IDEAS home Printed from https://ideas.repec.org/a/spr/reihed/v66y2025i4d10.1007_s11162-025-09844-8.html
   My bibliography  Save this article

Buyer Beware: Understanding and Validating Distributional Assumptions of K-Means in College Student Typology Research

Author

Listed:
  • Yiran Chen

    (University of Michigan)

Abstract

The k-means clustering method, while widely embraced in college student typology research, is often misunderstood and misapplied. Many researchers regard k-means as a near-universal solution for uncovering homogeneous student groups, believing its success hinges primarily on the selection of an appropriate k. This idealized view, however, starkly contrasts with reality. The effectiveness of k-means is fundamentally dependent on specific distributional assumptions: Data points must form compact, well-separated, hyperspherical clusters of approximately equal size. Violations of these assumptions may result in distorted representations of student characteristics, potentially impacting the interpretation of student needs and the design of educational interventions. Through case studies and simulations, this literature review explores the potential manifestation of these distortions in empirical research, revealing how inattention to distributional assumptions can lead to artificial groupings that masquerade as genuine student types. To safeguard against erroneous student classifications, silhouette analysis is recommended as a powerful validation tool capable of dissecting k-means outputs across multiple levels of granularity, allowing researchers to assess the methodological soundness of their clustering solution before drawing substantive conclusions. By shedding light on these frequently overlooked assumptions and offering more rigorous validation techniques, this paper cautions “buyers” of k-means to “beware” of its caveats, calling for a better-informed approach to its application.

Suggested Citation

  • Yiran Chen, 2025. "Buyer Beware: Understanding and Validating Distributional Assumptions of K-Means in College Student Typology Research," Research in Higher Education, Springer;Association for Institutional Research, vol. 66(4), pages 1-38, June.
  • Handle: RePEc:spr:reihed:v:66:y:2025:i:4:d:10.1007_s11162-025-09844-8
    DOI: 10.1007/s11162-025-09844-8
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11162-025-09844-8
    File Function: Abstract
    Download Restriction: Access to the full text of the articles in this series is restricted.

    File URL: https://libkey.io/10.1007/s11162-025-09844-8?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to

    for a different version of it.

