Exploratory factor analysis (EFA) is a statistical tool used for exploring the underlying structure of data. It was originally developed in the early 1900s during the attempt to determine whether intelligence is a unitary or multidimensional construct (Spearman, 1904). It has since served as a general-purpose dimension reduction tool with many applications. In the modern social sciences it is often used to explore the psychometric properties of an instrument or scale. Exploratory factor analysis examines all the pairwise relationships between individual variables (e.g., items on a scale) and seeks to extract latent factors from the measured variables. During the 110 years since Spearman’s seminal work in this area, few statistical techniques have been so widely used (or, unfortunately, misused).

The goal of this book is to explore best practices in applying EFA using SAS. We will review each of the major EFA steps (e.g., extraction, rotation), some associated practices (estimation of factor scores and higher-order factors), and some less common analyses that can inform the generalizability of EFA results (replication analyses and bootstrap analyses). We will review the SAS syntax for each task and highlight best practices according to research and practice. We will also demonstrate the procedures and analyses discussed throughout the book using real data, and we will occasionally survey some poor practices as a learning tool.

To get started in our exploration of EFA, we will first discuss the similarities and differences between EFA and principal components analysis (PCA), another technique that is commonly used for the same goal as EFA. We will then briefly summarize the steps to follow when conducting an EFA and conclude with a quick introduction to EFA in SAS.