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

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…

Virtual Levels and Role Models: N-Level Structural Equations Model of Reciprocal Ratings Data.

PubMed

Mehta, Paras D

2018-01-01

A general latent variable modeling framework called n-Level Structural Equations Modeling (NL-SEM) for dependent data-structures is introduced. NL-SEM is applicable to a wide range of complex multilevel data-structures (e.g., cross-classified, switching membership, etc.). Reciprocal dyadic ratings obtained in round-robin design involve complex set of dependencies that cannot be modeled within Multilevel Modeling (MLM) or Structural Equations Modeling (SEM) frameworks. The Social Relations Model (SRM) for round robin data is used as an example to illustrate key aspects of the NL-SEM framework. NL-SEM introduces novel constructs such as 'virtual levels' that allows a natural specification of latent variable SRMs. An empirical application of an explanatory SRM for personality using xxM, a software package implementing NL-SEM is presented. Results show that person perceptions are an integral aspect of personality. Methodological implications of NL-SEM for the analyses of an emerging class of contextual- and relational-SEMs are discussed.

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.

Implementing Restricted Maximum Likelihood Estimation in Structural Equation Models

ERIC Educational Resources Information Center

Cheung, Mike W.-L.

2013-01-01

Structural equation modeling (SEM) is now a generic modeling framework for many multivariate techniques applied in the social and behavioral sciences. Many statistical models can be considered either as special cases of SEM or as part of the latent variable modeling framework. One popular extension is the use of SEM to conduct linear mixed-effects…

Modeling Nonlinear Change via Latent Change and Latent Acceleration Frameworks: Examining Velocity and Acceleration of Growth Trajectories

ERIC Educational Resources Information Center

Grimm, Kevin; Zhang, Zhiyong; Hamagami, Fumiaki; Mazzocco, Michele

2013-01-01

We propose the use of the latent change and latent acceleration frameworks for modeling nonlinear growth in structural equation models. Moving to these frameworks allows for the direct identification of "rates of change" and "acceleration" in latent growth curves--information available indirectly through traditional growth…

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.

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

Validation of the Episodic Disability Framework with adults living with HIV.

PubMed

O'Brien, Kelly K; Hanna, Steven; Gardner, Sandra; Bayoumi, Ahmed M; Rueda, Sergio; Hart, Trevor A; Cooper, Curtis; Solomon, Patricia; Rourke, Sean B; Davis, Aileen M

2014-01-01

To assess the validity of dimensions of disability in the Episodic Disability Framework, a conceptual framework derived from the perspective of adults living with HIV. We conducted confirmatory factor analyses with 913 adults living with HIV in an observational cohort study called the Ontario HIV Treatment Network Cohort Study (OCS). We tested hypotheses that dimensions of disability in the Episodic Disability Framework were represented by a group of measured variables in the observational database. A model comprised of four latent variables and 43 indicator variables with one cross-loading was superior to models with fewer latent variables and more indicator variables and supported the validity of disability dimensions: physical health symptoms (represented by 21 indicator variables), mental health symptoms (10 variables), difficulties with day-to-day activities (5 variables) and challenges to social inclusion (8 variables). Overall goodness of fit statistics were χ(2 )= 2621.50 (p < 0.001), Comparative Fit Index = 0.912, Tucker Lewis Index = 0.907 and root mean square error of approximation = 0.048. Dimensions of disability correlated with each other ranging from r = 0.44 (between physical symptoms and challenges to social inclusion) to r = 0.81 (between physical symptoms and difficulties with day-to-day activities). This study supports the validity of four disability dimensions in the Episodic Disability Framework. This framework provides a new way to conceptualize disability and can lay the foundation for developing a future HIV disability measure for clinical and health services research. The Episodic Disability Framework is the first known conceptual framework of disability developed from the perspective of adults living with HIV. Results from this confirmatory factor analysis support the validity of four dimensions of disability experienced by adults living with HIV in the Episodic Disability Framework including: physical symptoms and impairments, mental health symptoms and impairments, difficulties carrying out day-to-day activities, and challenges to social inclusion. The Episodic Disability Framework provides a new way to conceptualize disability experienced by adults living with HIV. Clinicians can use this Framework to better understand episodic disability experienced by adults living with HIV. Clinicians can frame their assessments of disability to include physical and mental health symptoms and impairments as well as consider a patient's ability to participate in society, and indicate areas to apply interventions or strategies to prevent or mitigate disability experienced by adults living with HIV.

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.

Methods for integrating moderation and mediation: a general analytical framework using moderated path analysis.

PubMed

Edwards, Jeffrey R; Lambert, Lisa Schurer

2007-03-01

Studies that combine moderation and mediation are prevalent in basic and applied psychology research. Typically, these studies are framed in terms of moderated mediation or mediated moderation, both of which involve similar analytical approaches. Unfortunately, these approaches have important shortcomings that conceal the nature of the moderated and the mediated effects under investigation. This article presents a general analytical framework for combining moderation and mediation that integrates moderated regression analysis and path analysis. This framework clarifies how moderator variables influence the paths that constitute the direct, indirect, and total effects of mediated models. The authors empirically illustrate this framework and give step-by-step instructions for estimation and interpretation. They summarize the advantages of their framework over current approaches, explain how it subsumes moderated mediation and mediated moderation, and describe how it can accommodate additional moderator and mediator variables, curvilinear relationships, and structural equation models with latent variables. (c) 2007 APA, 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.

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.

A Spline Regression Model for Latent Variables

ERIC Educational Resources Information Center

Harring, Jeffrey R.

2014-01-01

Spline (or piecewise) regression models have been used in the past to account for patterns in observed data that exhibit distinct phases. The changepoint or knot marking the shift from one phase to the other, in many applications, is an unknown parameter to be estimated. As an extension of this framework, this research considers modeling the…

Power Analysis for Complex Mediational Designs Using Monte Carlo Methods

ERIC Educational Resources Information Center

Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.

2010-01-01

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex…

Stability of Teacher Value-Added Rankings across Measurement Model and Scaling Conditions

ERIC Educational Resources Information Center

Hawley, Leslie R.; Bovaird, James A.; Wu, ChaoRong

2017-01-01

Value-added assessment methods have been criticized by researchers and policy makers for a number of reasons. One issue includes the sensitivity of model results across different outcome measures. This study examined the utility of incorporating multivariate latent variable approaches within a traditional value-added framework. We evaluated the…

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…

Growth Modeling with Nonignorable Dropout: Alternative Analyses of the STAR*D Antidepressant Trial

ERIC Educational Resources Information Center

Muthen, Bengt; Asparouhov, Tihomir; Hunter, Aimee M.; Leuchter, Andrew F.

2011-01-01

This article uses a general latent variable framework to study a series of models for nonignorable missingness due to dropout. Nonignorable missing data modeling acknowledges that missingness may depend not only on covariates and observed outcomes at previous time points as with the standard missing at random assumption, but also on latent…

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…

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.

A Latent Class Approach to Estimating Test-Score Reliability

ERIC Educational Resources Information Center

van der Ark, L. Andries; van der Palm, Daniel W.; Sijtsma, Klaas

2011-01-01

This study presents a general framework for single-administration reliability methods, such as Cronbach's alpha, Guttman's lambda-2, and method MS. This general framework was used to derive a new approach to estimating test-score reliability by means of the unrestricted latent class model. This new approach is the latent class reliability…

Military health system efficiency: a review of history and recommendations for the future.

PubMed

Coppola, Nicholas; Satterwhite, Robin; Fulton, Lawrence V; Shanderson, Laurie L; Pasupathy, Rubini

2012-06-01

This article reviews the history of measuring military medical health care efficiency. No single approved definition or uniform framework has ever been offered or suggested defining military medical treatment facility efficiency over the last 225 years within the Department of Defense. The purpose of this article is to consolidate much of the existing research on the latent variable of military medical efficiency over the last two centuries, and to provide health care leaders a framework for understanding past and current practices in measuring efficiency in the military health care setting.

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.

POWER ANALYSIS FOR COMPLEX MEDIATIONAL DESIGNS USING MONTE CARLO METHODS

PubMed Central

Thoemmes, Felix; MacKinnon, David P.; Reiser, Mark R.

2013-01-01

Applied researchers often include mediation effects in applications of advanced methods such as latent variable models and linear growth curve models. Guidance on how to estimate statistical power to detect mediation for these models has not yet been addressed in the literature. We describe a general framework for power analyses for complex mediational models. The approach is based on the well known technique of generating a large number of samples in a Monte Carlo study, and estimating power as the percentage of cases in which an estimate of interest is significantly different from zero. Examples of power calculation for commonly used mediational models are provided. Power analyses for the single mediator, multiple mediators, three-path mediation, mediation with latent variables, moderated mediation, and mediation in longitudinal designs are described. Annotated sample syntax for Mplus is appended and tabled values of required sample sizes are shown for some models. PMID:23935262

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.

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.

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…

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

A Comparison of Methods for Estimating Relationships in the Change between Two Time Points for Latent Variables

ERIC Educational Resources Information Center

Finch, W. Holmes; Shim, Sungok Serena

2018-01-01

Collection and analysis of longitudinal data is an important tool in understanding growth and development over time in a whole range of human endeavors. Ideally, researchers working in the longitudinal framework are able to collect data at more than two points in time, as this will provide them with the potential for a deeper understanding of the…

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).

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 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,…

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.

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

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.

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…

Supervised Gamma Process Poisson Factorization

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

Anderson, Dylan Zachary

This thesis develops the supervised gamma process Poisson factorization (S- GPPF) framework, a novel supervised topic model for joint modeling of count matrices and document labels. S-GPPF is fully generative and nonparametric: document labels and count matrices are modeled under a uni ed probabilistic framework and the number of latent topics is controlled automatically via a gamma process prior. The framework provides for multi-class classification of documents using a generative max-margin classifier. Several recent data augmentation techniques are leveraged to provide for exact inference using a Gibbs sampling scheme. The first portion of this thesis reviews supervised topic modeling andmore » several key mathematical devices used in the formulation of S-GPPF. The thesis then introduces the S-GPPF generative model and derives the conditional posterior distributions of the latent variables for posterior inference via Gibbs sampling. The S-GPPF is shown to exhibit state-of-the-art performance for joint topic modeling and document classification on a dataset of conference abstracts, beating out competing supervised topic models. The unique properties of S-GPPF along with its competitive performance make it a novel contribution to supervised topic modeling.« less

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.

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.

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.

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…

Uncovering state-dependent relationships in shallow lakes using Bayesian latent variable regression.

PubMed

Vitense, Kelsey; Hanson, Mark A; Herwig, Brian R; Zimmer, Kyle D; Fieberg, John

2018-03-01

Ecosystems sometimes undergo dramatic shifts between contrasting regimes. Shallow lakes, for instance, can transition between two alternative stable states: a clear state dominated by submerged aquatic vegetation and a turbid state dominated by phytoplankton. Theoretical models suggest that critical nutrient thresholds differentiate three lake types: highly resilient clear lakes, lakes that may switch between clear and turbid states following perturbations, and highly resilient turbid lakes. For effective and efficient management of shallow lakes and other systems, managers need tools to identify critical thresholds and state-dependent relationships between driving variables and key system features. Using shallow lakes as a model system for which alternative stable states have been demonstrated, we developed an integrated framework using Bayesian latent variable regression (BLR) to classify lake states, identify critical total phosphorus (TP) thresholds, and estimate steady state relationships between TP and chlorophyll a (chl a) using cross-sectional data. We evaluated the method using data simulated from a stochastic differential equation model and compared its performance to k-means clustering with regression (KMR). We also applied the framework to data comprising 130 shallow lakes. For simulated data sets, BLR had high state classification rates (median/mean accuracy >97%) and accurately estimated TP thresholds and state-dependent TP-chl a relationships. Classification and estimation improved with increasing sample size and decreasing noise levels. Compared to KMR, BLR had higher classification rates and better approximated the TP-chl a steady state relationships and TP thresholds. We fit the BLR model to three different years of empirical shallow lake data, and managers can use the estimated bifurcation diagrams to prioritize lakes for management according to their proximity to thresholds and chance of successful rehabilitation. Our model improves upon previous methods for shallow lakes because it allows classification and regression to occur simultaneously and inform one another, directly estimates TP thresholds and the uncertainty associated with thresholds and state classifications, and enables meaningful constraints to be built into models. The BLR framework is broadly applicable to other ecosystems known to exhibit alternative stable states in which regression can be used to establish relationships between driving variables and state variables. © 2017 by the Ecological Society of America.

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…

Groundwater-fed irrigation impacts spatially distributed temporal scaling behavior of the natural system: a spatio-temporal framework for understanding water management impacts

NASA Astrophysics Data System (ADS)

Condon, Laura E.; Maxwell, Reed M.

2014-03-01

Regional scale water management analysis increasingly relies on integrated modeling tools. Much recent work has focused on groundwater-surface water interactions and feedbacks. However, to our knowledge, no study has explicitly considered impacts of management operations on the temporal dynamics of the natural system. Here, we simulate twenty years of hourly moisture dependent, groundwater-fed irrigation using a three-dimensional, fully integrated, hydrologic model (ParFlow-CLM). Results highlight interconnections between irrigation demand, groundwater oscillation frequency and latent heat flux variability not previously demonstrated. Additionally, the three-dimensional model used allows for novel consideration of spatial patterns in temporal dynamics. Latent heat flux and water table depth both display spatial organization in temporal scaling, an important finding given the spatial homogeneity and weak scaling observed in atmospheric forcings. Pumping and irrigation amplify high frequency (sub-annual) variability while attenuating low frequency (inter-annual) variability. Irrigation also intensifies scaling within irrigated areas, essentially increasing temporal memory in both the surface and the subsurface. These findings demonstrate management impacts that extend beyond traditional water balance considerations to the fundamental behavior of the system itself. This is an important step to better understanding groundwater’s role as a buffer for natural variability and the impact that water management has on this capacity.

Anxiety, Depression and Hopelessness in Adolescents: A Structural Equation Model

PubMed Central

Cunningham, Shaylyn; Gunn, Thelma; Alladin, Assen; Cawthorpe, David

2008-01-01

Objective This study tested a structural model, examining the relationship between a latent variable termed demoralization and measured variables (anxiety, depression and hopelessness) in a community sample of Canadian youth. Methods The combined sample consisted of data collected from four independent studies from 2001 to 2005. Nine hundred and seventy one (n=971) participants were high school students (grades 10–12) from three geographic locations: Calgary, Saskatchewan and Lethbridge. Participants completed the Beck Anxiety Inventory (BAI), Beck Depression Inventory-Revised (BDI-II), Beck Hopelessness Scale (BHS), and demographic survey. Structural equation modeling was used for statistical analysis. Results The analysis revealed that the final model, including depression, anxiety and hopelessness and one latent variable demoralization, fit the data (chi-square value, X2 (2) = 7.25, p< .001, goodness of fit indices (CFI=0.99, NFI=0.98) and standardized error (0.05). Overall, the findings suggest that close relationships exist among depression, anxiety, hopelessness and demoralization that is stable across demographic variables. Further, the model explains the relationship between sub-clinical anxiety, depression and hopelessness. Conclusion These findings contribute to a theoretical framework, which has implications for educational and clinical intervention. The present findings will help guide further preventative research on examining demoralization as a precursor to sub-clinical anxiety and depression. PMID:18769644

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…

Polytomous Latent Scales for the Investigation of the Ordering of Items

ERIC Educational Resources Information Center

Ligtvoet, Rudy; van der Ark, L. Andries; Bergsma, Wicher P.; Sijtsma, Klaas

2011-01-01

We propose three latent scales within the framework of nonparametric item response theory for polytomously scored items. Latent scales are models that imply an invariant item ordering, meaning that the order of the items is the same for each measurement value on the latent scale. This ordering property may be important in, for example,…

Mediation Analysis in a Latent Growth Curve Modeling Framework

ERIC Educational Resources Information Center

von Soest, Tilmann; Hagtvet, Knut A.

2011-01-01

This article presents several longitudinal mediation models in the framework of latent growth curve modeling and provides a detailed account of how such models can be constructed. Logical and statistical challenges that might arise when such analyses are conducted are also discussed. Specifically, we discuss how the initial status (intercept) and…

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

Maternal anaemia at delivery and haemoglobin evolution in children during their first 18 months of life using latent class analysis.

PubMed

Koura, Kobto G; Ouédraogo, Smaïla; Cottrell, Gilles; Le Port, Agnès; Massougbodji, Achille; Garcia, André

2012-01-01

Anaemia during pregnancy and at delivery is an important public health problem in low- and middle-income countries. Its association with the children's haemoglobin level over time remains unclear. Our goals were to identify distinct haemoglobin level trajectories using latent class analysis and to assess the association between these trajectories and maternal anaemia and other risk factors. A prospective study of children from birth to 18 months of life was conducted in a rural setting in Tori-Bossito, Benin. The main outcome measure was the haemoglobin levels repeatedly measured at 3, 6, 9, 12, 15 and 18 months. Variables were collected from the mothers at delivery and from their children at birth and during the follow-up. The analyses were performed by means of Latent Class Analysis which has never been used for this kind of data. All the analyses were performed with Stata software, version 11.0, using the generalized linear latent and mixed model (GLLAMM) framework. We showed that 33.7% of children followed a low haemoglobin trajectory and 66.3% a high trajectory during the first 18 months of life. Newborn anaemia, placental malaria, malaria attack, sickle cell trait and male gender were significantly associated with a lower children's haemoglobin level over time, whereas maternal age, children living in a polygamous family and with good feeding practices had a higher Hb level in the first18 months. We also showed that maternal anaemia was a predictor for 'low haemoglobin level trajectory' group membership but have no significant effect on children haemoglobin level over time. Latent Class Analyses framework seems well suited to analyse longitudinal data under the hypothesis that different subpopulations of subjects are present in the data, each with its own set of parameters, with distinctive evolutions that themselves may reflect distinctive aetiologies.

Maternal Anaemia at Delivery and Haemoglobin Evolution in Children during Their First 18 Months of Life Using Latent Class Analysis

PubMed Central

Koura, Kobto G.; Ouédraogo, Smaïla; Cottrell, Gilles; Le Port, Agnès; Massougbodji, Achille; Garcia, André

2012-01-01

Background Anaemia during pregnancy and at delivery is an important public health problem in low- and middle-income countries. Its association with the children’s haemoglobin level over time remains unclear. Our goals were to identify distinct haemoglobin level trajectories using latent class analysis and to assess the association between these trajectories and maternal anaemia and other risk factors. Method A prospective study of children from birth to 18 months of life was conducted in a rural setting in Tori-Bossito, Benin. The main outcome measure was the haemoglobin levels repeatedly measured at 3, 6, 9, 12, 15 and 18 months. Variables were collected from the mothers at delivery and from their children at birth and during the follow-up. The analyses were performed by means of Latent Class Analysis which has never been used for this kind of data. All the analyses were performed with Stata software, version 11.0, using the generalized linear latent and mixed model (GLLAMM) framework. Results We showed that 33.7% of children followed a low haemoglobin trajectory and 66.3% a high trajectory during the first 18 months of life. Newborn anaemia, placental malaria, malaria attack, sickle cell trait and male gender were significantly associated with a lower children’s haemoglobin level over time, whereas maternal age, children living in a polygamous family and with good feeding practices had a higher Hb level in the first18 months. We also showed that maternal anaemia was a predictor for ‘low haemoglobin level trajectory’ group membership but have no significant effect on children haemoglobin level over time. Conclusion Latent Class Analyses framework seems well suited to analyse longitudinal data under the hypothesis that different subpopulations of subjects are present in the data, each with its own set of parameters, with distinctive evolutions that themselves may reflect distinctive aetiologies. PMID:23185556

A Comparison of Approaches for the Analysis of Interaction Effects between Latent Variables Using Partial Least Squares Path Modeling

ERIC Educational Resources Information Center

Henseler, Jorg; Chin, Wynne W.

