Meta-Analytic Analysis of Invariance Across Samples: Introducing a Method That Does Not Require Raw Data

Abstract

Abstract Invariance of surveys across different groups means that the respondents interpret the items in the same way, as reflected in similar factor loadings, for example. Invariance can be assessed using various statistical procedures, such as Multi-Group Confirmatory Factor Analysis. However, these analyses require access to raw data. Here, we introduce a meta-analytic method that requires only the factor correlation matrices of samples as input. It compares the structures of intercorrelations of factors by correlating these values across two samples, yielding a value of overall similarity for how the factors intercorrelate in different samples. This method was tested in three different ways. We conclude that the method yields useful results and can assess invariance when raw data are not available.