{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T17:53:06Z","timestamp":1775152386028,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"4","license":[{"start":{"date-parts":[[2026,3,31]],"date-time":"2026-03-31T00:00:00Z","timestamp":1774915200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Computation"],"abstract":"<jats:p>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\u2014such as core\u2013peripheral positions and loading patterns\u2014may 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.<\/jats:p>","DOI":"10.3390\/computation14040080","type":"journal-article","created":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T16:51:02Z","timestamp":1775148662000},"page":"80","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Evaluating Psychometric Clustering Methods: A Machine-Learning Comparison of EFA and NCD"],"prefix":"10.3390","volume":"14","author":[{"given":"Jingyang","family":"Li","sequence":"first","affiliation":[{"name":"College of Education, University of Georgia, Athens, GA 30602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Zhenqiu (Laura)","family":"Lu","sequence":"additional","affiliation":[{"name":"College of Education, University of Georgia, Athens, GA 30602, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,31]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Bollen, K.A. (1989). Structural Equations with Latent Variables, John Wiley & Sons.","DOI":"10.1002\/9781118619179"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"611","DOI":"10.1207\/S15327906MBR3604_06","article-title":"Sample size in factor analysis: The role of model error","volume":"36","author":"MacCallum","year":"2001","journal-title":"Multivar. Behav. Res."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"189","DOI":"10.1037\/h0059402","article-title":"Current issues in factor analysis","volume":"37","author":"Thurstone","year":"1940","journal-title":"Psychol. Bull."},{"key":"ref_4","doi-asserted-by":"crossref","unstructured":"Wasserman, S., and Faust, K. (1994). Social Network Analysis: Methods and Applications, Cambridge University Press.","DOI":"10.1017\/CBO9780511815478"},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"7821","DOI":"10.1073\/pnas.122653799","article-title":"Community structure in social and biological networks","volume":"99","author":"Girvan","year":"2002","journal-title":"Proc. Natl. Acad. Sci. USA"},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"848","DOI":"10.1038\/s41567-022-01716-7","article-title":"20 years of network community detection","volume":"18","author":"Fortunato","year":"2022","journal-title":"Nat. Phys."},{"key":"ref_7","unstructured":"DeVellis, R.F., and Thorpe, C.T. (2021). Scale Development: Theory and Applications, Sage Publications."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"375","DOI":"10.1016\/S0378-8733(99)00019-2","article-title":"Models of core\/periphery structures","volume":"21","author":"Borgatti","year":"2000","journal-title":"Soc. Netw."},{"key":"ref_9","unstructured":"Borgatti, S.P., Agneessens, F., Johnson, J.C., and Everett, M.G. (2024). Analyzing Social Networks, Sage Publications."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"406","DOI":"10.1037\/h0069792","article-title":"Multiple factor analysis","volume":"38","author":"Thurstone","year":"1931","journal-title":"Psychol. Rev."},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"21","DOI":"10.1207\/s15327752jpa6401_2","article-title":"Domains and facets: Hierarchical personality assessment using the Revised NEO Personality Inventory","volume":"64","author":"Costa","year":"1995","journal-title":"J. Personal. Assess."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.3758\/s13428-023-02106-4","article-title":"Comparing community detection algorithms in psychometric networks: A Monte Carlo simulation","volume":"56","author":"Christensen","year":"2024","journal-title":"Behav. Res. Methods"},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"479","DOI":"10.3390\/psych3030032","article-title":"Estimating the stability of psychological dimensions via bootstrap exploratory graph analysis: A Monte Carlo simulation and tutorial","volume":"3","author":"Christensen","year":"2021","journal-title":"Psych"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Golino, H.F., and Epskamp, S. (2017). Exploratory graph analysis: A new approach for estimating the number of dimensions in psychological research. PLoS ONE, 12.","DOI":"10.1371\/journal.pone.0174035"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"292","DOI":"10.1037\/met0000255","article-title":"Investigating the performance of exploratory graph analysis and traditional techniques to identify the number of latent factors: A simulation and tutorial","volume":"25","author":"Golino","year":"2020","journal-title":"Psychol. Methods"},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"84","DOI":"10.1037\/1082-989X.4.1.84","article-title":"Sample size in factor analysis","volume":"4","author":"MacCallum","year":"1999","journal-title":"Psychol. Methods"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach. Learn."