The Experts below are selected from a list of 13452 Experts worldwide ranked by ideXlab platform
Tenko Raykov - One of the best experts on this subject based on the ideXlab platform.
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evaluation of variance inflation factors in regression models using Latent Variable Modeling methods
Educational and Psychological Measurement, 2019Co-Authors: Katerina M Marcoulides, Tenko RaykovAbstract:A procedure that can be used to evaluate the variance inflation factors and tolerance indices in linear regression models is discussed. The method permits both point and interval estimation of thes...
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discrete time survival analysis via Latent Variable Modeling a note on lagged depression links to stroke in middle and late life
Structural Equation Modeling, 2018Co-Authors: Tenko Raykov, Philip B Gorelick, Anna Zajacova, George A. MarcoulidesAbstract:This article is concerned with a Latent Variable Modeling approach to discrete time survival analysis that includes both time-invariant and time-varying covariates. The approach is illustrated with data from the Health and Retirement Study, which are utilized to study further the relationship of depression to stroke in middle and late life. Employing lag-1 depression scores as time-varying covariates, in addition to a set of relevant medical and demographic Variables as time-invariant covariates collected at baseline, the article addresses a particular aspect of the prominent vascular depression hypothesis representing an important area in aging research, gerontology, geriatrics, and medicine. The results indicate considerable links of immediately prior depression levels to subsequent occurrences of stroke in middle-aged and older adults. The findings complement those reported by Raykov, Gorelick, Zajacova, and Marcoulides (2017), and are consistent with that hypothesis implying depression as a potential ...
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testing criterion correlations with scale component measurement errors using Latent Variable Modeling
Structural Equation Modeling, 2017Co-Authors: Tenko Raykov, George A. Marcoulides, Siegfried GablerAbstract:A Latent Variable Modeling method for testing criterion correlations with measurement error terms in multicomponent measuring instruments is outlined. The approach is based on an application of the Benjamini–Hochberg multiple testing procedure and can be used when assumptions of validity estimation related procedures need to be examined. The method also allows studying the extent to which criterion validity coefficients might be due to the relationship between a presumed underlying Latent construct evaluated by a psychometric scale and a criterion Variable, or could be a consequence of the relation between measurement error in the overall scale score and the criterion. The discussed procedure is widely applicable with popular Latent Variable Modeling software, and is illustrated using a numerical example.
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on the potential of discrete time survival analysis using Latent Variable Modeling an application to the study of the vascular depression hypothesis
Structural Equation Modeling, 2017Co-Authors: Tenko Raykov, Philip B Gorelick, Anna Zajacova, George A. MarcoulidesAbstract:Analysis and Modeling of time to event data have been traditionally associated with nonparametric, semiparametric, or parametric statistical frameworks. Recent advances in Latent Variable Modeling have additionally provided unique analytic opportunities to methodologists and substantive researchers interested in survival time Modeling. As a consequence, discrete time survival analyses can now be readily carried out using Latent Variable Modeling, an approach that offers substantively important extensions to conventional survival models. Using data from the Health and Retirement Study, the discussed approach is applied to the study of the increasingly prominent vascular depression hypothesis in gerontology, geriatrics, and aging research, allowing examination of the unique predictive power of depression with respect to time to stroke in middle-aged and older adults.
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do two or more multicomponent instruments measure the same construct testing construct congruence using Latent Variable Modeling
Educational and Psychological Measurement, 2016Co-Authors: Tenko Raykov, George A. Marcoulides, Bing TongAbstract: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. The approach can be readily utilized in empirical settings with observed measure nonnormality and/or incomplete data sets. The procedure is based on testing model nesting restrictions, and it can be similarly employed to examine the collapsibility of Latent Variables evaluated by multidimensional measuring instruments. The outlined method is illustrated with two data examples.
George A. Marcoulides - One of the best experts on this subject based on the ideXlab platform.
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discrete time survival analysis via Latent Variable Modeling a note on lagged depression links to stroke in middle and late life
Structural Equation Modeling, 2018Co-Authors: Tenko Raykov, Philip B Gorelick, Anna Zajacova, George A. MarcoulidesAbstract:This article is concerned with a Latent Variable Modeling approach to discrete time survival analysis that includes both time-invariant and time-varying covariates. The approach is illustrated with data from the Health and Retirement Study, which are utilized to study further the relationship of depression to stroke in middle and late life. Employing lag-1 depression scores as time-varying covariates, in addition to a set of relevant medical and demographic Variables as time-invariant covariates collected at baseline, the article addresses a particular aspect of the prominent vascular depression hypothesis representing an important area in aging research, gerontology, geriatrics, and medicine. The results indicate considerable links of immediately prior depression levels to subsequent occurrences of stroke in middle-aged and older adults. The findings complement those reported by Raykov, Gorelick, Zajacova, and Marcoulides (2017), and are consistent with that hypothesis implying depression as a potential ...
