Classification methods such as exploratory factor analysis (EFA) and network community detection (NCD) are widely used to identify latent item groupings in multidimensional psychological assessments. However, direct comparisons between these approaches remain limited. In addition, evaluations of clustering methods often rely on overall classification metrics, which may obscure systematic differences in how well distinct types of items are recovered. Item characteristics—such as core–peripheral positions and loading patterns—may influence classification outcomes, yet few studies have examined how these item types interact with clustering methods. The present study addresses these gaps by comparing EFA and NCD within a unified machine-learning evaluation framework that varies sample size, latent structure, preprocessing strategy, and machine-learning classifier choice (Random Forests vs. Support Vector Machines). Results show that the performance of both EFA and NCD is influenced by sample size, item type, latent structure, and classifier choice. Moreover, the downstream classifier moderates how sensitive each method is to differences among item types. These findings highlight the importance of considering item-type heterogeneity when evaluating clustering methods and demonstrate the value of machine-learning-based frameworks for advancing psychometric classification approaches.
Li et al. (Tue,) studied this question.