Latent Profile Analysis of Human Values: What is the Optimal Number of Clusters?

Authors

  • Mikkel N. Schmidt
    Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
  • Daniel Seddig
    Institute of Sociology and Social Psychology (ISS), Faculty of Management, Economics and Social Sciences, University of Cologne, Cologne, Germany
  • Eldad Davidov
    Institute of Sociology and Social Psychology (ISS), Faculty of Management, Economics and Social Sciences, University of Cologne, Cologne, Germany; Department of Sociology, and URPP Social Networks, University of Zurich, Zurich, Switzerland
  • Morten Mørup
    Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
  • Kristoffer Jon Albers
    Department of Applied Mathematics and Computer Science, Technical University of Denmark, Kongens Lyngby, Denmark
  • Jan Michael Bauer
    Department of Management, Society and Communication, Copenhagen Business School, Frederiksberg, Denmark
  • Fumiko Kano Glückstad
    Department of Management, Society and Communication, Copenhagen Business School, Frederiksberg, Denmark

Abstract

Latent Profile Analysis (LPA) is a method to extract homogeneous clusters characterized by a common response profile. Previous works employing LPA to human value segmentation tend to select a small number of moderately homogeneous clusters based on model selection criteria such as Akaike information criterion, Bayesian information criterion and Entropy. The question is whether a small number of clusters is all that can be gleaned from the data. While some studies have carefully compared different statistical model selection criteria, there is currently no established criteria to assess if an increased number of clusters generates meaningful theoretical insights. This article examines the content and meaningfulness of the clusters extracted using two algorithms: Variational Bayesian LPA and Maximum Likelihood LPA. For both methods, our results point towards eight as the optimal number of clusters for characterizing distinctive Schwartz value typologies that generate meaningful insights and predict several external variables.