2010-01-01

In social and business sciences, the importance of the analysis of interaction effects between manifest as well as latent variables steadily increases. Researchers using partial least squares (PLS) to analyze interaction effects between latent variables need an overview of the available approaches as well as their suitability. This article…

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…

A general class of multinomial mixture models for anuran calling survey data

USGS Publications Warehouse

Royle, J. Andrew; Link, W.A.

2005-01-01

We propose a general framework for modeling anuran abundance using data collected from commonly used calling surveys. The data generated from calling surveys are indices of calling intensity (vocalization of males) that do not have a precise link to actual population size and are sensitive to factors that influence anuran behavior. We formulate a model for calling-index data in terms of the maximum potential calling index that could be observed at a site (the 'latent abundance class'), given its underlying breeding population, and we focus attention on estimating the distribution of this latent abundance class. A critical consideration in estimating the latent structure is imperfect detection, which causes the observed abundance index to be less than or equal to the latent abundance class. We specify a multinomial sampling model for the observed abundance index that is conditional on the latent abundance class. Estimation of the latent abundance class distribution is based on the marginal likelihood of the index data, having integrated over the latent class distribution. We apply the proposed modeling framework to data collected as part of the North American Amphibian Monitoring Program (NAAMP).

Exploring Latent Class Based on Growth Rates in Number Sense Ability

ERIC Educational Resources Information Center

Kim, Dongil; Shin, Jaehyun; Lee, Kijyung

2013-01-01

The purpose of this study was to explore latent class based on growth rates in number sense ability by using latent growth class modeling (LGCM). LGCM is one of the noteworthy methods for identifying growth patterns of the progress monitoring within the response to intervention framework in that it enables us to analyze latent sub-groups based not…

Expanding Stress Generation Theory: Test of a Transdiagnostic Model

PubMed Central

Conway, Christopher C.; Hammen, Constance; Brennan, Patricia A.

2016-01-01

Originally formulated to understand the recurrence of depressive disorders, the stress generation hypothesis has recently been applied in research on anxiety and externalizing disorders. Results from these investigations, in combination with findings of extensive comorbidity between depression and other mental disorders, suggest the need for an expansion of stress generation models to include the stress generating effects of transdiagnostic pathology as well as those of specific syndromes. Employing latent variable modeling techniques to parse the general and specific elements of commonly co-occurring Axis I syndromes, the current study examined the associations of transdiagnostic internalizing and externalizing dimensions with stressful life events over time. Analyses revealed that, after adjusting for the covariation between the dimensions, internalizing was a significant predictor of interpersonal dependent stress, whereas externalizing was a significant predictor of noninterpersonal dependent stress. Neither latent dimension was associated with the occurrence of independent, or fateful, stressful life events. At the syndrome level, once variance due to the internalizing factor was partialled out, unipolar depression contributed incrementally to the generation of interpersonal dependent stress. In contrast, the presence of panic disorder produced a “stress inhibition” effect, predicting reduced exposure to interpersonal dependent stress. Additionally, dysthymia was associated with an excess of noninterpersonal dependent stress. The latent variable modeling framework used here is discussed in terms of its potential as an integrative model for stress generation research. PMID:22428789

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.

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.

Synergistic Effects of Expectancy and Value on Homework Engagement: The Case for a Within-Person Perspective.

PubMed

Nagengast, Benjamin; Trautwein, Ulrich; Kelava, Augustin; Lüdtke, Oliver

2013-05-01

Historically, expectancy-value models of motivation assumed a synergistic relation between expectancy and value: motivation is high only when both expectancy and value are high. Motivational processes were studied from a within-person perspective, with expectancies and values being assessed or experimentally manipulated across multiple domains and the focus being placed on intraindividual differences. In contrast, contemporary expectancy-value models in educational psychology concentrate almost exclusively on linear effects of expectancy and value on motivational outcomes, with a focus on between-person differences. Recent advances in latent variable methodology allow both issues to be addressed in observational studies. Using the expectancy-value model of homework motivation as a theoretical framework, this study estimated multilevel structural equation models with latent interactions in a sample of 511 secondary school students and found synergistic effects between domain-specific homework expectancy and homework value in predicting homework engagement in 6 subjects. This approach not only brings the "×" back into expectancy-value theory but also reestablishes the within-person perspective as the appropriate level of analysis for latent expectancy-value models.

Distributed neural system for emotional intelligence revealed by lesion mapping.

PubMed

Barbey, Aron K; Colom, Roberto; Grafman, Jordan

2014-03-01

Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease.

Distributed neural system for emotional intelligence revealed by lesion mapping

PubMed Central

Colom, Roberto; Grafman, Jordan

2014-01-01

Cognitive neuroscience has made considerable progress in understanding the neural architecture of human intelligence, identifying a broadly distributed network of frontal and parietal regions that support goal-directed, intelligent behavior. However, the contributions of this network to social and emotional aspects of intellectual function remain to be well characterized. Here we investigated the neural basis of emotional intelligence in 152 patients with focal brain injuries using voxel-based lesion-symptom mapping. Latent variable modeling was applied to obtain measures of emotional intelligence, general intelligence and personality from the Mayer, Salovey, Caruso Emotional Intelligence Test (MSCEIT), the Wechsler Adult Intelligence Scale and the Neuroticism-Extroversion-Openness Inventory, respectively. Regression analyses revealed that latent scores for measures of general intelligence and personality reliably predicted latent scores for emotional intelligence. Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal and parietal brain regions. The results support an integrative framework for understanding the architecture of executive, social and emotional processes and make specific recommendations for the interpretation and application of the MSCEIT to the study of emotional intelligence in health and disease. PMID:23171618

Generalized Full-Information Item Bifactor Analysis

PubMed Central

Cai, Li; Yang, Ji Seung; Hansen, Mark

2011-01-01

Full-information item bifactor analysis is an important statistical method in psychological and educational measurement. Current methods are limited to single group analysis and inflexible in the types of item response models supported. We propose a flexible multiple-group item bifactor analysis framework that supports a variety of multidimensional item response theory models for an arbitrary mixing of dichotomous, ordinal, and nominal items. The extended item bifactor model also enables the estimation of latent variable means and variances when data from more than one group are present. Generalized user-defined parameter restrictions are permitted within or across groups. We derive an efficient full-information maximum marginal likelihood estimator. Our estimation method achieves substantial computational savings by extending Gibbons and Hedeker’s (1992) bifactor dimension reduction method so that the optimization of the marginal log-likelihood only requires two-dimensional integration regardless of the dimensionality of the latent variables. We use simulation studies to demonstrate the flexibility and accuracy of the proposed methods. We apply the model to study cross-country differences, including differential item functioning, using data from a large international education survey on mathematics literacy. PMID:21534682

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…

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…

Structural equation modeling of the inflammatory response to traffic air pollution

PubMed Central

Baja, Emmanuel S.; Schwartz, Joel D.; Coull, Brent A.; Wellenius, Gregory A.; Vokonas, Pantel S.; Suh, Helen H.

2015-01-01

Several epidemiological studies have reported conflicting results on the effect of traffic-related pollutants on markers of inflammation. In a Bayesian framework, we examined the effect of traffic pollution on inflammation using structural equation models (SEMs). We studied measurements of C-reactive protein (CRP), soluble vascular cell adhesion molecule-1 (sVCAM-1), and soluble intracellular adhesion molecule-1 (sICAM-1) for 749 elderly men from the Normative Aging Study. Using repeated measures SEMs, we fit a latent variable for traffic pollution that is reflected by levels of black carbon, carbon monoxide, nitrogen monoxide and nitrogen dioxide to estimate its effect on a latent variable for inflammation that included sICAM-1, sVCAM-1 and CRP. Exposure periods were assessed using 1-, 2-, 3-, 7-, 14- and 30-day moving averages previsit. We compared our findings using SEMs with those obtained using linear mixed models. Traffic pollution was related to increased inflammation for 3-, 7-, 14- and 30-day exposure periods. An inter-quartile range increase in traffic pollution was associated with a 2.3% (95% posterior interval (PI): 0.0–4.7%) increase in inflammation for the 3-day moving average, with the most significant association observed for the 30-day moving average (23.9%; 95% PI: 13.9–36.7%). Traffic pollution adversely impacts inflammation in the elderly. SEMs in a Bayesian framework can comprehensively incorporate multiple pollutants and health outcomes simultaneously in air pollution–cardiovascular epidemiological studies. PMID:23232970

Structural Equation Modeling: A Framework for Ocular and Other Medical Sciences Research

PubMed Central

Christ, Sharon L.; Lee, David J.; Lam, Byron L.; Diane, Zheng D.

2017-01-01

Structural equation modeling (SEM) is a modeling framework that encompasses many types of statistical models and can accommodate a variety of estimation and testing methods. SEM has been used primarily in social sciences but is increasingly used in epidemiology, public health, and the medical sciences. SEM provides many advantages for the analysis of survey and clinical data, including the ability to model latent constructs that may not be directly observable. Another major feature is simultaneous estimation of parameters in systems of equations that may include mediated relationships, correlated dependent variables, and in some instances feedback relationships. SEM allows for the specification of theoretically holistic models because multiple and varied relationships may be estimated together in the same model. SEM has recently expanded by adding generalized linear modeling capabilities that include the simultaneous estimation of parameters of different functional form for outcomes with different distributions in the same model. Therefore, mortality modeling and other relevant health outcomes may be evaluated. Random effects estimation using latent variables has been advanced in the SEM literature and software. In addition, SEM software has increased estimation options. Therefore, modern SEM is quite general and includes model types frequently used by health researchers, including generalized linear modeling, mixed effects linear modeling, and population average modeling. This article does not present any new information. It is meant as an introduction to SEM and its uses in ocular and other health research. PMID:24467557

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.

A Discrete Latent State Approach to Diagnostic Testing. Final Report on Contract Number N00014-81-K-0564.

ERIC Educational Resources Information Center

Paulson, James A.

This paper reports on a project which has developed the general latent class model as a framework for representation of item responses. This framework can be used to represent data in applications such as mastery tests and other kinds of achievement tests, where there is reason to believe that current foundations are deficient. Methods of…

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…

The Houdini Transformation: True, but Illusory.

PubMed

Bentler, Peter M; Molenaar, Peter C M

2012-01-01

Molenaar (2003, 2011) showed that a common factor model could be transformed into an equivalent model without factors, involving only observed variables and residual errors. He called this invertible transformation the Houdini transformation. His derivation involved concepts from time series and state space theory. This paper verifies the Houdini transformation on a general latent variable model using algebraic methods. The results show that the Houdini transformation is illusory, in the sense that the Houdini transformed model remains a latent variable model. Contrary to common knowledge, a model that is a path model with only observed variables and residual errors may, in fact, be a latent variable model.

The Houdini Transformation: True, but Illusory

PubMed Central

Bentler, Peter M.; Molenaar, Peter C. M.

2012-01-01

Molenaar (2003, 2011) showed that a common factor model could be transformed into an equivalent model without factors, involving only observed variables and residual errors. He called this invertible transformation the Houdini transformation. His derivation involved concepts from time series and state space theory. This paper verifies the Houdini transformation on a general latent variable model using algebraic methods. The results show that the Houdini transformation is illusory, in the sense that the Houdini transformed model remains a latent variable model. Contrary to common knowledge, a model that is a path model with only observed variables and residual errors may, in fact, be a latent variable model. PMID:23180888

Exploring New Pathways in Precipitation Assimilation

NASA Technical Reports Server (NTRS)

Hou, Arthur; Zhang, Sara Q.

2004-01-01

Precipitation assimilation poses a special challenge in that the forward model for rain in a global forecast system is based on parameterized physics, which can have large systematic errors that must be rectified to use precipitation data effectively within a standard statistical analysis framework. We examine some key issues in precipitation assimilation and describe several exploratory studies in assimilating rainfall and latent heating information in NASA's global data assimilation systems using the forecast model as a weak constraint. We present results from two research activities. The first is the assimilation of surface rainfall data using a time-continuous variational assimilation based on a column model of the full moist physics. The second is the assimilation of convective and stratiform latent heating retrievals from microwave sensors using a variational technique with physical parameters in the moist physics schemes as a control variable. We will show the impact of assimilating these data on analyses and forecasts. Among the lessons learned are (1) that the time-continuous application of moisture/temperature tendency corrections to mitigate model deficiencies offers an effective strategy for assimilating precipitation information, and (2) that the model prognostic variables must be allowed to directly respond to an improved rain and latent heating field within an analysis cycle to reap the full benefit of assimilating precipitation information. of microwave radiances versus retrieval information in raining areas, and initial efforts in developing ensemble techniques such as Kalman filter/smoother for precipitation assimilation. Looking to the future, we discuss new research directions including the assimilation

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…

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.…

The Role of Jahoda's Latent and Financial Benefits for Work Involvement: A Longitudinal Study

ERIC Educational Resources Information Center

Stiglbauer, Barbara; Batinic, Bernad

2012-01-01

This study examined the role of the latent and financial benefits of work as defined by Marie Jahoda (1982) in explaining a person's work involvement. Drawing upon theoretical frameworks on work commitment and work motivation, the latent benefits were expected to have a positive, whereas the financial benefits were expected to have a negative…

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

Accounting for standard errors of vision-specific latent trait in regression models.

PubMed

Wong, Wan Ling; Li, Xiang; Li, Jialiang; Wong, Tien Yin; Cheng, Ching-Yu; Lamoureux, Ecosse L

2014-07-11

To demonstrate the effectiveness of Hierarchical Bayesian (HB) approach in a modeling framework for association effects that accounts for SEs of vision-specific latent traits assessed using Rasch analysis. A systematic literature review was conducted in four major ophthalmic journals to evaluate Rasch analysis performed on vision-specific instruments. The HB approach was used to synthesize the Rasch model and multiple linear regression model for the assessment of the association effects related to vision-specific latent traits. The effectiveness of this novel HB one-stage "joint-analysis" approach allows all model parameters to be estimated simultaneously and was compared with the frequently used two-stage "separate-analysis" approach in our simulation study (Rasch analysis followed by traditional statistical analyses without adjustment for SE of latent trait). Sixty-six reviewed articles performed evaluation and validation of vision-specific instruments using Rasch analysis, and 86.4% (n = 57) performed further statistical analyses on the Rasch-scaled data using traditional statistical methods; none took into consideration SEs of the estimated Rasch-scaled scores. The two models on real data differed for effect size estimations and the identification of "independent risk factors." Simulation results showed that our proposed HB one-stage "joint-analysis" approach produces greater accuracy (average of 5-fold decrease in bias) with comparable power and precision in estimation of associations when compared with the frequently used two-stage "separate-analysis" procedure despite accounting for greater uncertainty due to the latent trait. Patient-reported data, using Rasch analysis techniques, do not take into account the SE of latent trait in association analyses. The HB one-stage "joint-analysis" is a better approach, producing accurate effect size estimations and information about the independent association of exposure variables with vision-specific latent traits. Copyright 2014 The Association for Research in Vision and Ophthalmology, Inc.

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.

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…

Longitudinal Model Building Using Latent Transition Analysis: An Example Using School Bullying Data.

PubMed

Ryoo, Ji Hoon; Wang, Cixin; Swearer, Susan M; Hull, Michael; Shi, Dingjing

2018-01-01

Applications of latent transition analysis (LTA) have emerged since the early 1990s, with numerous scientific findings being published in many areas, including social and behavioral sciences, education, and public health. Although LTA is effective as a statistical analytic tool for a person-centered model using longitudinal data, model building in LTA has often been subjective and confusing for applied researchers. To fill this gap in the literature, we review the components of LTA, recommend a framework of fitting LTA, and summarize what acceptable model evaluation tools should be used in practice. The proposed framework of fitting LTA consists of six steps depicted in Figure 1 from step 0 (exploring data) to step 5 (fitting distal variables). We also illustrate the framework of fitting LTA with data on concerns about school bullying from a sample of 1,180 students ranging from 5th to 9th grade (mean age = 12.2 years, SD = 1.29 years at Time 1) over three semesters. We identified four groups of students with distinct patterns of bullying concerns, and found that their concerns about bullying decreased and narrowed to specific concerns about rumors, gossip, and social exclusion over time. The data and command (syntax) files needed for reproducing the results using SAS PROC LCA and PROC LTA (Version 1.3.2) (2015) and Mplus 7.4 (Muthén and Muthén, 1998-2015) are provided as online supplementary materials.

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…

A Modularized Efficient Framework for Non-Markov Time Series Estimation

NASA Astrophysics Data System (ADS)

Schamberg, Gabriel; Ba, Demba; Coleman, Todd P.

2018-06-01

We present a compartmentalized approach to finding the maximum a-posteriori (MAP) estimate of a latent time series that obeys a dynamic stochastic model and is observed through noisy measurements. We specifically consider modern signal processing problems with non-Markov signal dynamics (e.g. group sparsity) and/or non-Gaussian measurement models (e.g. point process observation models used in neuroscience). Through the use of auxiliary variables in the MAP estimation problem, we show that a consensus formulation of the alternating direction method of multipliers (ADMM) enables iteratively computing separate estimates based on the likelihood and prior and subsequently "averaging" them in an appropriate sense using a Kalman smoother. As such, this can be applied to a broad class of problem settings and only requires modular adjustments when interchanging various aspects of the statistical model. Under broad log-concavity assumptions, we show that the separate estimation problems are convex optimization problems and that the iterative algorithm converges to the MAP estimate. As such, this framework can capture non-Markov latent time series models and non-Gaussian measurement models. We provide example applications involving (i) group-sparsity priors, within the context of electrophysiologic specrotemporal estimation, and (ii) non-Gaussian measurement models, within the context of dynamic analyses of learning with neural spiking and behavioral observations.

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

Using structural equation modeling for network meta-analysis.

PubMed

Tu, Yu-Kang; Wu, Yun-Chun

2017-07-14

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

Representing Heterogeneity in Structural Relationships Among Multiple Choice Variables Using a Latent Segmentation Approach

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

Garikapati, Venu; Astroza, Sebastian; Pendyala, Ram M.

Travel model systems often adopt a single decision structure that links several activity-travel choices together. The single decision structure is then used to predict activity-travel choices, with those downstream in the decision-making chain influenced by those upstream in the sequence. The adoption of a singular sequential causal structure to depict relationships among activity-travel choices in travel demand model systems ignores the possibility that some choices are made jointly as a bundle as well as the possible presence of structural heterogeneity in the population with respect to decision-making processes. As different segments in the population may adopt and follow different causalmore » decision-making mechanisms when making selected choices jointly, it would be of value to develop simultaneous equations model systems relating multiple endogenous choice variables that are able to identify population subgroups following alternative causal decision structures. Because the segments are not known a priori, they are considered latent and determined endogenously within a joint modeling framework proposed in this paper. The methodology is applied to a national mobility survey data set to identify population segments that follow different causal structures relating residential location choice, vehicle ownership, and car-share and mobility service usage. It is found that the model revealing three distinct latent segments best describes the data, confirming the efficacy of the modeling approach and the existence of structural heterogeneity in decision-making in the population. Future versions of activity-travel model systems should strive to incorporate such structural heterogeneity to better reflect varying decision processes across population subgroups.« less

A unifying framework for marginalized random intercept models of correlated binary outcomes

PubMed Central

Swihart, Bruce J.; Caffo, Brian S.; Crainiceanu, Ciprian M.

2013-01-01

We demonstrate that many current approaches for marginal modeling of correlated binary outcomes produce likelihoods that are equivalent to the copula-based models herein. These general copula models of underlying latent threshold random variables yield likelihood-based models for marginal fixed effects estimation and interpretation in the analysis of correlated binary data with exchangeable correlation structures. Moreover, we propose a nomenclature and set of model relationships that substantially elucidates the complex area of marginalized random intercept models for binary data. A diverse collection of didactic mathematical and numerical examples are given to illustrate concepts. PMID:25342871

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.

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

Latent constructs model explaining the attachment-linked variation in autobiographical remembering.

