and validity and to provide a short version. A heterogeneous sample of 458 adults from different Spanish-speaking countries completed the HIV-Internalized Stigma Scale and the HIV-Disclosure Concerns Scale, along with the Hospital Anxiety and Depression Scale, Rosenberg's Self-esteem Scale and other socio-demographic variables. Reliability and correlation analyses, exploratory factor analyses, path analyses with latent variables, and ANOVAs were conducted to test the scales' psychometric properties. The scales showed good reliability in terms of internal consistency and temporal stability, as well as good sensitivity and factorial and criterion validity. The HIV-Internalized Stigma Scale and the HIV-Disclosure Concerns Scale are reliable and valid means to assess these variables in several contexts.

Application of Local Linear Embedding to Nonlinear Exploratory Latent Structure Analysis

ERIC Educational Resources Information Center

Wang, Haonan; Iyer, Hari

2007-01-01

In this paper we discuss the use of a recent dimension reduction technique called Locally Linear Embedding, introduced by Roweis and Saul, for performing an exploratory latent structure analysis. The coordinate variables from the locally linear embedding describing the manifold on which the data reside serve as the latent variable scores. We…

Introduction to Latent Class Analysis with Applications

ERIC Educational Resources Information Center

Porcu, Mariano; Giambona, Francesca

2017-01-01

Latent class analysis (LCA) is a statistical method used to group individuals (cases, units) into classes (categories) of an unobserved (latent) variable on the basis of the responses made on a set of nominal, ordinal, or continuous observed variables. In this article, we introduce LCA in order to demonstrate its usefulness to early adolescence…

Mixture Distribution Latent State-Trait Analysis: Basic Ideas and Applications

ERIC Educational Resources Information Center

Courvoisier, Delphine S.; Eid, Michael; Nussbeck, Fridtjof W.

2007-01-01

Extensions of latent state-trait models for continuous observed variables to mixture latent state-trait models with and without covariates of change are presented that can separate individuals differing in their occasion-specific variability. An empirical application to the repeated measurement of mood states (N = 501) revealed that a model with 2…

Is decision-making ability related to food choice and facets of eating behaviour in adolescents?

PubMed

Macchi, Rosemarie; MacKew, Laura; Davis, Caroline

2017-09-01

To test the prediction that poor decision-making would predict poor eating-related behaviours, which in turn would relate to elevated body mass index (BMI) percentile. Associations among decision-making ability, eating behaviours, and BMI percentile were examined in a sample of 311 healthy male and female adolescents, aged 14-18 years. Structural equation modelling was used to test the proposed relationships. The predicted model was a good fit to the data and all paths between latent and indicator variables were significant. Impulsive responding significantly predicted poor food choice and overeating. No significant relationships emerged between eating-related variables and BMI percentile. Findings from this study extend the existing research in adults and offer a more comprehensive understanding of factors that may contribute to eating behaviours and weight status in teenagers. Copyright © 2017 Elsevier Ltd. All rights reserved.

The cognitive foundations of reading and arithmetic skills in 7- to 10-year-olds.

PubMed

Durand, Marianne; Hulme, Charles; Larkin, Rebecca; Snowling, Margaret

2005-06-01

A range of possible predictors of arithmetic and reading were assessed in a large sample (N=162) of children between ages 7 years 5 months and 10 years 4 months. A confirmatory factor analysis of the predictors revealed a good fit to a model consisting of four latent variables (verbal ability, nonverbal ability, search speed, and phonological memory) and two manifest variables (digit comparison and phoneme deletion). A path analysis showed that digit comparison and verbal ability were unique predictors of variations in arithmetic skills, whereas phoneme deletion and verbal ability were unique predictors of variations in reading skills. These results confirm earlier findings that phoneme deletion ability appears to be a critical foundation for learning to read (decode). In addition, variations in the speed of accessing numerical quantity information appear to be a critical foundation for the development of arithmetic skills.

Etiological Beliefs, Treatments, Stigmatizing Attitudes toward Schizophrenia. What Do Italians and Israelis Think?

PubMed

Mannarini, Stefania; Boffo, Marilisa; Rossi, Alessandro; Balottin, Laura

2017-01-01

Background: Although scientific research on the etiology of mental disorders has improved the knowledge of biogenetic and psychosocial aspects related to the onset of mental illness, stigmatizing attitudes and behaviors are still very prevalent and pose a significant social problem. Aim: The aim of this study was to deepen the knowledge of how attitudes toward people with mental illness are affected by specific personal beliefs and characteristics, such as culture and religion of the perceiver. More precisely, the main purpose is the definition of a structure of variables, namely perceived dangerousness, social closeness, and avoidance of the ill person, together with the beliefs about the best treatment to be undertaken and the sick person' gender, capable of describing the complexity of the stigma construct in particular as far as schizophrenia is concerned. Method: The study involved 305 university students, 183 from the University of Padua, Italy, and 122 from the University of Haifa, Israel. For the analyses, a latent class analysis (LCA) approach was chosen to identify a latent categorical structure accounting for the covariance between the observed variables. Such a latent structure was expected to be moderated by cultural background (Italy versus Israel) and religious beliefs, whereas causal beliefs, recommended treatment, dangerousness, social closeness, and public avoidance were the manifest variables, namely the observed indicators of the latent variable. Results: Two sets of results were obtained. First, the relevance of the manifest variables as indicators of the hypothesized latent variable was highlighted. Second, a two-latent-class categorical dimension represented by prejudicial attitudes, causal beliefs, and treatments concerning schizophrenia was found. Specifically, the differential effects of the two cultures and the religious beliefs on the latent structure and their relations highlighted the relevance of the observed variables as indicators of the expected latent variable. Conclusion: The present study contributes to the improvement of the understanding of how attitudes toward people with mental illness are affected by specific personal beliefs and characteristics of the perceiver. The definition of a structure of variables capable of describing the complexity of the stigma construct in particular as far as schizophrenia is concerned was achieved from a cross-cultural perspective.

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…

The Latent Structure of Dietary Restraint, Body Dissatisfaction, and Drive for Thinness: A Series of Taxometric Analyses

ERIC Educational Resources Information Center

Holm-Denoma, Jill M.; Richey, J. Anthony; Joiner, Thomas E., Jr.

2010-01-01

Although the latent structure of various eating disorders has been explored in previous studies, no published studies have examined the latent structure of theoretically relevant variables that have been shown to cut across eating disorder diagnoses. The current study examined 3 such variables (dietary restraint, body dissatisfaction, and drive…

Decayed and missing teeth and oral-health-related factors: predicting depression in homeless people.

PubMed

Coles, Emma; Chan, Karen; Collins, Jennifer; Humphris, Gerry M; Richards, Derek; Williams, Brian; Freeman, Ruth

2011-08-01

The objective of the study was to determine the effect of dental health status, dental anxiety and oral-health-related quality of life (OHRQoL) upon homeless people's experience of depression. A cross-sectional survey was conducted on a sample of homeless people in seven National Health Service Boards in Scotland. All participants completed a questionnaire to assess their depression, dental anxiety and OHRQoL using reliable and valid measures. Participants had an oral examination to assess their experience of tooth decay (decayed and missing teeth). Latent variable path analysis was conducted to determine the effects of dental health status on depression via dental anxiety and OHRQoL using intensive resampling methods. A total of 853 homeless people participated, of which 70% yielded complete data sets. Three latent variables, decayed and missing teeth, dental anxiety (Modified Dental Anxiety Scale: five items) and depression (Center for Epidemiological Studies Depression Scale: two factors), and a single variable for OHRQoL (Oral Health Impact Profile total scale) were used in a hybrid structural equation model. The variable decayed and missing teeth was associated with depression through indirect pathways (total standardised indirect effects=0.44, P<.001), via OHRQoL and dental anxiety (χ²=75.90, df=40, comparative fit index=0.985, Tucker-Lewis index=0.977, root mean square error of approximation=0.051 [90% confidence interval: 0.037-0.065]). Depression in Scottish homeless people is related to dental health status and oral-health-related factors. Decayed and missing teeth may influence depression primarily through the psychological constructs of OHRQoL and, to a lesser extent, dental anxiety. Copyright © 2010 Elsevier Inc. All rights reserved.

Building Coherent Validation Arguments for the Measurement of Latent Constructs with Unified Statistical Frameworks

ERIC Educational Resources Information Center

Rupp, Andre A.

2012-01-01

In the focus article of this issue, von Davier, Naemi, and Roberts essentially coupled: (1) a short methodological review of structural similarities of latent variable models with discrete and continuous latent variables; and (2) 2 short empirical case studies that show how these models can be applied to real, rather than simulated, large-scale…

Advanced Nonlinear Latent Variable Modeling: Distribution Analytic LMS and QML Estimators of Interaction and Quadratic Effects

ERIC Educational Resources Information Center

Kelava, Augustin; Werner, Christina S.; Schermelleh-Engel, Karin; Moosbrugger, Helfried; Zapf, Dieter; Ma, Yue; Cham, Heining; Aiken, Leona S.; West, Stephen G.

2011-01-01

Interaction and quadratic effects in latent variable models have to date only rarely been tested in practice. Traditional product indicator approaches need to create product indicators (e.g., x[superscript 2] [subscript 1], x[subscript 1]x[subscript 4]) to serve as indicators of each nonlinear latent construct. These approaches require the use of…

The Log-Linear Cognitive Diagnostic Model (LCDM) as a Special Case of The General Diagnostic Model (GDM). Research Report. ETS RR-14-40

ERIC Educational Resources Information Center

von Davier, Matthias

2014-01-01

Diagnostic models combine multiple binary latent variables in an attempt to produce a latent structure that provides more information about test takers' performance than do unidimensional latent variable models. Recent developments in diagnostic modeling emphasize the possibility that multiple skills may interact in a conjunctive way within the…

The strain path dependence of plastic deformation response of AA5754: Experiment and modeling

NASA Astrophysics Data System (ADS)

Pham, Minh-Son; Hu, Lin; Iadicola, Mark; Creuziger, Adam; Rollett, Anthony D.

2013-12-01

This work presents modeling of experiments on a balanced biaxial (BB) pre-strained AA5754 alloy, subsequently reloaded uniaxially along the rolling direction and transverse direction. The material exhibits a complex plastic deformation response during the change in strain path due to 1) crystallographic texture, 2) aging (interactions between dislocations and Mg atoms) and 3) recovery (annihilation and re-arrangement of dislocations). With a BB prestrain of about 5 %, the aging process is dominant, and the yield strength for uniaxially deformed samples is observed to be higher than the flow stress during BB straining. The strain hardening rate after changing path is, however, lower than that for pre-straining. Higher degrees of pre-straining make the dynamic recovery more active. The dynamic recovery at higher strain levels compensates for the aging effect, and results in: 1) a reduction of the yield strength, and 2) an increase in the hardening rate of re-strained specimens along other directions. The yield strength of deformed samples is further reduced if these samples are left at room temperature to let static recovery occur. The synergistic influences of texture condition, aging and recovery processes on the material response make the modeling of strain path dependence of mechanical behavior of AA5754 challenging. In this study, the influence of crystallographic texture is taken into account by incorporating the latent hardening into a visco-plastic self-consistent model. Different strengths of dislocation glide interaction models in 24 slip systems are used to represent the latent hardening. Moreover, the aging and recovery effects are also included into the latent hardening model by considering strong interactions between dislocations and dissolved atom Mg and the microstructural evolution. These microstructural considerations provide a powerful capability to successfully describe the strain path dependence of plastic deformation behavior of AA5754.

Alcohol and Cigarette Use From Ages 23 to 55: Links With Health and Well-Being in the Long-Term National Child Development Study

PubMed Central

Staff, Jeremy; Maggs, Jennifer

2017-01-01

Objective: Using longitudinal data from the National Child Development Study, an ongoing study of a nationally representative British cohort born in 1958 (n = 9,137; 51% female), we examined how patterns of alcohol and cigarette use from young adulthood (age 23) to midlife (age 55) are associated with health and well-being. Method: We first used a nonparametric multilevel latent class specification to identify eight unique paths of alcohol and cigarette use from ages 23 to 55, and then assessed how these long-term latent paths related to overall health, heart problems, chronic illness, and quality of life at midlife. Results: Results show that adults who consistently drank within current U.K. low-risk guidelines (i.e., not exceeding 14 units of alcohol per week) and abstained from smoking from young adulthood to midlife reported the best overall health and well-being compared with latent paths involving steady, light to moderate drinking and both current and prior smoking, increasing drinking and smoking, and infrequent drinking/abstention. Conclusions: British adults who consistently drank within new lower risk guidelines and abstained from smoking from young adulthood to midlife reported the best overall health and well-being across numerous indicators. However, apparent observed health benefits of stable low-dose alcohol use (vs. abstention) are weakened by the fact that by age 55 almost all alcohol “abstainers” in the National Child Development Study sample were former drinkers, and that respondents who followed infrequent drinking/abstention paths were the mostly likely to report poor health, psychological distress, and low educational qualifications in early adulthood. (J Stud. Alcohol Drugs, 78, 394–403, 2017) PMID:28499106

Measurement Model Specification Error in LISREL Structural Equation Models.

ERIC Educational Resources Information Center

Baldwin, Beatrice; Lomax, Richard

This LISREL study examines the robustness of the maximum likelihood estimates under varying degrees of measurement model misspecification. A true model containing five latent variables (two endogenous and three exogenous) and two indicator variables per latent variable was used. Measurement model misspecification considered included errors of…

Evaluating measurement models in clinical research: covariance structure analysis of latent variable models of self-conception.

PubMed

Hoyle, R H

1991-02-01

Indirect measures of psychological constructs are vital to clinical research. On occasion, however, the meaning of indirect measures of psychological constructs is obfuscated by statistical procedures that do not account for the complex relations between items and latent variables and among latent variables. Covariance structure analysis (CSA) is a statistical procedure for testing hypotheses about the relations among items that indirectly measure a psychological construct and relations among psychological constructs. This article introduces clinical researchers to the strengths and limitations of CSA as a statistical procedure for conceiving and testing structural hypotheses that are not tested adequately with other statistical procedures. The article is organized around two empirical examples that illustrate the use of CSA for evaluating measurement models with correlated error terms, higher-order factors, and measured and latent variables.

Unfinished Business in Clarifying Causal Measurement: Commentary on Bainter and Bollen

ERIC Educational Resources Information Center

Markus, Keith A.

2014-01-01

In a series of articles and comments, Kenneth Bollen and his collaborators have incrementally refined an account of structural equation models that (a) model a latent variable as the effect of several observed variables and (b) carry an interpretation of the observed variables as, in some sense, measures of the latent variable that they cause.…

Interexaminer variation of minutia markup on latent fingerprints.

PubMed

Ulery, Bradford T; Hicklin, R Austin; Roberts, Maria Antonia; Buscaglia, JoAnn

2016-07-01

Latent print examiners often differ in the number of minutiae they mark during analysis of a latent, and also during comparison of a latent with an exemplar. Differences in minutia counts understate interexaminer variability: examiners' markups may have similar minutia counts but differ greatly in which specific minutiae were marked. We assessed variability in minutia markup among 170 volunteer latent print examiners. Each provided detailed markup documenting their examinations of 22 latent-exemplar pairs of prints randomly assigned from a pool of 320 pairs. An average of 12 examiners marked each latent. The primary factors associated with minutia reproducibility were clarity, which regions of the prints examiners chose to mark, and agreement on value or comparison determinations. In clear areas (where the examiner was "certain of the location, presence, and absence of all minutiae"), median reproducibility was 82%; in unclear areas, median reproducibility was 46%. Differing interpretations regarding which regions should be marked (e.g., when there is ambiguity in the continuity of a print) contributed to variability in minutia markup: especially in unclear areas, marked minutiae were often far from the nearest minutia marked by a majority of examiners. Low reproducibility was also associated with differences in value or comparison determinations. Lack of standardization in minutia markup and unfamiliarity with test procedures presumably contribute to the variability we observed. We have identified factors accounting for interexaminer variability; implementing standards for detailed markup as part of documentation and focusing future training efforts on these factors may help to facilitate transparency and reduce subjectivity in the examination process. Published by Elsevier Ireland Ltd.

Multimethod latent class analysis

PubMed Central

Nussbeck, Fridtjof W.; Eid, Michael

2015-01-01

Correct and, hence, valid classifications of individuals are of high importance in the social sciences as these classifications are the basis for diagnoses and/or the assignment to a treatment. The via regia to inspect the validity of psychological ratings is the multitrait-multimethod (MTMM) approach. First, a latent variable model for the analysis of rater agreement (latent rater agreement model) will be presented that allows for the analysis of convergent validity between different measurement approaches (e.g., raters). Models of rater agreement are transferred to the level of latent variables. Second, the latent rater agreement model will be extended to a more informative MTMM latent class model. This model allows for estimating (i) the convergence of ratings, (ii) method biases in terms of differential latent distributions of raters and differential associations of categorizations within raters (specific rater bias), and (iii) the distinguishability of categories indicating if categories are satisfyingly distinct from each other. Finally, an empirical application is presented to exemplify the interpretation of the MTMM latent class model. PMID:26441714

Exploring heterogeneity in clinical trials with latent class analysis

PubMed Central

Abarda, Abdallah; Contractor, Ateka A.; Wang, Juan; Dayton, C. Mitchell

2018-01-01

Case-mix is common in clinical trials and treatment effect can vary across different subgroups. Conventionally, a subgroup analysis is performed by dividing the overall study population by one or two grouping variables. It is usually impossible to explore complex high-order intersections among confounding variables. Latent class analysis (LCA) provides a framework to identify latent classes by observed manifest variables. Distal clinical outcomes and treatment effect can be different across these classes. This paper provides a step-by-step tutorial on how to perform LCA with R. A simulated dataset is generated to illustrate the process. In the example, the classify-analyze approach is employed to explore the differential treatment effects on distal outcomes across latent classes. PMID:29955579

On the Asymptotic Relative Efficiency of Planned Missingness Designs.

PubMed

Rhemtulla, Mijke; Savalei, Victoria; Little, Todd D

2016-03-01

In planned missingness (PM) designs, certain data are set a priori to be missing. PM designs can increase validity and reduce cost; however, little is known about the loss of efficiency that accompanies these designs. The present paper compares PM designs to reduced sample (RN) designs that have the same total number of data points concentrated in fewer participants. In 4 studies, we consider models for both observed and latent variables, designs that do or do not include an "X set" of variables with complete data, and a full range of between- and within-set correlation values. All results are obtained using asymptotic relative efficiency formulas, and thus no data are generated; this novel approach allows us to examine whether PM designs have theoretical advantages over RN designs removing the impact of sampling error. Our primary findings are that (a) in manifest variable regression models, estimates of regression coefficients have much lower relative efficiency in PM designs as compared to RN designs, (b) relative efficiency of factor correlation or latent regression coefficient estimates is maximized when the indicators of each latent variable come from different sets, and (c) the addition of an X set improves efficiency in manifest variable regression models only for the parameters that directly involve the X-set variables, but it substantially improves efficiency of most parameters in latent variable models. We conclude that PM designs can be beneficial when the model of interest is a latent variable model; recommendations are made for how to optimize such a design.

Environmental risk perception, environmental concern and propensity to participate in organic farming programmes.

PubMed

Toma, Luiza; Mathijs, Erik

2007-04-01

This paper aims to identify the factors underlying farmers' propensity to participate in organic farming programmes in a Romanian rural region that confronts non-point source pollution. For this, we employ structural equation modelling with latent variables using a specific data set collected through an agri-environmental farm survey in 2001. The model includes one 'behavioural intention' latent variable ('propensity to participate in organic farming programmes') and five 'attitude' and 'socio-economic' latent variables ('socio-demographic characteristics', 'economic characteristics', 'agri-environmental information access', 'environmental risk perception' and 'general environmental concern'). The results indicate that, overall, the model has an adequate fit to the data. All loadings are statistically significant, supporting the theoretical basis for assignment of indicators for each latent variable. The significance tests for the structural model parameters show 'environmental risk perception' as the strongest determinant of farmers' propensity to participate in organic farming programmes.

Differences in environmental preferences towards cycling for transport among adults: a latent class analysis.

PubMed

Mertens, Lieze; Van Cauwenberg, Jelle; Ghekiere, Ariane; De Bourdeaudhuij, Ilse; Deforche, Benedicte; Van de Weghe, Nico; Van Dyck, Delfien

2016-08-12

Increasing cycling for transport can contribute to improve public health among adults. Micro-environmental factors (i.e. small-scaled street-setting features) may play an important role in affecting the street's appeal to cycle for transport. Understanding about the interplay between individuals and their physical environment is important to establish tailored environmental interventions. Therefore, the current study aimed to examine whether specific subgroups exist based on similarities in micro-environmental preferences to cycle for transport. Responses of 1950 middle-aged adults (45-65 years) on a series of choice tasks depicting potential cycling routes with manipulated photographs yielded three subgroups with different micro-environmental preferences using latent class analysis. Although latent class analysis revealed three different subgroups in the middle-aged adult population based on their environmental preferences, results indicated that cycle path type (i.e. a good separated cycle path) is the most important environmental factor for all participants and certainly for individuals who did not cycle for transport. Furthermore, only negligible differences were found between the importances of the other micro-environmental factors (i.e. traffic density, evenness of the cycle path, maintenance, vegetation and speed limits) regarding the two at risk subgroups and that providing a speed bump obviously has the least impact on the street's appeal to cycle for transport. Results from the current study indicate that only negligible differences were found between the three subgroups. Therefore, it might be suggested that tailored environmental interventions are not required in this research context.

Class Evolution Tree: A Graphical Tool to Support Decisions on the Number of Classes in Exploratory Categorical Latent Variable Modeling for Rehabilitation Research

ERIC Educational Resources Information Center

Kriston, Levente; Melchior, Hanne; Hergert, Anika; Bergelt, Corinna; Watzke, Birgit; Schulz, Holger; von Wolff, Alessa

2011-01-01

The aim of our study was to develop a graphical tool that can be used in addition to standard statistical criteria to support decisions on the number of classes in explorative categorical latent variable modeling for rehabilitation research. Data from two rehabilitation research projects were used. In the first study, a latent profile analysis was…

Data on the interexaminer variation of minutia markup on latent fingerprints.

PubMed

Ulery, Bradford T; Hicklin, R Austin; Roberts, Maria Antonia; Buscaglia, JoAnn

2016-09-01

The data in this article supports the research paper entitled "Interexaminer variation of minutia markup on latent fingerprints" [1]. The data in this article describes the variability in minutia markup during both analysis of the latents and comparison between latents and exemplars. The data was collected in the "White Box Latent Print Examiner Study," in which each of 170 volunteer latent print examiners provided detailed markup documenting their examinations of latent-exemplar pairs of prints randomly assigned from a pool of 320 pairs. Each examiner examined 22 latent-exemplar pairs; an average of 12 examiners marked each latent.

Monoamine Oxidase A (MAOA) Gene and Personality Traits from Late Adolescence through Early Adulthood: A Latent Variable Investigation

PubMed Central

Xu, Man K.; Gaysina, Darya; Tsonaka, Roula; Morin, Alexandre J. S.; Croudace, Tim J.; Barnett, Jennifer H.; Houwing-Duistermaat, Jeanine; Richards, Marcus; Jones, Peter B.

2017-01-01

Very few molecular genetic studies of personality traits have used longitudinal phenotypic data, therefore molecular basis for developmental change and stability of personality remains to be explored. We examined the role of the monoamine oxidase A gene (MAOA) on extraversion and neuroticism from adolescence to adulthood, using modern latent variable methods. A sample of 1,160 male and 1,180 female participants with complete genotyping data was drawn from a British national birth cohort, the MRC National Survey of Health and Development (NSHD). The predictor variable was based on a latent variable representing genetic variations of the MAOA gene measured by three SNPs (rs3788862, rs5906957, and rs979606). Latent phenotype variables were constructed using psychometric methods to represent cross-sectional and longitudinal phenotypes of extraversion and neuroticism measured at ages 16 and 26. In males, the MAOA genetic latent variable (AAG) was associated with lower extraversion score at age 16 (β = −0.167; CI: −0.289, −0.045; p = 0.007, FDRp = 0.042), as well as greater increase in extraversion score from 16 to 26 years (β = 0.197; CI: 0.067, 0.328; p = 0.003, FDRp = 0.036). No genetic association was found for neuroticism after adjustment for multiple testing. Although, we did not find statistically significant associations after multiple testing correction in females, this result needs to be interpreted with caution due to issues related to x-inactivation in females. The latent variable method is an effective way of modeling phenotype- and genetic-based variances and may therefore improve the methodology of molecular genetic studies of complex psychological traits. PMID:29075213

Monoamine Oxidase A (MAOA) Gene and Personality Traits from Late Adolescence through Early Adulthood: A Latent Variable Investigation.

PubMed

Xu, Man K; Gaysina, Darya; Tsonaka, Roula; Morin, Alexandre J S; Croudace, Tim J; Barnett, Jennifer H; Houwing-Duistermaat, Jeanine; Richards, Marcus; Jones, Peter B

2017-01-01

Very few molecular genetic studies of personality traits have used longitudinal phenotypic data, therefore molecular basis for developmental change and stability of personality remains to be explored. We examined the role of the monoamine oxidase A gene ( MAOA ) on extraversion and neuroticism from adolescence to adulthood, using modern latent variable methods. A sample of 1,160 male and 1,180 female participants with complete genotyping data was drawn from a British national birth cohort, the MRC National Survey of Health and Development (NSHD). The predictor variable was based on a latent variable representing genetic variations of the MAOA gene measured by three SNPs (rs3788862, rs5906957, and rs979606). Latent phenotype variables were constructed using psychometric methods to represent cross-sectional and longitudinal phenotypes of extraversion and neuroticism measured at ages 16 and 26. In males, the MAOA genetic latent variable (AAG) was associated with lower extraversion score at age 16 (β = -0.167; CI: -0.289, -0.045; p = 0.007, FDRp = 0.042), as well as greater increase in extraversion score from 16 to 26 years (β = 0.197; CI: 0.067, 0.328; p = 0.003, FDRp = 0.036). No genetic association was found for neuroticism after adjustment for multiple testing. Although, we did not find statistically significant associations after multiple testing correction in females, this result needs to be interpreted with caution due to issues related to x-inactivation in females. The latent variable method is an effective way of modeling phenotype- and genetic-based variances and may therefore improve the methodology of molecular genetic studies of complex psychological traits.

Group Comparisons in the Presence of Missing Data Using Latent Variable Modeling Techniques

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2010-01-01

A latent variable modeling approach for examining population similarities and differences in observed variable relationship and mean indexes in incomplete data sets is discussed. The method is based on the full information maximum likelihood procedure of model fitting and parameter estimation. The procedure can be employed to test group identities…

Stochastic Approximation Methods for Latent Regression Item Response Models

ERIC Educational Resources Information Center

von Davier, Matthias; Sinharay, Sandip

2010-01-01

This article presents an application of a stochastic approximation expectation maximization (EM) algorithm using a Metropolis-Hastings (MH) sampler to estimate the parameters of an item response latent regression model. Latent regression item response models are extensions of item response theory (IRT) to a latent variable model with covariates…

Estimation and Model Selection for Finite Mixtures of Latent Interaction Models

ERIC Educational Resources Information Center

Hsu, Jui-Chen

2011-01-01

Latent interaction models and mixture models have received considerable attention in social science research recently, but little is known about how to handle if unobserved population heterogeneity exists in the endogenous latent variables of the nonlinear structural equation models. The current study estimates a mixture of latent interaction…

Aggressiveness as a latent personality trait of domestic dogs: Testing local independence and measurement invariance.

PubMed

Goold, Conor; Newberry, Ruth C

2017-01-01

Studies of animal personality attempt to uncover underlying or "latent" personality traits that explain broad patterns of behaviour, often by applying latent variable statistical models (e.g., factor analysis) to multivariate data sets. Two integral, but infrequently confirmed, assumptions of latent variable models in animal personality are: i) behavioural variables are independent (i.e., uncorrelated) conditional on the latent personality traits they reflect (local independence), and ii) personality traits are associated with behavioural variables in the same way across individuals or groups of individuals (measurement invariance). We tested these assumptions using observations of aggression in four age classes (4-10 months, 10 months-3 years, 3-6 years, over 6 years) of male and female shelter dogs (N = 4,743) in 11 different contexts. A structural equation model supported the hypothesis of two positively correlated personality traits underlying aggression across contexts: aggressiveness towards people and aggressiveness towards dogs (comparative fit index: 0.96; Tucker-Lewis index: 0.95; root mean square error of approximation: 0.03). Aggression across contexts was moderately repeatable (towards people: intraclass correlation coefficient (ICC) = 0.479; towards dogs: ICC = 0.303). However, certain contexts related to aggressiveness towards people (but not dogs) shared significant residual relationships unaccounted for by latent levels of aggressiveness. Furthermore, aggressiveness towards people and dogs in different contexts interacted with sex and age. Thus, sex and age differences in displays of aggression were not simple functions of underlying aggressiveness. Our results illustrate that the robustness of traits in latent variable models must be critically assessed before making conclusions about the effects of, or factors influencing, animal personality. Our findings are of concern because inaccurate "aggressive personality" trait attributions can be costly to dogs, recipients of aggression and society in general.

Latent variable method for automatic adaptation to background states in motor imagery BCI

NASA Astrophysics Data System (ADS)

Dagaev, Nikolay; Volkova, Ksenia; Ossadtchi, Alexei

2018-02-01

Objective. Brain-computer interface (BCI) systems are known to be vulnerable to variabilities in background states of a user. Usually, no detailed information on these states is available even during the training stage. Thus there is a need in a method which is capable of taking background states into account in an unsupervised way. Approach. We propose a latent variable method that is based on a probabilistic model with a discrete latent variable. In order to estimate the model’s parameters, we suggest to use the expectation maximization algorithm. The proposed method is aimed at assessing characteristics of background states without any corresponding data labeling. In the context of asynchronous motor imagery paradigm, we applied this method to the real data from twelve able-bodied subjects with open/closed eyes serving as background states. Main results. We found that the latent variable method improved classification of target states compared to the baseline method (in seven of twelve subjects). In addition, we found that our method was also capable of background states recognition (in six of twelve subjects). Significance. Without any supervised information on background states, the latent variable method provides a way to improve classification in BCI by taking background states into account at the training stage and then by making decisions on target states weighted by posterior probabilities of background states at the prediction stage.

Latent class instrumental variables: A clinical and biostatistical perspective

PubMed Central

Baker, Stuart G.; Kramer, Barnett S.; Lindeman, Karen S.

2015-01-01

In some two-arm randomized trials, some participants receive the treatment assigned to the other arm as a result of technical problems, refusal of a treatment invitation, or a choice of treatment in an encouragement design. In some before-and-after studies, the availability of a new treatment changes from one time period to this next. Under assumptions that are often reasonable, the latent class instrumental variable (IV) method estimates the effect of treatment received in the aforementioned scenarios involving all-or-none compliance and all-or-none availability. Key aspects are four initial latent classes (sometimes called principal strata) based on treatment received if in each randomization group or time period, the exclusion restriction assumption (in which randomization group or time period is an instrumental variable), the monotonicity assumption (which drops an implausible latent class from the analysis), and the estimated effect of receiving treatment in one latent class (sometimes called efficacy, the local average treatment effect, or the complier average causal effect). Since its independent formulations in the biostatistics and econometrics literatures, the latent class IV method (which has no well-established name) has gained increasing popularity. We review the latent class IV method from a clinical and biostatistical perspective, focusing on underlying assumptions, methodological extensions, and applications in our fields of obstetrics and cancer research. PMID:26239275

Stigma and Psychological Well-being Among Older Adults With HIV: The Impact of Spirituality and Integrative Health Approaches.

PubMed

Porter, Kristen E; Brennan-Ing, Mark; Burr, Jeffrey A; Dugan, Elizabeth; Karpiak, Stephen E

2017-04-01

The National Institutes of Health calls for research that explores what it means to age optimally with HIV/AIDS as half of the U.S. people with HIV are aged 50 or older. This study applied the stress process model to examine the association between HIV stigma and psychological well-being and mediating resources (i.e., spirituality and complementary and integrative health [CIH]) approaches) in older adults with HIV. Using data from the Research on Older Adults with HIV (ROAH) study, structural equation modeling was used to estimate these relationships within a latent variable model. Namely, a direct negative association between HIV stigma and psychological well-being was hypothesized that would be mediated by spirituality and/or CIH use. The analyses showed that the model fits the data well [χ2 (137, N = 914) = 561.44, p = .000; comparative fit index = .964; root mean square error of approximation = .058, 95% confidence interval = .053 to .063]. All observed variables significantly loaded on their latent factor, and all paths were significant. Results indicated that spirituality and CIH use significantly mediated the negative association between HIV stigma and psychological well-being. Findings highlight the importance of spiritual and CIH interventions for older adults with HIV/AIDS. Practice recommendations are provided at the micro- and mesolevel. © The Author 2015. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The Role of Safety Culture in Influencing Provider Perceptions of Patient Safety.

PubMed

Bishop, Andrea C; Boyle, Todd A

2016-12-01

To determine how provider perceptions of safety culture influence their involvement in patient safety practices. Health-care providers were surveyed in 2 tertiary hospitals located in Atlantic Canada, composed of 4 units in total. The partial least squares (PLS) approach to structural equation modeling was used to analyze the data. Latent variables provider PLS model encompassed the hypothesized relationships between provider characteristics, safety culture, perceptions of patient safety practices, and actual performance of patient safety practices, using the Health Belief Model (HBM) as a guide. Data analysis was conducted using SmartPLS. A total of 113 health-care providers completed a survey out of an eligible 318, representing a response rate of 35.5%. The final PLS model showed acceptable internal consistency with all four latent variables having a composite reliability score above the recommended 0.70 cutoff value (safety culture = 0.86, threat = 0.76, expectations = 0.83, PS practices = 0.75). Discriminant validity was established, and all path coefficients were found to be significant at the α = 0.05 level using nonparametric bootstrapping. The survey results show that safety culture accounted for 34% of the variance in perceptions of threat and 42% of the variance in expectations. This research supports the role that safety culture plays in the promotion and maintenance of patient safety activities for health-care providers. As such, it is recommended that the introduction of new patient safety strategies follow a thorough exploration of an organization's safety culture.

Identification of Chinese medicine syndromes in persistent insomnia associated with major depressive disorder: a latent tree analysis.

PubMed

Yeung, Wing-Fai; Chung, Ka-Fai; Zhang, Nevin Lian-Wen; Zhang, Shi Ping; Yung, Kam-Ping; Chen, Pei-Xian; Ho, Yan-Yee

2016-01-01

Chinese medicine (CM) syndrome (zheng) differentiation is based on the co-occurrence of CM manifestation profiles, such as signs and symptoms, and pulse and tongue features. Insomnia is a symptom that frequently occurs in major depressive disorder despite adequate antidepressant treatment. This study aims to identify co-occurrence patterns in participants with persistent insomnia and major depressive disorder from clinical feature data using latent tree analysis, and to compare the latent variables with relevant CM syndromes. One hundred and forty-two participants with persistent insomnia and a history of major depressive disorder completed a standardized checklist (the Chinese Medicine Insomnia Symptom Checklist) specially developed for CM syndrome classification of insomnia. The checklist covers symptoms and signs, including tongue and pulse features. The clinical features assessed by the checklist were analyzed using Lantern software. CM practitioners with relevant experience compared the clinical feature variables under each latent variable with reference to relevant CM syndromes, based on a previous review of CM syndromes. The symptom data were analyzed to build the latent tree model and the model with the highest Bayes information criterion score was regarded as the best model. This model contained 18 latent variables, each of which divided participants into two clusters. Six clusters represented more than 50 % of the sample. The clinical feature co-occurrence patterns of these six clusters were interpreted as the CM syndromes Liver qi stagnation transforming into fire, Liver fire flaming upward, Stomach disharmony, Hyperactivity of fire due to yin deficiency, Heart-kidney noninteraction, and Qi deficiency of the heart and gallbladder. The clinical feature variables that contributed significant cumulative information coverage (at least 95 %) were identified. Latent tree model analysis on a sample of depressed participants with insomnia revealed 13 clinical feature co-occurrence patterns, four mutual-exclusion patterns, and one pattern with a single clinical feature variable.

MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data.

PubMed

Sella, Nadir; Verny, Louis; Uguzzoni, Guido; Affeldt, Séverine; Isambert, Hervé

2018-07-01

We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction. MIIC online can be freely accessed at https://miic.curie.fr. Supplementary data are available at Bioinformatics online.

Development of lifetime comorbidity in the WHO World Mental Health (WMH) Surveys

PubMed Central

Kessler, Ronald C.; Ormel, Johan; Petukhova, Maria; McLaughlin, Katie A.; Green, Jennifer Greif; Russo, Leo J.; Stein, Dan J.; Zaslavsky, Alan M; Aguilar-Gaxiola, Sergio; Alonso, Jordi; Andrade, Laura; Benjet, Corina; de Girolamo, Giovanni; de Graaf, Ron; Demyttenaere, Koen; Fayyad, John; Haro, Josep Maria; Hu, Chi yi; Karam, Aimee; Lee, Sing; Lepine, Jean-Pierre; Matchsinger, Herbert; Mihaescu-Pintia, Constanta; Posada-Villa, Jose; Sagar, Rajesh; Üstün, T. Bedirhan

2010-01-01

CONTEXT Although numerous studies have examined the role of latent variables in the structure of comorbidity among mental disorders, none has examined their role in the development of comorbidity. OBJECTIVE To study the role of latent variables in the development of comorbidity among 18 lifetime DSM-IV disorders in the WHO World Mental Health (WMH) surveys. SETTING/PARTICIPANTS Nationally or regionally representative community surveys in 14 countries with a total of 21,229 respondents. MAIN OUTCOME MEASURES First onset of 18 lifetime DSM-IV anxiety, mood, behavior, and substance disorders assessed retrospectively in the WHO Composite International Diagnostic Interview (CIDI). RESULTS Separate internalizing (anxiety and mood disorders) and externalizing (behavior and substance disorders) factors were found in exploratory factor analysis of lifetime disorders. Consistently significant positive time-lagged associations were found in survival analyses for virtually all temporally primary lifetime disorders predicting subsequent onset of other disorders. Within-domain (i.e., internalizing or externalizing) associations were generally stronger than between-domain associations. The vast majority of time-lagged associations were explained by a model that assumed the existence of mediating latent internalizing and externalizing variables. Specific phobia and obsessive-compulsive disorder (internalizing) and hyperactivity disorder and oppositional-defiant disorder (externalizing) were the most important predictors. A small number of residual associations remained significant after controlling the latent variables. CONCLUSIONS The good fit of the latent variable model suggests that common causal pathways account for most of the comorbidity among the disorders considered here. These common pathways should be the focus of future research on the development of comorbidity, although several important pair-wise associations that cannot be accounted for by latent variables also exist that warrant further focused study. PMID:21199968

Regression mixture models: Does modeling the covariance between independent variables and latent classes improve the results?

PubMed Central

Lamont, Andrea E.; Vermunt, Jeroen K.; Van Horn, M. Lee

2016-01-01

Regression mixture models are increasingly used as an exploratory approach to identify heterogeneity in the effects of a predictor on an outcome. In this simulation study, we test the effects of violating an implicit assumption often made in these models – i.e., independent variables in the model are not directly related to latent classes. Results indicated that the major risk of failing to model the relationship between predictor and latent class was an increase in the probability of selecting additional latent classes and biased class proportions. Additionally, this study tests whether regression mixture models can detect a piecewise relationship between a predictor and outcome. Results suggest that these models are able to detect piecewise relations, but only when the relationship between the latent class and the predictor is included in model estimation. We illustrate the implications of making this assumption through a re-analysis of applied data examining heterogeneity in the effects of family resources on academic achievement. We compare previous results (which assumed no relation between independent variables and latent class) to the model where this assumption is lifted. Implications and analytic suggestions for conducting regression mixture based on these findings are noted. PMID:26881956

A descriptivist approach to trait conceptualization and inference.

PubMed

Jonas, Katherine G; Markon, Kristian E

2016-01-01

In their recent article, How Functionalist and Process Approaches to Behavior Can Explain Trait Covariation, Wood, Gardner, and Harms (2015) underscore the need for more process-based understandings of individual differences. At the same time, the article illustrates a common error in the use and interpretation of latent variable models: namely, the misuse of models to arbitrate issues of causation and the nature of latent variables. Here, we explain how latent variables can be understood simply as parsimonious summaries of data, and how statistical inference can be based on choosing those summaries that minimize information required to represent the data using the model. Although Wood, Gardner, and Harms acknowledge this perspective, they underestimate its significance, including its importance to modeling and the conceptualization of psychological measurement. We believe this perspective has important implications for understanding individual differences in a number of domains, including current debates surrounding the role of formative versus reflective latent variables. (c) 2015 APA, all rights reserved).

Stochastic Approximation Methods for Latent Regression Item Response Models. Research Report. ETS RR-09-09

ERIC Educational Resources Information Center

von Davier, Matthias; Sinharay, Sandip

2009-01-01

This paper presents an application of a stochastic approximation EM-algorithm using a Metropolis-Hastings sampler to estimate the parameters of an item response latent regression model. Latent regression models are extensions of item response theory (IRT) to a 2-level latent variable model in which covariates serve as predictors of the…

A General Approach to Defining Latent Growth Components

ERIC Educational Resources Information Center

Mayer, Axel; Steyer, Rolf; Mueller, Horst

2012-01-01

We present a 3-step approach to defining latent growth components. In the first step, a measurement model with at least 2 indicators for each time point is formulated to identify measurement error variances and obtain latent variables that are purged from measurement error. In the second step, we use contrast matrices to define the latent growth…

Latent class instrumental variables: a clinical and biostatistical perspective.

PubMed

Baker, Stuart G; Kramer, Barnett S; Lindeman, Karen S

2016-01-15

In some two-arm randomized trials, some participants receive the treatment assigned to the other arm as a result of technical problems, refusal of a treatment invitation, or a choice of treatment in an encouragement design. In some before-and-after studies, the availability of a new treatment changes from one time period to this next. Under assumptions that are often reasonable, the latent class instrumental variable (IV) method estimates the effect of treatment received in the aforementioned scenarios involving all-or-none compliance and all-or-none availability. Key aspects are four initial latent classes (sometimes called principal strata) based on treatment received if in each randomization group or time period, the exclusion restriction assumption (in which randomization group or time period is an instrumental variable), the monotonicity assumption (which drops an implausible latent class from the analysis), and the estimated effect of receiving treatment in one latent class (sometimes called efficacy, the local average treatment effect, or the complier average causal effect). Since its independent formulations in the biostatistics and econometrics literatures, the latent class IV method (which has no well-established name) has gained increasing popularity. We review the latent class IV method from a clinical and biostatistical perspective, focusing on underlying assumptions, methodological extensions, and applications in our fields of obstetrics and cancer research. Copyright © 2015 John Wiley & Sons, Ltd.

Revealing unobserved factors underlying cortical activity with a rectified latent variable model applied to neural population recordings.

PubMed

Whiteway, Matthew R; Butts, Daniel A

2017-03-01

The activity of sensory cortical neurons is not only driven by external stimuli but also shaped by other sources of input to the cortex. Unlike external stimuli, these other sources of input are challenging to experimentally control, or even observe, and as a result contribute to variability of neural responses to sensory stimuli. However, such sources of input are likely not "noise" and may play an integral role in sensory cortex function. Here we introduce the rectified latent variable model (RLVM) in order to identify these sources of input using simultaneously recorded cortical neuron populations. The RLVM is novel in that it employs nonnegative (rectified) latent variables and is much less restrictive in the mathematical constraints on solutions because of the use of an autoencoder neural network to initialize model parameters. We show that the RLVM outperforms principal component analysis, factor analysis, and independent component analysis, using simulated data across a range of conditions. We then apply this model to two-photon imaging of hundreds of simultaneously recorded neurons in mouse primary somatosensory cortex during a tactile discrimination task. Across many experiments, the RLVM identifies latent variables related to both the tactile stimulation as well as nonstimulus aspects of the behavioral task, with a majority of activity explained by the latter. These results suggest that properly identifying such latent variables is necessary for a full understanding of sensory cortical function and demonstrate novel methods for leveraging large population recordings to this end. NEW & NOTEWORTHY The rapid development of neural recording technologies presents new opportunities for understanding patterns of activity across neural populations. Here we show how a latent variable model with appropriate nonlinear form can be used to identify sources of input to a neural population and infer their time courses. Furthermore, we demonstrate how these sources are related to behavioral contexts outside of direct experimental control. Copyright © 2017 the American Physiological Society.

The Impact of Ignoring the Level of Nesting Structure in Nonparametric Multilevel Latent Class Models

ERIC Educational Resources Information Center

Park, Jungkyu; Yu, Hsiu-Ting

2016-01-01

The multilevel latent class model (MLCM) is a multilevel extension of a latent class model (LCM) that is used to analyze nested structure data structure. The nonparametric version of an MLCM assumes a discrete latent variable at a higher-level nesting structure to account for the dependency among observations nested within a higher-level unit. In…

Measuring individual differences in responses to date-rape vignettes using latent variable models.

PubMed

Tuliao, Antover P; Hoffman, Lesa; McChargue, Dennis E

2017-01-01

Vignette methodology can be a flexible and powerful way to examine individual differences in response to dangerous real-life scenarios. However, most studies underutilize the usefulness of such methodology by analyzing only one outcome, which limits the ability to track event-related changes (e.g., vacillation in risk perception). The current study was designed to illustrate the dynamic influence of risk perception on exit point from a date-rape vignette. Our primary goal was to provide an illustrative example of how to use latent variable models for vignette methodology, including latent growth curve modeling with piecewise slopes, as well as latent variable measurement models. Through the combination of a step-by-step exposition in this text and corresponding model syntax available electronically, we detail an alternative statistical "blueprint" to enhance future violence research efforts using vignette methodology. Aggr. Behav. 43:60-73, 2017. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

TPSLVM: a dimensionality reduction algorithm based on thin plate splines.

PubMed

Jiang, Xinwei; Gao, Junbin; Wang, Tianjiang; Shi, Daming

2014-10-01

Dimensionality reduction (DR) has been considered as one of the most significant tools for data analysis. One type of DR algorithms is based on latent variable models (LVM). LVM-based models can handle the preimage problem easily. In this paper we propose a new LVM-based DR model, named thin plate spline latent variable model (TPSLVM). Compared to the well-known Gaussian process latent variable model (GPLVM), our proposed TPSLVM is more powerful especially when the dimensionality of the latent space is low. Also, TPSLVM is robust to shift and rotation. This paper investigates two extensions of TPSLVM, i.e., the back-constrained TPSLVM (BC-TPSLVM) and TPSLVM with dynamics (TPSLVM-DM) as well as their combination BC-TPSLVM-DM. Experimental results show that TPSLVM and its extensions provide better data visualization and more efficient dimensionality reduction compared to PCA, GPLVM, ISOMAP, etc.

Realist identification of group-level latent variables for perinatal social epidemiology theory building.

PubMed

Eastwood, John Graeme; Jalaludin, Bin Badrudin; Kemp, Lynn Ann; Phung, Hai Ngoc

2014-01-01

We have previously reported in this journal on an ecological study of perinatal depressive symptoms in South Western Sydney. In that article, we briefly reported on a factor analysis that was utilized to identify empirical indicators for analysis. In this article, we report on the mixed method approach that was used to identify those latent variables. Social epidemiology has been slow to embrace a latent variable approach to the study of social, political, economic, and cultural structures and mechanisms, partly for philosophical reasons. Critical realist ontology and epistemology have been advocated as an appropriate methodological approach to both theory building and theory testing in the health sciences. We describe here an emergent mixed method approach that uses qualitative methods to identify latent constructs followed by factor analysis using empirical indicators chosen to measure identified qualitative codes. Comparative analysis of the findings is reported together with a limited description of realist approaches to abstract reasoning.

Using SAS PROC CALIS to fit Level-1 error covariance structures of latent growth models.

PubMed

Ding, Cherng G; Jane, Ten-Der

2012-09-01

In the present article, we demonstrates the use of SAS PROC CALIS to fit various types of Level-1 error covariance structures of latent growth models (LGM). Advantages of the SEM approach, on which PROC CALIS is based, include the capabilities of modeling the change over time for latent constructs, measured by multiple indicators; embedding LGM into a larger latent variable model; incorporating measurement models for latent predictors; and better assessing model fit and the flexibility in specifying error covariance structures. The strength of PROC CALIS is always accompanied with technical coding work, which needs to be specifically addressed. We provide a tutorial on the SAS syntax for modeling the growth of a manifest variable and the growth of a latent construct, focusing the documentation on the specification of Level-1 error covariance structures. Illustrations are conducted with the data generated from two given latent growth models. The coding provided is helpful when the growth model has been well determined and the Level-1 error covariance structure is to be identified.

Heteroscedastic Latent Trait Models for Dichotomous Data.

PubMed

Molenaar, Dylan

2015-09-01

Effort has been devoted to account for heteroscedasticity with respect to observed or latent moderator variables in item or test scores. For instance, in the multi-group generalized linear latent trait model, it could be tested whether the observed (polychoric) covariance matrix differs across the levels of an observed moderator variable. In the case that heteroscedasticity arises across the latent trait itself, existing models commonly distinguish between heteroscedastic residuals and a skewed trait distribution. These models have valuable applications in intelligence, personality and psychopathology research. However, existing approaches are only limited to continuous and polytomous data, while dichotomous data are common in intelligence and psychopathology research. Therefore, in present paper, a heteroscedastic latent trait model is presented for dichotomous data. The model is studied in a simulation study, and applied to data pertaining alcohol use and cognitive ability.

Maximum Likelihood Estimation of Nonlinear Structural Equation Models with Ignorable Missing Data

ERIC Educational Resources Information Center

Lee, Sik-Yum; Song, Xin-Yuan; Lee, John C. K.

2003-01-01

The existing maximum likelihood theory and its computer software in structural equation modeling are established on the basis of linear relationships among latent variables with fully observed data. However, in social and behavioral sciences, nonlinear relationships among the latent variables are important for establishing more meaningful models…

Estimating and Visualizing Nonlinear Relations among Latent Variables: A Semiparametric Approach

ERIC Educational Resources Information Center

Pek, Jolynn; Sterba, Sonya K.; Kok, Bethany E.; Bauer, Daniel J.

2009-01-01

The graphical presentation of any scientific finding enhances its description, interpretation, and evaluation. Research involving latent variables is no exception, especially when potential nonlinear effects are suspect. This article has multiple aims. First, it provides a nontechnical overview of a semiparametric approach to modeling nonlinear…

Generalized Structured Component Analysis with Latent Interactions

ERIC Educational Resources Information Center

Hwang, Heungsun; Ho, Moon-Ho Ringo; Lee, Jonathan

2010-01-01

Generalized structured component analysis (GSCA) is a component-based approach to structural equation modeling. In practice, researchers may often be interested in examining the interaction effects of latent variables. However, GSCA has been geared only for the specification and testing of the main effects of variables. Thus, an extension of GSCA…

Behavioral Scale Reliability and Measurement Invariance Evaluation Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko

2004-01-01

A latent variable modeling approach to reliability and measurement invariance evaluation for multiple-component measuring instruments is outlined. An initial discussion deals with the limitations of coefficient alpha, a frequently used index of composite reliability. A widely and readily applicable structural modeling framework is next described…

Multilevel and Latent Variable Modeling with Composite Links and Exploded Likelihoods

ERIC Educational Resources Information Center

Rabe-Hesketh, Sophia; Skrondal, Anders

2007-01-01

Composite links and exploded likelihoods are powerful yet simple tools for specifying a wide range of latent variable models. Applications considered include survival or duration models, models for rankings, small area estimation with census information, models for ordinal responses, item response models with guessing, randomized response models,…

Evaluation of Validity and Reliability for Hierarchical Scales Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2012-01-01

A latent variable modeling method is outlined, which accomplishes estimation of criterion validity and reliability for a multicomponent measuring instrument with hierarchical structure. The approach provides point and interval estimates for the scale criterion validity and reliability coefficients, and can also be used for testing composite or…

Meta-Analysis of Scale Reliability Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2013-01-01

A latent variable modeling approach is outlined that can be used for meta-analysis of reliability coefficients of multicomponent measuring instruments. Important limitations of efforts to combine composite reliability findings across multiple studies are initially pointed out. A reliability synthesis procedure is discussed that is based on…

Diagnostic Procedures for Detecting Nonlinear Relationships between Latent Variables

ERIC Educational Resources Information Center

Bauer, Daniel J.; Baldasaro, Ruth E.; Gottfredson, Nisha C.

