Version 1
: Received: 30 April 2024 / Approved: 1 May 2024 / Online: 1 May 2024 (08:21:56 CEST)
How to cite:
Mindrila, D. Bayesian Structural Equation Modeling: Goodness of Fit and Estimation Precision across Sample Sizes. Preprints2024, 2024050065. https://doi.org/10.20944/preprints202405.0065.v1
Mindrila, D. Bayesian Structural Equation Modeling: Goodness of Fit and Estimation Precision across Sample Sizes. Preprints 2024, 2024050065. https://doi.org/10.20944/preprints202405.0065.v1
Mindrila, D. Bayesian Structural Equation Modeling: Goodness of Fit and Estimation Precision across Sample Sizes. Preprints2024, 2024050065. https://doi.org/10.20944/preprints202405.0065.v1
APA Style
Mindrila, D. (2024). Bayesian Structural Equation Modeling: Goodness of Fit and Estimation Precision across Sample Sizes. Preprints. https://doi.org/10.20944/preprints202405.0065.v1
Chicago/Turabian Style
Mindrila, D. 2024 "Bayesian Structural Equation Modeling: Goodness of Fit and Estimation Precision across Sample Sizes" Preprints. https://doi.org/10.20944/preprints202405.0065.v1
Abstract
The current study compared the goodness of fit and estimation precision of Bayes and maximum likelihood (ML) structural equation modeling across sample sizes. Data consisted of Markov Chain Monte Carlo generated samples of 50, 75, 100, 125, 150, 200, 250, 500, 750, 1000, and 1500 observations. The Bayes and ML methods were used to estimate a structural equation model with 40 parameters, including 12 continuous observed indicators, four latent variables, and three structural paths. Analyses were conducted with the Mplus 8.0 statistical software and were replicated 500 times with each of the 22 conditions (11 sample sizes x 2 estimation methods). Indices of model fit were the Bayes information criterion, the Tucker-Lewis index, the comparative fit index, the root mean square error of approximation, the posterior predictive p-value (for Bayes models), and the ꭓ2 test (for ML models). Results showed that as the sample size increased, both estimation methods exhibited improved model fit and enhanced estimation precision, and both methods performed well with samples of 200 or larger; however, ML demonstrated a marginally superior fit with smaller samples. These findings were not consistent with previous research and suggest that ML remains a better choice with continuous data and small samples.
Keywords
structural equation modeling; Bayesian analysis; maximum likelihood
Subject
Social Sciences, Education
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.