PubMed

Öner, Sezin; Gülgöz, Sami

2016-01-01

In the current study, we proposed a latent constructs model to characterise the qualitative aspects of autobiographical remembering and investigated the structural relations in the model that may vary across individuals. Primarily, we focused on the memories of romantic relationships and argued that attachment anxiety and avoidance would be reflected in the ways that individuals encode, rehearse, or remember autobiographical memories in close relationships. Participants reported two positive and two negative relationship-specific memories and rated the characteristics for each memory. As predicted, the basic memory model yielded appropriate fit, indicating that event characteristics (EC) predicted the frequency of rehearsal (RC) and phenomenology at retrieval (PC). When attachment variables were integrated, the model showed that rehearsal mediated the link between anxiety and PC, especially for negative memories. On the other hand, for avoidance EC was the key factor mediating the link between avoidance and RC, as well as PC. Findings were discussed with respect to autobiographical memory functions emphasising a systematically, integrated framework.

Discriminative parameter estimation for random walks segmentation.

PubMed

Baudin, Pierre-Yves; Goodman, Danny; Kumrnar, Puneet; Azzabou, Noura; Carlier, Pierre G; Paragios, Nikos; Kumar, M Pawan

2013-01-01

The Random Walks (RW) algorithm is one of the most efficient and easy-to-use probabilistic segmentation methods. By combining contrast terms with prior terms, it provides accurate segmentations of medical images in a fully automated manner. However, one of the main drawbacks of using the RW algorithm is that its parameters have to be hand-tuned. we propose a novel discriminative learning framework that estimates the parameters using a training dataset. The main challenge we face is that the training samples are not fully supervised. Specifically, they provide a hard segmentation of the images, instead of a probabilistic segmentation. We overcome this challenge by treating the optimal probabilistic segmentation that is compatible with the given hard segmentation as a latent variable. This allows us to employ the latent support vector machine formulation for parameter estimation. We show that our approach significantly outperforms the baseline methods on a challenging dataset consisting of real clinical 3D MRI volumes of skeletal muscles.

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…

Hyper-Spectral Image Analysis With Partially Latent Regression and Spatial Markov Dependencies

NASA Astrophysics Data System (ADS)

Deleforge, Antoine; Forbes, Florence; Ba, Sileye; Horaud, Radu

2015-09-01

Hyper-spectral data can be analyzed to recover physical properties at large planetary scales. This involves resolving inverse problems which can be addressed within machine learning, with the advantage that, once a relationship between physical parameters and spectra has been established in a data-driven fashion, the learned relationship can be used to estimate physical parameters for new hyper-spectral observations. Within this framework, we propose a spatially-constrained and partially-latent regression method which maps high-dimensional inputs (hyper-spectral images) onto low-dimensional responses (physical parameters such as the local chemical composition of the soil). The proposed regression model comprises two key features. Firstly, it combines a Gaussian mixture of locally-linear mappings (GLLiM) with a partially-latent response model. While the former makes high-dimensional regression tractable, the latter enables to deal with physical parameters that cannot be observed or, more generally, with data contaminated by experimental artifacts that cannot be explained with noise models. Secondly, spatial constraints are introduced in the model through a Markov random field (MRF) prior which provides a spatial structure to the Gaussian-mixture hidden variables. Experiments conducted on a database composed of remotely sensed observations collected from the Mars planet by the Mars Express orbiter demonstrate the effectiveness of the proposed model.

Psychosocial stress as a predictor of injury in elite junior soccer: a latent growth curve analysis.

PubMed

Ivarsson, Andreas; Johnson, Urban; Lindwall, Magnus; Gustafsson, Henrik; Altemyr, Mats

2014-07-01

To investigate by use of a latent growth curve analysis framework whether athletes' individual levels and changes in hassle and uplift levels over a 10-week period could predict injury outcome in an elite junior soccer population. A prospective design with repeated measurement points. Participants were 101 Swedish elite junior soccer players (67 males and 34 females). Ten sets of measures were taken on a weekly basis during which participants completed the Hassles and Uplifts Scale (HUS). Latent growth curve models were used to examine whether the level and change in psychological stress could predict the frequency of injury over the 10-week period. The results show that injury occurrence was significantly associated with both the initial level of daily hassle and the change in daily hassle. High initial daily hassle levels and a smaller decrease in daily hassles were associated with injury occurrence. Moreover, injury occurrence was significantly associated with a greater decrease in daily uplift. The findings highlight the importance of focusing on state variables using prospective designs and appropriate analysis of within-person change to detect complex and dynamic associations across time in injury-prediction research. Copyright © 2013 Sports Medicine Australia. Published by Elsevier Ltd. All rights reserved.

Uncovering a latent multinomial: Analysis of mark-recapture data with misidentification

USGS Publications Warehouse

Link, W.A.; Yoshizaki, J.; Bailey, L.L.; Pollock, K.H.

2010-01-01

Natural tags based on DNA fingerprints or natural features of animals are now becoming very widely used in wildlife population biology. However, classic capture-recapture models do not allow for misidentification of animals which is a potentially very serious problem with natural tags. Statistical analysis of misidentification processes is extremely difficult using traditional likelihood methods but is easily handled using Bayesian methods. We present a general framework for Bayesian analysis of categorical data arising from a latent multinomial distribution. Although our work is motivated by a specific model for misidentification in closed population capture-recapture analyses, with crucial assumptions which may not always be appropriate, the methods we develop extend naturally to a variety of other models with similar structure. Suppose that observed frequencies f are a known linear transformation f = A???x of a latent multinomial variable x with cell probability vector ?? = ??(??). Given that full conditional distributions [?? | x] can be sampled, implementation of Gibbs sampling requires only that we can sample from the full conditional distribution [x | f, ??], which is made possible by knowledge of the null space of A???. We illustrate the approach using two data sets with individual misidentification, one simulated, the other summarizing recapture data for salamanders based on natural marks. ?? 2009, The International Biometric Society.

Uncovering a Latent Multinomial: Analysis of Mark-Recapture Data with Misidentification

USGS Publications Warehouse

Link, W.A.; Yoshizaki, J.; Bailey, L.L.; Pollock, K.H.

2009-01-01

Natural tags based on DNA fingerprints or natural features of animals are now becoming very widely used in wildlife population biology. However, classic capture-recapture models do not allow for misidentification of animals which is a potentially very serious problem with natural tags. Statistical analysis of misidentification processes is extremely difficult using traditional likelihood methods but is easily handled using Bayesian methods. We present a general framework for Bayesian analysis of categorical data arising from a latent multinomial distribution. Although our work is motivated by a specific model for misidentification in closed population capture-recapture analyses, with crucial assumptions which may not always be appropriate, the methods we develop extend naturally to a variety of other models with similar structure. Suppose that observed frequencies f are a known linear transformation f=A'x of a latent multinomial variable x with cell probability vector pi= pi(theta). Given that full conditional distributions [theta | x] can be sampled, implementation of Gibbs sampling requires only that we can sample from the full conditional distribution [x | f, theta], which is made possible by knowledge of the null space of A'. We illustrate the approach using two data sets with individual misidentification, one simulated, the other summarizing recapture data for salamanders based on natural marks.

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.

Family Environment and Childhood Obesity: A New Framework with Structural Equation Modeling

PubMed Central

Huang, Hui; Wan Mohamed Radzi, Che Wan Jasimah bt; Salarzadeh Jenatabadi, Hashem

2017-01-01

The main purpose of the current article is to introduce a framework of the complexity of childhood obesity based on the family environment. A conceptual model that quantifies the relationships and interactions among parental socioeconomic status, family food security level, child’s food intake and certain aspects of parental feeding behaviour is presented using the structural equation modeling (SEM) concept. Structural models are analysed in terms of the direct and indirect connections among latent and measurement variables that lead to the child weight indicator. To illustrate the accuracy, fit, reliability and validity of the introduced framework, real data collected from 630 families from Urumqi (Xinjiang, China) were considered. The framework includes two categories of data comprising the normal body mass index (BMI) range and obesity data. The comparison analysis between two models provides some evidence that in obesity modeling, obesity data must be extracted from the dataset and analysis must be done separately from the normal BMI range. This study may be helpful for researchers interested in childhood obesity modeling based on family environment. PMID:28208833

Family Environment and Childhood Obesity: A New Framework with Structural Equation Modeling.

PubMed

Huang, Hui; Wan Mohamed Radzi, Che Wan Jasimah Bt; Salarzadeh Jenatabadi, Hashem

2017-02-13

The main purpose of the current article is to introduce a framework of the complexity of childhood obesity based on the family environment. A conceptual model that quantifies the relationships and interactions among parental socioeconomic status, family food security level, child's food intake and certain aspects of parental feeding behaviour is presented using the structural equation modeling (SEM) concept. Structural models are analysed in terms of the direct and indirect connections among latent and measurement variables that lead to the child weight indicator. To illustrate the accuracy, fit, reliability and validity of the introduced framework, real data collected from 630 families from Urumqi (Xinjiang, China) were considered. The framework includes two categories of data comprising the normal body mass index (BMI) range and obesity data. The comparison analysis between two models provides some evidence that in obesity modeling, obesity data must be extracted from the dataset and analysis must be done separately from the normal BMI range. This study may be helpful for researchers interested in childhood obesity modeling based on family environment.

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…

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.

Inferring oscillatory modulation in neural spike trains

PubMed Central

Arai, Kensuke; Kass, Robert E.

2017-01-01

Oscillations are observed at various frequency bands in continuous-valued neural recordings like the electroencephalogram (EEG) and local field potential (LFP) in bulk brain matter, and analysis of spike-field coherence reveals that spiking of single neurons often occurs at certain phases of the global oscillation. Oscillatory modulation has been examined in relation to continuous-valued oscillatory signals, and independently from the spike train alone, but behavior or stimulus triggered firing-rate modulation, spiking sparseness, presence of slow modulation not locked to stimuli and irregular oscillations with large variability in oscillatory periods, present challenges to searching for temporal structures present in the spike train. In order to study oscillatory modulation in real data collected under a variety of experimental conditions, we describe a flexible point-process framework we call the Latent Oscillatory Spike Train (LOST) model to decompose the instantaneous firing rate in biologically and behaviorally relevant factors: spiking refractoriness, event-locked firing rate non-stationarity, and trial-to-trial variability accounted for by baseline offset and a stochastic oscillatory modulation. We also extend the LOST model to accommodate changes in the modulatory structure over the duration of the experiment, and thereby discover trial-to-trial variability in the spike-field coherence of a rat primary motor cortical neuron to the LFP theta rhythm. Because LOST incorporates a latent stochastic auto-regressive term, LOST is able to detect oscillations when the firing rate is low, the modulation is weak, and when the modulating oscillation has a broad spectral peak. PMID:28985231

The role of cognitive reserve and memory self-efficacy in compensatory strategy use: A structural equation approach.

PubMed

Simon, Christa; Schmitter-Edgecombe, Maureen

2016-08-01

The use of compensatory strategies plays an important role in the ability of older adults to adapt to late-life memory changes. Even with the benefits associated with compensatory strategy use, little research has explored specific mechanisms associated with memory performance and compensatory strategies. Rather than an individual's objective memory performance directly predicting their use of compensatory strategies, it is possible that some other variables are indirectly influencing that relationship. The purpose of this study was to: (a) examine the moderating effects of cognitive reserve (CR) and (b) evaluate the potential mediating effects of memory self-efficacy on the relationship between objective memory performance and compensatory strategy use. Two structural equation models (SEM) were used to evaluate CR (latent moderator model) and memory self-efficacy (mediator model) in a sample of 155 community-dwelling older adults over the age of 55. The latent variable moderator model indicated that CR was not substantiated as a moderator variable in this sample (p = .861). However, memory self-efficacy significantly mediated the association between objective memory performance and compensatory strategy use (β = .22, 95% confidence interval, CI [.002, .437]). More specifically, better objective memory was associated with lower compensatory strategy use because of its relation to higher memory self-efficacy. These findings provide initial support for an explanatory framework of the relation between objective memory and compensatory strategy use in a healthy older adult population by identifying the importance of an individual's memory perceptions.

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.

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.

Abstract: Inference and Interval Estimation for Indirect Effects With Latent Variable Models.

PubMed

Falk, Carl F; Biesanz, Jeremy C

2011-11-30

Models specifying indirect effects (or mediation) and structural equation modeling are both popular in the social sciences. Yet relatively little research has compared methods that test for indirect effects among latent variables and provided precise estimates of the effectiveness of different methods. This simulation study provides an extensive comparison of methods for constructing confidence intervals and for making inferences about indirect effects with latent variables. We compared the percentile (PC) bootstrap, bias-corrected (BC) bootstrap, bias-corrected accelerated (BC a ) bootstrap, likelihood-based confidence intervals (Neale & Miller, 1997), partial posterior predictive (Biesanz, Falk, and Savalei, 2010), and joint significance tests based on Wald tests or likelihood ratio tests. All models included three reflective latent variables representing the independent, dependent, and mediating variables. The design included the following fully crossed conditions: (a) sample size: 100, 200, and 500; (b) number of indicators per latent variable: 3 versus 5; (c) reliability per set of indicators: .7 versus .9; (d) and 16 different path combinations for the indirect effect (α = 0, .14, .39, or .59; and β = 0, .14, .39, or .59). Simulations were performed using a WestGrid cluster of 1680 3.06GHz Intel Xeon processors running R and OpenMx. Results based on 1,000 replications per cell and 2,000 resamples per bootstrap method indicated that the BC and BC a bootstrap methods have inflated Type I error rates. Likelihood-based confidence intervals and the PC bootstrap emerged as methods that adequately control Type I error and have good coverage rates.

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…

Aptitude, Achievement and Competence in Medicine: A Latent Variable Path Model

ERIC Educational Resources Information Center

Collin, V. Terri; Violato, Claudio; Hecker, Kent

2009-01-01

To develop and test a latent variable path model of general achievement, aptitude for medicine and competence in medicine employing data from the Medical College Admission Test (MCAT), pre-medical undergraduate grade point average (UGPA) and demographic characteristics for competence in pre-clinical and measures of competence (United States…

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…

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.

Deformation-Aware Log-Linear Models

NASA Astrophysics Data System (ADS)

Gass, Tobias; Deselaers, Thomas; Ney, Hermann

In this paper, we present a novel deformation-aware discriminative model for handwritten digit recognition. Unlike previous approaches our model directly considers image deformations and allows discriminative training of all parameters, including those accounting for non-linear transformations of the image. This is achieved by extending a log-linear framework to incorporate a latent deformation variable. The resulting model has an order of magnitude less parameters than competing approaches to handling image deformations. We tune and evaluate our approach on the USPS task and show its generalization capabilities by applying the tuned model to the MNIST task. We gain interesting insights and achieve highly competitive results on both tasks.

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…

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.

The Latent Classes of Subclinical ADHD Symptoms: Convergences of Multiple Informant Reports

ERIC Educational Resources Information Center

Kobor, Andrea; Takacs, Adam; Urban, Robert; Csepe, Valeria

2012-01-01

The purpose of the present study was to conduct latent class analysis on the Hyperactivity scale of the Strengths and Difficulties Questionnaire in order to identify distinct subgroups of subclinical ADHD in a multi-informant framework. We hypothesized a similar structure between teachers and parents, and differences in symptom severity across…

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.

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…

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.

Trajectories of self-evaluation bias in primary and secondary school: Parental antecedents and academic consequences.

PubMed

Bonneville-Roussy, Arielle; Bouffard, Thérèse; Vezeau, Carole

2017-08-01

Using a longitudinal approach spanning nine years of children's formal education, this study investigated the developmental trajectories of self-evaluation bias of academic competence. The study also examined how parenting styles were associated with the trajectories of bias in mid-primary school, and how those trajectories predicted academic outcomes at the end of secondary school and the beginning of college. A total of 711 children in 4th and 5th grades (mean age=10.71years old; 358 girls) participated in this study. Using a latent class growth modeling framework, results indicated that children can be classified in three latent growth trajectories of self-evaluation bias: the optimistic, realistic and pessimistic trajectories. These trajectories differed in their initial status of bias and also in their development over time. Children's adherence to a specific trajectory was associated with parenting variables in childhood. Finally, the optimistic, realistic, or pessimistic trajectories distinctively predicted achievement and persistence. Copyright © 2017 Society for the Study of School Psychology. Published by Elsevier Ltd. All rights reserved.

Deep Processing Strategies and Critical Thinking: Developmental Trajectories Using Latent Growth Analyses

ERIC Educational Resources Information Center

Phan, Huy P.

2011-01-01

The author explored the developmental courses of deep learning approach and critical thinking over a 2-year period. Latent growth curve modeling (LGM) procedures were used to test and trace the trajectories of both theoretical frameworks over time. Participants were 264 (119 women, 145 men) university undergraduates. The Deep Learning subscale of…

Specification Search for Identifying the Correct Mean Trajectory in Polynomial Latent Growth Models

ERIC Educational Resources Information Center

Kim, Minjung; Kwok, Oi-Man; Yoon, Myeongsun; Willson, Victor; Lai, Mark H. C.

2016-01-01

This study investigated the optimal strategy for model specification search under the latent growth modeling (LGM) framework, specifically on searching for the correct polynomial mean or average growth model when there is no a priori hypothesized model in the absence of theory. In this simulation study, the effectiveness of different starting…

Singapore Primary Students' Pursuit of Multiple Achievement Goals: A Latent Profile Analysis

ERIC Educational Resources Information Center

Ning, Hoi Kwan

2018-01-01

Based on measures of approach and avoidance mastery and performance goals delineated in the 2 × 2 achievement goal framework, this study utilized a person-centered approach to examine Singapore primary students' (N = 819) multiple goals pursuit in the general school context. Latent profile analysis identified six types of students with distinct…

Dual gait generative models for human motion estimation from a single camera.

PubMed

Zhang, Xin; Fan, Guoliang

2010-08-01

This paper presents a general gait representation framework for video-based human motion estimation. Specifically, we want to estimate the kinematics of an unknown gait from image sequences taken by a single camera. This approach involves two generative models, called the kinematic gait generative model (KGGM) and the visual gait generative model (VGGM), which represent the kinematics and appearances of a gait by a few latent variables, respectively. The concept of gait manifold is proposed to capture the gait variability among different individuals by which KGGM and VGGM can be integrated together, so that a new gait with unknown kinematics can be inferred from gait appearances via KGGM and VGGM. Moreover, a new particle-filtering algorithm is proposed for dynamic gait estimation, which is embedded with a segmental jump-diffusion Markov Chain Monte Carlo scheme to accommodate the gait variability in a long observed sequence. The proposed algorithm is trained from the Carnegie Mellon University (CMU) Mocap data and tested on the Brown University HumanEva data with promising results.

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…

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.…

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.