2012-01-01

Structural equation models are commonly used to estimate relationships between latent variables. Almost universally, the fitted models specify that these relationships are linear in form. This assumption is rarely checked empirically, largely for lack of appropriate diagnostic techniques. This article presents and evaluates two procedures that can…

Estimation of the latent mediated effect with ordinal data using the limited-information and Bayesian full-information approaches.

PubMed

Chen, Jinsong; Zhang, Dake; Choi, Jaehwa

2015-12-01

It is common to encounter latent variables with ordinal data in social or behavioral research. Although a mediated effect of latent variables (latent mediated effect, or LME) with ordinal data may appear to be a straightforward combination of LME with continuous data and latent variables with ordinal data, the methodological challenges to combine the two are not trivial. This research covers model structures as complex as LME and formulates both point and interval estimates of LME for ordinal data using the Bayesian full-information approach. We also combine weighted least squares (WLS) estimation with the bias-corrected bootstrapping (BCB; Efron Journal of the American Statistical Association, 82, 171-185, 1987) method or the traditional delta method as the limited-information approach. We evaluated the viability of these different approaches across various conditions through simulation studies, and provide an empirical example to illustrate the approaches. We found that the Bayesian approach with reasonably informative priors is preferred when both point and interval estimates are of interest and the sample size is 200 or above.

The Relationship Between Executive Functions and Language Abilities in Children: A Latent Variables Approach

PubMed Central

Park, Ji Sook; Gangopadhyay, Ishanti; Davidson, Meghan M.; Weismer, Susan Ellis

2017-01-01

Purpose We aimed to outline the latent variables approach for measuring nonverbal executive function (EF) skills in school-age children, and to examine the relationship between nonverbal EF skills and language performance in this age group. Method Seventy-one typically developing children, ages 8 through 11, participated in the study. Three EF components, inhibition, updating, and task-shifting, were each indexed using 2 nonverbal tasks. A latent variables approach was used to extract latent scores that represented each EF construct. Children were also administered common standardized language measures. Multiple regression analyses were conducted to examine the relationship between EF and language skills. Results Nonverbal updating was associated with the Receptive Language Index on the Clinical Evaluation of Language Fundamentals–Fourth Edition (CELF-4). When composites denoting lexical–semantic and syntactic abilities were derived, nonverbal inhibition (but not shifting or updating) was found to predict children's syntactic abilities. These relationships held when the effects of age, IQ, and socioeconomic status were controlled. Conclusions The study makes a methodological contribution by explicating a method by which researchers can use the latent variables approach when measuring EF performance in school-age children. The study makes a theoretical and a clinical contribution by suggesting that language performance may be related to domain-general EFs. PMID:28306755

The Relationship Between Executive Functions and Language Abilities in Children: A Latent Variables Approach.

PubMed

Kaushanskaya, Margarita; Park, Ji Sook; Gangopadhyay, Ishanti; Davidson, Meghan M; Weismer, Susan Ellis

2017-04-14

We aimed to outline the latent variables approach for measuring nonverbal executive function (EF) skills in school-age children, and to examine the relationship between nonverbal EF skills and language performance in this age group. Seventy-one typically developing children, ages 8 through 11, participated in the study. Three EF components, inhibition, updating, and task-shifting, were each indexed using 2 nonverbal tasks. A latent variables approach was used to extract latent scores that represented each EF construct. Children were also administered common standardized language measures. Multiple regression analyses were conducted to examine the relationship between EF and language skills. Nonverbal updating was associated with the Receptive Language Index on the Clinical Evaluation of Language Fundamentals-Fourth Edition (CELF-4). When composites denoting lexical-semantic and syntactic abilities were derived, nonverbal inhibition (but not shifting or updating) was found to predict children's syntactic abilities. These relationships held when the effects of age, IQ, and socioeconomic status were controlled. The study makes a methodological contribution by explicating a method by which researchers can use the latent variables approach when measuring EF performance in school-age children. The study makes a theoretical and a clinical contribution by suggesting that language performance may be related to domain-general EFs.

Aggressiveness as a latent personality trait of domestic dogs: Testing local independence and measurement invariance

PubMed Central

2017-01-01

Studies of animal personality attempt to uncover underlying or “latent” personality traits that explain broad patterns of behaviour, often by applying latent variable statistical models (e.g., factor analysis) to multivariate data sets. Two integral, but infrequently confirmed, assumptions of latent variable models in animal personality are: i) behavioural variables are independent (i.e., uncorrelated) conditional on the latent personality traits they reflect (local independence), and ii) personality traits are associated with behavioural variables in the same way across individuals or groups of individuals (measurement invariance). We tested these assumptions using observations of aggression in four age classes (4–10 months, 10 months–3 years, 3–6 years, over 6 years) of male and female shelter dogs (N = 4,743) in 11 different contexts. A structural equation model supported the hypothesis of two positively correlated personality traits underlying aggression across contexts: aggressiveness towards people and aggressiveness towards dogs (comparative fit index: 0.96; Tucker-Lewis index: 0.95; root mean square error of approximation: 0.03). Aggression across contexts was moderately repeatable (towards people: intraclass correlation coefficient (ICC) = 0.479; towards dogs: ICC = 0.303). However, certain contexts related to aggressiveness towards people (but not dogs) shared significant residual relationships unaccounted for by latent levels of aggressiveness. Furthermore, aggressiveness towards people and dogs in different contexts interacted with sex and age. Thus, sex and age differences in displays of aggression were not simple functions of underlying aggressiveness. Our results illustrate that the robustness of traits in latent variable models must be critically assessed before making conclusions about the effects of, or factors influencing, animal personality. Our findings are of concern because inaccurate “aggressive personality” trait attributions can be costly to dogs, recipients of aggression and society in general. PMID:28854267

Estimators for longitudinal latent exposure models: examining measurement model assumptions.

PubMed

Sánchez, Brisa N; Kim, Sehee; Sammel, Mary D

2017-06-15

Latent variable (LV) models are increasingly being used in environmental epidemiology as a way to summarize multiple environmental exposures and thus minimize statistical concerns that arise in multiple regression. LV models may be especially useful when multivariate exposures are collected repeatedly over time. LV models can accommodate a variety of assumptions but, at the same time, present the user with many choices for model specification particularly in the case of exposure data collected repeatedly over time. For instance, the user could assume conditional independence of observed exposure biomarkers given the latent exposure and, in the case of longitudinal latent exposure variables, time invariance of the measurement model. Choosing which assumptions to relax is not always straightforward. We were motivated by a study of prenatal lead exposure and mental development, where assumptions of the measurement model for the time-changing longitudinal exposure have appreciable impact on (maximum-likelihood) inferences about the health effects of lead exposure. Although we were not particularly interested in characterizing the change of the LV itself, imposing a longitudinal LV structure on the repeated multivariate exposure measures could result in high efficiency gains for the exposure-disease association. We examine the biases of maximum likelihood estimators when assumptions about the measurement model for the longitudinal latent exposure variable are violated. We adapt existing instrumental variable estimators to the case of longitudinal exposures and propose them as an alternative to estimate the health effects of a time-changing latent predictor. We show that instrumental variable estimators remain unbiased for a wide range of data generating models and have advantages in terms of mean squared error. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.

Using Instrumental Variable (IV) Tests to Evaluate Model Specification in Latent Variable Structural Equation Models*

PubMed Central

Kirby, James B.; Bollen, Kenneth A.

2009-01-01

Structural Equation Modeling with latent variables (SEM) is a powerful tool for social and behavioral scientists, combining many of the strengths of psychometrics and econometrics into a single framework. The most common estimator for SEM is the full-information maximum likelihood estimator (ML), but there is continuing interest in limited information estimators because of their distributional robustness and their greater resistance to structural specification errors. However, the literature discussing model fit for limited information estimators for latent variable models is sparse compared to that for full information estimators. We address this shortcoming by providing several specification tests based on the 2SLS estimator for latent variable structural equation models developed by Bollen (1996). We explain how these tests can be used to not only identify a misspecified model, but to help diagnose the source of misspecification within a model. We present and discuss results from a Monte Carlo experiment designed to evaluate the finite sample properties of these tests. Our findings suggest that the 2SLS tests successfully identify most misspecified models, even those with modest misspecification, and that they provide researchers with information that can help diagnose the source of misspecification. PMID:20419054

Matrix completion by deep matrix factorization.

PubMed

Fan, Jicong; Cheng, Jieyu

2018-02-01

Conventional methods of matrix completion are linear methods that are not effective in handling data of nonlinear structures. Recently a few researchers attempted to incorporate nonlinear techniques into matrix completion but there still exists considerable limitations. In this paper, a novel method called deep matrix factorization (DMF) is proposed for nonlinear matrix completion. Different from conventional matrix completion methods that are based on linear latent variable models, DMF is on the basis of a nonlinear latent variable model. DMF is formulated as a deep-structure neural network, in which the inputs are the low-dimensional unknown latent variables and the outputs are the partially observed variables. In DMF, the inputs and the parameters of the multilayer neural network are simultaneously optimized to minimize the reconstruction errors for the observed entries. Then the missing entries can be readily recovered by propagating the latent variables to the output layer. DMF is compared with state-of-the-art methods of linear and nonlinear matrix completion in the tasks of toy matrix completion, image inpainting and collaborative filtering. The experimental results verify that DMF is able to provide higher matrix completion accuracy than existing methods do and DMF is applicable to large matrices. Copyright © 2017 Elsevier Ltd. All rights reserved.

Impact of marriage on HIV/AIDS risk behaviors among impoverished, at-risk couples: a multilevel latent variable approach.

PubMed

Stein, Judith A; Nyamathi, Adeline; Ullman, Jodie B; Bentler, Peter M

2007-01-01

Studies among normative samples generally demonstrate a positive impact of marriage on health behaviors and other related attitudes. In this study, we examine the impact of marriage on HIV/AIDS risk behaviors and attitudes among impoverished, highly stressed, homeless couples, many with severe substance abuse problems. A multilevel analysis of 368 high-risk sexually intimate married and unmarried heterosexual couples assessed individual and couple-level effects on social support, substance use problems, HIV/AIDS knowledge, perceived HIV/AIDS risk, needle-sharing, condom use, multiple sex partners, and HIV/AIDS testing. More variance was explained in the protective and risk variables by couple-level latent variable predictors than by individual latent variable predictors, although some gender effects were found (e.g., more alcohol problems among men). The couple-level variable of marriage predicted lower perceived risk, less deviant social support, and fewer sex partners but predicted more needle-sharing.

Psychometrics in Psychological Research: Role Model or Partner in Science?

ERIC Educational Resources Information Center

Sijtsma, Klaas

2006-01-01

This is a reaction to Borsboom's (2006) discussion paper on the issue that psychology takes so little notice of the modern developments in psychometrics, in particular, latent variable methods. Contrary to Borsboom, it is argued that latent variables are summaries of interesting data properties, that construct validation should involve studying…

An Alternative Approach for Nonlinear Latent Variable Models

ERIC Educational Resources Information Center

Mooijaart, Ab; Bentler, Peter M.

2010-01-01

In the last decades there has been an increasing interest in nonlinear latent variable models. Since the seminal paper of Kenny and Judd, several methods have been proposed for dealing with these kinds of models. This article introduces an alternative approach. The methodology involves fitting some third-order moments in addition to the means and…

Using Structural Equation Models with Latent Variables to Study Student Growth and Development.

ERIC Educational Resources Information Center

Pike, Gary R.

1991-01-01

Analysis of data on freshman-to-senior developmental gains in 722 University of Tennessee-Knoxville students provides evidence of the advantages of structural equation modeling with latent variables and suggests that the group differences identified by traditional analysis of variance and covariance techniques may be an artifact of measurement…

Bayesian Analysis of Structural Equation Models with Nonlinear Covariates and Latent Variables

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lee, Sik-Yum

2006-01-01

In this article, we formulate a nonlinear structural equation model (SEM) that can accommodate covariates in the measurement equation and nonlinear terms of covariates and exogenous latent variables in the structural equation. The covariates can come from continuous or discrete distributions. A Bayesian approach is developed to analyze the…

Evaluation of Reliability Coefficients for Two-Level Models via Latent Variable Analysis

ERIC Educational Resources Information Center

Raykov, Tenko; Penev, Spiridon

2010-01-01

A latent variable analysis procedure for evaluation of reliability coefficients for 2-level models is outlined. The method provides point and interval estimates of group means' reliability, overall reliability of means, and conditional reliability. In addition, the approach can be used to test simple hypotheses about these parameters. The…

Evaluation of Scale Reliability with Binary Measures Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko; Dimitrov, Dimiter M.; Asparouhov, Tihomir

2010-01-01

A method for interval estimation of scale reliability with discrete data is outlined. The approach is applicable with multi-item instruments consisting of binary measures, and is developed within the latent variable modeling methodology. The procedure is useful for evaluation of consistency of single measures and of sum scores from item sets…

Estimation of Latent Group Effects: Psychometric Technical Report No. 2.

ERIC Educational Resources Information Center

Mislevy, Robert J.

Conventional methods of multivariate normal analysis do not apply when the variables of interest are not observed directly, but must be inferred from fallible or incomplete data. For example, responses to mental test items may depend upon latent aptitude variables, which modeled in turn as functions of demographic effects in the population. A…

Global Convergence of the EM Algorithm for Unconstrained Latent Variable Models with Categorical Indicators

ERIC Educational Resources Information Center

Weissman, Alexander

2013-01-01

Convergence of the expectation-maximization (EM) algorithm to a global optimum of the marginal log likelihood function for unconstrained latent variable models with categorical indicators is presented. The sufficient conditions under which global convergence of the EM algorithm is attainable are provided in an information-theoretic context by…

A Comparison of Methods for Estimating Quadratic Effects in Nonlinear Structural Equation Models

ERIC Educational Resources Information Center

Harring, Jeffrey R.; Weiss, Brandi A.; Hsu, Jui-Chen

2012-01-01

Two Monte Carlo simulations were performed to compare methods for estimating and testing hypotheses of quadratic effects in latent variable regression models. The methods considered in the current study were (a) a 2-stage moderated regression approach using latent variable scores, (b) an unconstrained product indicator approach, (c) a latent…

Intraclass Correlation Coefficients in Hierarchical Designs: Evaluation Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko

2011-01-01

Interval estimation of intraclass correlation coefficients in hierarchical designs is discussed within a latent variable modeling framework. A method accomplishing this aim is outlined, which is applicable in two-level studies where participants (or generally lower-order units) are clustered within higher-order units. The procedure can also be…

Evaluation of Weighted Scale Reliability and Criterion Validity: A Latent Variable Modeling Approach

ERIC Educational Resources Information Center

Raykov, Tenko

2007-01-01

A method is outlined for evaluating the reliability and criterion validity of weighted scales based on sets of unidimensional measures. The approach is developed within the framework of latent variable modeling methodology and is useful for point and interval estimation of these measurement quality coefficients in counseling and education…

Multilevel Latent Class Analysis: Parametric and Nonparametric Models

ERIC Educational Resources Information Center

Finch, W. Holmes; French, Brian F.

2014-01-01

Latent class analysis is an analytic technique often used in educational and psychological research to identify meaningful groups of individuals within a larger heterogeneous population based on a set of variables. This technique is flexible, encompassing not only a static set of variables but also longitudinal data in the form of growth mixture…

Discriminant Validity Assessment: Use of Fornell & Larcker criterion versus HTMT Criterion

NASA Astrophysics Data System (ADS)

Hamid, M. R. Ab; Sami, W.; Mohmad Sidek, M. H.

2017-09-01

Assessment of discriminant validity is a must in any research that involves latent variables for the prevention of multicollinearity issues. Fornell and Larcker criterion is the most widely used method for this purpose. However, a new method has emerged for establishing the discriminant validity assessment through heterotrait-monotrait (HTMT) ratio of correlations method. Therefore, this article presents the results of discriminant validity assessment using these methods. Data from previous study was used that involved 429 respondents for empirical validation of value-based excellence model in higher education institutions (HEI) in Malaysia. From the analysis, the convergent, divergent and discriminant validity were established and admissible using Fornell and Larcker criterion. However, the discriminant validity is an issue when employing the HTMT criterion. This shows that the latent variables under study faced the issue of multicollinearity and should be looked into for further details. This also implied that the HTMT criterion is a stringent measure that could detect the possible indiscriminant among the latent variables. In conclusion, the instrument which consisted of six latent variables was still lacking in terms of discriminant validity and should be explored further.

Wealth, intelligence, politics and global fertility differentials.

PubMed

Meisenberg, Gerhard

2009-07-01

Demographic trends in today's world are dominated by large fertility differentials between nations, with 'less developed' nations having higher fertility than the more advanced nations. The present study investigates whether these fertility differences are related primarily to indicators of economic development, the intellectual level of the population, or political modernity in the form of liberal democracy. Results obtained with multiple regression, path models and latent variable models are compared. Both log-transformed GDP and measures of intelligence independently reduce fertility across all methods, whereas the effects of liberal democracy are weak and inconsistent. At present rates of fertility and mortality and in the absence of changes within countries, the average IQ of the young world population would decline by 1.34 points per decade and the average per capita income would decline by 0.79% per year.

An Index and Test of Linear Moderated Mediation.

PubMed

Hayes, Andrew F

2015-01-01

I describe a test of linear moderated mediation in path analysis based on an interval estimate of the parameter of a function linking the indirect effect to values of a moderator-a parameter that I call the index of moderated mediation. This test can be used for models that integrate moderation and mediation in which the relationship between the indirect effect and the moderator is estimated as linear, including many of the models described by Edwards and Lambert ( 2007 ) and Preacher, Rucker, and Hayes ( 2007 ) as well as extensions of these models to processes involving multiple mediators operating in parallel or in serial. Generalization of the method to latent variable models is straightforward. Three empirical examples describe the computation of the index and the test, and its implementation is illustrated using Mplus and the PROCESS macro for SPSS and SAS.

Research Productivity: Some Paths Less Travelled

ERIC Educational Resources Information Center

Martin, Brian

2009-01-01

Conventional approaches for fostering research productivity, such as recruitment and incentives, do relatively little to develop latent capacities in researchers. Six promising unorthodox approaches are the promotion of regular writing, tools for creativity, good luck, happiness, good health and crowd wisdom. These options challenge conventional…

Testing Specific Hypotheses Concerning Latent Group Differences in Multi-group Covariance Structure Analysis with Structured Means.

ERIC Educational Resources Information Center

Dolan, Conor V.; Molenaar, Peter C. M.

1994-01-01

In multigroup covariance structure analysis with structured means, the traditional latent selection model is formulated as a special case of phenotypic selection. Illustrations with real and simulated data demonstrate how one can test specific hypotheses concerning selection on latent variables. (SLD)

Spurious Latent Classes in the Mixture Rasch Model

ERIC Educational Resources Information Center

Alexeev, Natalia; Templin, Jonathan; Cohen, Allan S.

2011-01-01

Mixture Rasch models have been used to study a number of psychometric issues such as goodness of fit, response strategy differences, strategy shifts, and multidimensionality. Although these models offer the potential for improving understanding of the latent variables being measured, under some conditions overextraction of latent classes may…

Piecewise Linear-Linear Latent Growth Mixture Models with Unknown Knots

ERIC Educational Resources Information Center

Kohli, Nidhi; Harring, Jeffrey R.; Hancock, Gregory R.

2013-01-01

Latent growth curve models with piecewise functions are flexible and useful analytic models for investigating individual behaviors that exhibit distinct phases of development in observed variables. As an extension of this framework, this study considers a piecewise linear-linear latent growth mixture model (LGMM) for describing segmented change of…

Life course socioeconomic position and mid-late life cognitive function in Eastern Europe.

PubMed

Horvat, Pia; Richards, Marcus; Malyutina, Sofia; Pajak, Andrzej; Kubinova, Ruzena; Tamosiunas, Abdonas; Pikhart, Hynek; Peasey, Anne; Marmot, Michael G; Bobak, Martin

2014-05-01

To investigate whether the positive relation between socioeconomic position (SEP) across the life course and later life cognitive function observed in Western populations exists in former communist countries with apparently smaller income inequalities. Structural equation modeling analysis of cross-sectional data on 30,846 participants aged 45-78 years in four Central and Eastern European centers: Novosibirsk (Russia), Krakow (Poland), Kaunas (Lithuania), and six Czech towns from the HAPIEE (Health, Alcohol, and Psychosocial factors In Eastern Europe) study. SEP was measured using self-reported childhood (maternal education, household amenities), adult (education), and older adult (current material circumstances) indicators. Latent variable for cognition was constructed from word recall, animal naming, and letter search. Associations between SEP measures over the life course and cognition were similar across study centers. Education had the strongest direct association with cognition, followed by current material circumstances. Indirect path from education to cognition, mediated by current SEP, was small. Direct path from mother's education to cognition was significant but modest, and partially mediated by later SEP measures, particularly education. In these Eastern European populations, late life cognition reflected life course socioeconomic trajectories similarly to findings in Western countries.

Life Course Socioeconomic Position and Mid-Late Life Cognitive Function in Eastern Europe

PubMed Central

2014-01-01

Objectives. To investigate whether the positive relation between socioeconomic position (SEP) across the life course and later life cognitive function observed in Western populations exists in former communist countries with apparently smaller income inequalities. Method. Structural equation modeling analysis of cross-sectional data on 30,846 participants aged 45–78 years in four Central and Eastern European centers: Novosibirsk (Russia), Krakow (Poland), Kaunas (Lithuania), and six Czech towns from the HAPIEE (Health, Alcohol, and Psychosocial factors In Eastern Europe) study. SEP was measured using self-reported childhood (maternal education, household amenities), adult (education), and older adult (current material circumstances) indicators. Latent variable for cognition was constructed from word recall, animal naming, and letter search. Results. Associations between SEP measures over the life course and cognition were similar across study centers. Education had the strongest direct association with cognition, followed by current material circumstances. Indirect path from education to cognition, mediated by current SEP, was small. Direct path from mother’s education to cognition was significant but modest, and partially mediated by later SEP measures, particularly education. Discussion. In these Eastern European populations, late life cognition reflected life course socioeconomic trajectories similarly to findings in Western countries. PMID:24598045

Dissecting the variance-covariance structure in insect physiology: the multivariate association between metabolism and morphology in the nymphs of the sand cricket (Gryllus firmus).

PubMed

Nespolo, Roberto F; Castañeda, Luis E; Roff, Derek A

2005-08-01

Energy metabolism in animals has been largely studied in relation to exogenous sources of variation. However, because they give insight into the relationship between whole metabolism and lower organizational levels such as organs and tissues, examination of endogenous determinants of metabolism other than body mass is itself very important. We studied the multivariate association of body parts and several aspects of energy metabolism in an insect, the nymphs of the sand cricket, Gryllus firmus. By using a variety of both univariate and multivariate techniques, we explored the resultant variance-covariance matrix to build a path diagram with latent variables. After controlling for body mass, we found a significant canonical correlation between metabolism and morphology. According to the factor loadings and path coefficients, the most important contributions of morphology to the correlation were thorax and abdomen size measures, whereas the most important metabolic contribution was resting metabolism. Activity metabolism was mostly explained by body mass rather than body parts, which could be a result of resting rates being chronic consequences of the functioning of the metabolic machinery that the insect must maintain.

A new model of wheezing severity in young children using the validated ISAAC wheezing module: A latent variable approach with validation in independent cohorts.

PubMed

Brunwasser, Steven M; Gebretsadik, Tebeb; Gold, Diane R; Turi, Kedir N; Stone, Cosby A; Datta, Soma; Gern, James E; Hartert, Tina V

2018-01-01

The International Study of Asthma and Allergies in Children (ISAAC) Wheezing Module is commonly used to characterize pediatric asthma in epidemiological studies, including nearly all airway cohorts participating in the Environmental Influences on Child Health Outcomes (ECHO) consortium. However, there is no consensus model for operationalizing wheezing severity with this instrument in explanatory research studies. Severity is typically measured using coarsely-defined categorical variables, reducing power and potentially underestimating etiological associations. More precise measurement approaches could improve testing of etiological theories of wheezing illness. We evaluated a continuous latent variable model of pediatric wheezing severity based on four ISAAC Wheezing Module items. Analyses included subgroups of children from three independent cohorts whose parents reported past wheezing: infants ages 0-2 in the INSPIRE birth cohort study (Cohort 1; n = 657), 6-7-year-old North American children from Phase One of the ISAAC study (Cohort 2; n = 2,765), and 5-6-year-old children in the EHAAS birth cohort study (Cohort 3; n = 102). Models were estimated using structural equation modeling. In all cohorts, covariance patterns implied by the latent variable model were consistent with the observed data, as indicated by non-significant χ2 goodness of fit tests (no evidence of model misspecification). Cohort 1 analyses showed that the latent factor structure was stable across time points and child sexes. In both cohorts 1 and 3, the latent wheezing severity variable was prospectively associated with wheeze-related clinical outcomes, including physician asthma diagnosis, acute corticosteroid use, and wheeze-related outpatient medical visits when adjusting for confounders. We developed an easily applicable continuous latent variable model of pediatric wheezing severity based on items from the well-validated ISAAC Wheezing Module. This model prospectively associates with asthma morbidity, as demonstrated in two ECHO birth cohort studies, and provides a more statistically powerful method of testing etiologic hypotheses of childhood wheezing illness and asthma.

Impact of different eddy covariance sensors and set-up on the annual balance of CO2 and fluxes of CH4 and latent heat in the Arctic

NASA Astrophysics Data System (ADS)

Goodrich, J. P.; Zona, D.; Gioli, B.; Murphy, P.; Burba, G. G.; Oechel, W. C.

2015-12-01

Expanding eddy covariance measurements of CO2 and CH4 fluxes in the Arctic is critical for refining the global C budget. Continuous measurements are particularly challenging because of the remote locations, low power availability, and extreme weather conditions. The necessity for tailoring instrumentation at different sites further complicates the interpretation of results and may add uncertainty to estimates of annual CO2 budgets. We investigated the influence of different sensor combinations on FCO2, latent heat (LE), and FCH4, and assessed the differences in annual FCO2 estimated with different instrumentation at the same sites. Using data from four sites across the North Slope of Alaska, we resolved FCO2 and FCH4 to within 5% using different combinations of open- and closed-path gas analyzers and within 10% using heated and non-heated anemometers. A continuously heated anemometer increased data coverage relative to non-heated anemometers while resulting in comparable annual FCO2, despite over-estimating sensible heat fluxes by 15%. We also implemented an intermittent heating strategy whereby activation only when ice or snow blockage of the transducers was detected. This resulted in comparable data coverage (~ 60%) to the continuously heated anemometer, while avoiding potential over-estimation of sensible heat and gas fluxes. We found good agreement in FCO2 and FCH4 from two closed-path and one open-path gas analyzer, despite the need for large spectral corrections of closed-path fluxes and density and temperature corrections to open-path sensors. However, data coverage was generally greater when using closed-path, especially during cold seasons (36-40% vs 10-14% for the open path), when fluxes from Arctic regions are particularly uncertain and potentially critical to annual C budgets. Measurement of Arctic LE remains a challenge due to strong attenuation along sample tubes, even when heated, that could not be accounted for with spectral corrections.

Three Cs in measurement models: causal indicators, composite indicators, and covariates.

PubMed

Bollen, Kenneth A; Bauldry, Shawn

2011-09-01

In the last 2 decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that one can classify indicators into 2 categories: effect (reflective) indicators and causal (formative) indicators. We argue that the dichotomous view is too simple. Instead, there are effect indicators and 3 types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the "Three Cs"). Causal indicators have conceptual unity, and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variables. Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects, and composites are a matter of convenience. The failure to distinguish the Three Cs has led to confusion and questions, such as, Are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points.

Using structural equation modeling to investigate relationships among ecological variables

USGS Publications Warehouse

Malaeb, Z.A.; Kevin, Summers J.; Pugesek, B.H.

2000-01-01

Structural equation modeling is an advanced multivariate statistical process with which a researcher can construct theoretical concepts, test their measurement reliability, hypothesize and test a theory about their relationships, take into account measurement errors, and consider both direct and indirect effects of variables on one another. Latent variables are theoretical concepts that unite phenomena under a single term, e.g., ecosystem health, environmental condition, and pollution (Bollen, 1989). Latent variables are not measured directly but can be expressed in terms of one or more directly measurable variables called indicators. For some researchers, defining, constructing, and examining the validity of latent variables may be the end task of itself. For others, testing hypothesized relationships of latent variables may be of interest. We analyzed the correlation matrix of eleven environmental variables from the U.S. Environmental Protection Agency's (USEPA) Environmental Monitoring and Assessment Program for Estuaries (EMAP-E) using methods of structural equation modeling. We hypothesized and tested a conceptual model to characterize the interdependencies between four latent variables-sediment contamination, natural variability, biodiversity, and growth potential. In particular, we were interested in measuring the direct, indirect, and total effects of sediment contamination and natural variability on biodiversity and growth potential. The model fit the data well and accounted for 81% of the variability in biodiversity and 69% of the variability in growth potential. It revealed a positive total effect of natural variability on growth potential that otherwise would have been judged negative had we not considered indirect effects. That is, natural variability had a negative direct effect on growth potential of magnitude -0.3251 and a positive indirect effect mediated through biodiversity of magnitude 0.4509, yielding a net positive total effect of 0.1258. Natural variability had a positive direct effect on biodiversity of magnitude 0.5347 and a negative indirect effect mediated through growth potential of magnitude -0.1105 yielding a positive total effects of magnitude 0.4242. Sediment contamination had a negative direct effect on biodiversity of magnitude -0.1956 and a negative indirect effect on growth potential via biodiversity of magnitude -0.067. Biodiversity had a positive effect on growth potential of magnitude 0.8432, and growth potential had a positive effect on biodiversity of magnitude 0.3398. The correlation between biodiversity and growth potential was estimated at 0.7658 and that between sediment contamination and natural variability at -0.3769.

Representing general theoretical concepts in structural equation models: The role of composite variables

USGS Publications Warehouse

Grace, J.B.; Bollen, K.A.

2008-01-01

Structural equation modeling (SEM) holds the promise of providing natural scientists the capacity to evaluate complex multivariate hypotheses about ecological systems. Building on its predecessors, path analysis and factor analysis, SEM allows for the incorporation of both observed and unobserved (latent) variables into theoretically-based probabilistic models. In this paper we discuss the interface between theory and data in SEM and the use of an additional variable type, the composite. In simple terms, composite variables specify the influences of collections of other variables and can be helpful in modeling heterogeneous concepts of the sort commonly of interest to ecologists. While long recognized as a potentially important element of SEM, composite variables have received very limited use, in part because of a lack of theoretical consideration, but also because of difficulties that arise in parameter estimation when using conventional solution procedures. In this paper we present a framework for discussing composites and demonstrate how the use of partially-reduced-form models can help to overcome some of the parameter estimation and evaluation problems associated with models containing composites. Diagnostic procedures for evaluating the most appropriate and effective use of composites are illustrated with an example from the ecological literature. It is argued that an ability to incorporate composite variables into structural equation models may be particularly valuable in the study of natural systems, where concepts are frequently multifaceted and the influence of suites of variables are often of interest. ?? Springer Science+Business Media, LLC 2007.

A Second-Order Conditionally Linear Mixed Effects Model with Observed and Latent Variable Covariates

ERIC Educational Resources Information Center

Harring, Jeffrey R.; Kohli, Nidhi; Silverman, Rebecca D.; Speece, Deborah L.

2012-01-01

A conditionally linear mixed effects model is an appropriate framework for investigating nonlinear change in a continuous latent variable that is repeatedly measured over time. The efficacy of the model is that it allows parameters that enter the specified nonlinear time-response function to be stochastic, whereas those parameters that enter in a…

The Relationship between Executive Functions and Language Abilities in Children: A Latent Variables Approach

ERIC Educational Resources Information Center

Kaushanskaya, Margarita; Park, Ji Sook; Gangopadhyay, Ishanti; Davidson, Meghan M.; Weismer, Susan Ellis

2017-01-01

Purpose: We aimed to outline the latent variables approach for measuring nonverbal executive function (EF) skills in school-age children, and to examine the relationship between nonverbal EF skills and language performance in this age group. Method: Seventy-one typically developing children, ages 8 through 11, participated in the study. Three EF…

The Benefits of Latent Variable Modeling to Develop Norms for a Translated Version of a Standardized Scale

ERIC Educational Resources Information Center

Seo, Hyojeong; Shaw, Leslie A.; Shogren, Karrie A.; Lang, Kyle M.; Little, Todd D.

2017-01-01

This article demonstrates the use of structural equation modeling to develop norms for a translated version of a standardized scale, the Supports Intensity Scale-Children's Version (SIS-C). The latent variable norming method proposed is useful when the standardization sample for a translated version is relatively small to derive norms…

Interrater Agreement Evaluation: A Latent Variable Modeling Approach

ERIC Educational Resources Information Center

Raykov, Tenko; Dimitrov, Dimiter M.; von Eye, Alexander; Marcoulides, George A.

2013-01-01

A latent variable modeling method for evaluation of interrater agreement is outlined. The procedure is useful for point and interval estimation of the degree of agreement among a given set of judges evaluating a group of targets. In addition, the approach allows one to test for identity in underlying thresholds across raters as well as to identify…

Latent Variable Regression 4-Level Hierarchical Model Using Multisite Multiple-Cohorts Longitudinal Data. CRESST Report 801

ERIC Educational Resources Information Center

Choi, Kilchan

2011-01-01

This report explores a new latent variable regression 4-level hierarchical model for monitoring school performance over time using multisite multiple-cohorts longitudinal data. This kind of data set has a 4-level hierarchical structure: time-series observation nested within students who are nested within different cohorts of students. These…

Standard Errors of Estimated Latent Variable Scores with Estimated Structural Parameters

ERIC Educational Resources Information Center

Hoshino, Takahiro; Shigemasu, Kazuo

2008-01-01

The authors propose a concise formula to evaluate the standard error of the estimated latent variable score when the true values of the structural parameters are not known and must be estimated. The formula can be applied to factor scores in factor analysis or ability parameters in item response theory, without bootstrap or Markov chain Monte…

Cognitive Preconditions of Early Reading and Spelling: A Latent-Variable Approach with Longitudinal Data

ERIC Educational Resources Information Center

Preßler, Anna-Lena; Könen, Tanja; Hasselhorn, Marcus; Krajewski, Kristin

2014-01-01

The aim of the present study was to empirically disentangle the interdependencies of the impact of nonverbal intelligence, working memory capacities, and phonological processing skills on early reading decoding and spelling within a latent variable approach. In a sample of 127 children, these cognitive preconditions were assessed before the onset…

An Alternative Two Stage Least Squares (2SLS) Estimator for Latent Variable Equations.

ERIC Educational Resources Information Center

Bollen, Kenneth A.

1996-01-01

An alternative two-stage least squares (2SLS) estimator of the parameters in LISREL type models is proposed and contrasted with existing estimators. The new 2SLS estimator allows observed and latent variables to originate from nonnormal distributions, is consistent, has a known asymptotic covariance matrix, and can be estimated with standard…

Classical Item Analysis Using Latent Variable Modeling: A Note on a Direct Evaluation Procedure

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2011-01-01

A directly applicable latent variable modeling procedure for classical item analysis is outlined. The method allows one to point and interval estimate item difficulty, item correlations, and item-total correlations for composites consisting of categorical items. The approach is readily employed in empirical research and as a by-product permits…

A Direct Latent Variable Modeling Based Method for Point and Interval Estimation of Coefficient Alpha

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2015-01-01

A direct approach to point and interval estimation of Cronbach's coefficient alpha for multiple component measuring instruments is outlined. The procedure is based on a latent variable modeling application with widely circulated software. As a by-product, using sample data the method permits ascertaining whether the population discrepancy…

Introduction to the special section on mixture modeling in personality assessment.

PubMed

Wright, Aidan G C; Hallquist, Michael N

2014-01-01

Latent variable models offer a conceptual and statistical framework for evaluating the underlying structure of psychological constructs, including personality and psychopathology. Complex structures that combine or compare categorical and dimensional latent variables can be accommodated using mixture modeling approaches, which provide a powerful framework for testing nuanced theories about psychological structure. This special series includes introductory primers on cross-sectional and longitudinal mixture modeling, in addition to empirical examples applying these techniques to real-world data collected in clinical settings. This group of articles is designed to introduce personality assessment scientists and practitioners to a general latent variable framework that we hope will stimulate new research and application of mixture models to the assessment of personality and its pathology.

Assets as a Socioeconomic Status Index: Categorical Principal Components Analysis vs. Latent Class Analysis.

PubMed

Sartipi, Majid; Nedjat, Saharnaz; Mansournia, Mohammad Ali; Baigi, Vali; Fotouhi, Akbar

2016-11-01

Some variables like Socioeconomic Status (SES) cannot be directly measured, instead, so-called 'latent variables' are measured indirectly through calculating tangible items. There are different methods for measuring latent variables such as data reduction methods e.g. Principal Components Analysis (PCA) and Latent Class Analysis (LCA). The purpose of our study was to measure assets index- as a representative of SES- through two methods of Non-Linear PCA (NLPCA) and LCA, and to compare them for choosing the most appropriate model. This was a cross sectional study in which 1995 respondents filled the questionnaires about their assets in Tehran. The data were analyzed by SPSS 19 (CATPCA command) and SAS 9.2 (PROC LCA command) to estimate their socioeconomic status. The results were compared based on the Intra-class Correlation Coefficient (ICC). The 6 derived classes from LCA based on BIC, were highly consistent with the 6 classes from CATPCA (Categorical PCA) (ICC = 0.87, 95%CI: 0.86 - 0.88). There is no gold standard to measure SES. Therefore, it is not possible to definitely say that a specific method is better than another one. LCA is a complicated method that presents detailed information about latent variables and required one assumption (local independency), while NLPCA is a simple method, which requires more assumptions. Generally, NLPCA seems to be an acceptable method of analysis because of its simplicity and high agreement with LCA.

Variable-Length Computerized Adaptive Testing Using the Higher Order DINA Model

ERIC Educational Resources Information Center

Hsu, Chia-Ling; Wang, Wen-Chung

2015-01-01

Cognitive diagnosis models provide profile information about a set of latent binary attributes, whereas item response models yield a summary report on a latent continuous trait. To utilize the advantages of both models, higher order cognitive diagnosis models were developed in which information about both latent binary attributes and latent…

Testing Manifest Monotonicity Using Order-Constrained Statistical Inference

ERIC Educational Resources Information Center

Tijmstra, Jesper; Hessen, David J.; van der Heijden, Peter G. M.; Sijtsma, Klaas

2013-01-01

Most dichotomous item response models share the assumption of latent monotonicity, which states that the probability of a positive response to an item is a nondecreasing function of a latent variable intended to be measured. Latent monotonicity cannot be evaluated directly, but it implies manifest monotonicity across a variety of observed scores,…

Visualizing Confidence Bands for Semiparametrically Estimated Nonlinear Relations among Latent Variables

ERIC Educational Resources Information Center

Pek, Jolynn; Chalmers, R. Philip; Kok, Bethany E.; Losardo, Diane

2015-01-01

Structural equation mixture models (SEMMs), when applied as a semiparametric model (SPM), can adequately recover potentially nonlinear latent relationships without their specification. This SPM is useful for exploratory analysis when the form of the latent regression is unknown. The purpose of this article is to help users familiar with structural…

The Effect of Latent Binary Variables on the Uncertainty of the Prediction of a Dichotomous Outcome Using Logistic Regression Based Propensity Score Matching.

PubMed

Szekér, Szabolcs; Vathy-Fogarassy, Ágnes

2018-01-01

Logistic regression based propensity score matching is a widely used method in case-control studies to select the individuals of the control group. This method creates a suitable control group if all factors affecting the output variable are known. However, if relevant latent variables exist as well, which are not taken into account during the calculations, the quality of the control group is uncertain. In this paper, we present a statistics-based research in which we try to determine the relationship between the accuracy of the logistic regression model and the uncertainty of the dependent variable of the control group defined by propensity score matching. Our analyses show that there is a linear correlation between the fit of the logistic regression model and the uncertainty of the output variable. In certain cases, a latent binary explanatory variable can result in a relative error of up to 70% in the prediction of the outcome variable. The observed phenomenon calls the attention of analysts to an important point, which must be taken into account when deducting conclusions.

Three Cs in Measurement Models: Causal Indicators, Composite Indicators, and Covariates

PubMed Central

Bollen, Kenneth A.; Bauldry, Shawn

2013-01-01

In the last two decades attention to causal (and formative) indicators has grown. Accompanying this growth has been the belief that we can classify indicators into two categories, effect (reflective) indicators and causal (formative) indicators. This paper argues that the dichotomous view is too simple. Instead, there are effect indicators and three types of variables on which a latent variable depends: causal indicators, composite (formative) indicators, and covariates (the “three Cs”). Causal indicators have conceptual unity and their effects on latent variables are structural. Covariates are not concept measures, but are variables to control to avoid bias in estimating the relations between measures and latent variable(s). Composite (formative) indicators form exact linear combinations of variables that need not share a concept. Their coefficients are weights rather than structural effects and composites are a matter of convenience. The failure to distinguish the “three Cs” has led to confusion and questions such as: are causal and formative indicators different names for the same indicator type? Should an equation with causal or formative indicators have an error term? Are the coefficients of causal indicators less stable than effect indicators? Distinguishing between causal and composite indicators and covariates goes a long way toward eliminating this confusion. We emphasize the key role that subject matter expertise plays in making these distinctions. We provide new guidelines for working with these variable types, including identification of models, scaling latent variables, parameter estimation, and validity assessment. A running empirical example on self-perceived health illustrates our major points. PMID:21767021

Latent change models of adult cognition: are changes in processing speed and working memory associated with changes in episodic memory?

PubMed

Hertzog, Christopher; Dixon, Roger A; Hultsch, David F; MacDonald, Stuart W S

2003-12-01

The authors used 6-year longitudinal data from the Victoria Longitudinal Study (VLS) to investigate individual differences in amount of episodic memory change. Latent change models revealed reliable individual differences in cognitive change. Changes in episodic memory were significantly correlated with changes in other cognitive variables, including speed and working memory. A structural equation model for the latent change scores showed that changes in speed and working memory predicted changes in episodic memory, as expected by processing resource theory. However, these effects were best modeled as being mediated by changes in induction and fact retrieval. Dissociations were detected between cross-sectional ability correlations and longitudinal changes. Shuffling the tasks used to define the Working Memory latent variable altered patterns of change correlations.

Growth Modeling with Non-Ignorable Dropout: Alternative Analyses of the STAR*D Antidepressant Trial

PubMed Central

Muthén, Bengt; Asparouhov, Tihomir; Hunter, Aimee; Leuchter, Andrew

2011-01-01

This paper uses a general latent variable framework to study a series of models for non-ignorable missingness due to dropout. Non-ignorable missing data modeling acknowledges that missingness may depend on not only covariates and observed outcomes at previous time points as with the standard missing at random (MAR) assumption, but also on latent variables such as values that would have been observed (missing outcomes), developmental trends (growth factors), and qualitatively different types of development (latent trajectory classes). These alternative predictors of missing data can be explored in a general latent variable framework using the Mplus program. A flexible new model uses an extended pattern-mixture approach where missingness is a function of latent dropout classes in combination with growth mixture modeling using latent trajectory classes. A new selection model allows not only an influence of the outcomes on missingness, but allows this influence to vary across latent trajectory classes. Recommendations are given for choosing models. The missing data models are applied to longitudinal data from STAR*D, the largest antidepressant clinical trial in the U.S. to date. Despite the importance of this trial, STAR*D growth model analyses using non-ignorable missing data techniques have not been explored until now. The STAR*D data are shown to feature distinct trajectory classes, including a low class corresponding to substantial improvement in depression, a minority class with a U-shaped curve corresponding to transient improvement, and a high class corresponding to no improvement. The analyses provide a new way to assess drug efficiency in the presence of dropout. PMID:21381817

The Interface Between Theory and Data in Structural Equation Models

USGS Publications Warehouse

Grace, James B.; Bollen, Kenneth A.

2006-01-01

Structural equation modeling (SEM) holds the promise of providing natural scientists the capacity to evaluate complex multivariate hypotheses about ecological systems. Building on its predecessors, path analysis and factor analysis, SEM allows for the incorporation of both observed and unobserved (latent) variables into theoretically based probabilistic models. In this paper we discuss the interface between theory and data in SEM and the use of an additional variable type, the composite, for representing general concepts. In simple terms, composite variables specify the influences of collections of other variables and can be helpful in modeling general relationships of the sort commonly of interest to ecologists. While long recognized as a potentially important element of SEM, composite variables have received very limited use, in part because of a lack of theoretical consideration, but also because of difficulties that arise in parameter estimation when using conventional solution procedures. In this paper we present a framework for discussing composites and demonstrate how the use of partially reduced form models can help to overcome some of the parameter estimation and evaluation problems associated with models containing composites. Diagnostic procedures for evaluating the most appropriate and effective use of composites are illustrated with an example from the ecological literature. It is argued that an ability to incorporate composite variables into structural equation models may be particularly valuable in the study of natural systems, where concepts are frequently multifaceted and the influences of suites of variables are often of interest.

Partial Granger causality--eliminating exogenous inputs and latent variables.

PubMed

Guo, Shuixia; Seth, Anil K; Kendrick, Keith M; Zhou, Cong; Feng, Jianfeng

2008-07-15

Attempts to identify causal interactions in multivariable biological time series (e.g., gene data, protein data, physiological data) can be undermined by the confounding influence of environmental (exogenous) inputs. Compounding this problem, we are commonly only able to record a subset of all related variables in a system. These recorded variables are likely to be influenced by unrecorded (latent) variables. To address this problem, we introduce a novel variant of a widely used statistical measure of causality--Granger causality--that is inspired by the definition of partial correlation. Our 'partial Granger causality' measure is extensively tested with toy models, both linear and nonlinear, and is applied to experimental data: in vivo multielectrode array (MEA) local field potentials (LFPs) recorded from the inferotemporal cortex of sheep. Our results demonstrate that partial Granger causality can reveal the underlying interactions among elements in a network in the presence of exogenous inputs and latent variables in many cases where the existing conditional Granger causality fails.

The Fixed-Links Model in Combination with the Polynomial Function as a Tool for Investigating Choice Reaction Time Data

ERIC Educational Resources Information Center

Schweizer, Karl

2006-01-01

A model with fixed relations between manifest and latent variables is presented for investigating choice reaction time data. The numbers for fixation originate from the polynomial function. Two options are considered: the component-based (1 latent variable for each component of the polynomial function) and composite-based options (1 latent…

Estimation of Contextual Effects through Nonlinear Multilevel Latent Variable Modeling with a Metropolis-Hastings Robbins-Monro Algorithm

ERIC Educational Resources Information Center

Yang, Ji Seung; Cai, Li

2014-01-01

The main purpose of this study is to improve estimation efficiency in obtaining maximum marginal likelihood estimates of contextual effects in the framework of nonlinear multilevel latent variable model by adopting the Metropolis-Hastings Robbins-Monro algorithm (MH-RM). Results indicate that the MH-RM algorithm can produce estimates and standard…

On the Benefits of Latent Variable Modeling for Norming Scales: The Case of the "Supports Intensity Scale-Children's Version"

ERIC Educational Resources Information Center

Seo, Hyojeong; Little, Todd D.; Shogren, Karrie A.; Lang, Kyle M.

2016-01-01

Structural equation modeling (SEM) is a powerful and flexible analytic tool to model latent constructs and their relations with observed variables and other constructs. SEM applications offer advantages over classical models in dealing with statistical assumptions and in adjusting for measurement error. So far, however, SEM has not been fully used…

Do Two or More Multicomponent Instruments Measure the Same Construct? Testing Construct Congruence Using Latent Variable Modeling

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.; Tong, Bing

2016-01-01

A latent variable modeling procedure is discussed that can be used to test if two or more homogeneous multicomponent instruments with distinct components are measuring the same underlying construct. The method is widely applicable in scale construction and development research and can also be of special interest in construct validation studies.…

Improvement in latent variable indirect response modeling of multiple categorical clinical endpoints: application to modeling of guselkumab treatment effects in psoriatic patients.

PubMed

Hu, Chuanpu; Randazzo, Bruce; Sharma, Amarnath; Zhou, Honghui

2017-10-01

Exposure-response modeling plays an important role in optimizing dose and dosing regimens during clinical drug development. The modeling of multiple endpoints is made possible in part by recent progress in latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate the level of improvement achievable by jointly modeling two such endpoints in the latent variable IDR modeling framework through the sharing of model parameters. This is illustrated with an application to the exposure-response of guselkumab, a human IgG1 monoclonal antibody in clinical development that blocks IL-23. A Phase 2b study was conducted in 238 patients with psoriasis for which disease severity was assessed using Psoriasis Area and Severity Index (PASI) and Physician's Global Assessment (PGA) scores. A latent variable Type I IDR model was developed to evaluate the therapeutic effect of guselkumab dosing on 75, 90 and 100% improvement of PASI scores from baseline and PGA scores, with placebo effect empirically modeled. The results showed that the joint model is able to describe the observed data better with fewer parameters compared with the common approach of separately modeling the endpoints.

Construct validity evidence for the Male Role Norms Inventory-Short Form: A structural equation modeling approach using the bifactor model.

PubMed

Levant, Ronald F; Hall, Rosalie J; Weigold, Ingrid K; McCurdy, Eric R

2016-10-01

The construct validity of the Male Role Norms Inventory-Short Form (MRNI-SF) was assessed using a latent variable approach implemented with structural equation modeling (SEM). The MRNI-SF was specified as having a bifactor structure, and validation scales were also specified as latent variables. The latent variable approach had the advantages of separating effects of general and specific factors and controlling for some sources of measurement error. Data (N = 484) were from a diverse sample (38.8% men of color, 22.3% men of diverse sexualities) of community-dwelling and college men who responded to an online survey. The construct validity of the MRNI-SF General Traditional Masculinity Ideology factor was supported for all 4 of the proposed latent correlations with: (a) Male Role Attitudes Scale; (b) general factor of Conformity to Masculine Norms Inventory-46; (c) higher-order factor of Gender Role Conflict Scale; and (d) Personal Attributes Questionnaire-Masculinity Scale. Significant correlations with relevant other latent factors provided concurrent validity evidence for the MRNI-SF specific factors of Negativity toward Sexual Minorities, Importance of Sex, Restrictive Emotionality, and Toughness, with all 8 of the hypothesized relationships supported. However, 3 relationships concerning Dominance were not supported. (The construct validity of the remaining 2 MRNI-SF specific factors-Avoidance of Femininity and Self-Reliance through Mechanical Skills was not assessed.) Comparisons were made, and meaningful differences noted, between the latent correlations emphasized in this study and their raw variable counterparts. Results are discussed in terms of the advantages of an SEM approach and the unique characteristics of the bifactor model. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Using Design-Based Latent Growth Curve Modeling with Cluster-Level Predictor to Address Dependency

ERIC Educational Resources Information Center

Wu, Jiun-Yu; Kwok, Oi-Man; Willson, Victor L.

