Factor Analysis vs. PCA: Choosing the Right Tool

Factor Analysis vs. PCA: Choosing the Right Tool

Key Takeaways Factor analysis and principal component analysis are two techniques for dimensionality reduction that try to find a smaller set of variables that can explain the variation and correlation among large variables. Factor analysis is based on a causal model that assumes that there are latent factors that influence the observed variables, while PCA is based on a mathematical transformation that does not assume any causal model or latent variables. Factor analysis requires more assumptions and decisions than PCA, such as choosing between EFA and CFA, deciding how many factors to extract, choosing a method for estimating the factor loadings, and choosing a method for rotating the factors. PCA is more straightforward and objective than factor analysis, as it only requires deciding how many components to retain and interpreting the meaning of the components based on their loadings and correlations with the original variables Factor analysis can be useful for identifying the latent con…