Deciding The Number Of Dimensions In Explanatory Factor Analysis: A Brief Overview Of The Methods

Abstract

Exploratory factor analysis (EFA) finds its place in many scientific fields. With this analysis, information about the nature and structure of the measured feature can be obtained. It is possible to have information about the nature of the measured feature by fulfilling the requirements of this analysis. Correctly deciding on the number of dimensions in EFA can also be challenging for researchers. For this reason, this study presents information on the theoretical background of the factor retention methods used when deciding on the number of dimensions in EFA. In addition, it has been given information about which software is available for these methods. Moreover, there is information about which method gives more accurate results in the simulation studies. As a result, the number of dimensions can be decided by using traditional methods such as optimal parallel analysis, comparative data, or the average of partial correlations, as well as making use of machine learning methods (random forest or extreme gradient augmentation), which have found new uses in the literature, to researchers who will perform EFA.