Latent feature decompositions for integrative analysis of multi-platform genomic data

PubMed Central

Gregory, Karl B.; Momin, Amin A.; Coombes, Kevin R.; Baladandayuthapani, Veerabhadran

2015-01-01

Increased availability of multi-platform genomics data on matched samples has sparked research efforts to discover how diverse molecular features interact both within and between platforms. In addition, simultaneous measurements of genetic and epigenetic characteristics illuminate the roles their complex relationships play in disease progression and outcomes. However, integrative methods for diverse genomics data are faced with the challenges of ultra-high dimensionality and the existence of complex interactions both within and between platforms. We propose a novel modeling framework for integrative analysis based on decompositions of the large number of platform-specific features into a smaller number of latent features. Subsequently we build a predictive model for clinical outcomes accounting for both within- and between-platform interactions based on Bayesian model averaging procedures. Principal components, partial least squares and non-negative matrix factorization as well as sparse counterparts of each are used to define the latent features, and the performance of these decompositions is compared both on real and simulated data. The latent feature interactions are shown to preserve interactions between the original features and not only aid prediction but also allow explicit selection of outcome-related features. The methods are motivated by and applied to, a glioblastoma multiforme dataset from The Cancer Genome Atlas to predict patient survival times integrating gene expression, microRNA, copy number and methylation data. For the glioblastoma data, we find a high concordance between our selected prognostic genes and genes with known associations with glioblastoma. In addition, our model discovers several relevant cross-platform interactions such as copy number variation associated gene dosing and epigenetic regulation through promoter methylation. On simulated data, we show that our proposed method successfully incorporates interactions within and between genomic platforms to aid accurate prediction and variable selection. Our methods perform best when principal components are used to define the latent features. PMID:26146492

Generalized reduced rank latent factor regression for high dimensional tensor fields, and neuroimaging-genetic applications

PubMed Central

Tao, Chenyang; Nichols, Thomas E.; Hua, Xue; Ching, Christopher R.K.; Rolls, Edmund T.; Thompson, Paul M.; Feng, Jianfeng

2017-01-01

We propose a generalized reduced rank latent factor regression model (GRRLF) for the analysis of tensor field responses and high dimensional covariates. The model is motivated by the need from imaging-genetic studies to identify genetic variants that are associated with brain imaging phenotypes, often in the form of high dimensional tensor fields. GRRLF identifies from the structure in the data the effective dimensionality of the data, and then jointly performs dimension reduction of the covariates, dynamic identification of latent factors, and nonparametric estimation of both covariate and latent response fields. After accounting for the latent and covariate effects, GRLLF performs a nonparametric test on the remaining factor of interest. GRRLF provides a better factorization of the signals compared with common solutions, and is less susceptible to overfitting because it exploits the effective dimensionality. The generality and the flexibility of GRRLF also allow various statistical models to be handled in a unified framework and solutions can be efficiently computed. Within the field of neuroimaging, it improves the sensitivity for weak signals and is a promising alternative to existing approaches. The operation of the framework is demonstrated with both synthetic datasets and a real-world neuroimaging example in which the effects of a set of genes on the structure of the brain at the voxel level were measured, and the results compared favorably with those from existing approaches. PMID:27666385

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).

Latent Model Analysis of Substance Use and HIV Risk Behaviors among High-Risk Minority Adults

ERIC Educational Resources Information Center

Wang, Min Qi; Matthew, Resa F.; Chiu, Yu-Wen; Yan, Fang; Bellamy, Nikki D.

2007-01-01

Objectives: This study evaluated substance use and HIV risk profile using a latent model analysis based on ecological theory, inclusive of a risk and protective factor framework, in sexually active minority adults (N=1,056) who participated in a federally funded substance abuse and HIV prevention health initiative from 2002 to 2006. Methods: Data…

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…

Validating the European Health Literacy Survey Questionnaire in people with type 2 diabetes: Latent trait analyses applying multidimensional Rasch modelling and confirmatory factor analysis.

PubMed

Finbråten, Hanne Søberg; Pettersen, Kjell Sverre; Wilde-Larsson, Bodil; Nordström, Gun; Trollvik, Anne; Guttersrud, Øystein

2017-11-01

To validate the European Health Literacy Survey Questionnaire (HLS-EU-Q47) in people with type 2 diabetes mellitus. The HLS-EU-Q47 latent variable is outlined in a framework with four cognitive domains integrated in three health domains, implying 12 theoretically defined subscales. Valid and reliable health literacy measurers are crucial to effectively adapt health communication and education to individuals and groups of patients. Cross-sectional study applying confirmatory latent trait analyses. Using a paper-and-pencil self-administered approach, 388 adults responded in March 2015. The data were analysed using the Rasch methodology and confirmatory factor analysis. Response violation (response dependency) and trait violation (multidimensionality) of local independence were identified. Fitting the "multidimensional random coefficients multinomial logit" model, 1-, 3- and 12-dimensional Rasch models were applied and compared. Poor model fit and differential item functioning were present in some items, and several subscales suffered from poor targeting and low reliability. Despite multidimensional data, we did not observe any unordered response categories. Interpreting the domains as distinct but related latent dimensions, the data fit a 12-dimensional Rasch model and a 12-factor confirmatory factor model best. Therefore, the analyses did not support the estimation of one overall "health literacy score." To support the plausibility of claims based on the HLS-EU score(s), we suggest: removing the health care aspect to reduce the magnitude of multidimensionality; rejecting redundant items to avoid response dependency; adding "harder" items and applying a six-point rating scale to improve subscale targeting and reliability; and revising items to improve model fit and avoid bias owing to person factors. © 2017 John Wiley & Sons Ltd.

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.

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.

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.

Generalized Multilevel Structural Equation Modeling

ERIC Educational Resources Information Center

Rabe-Hesketh, Sophia; Skrondal, Anders; Pickles, Andrew

2004-01-01

A unifying framework for generalized multilevel structural equation modeling is introduced. The models in the framework, called generalized linear latent and mixed models (GLLAMM), combine features of generalized linear mixed models (GLMM) and structural equation models (SEM) and consist of a response model and a structural model for the latent…

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…

The Peer Interaction in Primary School Questionnaire: Testing for Measurement Equivalence and Latent Mean Differences in Bullying between Gender in Egypt, Saudi Arabia and the USA

ERIC Educational Resources Information Center

Hussein, Mohamed Habashy

2010-01-01

The Peer Interaction in Primary School Questionnaire (PIPSQ) was developed to assess individuals' levels of bullying and victimization. This study used the approach of latent means analysis (LMA) within the framework of structural equation modeling (SEM) to explore the factor structure and gender differences associated with the PIPSQ in a sample…

Analyzing latent state-trait and multiple-indicator latent growth curve models as multilevel structural equation models

PubMed Central

Geiser, Christian; Bishop, Jacob; Lockhart, Ginger; Shiffman, Saul; Grenard, Jerry L.

2013-01-01

Latent state-trait (LST) and latent growth curve (LGC) models are frequently used in the analysis of longitudinal data. Although it is well-known that standard single-indicator LGC models can be analyzed within either the structural equation modeling (SEM) or multilevel (ML; hierarchical linear modeling) frameworks, few researchers realize that LST and multivariate LGC models, which use multiple indicators at each time point, can also be specified as ML models. In the present paper, we demonstrate that using the ML-SEM rather than the SL-SEM framework to estimate the parameters of these models can be practical when the study involves (1) a large number of time points, (2) individually-varying times of observation, (3) unequally spaced time intervals, and/or (4) incomplete data. Despite the practical advantages of the ML-SEM approach under these circumstances, there are also some limitations that researchers should consider. We present an application to an ecological momentary assessment study (N = 158 youths with an average of 23.49 observations of positive mood per person) using the software Mplus (Muthén and Muthén, 1998–2012) and discuss advantages and disadvantages of using the ML-SEM approach to estimate the parameters of LST and multiple-indicator LGC models. PMID:24416023

Examining the Latent Structure of the Delis-Kaplan Executive Function System.

PubMed

Karr, Justin E; Hofer, Scott M; Iverson, Grant L; Garcia-Barrera, Mauricio A

2018-05-04

The current study aimed to determine whether the Delis-Kaplan Executive Function System (D-KEFS) taps into three executive function factors (inhibition, shifting, fluency) and to assess the relationship between these factors and tests of executive-related constructs less often measured in latent variable research: reasoning, abstraction, and problem solving. Participants included 425 adults from the D-KEFS standardization sample (20-49 years old; 50.1% female; 70.1% White). Eight alternative measurement models were compared based on model fit, with test scores assigned a priori to three factors: inhibition (Color-Word Interference, Tower), shifting (Trail Making, Sorting, Design Fluency), and fluency (Verbal/Design Fluency). The Twenty Questions, Word Context, and Proverb Tests were predicted in separate structural models. The three-factor model fit the data well (CFI = 0.938; RMSEA = 0.047), although a two-factor model, with shifting and fluency merged, fit similarly well (CFI = 0.929; RMSEA = 0.048). A bifactor model fit best (CFI = 0.977; RMSEA = 0.032) and explained the most variance in shifting indicators, but rarely converged among 5,000 bootstrapped samples. When the three first-order factors simultaneously predicted the criterion variables, only shifting was uniquely predictive (p < .05; R2 = 0.246-0.408). The bifactor significantly predicted all three criterion variables (p < .001; R2 = 0.141-242). Results supported a three-factor D-KEFS model (i.e., inhibition, shifting, and fluency), although shifting and fluency were highly related (r = 0.696). The bifactor showed superior fit, but converged less often than other models. Shifting best predicted tests of reasoning, abstraction, and problem solving. These findings support the validity of D-KEFS scores for measuring executive-related constructs and provide a framework through which clinicians can interpret D-KEFS results.

A Conceptual and Psychometric Framework for Distinguishing Categories and Dimensions

ERIC Educational Resources Information Center

De Boeck, Paul; Wilson, Mark; Acton, G. Scott

2005-01-01

An important, sometimes controversial feature of all psychological phenomena is whether they are categorical or dimensional. A conceptual and psychometric framework is described for distinguishing whether the latent structure behind manifest categories (e.g., psychiatric diagnoses, attitude groups, or stages of development) is category-like or…

Cross-Sectional Analysis of Longitudinal Mediation Processes.

PubMed

O'Laughlin, Kristine D; Martin, Monica J; Ferrer, Emilio

2018-01-01

Statistical mediation analysis can help to identify and explain the mechanisms behind psychological processes. Examining a set of variables for mediation effects is a ubiquitous process in the social sciences literature; however, despite evidence suggesting that cross-sectional data can misrepresent the mediation of longitudinal processes, cross-sectional analyses continue to be used in this manner. Alternative longitudinal mediation models, including those rooted in a structural equation modeling framework (cross-lagged panel, latent growth curve, and latent difference score models) are currently available and may provide a better representation of mediation processes for longitudinal data. The purpose of this paper is twofold: first, we provide a comparison of cross-sectional and longitudinal mediation models; second, we advocate using models to evaluate mediation effects that capture the temporal sequence of the process under study. Two separate empirical examples are presented to illustrate differences in the conclusions drawn from cross-sectional and longitudinal mediation analyses. Findings from these examples yielded substantial differences in interpretations between the cross-sectional and longitudinal mediation models considered here. Based on these observations, researchers should use caution when attempting to use cross-sectional data in place of longitudinal data for mediation analyses.

Applying a behavioral economic framework to understanding adolescent smoking.

PubMed

Audrain-McGovern, Janet; Rodriguez, Daniel; Tercyak, Kenneth P; Epstein, Leonard H; Goldman, Paula; Wileyto, E Paul

2004-03-01

Adolescents' choice to smoke may depend on substitute reinforcers for smoking, complementary activities to smoking, and individual differences in reinforcer value. The influence of these variables on smoking was determined among 983 adolescents. Substitutes were school involvement, academic performance, physical activity, and sports team participation: complements were peer smoking and substance use; delay discounting assessed individual differences in reinforcer value. Latent growth modeling indicated that substitute reinforcers reduced the odds of smoking progression almost two-fold, complementary reinforcers increased the odds by 1.14. and delay discounting indirectly influenced the odds of smoking progression through complementary reinforcers. Adolescents who smoke may have fewer reinforcers that protect against smoking and more reinforcers that promote smoking. Discounting of future rewards affects smoking through reinforcer type.

A Bayesian framework based on a Gaussian mixture model and radial-basis-function Fisher discriminant analysis (BayGmmKda V1.1) for spatial prediction of floods

NASA Astrophysics Data System (ADS)

Tien Bui, Dieu; Hoang, Nhat-Duc

2017-09-01

In this study, a probabilistic model, named as BayGmmKda, is proposed for flood susceptibility assessment in a study area in central Vietnam. The new model is a Bayesian framework constructed by a combination of a Gaussian mixture model (GMM), radial-basis-function Fisher discriminant analysis (RBFDA), and a geographic information system (GIS) database. In the Bayesian framework, GMM is used for modeling the data distribution of flood-influencing factors in the GIS database, whereas RBFDA is utilized to construct a latent variable that aims at enhancing the model performance. As a result, the posterior probabilistic output of the BayGmmKda model is used as flood susceptibility index. Experiment results showed that the proposed hybrid framework is superior to other benchmark models, including the adaptive neuro-fuzzy inference system and the support vector machine. To facilitate the model implementation, a software program of BayGmmKda has been developed in MATLAB. The BayGmmKda program can accurately establish a flood susceptibility map for the study region. Accordingly, local authorities can overlay this susceptibility map onto various land-use maps for the purpose of land-use planning or management.

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.

Determinants of harsh parenting in Mexico.

PubMed

Frías-Armenta, M; McCloskey, L A

1998-04-01

This paper presents a structural model of the determinants of harsh parenting among Mexican mothers. One hundred five mothers (46 from the community; 59 referred to agencies for child maltreatment) were recruited from Sonora (Northern) Mexico and interviewed. In this model the use of physical punishment was explained by (1) authoritarian parenting style (mothers' beliefs concerning the effective use of physical punishment and mothers' lack of disciplinary skills) and (2) family dysfunction (a latent variable constructed from reports of interspousal violence and the parents' use of alcohol and drugs). In addition, the indirect effects of demographic and historical variables on harsh parenting was included. The findings show that the most important factor influencing the use of physical punishment in these families was authoritarian parenting style, exerting a significant direct effect on the mothers' reports of their use of harsh punishment. Family dysfunction had an indirect effect through parenting style. Some sociodemographic variables also indirectly influenced the use of beliefs maternal punishment It is concluded that cultural beliefs play a major role in parenting within the framework of Mexican family relations.

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

Social inequality, lifestyles and health - a non-linear canonical correlation analysis based on the approach of Pierre Bourdieu.

PubMed

Grosse Frie, Kirstin; Janssen, Christian

2009-01-01

Based on the theoretical and empirical approach of Pierre Bourdieu, a multivariate non-linear method is introduced as an alternative way to analyse the complex relationships between social determinants and health. The analysis is based on face-to-face interviews with 695 randomly selected respondents aged 30 to 59. Variables regarding socio-economic status, life circumstances, lifestyles, health-related behaviour and health were chosen for the analysis. In order to determine whether the respondents can be differentiated and described based on these variables, a non-linear canonical correlation analysis (OVERALS) was performed. The results can be described on three dimensions; Eigenvalues add up to the fit of 1.444, which can be interpreted as approximately 50 % of explained variance. The three-dimensional space illustrates correspondences between variables and provides a framework for interpretation based on latent dimensions, which can be described by age, education, income and gender. Using non-linear canonical correlation analysis, health characteristics can be analysed in conjunction with socio-economic conditions and lifestyles. Based on Bourdieus theoretical approach, the complex correlations between these variables can be more substantially interpreted and presented.

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.

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

Hybrid generative-discriminative human action recognition by combining spatiotemporal words with supervised topic models

NASA Astrophysics Data System (ADS)

Sun, Hao; Wang, Cheng; Wang, Boliang

2011-02-01

We present a hybrid generative-discriminative learning method for human action recognition from video sequences. Our model combines a bag-of-words component with supervised latent topic models. A video sequence is represented as a collection of spatiotemporal words by extracting space-time interest points and describing these points using both shape and motion cues. The supervised latent Dirichlet allocation (sLDA) topic model, which employs discriminative learning using labeled data under a generative framework, is introduced to discover the latent topic structure that is most relevant to action categorization. The proposed algorithm retains most of the desirable properties of generative learning while increasing the classification performance though a discriminative setting. It has also been extended to exploit both labeled data and unlabeled data to learn human actions under a unified framework. We test our algorithm on three challenging data sets: the KTH human motion data set, the Weizmann human action data set, and a ballet data set. Our results are either comparable to or significantly better than previously published results on these data sets and reflect the promise of hybrid generative-discriminative learning approaches.

An application of the LC-LSTM framework to the self-esteem instability case.

PubMed

Alessandri, Guido; Vecchione, Michele; Donnellan, Brent M; Tisak, John

2013-10-01

The present research evaluates the stability of self-esteem as assessed by a daily version of the Rosenberg (Society and the adolescent self-image, Princeton University Press, Princeton, 1965) general self-esteem scale (RGSE). The scale was administered to 391 undergraduates for five consecutive days. The longitudinal data were analyzed using the integrated LC-LSTM framework that allowed us to evaluate: (1) the measurement invariance of the RGSE, (2) its stability and change across the 5-day assessment period, (3) the amount of variance attributable to stable and transitory latent factors, and (4) the criterion-related validity of these factors. Results provided evidence for measurement invariance, mean-level stability, and rank-order stability of daily self-esteem. Latent state-trait analyses revealed that variances in scores of the RGSE can be decomposed into six components: stable self-esteem (40 %), ephemeral (or temporal-state) variance (36 %), stable negative method variance (9 %), stable positive method variance (4 %), specific variance (1 %) and random error variance (10 %). Moreover, latent factors associated with daily self-esteem were associated with measures of depression, implicit self-esteem, and grade point average.

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…

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.

Providing detailed information about latent tuberculosis and compliance with the PPD test among healthcare workers in Israel: a randomized controlled study.

PubMed

Taubman, Danielle; Titler, Nava; Edelstein, Hana; Elias, Mazen; Saliba, Walid

2013-12-01

The compliance of screening for latent tuberculosis (TB) with the tuberculin purified protein derivative (PPD) test is very low among healthcare workers (HCWs) in Israel. This randomized controlled study uses the Health Belief Model (HBM) as a conceptual framework to examine whether providing more information about latent TB and the PPD test increases the response rate for PPD screening among HCWs. All candidate HCWs for latent TB screening were randomly allocated to one of the following two invitations to perform the PPD test: regular letter (control group, n=97), and a letter with information about latent TB and the PPD test (intervention group, n=196). 293 HCWs were included (185 nurses, and 108 physicians). Overall, 36 (12.3%) HCWs were compliant with the PPD test screening. Compliance with PPD testing in the intervention group was not statistically different from the control group, RR 0.87 (95% CI, 0.46-1.65). Compliance for latent TB screening is low among HCWs in northeastern Israel. Providing detailed information about latent TB was not associated with increased test compliance. Understanding existing disparities in screening rates and potential barriers to latent TB screening among HCWs is important in order to move forward and successfully increase screening rates. Copyright © 2013 Ministry of Health, Saudi Arabia. Published by Elsevier Ltd. All rights reserved.

Verbal task demands are key in explaining the relationship between paired-associate learning and reading ability.

PubMed

Clayton, Francina J; Sears, Claire; Davis, Alice; Hulme, Charles

2018-07-01

Paired-associate learning (PAL) tasks measure the ability to form a novel association between a stimulus and a response. Performance on such tasks is strongly associated with reading ability, and there is increasing evidence that verbal task demands may be critical in explaining this relationship. The current study investigated the relationships between different forms of PAL and reading ability. A total of 97 children aged 8-10 years completed a battery of reading assessments and six different PAL tasks (phoneme-phoneme, visual-phoneme, nonverbal-nonverbal, visual-nonverbal, nonword-nonword, and visual-nonword) involving both familiar phonemes and unfamiliar nonwords. A latent variable path model showed that PAL ability is captured by two correlated latent variables: auditory-articulatory and visual-articulatory. The auditory-articulatory latent variable was the stronger predictor of reading ability, providing support for a verbal account of the PAL-reading relationship. Copyright © 2018 The Authors. Published by Elsevier Inc. All rights reserved.

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

Architecture of cognitive flexibility revealed by lesion mapping

PubMed Central

Barbey, Aron K.; Colom, Roberto; Grafman, Jordan

2013-01-01

Neuroscience has made remarkable progress in understanding the architecture of human intelligence, identifying a distributed network of brain structures that support goal-directed, intelligent behavior. However, the neural foundations of cognitive flexibility and adaptive aspects of intellectual function remain to be well characterized. Here, we report a human lesion study (n = 149) that investigates the neural bases of key competencies of cognitive flexibility (i.e., mental flexibility and the fluent generation of new ideas) and systematically examine their contributions to a broad spectrum of cognitive and social processes, including psychometric intelligence (Wechsler Adult Intelligence Scale), emotional intelligence (Mayer, Salovey, Caruso Emotional Intelligence Test), and personality (Neuroticism–Extraversion–Openness Personality Inventory). Latent variable modeling was applied to obtain error-free indices of each factor, followed by voxel-based lesion-symptom mapping to elucidate their neural substrates. Regression analyses revealed that latent scores for psychometric intelligence reliably predict latent scores for cognitive flexibility (adjusted R2 = 0.94). Lesion mapping results further indicated that these convergent processes depend on a shared network of frontal, temporal, and parietal regions, including white matter association tracts, which bind these areas into an integrated system. A targeted analysis of the unique variance explained by cognitive flexibility further revealed selective damage within the right superior temporal gyrus, a region known to support insight and the recognition of novel semantic relations. The observed findings motivate an integrative framework for understanding the neural foundations of adaptive behavior, suggesting that core elements of cognitive flexibility emerge from a distributed network of brain regions that support specific competencies for human intelligence. PMID:23721727

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…

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.

