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Bayesian analysis of latent variable models using Mplus. Version 4. Retrieved from http://www.statmodel.com/download/BayesAdvantages18.pdf Asparouhov
 University of Barcelona
, 1996
"... In this paper we describe some of the modeling possibilities that are now available in Mplus Version 6 with the Bayesian methodology. This new methodology offers many new possibilities but also many challenges. The paper is intended to spur more research rather than to provide complete an ..."
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Cited by 22 (7 self)
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In this paper we describe some of the modeling possibilities that are now available in Mplus Version 6 with the Bayesian methodology. This new methodology offers many new possibilities but also many challenges. The paper is intended to spur more research rather than to provide complete an
Bayesian SEM: A more flexible representation of substantive theory. Submitted for publication. Retrieved from http://www.statmodel.com/download/ BSEMv4.pdf
, 2010
"... This paper proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, smallvariance priors. It is argued that this produces an analysis that bette ..."
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Cited by 16 (6 self)
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This paper proposes a new approach to factor analysis and structural equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, smallvariance priors. It is argued that this produces an analysis that better reflects substantive theories. The proposed Bayesian approach is particularly beneficial in applications where parameters are added to a conventional model such that a nonidentified model is obtained if maximumlikelihood estimation is applied. This approach is useful for measurement aspects of latent variable modeling such as with CFA and the measurement part of SEM. Two application areas are studied, crossloadings and residual correlations in CFA. The approach encompasses three elements: Model testing, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. 2 1
General Random Effect Latent Variable Modeling: Random Subjects, Items, Contexts, and Parameters
, 2012
"... Bayesian methodology is wellsuited for estimating latent variable models where subjects are not the only random mode, but also items and contexts. A general crossclassified structural equation model is presented where observations are nested within two independent clustering variables. The model i ..."
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Cited by 6 (3 self)
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Bayesian methodology is wellsuited for estimating latent variable models where subjects are not the only random mode, but also items and contexts. A general crossclassified structural equation model is presented where observations are nested within two independent clustering variables. The model includes continuous and categorical dependent variables as well as continuous latent variable. Random effects, intercepts and slopes, are used to model the clustering effects for both nesting structures. We describe the Bayesian methodology implemented in Mplus version 7 used to estimate such models. Bayesian methodology can also be used to estimate cluster specific structural equation models in twolevel data where all measurement and structural coefficients, including factor loadings and regression coefficients between factors can be estimated as cluster level random effects rather than fixed parameters. The maximumlikelihood estimation for such models is generally prohibitive due to the large dimension of numerical integration. We also discuss the effect of priors on the Bayesian estimation. In particular we show how a small variance prior can be used to easily identify more random effects than traditional ML methodology can, which can yield flexible structural models with many cluster specific coefficients. Applications are discussed such as multiple group analysis with large number of groups and measurement noninvariance, crosscultural research and Gtheory. 1 1
Item Response Modeling in Mplus: A MultiDimensional, MultiLevel, and MultiTimepoint Example
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Comparison of computational methods for highdimensional item factor analysis. Submitted for publication. www.statmodel.com
, 2012
"... In this article we conduct a simulation study to compare several methods for estimating confirmatory and exploratory item factor analysis using the software programs Mplus and IRTPRO. When the number of factors is bigger than three or four the standard numerical integration methodology used for comp ..."
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Cited by 3 (3 self)
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In this article we conduct a simulation study to compare several methods for estimating confirmatory and exploratory item factor analysis using the software programs Mplus and IRTPRO. When the number of factors is bigger than three or four the standard numerical integration methodology used for computing the maximumlikelihood estimates is intractable due to the exponentially large number of integration points needed to compute the likelihood. Several methods have been developed recently to overcome these computational problems however they have not been directly compared previously. In this paper we present a simulation study to compare maximum likelihood estimation based on Montecarlo integration, maximum likelihood estimation based on MetropolisHastings RobbinsMonro algorithm, maximum likelihood estimation based on twotier integration, Bayesian estimation and the weighted least square estimation. 1
New Methods for the Study of Measurement Invariance with Many Groups
, 2013
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"... NOTICE: this is the author¢s version of a work that was accepted for publication in Journal of Science and Medicine in Sport. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in ..."
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NOTICE: this is the author¢s version of a work that was accepted for publication in Journal of Science and Medicine in Sport. Changes resulting from the publishing process, such as peer review, editing, corrections, structural formatting, and other quality control mechanisms may not be reflected in this document. Changes may have been made to this work since it was submitted for publication. A definitive version was subsequently
Multiple Group Factor Analysis Alignment
"... There is a need for a new approach to multiplegroup factor analysis when there are many groups such as with country comparisons of achievement (PISA, TIMSS, PIRL) or crosscultural studies (ISSP, ESS etc). The goal of multiple group factor analysis is to study measurement invariance and also ..."
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There is a need for a new approach to multiplegroup factor analysis when there are many groups such as with country comparisons of achievement (PISA, TIMSS, PIRL) or crosscultural studies (ISSP, ESS etc). The goal of multiple group factor analysis is to study measurement invariance and also
Ordinal categorical data such as those measured using Likert scales (e.g., 3 or 5 point
"... Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies. Correspondence concerning this article should be addressed to Wei Wu, Psychology ..."
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Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the funding agencies. Correspondence concerning this article should be addressed to Wei Wu, Psychology