Latent Profiel Analysis of Human Values?

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

Research output: Contribution to conferenceConference abstract for conferenceResearchpeer-review


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 are 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
Publication date2022
Number of pages1
Publication statusPublished - 2022
EventEuropean Conference of Personality 2022 - Universidad Europea de Madrid, Madrid, Spain
Duration: 12 Jul 202215 Jul 2022
Conference number: 20


ConferenceEuropean Conference of Personality 2022
LocationUniversidad Europea de Madrid
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