Latent variable models for gene-environment interactions in longitudinal studies with multiple correlated exposures.

PubMed

Tao, Yebin; Sánchez, Brisa N; Mukherjee, Bhramar

2015-03-30

Many existing cohort studies designed to investigate health effects of environmental exposures also collect data on genetic markers. The Early Life Exposures in Mexico to Environmental Toxicants project, for instance, has been genotyping single nucleotide polymorphisms on candidate genes involved in mental and nutrient metabolism and also in potentially shared metabolic pathways with the environmental exposures. Given the longitudinal nature of these cohort studies, rich exposure and outcome data are available to address novel questions regarding gene-environment interaction (G × E). Latent variable (LV) models have been effectively used for dimension reduction, helping with multiple testing and multicollinearity issues in the presence of correlated multivariate exposures and outcomes. In this paper, we first propose a modeling strategy, based on LV models, to examine the association between repeated outcome measures (e.g., child weight) and a set of correlated exposure biomarkers (e.g., prenatal lead exposure). We then construct novel tests for G × E effects within the LV framework to examine effect modification of outcome-exposure association by genetic factors (e.g., the hemochromatosis gene). We consider two scenarios: one allowing dependence of the LV models on genes and the other assuming independence between the LV models and genes. We combine the two sets of estimates by shrinkage estimation to trade off bias and efficiency in a data-adaptive way. Using simulations, we evaluate the properties of the shrinkage estimates, and in particular, we demonstrate the need for this data-adaptive shrinkage given repeated outcome measures, exposure measures possibly repeated and time-varying gene-environment association. Copyright © 2014 John Wiley & Sons, Ltd.

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.

Factors influencing readiness to deploy in disaster response: findings from a cross-sectional survey of the Department of Veterans Affairs Disaster Emergency Medical Personnel System

PubMed Central

2014-01-01

Background The Disaster Emergency Medical Personnel System (DEMPS) program provides a system of volunteers whereby active or retired Department of Veterans Affairs (VA) personnel can register to be deployed to support other VA facilities or the nation during national emergencies or disasters. Both early and ongoing volunteer training is required to participate. Methods This study aims to identify factors that impact willingness to deploy in the event of an emergency. This analysis was based on responses from 2,385 survey respondents (response rate, 29%). Latent variable path models were developed and tested using the EQS structural equations modeling program. Background demographic variables of education, age, minority ethnicity, and female gender were used as predictors of intervening latent variables of DEMPS Volunteer Experience, Positive Attitude about Training, and Stress. The model had acceptable fit statistics, and all three intermediate latent variables significantly predicted the outcome latent variable Readiness to Deploy. Results DEMPS Volunteer Experience and a Positive Attitude about Training were associated with Readiness to Deploy. Stress was associated with decreased Readiness to Deploy. Female gender was negatively correlated with Readiness to Deploy; however, there was an indirect relationship between female gender and Readiness to Deploy through Positive Attitude about Training. Conclusions These findings suggest that volunteer emergency management response programs such as DEMPS should consider how best to address the factors that may make women less ready to deploy than men in order to ensure adequate gender representation among emergency responders. The findings underscore the importance of training opportunities to ensure that gender-sensitive support is a strong component of emergency response, and may apply to other emergency response programs such as the Medical Reserve Corps and the American Red Cross. PMID:25038628

Factors influencing readiness to deploy in disaster response: findings from a cross-sectional survey of the Department of Veterans Affairs Disaster Emergency Medical Personnel System.

PubMed

Zagelbaum, Nicole K; Heslin, Kevin C; Stein, Judith A; Ruzek, Josef; Smith, Robert E; Nyugen, Tam; Dobalian, Aram

2014-07-19

The Disaster Emergency Medical Personnel System (DEMPS) program provides a system of volunteers whereby active or retired Department of Veterans Affairs (VA) personnel can register to be deployed to support other VA facilities or the nation during national emergencies or disasters. Both early and ongoing volunteer training is required to participate. This study aims to identify factors that impact willingness to deploy in the event of an emergency. This analysis was based on responses from 2,385 survey respondents (response rate, 29%). Latent variable path models were developed and tested using the EQS structural equations modeling program. Background demographic variables of education, age, minority ethnicity, and female gender were used as predictors of intervening latent variables of DEMPS Volunteer Experience, Positive Attitude about Training, and Stress. The model had acceptable fit statistics, and all three intermediate latent variables significantly predicted the outcome latent variable Readiness to Deploy. DEMPS Volunteer Experience and a Positive Attitude about Training were associated with Readiness to Deploy. Stress was associated with decreased Readiness to Deploy. Female gender was negatively correlated with Readiness to Deploy; however, there was an indirect relationship between female gender and Readiness to Deploy through Positive Attitude about Training. These findings suggest that volunteer emergency management response programs such as DEMPS should consider how best to address the factors that may make women less ready to deploy than men in order to ensure adequate gender representation among emergency responders. The findings underscore the importance of training opportunities to ensure that gender-sensitive support is a strong component of emergency response, and may apply to other emergency response programs such as the Medical Reserve Corps and the American Red Cross.

Inferring genetic parameters on latent variables underlying milk yield and quality, protein composition, curd firmness and cheese-making traits in dairy cattle.

PubMed

Dadousis, C; Cipolat-Gotet, C; Bittante, G; Cecchinato, A

2018-02-01

We studied the genetics of cheese-related latent variables (factors; Fs) for application in dairy cattle breeding. In total, 26 traits, recorded in 1264 Brown Swiss cows, were analyzed through multivariate factor analysis (MFA). Traits analyzed were descriptors of milk quality and yield (including protein fractions) and measures of coagulation, curd firmness (CF), cheese yields (%CY) and nutrient recoveries in the curd (REC). A total of 10 Fs (mutual orthogonal with a varimax rotation) were obtained. To assess the practical use of the Fs into breeding, we inferred their genetic parameters using single and bivariate animal models under a Bayesian framework. Heritability estimates (intra-herd) varied between 0.11 and 0.72 (F3: Yield and F7: κ-β-CN, respectively). The Fs underlined basic characteristics of the cheese-making process, milk components and udder health, while retaining 74% of the original variability. The first two Fs were indicators of the CY percentage (F1: %CY) and the CF process (F2: CF t ), and presented similar heritability estimates: 0.268 and 0.295, respectively. The third factor was associated with the yield of milk and solids (F3: Yield) characterized by a low heritability (0.108) and the fourth with the cheese nitrogen (N) (F4: Cheese N) that conversely appeared to be characterized by a high heritability (0.618). Three Fs were associated with the proportion of the basic milk caseins on total milk protein (F5: as1-β-CN, F7: κ-β-CN, F8: as2-CN), also highly heritable (0.565, 0.723 and 0.397, respectively) and 1 factor with the phosphorylated form of the as1-CN (F9: as1-CN-Ph; 0.318). Moreover, 1 factor was linked to the whey protein α-LA (F10: α-LA; 0.147). An indicator factor of a cow's udder health (F6: Udder health) was also obtained and showed a moderate heritability (0.204). Although the Fs were phenotypically uncorrelated, considerable additive genetic correlations existed among them, with highest values observed between F10: α-LA and F6: Udder health (-0.67) as well as between F9: as1-CN-Ph and F3: Yield (-0.60). Our results show the usefulness of MFA in dairy cattle breeding. The ability to replace a large number of variables with a few latent indicators of the same biological meaning marks MFA as a valuable tool for developing breeding strategies to improve cow's cheese-related traits.

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.

Mixture class recovery in GMM under varying degrees of class separation: frequentist versus Bayesian estimation.

PubMed

Depaoli, Sarah

2013-06-01

Growth mixture modeling (GMM) represents a technique that is designed to capture change over time for unobserved subgroups (or latent classes) that exhibit qualitatively different patterns of growth. The aim of the current article was to explore the impact of latent class separation (i.e., how similar growth trajectories are across latent classes) on GMM performance. Several estimation conditions were compared: maximum likelihood via the expectation maximization (EM) algorithm and the Bayesian framework implementing diffuse priors, "accurate" informative priors, weakly informative priors, data-driven informative priors, priors reflecting partial-knowledge of parameters, and "inaccurate" (but informative) priors. The main goal was to provide insight about the optimal estimation condition under different degrees of latent class separation for GMM. Results indicated that optimal parameter recovery was obtained though the Bayesian approach using "accurate" informative priors, and partial-knowledge priors showed promise for the recovery of the growth trajectory parameters. Maximum likelihood and the remaining Bayesian estimation conditions yielded poor parameter recovery for the latent class proportions and the growth trajectories. (PsycINFO Database Record (c) 2013 APA, all rights reserved).

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.

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.

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.

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.

Psychometric Properties of the Problematic Internet Use Questionnaire Short-Form (PIUQ-SF-6) in a Nationally Representative Sample of Adolescents.

PubMed

Demetrovics, Zsolt; Király, Orsolya; Koronczai, Beatrix; Griffiths, Mark D; Nagygyörgy, Katalin; Elekes, Zsuzsanna; Tamás, Domokos; Kun, Bernadette; Kökönyei, Gyöngyi; Urbán, Róbert

2016-01-01

Despite the large number of measurement tools developed to assess problematic Internet use, numerous studies use measures with only modest investigation into their psychometric properties. The goal of the present study was to validate the short (6-item) version of the Problematic Internet Use Questionnaire (PIUQ) on a nationally representative adolescent sample (n = 5,005; mean age 16.4 years, SD = 0.87) and to determine a statistically established cut-off value. Data were collected within the framework of the European School Survey Project on Alcohol and Other Drugs project. Results showed an acceptable fit of the original three-factor structure to the data. In addition, a MIMIC model was carried out to justify the need for three distinct factors. The sample was divided into users at-risk of problematic Internet use and those with no-risk using a latent profile analysis. Two latent classes were obtained with 14.4% of adolescents belonging to the at-risk group. Concurrent and convergent validity were tested by comparing the two groups across a number of variables (i.e., time spent online, academic achievement, self-esteem, depressive symptoms, and preferred online activities). Using the at-risk latent profile analysis class as the gold standard, a cut-off value of 15 (out of 30) was suggested based on sensitivity and specificity analyses. In conclusion, the brief version of the (6-item) PIUQ also appears to be an appropriate measure to differentiate between Internet users at risk of developing problematic Internet use and those not at risk. Furthermore, due to its brevity, the shortened PIUQ is advantageous to utilize within large-scale surveys assessing many different behaviors and/or constructs by reducing the overall number of survey questions, and as a consequence, likely increasing completion rates.

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)

Demand modelling of passenger air travel: An analysis and extension. Volume 1: Background and summary

NASA Technical Reports Server (NTRS)

Jacobson, I. D.

1978-01-01

The framework for a model of travel demand which will be useful in predicting the total market for air travel between two cities is discussed. Variables to be used in determining the need for air transportation where none currently exists and the effect of changes in system characteristics on attracting latent demand are identified. Existing models are examined in order to provide insight into their strong points and shortcomings. Much of the existing behavioral research in travel demand is incorporated to allow the inclusion of non-economic factors, such as convenience. The model developed is characterized as a market segmentation model. This is a consequence of the strengths of disaggregation and its natural evolution to a usable aggregate formulation. The need for this approach both pedagogically and mathematically is discussed.

Brain Emotion Systems, Personality, Hopelessness, Self/Other Perception, and Gambling Cognition: A Structural Equation Model.

PubMed

Iliceto, Paolo; D'Antuono, Laura; Bowden-Jones, Henrietta; Giovani, Eleni; Giacolini, Teodosio; Candilera, Gabriella; Sabatello, Ugo; Panksepp, Jaak

2016-03-01

The aim of this study was to explore the relations between gambling, brain emotion systems, personality, self/other perception, and hopelessness in an Italian community. Dimensions of gambling, positive and negative emotions, self/other perception, personality and hopelessness were assessed in a community sample of 235 adults aged 19-59 years. Two structural models were tested. We found a significant correlation between problem gambling and impulsivity, which in association with aggressivity and negative personality dimensions may help explain the psychopathology factor, i.e. a latent variable involving neurotic personality, hopelessness, high sensation seeking, low metacognitive responsiveness, and disorganized patterns of interpersonal relationships. These results contribute to develop a theoretical framework of gambling in relation with personality factors and provide a new approach for clinical intervention of problem gambling that relies on a solid multidimensional perspective.

Similarity-based Regularized Latent Feature Model for Link Prediction in Bipartite Networks.

PubMed

Wang, Wenjun; Chen, Xue; Jiao, Pengfei; Jin, Di

2017-12-05

Link prediction is an attractive research topic in the field of data mining and has significant applications in improving performance of recommendation system and exploring evolving mechanisms of the complex networks. A variety of complex systems in real world should be abstractly represented as bipartite networks, in which there are two types of nodes and no links connect nodes of the same type. In this paper, we propose a framework for link prediction in bipartite networks by combining the similarity based structure and the latent feature model from a new perspective. The framework is called Similarity Regularized Nonnegative Matrix Factorization (SRNMF), which explicitly takes the local characteristics into consideration and encodes the geometrical information of the networks by constructing a similarity based matrix. We also develop an iterative scheme to solve the objective function based on gradient descent. Extensive experiments on a variety of real world bipartite networks show that the proposed framework of link prediction has a more competitive, preferable and stable performance in comparison with the state-of-art methods.

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…

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

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.

Systematic identification of latent disease-gene associations from PubMed articles.

PubMed

Zhang, Yuji; Shen, Feichen; Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research.

Systematic identification of latent disease-gene associations from PubMed articles

PubMed Central

Mojarad, Majid Rastegar; Li, Dingcheng; Liu, Sijia; Tao, Cui; Yu, Yue; Liu, Hongfang

2018-01-01

Recent scientific advances have accumulated a tremendous amount of biomedical knowledge providing novel insights into the relationship between molecular and cellular processes and diseases. Literature mining is one of the commonly used methods to retrieve and extract information from scientific publications for understanding these associations. However, due to large data volume and complicated associations with noises, the interpretability of such association data for semantic knowledge discovery is challenging. In this study, we describe an integrative computational framework aiming to expedite the discovery of latent disease mechanisms by dissecting 146,245 disease-gene associations from over 25 million of PubMed indexed articles. We take advantage of both Latent Dirichlet Allocation (LDA) modeling and network-based analysis for their capabilities of detecting latent associations and reducing noises for large volume data respectively. Our results demonstrate that (1) the LDA-based modeling is able to group similar diseases into disease topics; (2) the disease-specific association networks follow the scale-free network property; (3) certain subnetwork patterns were enriched in the disease-specific association networks; and (4) genes were enriched in topic-specific biological processes. Our approach offers promising opportunities for latent disease-gene knowledge discovery in biomedical research. PMID:29373609

Measurement of latent cognitive abilities involved in concept identification learning.

PubMed

Thomas, Michael L; Brown, Gregory G; Gur, Ruben C; Moore, Tyler M; Patt, Virginie M; Nock, Matthew K; Naifeh, James A; Heeringa, Steven; Ursano, Robert J; Stein, Murray B

2015-01-01

We used cognitive and psychometric modeling techniques to evaluate the construct validity and measurement precision of latent cognitive abilities measured by a test of concept identification learning: the Penn Conditional Exclusion Test (PCET). Item response theory parameters were embedded within classic associative- and hypothesis-based Markov learning models and were fitted to 35,553 Army soldiers' PCET data from the Army Study to Assess Risk and Resilience in Servicemembers (Army STARRS). Data were consistent with a hypothesis-testing model with multiple latent abilities-abstraction and set shifting. Latent abstraction ability was positively correlated with number of concepts learned, and latent set-shifting ability was negatively correlated with number of perseverative errors, supporting the construct validity of the two parameters. Abstraction was most precisely assessed for participants with abilities ranging from 1.5 standard deviations below the mean to the mean itself. Measurement of set shifting was acceptably precise only for participants making a high number of perseverative errors. The PCET precisely measures latent abstraction ability in the Army STARRS sample, especially within the range of mildly impaired to average ability. This precision pattern is ideal for a test developed to measure cognitive impairment as opposed to cognitive strength. The PCET also measures latent set-shifting ability, but reliable assessment is limited to the impaired range of ability, reflecting that perseverative errors are rare among cognitively healthy adults. Integrating cognitive and psychometric models can provide information about construct validity and measurement precision within a single analytical framework.

Information Foraging Theory: A Framework for Intelligence Analysis

DTIC Science & Technology

2014-11-01

oceanographic information, human intelligence (HUMINT), open-source intelligence ( OSINT ), and information provided by other governmental departments [1][5...Human Intelligence IFT Information Foraging Theory LSA Latent Semantic Similarity MVT Marginal Value Theorem OFT Optimal Foraging Theory OSINT

Stable isotope records of convection variability in the West Pacific Warm Pool from fast-growing stalagmites

NASA Astrophysics Data System (ADS)

Maupin, C. R.; Partin, J. W.; Quinn, T. M.; Shen, C.; Lin, K.; Taylor, F. W.; Sinclair, D. J.; Banner, J. L.

2010-12-01

The potential response of the tropical Pacific to ongoing anthropogenic global warming conditions is informed by instrumental data, model predictions and climate proxy evidence. However, these distinct lines of evidence lead to opposing predictions in terms of the nature of interannual (ENSO) variability in a warming world. Interpreted in an ENSO framework, warming in the tropical Pacific may elicit a zonally asymmetrical response and lead to an intensified Walker Circulation (more ‘La Niña - like’). Alternatively, discrepancies in the increasing rates of latent heat flux and rainfall due to warming conditions may in fact reduce Walker Circulation (more ‘El Niño - like’). However, in order for such a framework to be useful in the context of future climate change, some knowledge of the natural variability in the strength of Walker Circulation components is required. The extant instrumental data are not of sufficient temporal length to fully assess the spectrum of natural variability in such climate components. Oxygen isotope records from tropical speleothems have been successfully used to document the nature of precessional forcing on precipitation and atmospheric circulation patterns throughout the tropics. Typical stalagmite growth rates of 10-100 μm yr-1 allow decadally resolved records of δ18O variability on time scales of centuries to millennia and beyond. Here we present the initial results from calcite stalagmites of heretofore unprecedented growth rates (~1-4 mm yr-1) in a cave in northwest Guadalcanal, Solomon Islands (~9°S, 160°E). These stalagmites have been absolutely dated by U-Th techniques and indicate stalagmite growth spanning ~1650 to 2010 CE. The δ18O records from stalagmites provide evidence for changes in convection in the equatorial WPWP region of the SPCZ: the rising limb of the Pacific Walker Circulation, and therefore provide critical insight into changes in zonal atmospheric circulation across the Pacific.

Validation of the conceptual research utilization scale: an application of the standards for educational and psychological testing in healthcare.

PubMed

Squires, Janet E; Estabrooks, Carole A; Newburn-Cook, Christine V; Gierl, Mark

2011-05-19

There is a lack of acceptable, reliable, and valid survey instruments to measure conceptual research utilization (CRU). In this study, we investigated the psychometric properties of a newly developed scale (the CRU Scale). We used the Standards for Educational and Psychological Testing as a validation framework to assess four sources of validity evidence: content, response processes, internal structure, and relations to other variables. A panel of nine international research utilization experts performed a formal content validity assessment. To determine response process validity, we conducted a series of one-on-one scale administration sessions with 10 healthcare aides. Internal structure and relations to other variables validity was examined using CRU Scale response data from a sample of 707 healthcare aides working in 30 urban Canadian nursing homes. Principal components analysis and confirmatory factor analyses were conducted to determine internal structure. Relations to other variables were examined using: (1) bivariate correlations; (2) change in mean values of CRU with increasing levels of other kinds of research utilization; and (3) multivariate linear regression. Content validity index scores for the five items ranged from 0.55 to 1.00. The principal components analysis predicted a 5-item 1-factor model. This was inconsistent with the findings from the confirmatory factor analysis, which showed best fit for a 4-item 1-factor model. Bivariate associations between CRU and other kinds of research utilization were statistically significant (p < 0.01) for the latent CRU scale score and all five CRU items. The CRU scale score was also shown to be significant predictor of overall research utilization in multivariate linear regression. The CRU scale showed acceptable initial psychometric properties with respect to responses from healthcare aides in nursing homes. Based on our validity, reliability, and acceptability analyses, we recommend using a reduced (four-item) version of the CRU scale to yield sound assessments of CRU by healthcare aides. Refinement to the wording of one item is also needed. Planned future research will include: latent scale scoring, identification of variables that predict and are outcomes to conceptual research use, and longitudinal work to determine CRU Scale sensitivity to change.

