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KSC-N: Clustering of Hierarchical Time Profile Data

Published online by Cambridge University Press:  01 January 2025

Joke Heylen ,
Iven Van Mechelen ,
Philippe Verduyn  and
Eva Ceulemans
Joke Heylen*
Affiliation:
University of Leuven
Iven Van Mechelen
Affiliation:
University of Leuven
Philippe Verduyn
Affiliation:
University of Leuven
Eva Ceulemans
Affiliation:
University of Leuven
*
Correspondence should be made to Joke Heylen, Research Group of Methodology of Educational Sciences, University of Leuven, Tiensestraat 102, 3000 Leuven, Belgium. Email: [email protected]
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Abstract

Quite a few studies in the behavioral sciences result in hierarchical time profile data, with a number of time profiles being measured for each person under study. Associated research questions often focus on individual differences in profile repertoire, that is, differences between persons in the number and the nature of profile shapes that show up for each person. In this paper, we introduce a new method, called KSC-N, that parsimoniously captures such differences while neatly disentangling variability in shape and amplitude. KSC-N induces a few person clusters from the data and derives for each person cluster the types of profile shape that occur most for the persons in that cluster. An algorithm for fitting KSC-N is proposed and evaluated in a simulation study. Finally, the new method is applied to emotional intensity profile data.

Keywords

hierarchical datatime profilesshape and amplitude variabilityindividual differencesclusteringKSC
Type
Article
Information
Psychometrika , Volume 81 , Issue 2 , June 2016 , pp. 411 - 433
Copyright
Copyright © 2014 The Psychometric Society

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