A Comparison of Methods for Synthesizing Results from Previous Research to Obtain Priors for Bayesian Structural Equation Modeling

Abstract

Bayesian estimation of latent variable models provides some unique advantages to researchers working with small samples and complex models when compared with the more commonly used maximum likelihood approach. A key aspect of Bayesian modeling involves the selection of prior distributions for the parameters of interest. Prior research has demonstrated that using default priors, which are typically noninformative, may yield biased and inefficient estimates. Therefore, it is recommended that data analysts obtain useful, informative priors from prior research whenever possible. The goal of the current simulation study was to compare several methods designed to combine results from prior studies that will yield informative priors for regression coefficients in structural equation models. These methods include noninformative priors, Bayesian synthesis, pooled analysis, aggregated priors, standard meta-analysis, power priors, and the meta-analytic predictive methods. Results demonstrated that power priors and meta-analytic predictive priors, used in conjunction with Bayesian estimation, may yield the most accurate estimates of the latent structure coefficients. Implications for practice and suggestions for future research are discussed.