Selecting the Number of Factors in Exploratory Factor Analysis via out-of-sample Prediction Errors
Exploratory Factor Analysis (EFA) identifies a number of latent factors that explain correlations between observed variables. A key issue in the application of EFA is the selection of an adequate number of factors. This is a non-trivial problem because more factors always improve the fit of the model. Most methods for selecting the number of factors fall into two categories: either they analyze the patterns of eigenvalues of the correlation matrix, such as parallel analysis; or they frame the selection of the number of factors as a model selection problem and use approaches such as likelihood ratio tests or information criteria.