Identifying Latent Constructs Without Distributional Assumptions: A Probabilistic Model-Based Clustering Alternative to Traditional Factor Analysis

This study demonstrated that Extended Random Effect Maximal Interaction Two-Mode Clustering (E-ReMI), a probabilistic model-based clustering methodology for simultaneous grouping of both individuals and items, can capture latent constructs without decreasing the number of items or stringent distributional assumptions. The performance of E-ReMI relative to EFA was evaluated using simulated datasets to assess both the amount of variance accounted for by the reduced dimensionality of items, quantified by the coefficient of determination (R²), and the accuracy of item assignment to their latent constructs, determined by the Adjusted Rand Index (ARI).