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1023\/A:1022627411411","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1111\/j.2044-8317.1984.tb00789.x","article-title":"Asymptotically distribution-free methods for the analysis of covariance structures","volume":"37","author":"Browne","year":"1984","journal-title":"Br. J. Math. Stat. Psychol."},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"026113","DOI":"10.1103\/PhysRevE.69.026113","article-title":"Finding and evaluating community structure in networks","volume":"69","author":"Newman","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"066133","DOI":"10.1103\/PhysRevE.69.066133","article-title":"Fast algorithm for detecting community structure in networks","volume":"69","author":"Newman","year":"2004","journal-title":"Phys. Rev. E"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"P10008","DOI":"10.1088\/1742-5468\/2008\/10\/P10008","article-title":"Fast unfolding of communities in large networks","volume":"2008","author":"Blondel","year":"2008","journal-title":"J. Stat. Mech. Theory Exp."},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"White, S., and Smyth, P. (2005). A spectral clustering approach to finding communities in graphs. Proceedings of the 2005 SIAM International Conference on Data Mining, Society for industrial and applied Mathematics.","DOI":"10.1137\/1.9781611972757.25"},{"key":"ref_24","doi-asserted-by":"crossref","unstructured":"Xie, J., and Szymanski, B.K. (2013). Labelrank: A stabilized label propagation algorithm for community detection in networks. Proceedings of the 2013 IEEE 2nd Network Science Workshop (NSW), West Point, NY, USA, 29 April\u20131 May 2013, IEEE.","DOI":"10.1109\/NSW.2013.6609210"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"167","DOI":"10.1137\/120881683","article-title":"Core-periphery structure in networks","volume":"74","author":"Rombach","year":"2014","journal-title":"SIAM J. Appl. Math."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Schwaba, T., Rhemtulla, M., Hopwood, C.J., and Bleidorn, W. (2020). A facet atlas: Visualizing networks that describe the blends, cores, and peripheries of personality structure. PLoS ONE, 15.","DOI":"10.31234\/osf.io\/zskfu"},{"key":"ref_27","unstructured":"Stevens, J. (1992). Applied Multivariate Statistics for the Social Sciences, Lawrence Erlbaum Associates."},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"475","DOI":"10.1037\/0033-2909.115.3.475","article-title":"Applications of standard error estimates in unrestricted factor analysis: Significance tests for factor loadings and correlations","volume":"115","author":"Cudeck","year":"1994","journal-title":"Psychol. Bull."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"281","DOI":"10.1037\/met0000355","article-title":"Mixture multigroup factor analysis for unraveling factor loading noninvariance across many groups","volume":"27","author":"Vermunt","year":"2022","journal-title":"Psychol. Methods"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"557","DOI":"10.1177\/0013164409355695","article-title":"Factor loading estimation error and stability using exploratory factor analysis","volume":"70","author":"Sass","year":"2010","journal-title":"Educ. Psychol. Meas."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"1025","DOI":"10.1177\/0013164420906449","article-title":"Prior sensitivity in Bayesian structural equation modeling for sparse factor loading structures","volume":"80","author":"Liang","year":"2020","journal-title":"Educ. Psychol. Meas."}],"container-title":["Computation"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2079-3197\/14\/4\/80\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,4,2]],"date-time":"2026-04-02T17:07:35Z","timestamp":1775149655000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2079-3197\/14\/4\/80"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,31]]},"references-count":31,"journal-issue":{"issue":"4","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["computation14040080"],"URL":"https:\/\/doi.org\/10.3390\/computation14040080","relation":{},"ISSN":["2079-3197"],"issn-type":[{"value":"2079-3197","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,31]]}}}