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testing criterion correlations with scale component measurement errors using Latent Variable Modeling
Structural Equation Modeling, 2017Co-Authors: Tenko Raykov, George A. Marcoulides, Siegfried GablerAbstract:A Latent Variable Modeling method for testing criterion correlations with measurement error terms in multicomponent measuring instruments is outlined. The approach is based on an application of the Benjamini–Hochberg multiple testing procedure and can be used when assumptions of validity estimation related procedures need to be examined. The method also allows studying the extent to which criterion validity coefficients might be due to the relationship between a presumed underlying Latent construct evaluated by a psychometric scale and a criterion Variable, or could be a consequence of the relation between measurement error in the overall scale score and the criterion. The discussed procedure is widely applicable with popular Latent Variable Modeling software, and is illustrated using a numerical example.
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on the potential of discrete time survival analysis using Latent Variable Modeling an application to the study of the vascular depression hypothesis
Structural Equation Modeling, 2017Co-Authors: Tenko Raykov, Philip B Gorelick, Anna Zajacova, George A. MarcoulidesAbstract:Analysis and Modeling of time to event data have been traditionally associated with nonparametric, semiparametric, or parametric statistical frameworks. Recent advances in Latent Variable Modeling have additionally provided unique analytic opportunities to methodologists and substantive researchers interested in survival time Modeling. As a consequence, discrete time survival analyses can now be readily carried out using Latent Variable Modeling, an approach that offers substantively important extensions to conventional survival models. Using data from the Health and Retirement Study, the discussed approach is applied to the study of the increasingly prominent vascular depression hypothesis in gerontology, geriatrics, and aging research, allowing examination of the unique predictive power of depression with respect to time to stroke in middle-aged and older adults.
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do two or more multicomponent instruments measure the same construct testing construct congruence using Latent Variable Modeling
Educational and Psychological Measurement, 2016Co-Authors: Tenko Raykov, George A. Marcoulides, Bing TongAbstract: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. The approach can be readily utilized in empirical settings with observed measure nonnormality and/or incomplete data sets. The procedure is based on testing model nesting restrictions, and it can be similarly employed to examine the collapsibility of Latent Variables evaluated by multidimensional measuring instruments. The outlined method is illustrated with two data examples.
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examining population heterogeneity in finite mixture settings using Latent Variable Modeling
Structural Equation Modeling, 2016Co-Authors: Tenko Raykov, George A. Marcoulides, Chi ChangAbstract:A Latent Variable Modeling procedure for examining whether a studied population could be a mixture of 2 or more Latent classes is discussed. The approach can be used to evaluate a single-class model vis-a-vis competing models of increasing complexity for a given set of observed Variables without making any assumptions about their within-class interrelationships. The method is helpful in the initial stages of finite mixture analyses to assess whether models with 2 or more classes should be subsequently considered as opposed to a single-class model. The discussed procedure is illustrated with a numerical example.
Tihomir Asparouhov - One of the best experts on this subject based on the ideXlab platform.
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evaluation of scale reliability with binary measures using Latent Variable Modeling
Structural Equation Modeling, 2010Co-Authors: Tenko Raykov, Dimiter M Dimitrov, Tihomir AsparouhovAbstract: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 following the 2-parameter logistic model or the 1-parameter logistic model. An extension of the method is described for constructing confidence intervals of change in reliability due to instrument revision. The proposed procedure is illustrated with an example.
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sampling weights in Latent Variable Modeling
Structural Equation Modeling, 2005Co-Authors: Tihomir AsparouhovAbstract:This article reviews several basic statistical tools needed for Modeling data with sampling weights that are implemented in Mplus Version 3. These tools are illustrated in simulation studies for several Latent Variable models including factor analysis with continuous and categorical indicators, Latent class analysis, and growth models. The pseudomaximum likelihood estimation method is reviewed and illustrated with stratified cluster sampling. Additionally, the weighted least squares method for estimating structural equation models with categorical and continuous outcomes implemented in Mplus extended to incorporate sampling weights is also illustrated. The performance of several chi-square tests under unequal probability sampling is evaluated. Simulation studies compare the methods used in several statistical packages such as Mplus, HLM, SAS Proc Mixed, MLwiN, and the weighted sample statistics method used in other software packages.
