Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data
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arXiv
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| Auteurs principaux: | , , |
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| Format: | Preprint |
| Publié: |
2026
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| _version_ | 1866913101186596864 |
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| author | Pinto, Pedro Henrique Ramos Marques, Maria Jullyanna Ferreira Lopez, Luiz Carlos Serramo |
| author_facet | Pinto, Pedro Henrique Ramos Marques, Maria Jullyanna Ferreira Lopez, Luiz Carlos Serramo |
| contents | K-means clustering is widely used in psychological and psychometric research to identify profiles, subgroups, and potential typologies, yet its classical formulation does not test whether such groups exist as latent psychological categories. Instead, K-means partitions multidimensional space into regions around centroids, favoring compact, approximately spherical clusters defined by geometric distance. In this paper, we examine this limitation through a sequence of controlled simulated datasets. We then extend the analysis to the SMARVUS dataset, a large international psychometric dataset comprising survey responses from university students across 35 countries, to evaluate whether similar geometric partitioning patterns emerge in empirical psychological data. By contrasting simulated and empirical data, this paper argues that K-means can produce stable and visually coherent clustering solutions even in continuous Gaussian latent spaces without true subgroup structure. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2605_06989 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data Pinto, Pedro Henrique Ramos Marques, Maria Jullyanna Ferreira Lopez, Luiz Carlos Serramo Applications Artificial Intelligence Machine Learning Methodology 62H30, 62P15, 68T10, 68T09 K-means clustering is widely used in psychological and psychometric research to identify profiles, subgroups, and potential typologies, yet its classical formulation does not test whether such groups exist as latent psychological categories. Instead, K-means partitions multidimensional space into regions around centroids, favoring compact, approximately spherical clusters defined by geometric distance. In this paper, we examine this limitation through a sequence of controlled simulated datasets. We then extend the analysis to the SMARVUS dataset, a large international psychometric dataset comprising survey responses from university students across 35 countries, to evaluate whether similar geometric partitioning patterns emerge in empirical psychological data. By contrasting simulated and empirical data, this paper argues that K-means can produce stable and visually coherent clustering solutions even in continuous Gaussian latent spaces without true subgroup structure. |
| title | Drawing Lines in Psychological Space: What K-means Clustering Reveals in Simulated and Real Psychometric Data |
| topic | Applications Artificial Intelligence Machine Learning Methodology 62H30, 62P15, 68T10, 68T09 |
| url | https://arxiv.org/abs/2605.06989 |