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 observed data.

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 observed …

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 each …

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.

Exploratory factor analysis (EFA) is a multivariate statistical method that has become a fundamental tool in the development and validation of psychological theories and measurements.

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

Exploratory factor analysis (EFA), often just called “factor analysis,” is intended to help researchers understand the structure of complex data, and possibly reduce the complexity by combining items …

Exploratory factor analysis (EFA) is generally used to discover the factor structure of a measure and to examine its internal reliability. EFA is often recommended when researchers have no hypotheses …