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

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.

Optimal study design with identical power: an application of power equivalence to latent growth curve models.

PubMed

von Oertzen, Timo; Brandmaier, Andreas M

2013-06-01

Structural equation models have become a broadly applied data-analytic framework. Among them, latent growth curve models have become a standard method in longitudinal research. However, researchers often rely solely on rules of thumb about statistical power in their study designs. The theory of power equivalence provides an analytical answer to the question of how design factors, for example, the number of observed indicators and the number of time points assessed in repeated measures, trade off against each other while holding the power for likelihood-ratio tests on the latent structure constant. In this article, we present applications of power-equivalent transformations on a model with data from a previously published study on cognitive aging, and highlight consequences of participant attrition on power. PsycINFO Database Record (c) 2013 APA, all rights reserved.

The computational nature of memory modification.

PubMed

Gershman, Samuel J; Monfils, Marie-H; Norman, Kenneth A; Niv, Yael

2017-03-15

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature.

A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin.

PubMed

Ghanta, Sindhu; Jordan, Michael I; Kose, Kivanc; Brooks, Dana H; Rajadhyaksha, Milind; Dy, Jennifer G

2017-01-01

Segmenting objects of interest from 3D data sets is a common problem encountered in biological data. Small field of view and intrinsic biological variability combined with optically subtle changes of intensity, resolution, and low contrast in images make the task of segmentation difficult, especially for microscopy of unstained living or freshly excised thick tissues. Incorporating shape information in addition to the appearance of the object of interest can often help improve segmentation performance. However, the shapes of objects in tissue can be highly variable and design of a flexible shape model that encompasses these variations is challenging. To address such complex segmentation problems, we propose a unified probabilistic framework that can incorporate the uncertainty associated with complex shapes, variable appearance, and unknown locations. The driving application that inspired the development of this framework is a biologically important segmentation problem: the task of automatically detecting and segmenting the dermal-epidermal junction (DEJ) in 3D reflectance confocal microscopy (RCM) images of human skin. RCM imaging allows noninvasive observation of cellular, nuclear, and morphological detail. The DEJ is an important morphological feature as it is where disorder, disease, and cancer usually start. Detecting the DEJ is challenging, because it is a 2D surface in a 3D volume which has strong but highly variable number of irregularly spaced and variably shaped "peaks and valleys." In addition, RCM imaging resolution, contrast, and intensity vary with depth. Thus, a prior model needs to incorporate the intrinsic structure while allowing variability in essentially all its parameters. We propose a model which can incorporate objects of interest with complex shapes and variable appearance in an unsupervised setting by utilizing domain knowledge to build appropriate priors of the model. Our novel strategy to model this structure combines a spatial Poisson process with shape priors and performs inference using Gibbs sampling. Experimental results show that the proposed unsupervised model is able to automatically detect the DEJ with physiologically relevant accuracy in the range 10- 20 μm .

A Marked Poisson Process Driven Latent Shape Model for 3D Segmentation of Reflectance Confocal Microscopy Image Stacks of Human Skin

PubMed Central

Ghanta, Sindhu; Jordan, Michael I.; Kose, Kivanc; Brooks, Dana H.; Rajadhyaksha, Milind; Dy, Jennifer G.

2016-01-01

Segmenting objects of interest from 3D datasets is a common problem encountered in biological data. Small field of view and intrinsic biological variability combined with optically subtle changes of intensity, resolution and low contrast in images make the task of segmentation difficult, especially for microscopy of unstained living or freshly excised thick tissues. Incorporating shape information in addition to the appearance of the object of interest can often help improve segmentation performance. However, shapes of objects in tissue can be highly variable and design of a flexible shape model that encompasses these variations is challenging. To address such complex segmentation problems, we propose a unified probabilistic framework that can incorporate the uncertainty associated with complex shapes, variable appearance and unknown locations. The driving application which inspired the development of this framework is a biologically important segmentation problem: the task of automatically detecting and segmenting the dermal-epidermal junction (DEJ) in 3D reflectance confocal microscopy (RCM) images of human skin. RCM imaging allows noninvasive observation of cellular, nuclear and morphological detail. The DEJ is an important morphological feature as it is where disorder, disease and cancer usually start. Detecting the DEJ is challenging because it is a 2D surface in a 3D volume which has strong but highly variable number of irregularly spaced and variably shaped “peaks and valleys”. In addition, RCM imaging resolution, contrast and intensity vary with depth. Thus a prior model needs to incorporate the intrinsic structure while allowing variability in essentially all its parameters. We propose a model which can incorporate objects of interest with complex shapes and variable appearance in an unsupervised setting by utilizing domain knowledge to build appropriate priors of the model. Our novel strategy to model this structure combines a spatial Poisson process with shape priors and performs inference using Gibbs sampling. Experimental results show that the proposed unsupervised model is able to automatically detect the DEJ with physiologically relevant accuracy in the range 10 – 20µm. PMID:27723590

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.

Spatial path models with multiple indicators and multiple causes: mental health in US counties.

PubMed

Congdon, Peter

2011-06-01

This paper considers a structural model for the impact on area mental health outcomes (poor mental health, suicide) of spatially structured latent constructs: deprivation, social capital, social fragmentation and rurality. These constructs are measured by multiple observed effect indicators, with the constructs allowed to be correlated both between and within areas. However, in the scheme developed here, particular latent constructs may also be influenced by known variables, or, via path sequences, by other constructs, possibly nonlinearly. For example, area social capital may be measured by effect indicators (e.g. associational density, charitable activity), but influenced as causes by other constructs (e.g. area deprivation), and by observed features of the socio-ethnic structure of areas. A model incorporating these features is applied to suicide mortality and the prevalence of poor mental health in 3141 US counties, which are related to the latent spatial constructs and to observed variables (e.g. county ethnic mix). Copyright © 2011 Elsevier Ltd. All rights reserved.

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.

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.

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.

unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance

USGS Publications Warehouse

Fiske, Ian J.; Chandler, Richard B.

2011-01-01

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientific questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mechanisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unified modeling interface. The R package unmarked provides such a unified modeling framework, including tools for data exploration, model fitting, model criticism, post-hoc analysis, and model comparison.

Sensorimotor abilities predict on-field performance in professional baseball.

PubMed

Burris, Kyle; Vittetoe, Kelly; Ramger, Benjamin; Suresh, Sunith; Tokdar, Surya T; Reiter, Jerome P; Appelbaum, L Gregory

2018-01-08

Baseball players must be able to see and react in an instant, yet it is hotly debated whether superior performance is associated with superior sensorimotor abilities. In this study, we compare sensorimotor abilities, measured through 8 psychomotor tasks comprising the Nike Sensory Station assessment battery, and game statistics in a sample of 252 professional baseball players to evaluate the links between sensorimotor skills and on-field performance. For this purpose, we develop a series of Bayesian hierarchical latent variable models enabling us to compare statistics across professional baseball leagues. Within this framework, we find that sensorimotor abilities are significant predictors of on-base percentage, walk rate and strikeout rate, accounting for age, position, and league. We find no such relationship for either slugging percentage or fielder-independent pitching. The pattern of results suggests performance contributions from both visual-sensory and visual-motor abilities and indicates that sensorimotor screenings may be useful for player scouting.

Maximum likelihood estimation and EM algorithm of Copas-like selection model for publication bias correction.

PubMed

Ning, Jing; Chen, Yong; Piao, Jin

2017-07-01

Publication bias occurs when the published research results are systematically unrepresentative of the population of studies that have been conducted, and is a potential threat to meaningful meta-analysis. The Copas selection model provides a flexible framework for correcting estimates and offers considerable insight into the publication bias. However, maximizing the observed likelihood under the Copas selection model is challenging because the observed data contain very little information on the latent variable. In this article, we study a Copas-like selection model and propose an expectation-maximization (EM) algorithm for estimation based on the full likelihood. Empirical simulation studies show that the EM algorithm and its associated inferential procedure performs well and avoids the non-convergence problem when maximizing the observed likelihood. © The Author 2017. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Impact of missing data mechanism on the estimate of change: a case study on cognitive function and polypharmacy among older persons

PubMed Central

Lavikainen, Piia; Leskinen, Esko; Hartikainen, Sirpa; Möttönen, Jyrki; Sulkava, Raimo; Korhonen, Maarit J

2015-01-01

Longitudinal studies typically suffer from incompleteness of data. Attrition is a major problem in studies of older persons since participants may die during the study or are too frail to participate in follow-up examinations. Attrition is typically related to an individual’s health; therefore, ignoring it may lead to too optimistic inferences, for example, about cognitive decline or changes in polypharmacy. The objective of this study is to compare the estimates of level and slope of change in 1) cognitive function and 2) number of drugs in use between the assumptions of ignorable and non-ignorable missingness. This study demonstrates the usefulness of latent variable modeling framework. The results suggest that when the missing data mechanism is not known, it is preferable to conduct analyses both under ignorable and non-ignorable missing data assumptions. PMID:25678815

Impact of missing data mechanism on the estimate of change: a case study on cognitive function and polypharmacy among older persons.

PubMed

Lavikainen, Piia; Leskinen, Esko; Hartikainen, Sirpa; Möttönen, Jyrki; Sulkava, Raimo; Korhonen, Maarit J

2015-01-01

Longitudinal studies typically suffer from incompleteness of data. Attrition is a major problem in studies of older persons since participants may die during the study or are too frail to participate in follow-up examinations. Attrition is typically related to an individual's health; therefore, ignoring it may lead to too optimistic inferences, for example, about cognitive decline or changes in polypharmacy. The objective of this study is to compare the estimates of level and slope of change in 1) cognitive function and 2) number of drugs in use between the assumptions of ignorable and non-ignorable missingness. This study demonstrates the usefulness of latent variable modeling framework. The results suggest that when the missing data mechanism is not known, it is preferable to conduct analyses both under ignorable and non-ignorable missing data assumptions.

Genetic and Environmental Associations Between Procrastination and Internalizing/Externalizing Psychopathology.

PubMed

Gustavson, Daniel E; du Pont, Alta; Hatoum, Alexander S; Hyun Rhee, Soo; Kremen, William S; Hewitt, John K; Friedman, Naomi P

2017-09-01

Recent work on procrastination has begun to unravel the genetic and environmental correlates of this problematic behavior. However, little is known about how strongly procrastination is associated with internalizing and externalizing psychopathology, and the extent to which shared genetic/environmental factors or relevant personality constructs (e.g., fear of failure, impulsivity, and neuroticism) can inform the structure of these associations. The current study examined data from 764 young adult twins who completed questionnaires assessing procrastination and personality and structured interviews regarding psychopathology symptoms. Results indicated that procrastination was positively correlated with both internalizing and externalizing latent variables, and that these correlations were driven by shared genetic influences. Moreover, the association between procrastination and internalizing was accounted for by fear of failure and neuroticism, whereas the association between procrastination and externalizing was primarily explained by impulsivity. The role of procrastination in psychopathology is discussed using a framework that highlights common and broadband-specific variance.

Perceived Emotion Control Moderates the Relationship between Neuroticism and Generalized Anxiety Disorder

PubMed Central

Bourgeois, Michelle L.; Brown, Timothy A.

2015-01-01

The relationships between neuroticism, perceived emotion control, and generalized anxiety disorder (GAD) severity were examined in 293 individuals diagnosed with GAD at a specialty anxiety disorders clinic. Hierarchical regression analyses performed within a structural equation modeling framework revealed that (1) neuroticism and perceived emotion control both predicted a latent variable of GAD in the expected direction, and (2) perceived emotion control moderated the relationship between neuroticism and GAD severity, such that lower levels of perceived emotion control were associated with a stronger relationship between neuroticism and GAD severity. The other dimensions of perceived control (i.e., stress and threat control) did not moderate the effect of neuroticism on GAD severity. The findings are discussed with regard to their implications to conceptual models of the psychopathology of GAD, and theory-based differential relationships between dimensions of vulnerability, perceived control, and anxiety disorders. PMID:26236059

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.

Inferring network structure in non-normal and mixed discrete-continuous genomic data.

PubMed

Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran

2018-03-01

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach. © 2017, The International Biometric Society.

Inferring network structure in non-normal and mixed discrete-continuous genomic data

PubMed Central

Bhadra, Anindya; Rao, Arvind; Baladandayuthapani, Veerabhadran

2017-01-01

Inferring dependence structure through undirected graphs is crucial for uncovering the major modes of multivariate interaction among high-dimensional genomic markers that are potentially associated with cancer. Traditionally, conditional independence has been studied using sparse Gaussian graphical models for continuous data and sparse Ising models for discrete data. However, there are two clear situations when these approaches are inadequate. The first occurs when the data are continuous but display non-normal marginal behavior such as heavy tails or skewness, rendering an assumption of normality inappropriate. The second occurs when a part of the data is ordinal or discrete (e.g., presence or absence of a mutation) and the other part is continuous (e.g., expression levels of genes or proteins). In this case, the existing Bayesian approaches typically employ a latent variable framework for the discrete part that precludes inferring conditional independence among the data that are actually observed. The current article overcomes these two challenges in a unified framework using Gaussian scale mixtures. Our framework is able to handle continuous data that are not normal and data that are of mixed continuous and discrete nature, while still being able to infer a sparse conditional sign independence structure among the observed data. Extensive performance comparison in simulations with alternative techniques and an analysis of a real cancer genomics data set demonstrate the effectiveness of the proposed approach. PMID:28437848

Extending information retrieval methods to personalized genomic-based studies of disease.

PubMed

Ye, Shuyun; Dawson, John A; Kendziorski, Christina

2014-01-01

Genomic-based studies of disease now involve diverse types of data collected on large groups of patients. A major challenge facing statistical scientists is how best to combine the data, extract important features, and comprehensively characterize the ways in which they affect an individual's disease course and likelihood of response to treatment. We have developed a survival-supervised latent Dirichlet allocation (survLDA) modeling framework to address these challenges. Latent Dirichlet allocation (LDA) models have proven extremely effective at identifying themes common across large collections of text, but applications to genomics have been limited. Our framework extends LDA to the genome by considering each patient as a "document" with "text" detailing his/her clinical events and genomic state. We then further extend the framework to allow for supervision by a time-to-event response. The model enables the efficient identification of collections of clinical and genomic features that co-occur within patient subgroups, and then characterizes each patient by those features. An application of survLDA to The Cancer Genome Atlas ovarian project identifies informative patient subgroups showing differential response to treatment, and validation in an independent cohort demonstrates the potential for patient-specific inference.

Revisiting the natural history of tuberculosis. The inclusion of constant reinfection, host tolerance, and damage-response frameworks leads to a better understanding of latent infection and its evolution towards active disease.

PubMed

Cardona, Pere-Joan

2010-02-01

Once Mycobacterium tuberculosis infects a person it can persist for a long time in a process called latent tuberculosis infection (LTBI). LTBI has traditionally been considered to involve the bacilli remaining in a non-replicating state (dormant) in old lesions but still retaining their ability to induce reactivation and cause active tuberculosis (TB) once a disruption of the immune response takes place. The present review aims to challenge these concepts by including recent experimental data supporting LTBI as a constant endogenous reinfection process as well as the recently introduced concepts of damage-response and tolerance frameworks to explain TB induction. These frameworks highlight the key role of an exaggerated and intolerant host response against M. tuberculosis bacilli which induces the classical TB cavity in immunocompetent adults once the constant endogenous reinfection process has resulted in the presence of bacilli in the upper lobes, where they can grow faster and the immune response is delayed. This essay intends to provide new clues to understanding the induction of TB in non-immunosuppressed patients.

Beyond logistic regression: structural equations modelling for binary variables and its application to investigating unobserved confounders.

PubMed

Kupek, Emil

2006-03-15

Structural equation modelling (SEM) has been increasingly used in medical statistics for solving a system of related regression equations. However, a great obstacle for its wider use has been its difficulty in handling categorical variables within the framework of generalised linear models. A large data set with a known structure among two related outcomes and three independent variables was generated to investigate the use of Yule's transformation of odds ratio (OR) into Q-metric by (OR-1)/(OR+1) to approximate Pearson's correlation coefficients between binary variables whose covariance structure can be further analysed by SEM. Percent of correctly classified events and non-events was compared with the classification obtained by logistic regression. The performance of SEM based on Q-metric was also checked on a small (N = 100) random sample of the data generated and on a real data set. SEM successfully recovered the generated model structure. SEM of real data suggested a significant influence of a latent confounding variable which would have not been detectable by standard logistic regression. SEM classification performance was broadly similar to that of the logistic regression. The analysis of binary data can be greatly enhanced by Yule's transformation of odds ratios into estimated correlation matrix that can be further analysed by SEM. The interpretation of results is aided by expressing them as odds ratios which are the most frequently used measure of effect in medical statistics.

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 computational nature of memory modification

PubMed Central

Gershman, Samuel J; Monfils, Marie-H; Norman, Kenneth A; Niv, Yael

2017-01-01

Retrieving a memory can modify its influence on subsequent behavior. We develop a computational theory of memory modification, according to which modification of a memory trace occurs through classical associative learning, but which memory trace is eligible for modification depends on a structure learning mechanism that discovers the units of association by segmenting the stream of experience into statistically distinct clusters (latent causes). New memories are formed when the structure learning mechanism infers that a new latent cause underlies current sensory observations. By the same token, old memories are modified when old and new sensory observations are inferred to have been generated by the same latent cause. We derive this framework from probabilistic principles, and present a computational implementation. Simulations demonstrate that our model can reproduce the major experimental findings from studies of memory modification in the Pavlovian conditioning literature. DOI: http://dx.doi.org/10.7554/eLife.23763.001 PMID:28294944

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.

A Complex Network Perspective on Clinical Science

PubMed Central

Hofmann, Stefan G.; Curtiss, Joshua; McNally, Richard J.

2016-01-01

Contemporary classification systems for mental disorders assume that abnormal behaviors are expressions of latent disease entities. An alternative to the latent disease model is the complex network approach. Instead of assuming that symptoms arise from an underlying disease entity, the complex network approach holds that disorders exist as systems of interrelated elements of a network. This approach also provides a framework for the understanding of therapeutic change. Depending on the structure of the network, change can occur abruptly once the network reaches a critical threshold (the tipping point). Homogeneous and highly connected networks often recover more slowly from local perturbations when the network approaches the tipping point, allowing for the possibility to predict treatment change, relapse, and recovery. In this article we discuss the complex network approach as an alternative to the latent disease model, and we discuss its implications for classification, therapy, relapse, and recovery. PMID:27694457

A Framework for Estimating Causal Effects in Latent Class Analysis: Is There a Causal Link Between Early Sex and Subsequent Profiles of Delinquency?

PubMed Central

Lanza, Stephanie T.; Coffman, Donna L.

2013-01-01

Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p=0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p=0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work. PMID:23839479

A framework for estimating causal effects in latent class analysis: is there a causal link between early sex and subsequent profiles of delinquency?

