Predictive evaluation of human value segmentations

Kristoffer Jon Albers, Morten Mørup, Mikkel N. Schmidt*, Fumiko Kano Glückstad

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Abstrakt

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.

OriginalsprogEngelsk
TidsskriftJournal of Mathematical Sociology
ISSN0022-250X
DOI
StatusE-pub ahead of print - 17. sep. 2020

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