Exploratory Factor Analysis in R: Origins, Concepts, and Real-World Applications

In the world of data science and statistical modelling, we often encounter datasets with dozens — sometimes hundreds — of variables. While each variable carries information, interpreting them individually can become overwhelming. This is where Exploratory Factor Analysis (EFA) plays a powerful role. EFA helps us uncover hidden structures in the data by grouping correlated variables into meaningful underlying factors. This article explores the origins of factor analysis, explains its conceptual foundations, demonstrates its implementation in R, and discusses real-life applications and case studies where EFA has delivered valuable insights. The Origins of Factor Analysis Factor analysis traces its roots back to the early 20th century in the field of psychology. The method was first introduced by Charles Spearman in 1904. Spearman developed the concept while studying human intelligence. He observed that students who performed well in one cognitive test often performed well in others. This