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KSC-N: Clustering of Hierarchical Time Profile Data

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An Erratum to this article was published on 08 August 2016

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.

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Notes

  1. Before moving this time profile, we check whether this time profile is the only profile in its current profile cluster. If this is the case we move on to the time profile that fits its current cluster the second least, and so on.

  2. Again, taking into account the sizes of the person clusters (see footnote 1).

  3. As the beta distribution is only defined on the interval [0 1], we evaluate the probability density function in J points equally distributed between 0 and 1. These J points form the time points of the reference profile.

  4. On average, the mean congruence amounts to.38 for low congruence data sets and.81 for high congruence data sets.

  5. Participants also answered a number of appraisal and regulation questions regarding the emotional episode, but as we focus on individual differences these are not relevant for this study.

  6. Participants filled out additional trait questionnaires measuring fear and stress (DASS-21), self-esteem (JVGG), neuroticism and extraversion (NEO-FFI), life satisfaction (SWLS), and positive and negative affect (PANAS). Our focus, however, lies with depression

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Acknowledgments

This research was supported by Grant GOA/15/003 from the Research Fund of the University of Leuven. Philippe Verduyn is a Post-Doctoral Fellow of the Research Foundation—Flanders (FWO). The research leading to the results reported in this paper was supported in part by the Interuniversity Attraction Poles program financed by the Belgian government (IAP/P7/06).

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Authors and Affiliations

  1. Research Group of Methodology of Educational Sciences, University of Leuven, Tiensestraat 102, 3000 , Leuven, Belgium

    Joke Heylen & Eva Ceulemans

  2. Research Group of Quantitative Psychology and Individual Differences, University of Leuven, Tiensestraat 102, 3000, Leuven, Belgium

    Iven Van Mechelen & Philippe Verduyn

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  1. Joke Heylen
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  2. Iven Van Mechelen
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  3. Philippe Verduyn
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  4. Eva Ceulemans
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Correspondence to Joke Heylen.

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Heylen, J., Van Mechelen, I., Verduyn, P. et al. KSC-N: Clustering of Hierarchical Time Profile Data. Psychometrika 81, 411–433 (2016). https://doi.org/10.1007/s11336-014-9433-x

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