Exploratory Factor Analysis (EFA) is a statistical technique used to identify underlying relationships between variables in a dataset. It helps researchers uncover patterns by grouping related variables into factors, which can simplify data interpretation. EFA is often used in fields like psychology and social sciences to explore the structure of data without prior hypotheses. The process involves analyzing correlations among variables to determine how many factors are needed to explain the data. By reducing the number of variables, EFA aids in developing theories and models, making it easier to understand complex datasets and identify key influences on observed behaviors or outcomes.