title: KSC-N: Clustering of Hierarchical Time Profile Data.
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.
authors.affiliation: Research Group of Methodology of Educational Sciences, University of Leuven, Tiensestraat 102, 3000 , Leuven, Belgium. Joke.Heylen@ppw.kuleuven.be.
authors.forename: Joke
authors.initials: J
authors.lastname: Heylen
authors.affiliation: Research Group of Quantitative Psychology and Individual Differences, University of Leuven, Tiensestraat 102, 3000, Leuven, Belgium.
authors.forename: Iven
authors.initials: I
authors.lastname: Van Mechelen
authors.affiliation: Research Group of Quantitative Psychology and Individual Differences, University of Leuven, Tiensestraat 102, 3000, Leuven, Belgium.
authors.forename: Philippe
authors.initials: P
authors.lastname: Verduyn
authors.affiliation: Research Group of Methodology of Educational Sciences, University of Leuven, Tiensestraat 102, 3000 , Leuven, Belgium.
authors.forename: Eva
authors.initials: E
authors.lastname: Ceulemans
mesh_terms: D000465:Algorithms; D004644:Emotions; D006801:Humans; D007206:Individuality; D015233:Models, Statistical; D011594:Psychometrics; D013997:Time Factors
keywords: KSC; clustering; hierarchical data; individual differences; shape and amplitude variability; time profiles