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

Mikkel N. Schmidt, Daniel Seddig, Eldad Davidov, Morten Mørup, Kristoffer Jon Albers, Jan Michael Bauer, Fumiko Kano Glückstad*

*Corresponding author for this work

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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.
Original languageEnglish
JournalMethodology - European Journal of Research Methods for the Behavioral and Social Sciences
Issue number2
Pages (from-to)127-148
Number of pages22
Publication statusPublished - Jun 2021

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