Exploratory Factor Analysis
Daniel J. Denis · 2020
This chapter explains the nature of exploratory factor analysis (EFA), and shows difference between EFA and principal component analysis (PCA). It discusses the common factor analysis model, and its technical components. The chapter explains the nature of factor rotation in EFA, and why rotation is permissible in factor analysis. It addresses the issue of factor retention by using PCA Eigen values. The chapter also shows examples of the factor analysis by using the Holzinger and Swineford data. EFA is a dimension reduction technique similar in some respect to principal component surveyed, though different enough from PCA that the two should not in any real way be considered equivalent. While both in general have the goal of reducing the dimensionality of the data, the EFA model typically hypothesizes latent dimensions or constructs that underlie observed variables and their correlations.