Estimating the Number of Factors in Exploratory Factor Analysis via out-of-sample Prediction Errors

Exploratory Factor Analysis (EFA) is one of the most popular statistical models in psychological science. A key problem in EFA is to estimate the number of factors. In this paper, we present a new method for estimating the number of factors based on minimizing the out-of-sample prediction error of candidate factor models. We show in an extensive simulation study that our method slightly outperforms existing methods, including parallel analysis, the BIC, and Exploratory Graph Analysis. In addition, we show that among the high performance method our method is the one that is most robust across different specifications of the true factor model. We provide an implementation of our method in the R-package fspe.