    References listed on IDEAS

    as
    1. Robert Tibshirani & Guenther Walther & Trevor Hastie, 2001. "Estimating the number of clusters in a data set via the gap statistic," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 63(2), pages 411-423.
    2. Peter Riley Bahr & Yiran Chen & Rooney Columbus, 2023. "Community College Skills Builders: Prevalence, Characteristics, Behaviors, and Outcomes of Successful Non-Completing Students Across Four States," The Journal of Higher Education, Taylor & Francis Journals, vol. 94(1), pages 96-131, January.
    3. Amélie Rogiers & Emmelien Merchie & Hilde Van Keer, 2019. "Learner profiles in secondary education: Occurrence and relationship with performance and student characteristics," The Journal of Educational Research, Taylor & Francis Journals, vol. 112(3), pages 385-396, May.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Thiemo Fetzer & Samuel Marden, 2017. "Take What You Can: Property Rights, Contestability and Conflict," Economic Journal, Royal Economic Society, vol. 0(601), pages 757-783, May.
    2. Khanh Duong, 2024. "Is meritocracy just? New evidence from Boolean analysis and Machine learning," Journal of Computational Social Science, Springer, vol. 7(2), pages 1795-1821, October.
    3. Daniel Agness & Travis Baseler & Sylvain Chassang & Pascaline Dupas & Erik Snowberg, 2022. "Valuing the Time of the Self-Employed," Working Papers 2022-2, Princeton University. Economics Department..
    4. Batool, Fatima & Hennig, Christian, 2021. "Clustering with the Average Silhouette Width," Computational Statistics & Data Analysis, Elsevier, vol. 158(C).
    5. Nicoleta Serban & Huijing Jiang, 2012. "Multilevel Functional Clustering Analysis," Biometrics, The International Biometric Society, vol. 68(3), pages 805-814, September.
    6. Orietta Nicolis & Jean Paul Maidana & Fabian Contreras & Danilo Leal, 2024. "Analyzing the Impact of COVID-19 on Economic Sustainability: A Clustering Approach," Sustainability, MDPI, vol. 16(4), pages 1-30, February.
    7. Li, Pai-Ling & Chiou, Jeng-Min, 2011. "Identifying cluster number for subspace projected functional data clustering," Computational Statistics & Data Analysis, Elsevier, vol. 55(6), pages 2090-2103, June.
    8. Yaeji Lim & Hee-Seok Oh & Ying Kuen Cheung, 2019. "Multiscale Clustering for Functional Data," Journal of Classification, Springer;The Classification Society, vol. 36(2), pages 368-391, July.
    9. Forzani, Liliana & Gieco, Antonella & Tolmasky, Carlos, 2017. "Likelihood ratio test for partial sphericity in high and ultra-high dimensions," Journal of Multivariate Analysis, Elsevier, vol. 159(C), pages 18-38.
    10. Yujia Li & Xiangrui Zeng & Chien‐Wei Lin & George C. Tseng, 2022. "Simultaneous estimation of cluster number and feature sparsity in high‐dimensional cluster analysis," Biometrics, The International Biometric Society, vol. 78(2), pages 574-585, June.
    11. Vojtech Blazek & Michal Petruzela & Tomas Vantuch & Zdenek Slanina & Stanislav Mišák & Wojciech Walendziuk, 2020. "The Estimation of the Influence of Household Appliances on the Power Quality in a Microgrid System," Energies, MDPI, vol. 13(17), pages 1-21, August.
    12. Andrew Clark & Alexander Mihailov & Michael Zargham, 2024. "Complex Systems Modeling of Community Inclusion Currencies," Computational Economics, Springer;Society for Computational Economics, vol. 64(2), pages 1259-1294, August.
    13. Juliane Begenau & Emil N. Siriwardane, 2024. "Fee Variation in Private Equity," Journal of Finance, American Finance Association, vol. 79(2), pages 1199-1247, April.
    14. Nicoleta Serban, 2008. "Estimating and clustering curves in the presence of heteroscedastic errors," Journal of Nonparametric Statistics, Taylor & Francis Journals, vol. 20(7), pages 553-571.
    15. Caruso, Germán & Scartascini, Carlos & Tommasi, Mariano, 2015. "Are we all playing the same game? The economic effects of constitutions depend on the degree of institutionalization," European Journal of Political Economy, Elsevier, vol. 38(C), pages 212-228.
    16. Alessandro Crimi & Olivier Commowick & Adil Maarouf & Jean-Christophe Ferré & Elise Bannier & Ayman Tourbah & Isabelle Berry & Jean-Philippe Ranjeva & Gilles Edan & Christian Barillot, 2014. "Predictive Value of Imaging Markers at Multiple Sclerosis Disease Onset Based on Gadolinium- and USPIO-Enhanced MRI and Machine Learning," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-10, April.
    17. Mehmet Çağlar & Cem Gürler, 2022. "Sustainable Development Goals: A cluster analysis of worldwide countries," Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, Springer, vol. 24(6), pages 8593-8624, June.
    18. Elizabeth Tipton & Robert B. Olsen, "undated". "Enhancing the Generalizability of Impact Studies in Education," Mathematica Policy Research Reports 35d5625333dc480aba9765b3b, Mathematica Policy Research.
    19. Paloma Péligry & Xavier Ragot, 2025. "Evolution of Fiscal Systems: Convergence or Divergence?," LIS Working papers 895, LIS Cross-National Data Center in Luxembourg.
    20. Cyril Atkinson-Clement & Eléonore Pigalle, 2021. "What can we learn from Covid-19 pandemic’s impact on human behaviour? The case of France’s lockdown," Palgrave Communications, Palgrave Macmillan, vol. 8(1), pages 1-12, December.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:spr:reihed:v:66:y:2025:i:4:d:10.1007_s11162-025-09844-8. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.