Dimensionality Assessment in Exploratory Factor Analysis

In this project, we combine extensive data simulations and supervised machine learning to create new factor retention criteria that can accurately predict the dimensionality of a latent concept given various data characteristics. In simulation studies, these machine-learning-based approaches show a consistently higher accuracy than common factor retention criteria as well as higher replicability rates for empirical data sets.

Selected Publications and Preprints:

Goretzko, D., & Bühner, M. (2020). One model to rule them all? Using machine learning algorithms to determine the number of factors in exploratory factor analysis. Psychological Methods, 25(6), 776–786. https://doi.org/10.1037/met0000262

Goretzko, D., & Bühner, M. (2022). Factor retention using machine learning with ordinal data. Applied Psychological Measurement, 46(5), 406-421. https://doi.org/10.1177/01466216221089345

Goretzko, D., & Ruscio, J. (2024). The Comparison Data Forest – A new comparison data approach to determine the number of factors in exploratory factor analysis. Behavior Research Methods. https://doi.org/10.3758/s13428-023-02122-4

Goretzko, D. (2025). How many factors to retain in exploratory factor analysis? A critical overview of factor retention methods. Psychological Methods. https://doi.org/10.1037/met0000733

Goretzko, D., Partsch, M. V., & Sterner, P. (2025). Embrace the heterogeneity in EFA but be transparent about what you do – A commentary on Manapat et al. (2023). Psychological Methods. https://doi.org/10.1037/met0000759