Zhiqiang Ge - One of the best experts on this subject based on the ideXlab platform.
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Dynamic Probabilistic Latent Variable Model for Process Data Modeling and Regression Application
IEEE Transactions on Control Systems Technology, 2019Co-Authors: Zhiqiang Ge, Xinru ChenAbstract:Dynamic and uncertainty are two main features of the industrial process data which should be paid attention when carrying out process data Modeling and analytics. In this paper, the dynamical and uncertain data characteristics are both taken into consideration for the regression Modeling purpose. Based on the probabilistic Latent Variable Modeling framework, the linear dynamic system is introduced for incorporation of the dynamical data feature. The expectation-maximization Algorithm is introduced for parameter learning of the dynamical probabilistic Latent Variable model, based on which a new soft sensing scheme is then formulated for online prediction of key/quality Variables in the process. An industrial case study illustrates the necessity and effectiveness of introducing the dynamical data information into the probabilistic Latent Variable model.
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process data analytics via probabilistic Latent Variable models a tutorial review
Industrial & Engineering Chemistry Research, 2018Co-Authors: Zhiqiang GeAbstract:Dimensionality reduction is important for the high-dimensional nature of data in the process industry, which has made Latent Variable Modeling methods popular in recent years. By projecting high-dimensional data into a lower-dimensional space, Latent Variables models are able to extract key information from process data while simultaneously improving the efficiency of data analytics. Through a probabilistic viewpoint, this paper carries out a tutorial review of probabilistic Latent Variable models on process data analytics. Detailed illustrations of different kinds of basic probabilistic Latent Variable models (PLVM) are provided, as well as their research statuses. Additionally, more counterparts of those basic PLVMs are introduced and discussed for process data analytics. Several perspectives are highlighted for future research on this topic.
Bengt Muthén - One of the best experts on this subject based on the ideXlab platform.
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Latent Variable Modeling of growth with missing data and multilevel data
Multivariate Analysis: Future Directions 2, 2014Co-Authors: Bengt MuthénAbstract:Abstract Latent Variable Modeling in psychometrics is connected with mainstream statistical theory in the areas of random coefficients, missing data, and clustered data. An educational achievement example points to the need for integrating these developments in a single analysis framework. It is shown that existing methods and software for Latent Variable Modeling accomplish this.
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Latent Variable Modeling in heterogeneous populations
Department of Statistics UCLA, 2011Co-Authors: Bengt MuthénAbstract:PSYCHOMETR1KA--VOL.54, NO. 4, 557-585 SEPTEMBER 1989 Latent Variable Modeling IN H E T E R O G E N E O U S POPULATIONS BENGT O. MUTHI~N GRADUATE SCHOOL OF EDUCATION, UNIVERSITY OF CALIFORNIA, LOS ANGELES Common applications of Latent Variable analysis fail to recognize that data may be obtained from several populations with different sets of parameter values. This article describes the problem and gives an overview of methodology that can address heterogeneity. Artificial ex- amples of mixtures are given, where if the mixture is not recognized, strongly distorted results occur. MIMIC structural Modeling is shown to be a useful method for detecting and describing heterogeneity that cannot be handled in regular multiple-group analysis. Other useful methods instead take a random effects approach, describing heterogeneity in terms of random parameter variation across groups. These random effects models connect with emerging methodology for multilevel structural equation Modeling of hierarchical data. Examples are drawn from educa- tional achievement testing, psychopathology, and sociology of education. Estimation is carded out by the LISCOMP program. Key words: mixtures, covariance structures, multiple-group analysis, MIMIC, LISCOMP, ran- dom parameters, multilevel, hierarchical data. Introduction In preparing this presidential address, I decided to touch on not only what I have done but also some of what I am doing and would like to see done in the future, both in terms of my own research and that of other psychometricians. Before going into the specifics of my topic, the general themes will be described. In line with my own taste, I will concentrate on applied issues: Although applied is a relative term meaning different things to different people, I will present more general formulas, tables, and graphs than detailed derivations of theories and proofs. A second theme is Modeling. In line with my own interests, I will focus on the specifi- cation of models rather than details of estimation. In my view, too little psychometric effort is geared towards realistic Modeling which naturally should precede polishing of model parameter estimation. A final general theme is the standard statistical assump- tion of i.i.d., the assumption of identically and independently distributed observations. I will discuss analysis approaches that relax one or both of these assumptions. The presentation will also involve effects of ignoring