Journal article
Predictive evaluation of human value segmentations
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.
Language: | English |
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Publisher: | Routledge |
Year: | 2021 |
Pages: | 28-55 |
ISSN: | 15455874 and 0022250x |
Types: | Journal article |
DOI: | 10.1080/0022250X.2020.1811277 |
ORCIDs: | Albers, Kristoffer Jon , Mørup, Morten , Schmidt, Mikkel N. and 0000-0002-8618-2479 |