The article explains how exploratory factor analysis (EFA) is used to simplify complex data by identifying underlying patterns. EFA is different from principal component analysis (PCA) and focuses on finding hidden factors that influence observed variables. Factor rotation is a key concept in EFA, allowing for a clearer interpretation of the data. By using PCA Eigen values, researchers can determine how many factors to retain in the analysis. The Holzinger and Swineford data are used as examples to demonstrate EFA in action. EFA and PCA both aim to reduce data complexity, but EFA specifically looks for hidden dimensions that explain relationships between variables.