Meta-analysis of factor analyses: Comparison of univariate and multivariate approaches using correlation matrices and factor loadings.

摘要

Currently, more sophisticated techniques such as factor analyses are frequently applied in primary research thus may need to be meta-analyzed. This topic has been given little attention in the past due to its complexity. Because factor analysis is becoming more popular in research in many areas including education, social work, social science, and so on, the study of methods for the meta-analysis of factor analyses is also becoming more important. The first main purpose of this dissertation is to compare the results of seven different approaches to doing meta-analysis of confirmatory factor analyses. Specifically, five approaches are based on univariate meta-analysis methods. The next two approaches use multivariate meta-analysis to obtain the results of factor loadings and the standard errors of factor loadings. The results from each approach are compared. Given the fact that factor analyses are commonly used in many areas, the second purpose of this dissertation is to explore the appropriate approach or approaches to use for the meta-analysis of factor analyses, especially Confirmatory Factor Analysis (CFA). When the average sample size was small, the results of IRD, WMC, WMFL, and GLS-MFL approaches showed better performance than those of UMC, MFL, and GLS-MC approaches to estimating parameters. With large average sample sizes (larger than 150), the performance to estimate the parameters across all seven approaches seemed to be similar in this dissertation. Based on my simulation results, researchers who want to conduct meta-analytic confirmatory factor analysis can apply any of these approaches to synthesize the results from primary studies it their studies have n > 150.