Abstract:

Meta Analysis of Factor Analyses (MAFA) is defined as utilizing meta-analysis techniques to synthesize numerous primary studies with factor analysis paradigms. Although MAFA is a key stage in the process of producing and updating knowledge, it is not well known to the community of researchers. The five main MAFA techniques were summarized, including their basic premises, conditions for application, advantages and disadvantages. Typical examples corresponding to 1) pair-wise rotation of results to congruence (KHB), 2) multiple group confirmatory factor analysis (MGCFA), 3) factor analysis based on aggregate correlation matrix (ACMFA), 4) confirmatory factor analysis based on pooled correlation matrix (PCMCFA), and 5) exploratory factor analysis based on co-occurrence matrix of salient factor loadings (COEFA) are also presented The MAFA process can be divided into seven stages of which three, i.e., data extraction, data transformation, and data analysis, differ from other types of meta-analyses. Finally, several potential issues concerning the method itself and its application were also discussed.

Key words: factor analysis, meta analysis, co-occurrence matrix, factor loadings, KHB