Historical Measurement Information Can Be Used to Improve Estimation of Structural Parameters in Structural Equation Models With Small Samples.

This study investigates the incorporation of historical measurement information into structural equation models (SEM) with small samples to enhance the estimation of structural parameters. Given the availability of published factor analysis results with loading estimates and standard errors for popular scales, researchers may use this historical information as informative priors in Bayesian SEM (BSEM). We focus on estimating the correlation between two constructs using BSEM after generating data with significant bias in the Pearson correlation of their sum scores due to measurement error. Our findings indicate that incorporating historical information on measurement parameters as priors can improve the accuracy of correlation estimates, mainly when the true correlation is small-a common scenario in psychological research. Priors derived from meta-analytic estimates were especially effective, providing high accuracy and acceptable coverage. However, when the true correlation is large, weakly informative priors on all parameters yield the best results. These results suggest leveraging historical measurement information in BSEM can enhance structural parameter estimation.