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Mike W.l. Cheung  One of the best experts on this subject based on the ideXlab platform.

metasem an r package for meta analysis using Structural Equation modeling
Frontiers in Psychology, 2015CoAuthors: Mike W.l. CheungAbstract:The metaSEM package provides functions to conduct univariate, multivariate, and threelevel metaanalyses using a Structural Equation modeling (SEM) approach via the \texttt{OpenMx} package in R statistical platform. It also implements the twostage SEM approach to conducting fixed and randomeffects metaanalytic SEM on correlation or covariance matrices. This paper briefly outlines the theories and their implementations. It provides a summary on how metaanalyses can be formulated as Structural Equation models. The paper closes with a conclusion on several relevant topics to this SEMbased metaanalysis. Several examples are used to illustrate the procedures in the supplementary material.

Multivariate MetaAnalysis as Structural Equation Models
Structural Equation Modeling, 2013CoAuthors: Mike W.l. CheungAbstract:Multivariate metaanalysis has become increasingly popular in the educational, social, and medical sciences. It is because the outcome measures in a metaanalysis can involve more than one effect size. This article proposes 2 mathematically equivalent models to implement multivariate metaanalysis in Structural Equation modeling (SEM). Specifically, this article shows how multivariate fixed, random and mixedeffects metaanalyses can be formulated as Structural Equation models. metaSEM (a free R package based on OpenMx) and Mplus are used to implement the proposed procedures. A real data set is used to illustrate the procedures. Formulating multivariate metaanalysis as Structural Equation models provides many new research opportunities for methodological development in both metaanalysis and SEM. Issues related to and extensions on the SEMbased metaanalysis are discussed.
Tihomir Asparouhov  One of the best experts on this subject based on the ideXlab platform.

Dynamic Structural Equation Models
Structural Equation Modeling, 2017CoAuthors: Tihomir Asparouhov, Ellen L. Hamaker, Bengt MuthénAbstract:This article presents dynamic Structural Equation modeling (DSEM), which can be used to study the evolution of observed and latent variables as well as the Structural Equation models over time. DSEM is suitable for analyzing intensive longitudinal data where observations from multiple individuals are collected at many points in time. The modeling framework encompasses previously published DSEM models and is a comprehensive attempt to combine timeseries modeling with Structural Equation modeling. DSEM is estimated with Bayesian methods using the Markov chain Monte Carlo Gibbs sampler and the Metropolis–Hastings sampler. We provide a detailed description of the estimation algorithm as implemented in the Mplus software package. DSEM can be used for longitudinal analysis of any duration and with any number of observations across time. Simulation studies are used to illustrate the framework and study the performance of the estimation method. Methods for evaluating model fit are also discussed.

bayesian Structural Equation modeling a more flexible representation of substantive theory
Psychological Methods, 2012CoAuthors: Bengt Muthén, Tihomir AsparouhovAbstract:This article proposes a new approach to factor analysis and Structural Equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, 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 confirmatory factor analysis, and the measurement part of Structural Equation modeling. Two application areas are studied, crossloadings and residual correlations in confirmatory factor analysis. An example using a full Structural Equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The realdata analyses use data from Holzinger and Swineford’s (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.
Bengt Muthén  One of the best experts on this subject based on the ideXlab platform.

Dynamic Structural Equation Models
Structural Equation Modeling, 2017CoAuthors: Tihomir Asparouhov, Ellen L. Hamaker, Bengt MuthénAbstract:This article presents dynamic Structural Equation modeling (DSEM), which can be used to study the evolution of observed and latent variables as well as the Structural Equation models over time. DSEM is suitable for analyzing intensive longitudinal data where observations from multiple individuals are collected at many points in time. The modeling framework encompasses previously published DSEM models and is a comprehensive attempt to combine timeseries modeling with Structural Equation modeling. DSEM is estimated with Bayesian methods using the Markov chain Monte Carlo Gibbs sampler and the Metropolis–Hastings sampler. We provide a detailed description of the estimation algorithm as implemented in the Mplus software package. DSEM can be used for longitudinal analysis of any duration and with any number of observations across time. Simulation studies are used to illustrate the framework and study the performance of the estimation method. Methods for evaluating model fit are also discussed.

bayesian Structural Equation modeling a more flexible representation of substantive theory
Psychological Methods, 2012CoAuthors: Bengt Muthén, Tihomir AsparouhovAbstract:This article proposes a new approach to factor analysis and Structural Equation modeling using Bayesian analysis. The new approach replaces parameter specifications of exact zeros with approximate zeros based on informative, 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 confirmatory factor analysis, and the measurement part of Structural Equation modeling. Two application areas are studied, crossloadings and residual correlations in confirmatory factor analysis. An example using a full Structural Equation model is also presented, showing an efficient way to find model misspecification. The approach encompasses 3 elements: model testing using posterior predictive checking, model estimation, and model modification. Monte Carlo simulations and real data are analyzed using Mplus. The realdata analyses use data from Holzinger and Swineford’s (1939) classic mental abilities study, Big Five personality factor data from a British survey, and science achievement data from the National Educational Longitudinal Study of 1988.
Xiaohui Zheng  One of the best experts on this subject based on the ideXlab platform.

