Model-Selection-Based Approaches to Identifying the Optimal Number of Factors in Multilevel Exploratory Factor Analysis

This study examined the accuracy of commonly used model fit indexes in identifying number of factors in multilevel exploratory factor analysis using Monte Carlo simulations. Multilevel data were generated according to different scenarios of factor structures: cluster numbers, cluster sizes, and intraclass correlation coefficient (ICC) conditions. The results showed that when using the model-based approach, most of the commonly used fit indexes...