Issues and recommendations for exploratory factor analysis and principal component analysis

This commentary provides a brief mathematical review of exploratory factor analysis, the common factor model, and principal components analysis. Details and recommendations related to the goals, measurement scales, estimation technique, factor retention, item retention, and rotation of factors. For researchers interested in attempting to identify latent factors, exploratory factor analysis, the common factor model, is the appropriate analysis. For surveys with Likert-type scales weighted least squares with robust standard errors is recommended along with oblique rotation. Alternative techniques for analyzing the data, e.g., item response theory and machine learning, are briefly discussed. Finally, a basic check list for researchers and reviewers is provided.

Automated summary

This commentary provides a brief mathematical review of exploratory factor analysis, the common factor model, and principal components analysis, with details on goals, measurement scales, estimation techniques, factor retention, item retention, and rotation of factors. For researchers interested in identifying latent factors, exploratory factor analysis and the common factor model are recommended, along with the use of weighted least squares and oblique rotation. Other techniques such as item response theory and machine learning are briefly discussed, and a basic checklist for researchers and reviewers is provided.