ORIGINAL RESEARCH article
Front. Educ.
Sec. Assessment, Testing and Applied Measurement
A comparison of three approaches for clustering polytomous data in the presence of masking variables
Provisionally accepted
- 1Indiana University Bloomington, Bloomington, United States
- 2Loyola Marymount University, Los Angeles, United States
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To uncover the heterogeneity in a population, it is common yet important to partition individuals into distinct subgroups based on their responses to items in measurement tools. Various approaches have been introduced to tackle this clustering problem in psychology and education. To provide more guidance to practitioners, in this study, we compared the performance of three widely-applied approaches, including the latent class analysis (LCA), k-means and k-medians, in clustering polytomous items in the presence of masking variables. In the simulation conditions considered, we found that LCA coupled with Bayesian Information Criterion (BIC) outperformed other approaches and methods for determining the optimal number of subgroups. We also applied the three approaches to an empirical data set and obtained different conclusions regarding the number of subgroups. Additionally, we discussed the limitations of this study and future research directions.
Keywords: latent class analysis, K-means, K-medians, clustering, Polytomous data
Received: 12 Jun 2025; Accepted: 02 Dec 2025.
Copyright: © 2025 Huang, Botter and Sturm. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Sijia Huang
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