Factor retention in exploratory factor analysis based on LSTM

In psychological research, determining the dimensional structure and characteristics of psychological traits is of paramount importance. Exploratory Factor Analysis (EFA) serves as a critical statistical methodology for identifying latent dimensions. Accurately determining the number of factors constitutes a pivotal technical challenge in EFA; under- or over-extraction of factors invariably yields detrimental consequences. To address this challenge, the present study conceptualizes eigenvalues as sequential data and employs a deep neural network architecture based on Long Short-Term Memory (LSTM) networks. Comprehensive evaluation metrics (including accuracy, precision, recall, F1-score, and Kappa) all exceeded 83%. Rigorous validation through extensive simulation studies and empirical analyses confirmed the robust performance of LSTM across diverse data conditions. Results demonstrate that LSTM achieves substantially higher accuracy than Comparison Data Fit (CDF), Empirical Kaiser Criterion (EKC), and Parallel Analysis (PA) methods, with a mean improvement rate of 48.50% and a peak improvement of 171.09%. Furthermore, LSTM exhibits smaller bias and superior robustness relative to CDF, EKC, and PA. Researchers may utilize the R package LSTMfactors to apply the LSTM model trained in this study to empirical data analysis.