Multilevel Structural Equation modeling.
Handbook of Latent Variable and Related Models, 2020CoAuthors: Sophia Rabehesketh, Anders Skrondal, Xiaohui ZhengAbstract:Abstract In conventional Structural Equation models, all latent variables and indicators vary between units (typically subjects) and are assumed to be independent across units. The latter assumption is violated in multilevel settings where units are nested in clusters, leading to withincluster dependence. Different approaches to extending Structural Equation models for such multilevel settings are examined. The most common approach is to formulate separate withincluster and betweencluster models. An advantage of this setup is that it allows software for conventional Structural Equation models to be ‘tricked’ into estimating the model. However, the standard implementation of this approach does not permit crosslevel paths from latent or observed variables at a higher level to latent or observed variables at a lower level, and does not allow for indicators varying at higher levels. A multilevel regression (or path) model formulation is therefore suggested in which some of the response variables and some of the explanatory variables at the different levels are latent and measured by multiple indicators. The Generalized Linear Latent and Mixed Modeling (GLLAMM) framework allows such models to be specified by simply letting the usual Structural part of the model include latent and observed variables varying at different levels. Models of this kind are applied to the U.S. sample of the Program for International Student Assessment (PISA) 2000 to investigate the relationship between the schoollevel latent variable ‘teacher excellence’ and the studentlevel latent variable ‘reading ability’, each measured by multiple ordinal indicators.

Multilevel Structural Equation modeling
Handbook on Structural Structural Equation Models, 2008CoAuthors: Sophia Rabehesketh, Anders Skrondal, Xiaohui ZhengAbstract:In conventional Structural Equation models, all latent variables and indicators vary between units (typically subjects) and are assumed to be independent across units. The latter assumption is violated in multilevel settings where units are nested in clusters, leading to withincluster de pendence. Different approaches to extending Structural Equation models for such multilevel settings are examined. The most common approach is to formulate separate withincluster and betweencluster models. An advantage of this setup is that it allows software for conventional struc tural Equation models to be ‘tricked’ into estimating the model. However, the standard implementation of this approach does not permit crosslevel paths from latent or observed variables at a higher level to latent or ob served variables at a lower level, and does not allow for indicators varying at higher levels. A multilevel regression (or path) model formulation is therefore suggested in which some of the response variables and some of the explanatory variables at the different levels are latent and measured by multiple indicators. The Generalized Linear Latent and Mixed Modeling (GLLAMM) framework allows such models to be specified by simply letting the usual model for the Structural part of a Structural Equation model include latent and observed variables varying at different levels. Models of this kind are applied to the U.S. sample of the Program for International Student Assessment (PISA) 2000 to investigate the relation ship between the schoollevel latent variable ‘teacher excellence’ and the studentlevel latent variable ‘reading ability’, each measured by multiple ordinal indicators.
Paul D Allison  One of the best experts on this subject based on the ideXlab platform.

missing data techniques for Structural Equation modeling
Journal of Abnormal Psychology, 2003CoAuthors: Paul D AllisonAbstract:As with other statistical methods, missing data often create major problems for the estimation of Structural Equation models (SEMs). Conventional methods such as listwise or pairwise deletion generally do a poor job of using all the available information. However, Structural Equation modelers are fortunate that many programs for estimating SEMs now have maximum likelihood methods for handling missing data in an optimal fashion. In addition to maximum likelihood, this article also discusses multiple imputation. This method has statistical properties that are almost as good as those for maximum likelihood and can be applied to a much wider array of models and estimation methods. Virtually all methods of statistical analysis are plagued by problems with missing data, and Structural Equation modeling is no exception. It is well known that the use of inappropriate methods for handling missing data can lead to bias in parameter estimates (Jones, 1996), bias in standard errors and test statistics (Glasser, 1964), and inefficient use of the data (Afifi & Elashoff, 1966). This article surveys various methods that are available for handling missing data in the estimation of Structural Equation models (SEMs). After reviewing such conventional methods as listwise deletion, pairwise deletion, and regression imputation, I focus on the implementation of two newer methods, maximum likelihood and multiple imputation. These methods have much better statistical properties than conventional methods have under considerably weaker assumptions, a rare combination for new statistical