Exploratory factor analysis (EFA) is a statistical method used to uncover the underlying structure of a relatively large set of variables. EFA is a technique within factor analysis whose overarching goal is to identify the underlying relationships between measured variables.

Principal component analysis (PCA) is a technique for reducing the dimensionality of such datasets, increasing interpretability but at the same time minimizing information loss. It does so by creating new uncorrelated variables that successively maximize variance.

Differences between PCA and EFA

Parallel Analysis

It is often difficult to determine the number of factors or components that should be retained. Parallel analysis compares the eigenvalues from the solution from your data to the eigenvalues generated from a sampling of randomly generated data