PubMed

Butera, Nicole M; Lanza, Stephanie T; Coffman, Donna L

2014-06-01

Prevention scientists use latent class analysis (LCA) with increasing frequency to characterize complex behavior patterns and profiles of risk. Often, the most important research questions in these studies involve establishing characteristics that predict membership in the latent classes, thus describing the composition of the subgroups and suggesting possible points of intervention. More recently, prevention scientists have begun to adopt modern methods for drawing causal inference from observational data because of the bias that can be introduced by confounders. This same issue of confounding exists in any analysis of observational data, including prediction of latent class membership. This study demonstrates a straightforward approach to causal inference in LCA that builds on propensity score methods. We demonstrate this approach by examining the causal effect of early sex on subsequent delinquency latent classes using data from 1,890 adolescents in 11th and 12th grade from wave I of the National Longitudinal Study of Adolescent Health. Prior to the statistical adjustment for potential confounders, early sex was significantly associated with delinquency latent class membership for both genders (p = 0.02). However, the propensity score adjusted analysis indicated no evidence for a causal effect of early sex on delinquency class membership (p = 0.76) for either gender. Sample R and SAS code is included in an Appendix in the ESM so that prevention scientists may adopt this approach to causal inference in LCA in their own work.

A novel latent gaussian copula framework for modeling spatial correlation in quantized SAR imagery with applications to ATR

NASA Astrophysics Data System (ADS)

Thelen, Brian T.; Xique, Ismael J.; Burns, Joseph W.; Goley, G. Steven; Nolan, Adam R.; Benson, Jonathan W.

2017-04-01

With all of the new remote sensing modalities available, and with ever increasing capabilities and frequency of collection, there is a desire to fundamentally understand/quantify the information content in the collected image data relative to various exploitation goals, such as detection/classification. A fundamental approach for this is the framework of Bayesian decision theory, but a daunting challenge is to have significantly flexible and accurate multivariate models for the features and/or pixels that capture a wide assortment of distributions and dependen- cies. In addition, data can come in the form of both continuous and discrete representations, where the latter is often generated based on considerations of robustness to imaging conditions and occlusions/degradations. In this paper we propose a novel suite of "latent" models fundamentally based on multivariate Gaussian copula models that can be used for quantized data from SAR imagery. For this Latent Gaussian Copula (LGC) model, we derive an approximate, maximum-likelihood estimation algorithm and demonstrate very reasonable estimation performance even for the larger images with many pixels. However applying these LGC models to large dimen- sions/images within a Bayesian decision/classification theory is infeasible due to the computational/numerical issues in evaluating the true full likelihood, and we propose an alternative class of novel pseudo-likelihoood detection statistics that are computationally feasible. We show in a few simple examples that these statistics have the potential to provide very good and robust detection/classification performance. All of this framework is demonstrated on a simulated SLICY data set, and the results show the importance of modeling the dependencies, and of utilizing the pseudo-likelihood methods.

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…

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.

The efficiency of parameter estimation of latent path analysis using summated rating scale (SRS) and method of successive interval (MSI) for transformation of score to scale

NASA Astrophysics Data System (ADS)

Solimun, Fernandes, Adji Achmad Rinaldo; Arisoesilaningsih, Endang

2017-12-01

Research in various fields generally investigates systems and involves latent variables. One method to analyze the model representing the system is path analysis. The data of latent variables measured using questionnaires by applying attitude scale model yields data in the form of score, before analyzed should be transformation so that it becomes data of scale. Path coefficient, is parameter estimator, calculated from scale data using method of successive interval (MSI) and summated rating scale (SRS). In this research will be identifying which data transformation method is better. Path coefficients have smaller varieties are said to be more efficient. The transformation method that produces scaled data and used in path analysis capable of producing path coefficients (parameter estimators) with smaller varieties is said to be better. The result of analysis using real data shows that on the influence of Attitude variable to Intention Entrepreneurship, has relative efficiency (ER) = 1, where it shows that the result of analysis using data transformation of MSI and SRS as efficient. On the other hand, for simulation data, at high correlation between items (0.7-0.9), MSI method is more efficient 1.3 times better than SRS method.

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

Decoding brain activity using a large-scale probabilistic functional-anatomical atlas of human cognition

PubMed Central

Jones, Michael N.

2017-01-01

A central goal of cognitive neuroscience is to decode human brain activity—that is, to infer mental processes from observed patterns of whole-brain activation. Previous decoding efforts have focused on classifying brain activity into a small set of discrete cognitive states. To attain maximal utility, a decoding framework must be open-ended, systematic, and context-sensitive—that is, capable of interpreting numerous brain states, presented in arbitrary combinations, in light of prior information. Here we take steps towards this objective by introducing a probabilistic decoding framework based on a novel topic model—Generalized Correspondence Latent Dirichlet Allocation—that learns latent topics from a database of over 11,000 published fMRI studies. The model produces highly interpretable, spatially-circumscribed topics that enable flexible decoding of whole-brain images. Importantly, the Bayesian nature of the model allows one to “seed” decoder priors with arbitrary images and text—enabling researchers, for the first time, to generate quantitative, context-sensitive interpretations of whole-brain patterns of brain activity. PMID:29059185

A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing

PubMed Central

Wang, Guoli; Ebrahimi, Nader

2014-01-01

Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H, such that V ∼ W H. It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H. In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data. PMID:25821345

A unified statistical approach to non-negative matrix factorization and probabilistic latent semantic indexing.

PubMed

Devarajan, Karthik; Wang, Guoli; Ebrahimi, Nader

2015-04-01

Non-negative matrix factorization (NMF) is a powerful machine learning method for decomposing a high-dimensional nonnegative matrix V into the product of two nonnegative matrices, W and H , such that V ∼ W H . It has been shown to have a parts-based, sparse representation of the data. NMF has been successfully applied in a variety of areas such as natural language processing, neuroscience, information retrieval, image processing, speech recognition and computational biology for the analysis and interpretation of large-scale data. There has also been simultaneous development of a related statistical latent class modeling approach, namely, probabilistic latent semantic indexing (PLSI), for analyzing and interpreting co-occurrence count data arising in natural language processing. In this paper, we present a generalized statistical approach to NMF and PLSI based on Renyi's divergence between two non-negative matrices, stemming from the Poisson likelihood. Our approach unifies various competing models and provides a unique theoretical framework for these methods. We propose a unified algorithm for NMF and provide a rigorous proof of monotonicity of multiplicative updates for W and H . In addition, we generalize the relationship between NMF and PLSI within this framework. We demonstrate the applicability and utility of our approach as well as its superior performance relative to existing methods using real-life and simulated document clustering data.

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.

Multilevel Dynamic Generalized Structured Component Analysis for Brain Connectivity Analysis in Functional Neuroimaging Data.

PubMed

Jung, Kwanghee; Takane, Yoshio; Hwang, Heungsun; Woodward, Todd S

2016-06-01

We extend dynamic generalized structured component analysis (GSCA) to enhance its data-analytic capability in structural equation modeling of multi-subject time series data. Time series data of multiple subjects are typically hierarchically structured, where time points are nested within subjects who are in turn nested within a group. The proposed approach, named multilevel dynamic GSCA, accommodates the nested structure in time series data. Explicitly taking the nested structure into account, the proposed method allows investigating subject-wise variability of the loadings and path coefficients by looking at the variance estimates of the corresponding random effects, as well as fixed loadings between observed and latent variables and fixed path coefficients between latent variables. We demonstrate the effectiveness of the proposed approach by applying the method to the multi-subject functional neuroimaging data for brain connectivity analysis, where time series data-level measurements are nested within subjects.

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.

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.

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.

Principal Stratification: A Tool for Understanding Variation in Program Effects across Endogenous Subgroups

ERIC Educational Resources Information Center

Page, Lindsay C.; Feller, Avi; Grindal, Todd; Miratrix, Luke; Somers, Marie-Andree

2015-01-01

Increasingly, researchers are interested in questions regarding treatment-effect variation across partially or fully latent subgroups defined not by pretreatment characteristics but by postrandomization actions. One promising approach to address such questions is principal stratification. Under this framework, a researcher defines endogenous…

Linguistic Extensions of Topic Models

ERIC Educational Resources Information Center

Boyd-Graber, Jordan

2010-01-01

Topic models like latent Dirichlet allocation (LDA) provide a framework for analyzing large datasets where observations are collected into groups. Although topic modeling has been fruitfully applied to problems social science, biology, and computer vision, it has been most widely used to model datasets where documents are modeled as exchangeable…

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…

Psychometric Properties of the Problematic Internet Use Questionnaire Short-Form (PIUQ-SF-6) in a Nationally Representative Sample of Adolescents

PubMed Central

Demetrovics, Zsolt; Király, Orsolya; Koronczai, Beatrix; Griffiths, Mark D.; Nagygyörgy, Katalin; Elekes, Zsuzsanna; Tamás, Domokos; Kun, Bernadette; Kökönyei, Gyöngyi; Urbán, Róbert

2016-01-01

Despite the large number of measurement tools developed to assess problematic Internet use, numerous studies use measures with only modest investigation into their psychometric properties. The goal of the present study was to validate the short (6-item) version of the Problematic Internet Use Questionnaire (PIUQ) on a nationally representative adolescent sample (n = 5,005; mean age 16.4 years, SD = 0.87) and to determine a statistically established cut-off value. Data were collected within the framework of the European School Survey Project on Alcohol and Other Drugs project. Results showed an acceptable fit of the original three-factor structure to the data. In addition, a MIMIC model was carried out to justify the need for three distinct factors. The sample was divided into users at-risk of problematic Internet use and those with no-risk using a latent profile analysis. Two latent classes were obtained with 14.4% of adolescents belonging to the at-risk group. Concurrent and convergent validity were tested by comparing the two groups across a number of variables (i.e., time spent online, academic achievement, self-esteem, depressive symptoms, and preferred online activities). Using the at-risk latent profile analysis class as the gold standard, a cut-off value of 15 (out of 30) was suggested based on sensitivity and specificity analyses. In conclusion, the brief version of the (6-item) PIUQ also appears to be an appropriate measure to differentiate between Internet users at risk of developing problematic Internet use and those not at risk. Furthermore, due to its brevity, the shortened PIUQ is advantageous to utilize within large-scale surveys assessing many different behaviors and/or constructs by reducing the overall number of survey questions, and as a consequence, likely increasing completion rates. PMID:27504915

Designing a stable feedback control system for blind image deconvolution.

PubMed

Cheng, Shichao; Liu, Risheng; Fan, Xin; Luo, Zhongxuan

2018-05-01

Blind image deconvolution is one of the main low-level vision problems with wide applications. Many previous works manually design regularization to simultaneously estimate the latent sharp image and the blur kernel under maximum a posterior framework. However, it has been demonstrated that such joint estimation strategies may lead to the undesired trivial solution. In this paper, we present a novel perspective, using a stable feedback control system, to simulate the latent sharp image propagation. The controller of our system consists of regularization and guidance, which decide the sparsity and sharp features of latent image, respectively. Furthermore, the formational model of blind image is introduced into the feedback process to avoid the image restoration deviating from the stable point. The stability analysis of the system indicates the latent image propagation in blind deconvolution task can be efficiently estimated and controlled by cues and priors. Thus the kernel estimation used for image restoration becomes more precision. Experimental results show that our system is effective on image propagation, and can perform favorably against the state-of-the-art blind image deconvolution methods on different benchmark image sets and special blurred images. Copyright © 2018 Elsevier Ltd. All rights reserved.

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).

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

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.

Verbal Neuropsychological Functions in Aphasia: An Integrative Model

ERIC Educational Resources Information Center

Vigliecca, Nora Silvana; Báez, Sandra

2015-01-01

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

Discrete Latent Markov Models for Normally Distributed Response Data

ERIC Educational Resources Information Center

Schmittmann, Verena D.; Dolan, Conor V.; van der Maas, Han L. J.; Neale, Michael C.

2005-01-01

Van de Pol and Langeheine (1990) presented a general framework for Markov modeling of repeatedly measured discrete data. We discuss analogical single indicator models for normally distributed responses. In contrast to discrete models, which have been studied extensively, analogical continuous response models have hardly been considered. These…

How To Manage the Emerging Generational Divide in the Contemporary Knowledge-Rich Workplace.

ERIC Educational Resources Information Center

Novicevic, Milorad M.; Buckley, M. Ronald

2001-01-01

Addresses the manager's dilemmas and options in resolving emerging latent intergenerational conflict in the contemporary knowledge-rich workplace. Topics include a theoretical framework for generational divide management; the polarization in task requirements; social and environmental factors; differences in employee needs and expectations; and…

Advances in Methods for Assessing Longitudinal Change

ERIC Educational Resources Information Center

Grimm, Kevin J.; Mazza, Gina L.; Mazzocco, Michèle M. M.

2016-01-01

Educational research aims to understand how and why students change over time. With its emphasis on within-person change, latent change score models provide educational researchers with a more general and flexible framework for testing nuanced hypotheses regarding within-person change and between-person differences in within-person change. Models…

Validation of the conceptual research utilization scale: an application of the standards for educational and psychological testing in healthcare

PubMed Central

2011-01-01

Background There is a lack of acceptable, reliable, and valid survey instruments to measure conceptual research utilization (CRU). In this study, we investigated the psychometric properties of a newly developed scale (the CRU Scale). Methods We used the Standards for Educational and Psychological Testing as a validation framework to assess four sources of validity evidence: content, response processes, internal structure, and relations to other variables. A panel of nine international research utilization experts performed a formal content validity assessment. To determine response process validity, we conducted a series of one-on-one scale administration sessions with 10 healthcare aides. Internal structure and relations to other variables validity was examined using CRU Scale response data from a sample of 707 healthcare aides working in 30 urban Canadian nursing homes. Principal components analysis and confirmatory factor analyses were conducted to determine internal structure. Relations to other variables were examined using: (1) bivariate correlations; (2) change in mean values of CRU with increasing levels of other kinds of research utilization; and (3) multivariate linear regression. Results Content validity index scores for the five items ranged from 0.55 to 1.00. The principal components analysis predicted a 5-item 1-factor model. This was inconsistent with the findings from the confirmatory factor analysis, which showed best fit for a 4-item 1-factor model. Bivariate associations between CRU and other kinds of research utilization were statistically significant (p < 0.01) for the latent CRU scale score and all five CRU items. The CRU scale score was also shown to be significant predictor of overall research utilization in multivariate linear regression. Conclusions The CRU scale showed acceptable initial psychometric properties with respect to responses from healthcare aides in nursing homes. Based on our validity, reliability, and acceptability analyses, we recommend using a reduced (four-item) version of the CRU scale to yield sound assessments of CRU by healthcare aides. Refinement to the wording of one item is also needed. Planned future research will include: latent scale scoring, identification of variables that predict and are outcomes to conceptual research use, and longitudinal work to determine CRU Scale sensitivity to change. PMID:21595888

The forecasting of menstruation based on a state-space modeling of basal body temperature time series.

PubMed

Fukaya, Keiichi; Kawamori, Ai; Osada, Yutaka; Kitazawa, Masumi; Ishiguro, Makio

2017-09-20

Women's basal body temperature (BBT) shows a periodic pattern that associates with menstrual cycle. Although this fact suggests a possibility that daily BBT time series can be useful for estimating the underlying phase state as well as for predicting the length of current menstrual cycle, little attention has been paid to model BBT time series. In this study, we propose a state-space model that involves the menstrual phase as a latent state variable to explain the daily fluctuation of BBT and the menstruation cycle length. Conditional distributions of the phase are obtained by using sequential Bayesian filtering techniques. A predictive distribution of the next menstruation day can be derived based on this conditional distribution and the model, leading to a novel statistical framework that provides a sequentially updated prediction for upcoming menstruation day. We applied this framework to a real data set of women's BBT and menstruation days and compared prediction accuracy of the proposed method with that of previous methods, showing that the proposed method generally provides a better prediction. Because BBT can be obtained with relatively small cost and effort, the proposed method can be useful for women's health management. Potential extensions of this framework as the basis of modeling and predicting events that are associated with the menstrual cycles are discussed. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd. © 2017 The Authors. Statistics in Medicine Published by John Wiley & Sons Ltd.

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

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.

Hierarchical spatial capture-recapture models: Modeling population density from stratified populations

USGS Publications Warehouse

Royle, J. Andrew; Converse, Sarah J.

2014-01-01

Capture–recapture studies are often conducted on populations that are stratified by space, time or other factors. In this paper, we develop a Bayesian spatial capture–recapture (SCR) modelling framework for stratified populations – when sampling occurs within multiple distinct spatial and temporal strata.We describe a hierarchical model that integrates distinct models for both the spatial encounter history data from capture–recapture sampling, and also for modelling variation in density among strata. We use an implementation of data augmentation to parameterize the model in terms of a latent categorical stratum or group membership variable, which provides a convenient implementation in popular BUGS software packages.We provide an example application to an experimental study involving small-mammal sampling on multiple trapping grids over multiple years, where the main interest is in modelling a treatment effect on population density among the trapping grids.Many capture–recapture studies involve some aspect of spatial or temporal replication that requires some attention to modelling variation among groups or strata. We propose a hierarchical model that allows explicit modelling of group or strata effects. Because the model is formulated for individual encounter histories and is easily implemented in the BUGS language and other free software, it also provides a general framework for modelling individual effects, such as are present in SCR models.

General mixture item response models with different item response structures: Exposition with an application to Likert scales.

PubMed

Tijmstra, Jesper; Bolsinova, Maria; Jeon, Minjeong

2018-01-10

This article proposes a general mixture item response theory (IRT) framework that allows for classes of persons to differ with respect to the type of processes underlying the item responses. Through the use of mixture models, nonnested IRT models with different structures can be estimated for different classes, and class membership can be estimated for each person in the sample. If researchers are able to provide competing measurement models, this mixture IRT framework may help them deal with some violations of measurement invariance. To illustrate this approach, we consider a two-class mixture model, where a person's responses to Likert-scale items containing a neutral middle category are either modeled using a generalized partial credit model, or through an IRTree model. In the first model, the middle category ("neither agree nor disagree") is taken to be qualitatively similar to the other categories, and is taken to provide information about the person's endorsement. In the second model, the middle category is taken to be qualitatively different and to reflect a nonresponse choice, which is modeled using an additional latent variable that captures a person's willingness to respond. The mixture model is studied using simulation studies and is applied to an empirical example.

A Psychometric Evaluation of the DSM-IV Criteria for Antisocial Personality Disorder: Dimensionality, Local Reliability, and Differential Item Functioning Across Gender.

PubMed

Paap, Muirne C S; Braeken, Johan; Pedersen, Geir; Urnes, Øyvind; Karterud, Sigmund; Wilberg, Theresa; Hummelen, Benjamin

2017-12-01

This study aims at evaluating the psychometric properties of the antisocial personality disorder (ASPD) criteria in a large sample of patients, most of whom had one or more personality disorders (PD). PD diagnoses were assessed by experienced clinicians using the Structured Clinical Interview for Diagnostic and Statistical Manual of Mental Disorders, 4th edition, Axis II PDs. Analyses were performed within an item response theory framework. Results of the analyses indicated that ASPD is a unidimensional construct that can be measured reliably at the upper range of the latent trait scale. Differential item functioning across gender was restricted to two criteria and had little impact on the latent ASPD trait level. Patients fulfilling both the adult ASPD criteria and the conduct disorder criteria had similar latent trait distributions as patients fulfilling only the adult ASPD criteria. Overall, the ASPD items fit the purpose of a diagnostic instrument well, that is, distinguishing patients with moderate from those with high antisocial personality scores.

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.

A latent variable approach to study gene-environment interactions in the presence of multiple correlated exposures.

