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Research Article

Predictive evaluation of human value segmentations

ORCID Icon, ORCID Icon, ORCID Icon & ORCID Icon
Pages 28-55 | Received 27 May 2020, Accepted 13 Aug 2020, Published online: 17 Sep 2020
 

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.

Notes

Additional information

Funding

This work was supported by the Innovationsfonden [61579-00001A].

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