2014-01-01

The authors compared the effects of using the true Multilevel Latent Growth Curve Model (MLGCM) with single-level regular and design-based Latent Growth Curve Models (LGCM) with or without the higher-level predictor on various criterion variables for multilevel longitudinal data. They found that random effect estimates were biased when the…

A Vernacular for Linear Latent Growth Models

ERIC Educational Resources Information Center

Hancock, Gregory R.; Choi, Jaehwa

2006-01-01

In its most basic form, latent growth modeling (latent curve analysis) allows an assessment of individuals' change in a measured variable X over time. For simple linear models, as with other growth models, parameter estimates associated with the a construct (amount of X at a chosen temporal reference point) and b construct (growth in X per unit…

A Latent Transition Analysis Model for Assessing Change in Cognitive Skills

ERIC Educational Resources Information Center

Li, Feiming; Cohen, Allan; Bottge, Brian; Templin, Jonathan

2016-01-01

Latent transition analysis (LTA) was initially developed to provide a means of measuring change in dynamic latent variables. In this article, we illustrate the use of a cognitive diagnostic model, the DINA model, as the measurement model in a LTA, thereby demonstrating a means of analyzing change in cognitive skills over time. An example is…

Examining Measurement Invariance and Differential Item Functioning with Discrete Latent Construct Indicators: A Note on a Multiple Testing Procedure

ERIC Educational Resources Information Center

Raykov, Tenko; Dimitrov, Dimiter M.; Marcoulides, George A.; Li, Tatyana; Menold, Natalja

2018-01-01

A latent variable modeling method for studying measurement invariance when evaluating latent constructs with multiple binary or binary scored items with no guessing is outlined. The approach extends the continuous indicator procedure described by Raykov and colleagues, utilizes similarly the false discovery rate approach to multiple testing, and…

Alexithymia and psychosocial problems among Italian preadolescents. A latent class analysis approach.

PubMed

Mannarini, Stefania; Balottin, Laura; Toldo, Irene; Gatta, Michela

2016-10-01

The study, conducted on Italian preadolscents aged 11 to 13 belonging to the general population, aims to investigate the relationship between the emotional functioning, namely, alexithymia, and the risk of developing behavioral and emotional problems measured using the Strength and Difficulty Questionnaire. The latent class analysis approach allowed to identify two latent variables, accounting for the internalizing (emotional symptoms and difficulties in emotional awareness) and for the externalizing problems (conduct problems and hyperactivity, problematic relationships with peers, poor prosocial behaviors and externally oriented thinking). The two latent variables featured two latent classes: the difficulty in dealing with problems and the strength to face problems that was representative of most of the healthy participants with specific gender differences. Along with the analysis of psychopathological behaviors, the study of resilience and strengths can prove to be a key step in order to develop valuable preventive approaches to tackle psychiatric disorders. © 2016 Scandinavian Psychological Associations and John Wiley & Sons Ltd.

Estimating Brownian motion dispersal rate, longevity and population density from spatially explicit mark-recapture data on tropical butterflies.

PubMed

Tufto, Jarle; Lande, Russell; Ringsby, Thor-Harald; Engen, Steinar; Saether, Bernt-Erik; Walla, Thomas R; DeVries, Philip J

2012-07-01

1. We develop a Bayesian method for analysing mark-recapture data in continuous habitat using a model in which individuals movement paths are Brownian motions, life spans are exponentially distributed and capture events occur at given instants in time if individuals are within a certain attractive distance of the traps. 2. The joint posterior distribution of the dispersal rate, longevity, trap attraction distances and a number of latent variables representing the unobserved movement paths and time of death of all individuals is computed using Gibbs sampling. 3. An estimate of absolute local population density is obtained simply by dividing the Poisson counts of individuals captured at given points in time by the estimated total attraction area of all traps. Our approach for estimating population density in continuous habitat avoids the need to define an arbitrary effective trapping area that characterized previous mark-recapture methods in continuous habitat. 4. We applied our method to estimate spatial demography parameters in nine species of neotropical butterflies. Path analysis of interspecific variation in demographic parameters and mean wing length revealed a simple network of strong causation. Larger wing length increases dispersal rate, which in turn increases trap attraction distance. However, higher dispersal rate also decreases longevity, thus explaining the surprising observation of a negative correlation between wing length and longevity. © 2012 The Authors. Journal of Animal Ecology © 2012 British Ecological Society.

Reflected stochastic differential equation models for constrained animal movement

USGS Publications Warehouse

Hanks, Ephraim M.; Johnson, Devin S.; Hooten, Mevin B.

2017-01-01

Movement for many animal species is constrained in space by barriers such as rivers, shorelines, or impassable cliffs. We develop an approach for modeling animal movement constrained in space by considering a class of constrained stochastic processes, reflected stochastic differential equations. Our approach generalizes existing methods for modeling unconstrained animal movement. We present methods for simulation and inference based on augmenting the constrained movement path with a latent unconstrained path and illustrate this augmentation with a simulation example and an analysis of telemetry data from a Steller sea lion (Eumatopias jubatus) in southeast Alaska.

Modeling Bivariate Change in Individual Differences: Prospective Associations Between Personality and Life Satisfaction.

PubMed

Hounkpatin, Hilda Osafo; Boyce, Christopher J; Dunn, Graham; Wood, Alex M

2017-09-18

A number of structural equation models have been developed to examine change in 1 variable or the longitudinal association between 2 variables. The most common of these are the latent growth model, the autoregressive cross-lagged model, the autoregressive latent trajectory model, and the latent change score model. The authors first overview each of these models through evaluating their different assumptions surrounding the nature of change and how these assumptions may result in different data interpretations. They then, to elucidate these issues in an empirical example, examine the longitudinal association between personality traits and life satisfaction. In a representative Dutch sample (N = 8,320), with participants providing data on both personality and life satisfaction measures every 2 years over an 8-year period, the authors reproduce findings from previous research. However, some of the structural equation models overviewed have not previously been applied to the personality-life satisfaction relation. The extended empirical examination suggests intraindividual changes in life satisfaction predict subsequent intraindividual changes in personality traits. The availability of data sets with 3 or more assessment waves allows the application of more advanced structural equation models such as the autoregressive latent trajectory or the extended latent change score model, which accounts for the complex dynamic nature of change processes and allows stronger inferences on the nature of the association between variables. However, the choice of model should be determined by theories of change processes in the variables being studied. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Structural model to evaluate the effect of participation and satisfaction on ecotourism sustainability

NASA Astrophysics Data System (ADS)

Kencana, Eka N.; Manutami, T.

2017-10-01

This paper is directed to study the effect of local community participation and visitors’ satisfaction on ecotourism sustainability at Badung regency of Bali province, Indonesia. Two important aspects regarding ecotourism sustainability had been studied, i.e. (a) economic benefits for local people and (b) tourists’ satisfaction. Applying variance-based structural equation modeling, data were collected in July 2015 from local community leaders of Kiadan Village at Badung regency and tourists whom visited this village, were analysed. Four latent variables, namely (a) community participation, (b) economic benefits, (c) tourists’ satisfaction, and (d) ecotourism sustainability, were used to build structural model. The results showed sustainability of Kiadan’s ecotourism was significantly affected by local community participation and visitors’ satisfaction although community participation’s effect slightly greater than tourists’ satisfaction with path values for participation and satisfaction as much as 0.651 and 0.627, respectively.

Is There a Dark Side to Mindfulness? Relation of Mindfulness to Criminogenic Cognitions.

PubMed

Tangney, June P; Dobbins, Ashley E; Stuewig, Jeffrey B; Schrader, Shannon W

2017-10-01

In recent years, mindfulness-based interventions have been modified for use with inmate populations, but how this might relate to specific criminogenic cognitions has not been examined empirically. Theoretically, characteristics of mindfulness should be incompatible with distorted patterns of criminal thinking, but is this in fact the case? Among both 259 male jail inmates and 516 undergraduates, mindfulness was inversely related to the Criminogenic Cognitions Scale (CCS) through a latent variable of emotion regulation. However, in the jail sample, this mediational model also showed a direct, positive path from mindfulness to CCS, with an analogous, but nonsignificant trend in the college sample. Post hoc analyses indicate that the Nonjudgment of Self scale derived from the Mindfulness Inventory: Nine Dimensions (MI:ND) largely accounts for this apparently iatrogenic effect in both samples. Some degree of self-judgment is perhaps necessary and useful, especially among individuals involved in the criminal justice system.

Acculturation, enculturation, and Asian American college students' mental health and attitudes toward seeking professional psychological help.

PubMed

Miller, Matthew J; Yang, Minji; Hui, Kayi; Choi, Na-Yeun; Lim, Robert H

2011-07-01

In the present study, we tested a theoretically and empirically derived partially indirect effects acculturation and enculturation model of Asian American college students' mental health and attitudes toward seeking professional psychological help. Latent variable path analysis with 296 self-identified Asian American college students supported the partially indirect effects model and demonstrated the ways in which behavioral acculturation, behavioral enculturation, values acculturation, values enculturation, and acculturation gap family conflict related to mental health and attitudes toward seeking professional psychological help directly and indirectly through acculturative stress. We also tested a generational status moderator hypothesis to determine whether differences in model-implied relationships emerged across U.S.- (n = 185) and foreign-born (n = 107) participants. Consistent with this hypothesis, statistically significant differences in structural coefficients emerged across generational status. Limitations, future directions for research, and counseling implications are discussed.

Apathy and Type 2 Diabetes among American Indians: Exploring the Protective Effects of Traditional Cultural Involvement

PubMed Central

Carlson, Amanda E.; Aronson, Benjamin D.; Unzen, Michael; Lewis, Melissa; Benjamin, Gabrielle J.; Walls, Melissa L.

2017-01-01

In this study we examine relationships between traditional cultural factors, apathy, and health-related outcomes among a sample of American Indian adults with type 2 diabetes. Participants completed cross-sectional interviewer-assisted paper and pencil surveys. We tested a proposed model using latent variable path analysis in order to understand the relationships between cultural participation, apathy, frequency of high blood sugar symptoms, and health-related quality of life. The model revealed significant direct effects from cultural participation to apathy, and apathy to both health-related outcomes. No direct effect of cultural participation on either health-related outcome was found; however, cultural participation had a negative indirect effect through apathy on high blood sugar and positive indirect effects on health-related quality of life. This study highlights a potential pathway of cultural involvement to positive diabetes outcomes. PMID:28529223

High-Performance Psychometrics: The Parallel-E Parallel-M Algorithm for Generalized Latent Variable Models. Research Report. ETS RR-16-34

ERIC Educational Resources Information Center

von Davier, Matthias

2016-01-01

This report presents results on a parallel implementation of the expectation-maximization (EM) algorithm for multidimensional latent variable models. The developments presented here are based on code that parallelizes both the E step and the M step of the parallel-E parallel-M algorithm. Examples presented in this report include item response…

Cognitive Psychology Meets Psychometric Theory: On the Relation between Process Models for Decision Making and Latent Variable Models for Individual Differences

ERIC Educational Resources Information Center

van der Maas, Han L. J.; Molenaar, Dylan; Maris, Gunter; Kievit, Rogier A.; Borsboom, Denny

2011-01-01

This article analyzes latent variable models from a cognitive psychology perspective. We start by discussing work by Tuerlinckx and De Boeck (2005), who proved that a diffusion model for 2-choice response processes entails a 2-parameter logistic item response theory (IRT) model for individual differences in the response data. Following this line…

On the Benefits of Latent Variable Modeling for Norming Scales: The Case of the "Supports Intensity Scale--Children's Version"

ERIC Educational Resources Information Center

Seo, Hyojeong; Little, Todd D.; Shogren, Karrie A.; Lang, Kyle M.

2016-01-01

Structural equation modeling (SEM) is a powerful and flexible analytic tool to model latent constructs and their relations with observed variables and other constructs. SEM applications offer advantages over classical models in dealing with statistical assumptions and in adjusting for measurement error. So far, however, SEM has not been fully used…

Comparing Between- and Within-Group Variances in a Two-Level Study: A Latent Variable Modeling Approach to Evaluating Their Relationship

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.; Akaeze, Hope O.

2017-01-01

This note is concerned with examining the relationship between within-group and between-group variances in two-level nested designs. A latent variable modeling approach is outlined that permits point and interval estimation of their ratio and allows their comparison in a multilevel study. The procedure can also be used to test various hypotheses…

The "g" Factor and Cognitive Test Session Behavior: Using a Latent Variable Approach in Examining Measurement Invariance Across Age Groups on the WJ III

ERIC Educational Resources Information Center

Frisby, Craig L.; Wang, Ze

2016-01-01

Data from the standardization sample of the Woodcock-Johnson Psychoeducational Battery--Third Edition (WJ III) Cognitive standard battery and Test Session Observation Checklist items were analyzed to understand the relationship between g (general mental ability) and test session behavior (TSB; n = 5,769). Latent variable modeling methods were used…

The Longitudinal Structure of General and Specific Anxiety Dimensions in Children: Testing a Latent Trait-State-Occasion Model

ERIC Educational Resources Information Center

Olatunji, Bunmi O.; Cole, David A.

2009-01-01

In an 8-wave, 4-year longitudinal study, 787 children (Grades 3-6) completed the Revised Children's Manifest Anxiety Scale (C. R. Reynolds & B. O. Richmond, 1985), a measure of the Physiological Reactivity, Worry-Oversensitivity, and Social Alienation dimensions of anxiety. A latent variable (trait-state-occasion) model and a latent growth curve…

Causal Indicators Can Help to Interpret Factors

ERIC Educational Resources Information Center

Bentler, Peter M.

2016-01-01

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

Effects of additional data on Bayesian clustering.

PubMed

Yamazaki, Keisuke

2017-10-01

Hierarchical probabilistic models, such as mixture models, are used for cluster analysis. These models have two types of variables: observable and latent. In cluster analysis, the latent variable is estimated, and it is expected that additional information will improve the accuracy of the estimation of the latent variable. Many proposed learning methods are able to use additional data; these include semi-supervised learning and transfer learning. However, from a statistical point of view, a complex probabilistic model that encompasses both the initial and additional data might be less accurate due to having a higher-dimensional parameter. The present paper presents a theoretical analysis of the accuracy of such a model and clarifies which factor has the greatest effect on its accuracy, the advantages of obtaining additional data, and the disadvantages of increasing the complexity. Copyright © 2017 Elsevier Ltd. All rights reserved.

Elucidating the functional relationship between working memory capacity and psychometric intelligence: a fixed-links modeling approach for experimental repeated-measures designs.

PubMed

Thomas, Philipp; Rammsayer, Thomas; Schweizer, Karl; Troche, Stefan

2015-01-01

Numerous studies reported a strong link between working memory capacity (WMC) and fluid intelligence (Gf), although views differ in respect to how close these two constructs are related to each other. In the present study, we used a WMC task with five levels of task demands to assess the relationship between WMC and Gf by means of a new methodological approach referred to as fixed-links modeling. Fixed-links models belong to the family of confirmatory factor analysis (CFA) and are of particular interest for experimental, repeated-measures designs. With this technique, processes systematically varying across task conditions can be disentangled from processes unaffected by the experimental manipulation. Proceeding from the assumption that experimental manipulation in a WMC task leads to increasing demands on WMC, the processes systematically varying across task conditions can be assumed to be WMC-specific. Processes not varying across task conditions, on the other hand, are probably independent of WMC. Fixed-links models allow for representing these two kinds of processes by two independent latent variables. In contrast to traditional CFA where a common latent variable is derived from the different task conditions, fixed-links models facilitate a more precise or purified representation of the WMC-related processes of interest. By using fixed-links modeling to analyze data of 200 participants, we identified a non-experimental latent variable, representing processes that remained constant irrespective of the WMC task conditions, and an experimental latent variable which reflected processes that varied as a function of experimental manipulation. This latter variable represents the increasing demands on WMC and, hence, was considered a purified measure of WMC controlled for the constant processes. Fixed-links modeling showed that both the purified measure of WMC (β = .48) as well as the constant processes involved in the task (β = .45) were related to Gf. Taken together, these two latent variables explained the same portion of variance of Gf as a single latent variable obtained by traditional CFA (β = .65) indicating that traditional CFA causes an overestimation of the effective relationship between WMC and Gf. Thus, fixed-links modeling provides a feasible method for a more valid investigation of the functional relationship between specific constructs.

Cumulative burden of lifetime adversities: Trauma and mental health in low-SES African Americans and Latino/as.

PubMed

Myers, Hector F; Wyatt, Gail E; Ullman, Jodie B; Loeb, Tamra B; Chin, Dorothy; Prause, Nicole; Zhang, Muyu; Williams, John K; Slavich, George M; Liu, Honghu

2015-05-01

This study examined the utility of a lifetime cumulative adversities and trauma model in predicting the severity of mental health symptoms of depression, anxiety, and posttraumatic stress disorder. We also tested whether ethnicity and gender moderate the effects of this stress exposure construct on mental health using multigroup structural equation modeling. A sample of 500 low-socioeconomic status African American and Latino men and women with histories of adversities and trauma were recruited and assessed with a standard battery of self-report measures of stress and mental health. Multiple-group structural equation models indicated good overall model fit. As hypothesized, experiences of discrimination, childhood family adversities, childhood sexual abuse, other childhood trauma, and chronic stresses all loaded on the latent cumulative burden of adversities and trauma construct (CBAT). The CBAT stress exposure index in turn predicted the mental health status latent variable. Although there were several significant univariate ethnic and gender differences, and ethnic and gender differences were observed on several paths, there were no significant ethnic differences in the final model fit of the data. These findings highlight the deleterious consequences of cumulative stress and trauma for mental health and underscore a need to assess these constructs in selecting appropriate clinical interventions for reducing mental health disparities and improving human health. (c) 2015 APA, all rights reserved).

Childhood malnutrition in Egypt using geoadditive Gaussian and latent variable models.

PubMed

Khatab, Khaled

2010-04-01

Major progress has been made over the last 30 years in reducing the prevalence of malnutrition amongst children less than 5 years of age in developing countries. However, approximately 27% of children under the age of 5 in these countries are still malnourished. This work focuses on the childhood malnutrition in one of the biggest developing countries, Egypt. This study examined the association between bio-demographic and socioeconomic determinants and the malnutrition problem in children less than 5 years of age using the 2003 Demographic and Health survey data for Egypt. In the first step, we use separate geoadditive Gaussian models with the continuous response variables stunting (height-for-age), underweight (weight-for-age), and wasting (weight-for-height) as indicators of nutritional status in our case study. In a second step, based on the results of the first step, we apply the geoadditive Gaussian latent variable model for continuous indicators in which the 3 measurements of the malnutrition status of children are assumed as indicators for the latent variable "nutritional status".

The Theory of Planned Behavior within the Stages of the Transtheoretical Model: Latent Structural Modeling of Stage-Specific Prediction Patterns in Physical Activity

ERIC Educational Resources Information Center

Lippke, Sonia; Nigg, Claudio R.; Maddock, Jay E.

2007-01-01

This is the first study to test whether the stages of change of the transtheoretical model are qualitatively different through exploring discontinuity patterns in theory of planned behavior (TPB) variables using latent multigroup structural equation modeling (MSEM) with AMOS. Discontinuity patterns in terms of latent means and prediction patterns…

Multivariate Analysis of Genotype-Phenotype Association.

PubMed

Mitteroecker, Philipp; Cheverud, James M; Pavlicev, Mihaela

2016-04-01

With the advent of modern imaging and measurement technology, complex phenotypes are increasingly represented by large numbers of measurements, which may not bear biological meaning one by one. For such multivariate phenotypes, studying the pairwise associations between all measurements and all alleles is highly inefficient and prevents insight into the genetic pattern underlying the observed phenotypes. We present a new method for identifying patterns of allelic variation (genetic latent variables) that are maximally associated-in terms of effect size-with patterns of phenotypic variation (phenotypic latent variables). This multivariate genotype-phenotype mapping (MGP) separates phenotypic features under strong genetic control from less genetically determined features and thus permits an analysis of the multivariate structure of genotype-phenotype association, including its dimensionality and the clustering of genetic and phenotypic variables within this association. Different variants of MGP maximize different measures of genotype-phenotype association: genetic effect, genetic variance, or heritability. In an application to a mouse sample, scored for 353 SNPs and 11 phenotypic traits, the first dimension of genetic and phenotypic latent variables accounted for >70% of genetic variation present in all 11 measurements; 43% of variation in this phenotypic pattern was explained by the corresponding genetic latent variable. The first three dimensions together sufficed to account for almost 90% of genetic variation in the measurements and for all the interpretable genotype-phenotype association. Each dimension can be tested as a whole against the hypothesis of no association, thereby reducing the number of statistical tests from 7766 to 3-the maximal number of meaningful independent tests. Important alleles can be selected based on their effect size (additive or nonadditive effect on the phenotypic latent variable). This low dimensionality of the genotype-phenotype map has important consequences for gene identification and may shed light on the evolvability of organisms. Copyright © 2016 by the Genetics Society of America.

The Semantic Distance Task: Quantifying Semantic Distance with Semantic Network Path Length

ERIC Educational Resources Information Center

Kenett, Yoed N.; Levi, Effi; Anaki, David; Faust, Miriam

2017-01-01

Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We…

On Latent Growth Models for Composites and Their Constituents.

PubMed

Hancock, Gregory R; Mao, Xiulin; Kher, Hemant

2013-09-01

Over the last decade and a half, latent growth modeling has become an extremely popular and versatile technique for evaluating longitudinal change and its determinants. Most common among the models applied are those for a single measured variable over time. This model has been extended in a variety of ways, most relevant for the current work being the multidomain and the second-order latent growth models. Whereas the former allows for growth function characteristics to be modeled for multiple outcomes simultaneously, with the degree of growth characteristics' relations assessed within the model (e.g., cross-domain slope factor correlations), the latter models growth in latent outcomes, each of which has effect indicators repeated over time. But what if one has an outcome that is believed to be formative relative to its indicator variables rather than latent? In this case, where the outcome is a composite of multiple constituents, modeling change over time is less straightforward. This article provides analytical and applied details for simultaneously modeling growth in composites and their constituent elements, including a real data example using a general computer self-efficacy questionnaire.

DataHigh: Graphical user interface for visualizing and interacting with high-dimensional neural activity

PubMed Central

Cowley, Benjamin R.; Kaufman, Matthew T.; Butler, Zachary S.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

2014-01-01

Objective Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than three, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance DataHigh was developed to fulfill a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity. PMID:24216250

DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity

NASA Astrophysics Data System (ADS)

Cowley, Benjamin R.; Kaufman, Matthew T.; Butler, Zachary S.; Churchland, Mark M.; Ryu, Stephen I.; Shenoy, Krishna V.; Yu, Byron M.

2013-12-01

Objective. Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. Approach. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. Main results. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. Significance. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.

DataHigh: graphical user interface for visualizing and interacting with high-dimensional neural activity.

PubMed

Cowley, Benjamin R; Kaufman, Matthew T; Butler, Zachary S; Churchland, Mark M; Ryu, Stephen I; Shenoy, Krishna V; Yu, Byron M

2013-12-01

Analyzing and interpreting the activity of a heterogeneous population of neurons can be challenging, especially as the number of neurons, experimental trials, and experimental conditions increases. One approach is to extract a set of latent variables that succinctly captures the prominent co-fluctuation patterns across the neural population. A key problem is that the number of latent variables needed to adequately describe the population activity is often greater than 3, thereby preventing direct visualization of the latent space. By visualizing a small number of 2-d projections of the latent space or each latent variable individually, it is easy to miss salient features of the population activity. To address this limitation, we developed a Matlab graphical user interface (called DataHigh) that allows the user to quickly and smoothly navigate through a continuum of different 2-d projections of the latent space. We also implemented a suite of additional visualization tools (including playing out population activity timecourses as a movie and displaying summary statistics, such as covariance ellipses and average timecourses) and an optional tool for performing dimensionality reduction. To demonstrate the utility and versatility of DataHigh, we used it to analyze single-trial spike count and single-trial timecourse population activity recorded using a multi-electrode array, as well as trial-averaged population activity recorded using single electrodes. DataHigh was developed to fulfil a need for visualization in exploratory neural data analysis, which can provide intuition that is critical for building scientific hypotheses and models of population activity.

Inverse Ising problem in continuous time: A latent variable approach

NASA Astrophysics Data System (ADS)

Donner, Christian; Opper, Manfred

2017-12-01

We consider the inverse Ising problem: the inference of network couplings from observed spin trajectories for a model with continuous time Glauber dynamics. By introducing two sets of auxiliary latent random variables we render the likelihood into a form which allows for simple iterative inference algorithms with analytical updates. The variables are (1) Poisson variables to linearize an exponential term which is typical for point process likelihoods and (2) Pólya-Gamma variables, which make the likelihood quadratic in the coupling parameters. Using the augmented likelihood, we derive an expectation-maximization (EM) algorithm to obtain the maximum likelihood estimate of network parameters. Using a third set of latent variables we extend the EM algorithm to sparse couplings via L1 regularization. Finally, we develop an efficient approximate Bayesian inference algorithm using a variational approach. We demonstrate the performance of our algorithms on data simulated from an Ising model. For data which are simulated from a more biologically plausible network with spiking neurons, we show that the Ising model captures well the low order statistics of the data and how the Ising couplings are related to the underlying synaptic structure of the simulated network.

Path Finding on High-Dimensional Free Energy Landscapes

NASA Astrophysics Data System (ADS)

Díaz Leines, Grisell; Ensing, Bernd

2012-07-01

We present a method for determining the average transition path and the free energy along this path in the space of selected collective variables. The formalism is based upon a history-dependent bias along a flexible path variable within the metadynamics framework but with a trivial scaling of the cost with the number of collective variables. Controlling the sampling of the orthogonal modes recovers the average path and the minimum free energy path as the limiting cases. The method is applied to resolve the path and the free energy of a conformational transition in alanine dipeptide.

Measuring Latent Quantities

ERIC Educational Resources Information Center

McDonald, Roderick P.

2011-01-01

A distinction is proposed between measures and predictors of latent variables. The discussion addresses the consequences of the distinction for the true-score model, the linear factor model, Structural Equation Models, longitudinal and multilevel models, and item-response models. A distribution-free treatment of calibration and…

A general theoretical framework for interpreting patient-reported outcomes estimated from ordinally scaled item responses.

PubMed

Massof, Robert W

2014-10-01

A simple theoretical framework explains patient responses to items in rating scale questionnaires. Fixed latent variables position each patient and each item on the same linear scale. Item responses are governed by a set of fixed category thresholds, one for each ordinal response category. A patient's item responses are magnitude estimates of the difference between the patient variable and the patient's estimate of the item variable, relative to his/her personally defined response category thresholds. Differences between patients in their personal estimates of the item variable and in their personal choices of category thresholds are represented by random variables added to the corresponding fixed variables. Effects of intervention correspond to changes in the patient variable, the patient's response bias, and/or latent item variables for a subset of items. Intervention effects on patients' item responses were simulated by assuming the random variables are normally distributed with a constant scalar covariance matrix. Rasch analysis was used to estimate latent variables from the simulated responses. The simulations demonstrate that changes in the patient variable and changes in response bias produce indistinguishable effects on item responses and manifest as changes only in the estimated patient variable. Changes in a subset of item variables manifest as intervention-specific differential item functioning and as changes in the estimated person variable that equals the average of changes in the item variables. Simulations demonstrate that intervention-specific differential item functioning produces inefficiencies and inaccuracies in computer adaptive testing. © The Author(s) 2013 Reprints and permissions: sagepub.co.uk/journalsPermissions.nav.

Perceived family functioning, adolescent psychopathology and quality of life in the general population: a 6-month follow-up study.

PubMed

Jozefiak, Thomas; Wallander, Jan L

2016-04-01

The aim of the study was to investigate whether perceived family functioning of adolescent is moderating or mediating the longitudinal association of adolescent internalizing and externalizing psychopathology with quality of life (QoL) after 6 months in the general population. Using a cluster sampling technique in one Norwegian county 1331, 10- to 16-year-old students were included in the study (51 % girls). Parents completed the Child Behavior Checklist for the assessment of adolescent psychopathology at Time 1. The students completed the General Functioning Scale of the McMaster Family Assessment Device and the Inventory of Life Quality in Children and Adolescents at time 2 6 months later. Psychopathology, family functioning and QoL were treated as latent variables in a structural equation model adjusted for sex, age and parent education. The regression coefficients for paths from psychopathology decreased (β = .199 for the internalizing and β = .102 for the externalizing model) in each case when including the indirect path via family functioning compared with the direct path from psychopathology to QoL. The sum of indirect effects on QoL via family functioning was significant for internalizing β = 0.093 (95 % CI 0.054-0.133) and externalizing β = 0.119 (95 % CI 0.076-0.162) psychopathology. Family functioning significantly mediated the longitudinal association between psychopathology and QoL. Because the family remains an important social domain for adolescents, it must be an important consideration when attempting to reduce or alleviate psychopathology in youth and improve the quality of their life experience throughout this period.

On Fitting a Multivariate Two-Part Latent Growth Model

PubMed Central

Xu, Shu; Blozis, Shelley A.; Vandewater, Elizabeth A.

2017-01-01

A 2-part latent growth model can be used to analyze semicontinuous data to simultaneously study change in the probability that an individual engages in a behavior, and if engaged, change in the behavior. This article uses a Monte Carlo (MC) integration algorithm to study the interrelationships between the growth factors of 2 variables measured longitudinally where each variable can follow a 2-part latent growth model. A SAS macro implementing Mplus is developed to estimate the model to take into account the sampling uncertainty of this simulation-based computational approach. A sample of time-use data is used to show how maximum likelihood estimates can be obtained using a rectangular numerical integration method and an MC integration method. PMID:29333054

Interaction between Helicobacter pylori and latent toxoplasmosis and demographic variables on cognitive function in young to middle-aged adults.

PubMed

Gale, Shawn D; Erickson, Lance D; Brown, Bruce L; Hedges, Dawson W

2015-01-01

Helicobacter pylori and latent toxoplasmosis are widespread diseases that have been associated with cognitive deficits and Alzheimer's disease. We sought to determine whether interactions between Helicobacter pylori and latent toxoplasmosis, age, race-ethnicity, educational attainment, economic status, and general health predict cognitive function in young and middle-aged adults. To do so, we used multivariable regression and multivariate models to analyze data obtained from the United States' National Health and Nutrition Examination Survey from the Centers for Disease Control and Prevention, which can be weighted to represent the US population. In this sample, we found that 31.6 percent of women and 36.2 percent of men of the overall sample had IgG Antibodies against Helicobacter pylori, although the seroprevalence of Helicobacter pylori varied with sociodemographic variables. There were no main effects for Helicobacter pylori or latent toxoplasmosis for any of the cognitive measures in models adjusting for age, sex, race-ethnicity, educational attainment, economic standing, and self-rated health predicting cognitive function. However, interactions between Helicobacter pylori and race-ethnicity, educational attainment, latent toxoplasmosis in the fully adjusted models predicted cognitive function. People seropositive for both Helicobacter pylori and latent toxoplasmosis - both of which appear to be common in the general population - appear to be more susceptible to cognitive deficits than are people seropositive for either Helicobacter pylori and or latent toxoplasmosis alone, suggesting a synergistic effect between these two infectious diseases on cognition in young to middle-aged adults.

Latent lifestyle preferences and household location decisions

NASA Astrophysics Data System (ADS)

Walker, Joan L.; Li, Jieping

2007-04-01

Lifestyle, indicating preferences towards a particular way of living, is a key driver of the decision of where to live. We employ latent class choice models to represent this behavior, where the latent classes are the lifestyles and the choice model is the choice of residential location. Thus, we simultaneously estimate lifestyle groups and how lifestyle impacts location decisions. Empirical results indicate three latent lifestyle segments: suburban dwellers, urban dwellers, and transit-riders. The suggested lifestyle segments have intriguing policy implications. Lifecycle characteristics are used to predict lifestyle preferences, although there remain significant aspects that cannot be explained by observable variables.

On the role of precipitation latent heating in modulating the strength and width of the Hadley circulation

NASA Astrophysics Data System (ADS)

Mathew, Sneha Susan; Kumar, Karanam Kishore

2018-05-01

The latent heat released in the clouds over the tropics plays a vital role in driving the Hadley circulation (HC). The present study discusses the influence of latent heating (LH) on the HC parameters viz., centre, strength and total width by using precipitation LH profiles derived from the space-borne observations of the Precipitation Radar (PR) onboard Tropical Rain Measuring Mission (TRMM) and meridional stream function (MSF) derived from ECMWF-Interim reanalysis. The latitude of peak latent heating, width of the latent heating distribution and the total LH released within the ascending limb of the HC are estimated and their influence on the HC centre, strength and width is quantified, for the first time. The present results show that the latitude of peak LH significantly influences the position of the HC centre with correlation coefficient of 0.90. This high correlation between these two quantities seems to be due to their co-variability with the apparent motion of the Sun across the latitudes. The intensity of the HC in the NH as well as SH shows high correlation with the latitude of peak LH with coefficients - 0.85 and - 0.78, respectively. These results indicate that farther the latitude of peak LH from the equator in the summer hemisphere, stronger is the HC intensity in the winter hemisphere. The present analysis also reveals that the total LH released within the ascending limb of HC substantially influence the total width of the HC, with correlation coefficient 0.52, as compared to the other two LH parameters. This observation can be attributed to the fact that the HC is sensitive to the latent heat release in the mid-tropospheric levels in the tropics. An attempt is also made to investigate the degree of variability of these parameters after deseasonalization and results are discussed in the light of present understanding. The significance of the present study lies in providing the observational evidence for the influence of latent heating on the HC strength/width variability, quantitatively, for the first time using TRMM observations of precipitation latent heating.

Assessing factors related to waist circumference and obesity: application of a latent variable model.

PubMed

Dalvand, Sahar; Koohpayehzadeh, Jalil; Karimlou, Masoud; Asgari, Fereshteh; Rafei, Ali; Seifi, Behjat; Niksima, Seyed Hassan; Bakhshi, Enayatollah

2015-01-01

Because the use of BMI (Body Mass Index) alone as a measure of adiposity has been criticized, in the present study our aim was to fit a latent variable model to simultaneously examine the factors that affect waist circumference (continuous outcome) and obesity (binary outcome) among Iranian adults. Data included 18,990 Iranian individuals aged 20-65 years that are derived from the third National Survey of Noncommunicable Diseases Risk Factors in Iran. Using latent variable model, we estimated the relation of two correlated responses (waist circumference and obesity) with independent variables including age, gender, PR (Place of Residence), PA (physical activity), smoking status, SBP (Systolic Blood Pressure), DBP (Diastolic Blood Pressure), CHOL (cholesterol), FBG (Fasting Blood Glucose), diabetes, and FHD (family history of diabetes). All variables were related to both obesity and waist circumference (WC). Older age, female sex, being an urban resident, physical inactivity, nonsmoking, hypertension, hypercholesterolemia, hyperglycemia, diabetes, and having family history of diabetes were significant risk factors that increased WC and obesity. Findings from this study of Iranian adult settings offer more insights into factors associated with high WC and high prevalence of obesity in this population.

Distinguishing State Variability From Trait Change in Longitudinal Data: The Role of Measurement (Non)Invariance in Latent State-Trait Analyses

PubMed Central

Geiser, Christian; Keller, Brian T.; Lockhart, Ginger; Eid, Michael; Cole, David A.; Koch, Tobias

2014-01-01

Researchers analyzing longitudinal data often want to find out whether the process they study is characterized by (1) short-term state variability, (2) long-term trait change, or (3) a combination of state variability and trait change. Classical latent state-trait (LST) models are designed to measure reversible state variability around a fixed set-point or trait, whereas latent growth curve (LGC) models focus on long-lasting and often irreversible trait changes. In the present paper, we contrast LST and LGC models from the perspective of measurement invariance (MI) testing. We show that establishing a pure state-variability process requires (a) the inclusion of a mean structure and (b) establishing strong factorial invariance in LST analyses. Analytical derivations and simulations demonstrate that LST models with non-invariant parameters can mask the fact that a trait-change or hybrid process has generated the data. Furthermore, the inappropriate application of LST models to trait change or hybrid data can lead to bias in the estimates of consistency and occasion-specificity, which are typically of key interest in LST analyses. Four tips for the proper application of LST models are provided. PMID:24652650

Latent variable modeling to analyze the effects of process parameters on the dissolution of paracetamol tablet

PubMed Central

Sun, Fei; Xu, Bing; Zhang, Yi; Dai, Shengyun; Shi, Xinyuan; Qiao, Yanjiang

2017-01-01

ABSTRACT The dissolution is one of the critical quality attributes (CQAs) of oral solid dosage forms because it relates to the absorption of drug. In this paper, the influence of raw materials, granules and process parameters on the dissolution of paracetamol tablet was analyzed using latent variable modeling methods. The variability in raw materials and granules was understood based on the principle component analysis (PCA), respectively. A multi-block partial least squares (MBPLS) model was used to determine the critical factors affecting the dissolution. The results showed that the binder amount, the post granulation time, the API content in granule, the fill depth and the punch tip separation distance were the critical factors with variable importance in the projection (VIP) values larger than 1. The importance of each unit of the whole process was also ranked using the block importance in the projection (BIP) index. It was concluded that latent variable models (LVMs) were very useful tools to extract information from the available data and improve the understanding on dissolution behavior of paracetamol tablet. The obtained LVMs were also helpful to propose the process design space and to design control strategies in the further research. PMID:27689242

Multiple indicators, multiple causes measurement error models

DOE PAGES

Tekwe, Carmen D.; Carter, Randy L.; Cullings, Harry M.; ...

2014-06-25

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this study are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methodsmore » for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. Finally, as a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.« less

Multiple Indicators, Multiple Causes Measurement Error Models

PubMed Central

Tekwe, Carmen D.; Carter, Randy L.; Cullings, Harry M.; Carroll, Raymond J.

2014-01-01

Multiple Indicators, Multiple Causes Models (MIMIC) are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times however when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this paper are: (1) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model, (2) to develop likelihood based estimation methods for the MIMIC ME model, (3) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. As a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure. PMID:24962535

Multiple indicators, multiple causes measurement error models

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

Tekwe, Carmen D.; Carter, Randy L.; Cullings, Harry M.

Multiple indicators, multiple causes (MIMIC) models are often employed by researchers studying the effects of an unobservable latent variable on a set of outcomes, when causes of the latent variable are observed. There are times, however, when the causes of the latent variable are not observed because measurements of the causal variable are contaminated by measurement error. The objectives of this study are as follows: (i) to develop a novel model by extending the classical linear MIMIC model to allow both Berkson and classical measurement errors, defining the MIMIC measurement error (MIMIC ME) model; (ii) to develop likelihood-based estimation methodsmore » for the MIMIC ME model; and (iii) to apply the newly defined MIMIC ME model to atomic bomb survivor data to study the impact of dyslipidemia and radiation dose on the physical manifestations of dyslipidemia. Finally, as a by-product of our work, we also obtain a data-driven estimate of the variance of the classical measurement error associated with an estimate of the amount of radiation dose received by atomic bomb survivors at the time of their exposure.« less

Adolescent substance use behavior and suicidal behavior for boys and girls: a cross-sectional study by latent analysis approach.

PubMed

Wang, Peng-Wei; Yen, Cheng-Fang

2017-12-08

Adolescent suicidal behavior may consist of different symptoms, including suicidal ideation, suicidal planning and suicidal attempts. Adolescent substance use behavior may contribute to adolescent suicidal behavior. However, research on the relationships between specific substance use and individual suicidal behavior is insufficient, as adolescents may not use only one substance or develop only one facet of suicidal behavior. Latent variables permit us to describe the relationships between clusters of related behaviors more accurately than studying the relationships between specific behaviors. Thus, the aim of this study was to explore how adolescent substance use behavior contributes to suicidal behavior using latent variables representing adolescent suicidal and substance use behaviors. A total of 13,985 adolescents were recruited using a stratified random sampling strategy. The participants indicated whether they had experienced suicidal ideation, planning and attempts and reported their cigarette, alcohol, ketamine and MDMA use during the past year. Latent analysis was used to examine the relationship between substance use and suicidal behavior. Adolescents who used any one of the above substances exhibited more suicidal behavior. The results of latent variables analysis revealed that adolescent substance use contributed to suicidal behavior and that boys exhibited more severe substance use behavior than girls. However, there was no gender difference in the association between substance use and suicidal behavior. Substance use behavior in adolescents is related to more suicidal behavior. In addition, the contribution of substance use to suicidal behavior does not differ between genders.

Examining the integrity of measurement of cognitive abilities in the prediction of achievement: Comparisons and contrasts across variables from higher-order and bifactor models.

PubMed

Benson, Nicholas F; Kranzler, John H; Floyd, Randy G

2016-10-01

Prior research examining cognitive ability and academic achievement relations have been based on different theoretical models, have employed both latent variables as well as observed variables, and have used a variety of analytic methods. Not surprisingly, results have been inconsistent across studies. The aims of this study were to (a) examine how relations between psychometric g, Cattell-Horn-Carroll (CHC) broad abilities, and academic achievement differ across higher-order and bifactor models; (b) examine how well various types of observed scores corresponded with latent variables; and (c) compare two types of observed scores (i.e., refined and non-refined factor scores) as predictors of academic achievement. Results suggest that cognitive-achievement relations vary across theoretical models and that both types of factor scores tend to correspond well with the models on which they are based. However, orthogonal refined factor scores (derived from a bifactor model) have the advantage of controlling for multicollinearity arising from the measurement of psychometric g across all measures of cognitive abilities. Results indicate that the refined factor scores provide more precise representations of their targeted constructs than non-refined factor scores and maintain close correspondence with the cognitive-achievement relations observed for latent variables. Thus, we argue that orthogonal refined factor scores provide more accurate representations of the relations between CHC broad abilities and achievement outcomes than non-refined scores do. Further, the use of refined factor scores addresses calls for the application of scores based on latent variable models. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Associations Between Latent Classes of Interpersonal Polyvictimization and Polyperpetration and Sexual Risk Behaviors Among Young Pregnant Couples: A Dyadic Analysis.

PubMed

Willie, Tiara; Kershaw, Trace S

2018-05-24

Interpersonal violence victimization and perpetration have been associated with sexual risk behaviors among adolescents and young adults, but research is lacking on: (1) how patterns of interpersonal polyvictimization and polyperpetration are associated with sexual risk among young pregnant couples, and (2) how individual and partner experiences of violence differentially impact sexual risk. The current analyses used baseline data from a longitudinal study that followed 296 pregnant young couples from pregnancy to 12 months postpartum. Couples were recruited at obstetrics and gynecology clinics, and an ultrasound clinic in the U.S. Latent class analysis identified subgroups based on polyvictimization and polyperpetration. Using the Actor-Partner Interdependence Model, path analyses assessed actor-partner effects of class membership on sexual risk. Three latent classes were used for women: Class 1: Polyvictim-Polyperpetrator; Class 2: Nonvictim-Nonperpetrator; and Class 3: Community and Prior IPV Victim. Four latent classes were used for men: Class 1: Community and Prior IPV Victim; Class 2: Polyvictim-Nonpartner Perpetrator; Class 3: Prior IPV and Peer Victim; and Class 4: Nonvictim-Nonperpetrator. Path analyses revealed that females in Class 2 and their male partners had higher condom use than females in Class 3. Males in Class 2 had more sexual partners than males in Class 1. Among nonmonogamous couples, males in Class 2 were less likely to be involved with a female partner reporting unprotected sex than males in Class 1. Among nonmonogamous couples, females in Class 2 had more acts of unprotected sex than females in Class 1. Males in Class 4 were less likely to have concurrent sexual partners compared to males in Class 1. Risk reduction interventions should address both victimization and perpetration. Additional research is needed to understand how mechanisms driving differential sexual risk by patterns of interpersonal polyvictimization and polyperpetration.

The Influence of Social Interaction and Physical Health on the Association Between Hearing and Depression With Age and Gender

PubMed Central

Seeto, Mark

2017-01-01

Recent epidemiological data suggest the relation between hearing difficulty and depression is more evident in younger and middle-aged populations than in older adults. There are also suggestions that the relation may be more evident in specific subgroups; that is, other factors may influence a relationship between hearing and depression in different subgroups. Using cross-sectional data from the UK Biobank on 134,357 community-dwelling people and structural equation modelling, this study examined the potential mediating influence of social isolation and unemployment and the confounding influence of physical illness and cardiovascular conditions on the relation between a latent hearing variable and both a latent depressive episodes variable and a latent depressive symptoms variable. The models were stratified by age (40s, 50s, and 60s) and gender and further controlled for physical illness and professional support in associations involving social isolation and unemployment. The latent hearing variable was primarily defined by reported hearing difficulty in noise. For all subgroups, poor hearing was significantly related to both more depressive episodes and more depressive symptoms. In all models, the direct and generally small association exceeded the indirect associations via physical health and social interaction. Significant (depressive episodes) and near significant (depressive symptoms) higher direct associations were estimated for males in their 40s and 50s than for males in their 60s. There was at each age-group no significant difference in estimated associations across gender. Irrespective of the temporal order of variables, findings suggest that audiological services should facilitate psychosocial counselling. PMID:28752806

The Influence of Social Interaction and Physical Health on the Association Between Hearing and Depression With Age and Gender.

PubMed

Keidser, Gitte; Seeto, Mark

2017-01-01

Recent epidemiological data suggest the relation between hearing difficulty and depression is more evident in younger and middle-aged populations than in older adults. There are also suggestions that the relation may be more evident in specific subgroups; that is, other factors may influence a relationship between hearing and depression in different subgroups. Using cross-sectional data from the UK Biobank on 134,357 community-dwelling people and structural equation modelling, this study examined the potential mediating influence of social isolation and unemployment and the confounding influence of physical illness and cardiovascular conditions on the relation between a latent hearing variable and both a latent depressive episodes variable and a latent depressive symptoms variable. The models were stratified by age (40s, 50s, and 60s) and gender and further controlled for physical illness and professional support in associations involving social isolation and unemployment. The latent hearing variable was primarily defined by reported hearing difficulty in noise. For all subgroups, poor hearing was significantly related to both more depressive episodes and more depressive symptoms. In all models, the direct and generally small association exceeded the indirect associations via physical health and social interaction. Significant (depressive episodes) and near significant (depressive symptoms) higher direct associations were estimated for males in their 40s and 50s than for males in their 60s. There was at each age-group no significant difference in estimated associations across gender. Irrespective of the temporal order of variables, findings suggest that audiological services should facilitate psychosocial counselling.

Refining the tobacco dependence phenotype using the Wisconsin Inventory of Smoking Dependence Motives (WISDM)

PubMed Central

Piper, Megan E.; Bolt, Daniel M.; Kim, Su-Young; Japuntich, Sandra J.; Smith, Stevens S.; Niederdeppe, Jeff; Cannon, Dale S.; Baker, Timothy B.

2008-01-01

The construct of tobacco dependence is important from both scientific and public health perspectives, but it is poorly understood. The current research integrates person-centered analyses (e.g., latent profile analysis) and variable-centered analyses (e.g., exploratory factor analysis) to understand better the latent structure of dependence and to guide distillation of the phenotype. Using data from four samples of smokers (including treatment and non-treatment samples), latent profiles were derived using the Wisconsin Inventory of Smoking Dependence Motives (WISDM) subscale scores. Across all four samples, results revealed a unique latent profile that had relative elevations on four dependence motive subscales (Automaticity, Craving, Loss of Control, and Tolerance). Variable-centered analyses supported the uniqueness of these four subscales both as measures of a common factor distinct from that underlying the other nine subscales, and as the strongest predictors of relapse, withdrawal and other dependence criteria. Conversely, the remaining nine motives carried little unique predictive validity regarding dependence. Applications of a factor mixture model further support the presence of a unique class of smokers in relation to a common factor underlying the four subscales. The results illustrate how person-centered analyses may be useful as a supplement to variable-centered analyses for uncovering variables that are necessary and/or sufficient predictors of disorder criteria, as they may uncover small segments of a population in which the variables are uniquely distributed. The results also suggest that severe dependence is associated with a pattern of smoking that is heavy, pervasive, automatic and relatively unresponsive to instrumental contingencies. PMID:19025223

Scale Reliability Evaluation with Heterogeneous Populations

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2015-01-01

A latent variable modeling approach for scale reliability evaluation in heterogeneous populations is discussed. The method can be used for point and interval estimation of reliability of multicomponent measuring instruments in populations representing mixtures of an unknown number of latent classes or subpopulations. The procedure is helpful also…

Measurement of Psychological Disorders Using Cognitive Diagnosis Models

ERIC Educational Resources Information Center

Templin, Jonathan L.; Henson, Robert A.

2006-01-01

Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article…

A multilevel model for comorbid outcomes: obesity and diabetes in the US.

PubMed

Congdon, Peter

2010-02-01

Multilevel models are overwhelmingly applied to single health outcomes, but when two or more health conditions are closely related, it is important that contextual variation in their joint prevalence (e.g., variations over different geographic settings) is considered. A multinomial multilevel logit regression approach for analysing joint prevalence is proposed here that includes subject level risk factors (e.g., age, race, education) while also taking account of geographic context. Data from a US population health survey (the 2007 Behavioral Risk Factor Surveillance System or BRFSS) are used to illustrate the method, with a six category multinomial outcome defined by diabetic status and weight category (obese, overweight, normal). The influence of geographic context is partly represented by known geographic variables (e.g., county poverty), and partly by a model for latent area influences. In particular, a shared latent variable (common factor) approach is proposed to measure the impact of unobserved area influences on joint weight and diabetes status, with the latent variable being spatially structured to reflect geographic clustering in risk.

Latent Variable Modeling of Brain Gray Matter Volume and Psychopathy in Incarcerated Offenders

PubMed Central

Baskin-Sommers, Arielle R.; Neumann, Craig S.; Cope, Lora M.; Kiehl, Kent A.

2016-01-01

Advanced statistical modeling has become a prominent feature in psychological science and can be a useful approach for representing the neural architecture linked to psychopathology. Psychopathy, a disorder characterized by dysfunction in interpersonal-affective and impulsive-antisocial domains, is associated with widespread neural abnormalities. Several imaging studies suggest that underlying structural deficits in paralimbic regions are associated with psychopathy. While these studies are useful, they make assumptions about the organization of the brain and its relevance to individuals displaying psychopathic features. Capitalizing on statistical modeling, the present study (N=254) used latent variable methods to examine the structure of gray matter volume in male offenders, and assessed the latent relations between psychopathy and gray matter factors reflecting paralimbic and non-paralimbic regions. Results revealed good fit for a four-factor gray matter paralimbic model and these first-order factors were accounted for by a super-ordinate paralimbic ‘system’ factor. Moreover, a super-ordinate psychopathy factor significantly predicted the paralimbic, but not the non-paralimbic factor. The latent variable paralimbic model, specifically linked with psychopathy, goes beyond understanding of single brain regions within the system and provides evidence for psychopathy-related gray matter volume reductions in the paralimbic system as a whole. PMID:27269123

Interaction between Helicobacter pylori and Latent Toxoplasmosis and Demographic Variables on Cognitive Function in Young to Middle-Aged Adults

PubMed Central

Gale, Shawn D.; Erickson, Lance D.; Brown, Bruce L.; Hedges, Dawson W.

