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Abstract The increasing prevalence of exploratory factor analysis (EFA) applications in scholarly literature reflects its popularity and the convenience of computer‐assisted analysis. With advancements in computer hardware and software, the complexity and variations of EFA analysis have also grown. Despite the availability of sophisticated computer programming, the appropriate utilization of EFA necessitates users to make informed judgments. Additionally, users are responsible for searching and identifying suitable statistical software to accommodate their data and analysis requirements. This review aims to enhance understanding of the EFA technique and summarize the analysis options available for EFA in R packages. A total of 50 packages were examined in this study. Specifically, the review focuses on (1) diagnostic functions, (2) factor extraction, (3) factor retention, (4) factor rotation, and (5) complex data and technique features provided by these packages. The review summarizes the available function options in R packages by outlining these five crucial steps in conducting an EFA analysis. This synthesis offers an overview of the similarities and distinctive features of each package, serving as a valuable resource for users in selecting a suitable EFA technique. It is important to note that there is no definitive approach to conducting an exploratory factor analysis. Users need to deliberately select and combine appropriate techniques to achieve optimal results. This article is categorized under: Statistical and Graphical Methods of Data Analysis > Dimension Reduction Software for Computational Statistics > Software/Statistical Software
Govindasamy et al. (Wed,) studied this question.
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