i.i.d, violations, both in terms of distortions of regular analysis that maintains this assumption and, more importantly, in terms of information not uncovered by regular analysis. Before getting into specific Modeling issues, I will give a general description of my topic, including an outline of the content of the sections. Presidential address delivered at the Psychometric Society meetings in Los Angeles, USA and Leuven, Belgium, July 1989. The research was supported by Grant No. SES-8821668 from the National Science Foundation and by Grant No. OERI-G-86-003 from the Office for Educational Research and Improvement, Department of Education. I thank Leigh Burstein, Mike Hollis, Linda Muthrn, and Albert Satorra for helpful discussions and Tammy Tam, Jin-Wen Yang, Suk-Woo Kim, and Lynn Short for computational assistance. Designs were created by Arlette Collier, Rita Ling and Jennifer Edic-Bryant. Requests for reprints should be addressed to Bengt O. Muthrn, Graduate School of Education, Univer- sity of California, Los Angeles, California, 90024-1521. 0033-3123/89/1200-pa89500.75/0 © 1989 The Psychometric Society
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beyond sem general Latent Variable Modeling
Behaviormetrika, 2002Co-Authors: Bengt MuthénAbstract:This article gives an overview of statistical analysis with Latent Variables. Using traditional structural equation Modeling as a starting point, it shows how the idea of Latent Variables captures a wide variety of statistical concepts, including random effects, missing data, sources of variation in hierarchical data, finite mixtures. Latent classes, and clusters. These Latent Variable applications go beyond the traditional Latent Variable useage in psychometrics with its focus on measurement error and hypothetical constructs measured by multiple indicators. The article argues for the value of integrating statistical and psychometric Modeling ideas. Different applications are discussed in a unifying framework that brings together in one general model such different analysis types as factor models, growth curve models, multilevel models, Latent class models and discrete-time survival models. Several possible combinations and extensions of these models are made clear due to the unifying framework.
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integrating person centered and Variable centered analyses growth mixture Modeling with Latent trajectory classes
Alcoholism: Clinical and Experimental Research, 2000Co-Authors: Bengt Muthén, Linda K MuthenAbstract:Background: Many alcohol research questions require methods that take a person-centered approach because the interest is in finding heterogeneous groups of individuals, such as those who are susceptible to alcohol dependence and those who are not. A person-centered focus also is useful with longitudinal data to represent heterogeneity in developmental trajectories. In alcohol, drug, and mental health research the recognition of heterogeneity has led to theories of multiple developmental pathways. Methods: This paper gives a brief overview of new methods that integrate Variable- and person-centered analyses. Methods discussed include Latent class analysis, Latent transition analysis, Latent class growth analysis, growth mixture Modeling, and general growth mixture Modeling. These methods are presented in a general Latent Variable Modeling framework that expands traditional Latent Variable Modeling by including not only continuous Latent Variables but also categorical Latent Variables. Results: Four examples that use the National Longitudinal Survey of Youth (NLSY) data are presented to illustrate Latent class analysis, Latent class growth analysis, growth mixture Modeling, and general growth mixture Modeling. Latent class analysis of antisocial behavior found four classes. Four heavy drinking trajectory classes were found. The relationship between the Latent classes and background Variables and consequences was studied. Conclusions: Person-centered and Variable-centered analyses typically have been seen as different activities that use different types of models and software. This paper gives a brief overview of new methods that integrate Variable- and person-centered analyses. The general framework makes it possible to combine these models and to study new models serving as a stimulus for asking research questions that have both person- and Variable-centered aspects.
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longitudinal studies of achievement growth using Latent Variable Modeling
Learning and Individual Differences, 1998Co-Authors: Bengt Muthén, Siektoon KhooAbstract:This article gives a pedagogical description of growth Modeling of longitudinal data using Latent Variable methods. The growth Modeling is described using an example of mathematics achievement developing over grades 7 to 10 in two cohorts of students. The article describes the basic idea behind growth Modeling of individual differences in growth over time and applies it to mathematics achievement development as a function of background Variables such as gender, mother's education, and home resources. The Modeling ideas are described in words, diagrams, and formulas. The discussion covers Modeling that assesses the form of the growth, the influence of background Variables on the growth, multiple-cohort analysis, analysis with missing data, and multiple-group analysis of males and females. A corresponding set of analYses are performed on the mathematics data to illustrate the Modeling ideas.