2015-01-01

Helicobacter pylori and latent toxoplasmosis are widespread diseases that have been associated with cognitive deficits and Alzheimer’s disease. We sought to determine whether interactions between Helicobacter pylori and latent toxoplasmosis, age, race-ethnicity, educational attainment, economic status, and general health predict cognitive function in young and middle-aged adults. To do so, we used multivariable regression and multivariate models to analyze data obtained from the United States’ National Health and Nutrition Examination Survey from the Centers for Disease Control and Prevention, which can be weighted to represent the US population. In this sample, we found that 31.6 percent of women and 36.2 percent of men of the overall sample had IgG Antibodies against Helicobacter pylori, although the seroprevalence of Helicobacter pylori varied with sociodemographic variables. There were no main effects for Helicobacter pylori or latent toxoplasmosis for any of the cognitive measures in models adjusting for age, sex, race-ethnicity, educational attainment, economic standing, and self-rated health predicting cognitive function. However, interactions between Helicobacter pylori and race-ethnicity, educational attainment, latent toxoplasmosis in the fully adjusted models predicted cognitive function. People seropositive for both Helicobacter pylori and latent toxoplasmosis – both of which appear to be common in the general population – appear to be more susceptible to cognitive deficits than are people seropositive for either Helicobacter pylori and or latent toxoplasmosis alone, suggesting a synergistic effect between these two infectious diseases on cognition in young to middle-aged adults. PMID:25590622

Analyzing Longitudinal Item Response Data via the Pairwise Fitting Method

ERIC Educational Resources Information Center

Fu, Zhi-Hui; Tao, Jian; Shi, Ning-Zhong; Zhang, Ming; Lin, Nan

2011-01-01

Multidimensional item response theory (MIRT) models can be applied to longitudinal educational surveys where a group of individuals are administered different tests over time with some common items. However, computational problems typically arise as the dimension of the latent variables increases. This is especially true when the latent variable…

Teacher Support Resources, Need Satisfaction and Well-Being.

PubMed

Doménech-Betoret, Fernando; Lloret-Segura, Susana; Gómez-Artiga, Amparo

2015-03-03

Based on Job Demands-Resources Model (JD-R), this study examines the relationships among teacher support resources, psychological need satisfaction, engagement and burnout in a sample of 282 Spanish secondary school teachers. Nine teacher psychological needs were identified based on the study of Bess and on the Self-Determination Theory (SDT). Self-report questionnaires were used to measure the constructs selected for this study and their interrelationships were examined by structural equation modeling. The results reveal a good model fit to the data (NNFI = .88; CFI = .90; GFI = .90; RMSEA = .061). The analyses indicate a positive and significant effect of latent variable Psychological Need Satisfaction on engagement (β = .74, p < .05), and a negative and significant effect on burnout (β = -.78, p ≤ .05). Furthermore, the results show the mediator role played by Psychological Need Satisfaction in the relationship between teacher support resources and both engagement and burnout (additional paths did not improve the model fit: Δχ2(2) = 2.428, p = .29). Finally, practical implications of these findings are discussed.

Video Gaming Disorder and Sport and Exercise in Emerging Adulthood: A Longitudinal Study.

PubMed

Henchoz, Yves; Studer, Joseph; Deline, Stéphane; N'Goran, Alexandra A; Baggio, Stéphanie; Gmel, Gerhard

2016-01-01

Among the negative consequences of video gaming disorder, decreased participation in sport and exercise has received little attention. This study aimed to assess the longitudinal association between video gaming disorder and the level of sport and exercise in emerging adult men. A questionnaire was completed at baseline and 15-month follow-up by a representative national sample of 4,933 respondents. The seven items of the Game Addiction Scale were used to construct a latent variable representing video gaming disorder. Level of sport and exercise was also self-reported. Cross-lagged path modeling indicated a reciprocal causality between video gaming disorder and the level of sport and exercise, even after adjusting for a large set of confounders. These findings support the need for better promotion of sport and exercise among emerging adults in order to contribute to the prevention of video gaming disorder, and to raise the level of sport and exercise activity in addicted gamers.

Causal mediation analysis with a binary outcome and multiple continuous or ordinal mediators: Simulations and application to an alcohol intervention.

PubMed

Nguyen, Trang Quynh; Webb-Vargas, Yenny; Koning, Ina M; Stuart, Elizabeth A

We investigate a method to estimate the combined effect of multiple continuous/ordinal mediators on a binary outcome: 1) fit a structural equation model with probit link for the outcome and identity/probit link for continuous/ordinal mediators, 2) predict potential outcome probabilities, and 3) compute natural direct and indirect effects. Step 2 involves rescaling the latent continuous variable underlying the outcome to address residual mediator variance/covariance. We evaluate the estimation of risk-difference- and risk-ratio-based effects (RDs, RRs) using the ML, WLSMV and Bayes estimators in Mplus. Across most variations in path-coefficient and mediator-residual-correlation signs and strengths, and confounding situations investigated, the method performs well with all estimators, but favors ML/WLSMV for RDs with continuous mediators, and Bayes for RRs with ordinal mediators. Bayes outperforms WLSMV/ML regardless of mediator type when estimating RRs with small potential outcome probabilities and in two other special cases. An adolescent alcohol prevention study is used for illustration.

Body size as a latent variable in a structural equation model: thermal acclimation and energetics of the leaf-eared mouse.

PubMed

Nespolo, Roberto F; Arim, Matías; Bozinovic, Francisco

2003-07-01

Body size is one of the most important determinants of energy metabolism in mammals. However, the usual physiological variables measured to characterize energy metabolism and heat dissipation in endotherms are strongly affected by thermal acclimation, and are also correlated among themselves. In addition to choosing the appropriate measurement of body size, these problems create additional complications when analyzing the relationships among physiological variables such as basal metabolism, non-shivering thermogenesis, thermoregulatory maximum metabolic rate and minimum thermal conductance, body size dependence, and the effect of thermal acclimation on them. We measured these variables in Phyllotis darwini, a murid rodent from central Chile, under conditions of warm and cold acclimation. In addition to standard statistical analyses to determine the effect of thermal acclimation on each variable and the body-mass-controlled correlation among them, we performed a Structural Equation Modeling analysis to evaluate the effects of three different measurements of body size (body mass, m(b); body length, L(b) and foot length, L(f)) on energy metabolism and thermal conductance. We found that thermal acclimation changed the correlation among physiological variables. Only cold-acclimated animals supported our a priori path models, and m(b) appeared to be the best descriptor of body size (compared with L(b) and L(f)) when dealing with energy metabolism and thermal conductance. However, while m(b) appeared to be the strongest determinant of energy metabolism, there was an important and significant contribution of L(b) (but not L(f)) to thermal conductance. This study demonstrates how additional information can be drawn from physiological ecology and general organismal studies by applying Structural Equation Modeling when multiple variables are measured in the same individuals.

Intrinsic motivation, neurocognition and psychosocial functioning in schizophrenia: testing mediator and moderator effects.

PubMed

Nakagami, Eri; Xie, Bin; Hoe, Maanse; Brekke, John S

2008-10-01

This study examined the nature of the relationships among neurocognition, intrinsic motivation, and psychosocial functioning for persons with schizophrenia. Hypotheses concerning both mediator and moderator mechanisms were tested. 120 individuals diagnosed with schizophrenia were recruited as they entered outpatient psychosocial rehabilitation programs. Measures of psychosocial functioning and intrinsic motivation were administered at baseline. Measures of neurocognition were administered at baseline by testers blind to scores on other study variables. Data were analyzed using latent construct modeling to test for mediator and moderator effects. There were strong bivariate relationships between neurocognition, intrinsic motivation, and psychosocial functioning. The results demonstrated that intrinsic motivation strongly mediated the relationship between neurocognition and psychosocial functioning. This mediation was evidenced by: (i) the direct path from neurocognition to functional outcome no longer being statistically significant after the introduction of motivation into the model, (ii) the statistical significance of the indirect path from neurocognition through motivation to functional outcome. There was no support for the two moderation hypotheses: the level of neurocognition did not influence the relationship between intrinsic motivation and psychosocial functioning, nor did the level of intrinsic motivation influence the relationship between neurocognition and psychosocial functioning. Neurocognition influences psychosocial functioning through its relationship with intrinsic motivation. Intrinsic motivation is a critical mechanism for explaining the relationship between neurocognition and psychosocial functioning. Implications for the theoretical understanding and psychosocial treatment of intrinsic motivation in schizophrenia are discussed.

Association between latent toxoplasmosis and cognition in adults: a cross-sectional study.

PubMed

Gale, S D; Brown, B L; Erickson, L D; Berrett, A; Hedges, D W

2015-04-01

Latent infection from Toxoplasma gondii (T. gondii) is widespread worldwide and has been associated with cognitive deficits in some but not all animal models and in humans. We tested the hypothesis that latent toxoplasmosis is associated with decreased cognitive function in a large cross-sectional dataset, the National Health and Nutrition Examination Survey (NHANES). There were 4178 participants aged 20-59 years, of whom 19.1% had IgG antibodies against T. gondii. Two ordinary least squares (OLS) regression models adjusted for the NHANES complex sampling design and weighted to represent the US population were estimated for simple reaction time, processing speed and short-term memory or attention. The first model included only main effects of latent toxoplasmosis and demographic control variables, and the second added interaction terms between latent toxoplasmosis and the poverty-to-income ratio (PIR), educational attainment and race-ethnicity. We also used multivariate models to assess all three cognitive outcomes in the same model. Although the models evaluating main effects only demonstrated no association between latent toxoplasmosis and the cognitive outcomes, significant interactions between latent toxoplasmosis and the PIR, between latent toxoplasmosis and educational attainment, and between latent toxoplasmosis and race-ethnicity indicated that latent toxoplasmosis may adversely affect cognitive function in certain groups.

Repeatability and Reproducibility of Decisions by Latent Fingerprint Examiners

PubMed Central

Ulery, Bradford T.; Hicklin, R. Austin; Buscaglia, JoAnn; Roberts, Maria Antonia

2012-01-01

The interpretation of forensic fingerprint evidence relies on the expertise of latent print examiners. We tested latent print examiners on the extent to which they reached consistent decisions. This study assessed intra-examiner repeatability by retesting 72 examiners on comparisons of latent and exemplar fingerprints, after an interval of approximately seven months; each examiner was reassigned 25 image pairs for comparison, out of total pool of 744 image pairs. We compare these repeatability results with reproducibility (inter-examiner) results derived from our previous study. Examiners repeated 89.1% of their individualization decisions, and 90.1% of their exclusion decisions; most of the changed decisions resulted in inconclusive decisions. Repeatability of comparison decisions (individualization, exclusion, inconclusive) was 90.0% for mated pairs, and 85.9% for nonmated pairs. Repeatability and reproducibility were notably lower for comparisons assessed by the examiners as “difficult” than for “easy” or “moderate” comparisons, indicating that examiners' assessments of difficulty may be useful for quality assurance. No false positive errors were repeated (n = 4); 30% of false negative errors were repeated. One percent of latent value decisions were completely reversed (no value even for exclusion vs. of value for individualization). Most of the inter- and intra-examiner variability concerned whether the examiners considered the information available to be sufficient to reach a conclusion; this variability was concentrated on specific image pairs such that repeatability and reproducibility were very high on some comparisons and very low on others. Much of the variability appears to be due to making categorical decisions in borderline cases. PMID:22427888

Toward a Model-Based Approach to the Clinical Assessment of Personality Psychopathology

PubMed Central

Eaton, Nicholas R.; Krueger, Robert F.; Docherty, Anna R.; Sponheim, Scott R.

2015-01-01

Recent years have witnessed tremendous growth in the scope and sophistication of statistical methods available to explore the latent structure of psychopathology, involving continuous, discrete, and hybrid latent variables. The availability of such methods has fostered optimism that they can facilitate movement from classification primarily crafted through expert consensus to classification derived from empirically-based models of psychopathological variation. The explication of diagnostic constructs with empirically supported structures can then facilitate the development of assessment tools that appropriately characterize these constructs. Our goal in this paper is to illustrate how new statistical methods can inform conceptualization of personality psychopathology and therefore its assessment. We use magical thinking as example, because both theory and earlier empirical work suggested the possibility of discrete aspects to the latent structure of personality psychopathology, particularly forms of psychopathology involving distortions of reality testing, yet other data suggest that personality psychopathology is generally continuous in nature. We directly compared the fit of a variety of latent variable models to magical thinking data from a sample enriched with clinically significant variation in psychotic symptomatology for explanatory purposes. Findings generally suggested a continuous latent variable model best represented magical thinking, but results varied somewhat depending on different indices of model fit. We discuss the implications of the findings for classification and applied personality assessment. We also highlight some limitations of this type of approach that are illustrated by these data, including the importance of substantive interpretation, in addition to use of model fit indices, when evaluating competing structural models. PMID:24007309

Child involvement, alliance, and therapist flexibility: process variables in cognitive-behavioural therapy for anxiety disorders in childhood.

PubMed

Hudson, Jennifer L; Kendall, Philip C; Chu, Brian C; Gosch, Elizabeth; Martin, Erin; Taylor, Alan; Knight, Ashleigh

2014-01-01

This study examined the relations between treatment process variables and child anxiety outcomes. Independent raters watched/listened to taped therapy sessions of 151 anxiety-disordered (6-14 yr-old; M = 10.71) children (43% boys) and assessed process variables (child alliance, therapist alliance, child involvement, therapist flexibility and therapist functionality) within a manual-based cognitive-behavioural treatment. Latent growth modelling examined three latent variables (intercept, slope, and quadratic) for each process variable. Child age, gender, family income and ethnicity were examined as potential antecedents. Outcome was analyzed using factorially derived clinician, mother, father, child and teacher scores from questionnaire and structured diagnostic interviews at pretreatment, posttreatment and 12-month follow-up. Latent growth models demonstrated a concave quadratic curve for child involvement and therapist flexibility over time. A predominantly linear, downward slope was observed for alliance, and functional flexibility remained consistent over time. Increased alliance, child involvement and therapist flexibility showed some albeit inconsistent, associations with positive treatment outcome. Findings support the notion that maintaining the initial high level of alliance or involvement is important for clinical improvement. There is some support that progressively increasing alliance/involvement also positively impacts on treatment outcome. These findings were not consistent across outcome measurement points or reporters. Copyright © 2013 Elsevier Ltd. All rights reserved.

Ordinal probability effect measures for group comparisons in multinomial cumulative link models.

PubMed

Agresti, Alan; Kateri, Maria

2017-03-01

We consider simple ordinal model-based probability effect measures for comparing distributions of two groups, adjusted for explanatory variables. An "ordinal superiority" measure summarizes the probability that an observation from one distribution falls above an independent observation from the other distribution, adjusted for explanatory variables in a model. The measure applies directly to normal linear models and to a normal latent variable model for ordinal response variables. It equals Φ(β/2) for the corresponding ordinal model that applies a probit link function to cumulative multinomial probabilities, for standard normal cdf Φ and effect β that is the coefficient of the group indicator variable. For the more general latent variable model for ordinal responses that corresponds to a linear model with other possible error distributions and corresponding link functions for cumulative multinomial probabilities, the ordinal superiority measure equals exp(β)/[1+exp(β)] with the log-log link and equals approximately exp(β/2)/[1+exp(β/2)] with the logit link, where β is the group effect. Another ordinal superiority measure generalizes the difference of proportions from binary to ordinal responses. We also present related measures directly for ordinal models for the observed response that need not assume corresponding latent response models. We present confidence intervals for the measures and illustrate with an example. © 2016, The International Biometric Society.

Investigating Factorial Invariance of Latent Variables Across Populations When Manifest Variables Are Missing Completely

PubMed Central

Widaman, Keith F.; Grimm, Kevin J.; Early, Dawnté R.; Robins, Richard W.; Conger, Rand D.

2013-01-01

Difficulties arise in multiple-group evaluations of factorial invariance if particular manifest variables are missing completely in certain groups. Ad hoc analytic alternatives can be used in such situations (e.g., deleting manifest variables), but some common approaches, such as multiple imputation, are not viable. At least 3 solutions to this problem are viable: analyzing differing sets of variables across groups, using pattern mixture approaches, and a new method using random number generation. The latter solution, proposed in this article, is to generate pseudo-random normal deviates for all observations for manifest variables that are missing completely in a given sample and then to specify multiple-group models in a way that respects the random nature of these values. An empirical example is presented in detail comparing the 3 approaches. The proposed solution can enable quantitative comparisons at the latent variable level between groups using programs that require the same number of manifest variables in each group. PMID:24019738

Exploratory factor analysis of pathway copy number data with an application towards the integration with gene expression data.

PubMed

van Wieringen, Wessel N; van de Wiel, Mark A

2011-05-01

Realizing that genes often operate together, studies into the molecular biology of cancer shift focus from individual genes to pathways. In order to understand the regulatory mechanisms of a pathway, one must study its genes at all molecular levels. To facilitate such study at the genomic level, we developed exploratory factor analysis for the characterization of the variability of a pathway's copy number data. A latent variable model that describes the call probability data of a pathway is introduced and fitted with an EM algorithm. In two breast cancer data sets, it is shown that the first two latent variables of GO nodes, which inherit a clear interpretation from the call probabilities, are often related to the proportion of aberrations and a contrast of the probabilities of a loss and of a gain. Linking the latent variables to the node's gene expression data suggests that they capture the "global" effect of genomic aberrations on these transcript levels. In all, the proposed method provides an possibly insightful characterization of pathway copy number data, which may be fruitfully exploited to study the interaction between the pathway's DNA copy number aberrations and data from other molecular levels like gene expression.

Correcting Measurement Error in Latent Regression Covariates via the MC-SIMEX Method

ERIC Educational Resources Information Center

Rutkowski, Leslie; Zhou, Yan

2015-01-01

Given the importance of large-scale assessments to educational policy conversations, it is critical that subpopulation achievement is estimated reliably and with sufficient precision. Despite this importance, biased subpopulation estimates have been found to occur when variables in the conditioning model side of a latent regression model contain…

On the Power of Multivariate Latent Growth Curve Models to Detect Correlated Change

ERIC Educational Resources Information Center

Hertzog, Christopher; Lindenberger, Ulman; Ghisletta, Paolo; Oertzen, Timo von

2006-01-01

We evaluated the statistical power of single-indicator latent growth curve models (LGCMs) to detect correlated change between two variables (covariance of slopes) as a function of sample size, number of longitudinal measurement occasions, and reliability (measurement error variance). Power approximations following the method of Satorra and Saris…

Assessing Change in Latent Skills across Time with Longitudinal Cognitive Diagnosis Modeling: An Evaluation of Model Performance

ERIC Educational Resources Information Center

Kaya, Yasemin; Leite, Walter L.

2017-01-01

Cognitive diagnosis models are diagnostic models used to classify respondents into homogenous groups based on multiple categorical latent variables representing the measured cognitive attributes. This study aims to present longitudinal models for cognitive diagnosis modeling, which can be applied to repeated measurements in order to monitor…

A Latent Variable Approach to Executive Control in Healthy Ageing

ERIC Educational Resources Information Center

Adrover-Roig, Daniel; Sese, Albert; Barcelo, Francisco; Palmer, Alfonso

2012-01-01

It is a well-established finding that the central executive is fractionated in at least three separable component processes: Updating, Shifting, and Inhibition of information (Miyake et al., 2000). However, the fractionation of the central executive among the elderly has been less well explored, and Miyake's et al. latent structure has not yet…

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

ERIC Educational Resources Information Center

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

2011-01-01

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

(Latent) Transitions to Learning at University: A Latent Profile Transition Analysis of First-Year Japanese Students

ERIC Educational Resources Information Center

Fryer, Luke K.

2017-01-01

During the past decade, quantitative researchers have examined the first-year university experience from both variable-centred and person-centred perspectives. These studies have, however, generally been cross-sectional and therefore often failed to address how student learning changes during this transition. Furthermore, research has been…

A Comparison of Four Approaches to Account for Method Effects in Latent State-Trait Analyses

ERIC Educational Resources Information Center

Geiser, Christian; Lockhart, Ginger

2012-01-01

Latent state-trait (LST) analysis is frequently applied in psychological research to determine the degree to which observed scores reflect stable person-specific effects, effects of situations and/or person-situation interactions, and random measurement error. Most LST applications use multiple repeatedly measured observed variables as indicators…

A comparison of latent class, K-means, and K-median methods for clustering dichotomous data.

PubMed

Brusco, Michael J; Shireman, Emilie; Steinley, Douglas

2017-09-01

The problem of partitioning a collection of objects based on their measurements on a set of dichotomous variables is a well-established problem in psychological research, with applications including clinical diagnosis, educational testing, cognitive categorization, and choice analysis. Latent class analysis and K-means clustering are popular methods for partitioning objects based on dichotomous measures in the psychological literature. The K-median clustering method has recently been touted as a potentially useful tool for psychological data and might be preferable to its close neighbor, K-means, when the variable measures are dichotomous. We conducted simulation-based comparisons of the latent class, K-means, and K-median approaches for partitioning dichotomous data. Although all 3 methods proved capable of recovering cluster structure, K-median clustering yielded the best average performance, followed closely by latent class analysis. We also report results for the 3 methods within the context of an application to transitive reasoning data, in which it was found that the 3 approaches can exhibit profound differences when applied to real data. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Variation in working memory capacity, fluid intelligence, and episodic recall: a latent variable examination of differences in the dynamics of free recall.

PubMed

Unsworth, Nash

2009-09-01

A latent variable analysis was conducted to examine the nature of individual differences in the dynamics of free recall and cognitive abilities. Participants performed multiple measures of free recall, working memory capacity (WMC), and fluid intelligence (gF). For each free recall task, recall accuracy, recall latency, and number of intrusion errors were determined, and latent factors were derived for each. It was found that recall accuracy was negatively related to both recall latency and number of intrusions, and recall latency and number of intrusions were positively related. Furthermore, latent WMC and gF factors were positively related to recall accuracy, but negatively related to recall latency and number of intrusions. Finally, a cluster analysis revealed that subgroups of participants with deficits in focusing the search had deficits in recovering degraded representations or deficits in monitoring the products of retrieval. The results are consistent with the idea that variation in the dynamics of free recall, WMC, and gF are primarily due to differences in search set size, but differences in recovery and monitoring are also important.

A prospective examination of the path from child abuse and neglect to illicit drug use in middle adulthood: the potential mediating role of four risk factors.

PubMed

Wilson, Helen W; Widom, Cathy Spatz

2009-03-01

This study examines prostitution, homelessness, delinquency and crime, and school problems as potential mediators of the relationship between childhood abuse and neglect (CAN) and illicit drug use in middle adulthood. Children with documented cases of physical and sexual abuse and neglect (ages 0-11) during 1967-1971 were matched with non-maltreated children and followed into middle adulthood (approximate age 39). Mediators were assessed in young adulthood (approximate age 29) through in-person interviews between 1989 and 1995 and official arrest records through 1994 (N = 1,196). Drug use was assessed via self-reports of past year use of marijuana, psychedelics, cocaine, and/or heroin during 2000-2002 (N = 896). Latent variable structural equation modeling (SEM) was used to test: (1) a four-factor model with separate pathways from CAN to illicit drug use through each of the mediating risk factors and (2) a second-order model with a single mediating risk factor comprised of prostitution, homelessness, delinquency and crime, and poor school performance. Analyses were performed separately for women and men, controlling for race/ethnicity and early drug use. In the four-factor model for both men and women, CAN was significantly related to each of the mediators, but no paths from the mediators to drug use were significant. For women, the second-order risk factor mediated the relationship between CAN and illicit drug use in middle adulthood. For men, neither child abuse and neglect nor the second-order risk factor predicted drug use in middle adulthood. These results suggest that for women, the path from CAN to middle adulthood drug use is part of a general "problem behavior syndrome" evident earlier in life.

The potential of clustering methods to define intersection test scenarios: Assessing real-life performance of AEB.

PubMed

Sander, Ulrich; Lubbe, Nils

2018-04-01

Intersection accidents are frequent and harmful. The accident types 'straight crossing path' (SCP), 'left turn across path - oncoming direction' (LTAP/OD), and 'left-turn across path - lateral direction' (LTAP/LD) represent around 95% of all intersection accidents and one-third of all police-reported car-to-car accidents in Germany. The European New Car Assessment Program (Euro NCAP) have announced that intersection scenarios will be included in their rating from 2020; however, how these scenarios are to be tested has not been defined. This study investigates whether clustering methods can be used to identify a small number of test scenarios sufficiently representative of the accident dataset to evaluate Intersection Automated Emergency Braking (AEB). Data from the German In-Depth Accident Study (GIDAS) and the GIDAS-based Pre-Crash Matrix (PCM) from 1999 to 2016, containing 784 SCP and 453 LTAP/OD accidents, were analyzed with principal component methods to identify variables that account for the relevant total variances of the sample. Three different methods for data clustering were applied to each of the accident types, two similarity-based approaches, namely Hierarchical Clustering (HC) and Partitioning Around Medoids (PAM), and the probability-based Latent Class Clustering (LCC). The optimum number of clusters was derived for HC and PAM with the silhouette method. The PAM algorithm was both initiated with random start medoid selection and medoids from HC. For LCC, the Bayesian Information Criterion (BIC) was used to determine the optimal number of clusters. Test scenarios were defined from optimal cluster medoids weighted by their real-life representation in GIDAS. The set of variables for clustering was further varied to investigate the influence of variable type and character. We quantified how accurately each cluster variation represents real-life AEB performance using pre-crash simulations with PCM data and a generic algorithm for AEB intervention. The usage of different sets of clustering variables resulted in substantially different numbers of clusters. The stability of the resulting clusters increased with prioritization of categorical over continuous variables. For each different set of cluster variables, a strong in-cluster variance of avoided versus non-avoided accidents for the specified Intersection AEB was present. The medoids did not predict the most common Intersection AEB behavior in each cluster. Despite thorough analysis using various cluster methods and variable sets, it was impossible to reduce the diversity of intersection accidents into a set of test scenarios without compromising the ability to predict real-life performance of Intersection AEB. Although this does not imply that other methods cannot succeed, it was observed that small changes in the definition of a scenario resulted in a different avoidance outcome. Therefore, we suggest using limited physical testing to validate more extensive virtual simulations to evaluate vehicle safety. Copyright © 2018 Elsevier Ltd. All rights reserved.

Dish Stirling High Performance Thermal Storage FY14Q4 Quad Chart

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

Andraka, Charles E.

2014-10-01

The goals of this project are to demonstrate the feasibility of significant thermal storage for dish stirling systems to leverage their existing high performance to greater capacity; demonstrate key components of a latent storage and transport system enabling on-dish storage with low energy losses; and provide a technology path to a 25kW e system with 6 hours of storage.

Ambivalence over emotional expression, intrusive thoughts, and posttraumatic stress symptoms among Chinese American breast cancer survivors.

PubMed

Lu, Qian; Yeung, Nelson; Man, Jenny; Gallagher, Matthew W; Chu, Qiao; Deen, Sidra H

2017-10-01

Posttraumatic stress symptoms (PTSS) are common among breast cancer survivors. However, the association and the underlying mediating mechanism between psychosocial factors and PTSS were rarely investigated among breast cancer survivors. Previous studies have suggested the importance of emotional expression in cancer survivors' PTSS. This study examined the association between ambivalence over emotional expression (AEE; defined as the conflict between the desire to express feelings and the fear of its consequences) and PTSS, and proposed intrusive thoughts as the mediators in such an association. We tested this proposed mediation model among Chinese breast cancer survivors whose culture discourages emotional expression. Participants were 118 Chinese-speaking breast cancer survivors in the USA, who were diagnosed with breast cancer of stages 0-III within the past 5 years. They completed questionnaires measuring their levels of AEE, PTSS, and intrusive thoughts. AEE was positively associated with intrusive thoughts (r = 0.43, p < 01), which were positively associated with the arousal and avoidance subscales of PTSS (r = 0.68 and r = 0.62, respectively, p < .01). Path analysis supported a partial mediation model with an indirect effect from AEE to the latent variable of PTSS (with both arousal and avoidance as indicators) via intrusive thoughts (β = 0.29; 95% CI= 0.18, 0.42) and the direct effect from AEE to the latent variable of PTSS (β = 0.21, 95% CI = 0.07, 0.35), all p < .001. Those who are highly ambivalent about emotion expression tend to have higher PTSS, and this may be partially due to the lack of opportunities to discuss emotional events, thereby increasing the repetitive cancer-related negative thoughts. Intervention for PTSS should consider helping cancer patients to develop adaptive emotional regulation strategies to reduce the detrimental effects of cancer-related intrusive thoughts.

From loss to loneliness: The relationship between bereavement and depressive symptoms.

PubMed

Fried, Eiko I; Bockting, Claudi; Arjadi, Retha; Borsboom, Denny; Amshoff, Maximilian; Cramer, Angélique O J; Epskamp, Sacha; Tuerlinckx, Francis; Carr, Deborah; Stroebe, Margaret

2015-05-01

Spousal bereavement can cause a rise in depressive symptoms. This study empirically evaluates 2 competing explanations concerning how this causal effect is brought about: (a) a traditional latent variable explanation, in which loss triggers depression which then leads to symptoms; and (b) a novel network explanation, in which bereavement directly affects particular depression symptoms which then activate other symptoms. We used data from the Changing Lives of Older Couples (CLOC) study and compared depressive symptomatology, assessed via the 11-item Center for Epidemiologic Studies Depression Scale (CES-D), among those who lost their partner (N = 241) with a still-married control group (N = 274). We modeled the effect of partner loss on depressive symptoms either as an indirect effect through a latent variable, or as a direct effect in a network constructed through a causal search algorithm. Compared to the control group, widow(er)s' scores were significantly higher for symptoms of loneliness, sadness, depressed mood, and appetite loss, and significantly lower for happiness and enjoyed life. The effect of partner loss on these symptoms was not mediated by a latent variable. The network model indicated that bereavement mainly affected loneliness, which in turn activated other depressive symptoms. The direct effects of spousal loss on particular symptoms are inconsistent with the predictions of latent variable models, but can be explained from a network perspective. The findings support a growing body of literature showing that specific adverse life events differentially affect depressive symptomatology, and suggest that future studies should examine interventions that directly target such symptoms. (c) 2015 APA, all rights reserved).

A longitudinal study of mortality and air pollution for São Paulo, Brazil.

PubMed

Botter, Denise A; Jørgensen, Bent; Peres, Antonieta A Q

2002-09-01

We study the effects of various air-pollution variables on the daily death counts for people over 65 years in São Paulo, Brazil, from 1991 to 1993, controlling for meteorological variables. We use a state space model where the air-pollution variables enter via the latent process, and the meteorological variables via the observation equation. The latent process represents the potential mortality due to air pollution, and is estimated by Kalman filter techniques. The effect of air pollution on mortality is found to be a function of the variation in the sulphur dioxide level for the previous 3 days, whereas the other air-pollution variables (total suspended particulates, nitrogen dioxide, carbon monoxide, ozone) are not significant when sulphur dioxide is in the equation. There are significant effects of humidity and up to lag 3 of temperature, and a significant seasonal variation.

Characterising the latent structure and organisation of self-reported thoughts, feelings and behaviours in adolescents and young adults

PubMed Central

Neufeld, Sharon; Jones, Peter B.; Fonagy, Peter; Bullmore, Edward T.; Dolan, Raymond J.; Moutoussis, Michael; Toseeb, Umar; Goodyer, Ian M.

2017-01-01

Little is known about the underlying relationships between self-reported mental health items measuring both positive and negative emotional and behavioural symptoms at the population level in young people. Improved measurement of the full range of mental well-being and mental illness may aid in understanding the aetiological substrates underlying the development of both mental wellness as well as specific psychiatric diagnoses. A general population sample aged 14 to 24 years completed self-report questionnaires on anxiety, depression, psychotic-like symptoms, obsessionality and well-being. Exploratory and confirmatory factor models for categorical data and latent profile analyses were used to evaluate the structure of both mental wellness and illness items. First order, second order and bifactor structures were evaluated on 118 self-reported items obtained from 2228 participants. A bifactor solution was the best fitting latent variable model with one general latent factor termed ‘distress’ and five ‘distress independent’ specific factors defined as self-confidence, antisocial behaviour, worry, aberrant thinking, and mood. Next, six distinct subgroups were derived from a person-centred latent profile analysis of the factor scores. Finally, concurrent validity was assessed using information on hazardous behaviours (alcohol use, substance misuse, self-harm) and treatment for mental ill health: both discriminated between the latent traits and latent profile subgroups. The findings suggest a complex, multidimensional mental health structure in the youth population rather than the previously assumed first or second order factor structure. Additionally, the analysis revealed a low hazardous behaviour/low mental illness risk subgroup not previously described. Population sub-groups show greater validity over single variable factors in revealing mental illness risks. In conclusion, our findings indicate that the structure of self reported mental health is multidimensional in nature and uniquely finds improved prediction to mental illness risk within person-centred subgroups derived from the multidimensional latent traits. PMID:28403164

Characterising the latent structure and organisation of self-reported thoughts, feelings and behaviours in adolescents and young adults.

PubMed

St Clair, Michelle C; Neufeld, Sharon; Jones, Peter B; Fonagy, Peter; Bullmore, Edward T; Dolan, Raymond J; Moutoussis, Michael; Toseeb, Umar; Goodyer, Ian M

2017-01-01

Little is known about the underlying relationships between self-reported mental health items measuring both positive and negative emotional and behavioural symptoms at the population level in young people. Improved measurement of the full range of mental well-being and mental illness may aid in understanding the aetiological substrates underlying the development of both mental wellness as well as specific psychiatric diagnoses. A general population sample aged 14 to 24 years completed self-report questionnaires on anxiety, depression, psychotic-like symptoms, obsessionality and well-being. Exploratory and confirmatory factor models for categorical data and latent profile analyses were used to evaluate the structure of both mental wellness and illness items. First order, second order and bifactor structures were evaluated on 118 self-reported items obtained from 2228 participants. A bifactor solution was the best fitting latent variable model with one general latent factor termed 'distress' and five 'distress independent' specific factors defined as self-confidence, antisocial behaviour, worry, aberrant thinking, and mood. Next, six distinct subgroups were derived from a person-centred latent profile analysis of the factor scores. Finally, concurrent validity was assessed using information on hazardous behaviours (alcohol use, substance misuse, self-harm) and treatment for mental ill health: both discriminated between the latent traits and latent profile subgroups. The findings suggest a complex, multidimensional mental health structure in the youth population rather than the previously assumed first or second order factor structure. Additionally, the analysis revealed a low hazardous behaviour/low mental illness risk subgroup not previously described. Population sub-groups show greater validity over single variable factors in revealing mental illness risks. In conclusion, our findings indicate that the structure of self reported mental health is multidimensional in nature and uniquely finds improved prediction to mental illness risk within person-centred subgroups derived from the multidimensional latent traits.

The Effects of Model Misspecification and Sample Size on LISREL Maximum Likelihood Estimates.

ERIC Educational Resources Information Center

Baldwin, Beatrice

The robustness of LISREL computer program maximum likelihood estimates under specific conditions of model misspecification and sample size was examined. The population model used in this study contains one exogenous variable; three endogenous variables; and eight indicator variables, two for each latent variable. Conditions of model…

A Multilevel Model for Comorbid Outcomes: Obesity and Diabetes in the US

PubMed Central

Congdon, Peter

2010-01-01

Multilevel models are overwhelmingly applied to single health outcomes, but when two or more health conditions are closely related, it is important that contextual variation in their joint prevalence (e.g., variations over different geographic settings) is considered. A multinomial multilevel logit regression approach for analysing joint prevalence is proposed here that includes subject level risk factors (e.g., age, race, education) while also taking account of geographic context. Data from a US population health survey (the 2007 Behavioral Risk Factor Surveillance System or BRFSS) are used to illustrate the method, with a six category multinomial outcome defined by diabetic status and weight category (obese, overweight, normal). The influence of geographic context is partly represented by known geographic variables (e.g., county poverty), and partly by a model for latent area influences. In particular, a shared latent variable (common factor) approach is proposed to measure the impact of unobserved area influences on joint weight and diabetes status, with the latent variable being spatially structured to reflect geographic clustering in risk. PMID:20616977

Modeling Latent Interactions at Level 2 in Multilevel Structural Equation Models: An Evaluation of Mean-Centered and Residual-Centered Unconstrained Approaches

ERIC Educational Resources Information Center

Leite, Walter L.; Zuo, Youzhen

2011-01-01

Among the many methods currently available for estimating latent variable interactions, the unconstrained approach is attractive to applied researchers because of its relatively easy implementation with any structural equation modeling (SEM) software. Using a Monte Carlo simulation study, we extended and evaluated the unconstrained approach to…

Multilevel Latent Class Analysis: An Application of Adolescent Smoking Typologies with Individual and Contextual Predictors

ERIC Educational Resources Information Center

Henry, Kimberly L.; Muthen, Bengt

2010-01-01

Latent class analysis (LCA) is a statistical method used to identify subtypes of related cases using a set of categorical or continuous observed variables. Traditional LCA assumes that observations are independent. However, multilevel data structures are common in social and behavioral research and alternative strategies are needed. In this…

Using the Graded Response Model to Control Spurious Interactions in Moderated Multiple Regression

ERIC Educational Resources Information Center

Morse, Brendan J.; Johanson, George A.; Griffeth, Rodger W.

2012-01-01

Recent simulation research has demonstrated that using simple raw score to operationalize a latent construct can result in inflated Type I error rates for the interaction term of a moderated statistical model when the interaction (or lack thereof) is proposed at the latent variable level. Rescaling the scores using an appropriate item response…

Randomization-Based Inference about Latent Variables from Complex Samples: The Case of Two-Stage Sampling

ERIC Educational Resources Information Center

Li, Tiandong

2012-01-01

In large-scale assessments, such as the National Assessment of Educational Progress (NAEP), plausible values based on Multiple Imputations (MI) have been used to estimate population characteristics for latent constructs under complex sample designs. Mislevy (1991) derived a closed-form analytic solution for a fixed-effect model in creating…

Squeezing Interval Change From Ordinal Panel Data: Latent Growth Curves With Ordinal Outcomes

ERIC Educational Resources Information Center

Mehta, Paras D.; Neale, Michael C.; Flay, Brian R.

2004-01-01

A didactic on latent growth curve modeling for ordinal outcomes is presented. The conceptual aspects of modeling growth with ordinal variables and the notion of threshold invariance are illustrated graphically using a hypothetical example. The ordinal growth model is described in terms of 3 nested models: (a) multivariate normality of the…

www.common-metrics.org: a web application to estimate scores from different patient-reported outcome measures on a common scale.

PubMed

Fischer, H Felix; Rose, Matthias

2016-10-19

Recently, a growing number of Item-Response Theory (IRT) models has been published, which allow estimation of a common latent variable from data derived by different Patient Reported Outcomes (PROs). When using data from different PROs, direct estimation of the latent variable has some advantages over the use of sum score conversion tables. It requires substantial proficiency in the field of psychometrics to fit such models using contemporary IRT software. We developed a web application ( http://www.common-metrics.org ), which allows estimation of latent variable scores more easily using IRT models calibrating different measures on instrument independent scales. Currently, the application allows estimation using six different IRT models for Depression, Anxiety, and Physical Function. Based on published item parameters, users of the application can directly estimate latent trait estimates using expected a posteriori (EAP) for sum scores as well as for specific response patterns, Bayes modal (MAP), Weighted likelihood estimation (WLE) and Maximum likelihood (ML) methods and under three different prior distributions. The obtained estimates can be downloaded and analyzed using standard statistical software. This application enhances the usability of IRT modeling for researchers by allowing comparison of the latent trait estimates over different PROs, such as the Patient Health Questionnaire Depression (PHQ-9) and Anxiety (GAD-7) scales, the Center of Epidemiologic Studies Depression Scale (CES-D), the Beck Depression Inventory (BDI), PROMIS Anxiety and Depression Short Forms and others. Advantages of this approach include comparability of data derived with different measures and tolerance against missing values. The validity of the underlying models needs to be investigated in the future.

"Great expectations" of adoptive parents: theory extension through structural equation modeling.

PubMed

Foli, Karen J; Lim, Eunjung; South, Susan C; Sands, Laura P

2014-01-01

Most of the 2 million adoptive parents in the United States make the transition to parenting successfully. Adoptive parents who do not make the transition easily may put their children at risk for negative outcomes. The aim of this study was to further refine Foli's midrange theory of postadoption depression, which postulates that fulfillment of expectations is a principal contributor to parental emotional health status, aggravation, and bonding. The linked dataset (National Survey of Children's Health and National Survey of Adoptive Parents) was used for structural equation modeling. The sample consisted of 1,426 parents with adopted children who had been placed in the home more than 2 years before survey completion. Special services and child's behaviors were direct determinants of parental expectations, and parental expectations were direct determinants of parental aggravation and parentalbonding. As anticipated, parental expectations served as a mediator between child-related variables and parental outcomes. A path was also found between child's behaviors and special services and parental emotional health status. Child's past trauma was also associated with parental bonding. Parental expectations showed direct relationships with the latent variables of parental aggravation and bonding. Future research should examine factors associated with early transition when children have been in the adoptive home less than 2 years and include specific expectations held by parents.

Cognitive aging on latent constructs for visual processing capacity: a novel structural equation modeling framework with causal assumptions based on a theory of visual attention.

PubMed

Nielsen, Simon; Wilms, L Inge

2014-01-01

We examined the effects of normal aging on visual cognition in a sample of 112 healthy adults aged 60-75. A testbattery was designed to capture high-level measures of visual working memory and low-level measures of visuospatial attention and memory. To answer questions of how cognitive aging affects specific aspects of visual processing capacity, we used confirmatory factor analyses in Structural Equation Modeling (SEM; Model 2), informed by functional structures that were modeled with path analyses in SEM (Model 1). The results show that aging effects were selective to measures of visual processing speed compared to visual short-term memory (VSTM) capacity (Model 2). These results are consistent with some studies reporting selective aging effects on processing speed, and inconsistent with other studies reporting aging effects on both processing speed and VSTM capacity. In the discussion we argue that this discrepancy may be mediated by differences in age ranges, and variables of demography. The study demonstrates that SEM is a sensitive method to detect cognitive aging effects even within a narrow age-range, and a useful approach to structure the relationships between measured variables, and the cognitive functional foundation they supposedly represent.

The synergic role of sociotechnical and personal characteristics on work injuries in mines.

PubMed

Paul, P S; Maiti, J

2008-05-01

Occupational injuries in mines are attributed to many factors. In this study, an attempt was made to identify the various factors related to work injuries in mines and to estimate their effects on work injuries to mine workers. An accident path model was developed to estimate the pattern and strength of relationships amongst the personal and sociotechnical variables in accident/injury occurrences. The input data for the model were the correlation matrix of 18 variables, which were collected from the case study mines. The case study results showed that there are sequential interactions amongst the sociotechnical and personal factors leading to accidents/injuries in mines. Amongst the latent endogenous constructs, job dissatisfaction and safe work behaviour show a significant positive and negative direct relationship with work injury, respectively. However, the construct safety environment has a significant negative indirect relationship with work injury. The safety environment is negatively affected by work hazards and positively affected by social support. The safety environment also shows a significant negative relationship with job stress and job dissatisfaction. However, negative personality has no significant direct or indirect effect on work injury, but it has a significant negative relationship with safe work behaviour. The endogenous construct negative personality is positively influenced by job stress and negatively influenced by social support.

Simultaneous topography and tomography of latent fingerprints using full-field swept-source optical coherence tomography

NASA Astrophysics Data System (ADS)

Dubey, Satish Kumar; Singh Mehta, Dalip; Anand, Arun; Shakher, Chandra

2008-01-01

We demonstrate simultaneous topography and tomography of latent fingerprints using full-field swept-source optical coherence tomography (OCT). The swept-source OCT system comprises a superluminescent diode (SLD) as broad-band light source, an acousto-optic tunable filter (AOTF) as frequency tuning device, and a compact, nearly common-path interferometer. Both the amplitude and the phase map of the interference fringe signal are reconstructed. Optical sectioning of the latent fingerprint sample is obtained by selective Fourier filtering and the topography is retrieved from the phase map. Interferometry, selective filtering, low coherence and hence better resolution are some of the advantages of the proposed system over the conventional fingerprint detection techniques. The present technique is non-invasive in nature and does not require any physical or chemical processing. Therefore, the quality of the sample does not alter and hence the same fingerprint can be used for other types of forensic test. Exploitation of low-coherence interferometry for fingerprint detection itself provides an edge over other existing techniques as fingerprints can even be lifted from low-reflecting surfaces. The proposed system is very economical and compact.

Latent structure analysis of the process variables and pharmaceutical responses of an orally disintegrating tablet.

PubMed

Hayashi, Yoshihiro; Oshima, Etsuko; Maeda, Jin; Onuki, Yoshinori; Obata, Yasuko; Takayama, Kozo

2012-01-01

A multivariate statistical technique was applied to the design of an orally disintegrating tablet and to clarify the causal correlation among variables of the manufacturing process and pharmaceutical responses. Orally disintegrating tablets (ODTs) composed mainly of mannitol were prepared via the wet-granulation method using crystal transition from the δ to the β form of mannitol. Process parameters (water amounts (X(1)), kneading time (X(2)), compression force (X(3)), and amounts of magnesium stearate (X(4))) were optimized using a nonlinear response surface method (RSM) incorporating a thin plate spline interpolation (RSM-S). The results of a verification study revealed that the experimental responses, such as tensile strength and disintegration time, coincided well with the predictions. A latent structure analysis of the pharmaceutical formulations of the tablet performed using a Bayesian network led to the clear visualization of a causal connection among variables of the manufacturing process and tablet characteristics. The quantity of β-mannitol in the granules (Q(β)) was affected by X(2) and influenced all granule properties. The specific surface area of the granules was affected by X(1) and Q(β) and had an effect on all tablet characteristics. Moreover, the causal relationships among the variables were clarified by inferring conditional probability distributions. These techniques provide a better understanding of the complicated latent structure among variables of the manufacturing process and tablet characteristics.

Incorporating imperfect detection into joint models of communites: A response to Warton et al.

USGS Publications Warehouse

Beissinger, Steven R.; Iknayan, Kelly J.; Guillera-Arroita, Gurutzeta; Zipkin, Elise; Dorazio, Robert; Royle, Andy; Kery, Marc

2016-01-01

Warton et al. [1] advance community ecology by describing a statistical framework that can jointly model abundances (or distributions) across many taxa to quantify how community properties respond to environmental variables. This framework specifies the effects of both measured and unmeasured (latent) variables on the abundance (or occurrence) of each species. Latent variables are random effects that capture the effects of both missing environmental predictors and correlations in parameter values among different species. As presented in Warton et al., however, the joint modeling framework fails to account for the common problem of detection or measurement errors that always accompany field sampling of abundance or occupancy, and are well known to obscure species- and community-level inferences.

Radiation oncology career decision variables for graduating trainees seeking positions in 2003-2004

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

Wilson, Lynn D.; Flynn, Daniel F.; Haffty, Bruce G.

2005-06-01

Purpose: Radiation oncology trainees must consider an array of variables when deciding upon an academic or private practice career path. This prospective evaluation of the 2004 graduating radiation oncology trainees, evaluates such variables and provides additional descriptive data. Methods: A survey that included 15 questions (one subjective, eleven categorical, and 3 continuous variables) was mailed to the 144 graduating radiation oncology trainees in United States programs in January of 2004. Questions were designed to gather information regarding factors that may have influenced career path choices. The responses were anonymous, and no identifying information was sought. Survey data were collated andmore » analyzed for differences in both categorical and continuous variables as they related to choice of academic or private practice career path. Results: Sixty seven (47%) of the surveys were returned. Forty-five percent of respondents indicated pursuit of an academic career. All respondents participated in research during training with 73% participating in research publication authorship. Post graduate year-3 was the median in which career path was chosen, and 20% thought that a fellowship position was 'perhaps' necessary to secure an academic position. Thirty percent of the respondents revealed that the timing of the American Board of Radiology examination influenced their career path decision. Eighteen variables were offered as possibly influencing career path choice within the survey, and the top five identified by those seeking an academic path were: (1) colleagues, (2) clinical research, (3) teaching, (4) geography, (5) and support staff. For those seeking private practice, the top choices were: (1) lifestyle, (2) practice environment, (3) patient care, (4) geography, (5) colleagues. Female gender (p = 0.064), oral meeting presentation (p = 0.053), and international meeting presentation (p 0.066) were the variables most significantly associated with pursuing an academic career path. The following variables were ranked significantly differently in hierarchy (p < 0.05) by those seeking an academic versus private practice path with respect to having influence on the career decision: lifestyle, income, case-mix, autonomy, ability to sub-specialize, basic research, clinical research, teaching, patient care, board structure, practice environment, and mentoring. Conclusion: These data offer descriptive information regarding variables that lead to radiation oncology trainee career path decisions. Such information may be of use in modification of training programs to meet future personnel and programmatic needs within the specialty.« less

Variable Importance in Multivariate Group Comparisons.

ERIC Educational Resources Information Center

Huberty, Carl J.; Wisenbaker, Joseph M.

1992-01-01

Interpretations of relative variable importance in multivariate analysis of variance are discussed, with attention to (1) latent construct definition; (2) linear discriminant function scores; and (3) grouping variable effects. Two numerical ranking methods are proposed and compared by the bootstrap approach using two real data sets. (SLD)

Predictors of sexual risk behaviors among adolescent mothers in a human immunodeficiency virus prevention program.

PubMed

Koniak-Griffin, Deborah; Stein, Judith A

2006-03-01

The purpose of this study was to determine the following: (1) whether adolescent mothers in a human immunodeficiency virus (HIV) prevention program had significantly greater perceived self-efficacy and perceived behavioral control to use condoms, and more favorable outcome expectancies and subjective norms regarding condom use than those in a health education control group, 3 months after intervention; and (2) the impact of the 3-month postintervention theoretical variables on intentions to use condoms at 3 months and sexual risk behaviors at 6 months. Structural equation modeling with latent variables was used to assess the influence of theoretical variables and treatment condition using data from 496 participants (78% Latinas, 18% African-Americans) who completed questionnaires at baseline and at 3- and 6-month follow-up evaluations. Substantial improvements were shown by both groups, with a slight advantage for the HIV prevention group, on all theoretical variables between pretest and the follow-up evaluations. In the predictive model, the intervention group reported significantly fewer sex partners. By using intentions to use condoms as a mediator, greater self-efficacy, hedonistic beliefs, positive subjective norms, and less unprotected sex predicted intentions to use condoms, which, in turn, predicted less unprotected sex. Lower subjective norms modestly predicted multiple partners. Significant indirect paths mediated through intentions to use condoms were observed. These data support a relationship among several constructs from social cognitive theory and the theory of reasoned action, and subsequent sexual risk behaviors. HIV-prevention programs for adolescent mothers should be designed to include these theoretical constructs and to address contextual factors influencing their lives.

Multilevel Latent Class Analysis for Large-Scale Educational Assessment Data: Exploring the Relation between the Curriculum and Students' Mathematical Strategies

ERIC Educational Resources Information Center

Fagginger Auer, Marije F.; Hickendorff, Marian; Van Putten, Cornelis M.; Béguin, Anton A.; Heiser, Willem J.

