A Comparative Study on the Performance of GSCA and CSA in Parameter Recovery for Structural Equation Models With Ordinal Observed Variables

Date

2018

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Abstract

simulation based comparative study was designed to compare two alternative approaches to structural equation modeling—generalized structured component analysis (GSCA) with the alternating least squares (ALS) estimator vs. covariance structure analysis (CSA) with the maximum likelihood (ML) estimator or the weighted least squares mean and variance adjusted (WLSMV) estimator—in terms of parameter recovery with ordinal observed variables. The simulated conditions included the number of response categories in observed variables, distribution of ordinal observed variables, sample size, and model misspecification. The simulation outcomes focused on average root mean square error (RMSE) and average relative bias (RB) in parameter estimates. The results indicated that, by and large, GSCA-ALS recovered structural path coefficients more accurately than CSA-ML and CSA-WLSMV in either a correctly or incorrectly specified model, regardless of the number of response categories, observed variable distribution, and sample size. In terms of loadings, CSA-WLSMV outperformed GSCA-ALS and CSA-ML in almost all conditions. Implications and limitations of the current findings are discussed, as well as suggestions for future research.

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Keywords

Generalized Structured Component Analysis (GSCA), Alternating Least Squares Estimation, Maximum Likelihood Estimation, Diagonally Weighted Least Squares Estimation, Structural Equation Modeling (SEM), Covariance Structure Analysis, Monte Carlo Simulation

Citation

Jung, K., Panko, P., Lee, J., & Hwang, H. (2018). A comparative study on the performance of GSCA and CSA in parameter recovery for structural equation models with ordinal observed variables. Frontiers in Psychology–Quantitative Psychology and Measurement, 9:2461. https://doi.org/10.3389/fpsyg.2018.02461

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