Normal science and its tools: Reviewing the effects of exploratory factor analysis in management

Abstract

The aim of this study is to investigate how different methods of extraction, factor definition, and rotation of exploratory factor analysis affect the fit of measurement scales. For this purpose, we undertook a meta-analysis of 23 studies. Our results indicate that the Principal Components method provides greater explained variance, while the Maximum Likelihood method increases reliability. Of the rotations methods, Varimax provides greater reliability while Quartimax provides lower correlation between factors. In conclusion, this study highlights implications for quantitative research and suggests potential new studies.