In multivariate statistics, exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables.

There are two approaches to factor extraction which stems from different approaches to variance partitioning: a) principal components analysis and b) common factor analysis.

Mar 13, 2025 · Exploratory Factor Analysis (EFA) is a statistical technique used to identify underlying factors or latent variables that explain the pattern of correlations within a set of …

Dec 18, 2025 · Factor analysis is a method that aims to uncover structure in large variable sets. If you have a data set with many variables, some of them may be interrelated, i.e., correlate with …

In exploratory factor analysis (EFA, the focus of this resource page), each observed variable is potentially a measure of every factor, and the goal is to determine relationships (between …

Exploratory factor analysis (EFA) is a set of statistical procedures used to determine the number and nature of constructs required to account for the pattern of correlations among a set of …

In Exploratory Factor Analysis (EFA), we are essentially exploring the correlations between observed variables to uncover any interesting, important underlying (latent) factors that are …

Exploratory factor analysis is a statistical technique that is used to reduce data to a smaller set of summary variables and to explore the underlying theoretical structure of the phenomena. It …

Exploratory Factor Analysis (EFA) is a statistical technique primarily used to uncover * latent (unobserved) structures or relationships within a set of *observed variables. It allows …

Factor analysis is commonly used in psychometrics, personality psychology, biology, marketing, product management, operations research, finance, and machine learning. It may help to deal …