Welcome to the FactorAnalyzer documentation!

This is a Python module to perform exploratory and factor analysis (EFA), with several optional rotations. It also includes a class to perform confirmatory factor analysis (CFA), with certain pre-defined constraints. In exploratory factor analysis, factor extraction can be performed using a variety of estimation techniques. The factor_analyzer package allows users to perform EFA using either (1) a minimum residual (MINRES) solution, (2) a maximum likelihood (ML) solution, or (3) a principal factor solution. However, CFA can only be performed using an ML solution.

Both the EFA and CFA classes within this package are fully compatible with scikit-learn. Portions of this code are ported from the excellent R library psych, and the sem package provided inspiration for the CFA class.

Important

Please make sure to read the important notes section if you encounter any unexpected results.

Documentation

Indices and tables