2016-01-01

A first application of multilevel latent class analysis (MLCA) to educational large-scale assessment data is demonstrated. This statistical technique addresses several of the challenges that assessment data offers. Importantly, MLCA allows modeling of the often ignored teacher effects and of the joint influence of teacher and student variables.…

Consequences of Ignoring Guessing when Estimating the Latent Density in Item Response Theory

ERIC Educational Resources Information Center

Woods, Carol M.

2008-01-01

In Ramsay-curve item response theory (RC-IRT), the latent variable distribution is estimated simultaneously with the item parameters. In extant Monte Carlo evaluations of RC-IRT, the item response function (IRF) used to fit the data is the same one used to generate the data. The present simulation study examines RC-IRT when the IRF is imperfectly…

Students' Views on Mathematics in Single-Sex and Coed Classrooms in Ghana

ERIC Educational Resources Information Center

Bofah, Emmanuel Adu-tutu; Hannula, Markku S.

2016-01-01

In this study, we investigated students' views on themselves as learners of mathematics as a function of school-by-sex (N = 2034, MAge = 18.49, SDAge = 1.25; 12th-grade; 58.2% girls). Using latent variable Structural Equation Modeling (SEM), the measurement and structural equivalence as well as the equality of latent means of scores across…

The Information a Test Provides on an Ability Parameter. Research Report. ETS RR-07-18

ERIC Educational Resources Information Center

Haberman, Shelby J.

2007-01-01

In item-response theory, if a latent-structure model has an ability variable, then elementary information theory may be employed to provide a criterion for evaluation of the information the test provides concerning ability. This criterion may be considered even in cases in which the latent-structure model is not valid, although interpretation of…

An introduction to mixture item response theory models.

PubMed

De Ayala, R J; Santiago, S Y

2017-02-01

Mixture item response theory (IRT) allows one to address situations that involve a mixture of latent subpopulations that are qualitatively different but within which a measurement model based on a continuous latent variable holds. In this modeling framework, one can characterize students by both their location on a continuous latent variable as well as by their latent class membership. For example, in a study of risky youth behavior this approach would make it possible to estimate an individual's propensity to engage in risky youth behavior (i.e., on a continuous scale) and to use these estimates to identify youth who might be at the greatest risk given their class membership. Mixture IRT can be used with binary response data (e.g., true/false, agree/disagree, endorsement/not endorsement, correct/incorrect, presence/absence of a behavior), Likert response scales, partial correct scoring, nominal scales, or rating scales. In the following, we present mixture IRT modeling and two examples of its use. Data needed to reproduce analyses in this article are available as supplemental online materials at http://dx.doi.org/10.1016/j.jsp.2016.01.002. Copyright © 2016 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Latent Heating Structures Derived from TRMM

NASA Technical Reports Server (NTRS)

Tao, W.-K.; Smith, E. A.; Adler, R.; Hou, A.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.; Olson, W.; Satoh, S.

2004-01-01

Rainfall is the fundamental variable within the Earth's hydrological cycle because it is both the main forcing term leading to variations in continental and oceanic surface water budgets. The vertical distribution of latent heat release, which is accompanied with rain, modulates large-scale meridional and zonal circulations within the tropics as well as modifying the energetic efficiency of mid-latitude weather systems. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water.This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission 0. The TRMM observatory, whose development was a joint US-Japan space endeavor, was launched in November 1997. TRMM measurements provide an accurate account of rainfall over the global tropics, information which can be .used to estimate the four-dimensional structure of latent heating across the entire tropical and sub-tropical regions. Various algorithm methodologies for estimating latent heating based on rain rate measurements from TRMM observations are described. The strengths and weaknesses of these algorithms, as well as the latent heating products generated by these algorithms, are also discussed along with validation analyses of the products. The investigation paper provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, and concludes with remarks designed to stimulate further research on latent heating retrieval

An open-chain imaginary-time path-integral sampling approach to the calculation of approximate symmetrized quantum time correlation functions.

PubMed

Cendagorta, Joseph R; Bačić, Zlatko; Tuckerman, Mark E

2018-03-14

We introduce a scheme for approximating quantum time correlation functions numerically within the Feynman path integral formulation. Starting with the symmetrized version of the correlation function expressed as a discretized path integral, we introduce a change of integration variables often used in the derivation of trajectory-based semiclassical methods. In particular, we transform to sum and difference variables between forward and backward complex-time propagation paths. Once the transformation is performed, the potential energy is expanded in powers of the difference variables, which allows us to perform the integrals over these variables analytically. The manner in which this procedure is carried out results in an open-chain path integral (in the remaining sum variables) with a modified potential that is evaluated using imaginary-time path-integral sampling rather than requiring the generation of a large ensemble of trajectories. Consequently, any number of path integral sampling schemes can be employed to compute the remaining path integral, including Monte Carlo, path-integral molecular dynamics, or enhanced path-integral molecular dynamics. We believe that this approach constitutes a different perspective in semiclassical-type approximations to quantum time correlation functions. Importantly, we argue that our approximation can be systematically improved within a cumulant expansion formalism. We test this approximation on a set of one-dimensional problems that are commonly used to benchmark approximate quantum dynamical schemes. We show that the method is at least as accurate as the popular ring-polymer molecular dynamics technique and linearized semiclassical initial value representation for correlation functions of linear operators in most of these examples and improves the accuracy of correlation functions of nonlinear operators.

An open-chain imaginary-time path-integral sampling approach to the calculation of approximate symmetrized quantum time correlation functions

NASA Astrophysics Data System (ADS)

Cendagorta, Joseph R.; Bačić, Zlatko; Tuckerman, Mark E.

2018-03-01

We introduce a scheme for approximating quantum time correlation functions numerically within the Feynman path integral formulation. Starting with the symmetrized version of the correlation function expressed as a discretized path integral, we introduce a change of integration variables often used in the derivation of trajectory-based semiclassical methods. In particular, we transform to sum and difference variables between forward and backward complex-time propagation paths. Once the transformation is performed, the potential energy is expanded in powers of the difference variables, which allows us to perform the integrals over these variables analytically. The manner in which this procedure is carried out results in an open-chain path integral (in the remaining sum variables) with a modified potential that is evaluated using imaginary-time path-integral sampling rather than requiring the generation of a large ensemble of trajectories. Consequently, any number of path integral sampling schemes can be employed to compute the remaining path integral, including Monte Carlo, path-integral molecular dynamics, or enhanced path-integral molecular dynamics. We believe that this approach constitutes a different perspective in semiclassical-type approximations to quantum time correlation functions. Importantly, we argue that our approximation can be systematically improved within a cumulant expansion formalism. We test this approximation on a set of one-dimensional problems that are commonly used to benchmark approximate quantum dynamical schemes. We show that the method is at least as accurate as the popular ring-polymer molecular dynamics technique and linearized semiclassical initial value representation for correlation functions of linear operators in most of these examples and improves the accuracy of correlation functions of nonlinear operators.

Estimation of diagnostic test accuracy without full verification: a review of latent class methods

PubMed Central

Collins, John; Huynh, Minh

2014-01-01

The performance of a diagnostic test is best evaluated against a reference test that is without error. For many diseases, this is not possible, and an imperfect reference test must be used. However, diagnostic accuracy estimates may be biased if inaccurately verified status is used as the truth. Statistical models have been developed to handle this situation by treating disease as a latent variable. In this paper, we conduct a systematized review of statistical methods using latent class models for estimating test accuracy and disease prevalence in the absence of complete verification. PMID:24910172

Fall Risk, Supports and Services, and Falls Following a Nursing Home Discharge.

PubMed

Noureldin, Marwa; Hass, Zachary; Abrahamson, Kathleen; Arling, Greg

2017-09-04

Falls are a major source of morbidity and mortality among older adults; however, little is known regarding fall occurrence during a nursing home (NH) to community transition. This study sought to examine whether the presence of supports and services impacts the relationship between fall-related risk factors and fall occurrence post NH discharge. Participants in the Minnesota Return to Community Initiative who were assisted in achieving a community discharge (N = 1459) comprised the study sample. The main outcome was fall occurrence within 30 days of discharge. Factor analyses were used to estimate latent models from variables of interest. A structural equation model (SEM) was estimated to determine the relationship between the emerging latent variables and falls. Fifteen percent of participants fell within 30 days of NH discharge. Factor analysis of fall-related risk factors produced three latent variables: fall concerns/history; activities of daily living impairments; and use of high-risk medications. A supports/services latent variable also emerged that included caregiver support frequency, medication management assistance, durable medical equipment use, discharge location, and receipt of home health or skilled nursing services. In the SEM model, high-risk medications use and fall concerns/history had direct positive effects on falling. Receiving supports/services did not affect falling directly; however, it reduced the effect of high-risk medication use on falling (p < .05). Within the context of a state-implemented transition program, findings highlight the importance of supports/services in mitigating against medication-related risk of falling post NH discharge. © The Author 2017. Published by Oxford University Press on behalf of The Gerontological Society of America. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

The path from childhood behavioural disorders to felony offending: Investigating the role of adolescent drinking, peer marginalisation and school failure.

PubMed

Savolainen, Jukka; Mason, W Alex; Bolen, Jonathan D; Chmelka, Mary B; Hurtig, Tuula; Ebeling, Hanna; Nordström, Tanja; Taanila, Anja

2015-12-01

Although a pathway from childhood behavioural disorders to criminal offending is well established, the aetiological processes remain poorly understood. Also, it is not clear if attention deficit hyperactivity disorder (ADHD) is predictive of crime in the absence of comorbid disruptive behaviour disorder (DBD). We examined two research questions: (1) Does ADHD have a unique effect on the risk of criminal offending, independently of DBD? (2) Is the effect of childhood behavioural disorders on criminal offending direct or mediated by adolescent processes related to school experience, substance misuse and peers? Structural equation modelling, with latent variables, was applied to longitudinally collected data on 4644 men from the 1986 Northern Finland Birth Cohort Study. Both ADHD and DBD separately predicted felony conviction risk. Most of these effects were mediated by adolescent alcohol use and low academic performance. The effect of DBD was stronger and included a direct pathway to criminal offending. Findings were more consistent with the life course mediation hypothesis of pathways into crime than the behavioural continuity path, in that the effects of each disorder category were mediated by heavy drinking and educational failure. Preventing these adolescent risk outcomes may be an effective approach to closing pathways to criminal behaviour amongst behaviourally disordered children. However, as there was some evidence of a direct pathway from DBD, effective treatments targeting this disorder are also expected to reduce criminal offending. Copyright © 2014 John Wiley & Sons, Ltd.

A path model of sarcopenia on bone mass loss in elderly subjects.

PubMed

Rondanelli, M; Guido, D; Opizzi, A; Faliva, M A; Perna, S; Grassi, M

2014-01-01

Aging is associated with decreases in muscle mass, strength, power (sarcopenia) and bone mineral density (BMD). The aims of this study were to investigate in elderly the role of sarcopenia on BMD loss by a path model, including adiposity, inflammation, and malnutrition associations. Body composition and BMD were measured by dual X-ray absorptiometry in 159 elderly subjects (52 male/107 female; mean age 80.3 yrs). Muscle strength was determined with dynamometer. Serum albumin and PCR were also assessed. Structural equations examined the effect of sarcopenia (measured by Relative Skeletal Muscle Mass, Total Muscle Mass, Handgrip, Muscle Quality Score) on osteoporosis (measured by Vertebral and Femoral T-scores) in a latent variable model including adiposity (measured by Total Fat Mass, BMI, Ginoid/Android Fat), inflammation (PCR), and malnutrition (serum albumin). The sarcopenia assumed a role of moderator in the adiposity-osteoporosis relationship. Specifically, increasing the sarcopenia, the relationship adiposity-osteoporosis (β: -0.58) decrease in intensity. Adiposity also influences sarcopenia (β: -0.18). Malnutrition affects the inflammatory and the adiposity states (β: +0.61, and β: -0.30, respectively), while not influencing the sarcopenia. Thus, adiposity has a role as a mediator of the effect of malnutrition on both sarcopenia and osteoporosis. Malnutrition decreases adiposity; decreasing adiposity, in turn, increase the sarcopenia and osteoporosis. This study suggests such as in a group of elderly sarcopenia affects the link between adiposity and BMD, but not have a pure independent effect on osteoporosis.

Transformational Leadership and Organizational Citizenship Behavior: A Meta-Analytic Test of Underlying Mechanisms.

PubMed

Nohe, Christoph; Hertel, Guido

2017-01-01

Based on social exchange theory, we examined and contrasted attitudinal mediators (affective organizational commitment, job satisfaction) and relational mediators (trust in leader, leader-member exchange; LMX) of the positive relationship between transformational leadership and organizational citizenship behavior (OCB). Hypotheses were tested using meta-analytic path models with correlations from published meta-analyses (761 samples with 227,419 individuals overall). When testing single-mediator models, results supported our expectations that each of the mediators explained the relationship between transformational leadership and OCB. When testing a multi-mediator model, LMX was the strongest mediator. When testing a model with a latent attitudinal mechanism and a latent relational mechanism, the relational mechanism was the stronger mediator of the relationship between transformational leadership and OCB. Our findings help to better understand the underlying mechanisms of the relationship between transformational leadership and OCB.

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.

Deconstructing sub-clinical psychosis into latent-state and trait variables over a 30-year time span.

PubMed

Rössler, Wulf; Hengartner, Michael P; Ajdacic-Gross, Vladeta; Haker, Helene; Angst, Jules

2013-10-01

Our aim was to deconstruct the variance underlying the expression of sub-clinical psychosis symptoms into portions associated with latent time-dependent states and time-invariant traits. We analyzed data of 335 subjects from the general population of Zurich, Switzerland, who had been repeatedly measured between 1979 (age 20/21) and 2008 (age 49/50). We applied two measures of sub-clinical psychosis derived from the SCL-90-R, namely schizotypal signs (STS) and schizophrenia nuclear symptoms (SNS). Variance was decomposed with latent state-trait analysis and associations with covariates were examined with generalized linear models. At ages 19/20 and 49/50, the latent states underlying STS accounted for 48% and 51% of variance, whereas for SNS those estimates were 62% and 50%. Between those age classes, however, expression of sub-clinical psychosis was strongly associated with stable traits (75% and 89% of total variance in STS and SNS, respectively, at age 27/28). Latent states underlying variance in STS and SNS were particularly related to partnership problems over almost the entire observation period. STS was additionally related to employment problems, whereas drug-use was a strong predictor of states underlying both syndromes at age 19/20. The latent trait underlying expression of STS and SNS was particularly related to low sense of mastery and self-esteem and to high depressiveness. Although most psychosis symptoms are transient and episodic in nature, the variability in their expression is predominantly caused by stable traits. Those time-invariant and rather consistent effects are particularly influential around age 30, whereas the occasion-specific states appear to be particularly influential at ages 20 and 50. © 2013.

Fitting a Mixture Rasch Model to English as a Foreign Language Listening Tests: The Role of Cognitive and Background Variables in Explaining Latent Differential Item Functioning

ERIC Educational Resources Information Center

Aryadoust, Vahid

2015-01-01

The present study uses a mixture Rasch model to examine latent differential item functioning in English as a foreign language listening tests. Participants (n = 250) took a listening and lexico-grammatical test and completed the metacognitive awareness listening questionnaire comprising problem solving (PS), planning and evaluation (PE), mental…

An All-Fragments Grammar for Simple and Accurate Parsing

DTIC Science & Technology

2012-03-21

Tsujii. Probabilistic CFG with latent annotations. In Proceedings of ACL, 2005. Slav Petrov and Dan Klein. Improved Inference for Unlexicalized Parsing. In...Proceedings of NAACL-HLT, 2007. Slav Petrov and Dan Klein. Sparse Multi-Scale Grammars for Discriminative Latent Variable Parsing. In Proceedings of...EMNLP, 2008. Slav Petrov, Leon Barrett, Romain Thibaux, and Dan Klein. Learning Accurate, Compact, and Interpretable Tree Annotation. In Proceedings

Stability of Language in Childhood: A Multi-Age, -Domain, -Measure, and -Source Study

PubMed Central

Bornstein, Marc H.; Putnick, Diane L.

2011-01-01

The stability of language across childhood is traditionally assessed by exploring longitudinal relations between individual language measures. However, language encompasses many domains and varies with different sources (child speech, parental report, experimenter assessment). This study evaluated individual variation in multiple age-appropriate measures of child language derived from multiple sources and stability between their latent variables in 192 young children across more than 2 years. Structural equation modeling demonstrated the loading of multiple measures of child language from different sources on single latent variables of language at ages 20 and 48 months. A large stability coefficient (r = .84) obtained between the 2 language latent variables. This stability obtained even when accounting for family socioeconomic status, maternal verbal intelligence, education, speech, and tendency to respond in a socially desirable fashion, and child social competence. Stability was also equivalent for children in diverse childcare situations and for girls and boys. Across age, from the beginning of language acquisition to just before school entry, aggregating multiple age-appropriate methods and measures at each age and multiple reporters, children show strong stability of individual differences in general language development. PMID:22004343

Data-driven subtypes of major depressive disorder: a systematic review

PubMed Central

2012-01-01

Background According to current classification systems, patients with major depressive disorder (MDD) may have very different combinations of symptoms. This symptomatic diversity hinders the progress of research into the causal mechanisms and treatment allocation. Theoretically founded subtypes of depression such as atypical, psychotic, and melancholic depression have limited clinical applicability. Data-driven analyses of symptom dimensions or subtypes of depression are scarce. In this systematic review, we examine the evidence for the existence of data-driven symptomatic subtypes of depression. Methods We undertook a systematic literature search of MEDLINE, PsycINFO and Embase in May 2012. We included studies analyzing the depression criteria of the Diagnostic and Statistical Manual of Mental Disorders, fourth edition (DSM-IV) of adults with MDD in latent variable analyses. Results In total, 1176 articles were retrieved, of which 20 satisfied the inclusion criteria. These reports described a total of 34 latent variable analyses: 6 confirmatory factor analyses, 6 exploratory factor analyses, 12 principal component analyses, and 10 latent class analyses. The latent class techniques distinguished 2 to 5 classes, which mainly reflected subgroups with different overall severity: 62 of 71 significant differences on symptom level were congruent with a latent class solution reflecting severity. The latent class techniques did not consistently identify specific symptom clusters. Latent factor techniques mostly found a factor explaining the variance in the symptoms depressed mood and interest loss (11 of 13 analyses), often complemented by psychomotor retardation or fatigue (8 of 11 analyses). However, differences in found factors and classes were substantial. Conclusions The studies performed to date do not provide conclusive evidence for the existence of depressive symptom dimensions or symptomatic subtypes. The wide diversity of identified factors and classes might result either from the absence of patterns to be found, or from the theoretical and modeling choices preceding analysis. PMID:23210727

Path integrals and large deviations in stochastic hybrid systems.

PubMed

Bressloff, Paul C; Newby, Jay M

2014-04-01

We construct a path-integral representation of solutions to a stochastic hybrid system, consisting of one or more continuous variables evolving according to a piecewise-deterministic dynamics. The differential equations for the continuous variables are coupled to a set of discrete variables that satisfy a continuous-time Markov process, which means that the differential equations are only valid between jumps in the discrete variables. Examples of stochastic hybrid systems arise in biophysical models of stochastic ion channels, motor-driven intracellular transport, gene networks, and stochastic neural networks. We use the path-integral representation to derive a large deviation action principle for a stochastic hybrid system. Minimizing the associated action functional with respect to the set of all trajectories emanating from a metastable state (assuming that such a minimization scheme exists) then determines the most probable paths of escape. Moreover, evaluating the action functional along a most probable path generates the so-called quasipotential used in the calculation of mean first passage times. We illustrate the theory by considering the optimal paths of escape from a metastable state in a bistable neural network.

Epstein-Barr Virus Latent Membrane Protein 1 Genetic Variability in Peripheral Blood B Cells and Oropharyngeal Fluids

PubMed Central

Renzette, Nicholas; Somasundaran, Mohan; Brewster, Frank; Coderre, James; Weiss, Eric R.; McManus, Margaret; Greenough, Thomas; Tabak, Barbara; Garber, Manuel; Kowalik, Timothy F.

2014-01-01

ABSTRACT We report the diversity of latent membrane protein 1 (LMP1) gene founder sequences and the level of Epstein-Barr virus (EBV) genome variability over time and across anatomic compartments by using virus genomes amplified directly from oropharyngeal wash specimens and peripheral blood B cells during acute infection and convalescence. The intrahost nucleotide variability of the founder virus was 0.02% across the region sequences, and diversity increased significantly over time in the oropharyngeal compartment (P = 0.004). The LMP1 region showing the greatest level of variability in both compartments, and over time, was concentrated within the functional carboxyl-terminal activating regions 2 and 3 (CTAR2 and CTAR3). Interestingly, a deletion in a proline-rich repeat region (amino acids 274 to 289) of EBV commonly reported in EBV sequenced from cancer specimens was not observed in acute infectious mononucleosis (AIM) patients. Taken together, these data highlight the diversity in circulating EBV genomes and its potential importance in disease pathogenesis and vaccine design. IMPORTANCE This study is among the first to leverage an improved high-throughput deep-sequencing methodology to investigate directly from patient samples the degree of diversity in Epstein-Barr virus (EBV) populations and the extent to which viral genome diversity develops over time in the infected host. Significant variability of circulating EBV latent membrane protein 1 (LMP1) gene sequences was observed between cellular and oral wash samples, and this variability increased over time in oral wash samples. The significance of EBV genetic diversity in transmission and disease pathogenesis are discussed. PMID:24429365

Latent variable model for suicide risk in relation to social capital and socio-economic status.

PubMed

Congdon, Peter

2012-08-01

There is little evidence on the association between suicide outcomes (ideation, attempts, self-harm) and social capital. This paper investigates such associations using a structural equation model based on health survey data, and allowing for both individual and contextual risk factors. Social capital and other major risk factors for suicide, namely socioeconomic status and social isolation, are modelled as latent variables that are proxied (or measured) by observed indicators or question responses for survey subjects. These latent scales predict suicide risk in the structural component of the model. Also relevant to explaining suicide risk are contextual variables, such as area deprivation and region of residence, as well as the subject's demographic status. The analysis is based on the 2007 Adult Psychiatric Morbidity Survey and includes 7,403 English subjects. A Bayesian modelling strategy is used. Models with and without social capital as a predictor of suicide risk are applied. A benefit to statistical fit is demonstrated when social capital is added as a predictor. Social capital varies significantly by geographic context variables (neighbourhood deprivation, region), and this impacts on the direct effects of these contextual variables on suicide risk. In particular, area deprivation is not confirmed as a distinct significant influence. The model develops a suicidality risk score incorporating social capital, and the success of this risk score in predicting actual suicide events is demonstrated. Social capital as reflected in neighbourhood perceptions is a significant factor affecting risks of different types of self-harm and may mediate the effects of other contextual variables such as area deprivation.

Epstein-Barr virus latent membrane protein 1 genetic variability in peripheral blood B cells and oropharyngeal fluids.

PubMed

Renzette, Nicholas; Somasundaran, Mohan; Brewster, Frank; Coderre, James; Weiss, Eric R; McManus, Margaret; Greenough, Thomas; Tabak, Barbara; Garber, Manuel; Kowalik, Timothy F; Luzuriaga, Katherine

2014-04-01

We report the diversity of latent membrane protein 1 (LMP1) gene founder sequences and the level of Epstein-Barr virus (EBV) genome variability over time and across anatomic compartments by using virus genomes amplified directly from oropharyngeal wash specimens and peripheral blood B cells during acute infection and convalescence. The intrahost nucleotide variability of the founder virus was 0.02% across the region sequences, and diversity increased significantly over time in the oropharyngeal compartment (P = 0.004). The LMP1 region showing the greatest level of variability in both compartments, and over time, was concentrated within the functional carboxyl-terminal activating regions 2 and 3 (CTAR2 and CTAR3). Interestingly, a deletion in a proline-rich repeat region (amino acids 274 to 289) of EBV commonly reported in EBV sequenced from cancer specimens was not observed in acute infectious mononucleosis (AIM) patients. Taken together, these data highlight the diversity in circulating EBV genomes and its potential importance in disease pathogenesis and vaccine design. This study is among the first to leverage an improved high-throughput deep-sequencing methodology to investigate directly from patient samples the degree of diversity in Epstein-Barr virus (EBV) populations and the extent to which viral genome diversity develops over time in the infected host. Significant variability of circulating EBV latent membrane protein 1 (LMP1) gene sequences was observed between cellular and oral wash samples, and this variability increased over time in oral wash samples. The significance of EBV genetic diversity in transmission and disease pathogenesis are discussed.

Using Multigroup-Multiphase Latent State-Trait Models to Study Treatment-Induced Changes in Intra-Individual State Variability: An Application to Smokers' Affect.

PubMed

Geiser, Christian; Griffin, Daniel; Shiffman, Saul

2016-01-01

Sometimes, researchers are interested in whether an intervention, experimental manipulation, or other treatment causes changes in intra-individual state variability. The authors show how multigroup-multiphase latent state-trait (MG-MP-LST) models can be used to examine treatment effects with regard to both mean differences and differences in state variability. The approach is illustrated based on a randomized controlled trial in which N = 338 smokers were randomly assigned to nicotine replacement therapy (NRT) vs. placebo prior to quitting smoking. We found that post quitting, smokers in both the NRT and placebo group had significantly reduced intra-individual affect state variability with respect to the affect items calm and content relative to the pre-quitting phase. This reduction in state variability did not differ between the NRT and placebo groups, indicating that quitting smoking may lead to a stabilization of individuals' affect states regardless of whether or not individuals receive NRT.

Using Multigroup-Multiphase Latent State-Trait Models to Study Treatment-Induced Changes in Intra-Individual State Variability: An Application to Smokers' Affect

PubMed Central

Geiser, Christian; Griffin, Daniel; Shiffman, Saul

2016-01-01

Sometimes, researchers are interested in whether an intervention, experimental manipulation, or other treatment causes changes in intra-individual state variability. The authors show how multigroup-multiphase latent state-trait (MG-MP-LST) models can be used to examine treatment effects with regard to both mean differences and differences in state variability. The approach is illustrated based on a randomized controlled trial in which N = 338 smokers were randomly assigned to nicotine replacement therapy (NRT) vs. placebo prior to quitting smoking. We found that post quitting, smokers in both the NRT and placebo group had significantly reduced intra-individual affect state variability with respect to the affect items calm and content relative to the pre-quitting phase. This reduction in state variability did not differ between the NRT and placebo groups, indicating that quitting smoking may lead to a stabilization of individuals' affect states regardless of whether or not individuals receive NRT. PMID:27499744

A latent class distance association model for cross-classified data with a categorical response variable.

PubMed

Vera, José Fernando; de Rooij, Mark; Heiser, Willem J

2014-11-01

In this paper we propose a latent class distance association model for clustering in the predictor space of large contingency tables with a categorical response variable. The rows of such a table are characterized as profiles of a set of explanatory variables, while the columns represent a single outcome variable. In many cases such tables are sparse, with many zero entries, which makes traditional models problematic. By clustering the row profiles into a few specific classes and representing these together with the categories of the response variable in a low-dimensional Euclidean space using a distance association model, a parsimonious prediction model can be obtained. A generalized EM algorithm is proposed to estimate the model parameters and the adjusted Bayesian information criterion statistic is employed to test the number of mixture components and the dimensionality of the representation. An empirical example highlighting the advantages of the new approach and comparing it with traditional approaches is presented. © 2014 The British Psychological Society.

Precursors of Reading Difficulties in Czech and Slovak Children At-Risk of Dyslexia.

PubMed

Moll, Kristina; Thompson, Paul A; Mikulajova, Marina; Jagercikova, Zuzana; Kucharska, Anna; Franke, Helena; Hulme, Charles; Snowling, Margaret J

2016-05-01

Children with preschool language difficulties are at high risk of literacy problems; however, the nature of the relationship between delayed language development and dyslexia is not understood. Three hundred eight Slovak and Czech children were recruited into three groups: family risk of dyslexia, speech/language difficulties and controls, and were assessed three times from kindergarten until Grade 1. There was a twofold increase in probability of reading problems in each risk group. Precursors of 'dyslexia' included difficulties in oral language and code-related skills (phoneme awareness, letter-knowledge and rapid automatized naming); poor performance in phonological memory and vocabulary was observed in both affected and unaffected high-risk peers. A two-group latent variable path model shows that early language skills predict code-related skills, which in turn predict literacy skills. Findings suggest that dyslexia in Slavic languages has its origins in early language deficits, and children who succumb to reading problems show impaired code-related skills before the onset of formal reading instruction. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Emotion valence, intensity and emotion regulation in immigrants and majority members in the Netherlands.

PubMed

Stupar, Snežana; van de Vijver, Fons J R; Fontaine, Johnny R J

2015-08-01

We were interested in interethnic differences and similarities in how emotion regulation strategies (reappraisal, suppression and social sharing) can be predicted by emotion valence and intensity. The sample consisted of 389 Dutch majority members and members of five immigrant groups: 136 Turkish and Moroccan, 105 Antillean and Surinamese, 102 Indonesian, 313 Western and 150 other non-Western immigrants. In a path model with latent variables we confirmed that emotion regulation strategies were significantly and similarly related to emotion valence and intensity across the groups. Negative emotions were more reappraised and suppressed than positive emotions. Intensity was positively related to social sharing and negatively related to reappraisal and suppression. The Dutch majority group scored higher on emotion valence than Turkish and Moroccan immigrants. Also, the Dutch majority group scored lower on reappraisal than all non-Western groups, and lower on suppression than Turkish and Moroccan immigrants. We conclude that group differences reside more in mean scores on some components than in how antecedents are linked to regulation strategies. © 2014 International Union of Psychological Science.

Tourism sector preparedness in zones with a high seismic risk: Case study of the Capital Region of Japan

NASA Astrophysics Data System (ADS)

Lihui, W.; Wang, D.

2017-12-01

Japan is a country highly vulnerable to natural disasters, especially earthquakes. Tourism, as a strategic industry in Japan, is especially vulnerable to destructive earthquake disasters owing to the characteristics of vulnerability, sensitivity and substitutability. Here we aim to provide theoretical understanding of the perception and responses of tourism managers towards damaging disasters in tourism destinations with high seismic risks. We conducted surveys among the mangers of tourism businesses in the capital area of Japan in 2014 and applied structural equation modeling techniques to empirically test the proposed model with four latent variables, which are risk perception, threat knowledge, disaster preparedness and earthquake preparedness. Our results show that threat knowledge affects risk perception and disaster preparedness positively. In addition, disaster preparedness positively affects earthquake preparedness. However, the proposed paths from risk perception to disaster preparedness, risk perception to earthquake preparedness, and threat knowledge to earthquake preparedness were not statistically significant. Our results may provide references for policymakers in promoting crisis planning in tourism destination with high seismic risks.

Causal mediation analysis with a binary outcome and multiple continuous or ordinal mediators: Simulations and application to an alcohol intervention

PubMed Central

Nguyen, Trang Quynh; Webb-Vargas, Yenny; Koning, Ina M.; Stuart, Elizabeth A.

2016-01-01

We investigate a method to estimate the combined effect of multiple continuous/ordinal mediators on a binary outcome: 1) fit a structural equation model with probit link for the outcome and identity/probit link for continuous/ordinal mediators, 2) predict potential outcome probabilities, and 3) compute natural direct and indirect effects. Step 2 involves rescaling the latent continuous variable underlying the outcome to address residual mediator variance/covariance. We evaluate the estimation of risk-difference- and risk-ratio-based effects (RDs, RRs) using the ML, WLSMV and Bayes estimators in Mplus. Across most variations in path-coefficient and mediator-residual-correlation signs and strengths, and confounding situations investigated, the method performs well with all estimators, but favors ML/WLSMV for RDs with continuous mediators, and Bayes for RRs with ordinal mediators. Bayes outperforms WLSMV/ML regardless of mediator type when estimating RRs with small potential outcome probabilities and in two other special cases. An adolescent alcohol prevention study is used for illustration. PMID:27158217

Friendship Security, But Not Friendship Intimacy, Moderates the Stability of Anxiety During Preadolescence.

PubMed

Wood, Megan A; Bukowski, William M; Santo, Jonathan B

2017-01-01

A two-wave longitudinal study of 380 preadolescents (M age = 10.87) from largely middle-class schools in Montréal, Québec, Canada, assessed the hypothesis that friendship security, but not friendship intimacy, moderates the stability of anxiety during adolescence. This central but largely overlooked question about peer relations concerns which aspects of friendship account for the effects of friendship on emotional adjustment. Anxiety and friendship quality were measured via self-report questionnaires, employing the Network of Relationships Inventory for security and intimacy items. An index of friendship durability, which combined reciprocity and stability within first- and second-best friendship choices, was derived from sociometric measures. A latent variable path analysis examined with structural equation modeling showed that anxiety was less stable for children who perceived their friendships as secure. The moderating effect of intimacy was statistically nonsignificant. A follow-up analysis showed that the effects of security did not result from friendship durability. These findings provide support for the long-standing but previously unaddressed hypothesis that security, rather than intimacy, accounts for friendship's effect on anxiety reduction during early adolescence.

Latent Heating from TRMM Satellite Measurements

NASA Technical Reports Server (NTRS)

Tao, Wei-Kuo; Smith, E. A.; Adler, R.; Haddad, Z.; Hou, A.; Iguchi, T.; Kakar, R.; Krishnamurti, T.; Kummerow, C.; Lang, S.

2004-01-01

Rainfall production is the fundamental variable within the Earth's hydrological cycle because it is both the principal forcing term in surface water budgets and its energetics corollary, latent heating, is the principal source of atmospheric diabatic heating. Latent heat release itself is a consequence of phase changes between the vapor, liquid, and frozen states of water. The properties of the vertical distribution of latent heat release modulate large-scale meridional and zonal circulations within the tropics - as well as modifying the energetic efficiencies of midlatitude weather systems. This paper focuses on the retrieval of latent heat release from satellite measurements generated by the Tropical Rainfall Measuring Mission (TRMM) satellite observatory, which was launched in November 1997 as a joint American-Japanese space endeavor. Since then, TRMM measurements have been providing an accurate four-dimensional account of rainfall over the global tropics and sub-tropics, information which can be used to estimate the space-time structure of latent heating across the Earth's low latitudes. The paper examines how the observed TRMM distribution of rainfall has advanced an understanding of the global water and energy cycle and its consequent relationship to the atmospheric general circulation and climate via latent heat release. A set of algorithm methodologies that are being used to estimate latent heating based on rain rate retrievals from the TRMM observations are described. The characteristics of these algorithms and the latent heating products that can be generated from them are also described, along with validation analyses of the heating products themselves. Finally, the investigation provides an overview of how TRMM-derived latent heating information is currently being used in conjunction with global weather and climate models, concluding with remarks intended to stimulate further research on latent heating retrieval from satellites.

A Bayesian Approach to More Stable Estimates of Group-Level Effects in Contextual Studies.

PubMed

Zitzmann, Steffen; Lüdtke, Oliver; Robitzsch, Alexander

2015-01-01

Multilevel analyses are often used to estimate the effects of group-level constructs. However, when using aggregated individual data (e.g., student ratings) to assess a group-level construct (e.g., classroom climate), the observed group mean might not provide a reliable measure of the unobserved latent group mean. In the present article, we propose a Bayesian approach that can be used to estimate a multilevel latent covariate model, which corrects for the unreliable assessment of the latent group mean when estimating the group-level effect. A simulation study was conducted to evaluate the choice of different priors for the group-level variance of the predictor variable and to compare the Bayesian approach with the maximum likelihood approach implemented in the software Mplus. Results showed that, under problematic conditions (i.e., small number of groups, predictor variable with a small ICC), the Bayesian approach produced more accurate estimates of the group-level effect than the maximum likelihood approach did.

Sensitivity of Latent Heating Profiles to Environmental Conditions: Implications for TRMM and Climate Research

NASA Technical Reports Server (NTRS)

Shepherd, J. Marshall; Einaudi, Franco (Technical Monitor)

2000-01-01

The Tropical Rainfall Measuring Mission (TRMM) as a part of NASA's Earth System Enterprise is the first mission dedicated to measuring tropical rainfall through microwave and visible sensors, and includes the first spaceborne rain radar. Tropical rainfall comprises two-thirds of global rainfall. It is also the primary distributor of heat through the atmosphere's circulation. It is this circulation that defines Earth's weather and climate. Understanding rainfall and its variability is crucial to understanding and predicting global climate change. Weather and climate models need an accurate assessment of the latent heating released as tropical rainfall occurs. Currently, cloud model-based algorithms are used to derive latent heating based on rainfall structure. Ultimately, these algorithms can be applied to actual data from TRMM. This study investigates key underlying assumptions used in developing the latent heating algorithms. For example, the standard algorithm is highly dependent on a system's rainfall amount and structure. It also depends on an a priori database of model-derived latent heating profiles based on the aforementioned rainfall characteristics. Unanswered questions remain concerning the sensitivity of latent heating profiles to environmental conditions (both thermodynamic and kinematic), regionality, and seasonality. This study investigates and quantifies such sensitivities and seeks to determine the optimal latent heating profile database based on the results. Ultimately, the study seeks to produce an optimized latent heating algorithm based not only on rainfall structure but also hydrometeor profiles.

Anxiety, bulimia, drug and alcohol addiction, depression, and schizophrenia: what do you think about their aetiology, dangerousness, social distance, and treatment? A latent class analysis approach.

PubMed

Mannarini, Stefania; Boffo, Marilisa

2015-01-01

Mental illness stigma is a serious societal problem and a critical impediment to treatment seeking for mentally ill people. To improve the understanding of mental illness stigma, this study focuses on the simultaneous analysis of people's aetiological beliefs, attitudes (i.e. perceived dangerousness and social distance), and recommended treatments related to several mental disorders by devising an over-arching latent structure that could explain the relations among these variables. Three hundred and sixty university students randomly received an unlabelled vignette depicting one of six mental disorders to be evaluated on the four variables on a Likert-type scale. A one-factor Latent Class Analysis (LCA) model was hypothesized, which comprised the four manifest variables as indicators and the mental disorder as external variable. The main findings were the following: (a) a one-factor LCA model was retrieved; (b) alcohol and drug addictions are the most strongly stigmatized; (c) a realistic opinion about the causes and treatment of schizophrenia, anxiety, bulimia, and depression was associated to lower prejudicial attitudes and social rejection. Beyond the general appraisal of mental illness an individual might have, the results generally point to the acknowledgement of the specific features of different diagnostic categories. The implications of the present results are discussed in the framework of a better understanding of mental illness stigma.

Robust Bayesian clustering.

PubMed

Archambeau, Cédric; Verleysen, Michel

2007-01-01

A new variational Bayesian learning algorithm for Student-t mixture models is introduced. This algorithm leads to (i) robust density estimation, (ii) robust clustering and (iii) robust automatic model selection. Gaussian mixture models are learning machines which are based on a divide-and-conquer approach. They are commonly used for density estimation and clustering tasks, but are sensitive to outliers. The Student-t distribution has heavier tails than the Gaussian distribution and is therefore less sensitive to any departure of the empirical distribution from Gaussianity. As a consequence, the Student-t distribution is suitable for constructing robust mixture models. In this work, we formalize the Bayesian Student-t mixture model as a latent variable model in a different way from Svensén and Bishop [Svensén, M., & Bishop, C. M. (2005). Robust Bayesian mixture modelling. Neurocomputing, 64, 235-252]. The main difference resides in the fact that it is not necessary to assume a factorized approximation of the posterior distribution on the latent indicator variables and the latent scale variables in order to obtain a tractable solution. Not neglecting the correlations between these unobserved random variables leads to a Bayesian model having an increased robustness. Furthermore, it is expected that the lower bound on the log-evidence is tighter. Based on this bound, the model complexity, i.e. the number of components in the mixture, can be inferred with a higher confidence.

Path Analysis and Residual Plotting as Methods of Environmental Scanning in Higher Education: An Illustration with Applications and Enrollments.

ERIC Educational Resources Information Center

Morcol, Goktug; McLaughlin, Gerald W.

1990-01-01

The study proposes using path analysis and residual plotting as methods supporting environmental scanning in strategic planning for higher education institutions. Path models of three levels of independent variables are developed. Dependent variables measuring applications and enrollments at Virginia Polytechnic Institute and State University are…

Thermal control of low-pressure fractionation processes. [in basaltic magma solidification

NASA Technical Reports Server (NTRS)

Usselman, T. M.; Hodge, D. S.

1978-01-01

Thermal models detailing the solidification paths for shallow basaltic magma chambers (both open and closed systems) were calculated using finite-difference techniques. The total solidification time for closed chambers are comparable to previously published calculations; however, the temperature-time paths are not. These paths are dependent on the phase relations and the crystallinity of the system, because both affect the manner in which the latent heat of crystallization is distributed. In open systems, where a chamber would be periodically replenished with additional parental liquid, calculations indicate that the possibility is strong that a steady-state temperature interval is achieved near a major phase boundary. In these cases it is straightforward to analyze fractionation models of the basaltic liquid evolution and their corresponding cumulate sequences. This steady thermal fractionating state can be invoked to explain large amounts of erupted basalts of similar composition over long time periods from the same volcanic center and some rhythmically layered basic cumulate sequences.

Identifying Useful Auxiliary Variables for Incomplete Data Analyses: A Note on a Group Difference Examination Approach

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2014-01-01

This research note contributes to the discussion of methods that can be used to identify useful auxiliary variables for analyses of incomplete data sets. A latent variable approach is discussed, which is helpful in finding auxiliary variables with the property that if included in subsequent maximum likelihood analyses they may enhance considerably…

Maximum Likelihood Analysis of Nonlinear Structural Equation Models with Dichotomous Variables

ERIC Educational Resources Information Center

Song, Xin-Yuan; Lee, Sik-Yum

2005-01-01

In this article, a maximum likelihood approach is developed to analyze structural equation models with dichotomous variables that are common in behavioral, psychological and social research. To assess nonlinear causal effects among the latent variables, the structural equation in the model is defined by a nonlinear function. The basic idea of the…

Causal Models with Unmeasured Variables: An Introduction to LISREL.

ERIC Educational Resources Information Center

Wolfle, Lee M.

Whenever one uses ordinary least squares regression, one is making an implicit assumption that all of the independent variables have been measured without error. Such an assumption is obviously unrealistic for most social data. One approach for estimating such regression models is to measure implied coefficients between latent variables for which…

Least Principal Components Analysis (LPCA): An Alternative to Regression Analysis.

ERIC Educational Resources Information Center

Olson, Jeffery E.

Often, all of the variables in a model are latent, random, or subject to measurement error, or there is not an obvious dependent variable. When any of these conditions exist, an appropriate method for estimating the linear relationships among the variables is Least Principal Components Analysis. Least Principal Components are robust, consistent,…

Inference for finite-sample trajectories in dynamic multi-state site-occupancy models using hidden Markov model smoothing

USGS Publications Warehouse

Fiske, Ian J.; Royle, J. Andrew; Gross, Kevin

2014-01-01

Ecologists and wildlife biologists increasingly use latent variable models to study patterns of species occurrence when detection is imperfect. These models have recently been generalized to accommodate both a more expansive description of state than simple presence or absence, and Markovian dynamics in the latent state over successive sampling seasons. In this paper, we write these multi-season, multi-state models as hidden Markov models to find both maximum likelihood estimates of model parameters and finite-sample estimators of the trajectory of the latent state over time. These estimators are especially useful for characterizing population trends in species of conservation concern. We also develop parametric bootstrap procedures that allow formal inference about latent trend. We examine model behavior through simulation, and we apply the model to data from the North American Amphibian Monitoring Program.

Modeling Heterogeneity in Relationships between Initial Status and Rates of Change: Latent Variable Regression in a Three-Level Hierarchical Model. CSE Report 647

ERIC Educational Resources Information Center

Choi, Kilchan; Seltzer, Michael

2005-01-01

In studies of change in education and numerous other fields, interest often centers on how differences in the status of individuals at the start of a time period of substantive interest relate to differences in subsequent change. This report presents a fully Bayesian approach to estimating three-level hierarchical models in which latent variable…

Epilepsy and the Wnt Signaling Pathway

DTIC Science & Technology

2015-06-01

status epilepticus (SE), head injury, infection or stroke). This is followed by a variable (months to years in humans) “latent period” followed by the...TERMS Status Epilepticus , Wnt Signaling, Epileptogenesis 16. SECURITY CLASSIFICATION OF: U 17. LIMITATION OF ABSTRACTU U 18. NUMBER OF PAGES 4...disease sub-type. In this grant, we will investigate the mechanisms of Status Epilepticus (SE) and the ensuing latent period in animal models of

Individual heterogeneity in reproductive rates and cost of reproduction in a long-lived vertebrate

PubMed Central

Chambert, Thierry; Rotella, Jay J; Higgs, Megan D; Garrott, Robert A

2013-01-01

Individual variation in reproductive success is a key feature of evolution, but also has important implications for predicting population responses to variable environments. Although such individual variation in reproductive outcomes has been reported in numerous studies, most analyses to date have not considered whether these realized differences were due to latent individual heterogeneity in reproduction or merely random chance causing different outcomes among like individuals. Furthermore, latent heterogeneity in fitness components might be expressed differently in contrasted environmental conditions, an issue that has only rarely been investigated. Here, we assessed (i) the potential existence of latent individual heterogeneity and (ii) the nature of its expression (fixed vs. variable) in a population of female Weddell seals (Leptonychotes weddellii), using a hierarchical modeling approach on a 30-year mark–recapture data set consisting of 954 individual encounter histories. We found strong support for the existence of latent individual heterogeneity in the population, with “robust” individuals expected to produce twice as many pups as “frail” individuals. Moreover, the expression of individual heterogeneity appeared consistent, with only mild evidence that it might be amplified when environmental conditions are severe. Finally, the explicit modeling of individual heterogeneity allowed us to detect a substantial cost of reproduction that was not evidenced when the heterogeneity was ignored. PMID:23919151

Comparison of CTT and Rasch-based approaches for the analysis of longitudinal Patient Reported Outcomes.

PubMed

Blanchin, Myriam; Hardouin, Jean-Benoit; Le Neel, Tanguy; Kubis, Gildas; Blanchard, Claire; Mirallié, Eric; Sébille, Véronique

2011-04-15

Health sciences frequently deal with Patient Reported Outcomes (PRO) data for the evaluation of concepts, in particular health-related quality of life, which cannot be directly measured and are often called latent variables. Two approaches are commonly used for the analysis of such data: Classical Test Theory (CTT) and Item Response Theory (IRT). Longitudinal data are often collected to analyze the evolution of an outcome over time. The most adequate strategy to analyze longitudinal latent variables, which can be either based on CTT or IRT models, remains to be identified. This strategy must take into account the latent characteristic of what PROs are intended to measure as well as the specificity of longitudinal designs. A simple and widely used IRT model is the Rasch model. The purpose of our study was to compare CTT and Rasch-based approaches to analyze longitudinal PRO data regarding type I error, power, and time effect estimation bias. Four methods were compared: the Score and Mixed models (SM) method based on the CTT approach, the Rasch and Mixed models (RM), the Plausible Values (PV), and the Longitudinal Rasch model (LRM) methods all based on the Rasch model. All methods have shown comparable results in terms of type I error, all close to 5 per cent. LRM and SM methods presented comparable power and unbiased time effect estimations, whereas RM and PV methods showed low power and biased time effect estimations. This suggests that RM and PV methods should be avoided to analyze longitudinal latent variables. Copyright © 2010 John Wiley & Sons, Ltd.

Illustration of Step-Wise Latent Class Modeling With Covariates and Taxometric Analysis in Research Probing Children's Mental Models in Learning Sciences

PubMed Central

Stamovlasis, Dimitrios; Papageorgiou, George; Tsitsipis, Georgios; Tsikalas, Themistoklis; Vaiopoulou, Julie

2018-01-01

This paper illustrates two psychometric methods, latent class analysis (LCA) and taxometric analysis (TA) using empirical data from research probing children's mental representation in science learning. LCA is used to obtain a typology based on observed variables and to further investigate how the encountered classes might be related to external variables, where the effectiveness of classification process and the unbiased estimations of parameters become the main concern. In the step-wise LCA, the class membership is assigned and subsequently its relationship with covariates is established. This leading-edge modeling approach suffers from severe downward-biased estimations. The illustration of LCA is focused on alternative bias correction approaches and demonstrates the effect of modal and proportional class-membership assignment along with BCH and ML correction procedures. The illustration of LCA is presented with three covariates, which are psychometric variables operationalizing formal reasoning, divergent thinking and field dependence-independence, respectively. Moreover, taxometric analysis, a method designed to detect the type of the latent structural model, categorical or dimensional, is introduced, along with the relevant basic concepts and tools. TA was applied complementarily in the same data sets to answer the fundamental hypothesis about children's naïve knowledge on the matters under study and it comprises an additional asset in building theory which is fundamental for educational practices. Taxometric analysis provided results that were ambiguous as far as the type of the latent structure. This finding initiates further discussion and sets a problematization within this framework rethinking fundamental assumptions and epistemological issues. PMID:29713300

Structural Equations and Path Analysis for Discrete Data.

ERIC Educational Resources Information Center

Winship, Christopher; Mare, Robert D.

1983-01-01

Presented is an approach to causal models in which some or all variables are discretely measured, showing that path analytic methods permit quantification of causal relationships among variables with the same flexibility and power of interpretation as is feasible in models including only continuous variables. Examples are provided. (Author/IS)

Forecast model applications of retrieved three dimensional liquid water fields

NASA Technical Reports Server (NTRS)

Raymond, William H.; Olson, William S.

1990-01-01

Forecasts are made for tropical storm Emily using heating rates derived from the SSM/I physical retrievals described in chapters 2 and 3. Average values of the latent heating rates from the convective and stratiform cloud simulations, used in the physical retrieval, are obtained for individual 1.1 km thick vertical layers. Then, the layer-mean latent heating rates are regressed against the slant path-integrated liquid and ice precipitation water contents to determine the best fit two parameter regression coefficients for each layer. The regression formulae and retrieved precipitation water contents are utilized to infer the vertical distribution of heating rates for forecast model applications. In the forecast model, diabatic temperature contributions are calculated and used in a diabatic initialization, or in a diabatic initialization combined with a diabatic forcing procedure. Our forecasts show that the time needed to spin-up precipitation processes in tropical storm Emily is greatly accelerated through the application of the data.

Transformational Leadership and Organizational Citizenship Behavior: A Meta-Analytic Test of Underlying Mechanisms

PubMed Central

Nohe, Christoph; Hertel, Guido

2017-01-01

Based on social exchange theory, we examined and contrasted attitudinal mediators (affective organizational commitment, job satisfaction) and relational mediators (trust in leader, leader-member exchange; LMX) of the positive relationship between transformational leadership and organizational citizenship behavior (OCB). Hypotheses were tested using meta-analytic path models with correlations from published meta-analyses (761 samples with 227,419 individuals overall). When testing single-mediator models, results supported our expectations that each of the mediators explained the relationship between transformational leadership and OCB. When testing a multi-mediator model, LMX was the strongest mediator. When testing a model with a latent attitudinal mechanism and a latent relational mechanism, the relational mechanism was the stronger mediator of the relationship between transformational leadership and OCB. Our findings help to better understand the underlying mechanisms of the relationship between transformational leadership and OCB. PMID:28848478

Device for adapting continuously variable transmissions to infinitely variable transmissions with forward-neutral-reverse capabilities

DOEpatents

Wilkes, Donald F.; Purvis, James W.; Miller, A. Keith

1997-01-01

An infinitely variable transmission is capable of operating between a maximum speed in one direction and a minimum speed in an opposite direction, including a zero output angular velocity, while being supplied with energy at a constant angular velocity. Input energy is divided between a first power path carrying an orbital set of elements and a second path that includes a variable speed adjustment mechanism. The second power path also connects with the orbital set of elements in such a way as to vary the rate of angular rotation thereof. The combined effects of power from the first and second power paths are combined and delivered to an output element by the orbital element set. The transmission can be designed to operate over a preselected ratio of forward to reverse output speeds.