PubMed

Sánchez, Brisa N; Kang, Shan; Mukherjee, Bhramar

2012-06-01

Many existing cohort studies initially designed to investigate disease risk as a function of environmental exposures have collected genomic data in recent years with the objective of testing for gene-environment interaction (G × E) effects. In environmental epidemiology, interest in G × E arises primarily after a significant effect of the environmental exposure has been documented. Cohort studies often collect rich exposure data; as a result, assessing G × E effects in the presence of multiple exposure markers further increases the burden of multiple testing, an issue already present in both genetic and environment health studies. Latent variable (LV) models have been used in environmental epidemiology to reduce dimensionality of the exposure data, gain power by reducing multiplicity issues via condensing exposure data, and avoid collinearity problems due to presence of multiple correlated exposures. We extend the LV framework to characterize gene-environment interaction in presence of multiple correlated exposures and genotype categories. Further, similar to what has been done in case-control G × E studies, we use the assumption of gene-environment (G-E) independence to boost the power of tests for interaction. The consequences of making this assumption, or the issue of how to explicitly model G-E association has not been previously investigated in LV models. We postulate a hierarchy of assumptions about the LV model regarding the different forms of G-E dependence and show that making such assumptions may influence inferential results on the G, E, and G × E parameters. We implement a class of shrinkage estimators to data adaptively trade-off between the most restrictive to most flexible form of G-E dependence assumption and note that such class of compromise estimators can serve as a benchmark of model adequacy in LV models. We demonstrate the methods with an example from the Early Life Exposures in Mexico City to Neuro-Toxicants Study of lead exposure, iron metabolism genes, and birth weight. © 2011, The International Biometric Society.

Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data.

PubMed

Buettner, Florian; Moignard, Victoria; Göttgens, Berthold; Theis, Fabian J

2014-07-01

High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data. We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA. The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm. © The Author 2014. Published by Oxford University Press. All rights reserved.

Probabilistic PCA of censored data: accounting for uncertainties in the visualization of high-throughput single-cell qPCR data

PubMed Central

Buettner, Florian; Moignard, Victoria; Göttgens, Berthold; Theis, Fabian J.

2014-01-01

Motivation: High-throughput single-cell quantitative real-time polymerase chain reaction (qPCR) is a promising technique allowing for new insights in complex cellular processes. However, the PCR reaction can be detected only up to a certain detection limit, whereas failed reactions could be due to low or absent expression, and the true expression level is unknown. Because this censoring can occur for high proportions of the data, it is one of the main challenges when dealing with single-cell qPCR data. Principal component analysis (PCA) is an important tool for visualizing the structure of high-dimensional data as well as for identifying subpopulations of cells. However, to date it is not clear how to perform a PCA of censored data. We present a probabilistic approach that accounts for the censoring and evaluate it for two typical datasets containing single-cell qPCR data. Results: We use the Gaussian process latent variable model framework to account for censoring by introducing an appropriate noise model and allowing a different kernel for each dimension. We evaluate this new approach for two typical qPCR datasets (of mouse embryonic stem cells and blood stem/progenitor cells, respectively) by performing linear and non-linear probabilistic PCA. Taking the censoring into account results in a 2D representation of the data, which better reflects its known structure: in both datasets, our new approach results in a better separation of known cell types and is able to reveal subpopulations in one dataset that could not be resolved using standard PCA. Availability and implementation: The implementation was based on the existing Gaussian process latent variable model toolbox (https://github.com/SheffieldML/GPmat); extensions for noise models and kernels accounting for censoring are available at http://icb.helmholtz-muenchen.de/censgplvm. Contact: fbuettner.phys@gmail.com Supplementary information: Supplementary data are available at Bioinformatics online. PMID:24618470

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 Potential of Growth Mixture Modelling

ERIC Educational Resources Information Center

Muthen, Bengt

2006-01-01

The authors of the paper on growth mixture modelling (GMM) give a description of GMM and related techniques as applied to antisocial behaviour. They bring up the important issue of choice of model within the general framework of mixture modelling, especially the choice between latent class growth analysis (LCGA) techniques developed by Nagin and…

Democracy and Education, Schools and Communities Initiative: Conceptual Framework and Preliminary Findings.

ERIC Educational Resources Information Center

Koliba, Christopher

Nine rural Vermont schools committed to linking to their local communities are participating in an ongoing study of how manifest curricula (what is taught) and latent curricula (how classes are taught and schools are governed) influence development of student dispositions toward democratic participation. This paper presents preliminary findings…

Assessing Person-Centered Outcomes in Practice Research: A Latent Transition Profile Framework

ERIC Educational Resources Information Center

Thompson, Aaron M.; Macy, Rebecca J.; Fraser, Mark W.

2011-01-01

Advances in statistics provide new methods for analyzing practice data. These advances include person-centered methods (PCMs) that identify subgroups of research participants with similar characteristics. PCMs derive from a frame of reference that is similar to the risk factor perspective in practice. In practice, the delivery of services is often…

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.

Organizing Books and Authors by Multilayer SOM.

PubMed

Zhang, Haijun; Chow, Tommy W S; Wu, Q M Jonathan

2016-12-01

This paper introduces a new framework for the organization of electronic books (e-books) and their corresponding authors using a multilayer self-organizing map (MLSOM). An author is modeled by a rich tree-structured representation, and an MLSOM-based system is used as an efficient solution to the organizational problem of structured data. The tree-structured representation formulates author features in a hierarchy of author biography, books, pages, and paragraphs. To efficiently tackle the tree-structured representation, we used an MLSOM algorithm that serves as a clustering technique to handle e-books and their corresponding authors. A book and author recommender system is then implemented using the proposed framework. The effectiveness of our approach was examined in a large-scale data set containing 3868 authors along with the 10500 e-books that they wrote. We also provided visualization results of MLSOM for revealing the relevance patterns hidden from presented author clusters. The experimental results corroborate that the proposed method outperforms other content-based models (e.g., rate adapting poisson, latent Dirichlet allocation, probabilistic latent semantic indexing, and so on) and offers a promising solution to book recommendation, author recommendation, and visualization.

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…

Perceptions of care in women sent home in latent labor.

PubMed

Hosek, Claire; Faucher, Mary Ann; Lankford, Janice; Alexander, James

2014-01-01

To assess perceptions of care from woman discharged from an obstetrical (OB) triage unit or a labor and delivery unit with a diagnosis of false or latent labor in order to determine factors that may increase or decrease the woman's satisfaction with care. Descriptive, convenience sample. One hundred low-income pregnant women at term presenting for care in latent labor consented to participate in a telephone survey. The survey was based on the relevant research about care of women in early labor and the Donabedian quality improvement framework assessing structure, process, and outcomes of care. Forty-one percent of women did not want to be discharged home in latent labor. Common reasons included women stating they were in too much pain or they were living too far from the birth setting. Eating, drinking, and comfort measures were the most common measures women cited that would have made them feel better when in the hospital. A reoccurring response from women was their desire for very clear and specific written instructions about how to stay comfortable at home and when to return to the hospital. Comfort measures in the birth setting, including in triage, should include a variety of options including ambulation and oral nutrition. Detailed and specific written instructions about early labor and staying comfortable while at home have value for women in this survey regarding their perceptions of care. Results from this survey of low-income women suggest that a subset of women in latent labor just do not want to go home and this may be related to having too much pain and/or travel distance to the hospital. Hospital birth settings also have an opportunity to create a care environment that provides services and embodies attributes that women report as important for their satisfaction with care in latent labor.

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

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.

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

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

Unmarked: An R package for fitting hierarchical models of wildlife occurrence and abundance

USGS Publications Warehouse

Fiske, I.J.; Chandler, R.B.

2011-01-01

Ecological research uses data collection techniques that are prone to substantial and unique types of measurement error to address scientic questions about species abundance and distribution. These data collection schemes include a number of survey methods in which unmarked individuals are counted, or determined to be present, at spatially- referenced sites. Examples include site occupancy sampling, repeated counts, distance sampling, removal sampling, and double observer sampling. To appropriately analyze these data, hierarchical models have been developed to separately model explanatory variables of both a latent abundance or occurrence process and a conditional detection process. Because these models have a straightforward interpretation paralleling mecha- nisms under which the data arose, they have recently gained immense popularity. The common hierarchical structure of these models is well-suited for a unied modeling in- terface. The R package unmarked provides such a unied modeling framework, including tools for data exploration, model tting, model criticism, post-hoc analysis, and model comparison.

Developmental relations between reading fluency and reading comprehension: A longitudinal study from grade one to two

PubMed Central

Kim, Young-Suk; Wagner, Richard K.; Lopez, Danielle

2012-01-01

From a developmental framework, relations among list reading fluency, oral and silent reading fluency, listening comprehension, and reading comprehension might be expected to change as children’s reading skills develop. We examined developmental relations among these constructs in a latent-variable longitudinal study of first- and second-grade students. Results showed that list reading fluency was uniquely related to reading comprehension in grade one, but not in grade two after accounting for text reading fluency (oral or silent) and listening comprehension. In contrast, text reading fluency was uniquely related to reading comprehension in grade two, but not in grade one, after accounting for list reading fluency and listening comprehension. When oral and silent reading fluency were compared, oral reading fluency was uniquely related to reading comprehension after accounting for silent reading fluency in grade one whereas in grade two, silent reading fluency was uniquely related to reading comprehension after accounting for oral reading fluency. PMID:22726256

Generalized species sampling priors with latent Beta reinforcements

PubMed Central

Airoldi, Edoardo M.; Costa, Thiago; Bassetti, Federico; Leisen, Fabrizio; Guindani, Michele

2014-01-01

Many popular Bayesian nonparametric priors can be characterized in terms of exchangeable species sampling sequences. However, in some applications, exchangeability may not be appropriate. We introduce a novel and probabilistically coherent family of non-exchangeable species sampling sequences characterized by a tractable predictive probability function with weights driven by a sequence of independent Beta random variables. We compare their theoretical clustering properties with those of the Dirichlet Process and the two parameters Poisson-Dirichlet process. The proposed construction provides a complete characterization of the joint process, differently from existing work. We then propose the use of such process as prior distribution in a hierarchical Bayes modeling framework, and we describe a Markov Chain Monte Carlo sampler for posterior inference. We evaluate the performance of the prior and the robustness of the resulting inference in a simulation study, providing a comparison with popular Dirichlet Processes mixtures and Hidden Markov Models. Finally, we develop an application to the detection of chromosomal aberrations in breast cancer by leveraging array CGH data. PMID:25870462

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing.

PubMed

Pajak, Bozena; Fine, Alex B; Kleinschmidt, Dave F; Jaeger, T Florian

2016-12-01

We present a framework of second and additional language (L2/L n ) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/L n learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/L n acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/L n learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa.

Learning Additional Languages as Hierarchical Probabilistic Inference: Insights From First Language Processing

PubMed Central

Pajak, Bozena; Fine, Alex B.; Kleinschmidt, Dave F.; Jaeger, T. Florian

2015-01-01

We present a framework of second and additional language (L2/Ln) acquisition motivated by recent work on socio-indexical knowledge in first language (L1) processing. The distribution of linguistic categories covaries with socio-indexical variables (e.g., talker identity, gender, dialects). We summarize evidence that implicit probabilistic knowledge of this covariance is critical to L1 processing, and propose that L2/Ln learning uses the same type of socio-indexical information to probabilistically infer latent hierarchical structure over previously learned and new languages. This structure guides the acquisition of new languages based on their inferred place within that hierarchy, and is itself continuously revised based on new input from any language. This proposal unifies L1 processing and L2/Ln acquisition as probabilistic inference under uncertainty over socio-indexical structure. It also offers a new perspective on crosslinguistic influences during L2/Ln learning, accommodating gradient and continued transfer (both negative and positive) from previously learned to novel languages, and vice versa. PMID:28348442

Cultural influences on positive father involvement in two-parent Mexican-origin families.

PubMed

Cruz, Rick A; King, Kevin M; Widaman, Keith F; Leu, Janxin; Cauce, Ana Mari; Conger, Rand D

2011-10-01

A growing body of research documents the importance of positive father involvement in children's development. However, research on fathers in Latino families is sparse, and research contextualizing the father-child relationship within a cultural framework is needed. The present study examined how fathers' cultural practices and values predicted their fifth-grade children's report of positive father involvement in a sample of 450 two-parent Mexican-origin families. Predictors included Spanish- and English-language use, Mexican and American cultural values, and positive machismo (i.e., culturally related attitudes about the father's role within the family). Positive father involvement was measured by the child's report of his or her father's monitoring, educational involvement, and warmth. Latent variable regression analyses showed that fathers' machismo attitudes were positively related to children's report of positive father involvement and that this association was similar across boys and girls. The results of this study suggest an important association between fathers' cultural values about men's roles and responsibilities within a family and their children's perception of positive fathering.

The Influence of Static and Dynamic Intrapersonal Factors on Longitudinal Patterns of Peer Victimization through Mid-adolescence: a Latent Transition Analysis.

PubMed

Haltigan, John D; Vaillancourt, Tracy

2018-01-01

Using 6 cycles (grade 5 through grade 10) of data obtained from a large prospective sample of Canadian school children (N = 700; 52.6% girls), we replicated previous findings concerning the empirical definition of peer victimization (i.e., being bullied) and examined static and dynamic intrapersonal factors associated with its emergence and experiential continuity through mid-adolescence. Latent class analyses consistently revealed a low victimization and an elevated victimization class across time, supporting previous work suggesting peer victimization was defined by degree rather than by type (e.g., physical). Using latent transition analyses (LTA), we found that child sex, parent-perceived pubertal development, and internalizing symptoms influenced the probability of transitioning from the low to the elevated victimization class across time. Higher-order extensions within the LTA modeling framework revealed a lasting effect of grade 5 victimization status on grade 10 victimization status and a large effect of chronic victimization on later parent-reported youth internalizing symptoms (net of prior parent-reported internalizing symptoms) in later adolescence (grade 11). Implications of the current findings for the experience of peer victimization, as well as the application of latent transition analysis as a useful approach for peer victimization research, are discussed.

Relating Memory To Functional Performance In Normal Aging to Dementia Using Hierarchical Bayesian Cognitive Processing Models

PubMed Central

Shankle, William R.; Pooley, James P.; Steyvers, Mark; Hara, Junko; Mangrola, Tushar; Reisberg, Barry; Lee, Michael D.

2012-01-01

Determining how cognition affects functional abilities is important in Alzheimer’s disease and related disorders (ADRD). 280 patients (normal or ADRD) received a total of 1,514 assessments using the Functional Assessment Staging Test (FAST) procedure and the MCI Screen (MCIS). A hierarchical Bayesian cognitive processing (HBCP) model was created by embedding a signal detection theory (SDT) model of the MCIS delayed recognition memory task into a hierarchical Bayesian framework. The SDT model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the six FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. HBCP models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition to a continuous measure of functional severity for both individuals and FAST groups. Such a translation links two levels of brain information processing, and may enable more accurate correlations with other levels, such as those characterized by biomarkers. PMID:22407225

Relating memory to functional performance in normal aging to dementia using hierarchical Bayesian cognitive processing models.

PubMed

Shankle, William R; Pooley, James P; Steyvers, Mark; Hara, Junko; Mangrola, Tushar; Reisberg, Barry; Lee, Michael D

2013-01-01

Determining how cognition affects functional abilities is important in Alzheimer disease and related disorders. A total of 280 patients (normal or Alzheimer disease and related disorders) received a total of 1514 assessments using the functional assessment staging test (FAST) procedure and the MCI Screen. A hierarchical Bayesian cognitive processing model was created by embedding a signal detection theory model of the MCI Screen-delayed recognition memory task into a hierarchical Bayesian framework. The signal detection theory model used latent parameters of discriminability (memory process) and response bias (executive function) to predict, simultaneously, recognition memory performance for each patient and each FAST severity group. The observed recognition memory data did not distinguish the 6 FAST severity stages, but the latent parameters completely separated them. The latent parameters were also used successfully to transform the ordinal FAST measure into a continuous measure reflecting the underlying continuum of functional severity. Hierarchical Bayesian cognitive processing models applied to recognition memory data from clinical practice settings accurately translated a latent measure of cognition into a continuous measure of functional severity for both individuals and FAST groups. Such a translation links 2 levels of brain information processing and may enable more accurate correlations with other levels, such as those characterized by biomarkers.

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…

Dissecting the Illegal Ivory Trade: An Analysis of Ivory Seizures Data

PubMed Central

Underwood, Fiona M.; Burn, Robert W.; Milliken, Tom

2013-01-01

Reliable evidence of trends in the illegal ivory trade is important for informing decision making for elephants but it is difficult to obtain due to the covert nature of the trade. The Elephant Trade Information System, a global database of reported seizures of illegal ivory, holds the only extensive information on illicit trade available. However inherent biases in seizure data make it difficult to infer trends; countries differ in their ability to make and report seizures and these differences cannot be directly measured. We developed a new modelling framework to provide quantitative evidence on trends in the illegal ivory trade from seizures data. The framework used Bayesian hierarchical latent variable models to reduce bias in seizures data by identifying proxy variables that describe the variability in seizure and reporting rates between countries and over time. Models produced bias-adjusted smoothed estimates of relative trends in illegal ivory activity for raw and worked ivory in three weight classes. Activity is represented by two indicators describing the number of illegal ivory transactions – Transactions Index – and the total weight of illegal ivory transactions – Weights Index – at global, regional or national levels. Globally, activity was found to be rapidly increasing and at its highest level for 16 years, more than doubling from 2007 to 2011 and tripling from 1998 to 2011. Over 70% of the Transactions Index is from shipments of worked ivory weighing less than 10 kg and the rapid increase since 2007 is mainly due to increased consumption in China. Over 70% of the Weights Index is from shipments of raw ivory weighing at least 100 kg mainly moving from Central and East Africa to Southeast and East Asia. The results tie together recent findings on trends in poaching rates, declining populations and consumption and provide detailed evidence to inform international decision making on elephants. PMID:24250744

Dissecting the illegal ivory trade: an analysis of ivory seizures data.

PubMed

Underwood, Fiona M; Burn, Robert W; Milliken, Tom

2013-01-01

Reliable evidence of trends in the illegal ivory trade is important for informing decision making for elephants but it is difficult to obtain due to the covert nature of the trade. The Elephant Trade Information System, a global database of reported seizures of illegal ivory, holds the only extensive information on illicit trade available. However inherent biases in seizure data make it difficult to infer trends; countries differ in their ability to make and report seizures and these differences cannot be directly measured. We developed a new modelling framework to provide quantitative evidence on trends in the illegal ivory trade from seizures data. The framework used Bayesian hierarchical latent variable models to reduce bias in seizures data by identifying proxy variables that describe the variability in seizure and reporting rates between countries and over time. Models produced bias-adjusted smoothed estimates of relative trends in illegal ivory activity for raw and worked ivory in three weight classes. Activity is represented by two indicators describing the number of illegal ivory transactions--Transactions Index--and the total weight of illegal ivory transactions--Weights Index--at global, regional or national levels. Globally, activity was found to be rapidly increasing and at its highest level for 16 years, more than doubling from 2007 to 2011 and tripling from 1998 to 2011. Over 70% of the Transactions Index is from shipments of worked ivory weighing less than 10 kg and the rapid increase since 2007 is mainly due to increased consumption in China. Over 70% of the Weights Index is from shipments of raw ivory weighing at least 100 kg mainly moving from Central and East Africa to Southeast and East Asia. The results tie together recent findings on trends in poaching rates, declining populations and consumption and provide detailed evidence to inform international decision making on elephants.

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

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.

Abstract Conceptual Feature Ratings Predict Gaze within Written Word Arrays: Evidence from a Visual Wor(l)d Paradigm

ERIC Educational Resources Information Center

Primativo, Silvia; Reilly, Jamie; Crutch, Sebastian J

2017-01-01

The Abstract Conceptual Feature (ACF) framework predicts that word meaning is represented within a high-dimensional semantic space bounded by weighted contributions of perceptual, affective, and encyclopedic information. The ACF, like latent semantic analysis, is amenable to distance metrics between any two words. We applied predictions of the ACF…

Young-Age Gender Differences in Mathematics Mediated by Independent Control or Uncontrollability

ERIC Educational Resources Information Center

Zirk-Sadowski, Jan; Lamptey, Charlotte; Devine, Amy; Haggard, Mark; Szucs, Dénes

2014-01-01

We studied whether the origins of math anxiety can be related to a biologically supported framework of stress induction: (un)controllability perception, here indicated by self-reported independent efforts in mathematics. Math anxiety was tested in 182 children (8- to 11-year-olds). "Latent factor modeling" was used to test hypotheses on…