Examination of heterogeneous societies: Identifying subpopulations by contrasting cultures

Fumiko Kano Glückstad, Mikkel Nørgaard Schmidt, Morten Mørup

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The recent development of data analytic tools rooted around the Multi-Group Latent Class Analysis (MGLCA) has enabled the examination of heterogeneous datasets in a cross-cultural context. Although the MGLCA is considered as an established and popular cross-cultural data analysis approach, the infinite relational model (IRM) is a new and disruptive type of unsupervised clustering approach that has been developed recently by cognitive psychologists and computer scientists. In this article, an extended version of the IRM coined the multinominal IRM—or mIRM in short—is applied to a cross-cultural analysis of survey data available from the World Value Survey organization. Specifically, the present work analyzes response patterns of the Portrait Value Questionnaire (PVQ) representing Schwartz’s 10 basic values of Japanese and Swedes. The applied model exposes heterogeneous structures of the two societies consisting of fine-grained response patterns expressed by the respective subpopulations and extracts latent typological structures contrasting and highlighting similarities and differences between these two societies. In the final section, we discuss similarities and differences identified between the MGLCA and the mIRM approaches, which indicate potential applications and contributions of the mIRM and the general IRM framework for future cross-cultural data analyses.
Original languageEnglish
JournalJournal of Cross-Cultural Psychology
Issue number1
Pages (from-to)39– 57
Number of pages19
Publication statusPublished - 2017


  • Heterogeneity
  • Cross-cultural data analysis
  • Intracultural data analysis
  • Clustering
  • Data structuring
  • Unsupervised machine learning
  • Nonparametric Bayesian relational modeling
  • Infinite relational model
  • World Value Survey
  • Schwartz’s theory of the 10 basic human values


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