On accommodating spatial interactions in a Generalized Heterogeneous Data Model (GHDM) of mixed types of dependent variables.

DOT National Transportation Integrated Search

2015-12-01

We develop an econometric framework for incorporating spatial dependence in integrated model systems of latent variables and multidimensional mixed data outcomes. The framework combines Bhats Generalized Heterogeneous Data Model (GHDM) with a spat...

Should "Multiple Imputations" Be Treated as "Multiple Indicators"?

ERIC Educational Resources Information Center

Mislevy, Robert J.

1993-01-01

Multiple imputations for latent variables are constructed so that analyses treating them as true variables have the correct expectations for population characteristics. Analyzing multiple imputations in accordance with their construction yields correct estimates of population characteristics, whereas analyzing them as multiple indicators generally…

Measurement Models for Reasoned Action Theory.

PubMed

Hennessy, Michael; Bleakley, Amy; Fishbein, Martin

2012-03-01

Quantitative researchers distinguish between causal and effect indicators. What are the analytic problems when both types of measures are present in a quantitative reasoned action analysis? To answer this question, we use data from a longitudinal study to estimate the association between two constructs central to reasoned action theory: behavioral beliefs and attitudes toward the behavior. The belief items are causal indicators that define a latent variable index while the attitude items are effect indicators that reflect the operation of a latent variable scale. We identify the issues when effect and causal indicators are present in a single analysis and conclude that both types of indicators can be incorporated in the analysis of data based on the reasoned action approach.

Application of latent variable model in Rosenberg self-esteem scale.

PubMed

Leung, Shing-On; Wu, Hui-Ping

2013-01-01

Latent Variable Models (LVM) are applied to Rosenberg Self-Esteem Scale (RSES). Parameter estimations automatically give negative signs hence no recoding is necessary for negatively scored items. Bad items can be located through parameter estimate, item characteristic curves and other measures. Two factors are extracted with one on self-esteem and the other on the degree to take moderate views, with the later not often being covered in previous studies. A goodness-of-fit measure based on two-way margins is used but more works are needed. Results show that scaling provided by models with more formal statistical ground correlated highly with conventional method, which may provide justification for usual practice.

The Role of Global Hydrologic Processes in Interannual and Long-Term Climate Variability

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.

1997-01-01

The earth's climate and its variability is linked inextricably with the presence of water on our planet. El Nino / Southern Oscillation-- the major mode of interannual variability-- is characterized by strong perturbations in oceanic evaporation, tropical rainfall, and radiation. On longer time scales, the major feedback mechanism in CO2-induced global warming is actually that due to increased water vapor holding capacity of the atmosphere. The global hydrologic cycle effects on climate are manifested through influence of cloud and water vapor on energy fluxes at the top of atmosphere and at the surface. Surface moisture anomalies retain the "memory" of past precipitation anomalies and subsequently alter the partitioning of latent and sensible heat fluxes at the surface. At the top of atmosphere, water vapor and cloud perturbations alter the net amount of radiation that the earth's climate system receives. These pervasive linkages between water, radiation, and surface processes present major complexities for observing and modeling climate variations. Major uncertainties in the observations include vertical structure of clouds and water vapor, surface energy balance, and transport of water and heat by wind fields. Modeling climate variability and change on a physical basis requires accurate by simplified submodels of radiation, cloud formation, radiative exchange, surface biophysics, and oceanic energy flux. In the past, we m safely say that being "data poor' has limited our depth of understanding and impeded model validation and improvement. Beginning with pre-EOS data sets, many of these barriers are being removed. EOS platforms with the suite of measurements dedicated to specific science questions are part of our most cost effective path to improved understanding and predictive capability. This talk will highlight some of the major questions confronting global hydrology and the prospects for significant progress afforded by EOS-era measurements.

Acute alcohol effects on set-shifting and its moderation by baseline individual differences: a latent variable analysis.

PubMed

Korucuoglu, Ozlem; Sher, Kenneth J; Wood, Phillip K; Saults, John Scott; Altamirano, Lee; Miyake, Akira; Bartholow, Bruce D

2017-03-01

To compare the acute effects of alcohol on set-shifting task performance (relative to sober baseline performance) during ascending and descending limb breath alcohol concentration (BrAC), as well as possible moderation of these effects by baseline individual differences. Shifting performance was tested during an initial baseline and a subsequent drinking session, during which participants were assigned randomly to one of three beverage conditions (alcohol, placebo or control) and one of two BrAC limb conditions [ascending and descending (A/D) or descending-only (D-only)]. A human experimental laboratory on the University of Missouri campus in Columbia, MO, USA. A total of 222 moderate-drinking adults (ages 21-30 years) recruited from Columbia, MO and tested between 2010 and 2013. The outcome measure was performance on set-shifting tasks under the different beverage and limb conditions. Shifting performance assessed at baseline was a key moderator. Although performance improved across sessions, this improvement was reduced in the alcohol compared with no-alcohol groups (post-drink latent mean comparison across groups, all Ps ≤ 0.05), and this effect was more pronounced in individuals with lower pre-drink performance (comparison of pre- to post-drink path coefficients across groups, all Ps ≤ 0.05). In the alcohol group, performance was better on descending compared with ascending limb (P ≤ 0.001), but descending limb performance did not differ across the A/D and D-only groups. Practising tasks before drinking moderates the acute effects of alcohol on the ability to switch between tasks. Greater impairment in shifting ability on descending compared with ascending breath alcohol concentration is not related to task practice. © 2016 Society for the Study of Addiction.

Longitudinal trajectories of benefit finding in adolescents with Type 1 diabetes.

PubMed

Rassart, Jessica; Luyckx, Koen; Berg, Cynthia A; Oris, Leen; Wiebe, Deborah J

2017-10-01

Benefit finding, which refers to perceiving positive life changes resulting from adversity, has been associated with better psychosocial well-being in different chronic illnesses. However, little research to date has examined how benefit finding develops in the context of Type 1 diabetes (T1D). The present study aimed to identify trajectories of benefit finding across adolescence and to investigate prospective associations with depressive symptoms, self-care, and metabolic control. Adolescents with T1D aged 10 to 14 (Mage = 12.49 years, 54% girls) participated in a 4-wave longitudinal study spanning 1.5 years (N = 252 at Time 1). Adolescents filled out questionnaires on benefit finding, self-care, depressive symptoms, and illness perceptions. HbA1c values were obtained through point of care assays. We used latent growth curve modeling (LGCM) and latent class growth analysis (LCGA) to examine the development of benefit finding. Cross-lagged path analysis and multi-group LGCM were used to examine prospective associations among the study variables. Adolescents reported moderate levels of benefit finding which decreased over time. Three benefit finding trajectory classes were identified: low and decreasing, moderate and decreasing, and high and stable. These trajectory classes differed in terms of self-care, perceived personal and treatment control, and perceptions of illness cyclicality. Higher levels of benefit finding predicted relative increases in self-care 6 months later. Benefit finding was not prospectively related to depressive symptoms and metabolic control. Benefit finding may serve as a protective factor for adolescents with Type 1 diabetes and may motivate these adolescents to more closely follow their treatment regimen. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Differentiating Immune Cell Targets in Gut-Associated Lymphoid Tissue for HIV Cure.

PubMed

Khan, Shahzada; Telwatte, Sushama; Trapecar, Martin; Yukl, Steven; Sanjabi, Shomyseh

2017-11-01

The single greatest challenge to an HIV cure is the persistence of latently infected cells containing inducible, replication-competent proviral genomes, which constitute only a small fraction of total or infected cells in the body. Although resting CD4 + T cells in the blood are a well-known source of viral rebound, more than 90% of the body's lymphocytes reside elsewhere. Many are in gut tissue, where HIV DNA levels per million CD4 + T cells are considerably higher than in the blood. Despite the significant contribution of gut tissue to viral replication and persistence, little is known about the cell types that support persistence of HIV in the gut; importantly, T cells in the gut have phenotypic, functional, and survival properties that are distinct from T cells in other tissues. The mechanisms by which latency is established and maintained will likely depend on the location and cytokine milieu surrounding the latently infected cells in each compartment. Therefore, successful HIV cure strategies require identification and characterization of the exact cell types that support viral persistence, particularly in the gut. In this review, we describe the seeding of the latent HIV reservoir in the gut mucosa; highlight the evidence for compartmentalization and depletion of T cells; summarize the immunologic consequences of HIV infection within the gut milieu; propose how the damaged gut environment may promote the latent HIV reservoir; and explore several immune cell targets in the gut and their place on the path toward HIV cure.

Self-Consciousness and Assertiveness as Explanatory Variables of L2 Oral Ability: A Latent Variable Approach

ERIC Educational Resources Information Center

Ockey, Gary

2011-01-01

Drawing on current theories in personality, second-language (L2) oral ability, and psychometrics, this study investigates the extent to which self-consciousness and assertiveness are explanatory variables of L2 oral ability. Three hundred sixty first-year Japanese university students who were studying English as a foreign language participated in…

The Potential for Differential Findings among Invariance Testing Strategies for Multisample Measured Variable Path Models

ERIC Educational Resources Information Center

Mann, Heather M.; Rutstein, Daisy W.; Hancock, Gregory R.

2009-01-01

Multisample measured variable path analysis is used to test whether causal/structural relations among measured variables differ across populations. Several invariance testing approaches are available for assessing cross-group equality of such relations, but the associated test statistics may vary considerably across methods. This study is a…

Accelerometry in persons with multiple sclerosis: measurement of physical activity or walking mobility?

PubMed

Weikert, Madeline; Motl, Robert W; Suh, Yoojin; McAuley, Edward; Wynn, Daniel

2010-03-15

Motion sensors such as accelerometers have been recognized as an ideal measure of physical activity in persons with MS. This study examined the hypothesis that accelerometer movement counts represent a measure of both physical activity and walking mobility in individuals with MS. The sample included 269 individuals with a definite diagnosis of relapsing-remitting MS who completed the Godin Leisure-Time Exercise Questionnaire (GLTEQ), International Physical Activity Questionnaire (IPAQ), Multiple Sclerosis Walking Scale-12 (MSWS-12), Patient Determined Disease Steps (PDDS), and then wore an ActiGraph accelerometer for 7days. The data were analyzed using bivariate correlation and confirmatory factor analysis. The results indicated that (a) the GLTEQ and IPAQ scores were strongly correlated and loaded significantly on a physical activity latent variable, (b) the MSWS-12 and PDDS scores strongly correlated and loaded significantly on a walking mobility latent variable, and (c) the accelerometer movement counts correlated similarly with the scores from the four self-report questionnaires and cross-loaded on both physical activity and walking mobility latent variables. Our data suggest that accelerometers are measuring both physical activity and walking mobility in persons with MS, whereas self-report instruments are measuring either physical activity or walking mobility in this population.

A call for theory to support the use of causal-formative indicators: A commentary on Bollen and Diamantopoulos (2017).

PubMed

Hardin, Andrew

2017-09-01

In this issue, Bollen and Diamantopoulos (2017) defend causal-formative indicators against several common criticisms leveled by scholars who oppose their use. In doing so, the authors make several convincing assertions: Constructs exist independently from their measures; theory determines whether indicators cause or measure latent variables; and reflective and causal-formative indicators are both subject to interpretational confounding. However, despite being a well-reasoned, comprehensive defense of causal-formative indicators, no single article can address all of the issues associated with this debate. Thus, Bollen and Diamantopoulos leave a few fundamental issues unresolved. For example, how can researchers establish the reliability of indicators that may include measurement error? Moreover, how should researchers interpret disturbance terms that capture sources of influence related to both the empirical definition of the latent variable and to the theoretical definition of the construct? Relatedly, how should researchers reconcile the requirement for a census of causal-formative indicators with the knowledge that indicators are likely missing from the empirically estimated latent variable? This commentary develops 6 related research questions to draw attention to these fundamental issues, and to call for future research that can lead to the development of theory to guide the use of causal-formative indicators. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Comparing hierarchical models via the marginalized deviance information criterion.

PubMed

Quintero, Adrian; Lesaffre, Emmanuel

2018-07-20

Hierarchical models are extensively used in pharmacokinetics and longitudinal studies. When the estimation is performed from a Bayesian approach, model comparison is often based on the deviance information criterion (DIC). In hierarchical models with latent variables, there are several versions of this statistic: the conditional DIC (cDIC) that incorporates the latent variables in the focus of the analysis and the marginalized DIC (mDIC) that integrates them out. Regardless of the asymptotic and coherency difficulties of cDIC, this alternative is usually used in Markov chain Monte Carlo (MCMC) methods for hierarchical models because of practical convenience. The mDIC criterion is more appropriate in most cases but requires integration of the likelihood, which is computationally demanding and not implemented in Bayesian software. Therefore, we consider a method to compute mDIC by generating replicate samples of the latent variables that need to be integrated out. This alternative can be easily conducted from the MCMC output of Bayesian packages and is widely applicable to hierarchical models in general. Additionally, we propose some approximations in order to reduce the computational complexity for large-sample situations. The method is illustrated with simulated data sets and 2 medical studies, evidencing that cDIC may be misleading whilst mDIC appears pertinent. Copyright © 2018 John Wiley & Sons, Ltd.

Effect of Scintillometer Height on Structure Parameter of the Refractive Index of Air Measurements

NASA Astrophysics Data System (ADS)

Gowda, P. H.; Howell, T. A.; Hartogensis, O.; Basu, S.; Scanlon, B. R.

2009-12-01

Scintillometers measure amount of scintillations by emitting a beam of light over a horizontal path and expresses as the atmospheric turbulence structure parameter as the refractive index of air (Cn2). Cn2 represents the turbulent strength of the atmosphere and describes the ability of the atmosphere to transport heat and humidity. The main objective of this study was to evaluate the effect of scintillometer height on Cn2 measurements and on the estimation of latent heat fluxes. The study was conducted during the 2009 summer growing season in the USDA-ARS Conservation and Production Research Laboratory (CPRL) at Bushland [350 11' N, 1020 06' W; 1,170 m elevation MSL], Texas. Field experiment consisted of two steps: (1) cross-calibration of scintillometers and (2) measurement of Cn2 at different heights. In the first step, three large aperture scintillometers (LAS) were deployed across two large lysimeter fields with bare soil surfaces. During the 3-week cross-calibration period, all three scintillometers were installed at a 2-m height with a path length of 420 m. Cn2 was monitored at a 1-min interval and averaged for 15-min periods. Cn2 measurements were synchronized with weather station and weighing lysimeter measurements. After the cross-calibration period, scintillometers were installed at 2-, 2.5- and 3-m heights, and Cn2 measurements were continued for another 3-week period. In addition to the Cn2 measurements, net radiation (Rn) and soil heat fluxes (G) were measured in both lysimeter fields. Cn2 values were corrected for inner scale dependence before cross calibration and estimation of sensible heat fluxes. Measurements of wind speed, air temperature, and relative humidity were used with Cn2 data to derive sensible heat fluxes. Latent heat fluxes were estimated as a residual from the energy balance and compared with lysimeter data. Results of cross calibration and effects of scintillometer height on the estimation of latent heat fluxes were reported and discussed.

Prevalence and risk factors for latent tuberculosis infection among healthcare workers in Nampula Central Hospital, Mozambique.

PubMed

Belo, Celso; Naidoo, Saloshni

2017-06-08

Healthcare workers in high tuberculosis burdened countries are occupationally exposed to the tuberculosis disease with uncomplicated and complicated tuberculosis on the increase among them. Most of them acquire Mycobacterium tuberculosis but do not progress to the active disease - latent tuberculosis infection. The objective of this study was to assess the prevalence and risk factors associated with latent tuberculosis infection among healthcare workers in Nampula Central Hospital, Mozambique. This cross-sectional study of healthcare workers was conducted between 2014 and 2015. Participants (n = 209) were administered a questionnaire on demographics and occupational tuberculosis exposure and had a tuberculin skin test administered. Multivariate linear and logistic regression tested for associations between independent variables and dependent outcomes (tuberculin skin test induration and latent tuberculosis infection status). The prevalence of latent tuberculosis infection was 34.4%. Latent tuberculosis infection was highest in those working for more than eight years (39.3%), those who had no BCG vaccination (39.6%) and were immunocompromised (78.1%). Being immunocompromised was significantly associated with latent tuberculosis infection (OR 5.97 [95% CI 1.89; 18.87]). Positive but non-significant associations occurred with working in the medical domain (OR 1.02 [95% CI 0.17; 6.37]), length of employment > eight years (OR 1.97 [95% CI 0.70; 5.53]) and occupational contact with tuberculosis patients (OR 1.24 [95% CI 0.47; 3.27]). Personal and occupational factors were positively associated with latent tuberculosis infection among healthcare workers in Mozambique.

Examining the DSM-5 Section III Criteria for Obsessive-Compulsive Personality Disorder in a Community Sample.

PubMed

Liggett, Jacqueline; Sellbom, Martin; Carmichael, Kieran L C

2017-12-01

The current study examined the extent to which the trait-based operationalization of obsessive-compulsive personality disorder (OCPD) in Section III of the DSM-5 describes the same construct as the one described in Section II. A community sample of 313 adults completed a series of personality inventories indexing the DSM-5 Sections II and III diagnostic criteria for OCPD, in addition to a measure of functional impairment modelled after the criteria in Section III. Results indicated that latent constructs representing Section II and Section III OCPD overlapped substantially (r = .75, p < .001). Hierarchical latent regression models revealed that at least three of the four DSM-5 Section III facets (Rigid Perfectionism, Perseveration, and Intimacy Avoidance) uniquely accounted for a large proportion of variance (53%) in a latent Section II OCPD variable. Further, Anxiousness and (low) Impulsivity, as well as self and interpersonal impairment, augmented the prediction of latent OCPD scores.

Estimating Interaction Effects With Incomplete Predictor Variables

PubMed Central

Enders, Craig K.; Baraldi, Amanda N.; Cham, Heining

2014-01-01

The existing missing data literature does not provide a clear prescription for estimating interaction effects with missing data, particularly when the interaction involves a pair of continuous variables. In this article, we describe maximum likelihood and multiple imputation procedures for this common analysis problem. We outline 3 latent variable model specifications for interaction analyses with missing data. These models apply procedures from the latent variable interaction literature to analyses with a single indicator per construct (e.g., a regression analysis with scale scores). We also discuss multiple imputation for interaction effects, emphasizing an approach that applies standard imputation procedures to the product of 2 raw score predictors. We thoroughly describe the process of probing interaction effects with maximum likelihood and multiple imputation. For both missing data handling techniques, we outline centering and transformation strategies that researchers can implement in popular software packages, and we use a series of real data analyses to illustrate these methods. Finally, we use computer simulations to evaluate the performance of the proposed techniques. PMID:24707955

Social skills: a resource for more social support, lower depression levels, higher quality of life, and participation in individuals with spinal cord injury?

PubMed

Müller, Rachel; Peter, Claudio; Cieza, Alarcos; Post, Marcel W; Van Leeuwen, Christel M; Werner, Christina S; Geyh, Szilvia

2015-03-01

To examine the relevance of social skills and their different dimensions (ie, expressivity, sensitivity, control) in relation to social support, depression, participation, and quality of life (QOL) in individuals with spinal cord injury (SCI). Cross-sectional data collection within the Swiss Spinal Cord Injury Cohort. Community-based. Individuals with SCI (N=503). Not applicable. Depression, participation, and QOL were measured using the Hospital Anxiety and Depression Scale, the Utrecht Scale for Evaluation of Rehabilitation-Participation, and 5 selected items of the World Health Organization Quality of Life Scale. The Social Skills Inventory and the Social Support Questionnaire were used to assess social skills (expressivity, sensitivity, control) and social support, respectively. Structural equation modeling was conducted. In model 1 (χ(2)=27.81; df=19; P=.087; root mean square error of approximation=.033; 90% confidence interval=.000-.052), social skills as a latent variable was related to social support (β=.31; R(2)=.10), depression (β=-.31; total R(2)=.42), and QOL (β=.46; R(2)=.25). Social support partially mediated the effect of social skills on QOL (indirect effect: β=.04; P=.02) but not on depression or participation. In model 2 (χ(2)=27.96; df=19; P=.084; root mean square error of approximation=.031; 90% confidence interval=.000-.053), the social skills dimension expressivity showed a path coefficient of β=.20 to social support and β=.18 to QOL. Sensitivity showed a negative path coefficient to QOL (β=-.15) and control a path coefficient of β=-.15 to depression and β=.24 to QOL. Social skills are a resource related to more social support, lower depression scores, and higher QOL. Copyright © 2015 American Congress of Rehabilitation Medicine. Published by Elsevier Inc. All rights reserved.

Accounting for measurement error in human life history trade-offs using structural equation modeling.

PubMed

Helle, Samuli

2018-03-01

Revealing causal effects from correlative data is very challenging and a contemporary problem in human life history research owing to the lack of experimental approach. Problems with causal inference arising from measurement error in independent variables, whether related either to inaccurate measurement technique or validity of measurements, seem not well-known in this field. The aim of this study is to show how structural equation modeling (SEM) with latent variables can be applied to account for measurement error in independent variables when the researcher has recorded several indicators of a hypothesized latent construct. As a simple example of this approach, measurement error in lifetime allocation of resources to reproduction in Finnish preindustrial women is modelled in the context of the survival cost of reproduction. In humans, lifetime energetic resources allocated in reproduction are almost impossible to quantify with precision and, thus, typically used measures of lifetime reproductive effort (e.g., lifetime reproductive success and parity) are likely to be plagued by measurement error. These results are contrasted with those obtained from a traditional regression approach where the single best proxy of lifetime reproductive effort available in the data is used for inference. As expected, the inability to account for measurement error in women's lifetime reproductive effort resulted in the underestimation of its underlying effect size on post-reproductive survival. This article emphasizes the advantages that the SEM framework can provide in handling measurement error via multiple-indicator latent variables in human life history studies. © 2017 Wiley Periodicals, Inc.

Physician communication in the operating room.

PubMed

Kirschbaum, Kristin A; Rask, John P; Fortner, Sally A; Kulesher, Robert; Nelson, Michael T; Yen, Tony; Brennan, Matthew

2015-01-01

In this study, communication research was conducted with multidisciplinary groups of operating-room physicians. Theoretical frameworks from intercultural communication and rhetoric were used to (a) measure latent cultural communication variables and (b) conduct communication training with the physicians. A six-step protocol guided the research with teams of physicians from different surgical specialties: anesthesiologists, general surgeons, and obstetrician-gynecologists (n = 85). Latent cultural communication variables were measured by surveys administered to physicians before and after completion of the protocol. The centerpiece of the 2-hour research protocol was an instructional session that informed the surgical physicians about rhetorical choices that support participatory communication. Post-training results demonstrated scores increased on communication variables that contribute to collaborative communication and teamwork among the physicians. This study expands health communication research through application of combined intercultural and rhetorical frameworks, and establishes new ways communication theory can contribute to medical education.

Latent class analysis derived subgroups of low back pain patients - do they have prognostic capacity?

PubMed

Molgaard Nielsen, Anne; Hestbaek, Lise; Vach, Werner; Kent, Peter; Kongsted, Alice

2017-08-09

Heterogeneity in patients with low back pain is well recognised and different approaches to subgrouping have been proposed. One statistical technique that is increasingly being used is Latent Class Analysis as it performs subgrouping based on pattern recognition with high accuracy. Previously, we developed two novel suggestions for subgrouping patients with low back pain based on Latent Class Analysis of patient baseline characteristics (patient history and physical examination), which resulted in 7 subgroups when using a single-stage analysis, and 9 subgroups when using a two-stage approach. However, their prognostic capacity was unexplored. This study (i) determined whether the subgrouping approaches were associated with the future outcomes of pain intensity, pain frequency and disability, (ii) assessed whether one of these two approaches was more strongly or more consistently associated with these outcomes, and (iii) assessed the performance of the novel subgroupings as compared to the following variables: two existing subgrouping tools (STarT Back Tool and Quebec Task Force classification), four baseline characteristics and a group of previously identified domain-specific patient categorisations (collectively, the 'comparator variables'). This was a longitudinal cohort study of 928 patients consulting for low back pain in primary care. The associations between each subgroup approach and outcomes at 2 weeks, 3 and 12 months, and with weekly SMS responses were tested in linear regression models, and their prognostic capacity (variance explained) was compared to that of the comparator variables listed above. The two previously identified subgroupings were similarly associated with all outcomes. The prognostic capacity of both subgroupings was better than that of the comparator variables, except for participants' recovery beliefs and the domain-specific categorisations, but was still limited. The explained variance ranged from 4.3%-6.9% for pain intensity and from 6.8%-20.3% for disability, and highest at the 2 weeks follow-up. Latent Class-derived subgroups provided additional prognostic information when compared to a range of variables, but the improvements were not substantial enough to warrant further development into a new prognostic tool. Further research could investigate if these novel subgrouping approaches may help to improve existing tools that subgroup low back pain patients.

Robust Measurement via A Fused Latent and Graphical Item Response Theory Model.

PubMed

Chen, Yunxiao; Li, Xiaoou; Liu, Jingchen; Ying, Zhiliang

2018-03-12

Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large. Results in the literature and simulation studies in this paper reveal that misspecifying the local independence assumption may result in inaccurate measurements and differential item functioning. To provide more robust measurements, we propose an integrated approach by adding a graphical component to a multidimensional IRT model that can offset the effect of unknown local dependence. The new model contains a confirmatory latent variable component, which measures the targeted latent traits, and a graphical component, which captures the local dependence. An efficient proximal algorithm is proposed for the parameter estimation and structure learning of the local dependence. This approach can substantially improve the measurement, given no prior information on the local dependence structure. The model can be applied to measure both a unidimensional latent trait and multidimensional latent traits.

Mean Comparison: Manifest Variable versus Latent Variable

ERIC Educational Resources Information Center

Yuan, Ke-Hai; Bentler, Peter M.

2006-01-01

An extension of multiple correspondence analysis is proposed that takes into account cluster-level heterogeneity in respondents' preferences/choices. The method involves combining multiple correspondence analysis and k-means in a unified framework. The former is used for uncovering a low-dimensional space of multivariate categorical variables…

The choice of product indicators in latent variable interaction models: post hoc analyses.

PubMed

Foldnes, Njål; Hagtvet, Knut Arne

2014-09-01

The unconstrained product indicator (PI) approach is a simple and popular approach for modeling nonlinear effects among latent variables. This approach leaves the practitioner to choose the PIs to be included in the model, introducing arbitrariness into the modeling. In contrast to previous Monte Carlo studies, we evaluated the PI approach by 3 post hoc analyses applied to a real-world case adopted from a research effort in social psychology. The measurement design applied 3 and 4 indicators for the 2 latent 1st-order variables, leaving the researcher with a choice among more than 4,000 possible PI configurations. Sixty so-called matched-pair configurations that have been recommended in previous literature are of special interest. In the 1st post hoc analysis we estimated the interaction effect for all PI configurations, keeping the real-world sample fixed. The estimated interaction effect was substantially affected by the choice of PIs, also across matched-pair configurations. Subsequently, a post hoc Monte Carlo study was conducted, with varying sample sizes and data distributions. Convergence, bias, Type I error and power of the interaction test were investigated for each matched-pair configuration and the all-pairs configuration. Variation in estimates across matched-pair configurations for a typical sample was substantial. The choice of specific configuration significantly affected convergence and the interaction test's outcome. The all-pairs configuration performed overall better than the matched-pair configurations. A further advantage of the all-pairs over the matched-pairs approach is its unambiguity. The final study evaluates the all-pairs configuration for small sample sizes and compares it to the non-PI approach of latent moderated structural equations. PsycINFO Database Record (c) 2014 APA, all rights reserved.

Metric and structural equivalence of core cognitive abilities measured with the Wechsler Adult Intelligence Scale-III in the United States and Australia.

PubMed

Bowden, Stephen C; Lissner, Dianne; McCarthy, Kerri A L; Weiss, Lawrence G; Holdnack, James A

2007-10-01

Equivalence of the psychological model underlying Wechsler Adult Intelligence Scale-Third Edition (WAIS-III) scores obtained in the United States and Australia was examined in this study. Examination of metric invariance involves testing the hypothesis that all components of the measurement model relating observed scores to latent variables are numerically equal in different samples. The assumption of metric invariance is necessary for interpretation of scores derived from research studies that seek to generalize patterns of convergent and divergent validity and patterns of deficit or disability. An Australian community volunteer sample was compared to the US standardization data. A pattern of strict metric invariance was observed across samples. In addition, when the effects of different demographic characteristics of the US and Australian samples were included, structural parameters reflecting values of the latent cognitive variables were found not to differ. These results provide important evidence for the equivalence of measurement of core cognitive abilities with the WAIS-III and suggest that latent cognitive abilities in the US and Australia do not differ.

Discriminative latent models for recognizing contextual group activities.

PubMed

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N; Mori, Greg

2012-08-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities.

Discriminative Latent Models for Recognizing Contextual Group Activities

PubMed Central

Lan, Tian; Wang, Yang; Yang, Weilong; Robinovitch, Stephen N.; Mori, Greg

2012-01-01

In this paper, we go beyond recognizing the actions of individuals and focus on group activities. This is motivated from the observation that human actions are rarely performed in isolation; the contextual information of what other people in the scene are doing provides a useful cue for understanding high-level activities. We propose a novel framework for recognizing group activities which jointly captures the group activity, the individual person actions, and the interactions among them. Two types of contextual information, group-person interaction and person-person interaction, are explored in a latent variable framework. In particular, we propose three different approaches to model the person-person interaction. One approach is to explore the structures of person-person interaction. Differently from most of the previous latent structured models, which assume a predefined structure for the hidden layer, e.g., a tree structure, we treat the structure of the hidden layer as a latent variable and implicitly infer it during learning and inference. The second approach explores person-person interaction in the feature level. We introduce a new feature representation called the action context (AC) descriptor. The AC descriptor encodes information about not only the action of an individual person in the video, but also the behavior of other people nearby. The third approach combines the above two. Our experimental results demonstrate the benefit of using contextual information for disambiguating group activities. PMID:22144516

Intraclass Correlation Coefficients in Hierarchical Design Studies with Discrete Response Variables: A Note on a Direct Interval Estimation Procedure

ERIC Educational Resources Information Center

Raykov, Tenko; Marcoulides, George A.

2015-01-01

A latent variable modeling procedure that can be used to evaluate intraclass correlation coefficients in two-level settings with discrete response variables is discussed. The approach is readily applied when the purpose is to furnish confidence intervals at prespecified confidence levels for these coefficients in setups with binary or ordinal…

Families as Natural Experiments: A Procedure for Estimating the Potentially Biasing Influence of Families on Relationships Between Variables.

ERIC Educational Resources Information Center

Teachman, Jay D.

1995-01-01

Argues that data on siblings provide a way to account for the impact of unmeasured, omitted variables on relationships of interest because families form a sort of natural experiment, with similar experiences and common genetic heritage. Proposes a latent-variable structural equation approach to the problem, which provides estimates of both within-…

Measurement Models for Reasoned Action Theory

PubMed Central

Hennessy, Michael; Bleakley, Amy; Fishbein, Martin

2012-01-01

Quantitative researchers distinguish between causal and effect indicators. What are the analytic problems when both types of measures are present in a quantitative reasoned action analysis? To answer this question, we use data from a longitudinal study to estimate the association between two constructs central to reasoned action theory: behavioral beliefs and attitudes toward the behavior. The belief items are causal indicators that define a latent variable index while the attitude items are effect indicators that reflect the operation of a latent variable scale. We identify the issues when effect and causal indicators are present in a single analysis and conclude that both types of indicators can be incorporated in the analysis of data based on the reasoned action approach. PMID:23243315

A Two-Step Approach to Analyze Satisfaction Data

ERIC Educational Resources Information Center

Ferrari, Pier Alda; Pagani, Laura; Fiorio, Carlo V.

2011-01-01

In this paper a two-step procedure based on Nonlinear Principal Component Analysis (NLPCA) and Multilevel models (MLM) for the analysis of satisfaction data is proposed. The basic hypothesis is that observed ordinal variables describe different aspects of a latent continuous variable, which depends on covariates connected with individual and…

Factorial versus Typological Models: A Comparison of Methods for Personality Data

ERIC Educational Resources Information Center

von Davier, Matthias; Naemi, Bobby; Roberts, Richard D.

2012-01-01

This article describes an exploration of the distinction between typological and factorial latent variables in the domain of personality theory. Traditionally, many personality variables have been considered to be factorial in nature, even though there are examples of typological constructs dating back to Hippocrates. Recently, some…

A Latent-Variable Causal Model of Faculty Reputational Ratings.

ERIC Educational Resources Information Center

King, Suzanne; Wolfle, Lee M.

A reanalysis was conducted of Saunier's research (1985) on sources of variation in the National Research Council (NRC) reputational ratings of university faculty. Saunier conducted a stepwise regression analysis using 12 predictor variables. Due to problems with multicollinearity and because of the atheoretical nature of stepwise regression,…

Specifying and Refining a Complex Measurement Model.

ERIC Educational Resources Information Center

Levy, Roy; Mislevy, Robert J.

This paper aims to describe a Bayesian approach to modeling and estimating cognitive models both in terms of statistical machinery and actual instrument development. Such a method taps the knowledge of experts to provide initial estimates for the probabilistic relationships among the variables in a multivariate latent variable model and refines…

Undergraduate Nurse Variables that Predict Academic Achievement and Clinical Competence in Nursing

ERIC Educational Resources Information Center

Blackman, Ian; Hall, Margaret; Darmawan, I Gusti Ngurah.

2007-01-01

A hypothetical model was formulated to explore factors that influenced academic and clinical achievement for undergraduate nursing students. Sixteen latent variables were considered including the students' background, gender, type of first language, age, their previous successes with their undergraduate nursing studies and status given for…

Environmental, morphological, and productive characterization of Sardinian goats and use of latent explanatory factors for population analysis.

PubMed

Vacca, G M; Paschino, P; Dettori, M L; Bergamaschi, M; Cipolat-Gotet, C; Bittante, G; Pazzola, M

2016-09-01

Dairy goat farming is practiced worldwide, within a range of different farming systems. Here we investigated the effects of environmental factors and morphology on milk traits of the Sardinian goat population. Sardinian goats are currently reared in Sardinia (Italy) in a low-input context, similar to many goat farming systems, especially in developing countries. Milk and morphological traits from 1,050 Sardinian goats from 42 farms were recorded. We observed a high variability regarding morphological traits, such as coat color, ear length and direction, horn presence, and udder shape. Such variability derived partly from the unplanned repeated crossbreeding of the native Sardinian goats with exotic breeds, especially Maltese goats. The farms located in the mountains were characterized by the traditional farming system and the lowest percentage of crossbred goats. Explanatory factors analysis was used to summarize the interrelated measured milk variables. The explanatory factor related to fat, protein, and energy content of milk (the "Quality" latent variable) explained about 30% of the variance of the whole data set of measured milk traits followed by the "Hygiene" (19%), "Production" (19%), and "Acidity" (11%) factors. The "Quality" and "Hygiene" factors were not affected by any of the farm classification items, whereas "Production" and "Acidity" were affected only by altitude and size of herds, respectively, indicating the adaptation of the local goat population to different environmental conditions. The use of latent explanatory factor analysis allowed us to clearly explain the large variability of milk traits, revealing that the Sardinian goat population cannot be divided into subpopulations based on milk attitude The factors, properly integrated with genetic data, may be useful tools in future selection programs.

Individual Differences in Childhood Sleep Problems Predict Later Cognitive Executive Control

PubMed Central

Friedman, Naomi P.; Corley, Robin P.; Hewitt, John K.; Wright, Kenneth P.

2009-01-01

Study Objective: To determine whether individual differences in developmental patterns of general sleep problems are associated with 3 executive function abilities—inhibiting, updating working memory, and task shifting—in late adolescence. Participants: 916 twins (465 female, 451 male) and parents from the Colorado Longitudinal Twin Study. Measurements and Results: Parents reported their children's sleep problems at ages 4 years, 5 y, 7 y, and 9–16 y based on a 7-item scale from the Child-Behavior Checklist; a subset of children (n = 568) completed laboratory assessments of executive functions at age 17. Latent variable growth curve analyses were used to model individual differences in longitudinal trajectories of childhood sleep problems. Sleep problems declined over time, with ~70% of children having ≥ 1 problem at age 4 and ~33% of children at age 16. However, significant individual differences in both the initial levels of problems (intercept) and changes across time (slope) were observed. When executive function latent variables were added to the model, the intercept did not significantly correlate with the later executive function latent variables; however, the slope variable significantly (P < 0.05) negatively correlated with inhibiting (r = −0.27) and updating (r = −0.21), but not shifting (r = −0.10) abilities. Further analyses suggested that the slope variable predicted the variance common to the 3 executive functions (r = −0.29). Conclusions: Early levels of sleep problems do not seem to have appreciable implications for later executive functioning. However, individuals whose sleep problems decrease more across time show better general executive control in late adolescence. Citation: Friedman NP; Corley RP; Hewitt JK; Wright KP. Individual differences in childhood sleep problems predict later cognitive executive control. SLEEP 2009;32(3):323-333. PMID:19294952

High-Temperature Phase Change Materials (PCM) Candidates for Thermal Energy Storage (TES) Applications

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

Gomez, J. C.

2011-09-01

It is clearly understood that lower overall costs are a key factor to make renewable energy technologies competitive with traditional energy sources. Energy storage technology is one path to increase the value and reduce the cost of all renewable energy supplies. Concentrating solar power (CSP) technologies have the ability to dispatch electrical output to match peak demand periods by employing thermal energy storage (TES). Energy storage technologies require efficient materials with high energy density. Latent heat TES systems using phase change material (PCM) are useful because of their ability to charge and discharge a large amount of heat from amore » small mass at constant temperature during a phase transformation like melting-solidification. PCM technology relies on the energy absorption/liberation of the latent heat during a physical transformation. The main objective of this report is to provide an assessment of molten salts and metallic alloys proposed as candidate PCMs for TES applications, particularly in solar parabolic trough electrical power plants at a temperature range from 300..deg..C to 500..deg.. C. The physical properties most relevant for PCMs service were reviewed from the candidate selection list. Some of the PCM candidates were characterized for: chemical stability with some container materials; phase change transformation temperatures; and latent heats.« less

Using Trait-State Models to Evaluate the Longitudinal Consistency of Global Self-Esteem From Adolescence to Adulthood.

PubMed

Donnellan, M Brent; Kenny, David A; Trzesniewski, Kali H; Lucas, Richard E; Conger, Rand D

2012-12-01

The present research used a latent variable trait-state model to evaluate the longitudinal consistency of self-esteem during the transition from adolescence to adulthood. Analyses were based on ten administrations of the Rosenberg Self-Esteem scale (Rosenberg, 1965) spanning the ages of approximately 13 to 32 for a sample of 451 participants. Results indicated that a completely stable trait factor and an autoregressive trait factor accounted for the majority of the variance in latent self-esteem assessments, whereas state factors accounted for about 16% of the variance in repeated assessments of latent self-esteem. The stability of individual differences in self-esteem increased with age consistent with the cumulative continuity principle of personality development.

Using Trait-State Models to Evaluate the Longitudinal Consistency of Global Self-Esteem From Adolescence to Adulthood

PubMed Central

Donnellan, M. Brent; Kenny, David A.; Trzesniewski, Kali H.; Lucas, Richard E.; Conger, Rand D.

2012-01-01

The present research used a latent variable trait-state model to evaluate the longitudinal consistency of self-esteem during the transition from adolescence to adulthood. Analyses were based on ten administrations of the Rosenberg Self-Esteem scale (Rosenberg, 1965) spanning the ages of approximately 13 to 32 for a sample of 451 participants. Results indicated that a completely stable trait factor and an autoregressive trait factor accounted for the majority of the variance in latent self-esteem assessments, whereas state factors accounted for about 16% of the variance in repeated assessments of latent self-esteem. The stability of individual differences in self-esteem increased with age consistent with the cumulative continuity principle of personality development. PMID:23180899

Symptom Cluster Research With Biomarkers and Genetics Using Latent Class Analysis.

PubMed

Conley, Samantha

2017-12-01

The purpose of this article is to provide an overview of latent class analysis (LCA) and examples from symptom cluster research that includes biomarkers and genetics. A review of LCA with genetics and biomarkers was conducted using Medline, Embase, PubMed, and Google Scholar. LCA is a robust latent variable model used to cluster categorical data and allows for the determination of empirically determined symptom clusters. Researchers should consider using LCA to link empirically determined symptom clusters to biomarkers and genetics to better understand the underlying etiology of symptom clusters. The full potential of LCA in symptom cluster research has not yet been realized because it has been used in limited populations, and researchers have explored limited biologic pathways.

Estimating ocean-air heat fluxes during cold air outbreaks by satellite

NASA Technical Reports Server (NTRS)

Chou, S. H.; Atlas, D.

1981-01-01

Nomograms of mean column heating due to surface sensible and latent heat fluxes were developed. Mean sensible heating of the cloud free region is related to the cloud free path (CFP, the distance from the shore to the first cloud formation) and the difference between land air and sea surface temperatures, theta sub 1 and theta sub 0, respectively. Mean latent heating is related to the CFP and the difference between land air and sea surface humidities q sub 1 and q sub 0 respectively. Results are also applicable to any path within the cloud free region. Corresponding heat fluxes may be obtained by multiplying the mean heating by the mean wind speed in the boundary layer. The sensible heating estimated by the present method is found to be in good agreement with that computed from the bulk transfer formula. The sensitivity of the solutions to the variations in the initial coastal soundings and large scale subsidence is also investigated. The results are not sensitive to divergence but are affected by the initial lapse rate of potential temperature; the greater the stability, the smaller the heating, other things being equal. Unless one knows the lapse rate at the shore, this requires another independent measurement. For this purpose the downwind slope of the square of the boundary layer height is used, the mean value of which is also directly proportional to the mean sensible heating. The height of the boundary layer should be measurable by future spaceborn lidar systems.

Robotic Online Path Planning on Point Cloud.

PubMed

Liu, Ming

2016-05-01

This paper deals with the path-planning problem for mobile wheeled- or tracked-robot which drive in 2.5-D environments, where the traversable surface is usually considered as a 2-D-manifold embedded in a 3-D ambient space. Specially, we aim at solving the 2.5-D navigation problem using raw point cloud as input. The proposed method is independent of traditional surface parametrization or reconstruction methods, such as a meshing process, which generally has high-computational complexity. Instead, we utilize the output of 3-D tensor voting framework on the raw point clouds. The computation of tensor voting is accelerated by optimized implementation on graphics computation unit. Based on the tensor voting results, a novel local Riemannian metric is defined using the saliency components, which helps the modeling of the latent traversable surface. Using the proposed metric, we prove that the geodesic in the 3-D tensor space leads to rational path-planning results by experiments. Compared to traditional methods, the results reveal the advantages of the proposed method in terms of smoothing the robot maneuver while considering the minimum travel distance.

Influence of science and technology magnet middle schools on students' motivation and achievement in science

NASA Astrophysics Data System (ADS)

Allen, David

Some informal discussions among educators regarding motivation of students and academic performance have included the topic of magnet schools. The premise is that a focused theme, such as an aspect of science, positively affects student motivation and academic achievement. However, there is limited research involving magnet schools and their influence on student motivation and academic performance. This study provides empirical data for the discussion about magnet schools influence on motivation and academic ability. This study utilized path analysis in a structural equation modeling framework to simultaneously investigate the relationships between demographic exogenous independent variables, the independent variable of attending a science or technology magnet middle school, and the dependent variables of motivation to learn science and academic achievement in science. Due to the categorical nature of the variables, Bayesian statistical analysis was used to calculate the path coefficients and the standardized effects for each relationship in the model. The coefficients of determination were calculated to determine the amount of variance each path explained. Only five of 21 paths had statistical significance. Only one of the five statistically significant paths (Attended Magnet School to Motivation to Learn Science) explained a noteworthy amount (45.8%) of the variance.

Improvement in latent variable indirect response joint modeling of a continuous and a categorical clinical endpoint in rheumatoid arthritis.

PubMed

Hu, Chuanpu; Zhou, Honghui

2016-02-01

Improving the quality of exposure-response modeling is important in clinical drug development. The general joint modeling of multiple endpoints is made possible in part by recent progress on the latent variable indirect response (IDR) modeling for ordered categorical endpoints. This manuscript aims to investigate, when modeling a continuous and a categorical clinical endpoint, the level of improvement achievable by joint modeling in the latent variable IDR modeling framework through the sharing of model parameters for the individual endpoints, guided by the appropriate representation of drug and placebo mechanism. This was illustrated with data from two phase III clinical trials of intravenously administered mAb X for the treatment of rheumatoid arthritis, with the 28-joint disease activity score (DAS28) and 20, 50, and 70% improvement in the American College of Rheumatology (ACR20, ACR50, and ACR70) disease severity criteria were used as efficacy endpoints. The joint modeling framework led to a parsimonious final model with reasonable performance, evaluated by visual predictive check. The results showed that, compared with the more common approach of separately modeling the endpoints, it is possible for the joint model to be more parsimonious and yet better describe the individual endpoints. In particular, the joint model may better describe one endpoint through subject-specific random effects that would not have been estimable from data of this endpoint alone.

Ascertainment-adjusted parameter estimation approach to improve robustness against misspecification of health monitoring methods

NASA Astrophysics Data System (ADS)

Juesas, P.; Ramasso, E.

2016-12-01

Condition monitoring aims at ensuring system safety which is a fundamental requirement for industrial applications and that has become an inescapable social demand. This objective is attained by instrumenting the system and developing data analytics methods such as statistical models able to turn data into relevant knowledge. One difficulty is to be able to correctly estimate the parameters of those methods based on time-series data. This paper suggests the use of the Weighted Distribution Theory together with the Expectation-Maximization algorithm to improve parameter estimation in statistical models with latent variables with an application to health monotonic under uncertainty. The improvement of estimates is made possible by incorporating uncertain and possibly noisy prior knowledge on latent variables in a sound manner. The latent variables are exploited to build a degradation model of dynamical system represented as a sequence of discrete states. Examples on Gaussian Mixture Models, Hidden Markov Models (HMM) with discrete and continuous outputs are presented on both simulated data and benchmarks using the turbofan engine datasets. A focus on the application of a discrete HMM to health monitoring under uncertainty allows to emphasize the interest of the proposed approach in presence of different operating conditions and fault modes. It is shown that the proposed model depicts high robustness in presence of noisy and uncertain prior.

The Depression Anxiety Stress Scales (DASS): normative data and latent structure in a large non-clinical sample.

PubMed

Crawford, John R; Henry, Julie D

2003-06-01

To provide UK normative data for the Depression Anxiety and Stress Scale (DASS) and test its convergent, discriminant and construct validity. Cross-sectional, correlational and confirmatory factor analysis (CFA). The DASS was administered to a non-clinical sample, broadly representative of the general adult UK population (N = 1,771) in terms of demographic variables. Competing models of the latent structure of the DASS were derived from theoretical and empirical sources and evaluated using confirmatory factor analysis. Correlational analysis was used to determine the influence of demographic variables on DASS scores. The convergent and discriminant validity of the measure was examined through correlating the measure with two other measures of depression and anxiety (the HADS and the sAD), and a measure of positive and negative affectivity (the PANAS). The best fitting model (CFI =.93) of the latent structure of the DASS consisted of three correlated factors corresponding to the depression, anxiety and stress scales with correlated error permitted between items comprising the DASS subscales. Demographic variables had only very modest influences on DASS scores. The reliability of the DASS was excellent, and the measure possessed adequate convergent and discriminant validity Conclusions: The DASS is a reliable and valid measure of the constructs it was intended to assess. The utility of this measure for UK clinicians is enhanced by the provision of large sample normative data.

Enterprise Systems Analysis

DTIC Science & Technology

2017-04-30

practices in latent variable theory, it is not surprising that effective measurement programs present methodological typing and considering of experimental ...7 3.3 Methodology ...8 Revised Enterprise Modeling Methodology ................................................................ 128 9 Conclusions

Applying the Theory of Planned Behavior to Explain Women's Role in Prostate Cancer Screening.

PubMed

Di Sarra, Luca; Ghezzi, Valerio; Eastland, Taryn Yolanda; Antonini, Francesca; Scialó, Gennaro; Zega, Maurizio; Alvaro, Rosaria

2015-01-01

To test the suitability of the theory of planned behavior (TPB) for explaining Italian women's role in prostatic cancer screening promotion. A descriptive, cross-sectional, online self-report survey was conducted with a convenience sample of 235 Italian women. Variables included attitudes women's role, perceived behavioral control, subjective norm, behavioral intention, and prostate cancer screening promotion behavior. A survey composed of the Eastland Prostate Cancer Survey subscales that were consistent with the TPB was distributed via e-mail to potential participants. The survey was live for 12 weeks (March 2013 to May 2013). Responses were collated with eSurv.org. Data were analyzed using latent path analysis and structural equation modeling. Behavioral intentions in promoting prostate cancer screening significantly predicted the likelihood of the Italian women to adopt self-reported prostate cancer screening promotion behaviors. In addition, the exclusive direct impact of the intentions explained 39% of the variance in self-reported behaviors. The TPB could represent a good framework to explain the role of Italian women in prevention behaviors related to the prostatic screening domain. Consistent with literature findings in social and nursing sciences, the intention to promote prostate cancer screening was a powerful "predictor" of the behavior itself.

Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level

PubMed Central

Savalei, Victoria; Rhemtulla, Mijke

2017-01-01

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately handle missing data at the item level. Item-level multiple imputation (MI), however, can handle such missing data straightforwardly. In this article, we develop an analytic approach for dealing with item-level missing data—that is, one that obtains a unique set of parameter estimates directly from the incomplete data set and does not require imputations. The proposed approach is a variant of the two-stage maximum likelihood (TSML) methodology, and it is the analytic equivalent of item-level MI. We compare the new TSML approach to three existing alternatives for handling item-level missing data: scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI. We find that the TSML approach is the best analytic approach, and its performance is similar to item-level MI. We recommend its implementation in popular software and its further study. PMID:29276371

An ARM data-oriented diagnostics package to evaluate the climate model simulation

NASA Astrophysics Data System (ADS)

Zhang, C.; Xie, S.

2016-12-01

A set of diagnostics that utilize long-term high frequency measurements from the DOE Atmospheric Radiation Measurement (ARM) program is developed for evaluating the regional simulation of clouds, radiation and precipitation in climate models. The diagnostics results are computed and visualized automatically in a python-based package that aims to serve as an easy entry point for evaluating climate simulations using the ARM data, as well as the CMIP5 multi-model simulations. Basic performance metrics are computed to measure the accuracy of mean state and variability of simulated regional climate. The evaluated physical quantities include vertical profiles of clouds, temperature, relative humidity, cloud liquid water path, total column water vapor, precipitation, sensible and latent heat fluxes, radiative fluxes, aerosol and cloud microphysical properties. Process-oriented diagnostics focusing on individual cloud and precipitation-related phenomena are developed for the evaluation and development of specific model physical parameterizations. Application of the ARM diagnostics package will be presented in the AGU session. This work is performed under the auspices of the U.S. Department of Energy by Lawrence Livermore National Laboratory under Contract DE-AC52-07NA27344, IM release number is: LLNL-ABS-698645.

Normal Theory Two-Stage ML Estimator When Data Are Missing at the Item Level.

