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Predictive evaluation of human value segmentations

Author

Listed:
  • Kristoffer Jon Albers
  • Morten Mørup
  • Mikkel N. Schmidt
  • Fumiko Kano Glückstad

Abstract

Data-driven segmentation is an important tool for analyzing patterns of associations in social survey data; however, it remains a challenge to compare the quality of segmentations obtained by different methods. We present a statistical framework for quantifying the quality of segmentations of human values, by evaluating their ability to predict held-out data. By comparing clusterings of human values survey data from the forth round of European Social Study (ESS-4), we show that demographic markers such as age or country predict better than random, yet are outperformed by data-driven segmentation methods. We show that a Bayesian version of Latent Class Analysis (LCA) outperforms the standard maximum likelihood LCA in predictive performance and is more robust for different number of clusters.

Suggested Citation

  • Kristoffer Jon Albers & Morten Mørup & Mikkel N. Schmidt & Fumiko Kano Glückstad, 2022. "Predictive evaluation of human value segmentations," The Journal of Mathematical Sociology, Taylor & Francis Journals, vol. 46(1), pages 28-55, January.
    • Handle: RePEc:taf:gmasxx:v:46:y:2022:i:1:p:28-55
    DOI: 10.1080/0022250X.2020.1811277
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