Residual Structural Equation Models

Tihomir Asparouhov · Bengt Muthén

73 citations0/0 files storedStructural Equation Modeling A Multidisciplinary Journal

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Psychometric Methodologies and Testing Statistical Methods and Bayesian Inference Mental Health Research Topics

Abstract

The residual variables in a structural equation model can be used to create a secondary structural model which we call the residual structural equation model (RSEM). We describe the maximum-likelihood, weighted least squares and Bayesian estimations for RSEM. The methodology is illustrated with several examples and simulation studies. We discuss the implementation of RSEM in the Mplus software package and provide scripts for the simulation studies. The RSEM framework is utilized to estimate and simplify popular models such as the random intercept cross-lagged panel model (RI-CLPM) and the latent curve model with structured residuals (LCM-SR). We discuss in details RSEM models with categorical observed variables as well as categorical latent variables in the context of mixture modeling.

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References

1994Testing Structural Equation Models.1977Time Series Analysis: Forecasting and Control 2013Handbook of Structural Equation Modeling 2012Bayesian structural equation modeling: A more flexible representation of substantive theory.1978Structural Analysis of Covariance and Correlation Matrices 2013The separation of between-person and within-person components of individual change over time: A latent curve model with structured residuals.1989Model Modification 2015Bayesian Structural Equation Modeling With Cross-Loadings and Residual Covariances

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