PubMed

Savalei, Victoria; Rhemtulla, Mijke

2017-08-01

In many modeling contexts, the variables in the model are linear composites of the raw items measured for each participant; for instance, regression and path analysis models rely on scale scores, and structural equation models often use parcels as indicators of latent constructs. Currently, no analytic estimation method exists to appropriately handle missing data at the item level. Item-level multiple imputation (MI), however, can handle such missing data straightforwardly. In this article, we develop an analytic approach for dealing with item-level missing data-that is, one that obtains a unique set of parameter estimates directly from the incomplete data set and does not require imputations. The proposed approach is a variant of the two-stage maximum likelihood (TSML) methodology, and it is the analytic equivalent of item-level MI. We compare the new TSML approach to three existing alternatives for handling item-level missing data: scale-level full information maximum likelihood, available-case maximum likelihood, and item-level MI. We find that the TSML approach is the best analytic approach, and its performance is similar to item-level MI. We recommend its implementation in popular software and its further study.

An empirical evaluation of two theoretically-based hypotheses on the directional association between self-worth and hope.

PubMed

McDavid, Lindley; McDonough, Meghan H; Smith, Alan L

2015-06-01

Fostering self-worth and hope are important goals of positive youth development (PYD) efforts, yet intervention design is complicated by contrasting theoretical hypotheses regarding the directional association between these constructs. Therefore, within a longitudinal design we tested: (1) that self-worth predicts changes in hope (self theory; Harter, 1999), and (2) that hope predicts changes in self-worth (hope theory; Snyder, 2002) over time. Youth (N = 321; Mage = 10.33 years) in a physical activity-based PYD program completed surveys 37-45 days prior to and on the second day and third-to-last day of the program. A latent variable panel model that included autoregressive and cross-lagged paths indicated that self-worth was a significant predictor of change in hope, but hope did not predict change in self-worth. Therefore, the directional association between self-worth and hope is better explained by self-theory and PYD programs should aim to enhance perceptions of self-worth to build perceptions of hope. Copyright © 2015 The Foundation for Professionals in Services for Adolescents. Published by Elsevier Ltd. All rights reserved.

Global Qualitative Flow-Path Modeling for Local State Determination in Simulation and Analysis

NASA Technical Reports Server (NTRS)

Malin, Jane T. (Inventor); Fleming, Land D. (Inventor)

1998-01-01

For qualitative modeling and analysis, a general qualitative abstraction of power transmission variables (flow and effort) for elements of flow paths includes information on resistance, net flow, permissible directions of flow, and qualitative potential is discussed. Each type of component model has flow-related variables and an associated internal flow map, connected into an overall flow network of the system. For storage devices, the implicit power transfer to the environment is represented by "virtual" circuits that include an environmental junction. A heterogeneous aggregation method simplifies the path structure. A method determines global flow-path changes during dynamic simulation and analysis, and identifies corresponding local flow state changes that are effects of global configuration changes. Flow-path determination is triggered by any change in a flow-related device variable in a simulation or analysis. Components (path elements) that may be affected are identified, and flow-related attributes favoring flow in the two possible directions are collected for each of them. Next, flow-related attributes are determined for each affected path element, based on possibly conflicting indications of flow direction. Spurious qualitative ambiguities are minimized by using relative magnitudes and permissible directions of flow, and by favoring flow sources over effort sources when comparing flow tendencies. The results are output to local flow states of affected components.

Sleep schedules and school performance in Indigenous Australian children.

PubMed

Blunden, Sarah; Magee, Chris; Attard, Kelly; Clarkson, Larissa; Caputi, Peter; Skinner, Timothy

2018-04-01

Sleep duration and sleep schedule variability have been related to negative health and well-being outcomes in children, but little is known about Australian Indigenous children. Data for children aged 7-9 years came from the Australian Longitudinal Study of Indigenous Children and the National Assessment Program-Literacy and Numeracy (NAPLAN). Latent class analysis determined sleep classes taking into account sleep duration, bedtimes, waketimes, and variability in bedtimes from weekdays to weekends. Regression models tested whether the sleep classes were cross-sectionally associated with grade 3 NAPLAN scores. Latent change score modeling then examined whether the sleep classes predicted changes in NAPLAN performance from grades 3 to 5. Five sleep schedule classes were identified: normative sleep, early risers, long sleep, variable sleep, and short sleep. Overall, long sleepers performed best, with those with reduced sleep (short sleepers and early risers) performing the worse on grammar, numeracy, and writing performance. Latent change score results also showed that long sleepers performed best in spelling and writing and short sleepers and typical sleepers performed the worst over time. In this sample of Australian Indigenous children, short sleep was associated with poorer school performance compared with long sleep, with this performance worsening over time for some performance indicators. Other sleep schedules (eg, early wake times and variable sleep) also had some relationships with school performance. As sleep scheduling is modifiable, this offers opportunity for improvement in sleep and thus performance outcomes for these and potentially all children. Copyright © 2018 National Sleep Foundation. Published by Elsevier Inc. All rights reserved.

Bayesian inference of uncertainties in precipitation-streamflow modeling in a snow affected catchment

NASA Astrophysics Data System (ADS)

Koskela, J. J.; Croke, B. W. F.; Koivusalo, H.; Jakeman, A. J.; Kokkonen, T.

2012-11-01

Bayesian inference is used to study the effect of precipitation and model structural uncertainty on estimates of model parameters and confidence limits of predictive variables in a conceptual rainfall-runoff model in the snow-fed Rudbäck catchment (142 ha) in southern Finland. The IHACRES model is coupled with a simple degree day model to account for snow accumulation and melt. The posterior probability distribution of the model parameters is sampled by using the Differential Evolution Adaptive Metropolis (DREAM(ZS)) algorithm and the generalized likelihood function. Precipitation uncertainty is taken into account by introducing additional latent variables that were used as multipliers for individual storm events. Results suggest that occasional snow water equivalent (SWE) observations together with daily streamflow observations do not contain enough information to simultaneously identify model parameters, precipitation uncertainty and model structural uncertainty in the Rudbäck catchment. The addition of an autoregressive component to account for model structure error and latent variables having uniform priors to account for input uncertainty lead to dubious posterior distributions of model parameters. Thus our hypothesis that informative priors for latent variables could be replaced by additional SWE data could not be confirmed. The model was found to work adequately in 1-day-ahead simulation mode, but the results were poor in the simulation batch mode. This was caused by the interaction of parameters that were used to describe different sources of uncertainty. The findings may have lessons for other cases where parameterizations are similarly high in relation to available prior information.

Medical University admission test: a confirmatory factor analysis of the results.

PubMed

Luschin-Ebengreuth, Marion; Dimai, Hans P; Ithaler, Daniel; Neges, Heide M; Reibnegger, Gilbert

2016-05-01

The Graz Admission Test has been applied since the academic year 2006/2007. The validity of the Test was demonstrated by a significant improvement of study success and a significant reduction of dropout rate. The purpose of this study was a detailed analysis of the internal correlation structure of the various components of the Graz Admission Test. In particular, the question investigated was whether or not the various test parts constitute a suitable construct which might be designated as "Basic Knowledge in Natural Science." This study is an observational investigation, analyzing the results of the Graz Admission Test for the study of human medicine and dentistry. A total of 4741 applicants were included in the analysis. Principal component factor analysis (PCFA) as well as techniques from structural equation modeling, specifically confirmatory factor analysis (CFA), were employed to detect potential underlying latent variables governing the behavior of the measured variables. PCFA showed good clustering of the science test parts, including also text comprehension. A putative latent variable "Basic Knowledge in Natural Science," investigated by CFA, was indeed shown to govern the response behavior of the applicants in biology, chemistry, physics, and mathematics as well as text comprehension. The analysis of the correlation structure of the various test parts confirmed that the science test parts together with text comprehension constitute a satisfactory instrument for measuring a latent construct variable "Basic Knowledge in Natural Science." The present results suggest the fundamental importance of basic science knowledge for results obtained in the framework of the admission process for medical universities.

Obtaining systematic teacher reports of disruptive behavior disorders utilizing DSM-IV.

PubMed

Wolraich, M L; Feurer, I D; Hannah, J N; Baumgaertel, A; Pinnock, T Y

1998-04-01

This study examines the psychometric properties of the Vanderbilt AD/HD Diagnostic Teacher Rating Scale (VADTRS) and provides preliminary normative data from a large, geographically defined population. The VADTRS consists of the complete list of DSM-IV AD/HD symptoms, a screen for other disruptive behavior disorders, anxiety and depression, and ratings of academic and classroom behavior performance. Teachers in one suburban county completed the scale for their students during 2 consecutive years. Statistical methods included (a) exploratory and confirmatory latent variable analyses of item data, (b) evaluation of the internal consistency of the latent dimensions, (c) evaluation of latent structure concordance between school year samples, and (d) preliminary evaluation of criterion-related validity. The instrument comprises four behavioral dimensions and two performance dimensions. The behavioral dimensions were concordant between school years and were consistent with a priori DSM-IV diagnostic criteria. Correlations between latent dimensions and relevant, known disorders or problems varied from .25 to .66.

An IRT Model with a Parameter-Driven Process for Change

ERIC Educational Resources Information Center

Rijmen, Frank; De Boeck, Paul; van der Maas, Han L. J.

2005-01-01

An IRT model with a parameter-driven process for change is proposed. Quantitative differences between persons are taken into account by a continuous latent variable, as in common IRT models. In addition, qualitative inter-individual differences and auto-dependencies are accounted for by assuming within-subject variability with respect to the…

Beyond IQ: A Latent State-Trait Analysis of General Intelligence, Dynamic Decision Making, and Implicit Learning

ERIC Educational Resources Information Center

Danner, Daniel; Hagemann, Dirk; Schankin, Andrea; Hager, Marieke; Funke, Joachim

2011-01-01

The present study investigated cognitive performance measures beyond IQ. In particular, we investigated the psychometric properties of dynamic decision making variables and implicit learning variables and their relation with general intelligence and professional success. N = 173 employees from different companies and occupational groups completed…

Characterizing Student Expectations: A Small Empirical Study

ERIC Educational Resources Information Center

Warwick, Jonathan

2016-01-01

This paper describes the results of a small empirical study (n = 130), in which undergraduate students in the Business Faculty of a UK university were asked to express views and expectations relating to the study of a mathematics. Factor analysis is used to identify latent variables emerging from clusters of the measured variables and these are…

Adolescent Substance Use, Sleep, and Academic Achievement: Evidence of Harm Due to Caffeine

ERIC Educational Resources Information Center

James, Jack E.; Kristjansson, Alfgeir Logi; Sigfusdottir, Inga Dora

2011-01-01

Using academic achievement as the key outcome variable, 7377 Icelandic adolescents were surveyed for cigarette smoking, alcohol use, daytime sleepiness, caffeine use, and potential confounders. Structural equation modeling (SEM) was used to examine direct and indirect effects of measured and latent variables in two models: the first with caffeine…

Use of Partial Least Squares improves the efficacy of removing unwanted variability in differential expression analyses based on RNA-Seq data.

PubMed

Chakraborty, Sutirtha

2018-05-26

RNA-Seq technology has revolutionized the face of gene expression profiling by generating read count data measuring the transcript abundances for each queried gene on multiple experimental subjects. But on the downside, the underlying technical artefacts and hidden biological profiles of the samples generate a wide variety of latent effects that may potentially distort the actual transcript/gene expression signals. Standard normalization techniques fail to correct for these hidden variables and lead to flawed downstream analyses. In this work I demonstrate the use of Partial Least Squares (built as an R package 'SVAPLSseq') to correct for the traces of extraneous variability in RNA-Seq data. A novel and thorough comparative analysis of the PLS based method is presented along with some of the other popularly used approaches for latent variable correction in RNA-Seq. Overall, the method is found to achieve a substantially improved estimation of the hidden effect signatures in the RNA-Seq transcriptome expression landscape compared to other available techniques. Copyright © 2017. Published by Elsevier Inc.

Stability of Core Language Skill Stability of Core Language Skill from Early Childhood to Adolescence: A Latent Variable Approach

PubMed Central

Bornstein, Marc H.; Hahn, Chun-Shin; Putnick, Diane L.; Suwalsky, Joan T. D.

2014-01-01

This four-wave prospective longitudinal study evaluated stability of language in 324 children from early childhood to adolescence. Structural equation modeling supported loadings of multiple age-appropriate multi-source measures of child language on single-factor core language skills at 20 months and 4, 10, and 14 years. Large stability coefficients (standardized indirect effect = .46) were obtained between language latent variables from early childhood to adolescence and accounting for child nonverbal intelligence and social competence and maternal verbal intelligence, education, speech, and social desirability. Stability coefficients were similar for girls and boys. Stability of core language skill was stronger from 4 to 10 to 14 years than from 20 months to 4 years, so early intervention to improve lagging language is recommended. PMID:25165797

Long-Term Stability of Core Language Skill in Children with Contrasting Language Skills

PubMed Central

Bornstein, Marc H.; Hahn, Chun-Shin; Putnick, Diane L.

2016-01-01

This four-wave longitudinal study evaluated stability of core language skill in 421 European American and African American children, half of whom were identified as low (n = 201) and half of whom were average-to-high (n = 220) in later language skill. Structural equation modeling supported loadings of multivariate age-appropriate multisource measures of child language on single latent variables of core language skill at 15 and 25 months and 5 and 11 years. Significant stability coefficients were obtained between language latent variables for children of low and average-to-high language skill, even accounting for child positive social interaction and nonverbal intelligence, maternal education and language, and family home environment. Prospects for children with different language skills and intervention implications are discussed. PMID:26998572

CORRECTING FOR MEASUREMENT ERROR IN LATENT VARIABLES USED AS PREDICTORS*

PubMed Central

Schofield, Lynne Steuerle

2015-01-01

This paper represents a methodological-substantive synergy. A new model, the Mixed Effects Structural Equations (MESE) model which combines structural equations modeling and item response theory is introduced to attend to measurement error bias when using several latent variables as predictors in generalized linear models. The paper investigates racial and gender disparities in STEM retention in higher education. Using the MESE model with 1997 National Longitudinal Survey of Youth data, I find prior mathematics proficiency and personality have been previously underestimated in the STEM retention literature. Pre-college mathematics proficiency and personality explain large portions of the racial and gender gaps. The findings have implications for those who design interventions aimed at increasing the rates of STEM persistence among women and under-represented minorities. PMID:26977218

Do recognizable lifetime eating disorder phenotypes naturally occur in a culturally asian population? A combined latent profile and taxometric approach.

PubMed

Thomas, Jennifer J; Eddy, Kamryn T; Ruscio, John; Ng, King Lam; Casale, Kristen E; Becker, Anne E; Lee, Sing

2015-05-01

We examined whether empirically derived eating disorder (ED) categories in Hong Kong Chinese patients (N = 454) would be consistent with recognizable lifetime ED phenotypes derived from latent structure models of European and American samples. We performed latent profile analysis (LPA) using indicator variables from data collected during routine assessment, and then applied taxometric analysis to determine whether latent classes were qualitatively versus quantitatively distinct. Latent profile analysis identified four classes: (i) binge/purge (47%); (ii) non-fat-phobic low-weight (34%); (iii) fat-phobic low-weight (12%); and (iv) overweight disordered eating (6%). Taxometric analysis identified qualitative (categorical) distinctions between the binge/purge and non-fat-phobic low-weight classes, and also between the fat-phobic and non-fat-phobic low-weight classes. Distinctions between the fat-phobic low-weight and binge/purge classes were indeterminate. Empirically derived categories in Hong Kong showed recognizable correspondence with recognizable lifetime ED phenotypes. Although taxometric findings support two distinct classes of low weight EDs, LPA findings also support heterogeneity among non-fat-phobic individuals. Copyright © 2015 John Wiley & Sons, Ltd and Eating Disorders Association.

Effects of categorization method, regression type, and variable distribution on the inflation of Type-I error rate when categorizing a confounding variable.

PubMed

Barnwell-Ménard, Jean-Louis; Li, Qing; Cohen, Alan A

2015-03-15

The loss of signal associated with categorizing a continuous variable is well known, and previous studies have demonstrated that this can lead to an inflation of Type-I error when the categorized variable is a confounder in a regression analysis estimating the effect of an exposure on an outcome. However, it is not known how the Type-I error may vary under different circumstances, including logistic versus linear regression, different distributions of the confounder, and different categorization methods. Here, we analytically quantified the effect of categorization and then performed a series of 9600 Monte Carlo simulations to estimate the Type-I error inflation associated with categorization of a confounder under different regression scenarios. We show that Type-I error is unacceptably high (>10% in most scenarios and often 100%). The only exception was when the variable categorized was a continuous mixture proxy for a genuinely dichotomous latent variable, where both the continuous proxy and the categorized variable are error-ridden proxies for the dichotomous latent variable. As expected, error inflation was also higher with larger sample size, fewer categories, and stronger associations between the confounder and the exposure or outcome. We provide online tools that can help researchers estimate the potential error inflation and understand how serious a problem this is. Copyright © 2014 John Wiley & Sons, Ltd.

The NEO Five-Factor Inventory: latent structure and relationships with dimensions of anxiety and depressive disorders in a large clinical sample.

PubMed

Rosellini, Anthony J; Brown, Timothy A

2011-03-01

The present study evaluated the latent structure of the NEO Five-Factor Inventory (NEO FFI) and relations between the five-factor model (FFM) of personality and dimensions of DSM-IV anxiety and depressive disorders (panic disorder, generalized anxiety disorder [GAD], obsessive-compulsive disorder, social phobia [SOC], major depressive disorder [MDD]) in a large sample of outpatients (N = 1,980). Exploratory structural equation modeling (ESEM) was used to show that a five-factor solution provided acceptable model fit, albeit with some poorly functioning items. Neuroticism demonstrated significant positive associations with all but one of the disorder constructs whereas Extraversion was inversely related to SOC and MDD. Conscientiousness was inversely related to MDD but demonstrated a positive relationship with GAD. Results are discussed in regard to potential revisions to the NEO FFI, the evaluation of other NEO instruments using ESEM, and clinical implications of structural paths between FFM domains and specific emotional disorders.

The NEO Five-Factor Inventory: Latent Structure and Relationships With Dimensions of Anxiety and Depressive Disorders in a Large Clinical Sample

PubMed Central

Rosellini, Anthony J.; Brown, Timothy A.

2017-01-01

The present study evaluated the latent structure of the NEO Five-Factor Inventory (NEO FFI) and relations between the five-factor model (FFM) of personality and dimensions of DSM-IV anxiety and depressive disorders (panic disorder, generalized anxiety disorder [GAD], obsessive–compulsive disorder, social phobia [SOC], major depressive disorder [MDD]) in a large sample of outpatients (N = 1,980). Exploratory structural equation modeling (ESEM) was used to show that a five-factor solution provided acceptable model fit, albeit with some poorly functioning items. Neuroticism demonstrated significant positive associations with all but one of the disorder constructs whereas Extraversion was inversely related to SOC and MDD. Conscientiousness was inversely related to MDD but demonstrated a positive relationship with GAD. Results are discussed in regard to potential revisions to the NEO FFI, the evaluation of other NEO instruments using ESEM, and clinical implications of structural paths between FFM domains and specific emotional disorders. PMID:20881102

Multidimensional model of apathy in older adults using partial least squares--path modeling.

PubMed

Raffard, Stéphane; Bortolon, Catherine; Burca, Marianna; Gely-Nargeot, Marie-Christine; Capdevielle, Delphine

2016-06-01

Apathy defined as a mental state characterized by a lack of goal-directed behavior is prevalent and associated with poor functioning in older adults. The main objective of this study was to identify factors contributing to the distinct dimensions of apathy (cognitive, emotional, and behavioral) in older adults without dementia. One hundred and fifty participants (mean age, 80.42) completed self-rated questionnaires assessing apathy, emotional distress, anticipatory pleasure, motivational systems, physical functioning, quality of life, and cognitive functioning. Data were analyzed using partial least squares variance-based structural equation modeling in order to examine factors contributing to the three different dimensions of apathy in our sample. Overall, the different facets of apathy were associated with cognitive functioning, anticipatory pleasure, sensitivity to reward, and physical functioning, but the contribution of these different factors to the three dimensions of apathy differed significantly. More specifically, the impact of anticipatory pleasure and physical functioning was stronger for the cognitive than for emotional apathy. Conversely, the impact of sensibility to reward, although small, was slightly stronger on emotional apathy. Regarding behavioral apathy, again we found similar latent variables except for the cognitive functioning whose impact was not statistically significant. Our results highlight the need to take into account various mechanisms involved in the different facets of apathy in older adults without dementia, including not only cognitive factors but also motivational variables and aspects related to physical disability. Clinical implications are discussed.

A two-stage path planning approach for multiple car-like robots based on PH curves and a modified harmony search algorithm

NASA Astrophysics Data System (ADS)

Zeng, Wenhui; Yi, Jin; Rao, Xiao; Zheng, Yun

2017-11-01

In this article, collision-avoidance path planning for multiple car-like robots with variable motion is formulated as a two-stage objective optimization problem minimizing both the total length of all paths and the task's completion time. Accordingly, a new approach based on Pythagorean Hodograph (PH) curves and Modified Harmony Search algorithm is proposed to solve the two-stage path-planning problem subject to kinematic constraints such as velocity, acceleration, and minimum turning radius. First, a method of path planning based on PH curves for a single robot is proposed. Second, a mathematical model of the two-stage path-planning problem for multiple car-like robots with variable motion subject to kinematic constraints is constructed that the first-stage minimizes the total length of all paths and the second-stage minimizes the task's completion time. Finally, a modified harmony search algorithm is applied to solve the two-stage optimization problem. A set of experiments demonstrate the effectiveness of the proposed approach.

Using Latent Sleepiness to Evaluate an Important Effect of Promethazine

NASA Technical Reports Server (NTRS)

Feiveson, Alan H.; Hayat, Matthew; Vksman, Zalman; Putcha, Laksmi

2007-01-01

Astronauts often use promethazine (PMZ) to counteract space motion sickness; however PMZ may cause drowsiness, which might impair cognitive function. In a NASA ground study, subjects received PMZ and their cognitive performance was then monitored over time. Subjects also reported sleepiness using the Karolinska Sleepiness Score (KSS), which ranges from 1 - 9. A problem arises when using KSS to establish an association between true sleepiness and performance because KSS scores tend to overly concentrate on the values 3 (fairly awake) and 7 (moderately tired). Therefore, we defined a latent sleepiness measure as a continuous random variable describing a subject s actual, but unobserved true state of sleepiness through time. The latent sleepiness and observed KSS are associated through a conditional probability model, which when coupled with demographic factors, predicts performance.

Factor Models for Ordinal Variables With Covariate Effects on the Manifest and Latent Variables: A Comparison of LISREL and IRT Approaches

ERIC Educational Resources Information Center

Moustaki, Irini; Joreskog, Karl G.; Mavridis, Dimitris

2004-01-01

We consider a general type of model for analyzing ordinal variables with covariate effects and 2 approaches for analyzing data for such models, the item response theory (IRT) approach and the PRELIS-LISREL (PLA) approach. We compare these 2 approaches on the basis of 2 examples, 1 involving only covariate effects directly on the ordinal variables…

The construct of sexual openness for females in steady intimate relationships.

PubMed

Rausch, Diana; Dekker, Arne; Rettenberger, Martin

2017-01-01

The analysis of open-minded attitudes towards sexuality in general requires a construct based on attitudinal dimensions. Although several existing studies involve sexual attitudes, they differ substantially and standardized conceptual work is missing. Thus, the authors introduce the latent variable sexual openness to develop a construct based on self-oriented attitudes towards different sexual topics. Available survey data of female German students in a steady relationship allowed providing a first empirical test for the applicability of this construct. Five subdimensions are acknowledged central for sexual openness: sexual practices, masturbation, bisexuality, permissiveness, and pornography consumption. Confirmatory factor analysis and correlations confirmed the idea of an underlying mechanism with an impact on all five variables. Though further validation of the construct of sexual openness is required, the findings strongly support the notion of an overarching latent attitude variable, which influences the individual relation to everything sexual. The results were compared to other studies and potential approaches for future analyses were proposed.

Estimating Causal Effects with Ancestral Graph Markov Models

PubMed Central

Malinsky, Daniel; Spirtes, Peter

2017-01-01

We present an algorithm for estimating bounds on causal effects from observational data which combines graphical model search with simple linear regression. We assume that the underlying system can be represented by a linear structural equation model with no feedback, and we allow for the possibility of latent variables. Under assumptions standard in the causal search literature, we use conditional independence constraints to search for an equivalence class of ancestral graphs. Then, for each model in the equivalence class, we perform the appropriate regression (using causal structure information to determine which covariates to include in the regression) to estimate a set of possible causal effects. Our approach is based on the “IDA” procedure of Maathuis et al. (2009), which assumes that all relevant variables have been measured (i.e., no unmeasured confounders). We generalize their work by relaxing this assumption, which is often violated in applied contexts. We validate the performance of our algorithm on simulated data and demonstrate improved precision over IDA when latent variables are present. PMID:28217244

The construct of sexual openness for females in steady intimate relationships

PubMed Central

Rausch, Diana; Dekker, Arne; Rettenberger, Martin

2017-01-01

The analysis of open-minded attitudes towards sexuality in general requires a construct based on attitudinal dimensions. Although several existing studies involve sexual attitudes, they differ substantially and standardized conceptual work is missing. Thus, the authors introduce the latent variable sexual openness to develop a construct based on self-oriented attitudes towards different sexual topics. Available survey data of female German students in a steady relationship allowed providing a first empirical test for the applicability of this construct. Five subdimensions are acknowledged central for sexual openness: sexual practices, masturbation, bisexuality, permissiveness, and pornography consumption. Confirmatory factor analysis and correlations confirmed the idea of an underlying mechanism with an impact on all five variables. Though further validation of the construct of sexual openness is required, the findings strongly support the notion of an overarching latent attitude variable, which influences the individual relation to everything sexual. The results were compared to other studies and potential approaches for future analyses were proposed. PMID:28636608

Measurement of psychological disorders using cognitive diagnosis models.

PubMed

Templin, Jonathan L; Henson, Robert A

2006-09-01

Cognitive diagnosis models are constrained (multiple classification) latent class models that characterize the relationship of questionnaire responses to a set of dichotomous latent variables. Having emanated from educational measurement, several aspects of such models seem well suited to use in psychological assessment and diagnosis. This article presents the development of a new cognitive diagnosis model for use in psychological assessment--the DINO (deterministic input; noisy "or" gate) model--which, as an illustrative example, is applied to evaluate and diagnose pathological gamblers. As part of this example, a demonstration of the estimates obtained by cognitive diagnosis models is provided. Such estimates include the probability an individual meets each of a set of dichotomous Diagnostic and Statistical Manual of Mental Disorders (text revision [DSM-IV-TR]; American Psychiatric Association, 2000) criteria, resulting in an estimate of the probability an individual meets the DSM-IV-TR definition for being a pathological gambler. Furthermore, a demonstration of how the hypothesized underlying factors contributing to pathological gambling can be measured with the DINO model is presented, through use of a covariance structure model for the tetrachoric correlation matrix of the dichotomous latent variables representing DSM-IV-TR criteria. Copyright 2006 APA

Multilevel structural equation models for assessing moderation within and across levels of analysis.

PubMed

Preacher, Kristopher J; Zhang, Zhen; Zyphur, Michael J

2016-06-01

Social scientists are increasingly interested in multilevel hypotheses, data, and statistical models as well as moderation or interactions among predictors. The result is a focus on hypotheses and tests of multilevel moderation within and across levels of analysis. Unfortunately, existing approaches to multilevel moderation have a variety of shortcomings, including conflated effects across levels of analysis and bias due to using observed cluster averages instead of latent variables (i.e., "random intercepts") to represent higher-level constructs. To overcome these problems and elucidate the nature of multilevel moderation effects, we introduce a multilevel structural equation modeling (MSEM) logic that clarifies the nature of the problems with existing practices and remedies them with latent variable interactions. This remedy uses random coefficients and/or latent moderated structural equations (LMS) for unbiased tests of multilevel moderation. We describe our approach and provide an example using the publicly available High School and Beyond data with Mplus syntax in Appendix. Our MSEM method eliminates problems of conflated multilevel effects and reduces bias in parameter estimates while offering a coherent framework for conceptualizing and testing multilevel moderation effects. (PsycINFO Database Record (c) 2016 APA, all rights reserved).

Human Life History Strategies.

PubMed

Chua, Kristine J; Lukaszewski, Aaron W; Grant, DeMond M; Sng, Oliver

2017-01-01

Human life history (LH) strategies are theoretically regulated by developmental exposure to environmental cues that ancestrally predicted LH-relevant world states (e.g., risk of morbidity-mortality). Recent modeling work has raised the question of whether the association of childhood family factors with adult LH variation arises via (i) direct sampling of external environmental cues during development and/or (ii) calibration of LH strategies to internal somatic condition (i.e., health), which itself reflects exposure to variably favorable environments. The present research tested between these possibilities through three online surveys involving a total of over 26,000 participants. Participants completed questionnaires assessing components of self-reported environmental harshness (i.e., socioeconomic status, family neglect, and neighborhood crime), health status, and various LH-related psychological and behavioral phenotypes (e.g., mating strategies, paranoia, and anxiety), modeled as a unidimensional latent variable. Structural equation models suggested that exposure to harsh ecologies had direct effects on latent LH strategy as well as indirect effects on latent LH strategy mediated via health status. These findings suggest that human LH strategies may be calibrated to both external and internal cues and that such calibrational effects manifest in a wide range of psychological and behavioral phenotypes.

Why aren’t they happy? An analysis of end-user satisfaction with Electronic health records

PubMed Central

Unni, Prasad; Staes, Catherine; Weeks, Howard; Kramer, Heidi; Borbolla, Damion; Slager, Stacey; Taft, Teresa; Chidambaram, Valliammai; Weir, Charlene

2016-01-01

Introduction. Implementations of electronic health records (EHR) have been met with mixed outcome reviews. Complaints about these systems have led to many attempts to have useful measures of end-user satisfaction. However, most user satisfaction assessments do not focus on high-level reasoning, despite the complaints of many physicians. Our study attempts to identify some of these determinants. Method. We developed a user satisfaction survey instrument, based on pre-identified and important clinical and non-clinical clinician tasks. We surveyed a sample of in-patient physicians and focused on using exploratory factor analyses to identify underlying high-level cognitive tasks. We used the results to create unique, orthogonal variables representative of latent structure predictive of user satisfaction. Results. Our findings identified 3 latent high-level tasks that were associated with end-user satisfaction: a) High- level clinical reasoning b) Communicate/coordinate care and c) Follow the rules/compliance. Conclusion: We were able to successfully identify latent variables associated with satisfaction. Identification of communicability and high-level clinical reasoning as important factors determining user satisfaction can lead to development and design of more usable electronic health records with higher user satisfaction. PMID:28269962

Causal mediation analysis with a latent mediator.

PubMed

Albert, Jeffrey M; Geng, Cuiyu; Nelson, Suchitra

2016-05-01

Health researchers are often interested in assessing the direct effect of a treatment or exposure on an outcome variable, as well as its indirect (or mediation) effect through an intermediate variable (or mediator). For an outcome following a nonlinear model, the mediation formula may be used to estimate causally interpretable mediation effects. This method, like others, assumes that the mediator is observed. However, as is common in structural equations modeling, we may wish to consider a latent (unobserved) mediator. We follow a potential outcomes framework and assume a generalized structural equations model (GSEM). We provide maximum-likelihood estimation of GSEM parameters using an approximate Monte Carlo EM algorithm, coupled with a mediation formula approach to estimate natural direct and indirect effects. The method relies on an untestable sequential ignorability assumption; we assess robustness to this assumption by adapting a recently proposed method for sensitivity analysis. Simulation studies show good properties of the proposed estimators in plausible scenarios. Our method is applied to a study of the effect of mother education on occurrence of adolescent dental caries, in which we examine possible mediation through latent oral health behavior. © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

The Spatial and Temporal Variability of Meltwater Flow Paths: Insights From a Grid of Over 100 Snow Lysimeters

NASA Astrophysics Data System (ADS)

Webb, R. W.; Williams, M. W.; Erickson, T. A.

2018-02-01

Snowmelt is an important part of the hydrologic cycle and ecosystem dynamics for headwater systems. However, the physical process of water flow through snow is a poorly understood aspect of snow hydrology as meltwater flow paths tend to be highly complex. Meltwater flow paths diverge and converge as percolating meltwater reaches stratigraphic layer interfaces creating high spatial variability. Additionally, a snowpack is temporally heterogeneous due to rapid localized metamorphism that occurs during melt. This study uses a snowmelt lysimeter array at tree line in the Niwot Ridge study area of northern Colorado. The array is designed to address the issue of spatial and temporal variability of basal discharge at 105 locations over an area of 1,300 m2. Observed coefficients of variation ranged from 0 to almost 10 indicating more variability than previously observed, though this variability decreased throughout each melt season. Snowmelt basal discharge also significantly increases as snow depth decreases displaying a cluster pattern that peaks during weeks 3-5 of the snowmelt season. These results are explained by the flow of meltwater along snow layer interfaces. As the snowpack becomes less stratified through the melt season, the pattern transforms from preferential flow paths to uniform matrix flow. Correlation ranges of the observed basal discharge correspond to a mean representative elementary area of 100 m2, or a characteristic length of 10 m. Snowmelt models representing processes at scales less than this will need to explicitly incorporate the spatial variability of snowmelt discharge and meltwater flow paths through snow between model pixels.

Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis

PubMed Central

BOYSAN, Murat

2014-01-01

Introduction There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Method Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson’s product-moment correlation coefficients. Results The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. Conclusion It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism. PMID:28360635

Dissociative Experiences are Associated with Obsessive-Compulsive Symptoms in a Non-clinical Sample: A Latent Profile Analysis.

PubMed

Boysan, Murat

2014-09-01

There has been a burgeoning literature considering the significant associations between obsessive-compulsive symptoms and dissociative experiences. In this study, the relationsips between dissociative symtomotology and dimensions of obsessive-compulsive symptoms were examined in homogeneous sub-groups obtained with latent class algorithm in an undergraduate Turkish sample. Latent profile analysis, a recently developed classification method based on latent class analysis, was applied to the Dissociative Experiences Scale (DES) item-response data from 2976 undergraduates. Differences in severity of obsessive-compulsive symptoms, anxiety and depression across groups were evaluated by running multinomial logistic regression analyses. Associations between latent class probabilities and psychological variables in terms of obsessive-compulsive sub-types, anxiety, and depression were assessed by computing Pearson's product-moment correlation coefficients. The findings of the latent profile analysis supported further evidence for discontinuity model of dissociative experiences. The analysis empirically justified the distinction among three sub-groups based on the DES items. A marked proportion of the sample (42%) was assigned to the high dissociative class. In the further analyses, all sub-types of obsessive-compulsive symptoms significantly differed across latent classes. Regarding the relationships between obsessive-compulsive symptoms and dissociative symptomatology, low dissociation appeared to be a buffering factor dealing with obsessive-compulsive symptoms; whereas high dissociation appeared to be significantly associated with high levels of obsessive-compulsive symptoms. It is concluded that the concept of dissociation can be best understood in a typological approach that dissociative symptomatology not only exacerbates obsessive-compulsive symptoms but also serves as an adaptive coping mechanism.

Relevance and acceptability of using the Quantiferon gold test (QGIT) to screen CD4 blood draws for latent TB infection among PLHIV in South Africa: formative qualitative research findings from the TEKO trial.

PubMed

Kerrigan, Deanna; Tudor, Carrie; Motlhaoleng, Katlego; Lebina, Limakatso; Qomfu, Cokiswa; Variava, Ebrahim; Chon, Sandy; Martinson, Neil; Golub, Jonathan E

2018-04-16

Tuberculosis (TB) is the leading cause of mortality among people living with HIV (PLHIV), despite the availability of effective preventive therapy. The TEKO trial is assessing the impact of using a blood test, Quantiferon-TB Gold In-Tube Test (QGIT), to screen for latent TB compared to the Tuberculin Screening Test (TST) among PLHIV in South Africa. Fifty-six qualitative interviews were conducted with PLHIV and clinical providers participating in the TEKO trial. We explored TB screening, diagnosis, and treatment guidelines and processes and the use of the QGIT to screen for latent TB infection at the time of CD4 blood draw. Thematic content analysis was conducted. Considerable variability in TB screening procedures was documented due to lack of personnel and clarity regarding current national TB guidelines for PLHIV. Few clinics had started using the TST per national guidelines and many patients had never heard of isoniazid preventive therapy (IPT). Nearly all participants supported the idea of latent TB screening using routine blood drawn for CD4 counts. Findings indicate that screening for latent TB infection using QGIT from blood drawn for CD4 counts among PLHIV is an acceptable approach to increase latent TB detection given the challenges associated with ensuring systematic latent TB screening in overburdened public clinics. The results presented here were from formative research related to the TEKO trial (Identifier NCT02119130 , registered 10 April 2014).

The Rasch Rating Model and the Disordered Threshold Controversy

ERIC Educational Resources Information Center

Adams, Raymond J.; Wu, Margaret L.; Wilson, Mark

2012-01-01

The Rasch rating (or partial credit) model is a widely applied item response model that is used to model ordinal observed variables that are assumed to collectively reflect a common latent variable. In the application of the model there is considerable controversy surrounding the assessment of fit. This controversy is most notable when the set of…

Testing for Two-Way Interactions in the Multigroup Common Factor Model

ERIC Educational Resources Information Center

van Smeden, Maarten; Hessen, David J.

2013-01-01

In this article, a 2-way multigroup common factor model (MG-CFM) is presented. The MG-CFM can be used to estimate interaction effects between 2 grouping variables on 1 or more hypothesized latent variables. For testing the significance of such interactions, a likelihood ratio test is presented. In a simulation study, the robustness of the…

Collinear Latent Variables in Multilevel Confirmatory Factor Analysis: A Comparison of Maximum Likelihood and Bayesian Estimations

ERIC Educational Resources Information Center

Can, Seda; van de Schoot, Rens; Hox, Joop

2015-01-01

Because variables may be correlated in the social and behavioral sciences, multicollinearity might be problematic. This study investigates the effect of collinearity manipulated in within and between levels of a two-level confirmatory factor analysis by Monte Carlo simulation. Furthermore, the influence of the size of the intraclass correlation…

Structural Modeling of Variables Related to Parental Support in Mexican Children's Perfomance on Reading and Writing

ERIC Educational Resources Information Center

Bazan-Ramirez, Aldo; Castellanos-Simons, Doris; Lopez-Valenzuela, Mercedes

2010-01-01

This paper aims at analysing the structural relationships among some latent and observed variables related to the assessment of written language performance in 139 fourth grade students of Elementary School selected from nine public schools of the northwest of Mexico. Questionnaires were also applied to the children's parents and teachers. The…

Improving the Quality of Ability Estimates through Multidimensional Scoring and Incorporation of Ancillary Variables

ERIC Educational Resources Information Center

de la Torre, Jimmy

2009-01-01

For one reason or another, various sources of information, namely, ancillary variables and correlational structure of the latent abilities, which are usually available in most testing situations, are ignored in ability estimation. A general model that incorporates these sources of information is proposed in this article. The model has a general…

Measuring Student Variables Useful in the Study of Performance in an Online Learning Environment.

ERIC Educational Resources Information Center

Kennedy, Cathleen A.

This paper discusses the measurement of unobservable or latent variables of students and how they contribute to learning in an online environment. It also examines the construct validity of two questionnaires: the College Experience Survey and the Computer Experience Study, which both measure different aspects of student attitudes and behavior…

Highways and Byways: The Career Paths of Senior Student Affairs Officers

ERIC Educational Resources Information Center

Tull, Ashley; Miller, Michael T.

2009-01-01

The highways and byways, or career paths, to the Senior Student Affairs Officer (SSAO) position differ based on a variety of variables. This study examined several variables including the induction or graduate preparation process, professional pathways, and professional and academic involvement of more than half of current land grant SSAOs. Data…

Cognitive declines in healthy aging: evidence from multiple aspects of interference resolution.

PubMed

Pettigrew, Corinne; Martin, Randi C

2014-06-01

The present study tested the hypothesis that older adults show age-related deficits in interference resolution, also referred to as inhibitory control. Although oftentimes considered as a unitary aspect of executive function, various lines of work support the notion that interference resolution may be better understood as multiple constructs, including resistance to proactive interference (PI) and response-distractor inhibition (e.g., Friedman & Miyake, 2004). Using this dichotomy, the present study assessed whether older adults (relative to younger adults) show impaired performance across both, 1, or neither of these interference resolution constructs. To do so, we used multiple tasks to tap each construct and examined age effects at both the single task and latent variable levels. Older adults consistently demonstrated exaggerated interference effects across resistance to PI tasks. Although the results for the response-distractor inhibition tasks were less consistent at the individual task level analyses, age effects were evident on multiple tasks, as well as at the latent variable level. However, results of the latent variable modeling suggested declines in interference resolution are best explained by variance that is common to the 2 interference resolution constructs measured herein. Furthermore, the effect of age on interference resolution was found to be both distinct from declines in working memory, and independent of processing speed. These findings suggest multiple cognitive domains are independently sensitive to age, but that declines in the interference resolution constructs measured herein may originate from a common cause. PsycINFO Database Record (c) 2014 APA, all rights reserved.

CLUSTERING SOUTH AFRICAN HOUSEHOLDS BASED ON THEIR ASSET STATUS USING LATENT VARIABLE MODELS

PubMed Central

McParland, Damien; Gormley, Isobel Claire; McCormick, Tyler H.; Clark, Samuel J.; Kabudula, Chodziwadziwa Whiteson; Collinson, Mark A.

2014-01-01

The Agincourt Health and Demographic Surveillance System has since 2001 conducted a biannual household asset survey in order to quantify household socio-economic status (SES) in a rural population living in northeast South Africa. The survey contains binary, ordinal and nominal items. In the absence of income or expenditure data, the SES landscape in the study population is explored and described by clustering the households into homogeneous groups based on their asset status. A model-based approach to clustering the Agincourt households, based on latent variable models, is proposed. In the case of modeling binary or ordinal items, item response theory models are employed. For nominal survey items, a factor analysis model, similar in nature to a multinomial probit model, is used. Both model types have an underlying latent variable structure—this similarity is exploited and the models are combined to produce a hybrid model capable of handling mixed data types. Further, a mixture of the hybrid models is considered to provide clustering capabilities within the context of mixed binary, ordinal and nominal response data. The proposed model is termed a mixture of factor analyzers for mixed data (MFA-MD). The MFA-MD model is applied to the survey data to cluster the Agincourt households into homogeneous groups. The model is estimated within the Bayesian paradigm, using a Markov chain Monte Carlo algorithm. Intuitive groupings result, providing insight to the different socio-economic strata within the Agincourt region. PMID:25485026

Does partial Granger causality really eliminate the influence of exogenous inputs and latent variables?

PubMed

Roelstraete, Bjorn; Rosseel, Yves

2012-04-30

Partial Granger causality was introduced by Guo et al. (2008) who showed that it could better eliminate the influence of latent variables and exogenous inputs than conditional G-causality. In the recent literature we can find some reviews and applications of this type of Granger causality (e.g. Smith et al., 2011; Bressler and Seth, 2010; Barrett et al., 2010). These articles apparently do not take into account a serious flaw in the original work on partial G-causality, being the negative F values that were reported and even proven to be plausible. In our opinion, this undermines the credibility of the obtained results and thus the validity of the approach. Our study is aimed to further validate partial G-causality and to find an answer why negative partial Granger causality estimates were reported. Time series were simulated from the same toy model as used in the original paper and partial and conditional causal measures were compared in the presence of confounding variables. Inference was done parametrically and using non-parametric block bootstrapping. We counter the proof that partial Granger F values can be negative, but the main conclusion of the original article remains. In the presence of unknown latent and exogenous influences, it appears that partial G-causality will better eliminate their influence than conditional G-causality, at least when non-parametric inference is used. Copyright © 2012 Elsevier B.V. All rights reserved.

Variability in Proactive and Reactive Cognitive Control Processes Across the Adult Lifespan

PubMed Central

Karayanidis, Frini; Whitson, Lisa Rebecca; Heathcote, Andrew; Michie, Patricia T.

2011-01-01

Task-switching paradigms produce a highly consistent age-related increase in mixing cost [longer response time (RT) on repeat trials in mixed-task than single-task blocks] but a less consistent age effect on switch cost (longer RT on switch than repeat trials in mixed-task blocks). We use two approaches to examine the adult lifespan trajectory of control processes contributing to mixing cost and switch cost: latent variables derived from an evidence accumulation model of choice, and event-related potentials (ERP) that temporally differentiate proactive (cue-driven) and reactive (target-driven) control processes. Under highly practiced and prepared task conditions, aging was associated with increasing RT mixing cost but reducing RT switch cost. Both effects were largely due to the same cause: an age effect for mixed-repeat trials. In terms of latent variables, increasing age was associated with slower non-decision processes, slower rate of evidence accumulation about the target, and higher response criterion. Age effects on mixing costs were evident only on response criterion, the amount of evidence required to trigger a decision, whereas age effects on switch cost were present for all three latent variables. ERPs showed age-related increases in preparation for mixed-repeat trials, anticipatory attention, and post-target interference. Cue-locked ERPs that are linked to proactive control were associated with early emergence of age differences in response criterion. These results are consistent with age effects on strategic processes controlling decision caution. Consistent with an age-related decline in cognitive flexibility, younger adults flexibly adjusted response criterion from trial-to-trial on mixed-task blocks, whereas older adults maintained a high criterion for all trials. PMID:22073037

Genetic and environmental influences on last-year major depression in adulthood: a highly heritable stable liability but strong environmental effects on 1-year prevalence.

PubMed

Kendler, K S; Gardner, C O

2017-07-01

This study seeks to clarify the contribution of temporally stable and occasion-specific genetic and environmental influences on risk for major depression (MD). Our sample was 2153 members of female-female twin pairs from the Virginia Twin Registry. We examined four personal interview waves conducted over an 8-year period with MD in the last year defined by DSM-IV criteria. We fitted a structural equation model to the data using classic Mx. The model included genetic and environmental risk factors for a latent, stable vulnerability to MD and for episodes in each of the four waves. The best-fit model was simple and included genetic and unique environmental influences on the latent liability to MD and unique wave-specific environmental effects. The path from latent liability to MD in the last year was constant over time, moderate in magnitude (+0.65) and weaker than the impact of occasion-specific environmental effects (+0.76). Heritability of the latent stable liability to MD was much higher (78%) than that estimated for last-year MD (32%). Of the total unique environmental influences on MD, 13% reflected enduring consequences of earlier environmental insults, 17% diagnostic error and 70% wave-specific short-lived environmental stressors. Both genetic influences on MD and MD heritability are stable over middle adulthood. However, the largest influence on last-year MD is short-lived environmental effects. As predicted by genetic theory, the heritability of MD is increased substantially by measurement at multiple time points largely through the reduction of the effects of measurement error and short-term environmental risk factors.

Latent class analysis reveals clinically relevant atopy phenotypes in 2 birth cohorts.

PubMed

Hose, Alexander J; Depner, Martin; Illi, Sabina; Lau, Susanne; Keil, Thomas; Wahn, Ulrich; Fuchs, Oliver; Pfefferle, Petra Ina; Schmaußer-Hechfellner, Elisabeth; Genuneit, Jon; Lauener, Roger; Karvonen, Anne M; Roduit, Caroline; Dalphin, Jean-Charles; Riedler, Josef; Pekkanen, Juha; von Mutius, Erika; Ege, Markus J

2017-06-01

Phenotypes of childhood-onset asthma are characterized by distinct trajectories and functional features. For atopy, definition of phenotypes during childhood is less clear. We sought to define phenotypes of atopic sensitization over the first 6 years of life using a latent class analysis (LCA) integrating 3 dimensions of atopy: allergen specificity, time course, and levels of specific IgE (sIgE). Phenotypes were defined by means of LCA in 680 children of the Multizentrische Allergiestudie (MAS) and 766 children of the Protection against allergy: Study in Rural Environments (PASTURE) birth cohorts and compared with classical nondisjunctive definitions of seasonal, perennial, and food sensitization with respect to atopic diseases and lung function. Cytokine levels were measured in the PASTURE cohort. The LCA classified predominantly by type and multiplicity of sensitization (food vs inhalant), allergen combinations, and sIgE levels. Latent classes were related to atopic disease manifestations with higher sensitivity and specificity than the classical definitions. LCA detected consistently in both cohorts a distinct group of children with severe atopy characterized by high seasonal sIgE levels and a strong propensity for asthma; hay fever; eczema; and impaired lung function, also in children without an established asthma diagnosis. Severe atopy was associated with an increased IL-5/IFN-γ ratio. A path analysis among sensitized children revealed that among all features of severe atopy, only excessive sIgE production early in life affected asthma risk. LCA revealed a set of benign, symptomatic, and severe atopy phenotypes. The severe phenotype emerged as a latent condition with signs of a dysbalanced immune response. It determined high asthma risk through excessive sIgE production and directly affected impaired lung function. Copyright © 2016 American Academy of Allergy, Asthma & Immunology. Published by Elsevier Inc. All rights reserved.

Subgroups of adolescents differing in physical and social environmental preferences towards cycling for transport: A latent class analysis.

PubMed

Verhoeven, Hannah; Ghekiere, Ariane; Van Cauwenberg, Jelle; Van Dyck, Delfien; De Bourdeaudhuij, Ilse; Clarys, Peter; Deforche, Benedicte

2018-07-01

In order to be able to tailor environmental interventions to adolescents at risk for low levels of physical activity, the aim of the present study is to identify subgroups of adolescents with different physical and social environmental preferences towards cycling for transport and to determine differences in individual characteristics between these subgroups. In this experimental study, 882 adolescents (12-16 years) completed 15 choice tasks with manipulated photographs. Participants chose between two possible routes to cycle to a friend's house which differed in seven physical micro-environmental factors, cycling distance and co-participation in cycling (i.e. cycling alone or with a friend). Latent class analysis was performed. Data were collected from March till October 2016 across Flanders (Belgium). Three subgroups could be identified. Subgroup 1 attached most importance to separation of the cycle path and safety-related aspects. Subgroup 2 attached most importance to being able to cycle together with a friend and had the highest percentage of regular cyclists. In subgroup 3, the importance of cycling distance clearly stood out. This subgroup included the lowest percentage of regular cyclists. Results showed that in order to stimulate the least regular cyclists, and thus also the subgroup most at risk for low levels of active transport, cycling distances should be as short as possible. In general, results showed that providing well-separated cycle paths which enable adolescents to cycle side by side and introducing shortcuts for cyclists may encourage different subgroups of adolescents to cycle for transport without discouraging other subgroups. Copyright © 2018 Elsevier Inc. All rights reserved.

Spatial-Orientation Priming Impedes Rather than Facilitates the Spontaneous Control of Hand-Retraction Speeds in Patients with Parkinson’s Disease

PubMed Central

Yanovich, Polina; Isenhower, Robert W.; Sage, Jacob; Torres, Elizabeth B.

