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Generalized Structured Component Analysis with Latent Interactions
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Hwang, Heungsun; Ho, Moon-Ho Ringo; Lee, Jonathan – Psychometrika, 2010
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…
Descriptors: Monte Carlo Methods, Structural Equation Models, Interaction, Researchers
Bayesian Semiparametric Structural Equation Models with Latent Variables
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Yang, Mingan; Dunson, David B. – Psychometrika, 2010
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…
Descriptors: Structural Equation Models, Markov Processes, Item Response Theory, Bayesian Statistics
Regularized Generalized Structured Component Analysis
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Hwang, Heungsun – Psychometrika, 2009
Generalized structured component analysis (GSCA) has been proposed as a component-based approach to structural equation modeling. In practice, GSCA may suffer from multi-collinearity, i.e., high correlations among exogenous variables. GSCA has yet no remedy for this problem. Thus, a regularized extension of GSCA is proposed that integrates a ridge…
Descriptors: Monte Carlo Methods, Structural Equation Models, Least Squares Statistics, Computation
A Markov Chain Monte Carlo Approach to Confirmatory Item Factor Analysis
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Edwards, Michael C. – Psychometrika, 2010
Item factor analysis has a rich tradition in both the structural equation modeling and item response theory frameworks. The goal of this paper is to demonstrate a novel combination of various Markov chain Monte Carlo (MCMC) estimation routines to estimate parameters of a wide variety of confirmatory item factor analysis models. Further, I show…
Descriptors: Structural Equation Models, Markov Processes, Factor Analysis, Item Response Theory
Asymptotic Biases in Exploratory Factor Analysis and Structural Equation Modeling
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Ogasawara, Haruhiko – Psychometrika, 2004
Formulas for the asymptotic biases of the parameter estimates in structural equation models are provided in the case of the Wishart maximum likelihood estimation for normally and nonnormally distributed variables. When multivariate normality is satisfied, considerable simplification is obtained for the models of unstandardized variables. Formulas…
Descriptors: Evaluation Methods, Bias, Factor Analysis, Structural Equation Models
Maximum Likelihood Methods in Treating Outliers and Symmetrically Heavy-Tailed Distributions for Nonlinear Structural Equation Models with Missing Data
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Lee, Sik-Yum; Xia, Ye-Mao – Psychometrika, 2006
By means of more than a dozen user friendly packages, structural equation models (SEMs) are widely used in behavioral, education, social, and psychological research. As the underlying theory and methods in these packages are vulnerable to outliers and distributions with longer-than-normal tails, a fundamental problem in the field is the…
Descriptors: Maximum Likelihood Statistics, Statistical Distributions, Structural Equation Models, Robustness (Statistics)