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.
Formative Model (FM) refers to the measurement model in which the variation of latent variable is caused by the index variation, while Reflective Model (RM) refers to the measurement model in which the index variation is caused by the variation of latent variable. There are distinctions between FM and RM in definition, identification, estimation, the evaluation of reliability and validity and application. Wrong model definition may cause the deviation of parameters estimation, so as to lower the efficiency of statistical result. Therefore prudent consideration should be taken to the relationship between index and latent variable in order to choose the appropriate measurement model. At last, the future research should focus on the clarification of the differences between FM and RM, the effect of misuse, the improvement of the identification and estimation, the reliability and validity evaluation, the explanation of the meaning of variables and the theoretical interpretation and model estimaton of high-level FM.