2013-01-01

Background Often in Parkinson’s disease (PD) motor-related problems overshadow latent non-motor deficits as it is difficult to dissociate one from the other with commonly used observational inventories. Here we ask if the variability patterns of hand speed and acceleration would be revealing of deficits in spatial-orientation related decisions as patients performed a familiar reach-to-grasp task. To this end we use spatial-orientation priming which normally facilitates motor-program selection and asked whether in PD spatial-orientation priming helps or hinders performance. Methods To dissociate spatial-orientation- and motor-related deficits participants performed two versions of the task. The biomechanical version (DEFAULT) required the same postural- and hand-paths as the orientation-priming version (primed-UP). Any differences in the patients here could not be due to motor issues as the tasks were biomechanically identical. The other priming version (primed-DOWN) however required additional spatial and postural processing. We assessed in all three cases both the forward segment deliberately aimed towards the spatial-target and the retracting segment, spontaneously bringing the hand to rest without an instructed goal. Results and Conclusions We found that forward and retracting segments belonged in two different statistical classes according to the fluctuations of speed and acceleration maxima. Further inspection revealed conservation of the forward (voluntary) control of speed but in PD a discontinuity of this control emerged during the uninstructed retractions which was absent in NC. Two PD groups self-emerged: one group in which priming always affected the retractions and the other in which only the more challenging primed-DOWN condition was affected. These PD-groups self-formed according to the speed variability patterns, which systematically changed along a gradient that depended on the priming, thus dissociating motor from spatial-orientation issues. Priming did not facilitate the motor task in PD but it did reveal a breakdown in the spatial-orientation decision that was independent of the motor-postural path. PMID:23843963

A Comparison of Latent Heat Fluxes over Global Oceans for Four Flux Products

NASA Technical Reports Server (NTRS)

Chou, Shu-Hsien; Nelkin, Eric; Ardizzone, Joe; Atlas, Robert M.

2003-01-01

To improve our understanding of global energy and water cycle variability, and to improve model simulations of climate variations, it is vital to have accurate latent heat fluxes (LHF) over global oceans. Monthly LHF, 10-m wind speed (U10m), 10-m specific humidity (Q10h), and sea-air humidity difference (Qs-Q10m) of GSSTF2 (version 2 Goddard Satellite-based Surface Turbulent Fluxes) over global Oceans during 1992-93 are compared with those of HOAPS (Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data), NCEP (NCEP/NCAR reanalysis). The mean differences, standard deviations of differences, and temporal correlation of these monthly variables over global Oceans during 1992-93 between GSSTF2 and each of the three datasets are analyzed. The large-scale patterns of the 2yr-mean fields for these variables are similar among these four datasets, but significant quantitative differences are found. The temporal correlation is higher in the northern extratropics than in the south for all variables, with the contrast being especially large for da Silva as a result of more missing ship data in the south. The da Silva has extremely low temporal correlation and large differences with GSSTF2 for all variables in the southern extratropics, indicating that da Silva hardly produces a realistic variability in these variables. The NCEP has extremely low temporal correlation (0.27) and large spatial variations of differences with GSSTF2 for Qs-Q10m in the tropics, which causes the low correlation for LHF. Over the tropics, the HOAPS LHF is significantly smaller than GSSTF2 by approx. 31% (37 W/sq m), whereas the other two datasets are comparable to GSSTF2. This is because the HOAPS has systematically smaller LHF than GSSTF2 in space, while the other two datasets have very large spatial variations of large positive and negative LHF differences with GSSTF2 to cancel and to produce smaller regional-mean differences. Our analyses suggest that the GSSTF2 latent heat flux, surface air humidity, and winds are likely to be more realistic than the other three flux datasets examined, although those of GSSTF2 are still subject to regional biases.

Modeling and impacts of the latent heat of phase change and specific heat for phase change materials

NASA Astrophysics Data System (ADS)

Scoggin, J.; Khan, R. S.; Silva, H.; Gokirmak, A.

2018-05-01

We model the latent heats of crystallization and fusion in phase change materials with a unified latent heat of phase change, ensuring energy conservation by coupling the heat of phase change with amorphous and crystalline specific heats. We demonstrate the model with 2-D finite element simulations of Ge2Sb2Te5 and find that the heat of phase change increases local temperature up to 180 K in 300 nm × 300 nm structures during crystallization, significantly impacting grain distributions. We also show in electrothermal simulations of 45 nm confined and 10 nm mushroom cells that the higher amorphous specific heat predicted by this model increases nucleation probability at the end of reset operations. These nuclei can decrease set time, leading to variability, as demonstrated for the mushroom cell.

Simulation Of Research And Development Projects

NASA Technical Reports Server (NTRS)

Miles, Ralph F.

1987-01-01

Measures of preference for alternative project plans calculated. Simulation of Research and Development Projects (SIMRAND) program aids in optimal allocation of research and development resources needed to achieve project goals. Models system subsets or project tasks as various network paths to final goal. Each path described in terms of such task variables as cost per hour, cost per unit, and availability of resources. Uncertainty incorporated by treating task variables as probabilistic random variables. Written in Microsoft FORTRAN 77.

Reactivation of Latent Tuberculosis in Cynomolgus Macaques Infected with SIV Is Associated with Early Peripheral T Cell Depletion and Not Virus Load

PubMed Central

Klein, Edwin; Janssen, Chris; Phuah, Jiayao; Sturgeon, Timothy J.; Montelaro, Ronald C.; Lin, Philana Ling; Flynn, JoAnne L.

2010-01-01

HIV-infected individuals with latent Mycobacterium tuberculosis (Mtb) infection are at significantly greater risk of reactivation tuberculosis (TB) than HIV-negative individuals with latent TB, even while CD4 T cell numbers are well preserved. Factors underlying high rates of reactivation are poorly understood and investigative tools are limited. We used cynomolgus macaques with latent TB co-infected with SIVmac251 to develop the first animal model of reactivated TB in HIV-infected humans to better explore these factors. All latent animals developed reactivated TB following SIV infection, with a variable time to reactivation (up to 11 months post-SIV). Reactivation was independent of virus load but correlated with depletion of peripheral T cells during acute SIV infection. Animals experiencing reactivation early after SIV infection (<17 weeks) had fewer CD4 T cells in the periphery and airways than animals reactivating in later phases of SIV infection. Co-infected animals had fewer T cells in involved lungs than SIV-negative animals with active TB despite similar T cell numbers in draining lymph nodes. Granulomas from these animals demonstrated histopathologic characteristics consistent with a chronically active disease process. These results suggest initial T cell depletion may strongly influence outcomes of HIV-Mtb co-infection. PMID:20224771

Measuring the environmental awareness of young farmers

NASA Astrophysics Data System (ADS)

Kountios, G.; Ragkos, A.; Padadavid, G.; Hadjimitsis, D.

2017-09-01

Young farmers in Europe, especially the beneficiaries of Common Agricultural Policy (CAP) funding schemes, are considered as the ones who could ensure the sustainability of the European Model of Agriculture. Economic efficiency and competitiveness, aversion of depopulation of rural areas and environmental protection constitute some of the key objectives of the CAP and young farmers are expected to play a role to all of them. This study proposes a way of measuring the potential of young farmers to contribute to the latter objectives of the CAP by estimating their environmental attitudes. Data from a questionnaire survey of 492 Greek young farmers were used to design a latent construct measuring their environmental attitudes. The latent construct was designed by means of an Explanatory Factor Analysis (EFA) using the responses to a set of 12 Likert-scale items. The results the EFA yielded a latent construct with three factors related to "Environmental pollution and policies (EPP)", "Environmental factors and food quality (EFF)" and "Farming practices and the environment". These results were validated through a CFA where 8 items in total were categorized in the three factors (latent variables). The utilization of the latent construct for the effective implementation of CAP measures could ameliorate the relationships of agriculture and environment in general.

Visual, motor and attentional influences on proprioceptive contributions to perception of hand path rectilinearity during reaching

PubMed Central

Scheidt, Robert A.; Lillis, Kyle P.; Emerson, Scott J.

2010-01-01

We examined how proprioceptive contributions to perception of hand path straightness are influenced by visual, motor and attentional sources of performance variability during horizontal planar reaching. Subjects held the handle of a robot that constrained goal-directed movements of the hand to paths of controlled curvature. Subjects attempted to detect the presence of hand path curvature during both active (subject-driven) and passive (robot-driven) movements that either required active muscle force production or not. Subjects were less able to discriminate curved from straight paths when actively reaching for a target vs. when the robot moved their hand through the same curved paths. This effect was especially evident during robot-driven movements requiring concurrent activation of lengthening but not shortening muscles. Subjects were less likely to report curvature and were more variable in reporting when movements appeared straight in a novel “visual channel” condition previously shown to block adaptive updating of motor commands in response to deviations from a straight-line hand path. Similarly compromised performance was obtained when subjects simultaneously performed a distracting secondary task (key pressing with the contralateral hand). The effects compounded when these last two treatments were combined. It is concluded that environmental, intrinsic and attentional factors all impact the ability to detect deviations from a rectilinear hand path during goal-directed movement by decreasing proprioceptive contributions to limb state estimation. In contrast, response variability increased only in experimental conditions thought to impose additional attentional demands on the observer. Implications of these results for perception and other sensorimotor behaviors are discussed. PMID:20532489

Prediction of first episode of panic attack among white-collar workers.

PubMed

Watanabe, Akira; Nakao, Kazuhisa; Tokuyama, Madoka; Takeda, Masatoshi

2005-04-01

The purpose of the present study was to elucidate a longitudinal matrix of the etiology for first-episode panic attack among white-collar workers. A path model was designed for this purpose. A 5-year, open-cohort study was carried out in a Japanese company. To evaluate the risk factors associated with the onset of a first episode of panic attack, the odds ratios of a new episode of panic attack were calculated by logistic regression. The path model contained five predictor variables: gender difference, overprotection, neuroticism, lifetime history of major depression, and recent stressful life events. The logistic regression analysis indicated that a person with a lifetime history of major depression and recent stressful life events had a fivefold and a threefold higher risk of panic attacks at follow up, respectively. The path model for the prediction of a first episode of panic attack fitted the data well. However, this model presented low accountability for the variance in the ultimate dependent variables, the first episode of panic attack. Three predictors (neuroticism, lifetime history of major depression, and recent stressful life events) had a direct effect on the risk for a first episode of panic attack, whereas gender difference and overprotection had no direct effect. The present model could not fully predict first episodes of panic attack in white-collar workers. To make a path model for the prediction of the first episode of panic attack, other strong predictor variables, which were not surveyed in the present study, are needed. It is suggested that genetic variables are among the other strong predictor variables. A new path model containing genetic variables (e.g. family history etc.) will be needed to predict the first episode of panic attack.

Fast exploration of an optimal path on the multidimensional free energy surface

PubMed Central

Chen, Changjun

2017-01-01

In a reaction, determination of an optimal path with a high reaction rate (or a low free energy barrier) is important for the study of the reaction mechanism. This is a complicated problem that involves lots of degrees of freedom. For simple models, one can build an initial path in the collective variable space by the interpolation method first and then update the whole path constantly in the optimization. However, such interpolation method could be risky in the high dimensional space for large molecules. On the path, steric clashes between neighboring atoms could cause extremely high energy barriers and thus fail the optimization. Moreover, performing simulations for all the snapshots on the path is also time-consuming. In this paper, we build and optimize the path by a growing method on the free energy surface. The method grows a path from the reactant and extends its length in the collective variable space step by step. The growing direction is determined by both the free energy gradient at the end of the path and the direction vector pointing at the product. With fewer snapshots on the path, this strategy can let the path avoid the high energy states in the growing process and save the precious simulation time at each iteration step. Applications show that the presented method is efficient enough to produce optimal paths on either the two-dimensional or the twelve-dimensional free energy surfaces of different small molecules. PMID:28542475

A Random Walk Down University Avenue: Life Paths, Life Events, and Personality Trait Change at the Transition to University Life

PubMed Central

Lüdtke, Oliver; Roberts, Brent W.; Trautwein, Ulrich; Nagy, Gabriel

2013-01-01

This longitudinal study examined the relation between continuity and change in the Big Five personality traits and life events. Approximately 2,000 German students were tracked from high school to university or to vocational training or work, with 3 assessments over 4 years. Life events were reported retrospectively at the 2nd and 3rd assessment. Latent curve analyses were used to assess change in personality traits, revealing 3 main findings. First, mean-level changes in the Big Five factors over the 4 years were in line with the maturity principle, indicating increasing psychological maturity from adolescence to young adulthood. Second, personality development was characterized by substantive individual differences relating to the life path followed; participants on a more vocationally oriented path showed higher increases in conscientiousness and lower increases in agreeableness than their peers at university. Third, initial level and change in the Big Five factors (especially Neuroticism and Extraversion) were linked to the occurrence of aggregated as well as single positive and negative life events. The analyses suggest that individual differences in personality development are associated with life transitions and individual life experiences. PMID:21744977

Insights into the latent multinomial model through mark-resight data on female grizzly bears with cubs-of-the-year

USGS Publications Warehouse

Higgs, Megan D.; Link, William; White, Gary C.; Haroldson, Mark A.; Bjornlie, Daniel D.

2013-01-01

Mark-resight designs for estimation of population abundance are common and attractive to researchers. However, inference from such designs is very limited when faced with sparse data, either from a low number of marked animals, a low probability of detection, or both. In the Greater Yellowstone Ecosystem, yearly mark-resight data are collected for female grizzly bears with cubs-of-the-year (FCOY), and inference suffers from both limitations. To overcome difficulties due to sparseness, we assume homogeneity in sighting probabilities over 16 years of bi-annual aerial surveys. We model counts of marked and unmarked animals as multinomial random variables, using the capture frequencies of marked animals for inference about the latent multinomial frequencies for unmarked animals. We discuss undesirable behavior of the commonly used discrete uniform prior distribution on the population size parameter and provide OpenBUGS code for fitting such models. The application provides valuable insights into subtleties of implementing Bayesian inference for latent multinomial models. We tie the discussion to our application, though the insights are broadly useful for applications of the latent multinomial model.

Predicting Viral Infection From High-Dimensional Biomarker Trajectories

PubMed Central

Chen, Minhua; Zaas, Aimee; Woods, Christopher; Ginsburg, Geoffrey S.; Lucas, Joseph; Dunson, David; Carin, Lawrence

2013-01-01

There is often interest in predicting an individual’s latent health status based on high-dimensional biomarkers that vary over time. Motivated by time-course gene expression array data that we have collected in two influenza challenge studies performed with healthy human volunteers, we develop a novel time-aligned Bayesian dynamic factor analysis methodology. The time course trajectories in the gene expressions are related to a relatively low-dimensional vector of latent factors, which vary dynamically starting at the latent initiation time of infection. Using a nonparametric cure rate model for the latent initiation times, we allow selection of the genes in the viral response pathway, variability among individuals in infection times, and a subset of individuals who are not infected. As we demonstrate using held-out data, this statistical framework allows accurate predictions of infected individuals in advance of the development of clinical symptoms, without labeled data and even when the number of biomarkers vastly exceeds the number of individuals under study. Biological interpretation of several of the inferred pathways (factors) is provided. PMID:23704802

Phenotypic factor analysis of psychopathology reveals a new body-related transdiagnostic factor.

PubMed

Pezzoli, Patrizia; Antfolk, Jan; Santtila, Pekka

2017-01-01

Comorbidity challenges the notion of mental disorders as discrete categories. An increasing body of literature shows that symptoms cut across traditional diagnostic boundaries and interact in shaping the latent structure of psychopathology. Using exploratory and confirmatory factor analysis, we reveal the latent sources of covariation among nine measures of psychopathological functioning in a population-based sample of 13024 Finnish twins and their siblings. By implementing unidimensional, multidimensional, second-order, and bifactor models, we illustrate the relationships between observed variables, specific, and general latent factors. We also provide the first investigation to date of measurement invariance of the bifactor model of psychopathology across gender and age groups. Our main result is the identification of a distinct "Body" factor, alongside the previously identified Internalizing and Externalizing factors. We also report relevant cross-disorder associations, especially between body-related psychopathology and trait anger, as well as substantial sex and age differences in observed and latent means. The findings expand the meta-structure of psychopathology, with implications for empirical and clinical practice, and demonstrate shared mechanisms underlying attitudes towards nutrition, self-image, sexuality and anger, with gender- and age-specific features.

Multilayer Joint Gait-Pose Manifolds for Human Gait Motion Modeling.

PubMed

Ding, Meng; Fan, Guolian

2015-11-01

We present new multilayer joint gait-pose manifolds (multilayer JGPMs) for complex human gait motion modeling, where three latent variables are defined jointly in a low-dimensional manifold to represent a variety of body configurations. Specifically, the pose variable (along the pose manifold) denotes a specific stage in a walking cycle; the gait variable (along the gait manifold) represents different walking styles; and the linear scale variable characterizes the maximum stride in a walking cycle. We discuss two kinds of topological priors for coupling the pose and gait manifolds, i.e., cylindrical and toroidal, to examine their effectiveness and suitability for motion modeling. We resort to a topologically-constrained Gaussian process (GP) latent variable model to learn the multilayer JGPMs where two new techniques are introduced to facilitate model learning under limited training data. First is training data diversification that creates a set of simulated motion data with different strides. Second is the topology-aware local learning to speed up model learning by taking advantage of the local topological structure. The experimental results on the Carnegie Mellon University motion capture data demonstrate the advantages of our proposed multilayer models over several existing GP-based motion models in terms of the overall performance of human gait motion modeling.

Validity test and its consistency in the construction of patient loyalty model

NASA Astrophysics Data System (ADS)

Yanuar, Ferra

2016-04-01

The main objective of this present study is to demonstrate the estimation of validity values and its consistency based on structural equation model. The method of estimation was then implemented to an empirical data in case of the construction the patient loyalty model. In the hypothesis model, service quality, patient satisfaction and patient loyalty were determined simultaneously, each factor were measured by any indicator variables. The respondents involved in this study were the patients who ever got healthcare at Puskesmas in Padang, West Sumatera. All 394 respondents who had complete information were included in the analysis. This study found that each construct; service quality, patient satisfaction and patient loyalty were valid. It means that all hypothesized indicator variables were significant to measure their corresponding latent variable. Service quality is the most measured by tangible, patient satisfaction is the most mesured by satisfied on service and patient loyalty is the most measured by good service quality. Meanwhile in structural equation, this study found that patient loyalty was affected by patient satisfaction positively and directly. Service quality affected patient loyalty indirectly with patient satisfaction as mediator variable between both latent variables. Both structural equations were also valid. This study also proved that validity values which obtained here were also consistence based on simulation study using bootstrap approach.

Evaluation of Two Types of Differential Item Functioning in Factor Mixture Models with Binary Outcomes

ERIC Educational Resources Information Center

Lee, HwaYoung; Beretvas, S. Natasha

2014-01-01

Conventional differential item functioning (DIF) detection methods (e.g., the Mantel-Haenszel test) can be used to detect DIF only across observed groups, such as gender or ethnicity. However, research has found that DIF is not typically fully explained by an observed variable. True sources of DIF may include unobserved, latent variables, such as…

Traumatic Brain Injury and PTSD Screening Efforts Evaluated Using Latent Class Analysis

DTIC Science & Technology

2014-01-01

Pastorek, & Thornton, 2009; Breslau, Reboussin, Anthony, & Storr, 2005; Naifeh, Richardson, Del Ben, & Elhai, 2010; Shevlin, Armour , Murphy, Houston...about a wide range of demographic characteristics, environmental exposures, and com- bat experiences. The Post-Deployment Health Reassessment (PDHRA...treatment, responses from the PDHRA were used. Demographic variables. Demographic variables included age, military pay grade, and branch of service. All

The Double ABCX Model of Family Stress and Adaptation: An Empirical Test by Analysis of Structural Equations with Latent Variables.

ERIC Educational Resources Information Center

Lavee, Yoav; And Others

1985-01-01

Examined relationships among major variables of the Double ABCX model of family stress and adaptation using data on Army families' adaptation to the crisis of relocation overseas. Results support the notion of pile-up of demands. Family system resources and social support are both found to facilitate adaptation. (Author/BL)

Assessing Fit of Models with Discrete Proficiency Variable in Educational Assessment. Research Report. RR-04-07

ERIC Educational Resources Information Center

Sinharay, Sandip; Almond, Russell; Yan, Duanli

2004-01-01

Model checking is a crucial part of any statistical analysis. As educators tie models for testing to cognitive theory of the domains, there is a natural tendency to represent participant proficiencies with latent variables representing the presence or absence of the knowledge, skills, and proficiencies to be tested (Mislevy, Almond, Yan, &…

Pathways from Parental Stimulation of Children's Curiosity to High School Science Course Accomplishments and Science Career Interest and Skill

ERIC Educational Resources Information Center

Gottfried, Adele Eskeles; Preston, Kathleen Suzanne Johnson; Gottfried, Allen W.; Oliver, Pamella H.; Delany, Danielle E.; Ibrahim, Sirena M.

2016-01-01

Curiosity is fundamental to scientific inquiry and pursuance. Parents are important in encouraging children's involvement in science. This longitudinal study examined pathways from parental stimulation of children's curiosity per se to their science acquisition (SA). A latent variable of SA was indicated by the inter-related variables of high…

Zipf’s Law Arises Naturally When There Are Underlying, Unobserved Variables

PubMed Central

Corradi, Nicola

2016-01-01

Zipf’s law, which states that the probability of an observation is inversely proportional to its rank, has been observed in many domains. While there are models that explain Zipf’s law in each of them, those explanations are typically domain specific. Recently, methods from statistical physics were used to show that a fairly broad class of models does provide a general explanation of Zipf’s law. This explanation rests on the observation that real world data is often generated from underlying causes, known as latent variables. Those latent variables mix together multiple models that do not obey Zipf’s law, giving a model that does. Here we extend that work both theoretically and empirically. Theoretically, we provide a far simpler and more intuitive explanation of Zipf’s law, which at the same time considerably extends the class of models to which this explanation can apply. Furthermore, we also give methods for verifying whether this explanation applies to a particular dataset. Empirically, these advances allowed us extend this explanation to important classes of data, including word frequencies (the first domain in which Zipf’s law was discovered), data with variable sequence length, and multi-neuron spiking activity. PMID:27997544

The importance of position and path repeatability on force at the knee during six-DOF joint motion.

PubMed

Darcy, Shon P; Gil, Jorge E; Woo, Savio L-Y; Debski, Richard E

2009-06-01

Mechanical devices, such as robotic manipulators have been designed to measure joint and ligament function because of their ability to position a diarthrodial joint in six degrees-of-freedom with fidelity. However, the precision and performance of these testing devices vary. Therefore, the objective of this study was to determine the effect of systematic errors in position and path repeatability of two high-payload robotic manipulators (Manipulators 1 and 2) on the resultant forces at the knee. Using a porcine knee, the position and path repeatability of these manipulators were determined during passive flexion-extension with a coordinate measuring machine. The position repeatability of Manipulator 1 was 0.3 mm in position and 0.2 degrees in orientation while Manipulator 2 had a better position repeatability of 0.1 mm in position and 0.1 degrees in orientation throughout the range of positions examined. The corresponding variability in the resultant force at the knee for these assigned positions was 32+/-33 N for Manipulator 1 and 4+/-1 N for Manipulator 2. Furthermore, the repeatability of the trajectory of each manipulator while moving between assigned positions (path repeatability) was 0.8 mm for Manipulator 1 while the path repeatability for Manipulator 2 was improved (0.1 mm). These path discrepancies produced variability in the resultant force at the knee of 44+/-24 and 21+/-8 N, respectively, for Manipulators 1 and 2 primarily due to contact between the articular surfaces of the tibia and femur. Therefore, improved position and path repeatability yields lower variability in the resultant forces at the knee. Although position repeatability has been the most common criteria for evaluating biomechanical testing devices, the current study has clearly demonstrated that path repeatability can have an even larger effect on the variability in resultant force at the knee. Consequently, the repeatability of the path followed by the joint throughout its prescribed trajectory is as important as the repeatability of the joint at reaching positions making up its trajectory, particularly when joint contact occurs.

LQTA-QSAR: a new 4D-QSAR methodology.

PubMed

Martins, João Paulo A; Barbosa, Euzébio G; Pasqualoto, Kerly F M; Ferreira, Márcia M C

2009-06-01

A novel 4D-QSAR approach which makes use of the molecular dynamics (MD) trajectories and topology information retrieved from the GROMACS package is presented in this study. This new methodology, named LQTA-QSAR (LQTA, Laboratório de Quimiometria Teórica e Aplicada), has a module (LQTAgrid) that calculates intermolecular interaction energies at each grid point considering probes and all aligned conformations resulting from MD simulations. These interaction energies are the independent variables or descriptors employed in a QSAR analysis. The comparison of the proposed methodology to other 4D-QSAR and CoMFA formalisms was performed using a set of forty-seven glycogen phosphorylase b inhibitors (data set 1) and a set of forty-four MAP p38 kinase inhibitors (data set 2). The QSAR models for both data sets were built using the ordered predictor selection (OPS) algorithm for variable selection. Model validation was carried out applying y-randomization and leave-N-out cross-validation in addition to the external validation. PLS models for data set 1 and 2 provided the following statistics: q(2) = 0.72, r(2) = 0.81 for 12 variables selected and 2 latent variables and q(2) = 0.82, r(2) = 0.90 for 10 variables selected and 5 latent variables, respectively. Visualization of the descriptors in 3D space was successfully interpreted from the chemical point of view, supporting the applicability of this new approach in rational drug design.

Effect of the environment on participation in spinal cord injuries/disorders: The mediating impact of resilience, grief, and self-efficacy.

PubMed

Locatelli, Sara M; Etingen, Bella; Miskevics, Scott; LaVela, Sherri L

2017-08-01

The objective of the study was to test the hypothesis that the influence of environmental barriers on participation will be fully mediated by psychosocial factors (resilience, grief or loss, and self-efficacy) among individuals with spinal cord injuries and disorders. This was a cross-sectional mailed survey with a national sample of veterans with spinal cord injuries and disorders (n = 565), which included measures of demographics, perceived environmental barriers, participation, resilience, grief or loss, and self-efficacy. Data were analyzed using a latent variable path analysis; the model fit was assessed using χ2, normed χ2, root mean square error of approximation (RMSEA; <0.07), Comparative Fit Index (CFI; ≥0.95), and Tucker-Lewis Index (TLI; ≥0.95). Participants were mostly male (93.9%), Caucasian or White (74.2%), and, on average, 62.4 years of age (SD = 10.48). Almost 62% had paraplegia, and 38.3% had a motor or sensory complete injury. The hypothesized model showed excellent fit, χ2(42) = 153.40, normed χ2 = 3.65, RMSEA = 0.07, CFI = 0.96, TLI = 0.95. To assess for full mediation, a direct path was added from environment to participation. This model showed nearly identical fit, χ2(41) = 153.39, normed χ2 = 3.74, RMSEA = 0.07, CFI = 0.96, TLI = 0.95; the path from environment to participation was not significant, unstandardized coefficient = -0.04, p = .92. Examination of indirect effects of the hypothesized model demonstrated that environment significantly influences participation through psychosocial factors, unstandardized coefficient = -4.85, p < .001. Together these results support full mediation. In conclusion the present study expands upon past research by suggesting that perceived environmental barriers play a role in internal factors, such as resilience and self-efficacy, which in turn influence participation. Interventions to improve environmental barriers may be warranted, but attention must also be paid to resilience, grief or loss, and self-efficacy to increase participation. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

A Path Analysis Model Pertinent to Undergraduates' Academic Success: Examining Academic Confidence, Psychological Capital and Academic Coping Factors

ERIC Educational Resources Information Center

Kirikkanat, Berke; Soyer, Makbule Kali

2018-01-01

The major purpose of this study was to create a path analysis model of academic success in a group of university students, which included the variables of academic confidence and psychological capital with a mediator variable--academic coping. 400 undergraduates from Marmara University and Istanbul Commerce University who were in sophomore, junior…

Personal and Financial Risk Typologies Among Women Who Engage in Sex Work in Mongolia: A Latent Class Analysis.

PubMed

Offringa, Reid; Tsai, Laura Cordisco; Aira, Toivgoo; Riedel, Marion; Witte, Susan S

2017-08-01

Women engaged in sex work bear a disproportionate burden of HIV infection worldwide, particularly in low- to middle-income countries. Stakeholders interested in promoting prevention and treatment programs are challenged to efficiently and effectively target heterogeneous groups of women. This problem is particularly difficult because it is nearly impossible to know how those groups are composed a priori. Although grouping based on individual variables (e.g., age or place of solicitation) can describe a sample of women engaged in sex work, selecting these variables requires a strong intuitive understanding of the population. Furthermore, this approach is difficult to quantify and has the potential to reinforce preconceived notions, rather than generate new information. We aimed to investigate groupings of women engaged in sex work. The data were collected from a sample of 204 women who were referred to an HIV prevention intervention in Ulaanbaatar, Mongolia. Latent class analysis was used to create subgroups of women engaged in sex work, based on personal and financial risk factors. This analysis found three latent classes, representing unique response pattern profiles of personal and financial risk. The current study approached typology research in a novel, more empirical way and provided a description of different subgroups, which may respond differently to HIV risk interventions.

Latent transition analysis of pre-service teachers' efficacy in mathematics and science

NASA Astrophysics Data System (ADS)

Ward, Elizabeth Kennedy

This study modeled changes in pre-service teacher efficacy in mathematics and science over the course of the final year of teacher preparation using latent transition analysis (LTA), a longitudinal form of analysis that builds on two modeling traditions (latent class analysis (LCA) and auto-regressive modeling). Data were collected using the STEBI-B, MTEBI-r, and the ABNTMS instruments. The findings suggest that LTA is a viable technique for use in teacher efficacy research. Teacher efficacy is modeled as a construct with two dimensions: personal teaching efficacy (PTE) and outcome expectancy (OE). Findings suggest that the mathematics and science teaching efficacy (PTE) of pre-service teachers is a multi-class phenomena. The analyses revealed a four-class model of PTE at the beginning and end of the final year of teacher training. Results indicate that when pre-service teachers transition between classes, they tend to move from a lower efficacy class into a higher efficacy class. In addition, the findings suggest that time-varying variables (attitudes and beliefs) and time-invariant variables (previous coursework, previous experiences, and teacher perceptions) are statistically significant predictors of efficacy class membership. Further, analyses suggest that the measures used to assess outcome expectancy are not suitable for LCA and LTA procedures.

Control and design heat flux bending in thermal devices with transformation optics.

PubMed

Xu, Guoqiang; Zhang, Haochun; Jin, Yan; Li, Sen; Li, Yao

2017-04-17

We propose a fundamental latent function of control heat transfer and heat flux density vectors at random positions on thermal materials by applying transformation optics. The expressions for heat flux bending are obtained, and the factors influencing them are investigated in both 2D and 3D cloaking schemes. Under certain conditions, more than one degree of freedom of heat flux bending exists corresponding to the temperature gradients of the 3D domain. The heat flux path can be controlled in random space based on the geometrical azimuths, radial positions, and thermal conductivity ratios of the selected materials.

Similarities and differences between mind-wandering and external distraction: a latent variable analysis of lapses of attention and their relation to cognitive abilities.

PubMed

Unsworth, Nash; McMillan, Brittany D

2014-07-01

The current study examined the extent to which task-unrelated thoughts represent both vulnerability to mind-wandering and susceptibility to external distraction from an individual difference perspective. Participants performed multiple measures of attention control, working memory capacity, and fluid intelligence. Task-unrelated thoughts were assessed using thought probes during the attention control tasks. Using latent variable techniques, the results suggested that mind-wandering and external distraction reflect distinct, yet correlated constructs, both of which are related to working memory capacity and fluid intelligence. Furthermore, the results suggest that the common variance shared by mind-wandering, external distraction, and attention control is what primarily accounts for their relation with working memory capacity and fluid intelligence. These results support the notion that lapses of attention are strongly related to cognitive abilities. Copyright © 2014 Elsevier B.V. All rights reserved.

Mixture modeling methods for the assessment of normal and abnormal personality, part I: cross-sectional models.

PubMed

Hallquist, Michael N; Wright, Aidan G C

2014-01-01

Over the past 75 years, the study of personality and personality disorders has been informed considerably by an impressive array of psychometric instruments. Many of these tests draw on the perspective that personality features can be conceptualized in terms of latent traits that vary dimensionally across the population. A purely trait-oriented approach to personality, however, might overlook heterogeneity that is related to similarities among subgroups of people. This article describes how factor mixture modeling (FMM), which incorporates both categories and dimensions, can be used to represent person-oriented and trait-oriented variability in the latent structure of personality. We provide an overview of different forms of FMM that vary in the degree to which they emphasize trait- versus person-oriented variability. We also provide practical guidelines for applying FMM to personality data, and we illustrate model fitting and interpretation using an empirical analysis of general personality dysfunction.

Measuring Five Dimensions of Religiosity across Adolescence

PubMed Central

Pearce, Lisa D.; Hayward, George M.; Pearlman, Jessica A.

2017-01-01

This paper theorizes and tests a latent variable model of adolescent religiosity in which five dimensions of religiosity are interrelated: religious beliefs, religious exclusivity, external religiosity, private practice, and religious salience. Research often theorizes overlapping and independent influences of single items or dimensions of religiosity on outcomes such as adolescent sexual behavior, but rarely operationalizes the dimensions in a measurement model accounting for their associations with each other and across time. We use longitudinal structural equation modeling (SEM) with latent variables to analyze data from two waves of the National Study of Youth and Religion. We test our hypothesized measurement model as compared to four alternate measurement models and find that our proposed model maintains superior fit. We then discuss the associations between the five dimensions of religiosity we measure and how these change over time. Our findings suggest how future research might better operationalize multiple dimensions of religiosity in studies of the influence of religion in adolescence. PMID:28931956

Conceptualising computerized adaptive testing for measurement of latent variables associated with physical objects

NASA Astrophysics Data System (ADS)

Camargo, F. R.; Henson, B.

2015-02-01

The notion of that more or less of a physical feature affects in different degrees the users' impression with regard to an underlying attribute of a product has frequently been applied in affective engineering. However, those attributes exist only as a premise that cannot directly be measured and, therefore, inferences based on their assessment are error-prone. To establish and improve measurement of latent attributes it is presented in this paper the concept of a stochastic framework using the Rasch model for a wide range of independent variables referred to as an item bank. Based on an item bank, computerized adaptive testing (CAT) can be developed. A CAT system can converge into a sequence of items bracketing to convey information at a user's particular endorsement level. It is through item banking and CAT that the financial benefits of using the Rasch model in affective engineering can be realised.

Architecture of fluid intelligence and working memory revealed by lesion mapping.

PubMed

Barbey, Aron K; Colom, Roberto; Paul, Erick J; Grafman, Jordan

2014-03-01

Although cognitive neuroscience has made valuable progress in understanding the role of the prefrontal cortex in human intelligence, the functional networks that support adaptive behavior and novel problem solving remain to be well characterized. Here, we studied 158 human brain lesion patients to investigate the cognitive and neural foundations of key competencies for fluid intelligence and working memory. We administered a battery of neuropsychological tests, including the Wechsler Adult Intelligence Scale (WAIS) and the N-Back task. Latent variable modeling was applied to obtain error-free scores of fluid intelligence and working memory, followed by voxel-based lesion-symptom mapping to elucidate their neural substrates. The observed latent variable modeling and lesion results support an integrative framework for understanding the architecture of fluid intelligence and working memory and make specific recommendations for the interpretation and application of the WAIS and N-Back task to the study of fluid intelligence in health and disease.

Computational analysis of cell-to-cell heterogeneity in single-cell RNA-sequencing data reveals hidden subpopulations of cells.

PubMed

Buettner, Florian; Natarajan, Kedar N; Casale, F Paolo; Proserpio, Valentina; Scialdone, Antonio; Theis, Fabian J; Teichmann, Sarah A; Marioni, John C; Stegle, Oliver

2015-02-01

Recent technical developments have enabled the transcriptomes of hundreds of cells to be assayed in an unbiased manner, opening up the possibility that new subpopulations of cells can be found. However, the effects of potential confounding factors, such as the cell cycle, on the heterogeneity of gene expression and therefore on the ability to robustly identify subpopulations remain unclear. We present and validate a computational approach that uses latent variable models to account for such hidden factors. We show that our single-cell latent variable model (scLVM) allows the identification of otherwise undetectable subpopulations of cells that correspond to different stages during the differentiation of naive T cells into T helper 2 cells. Our approach can be used not only to identify cellular subpopulations but also to tease apart different sources of gene expression heterogeneity in single-cell transcriptomes.

The value of item response theory in clinical assessment: a review.

PubMed

Thomas, Michael L

2011-09-01

Item response theory (IRT) and related latent variable models represent modern psychometric theory, the successor to classical test theory in psychological assessment. Although IRT has become prevalent in the measurement of ability and achievement, its contributions to clinical domains have been less extensive. Applications of IRT to clinical assessment are reviewed to appraise its current and potential value. Benefits of IRT include comprehensive analyses and reduction of measurement error, creation of computer adaptive tests, meaningful scaling of latent variables, objective calibration and equating, evaluation of test and item bias, greater accuracy in the assessment of change due to therapeutic intervention, and evaluation of model and person fit. The theory may soon reinvent the manner in which tests are selected, developed, and scored. Although challenges remain to the widespread implementation of IRT, its application to clinical assessment holds great promise. Recommendations for research, test development, and clinical practice are provided.

Day-to-day affect is surprisingly stable: A two-year longitudinal study of well-being

PubMed Central

Hudson, Nathan W.; Lucas, Richard E.; Donnellan, M. Brent

2017-01-01

Previous research suggests global assessments of cognitive well-being—life satisfaction—are relatively stable over time. Far fewer studies have examined the extent to which experiential measures of affective well-being—the moods/emotions people regularly experience—are stable, especially over extended periods of time. The present study used longitudinal data from a representative sample of Germans to investigate the long-term stability of different components of well-being. Participants provided global ratings of life satisfaction and affect, along with experiential measures of well-being up to three times over two years. Results indicated between one third and one half of the variance in people’s daily affect was attributable to trait-like latent variables. Replicating meta-analytic findings, 50% of the variance in global measures of well-being was attributable to trait-like latent variables. PMID:29238453

The semantic distance task: Quantifying semantic distance with semantic network path length.

PubMed

Kenett, Yoed N; Levi, Effi; Anaki, David; Faust, Miriam

2017-09-01

Semantic distance is a determining factor in cognitive processes, such as semantic priming, operating upon semantic memory. The main computational approach to compute semantic distance is through latent semantic analysis (LSA). However, objections have been raised against this approach, mainly in its failure at predicting semantic priming. We propose a novel approach to computing semantic distance, based on network science methodology. Path length in a semantic network represents the amount of steps needed to traverse from 1 word in the network to the other. We examine whether path length can be used as a measure of semantic distance, by investigating how path length affect performance in a semantic relatedness judgment task and recall from memory. Our results show a differential effect on performance: Up to 4 steps separating between word-pairs, participants exhibit an increase in reaction time (RT) and decrease in the percentage of word-pairs judged as related. From 4 steps onward, participants exhibit a significant decrease in RT and the word-pairs are dominantly judged as unrelated. Furthermore, we show that as path length between word-pairs increases, success in free- and cued-recall decreases. Finally, we demonstrate how our measure outperforms computational methods measuring semantic distance (LSA and positive pointwise mutual information) in predicting participants RT and subjective judgments of semantic strength. Thus, we provide a computational alternative to computing semantic distance. Furthermore, this approach addresses key issues in cognitive theory, namely the breadth of the spreading activation process and the effect of semantic distance on memory retrieval. (PsycINFO Database Record (c) 2017 APA, all rights reserved).

Discordant inflammation and pain in early and established rheumatoid arthritis: Latent Class Analysis of Early Rheumatoid Arthritis Network and British Society for Rheumatology Biologics Register data.

PubMed

McWilliams, Daniel F; Ferguson, Eamonn; Young, Adam; Kiely, Patrick D W; Walsh, David A

2016-12-13

Rheumatoid arthritis (RA) disease activity is often measured using the 28-joint Disease Activity Score (DAS28). We aimed to identify and independently verify subgroups of people with RA that may be discordant with respect to self-reported and objective disease state, with potentially different clinical needs. Data were derived from three cohorts: (1) the Early Rheumatoid Arthritis Network (ERAN) and the British Society for Rheumatology Biologics Register (BSRBR), (2) those commencing tumour necrosis factor (TNF)-α inhibitors and (3) those using non-biologic drugs. In latent class analysis, we used variables related to pain, central pain mechanisms or inflammation (pain, vitality, mental health, erythrocyte sedimentation rate, swollen joint count, tender joint count, visual analogue scale of general health). Clinically relevant outcomes were examined. Five, four and four latent classes were found in the ERAN, BSRBR TNF inhibitor and non-biologic cohorts, respectively. The proportions of people assigned with >80% probability into latent classes were 76%, 58% and 72% in the ERAN, TNF inhibitor and non-biologic cohorts, respectively. The latent classes displayed either concordance between measures indicative of mild, moderate or severe disease activity; discordantly worse patient-reported measures despite less markedly elevated inflammation; or discordantly less severe patient-reported measures despite elevated inflammation. Latent classes with discordantly worse patient-reported measures represented 12%, 40% and 21% of the ERAN, TNF inhibitor and non-biologic cohorts, respectively; contained more females; and showed worse function. In those latent classes with worse scores at baseline, DAS28 and function improved over 1 year (p < 0.001 for all comparisons), and scores differed less at follow-up than at baseline. Discordant latent classes can be identified in people with RA, and these findings are robust across three cohorts with varying disease duration and activity. These findings could be used to identify a sizeable subgroup of people with RA who might gain added benefit from pain management strategies.

Variable-centered and person-centered approaches to studying Mexican-origin mother-daughter cultural orientation dissonance.

PubMed

Bámaca-Colbert, Mayra Y; Gayles, Jochebed G

2010-11-01

The overall aim of the current study was to identify the methodological approach and corresponding analytic procedure that best elucidated the associations among Mexican-origin mother-daughter cultural orientation dissonance, family functioning, and adolescent adjustment. To do so, we employed, and compared, two methodological approaches (i.e., variable-centered and person-centered) via four analytic procedures (i.e., difference score, interactive, matched/mismatched grouping, and latent profiles). The sample consisted of 319 girls in the 7th or 10th grade and their mother or mother figure from a large Southwestern, metropolitan area in the US. Family factors were found to be important predictors of adolescent adjustment in all models. Although some findings were similar across all models, overall, findings suggested that the latent profile procedure best elucidated the associations among the variables examined in this study. In addition, associations were present across early and middle adolescents, with a few findings being only present for one group. Implications for using these analytic procedures in studying cultural and family processes are discussed.

Testing manifest monotonicity using order-constrained statistical inference.

PubMed

Tijmstra, Jesper; Hessen, David J; van der Heijden, Peter G M; Sijtsma, Klaas

2013-01-01

Most dichotomous item response models share the assumption of latent monotonicity, which states that the probability of a positive response to an item is a nondecreasing function of a latent variable intended to be measured. Latent monotonicity cannot be evaluated directly, but it implies manifest monotonicity across a variety of observed scores, such as the restscore, a single item score, and in some cases the total score. In this study, we show that manifest monotonicity can be tested by means of the order-constrained statistical inference framework. We propose a procedure that uses this framework to determine whether manifest monotonicity should be rejected for specific items. This approach provides a likelihood ratio test for which the p-value can be approximated through simulation. A simulation study is presented that evaluates the Type I error rate and power of the test, and the procedure is applied to empirical data.

Comparison of measured and modeled radiation, heat and water vapor fluxes: FIFE pilot study

NASA Technical Reports Server (NTRS)

Blad, Blaine L.; Hubbard, Kenneth G.; Verma, Shashi B.; Starks, Patrick; Norman, John M.; Walter-Shea, Elizabeth

1987-01-01

The feasibility of using radio frequency receivers to collect data from automated weather stations to model fluxes of latent heat, sensible heat, and radiation using routine weather data collected by automated weather stations was tested and the estimated fluxes were compared with fluxes measured over wheat. The model Cupid was used to model the fluxes. Two or more automated weather stations, interrogated by radio frequency and other means, were utilized to examine some of the climatic variability of the First ISLSCP (International Satellite Land-Surface Climatology Project) Field Experiment (FIFE) site, to measure and model reflected and emitted radiation streams from various locations at the site and to compare modeled latent and sensible heat fluxes with measured values. Some bidirectional reflected and emitted radiation data were collected from 23 locations throughout the FIFE site. Analysis of these data along with analysis of the measured sensible and latent heat fluxes is just beginning.

The Coach-Athlete Relationship Questionnaire (CART-Q): development and initial validation.

PubMed

Jowett, Sophia; Ntoumanis, Nikos

2004-08-01

The purpose of the present study was to develop and validate a self-report instrument that measures the nature of the coach-athlete relationship. Jowett et al.'s (Jowett & Meek, 2000; Jowett, in press) qualitative case studies and relevant literature were used to generate items for an instrument that measures affective, cognitive, and behavioral aspects of the coach-athlete relationship. Two studies were carried out in an attempt to assess content, predictive, and construct validity, as well as internal consistency, of the Coach-Athlete Relationship Questionnaire (CART-Q), using two independent British samples. Principal component analysis and confirmatory factor analysis were used to reduce the number of items, identify principal components, and confirm the latent structure of the CART-Q. Results supported the multidimensional nature of the coach-athlete relationship. The latent structure of the CART-Q was underlined by the latent variables of coaches' and athletes' Closeness (emotions), Commitment (cognitions), and Complementarity (behaviors).

Latent log-linear models for handwritten digit classification.

PubMed

Deselaers, Thomas; Gass, Tobias; Heigold, Georg; Ney, Hermann

2012-06-01

We present latent log-linear models, an extension of log-linear models incorporating latent variables, and we propose two applications thereof: log-linear mixture models and image deformation-aware log-linear models. The resulting models are fully discriminative, can be trained efficiently, and the model complexity can be controlled. Log-linear mixture models offer additional flexibility within the log-linear modeling framework. Unlike previous approaches, the image deformation-aware model directly considers image deformations and allows for a discriminative training of the deformation parameters. Both are trained using alternating optimization. For certain variants, convergence to a stationary point is guaranteed and, in practice, even variants without this guarantee converge and find models that perform well. We tune the methods on the USPS data set and evaluate on the MNIST data set, demonstrating the generalization capabilities of our proposed models. Our models, although using significantly fewer parameters, are able to obtain competitive results with models proposed in the literature.

Variable- and Person-Centered Approaches to the Analysis of Early Adolescent Substance Use: Linking Peer, Family, and Intervention Effects with Developmental Trajectories

ERIC Educational Resources Information Center

Connell, Arin M.; Dishion, Thomas J.; Deater-Deckard, Kirby

2006-01-01

This 4-year study of 698 young adolescents examined the covariates of early onset substance use from Grade 6 through Grade 9. The youth were randomly assigned to a family-centered Adolescent Transitions Program (ATP) condition. Variable-centered (zero-inflated Poisson growth model) and person-centered (latent growth mixture model) approaches were…

Integrative Lifecourse and Genetic Analysis of Military Working Dogs

DTIC Science & Technology

2015-12-01

done as the samples are collected in order to avoid experimental variability and batch effects . Detailed description and discussion of this task...associated loss of power to detect all associations but those of large effect sizes) and latent variables (e.g., population structure – addressed in...processes associated with tissue development and maintenance are thus grouped with external environmental effects . This in turn suggests how those

When Can Categorical Variables Be Treated as Continuous? A Comparison of Robust Continuous and Categorical SEM Estimation Methods under Suboptimal Conditions

ERIC Educational Resources Information Center

Rhemtulla, Mijke; Brosseau-Liard, Patricia E.; Savalei, Victoria

2012-01-01

A simulation study compared the performance of robust normal theory maximum likelihood (ML) and robust categorical least squares (cat-LS) methodology for estimating confirmatory factor analysis models with ordinal variables. Data were generated from 2 models with 2-7 categories, 4 sample sizes, 2 latent distributions, and 5 patterns of category…

Within-Subject Interlaboratory Variability of QuantiFERON-TB Gold In-Tube Tests

DTIC Science & Technology

2012-09-06

QuantiFERONH-TB Gold In-Tube test (QFT-GIT) is a viable alternative to the tuberculin skin test (TST) for detecting Mycobacterium tuberculosis infection...viable alternative to the tuberculin skin test (TST) for detecting Mycobacterium tuberculosis infection. However, within-subject variability may limit test...release assays (IGRAs) are designed to detect both latent Mycobacterium tuberculosis infection (LTBI) and infections manifesting as active

Asymptomatic Alzheimer disease: Defining resilience.

PubMed

Hohman, Timothy J; McLaren, Donald G; Mormino, Elizabeth C; Gifford, Katherine A; Libon, David J; Jefferson, Angela L

2016-12-06

To define robust resilience metrics by leveraging CSF biomarkers of Alzheimer disease (AD) pathology within a latent variable framework and to demonstrate the ability of such metrics to predict slower rates of cognitive decline and protection against diagnostic conversion. Participants with normal cognition (n = 297) and mild cognitive impairment (n = 432) were drawn from the Alzheimer's Disease Neuroimaging Initiative. Resilience metrics were defined at baseline by examining the residuals when regressing brain aging outcomes (hippocampal volume and cognition) on CSF biomarkers. A positive residual reflected better outcomes than expected for a given level of pathology (high resilience). Residuals were integrated into a latent variable model of resilience and validated by testing their ability to independently predict diagnostic conversion, cognitive decline, and the rate of ventricular dilation. Latent variables of resilience predicted a decreased risk of conversion (hazard ratio < 0.54, p < 0.0001), slower cognitive decline (β > 0.02, p < 0.001), and slower rates of ventricular dilation (β < -4.7, p < 2 × 10 -15 ). These results were significant even when analyses were restricted to clinically normal individuals. Furthermore, resilience metrics interacted with biomarker status such that biomarker-positive individuals with low resilience showed the greatest risk of subsequent decline. Robust phenotypes of resilience calculated by leveraging AD biomarkers and baseline brain aging outcomes provide insight into which individuals are at greatest risk of short-term decline. Such comprehensive definitions of resilience are needed to further our understanding of the mechanisms that protect individuals from the clinical manifestation of AD dementia, especially among biomarker-positive individuals. © 2016 American Academy of Neurology.

Path-integral methods for analyzing the effects of fluctuations in stochastic hybrid neural networks.

PubMed

Bressloff, Paul C

2015-01-01

We consider applications of path-integral methods to the analysis of a stochastic hybrid model representing a network of synaptically coupled spiking neuronal populations. The state of each local population is described in terms of two stochastic variables, a continuous synaptic variable and a discrete activity variable. The synaptic variables evolve according to piecewise-deterministic dynamics describing, at the population level, synapses driven by spiking activity. The dynamical equations for the synaptic currents are only valid between jumps in spiking activity, and the latter are described by a jump Markov process whose transition rates depend on the synaptic variables. We assume a separation of time scales between fast spiking dynamics with time constant [Formula: see text] and slower synaptic dynamics with time constant τ. This naturally introduces a small positive parameter [Formula: see text], which can be used to develop various asymptotic expansions of the corresponding path-integral representation of the stochastic dynamics. First, we derive a variational principle for maximum-likelihood paths of escape from a metastable state (large deviations in the small noise limit [Formula: see text]). We then show how the path integral provides an efficient method for obtaining a diffusion approximation of the hybrid system for small ϵ. The resulting Langevin equation can be used to analyze the effects of fluctuations within the basin of attraction of a metastable state, that is, ignoring the effects of large deviations. We illustrate this by using the Langevin approximation to analyze the effects of intrinsic noise on pattern formation in a spatially structured hybrid network. In particular, we show how noise enlarges the parameter regime over which patterns occur, in an analogous fashion to PDEs. Finally, we carry out a [Formula: see text]-loop expansion of the path integral, and use this to derive corrections to voltage-based mean-field equations, analogous to the modified activity-based equations generated from a neural master equation.