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CLASSI: A classification model for the study of sequential processes and individual differences therein

Published online by Cambridge University Press:  01 January 2025

Eva Ceulemans  and
Iven Van Mechelen
Eva Ceulemans*
Affiliation:
Katholieke Universiteit Leuven
Iven Van Mechelen
Affiliation:
Katholieke Universiteit Leuven
*
Requests for reprints should be sent to Eva Ceulemans, Department of Psychology, Katholieke Universiteit Leuven, Tiensestraat 102, 3000 Leuven, Belgium. E-mail: [email protected]
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Abstract

In psychological research, one often aims at explaining individual differences in S-R profiles, that is, individual differences in the responses (R) with which people react to specific stimuli (S). To this end, researchers often postulate an underlying sequential process, which boils down to the specification of a set of mediating variables (M) and the processes that link these mediating variables to the stimuli and responses under study. Obviously, a crucial task is to chart how the individual differences in the S-R profiles are caused by individual differences in the S-M link and/or by individual differences in the M-R link. In this paper we propose a new model, called CLASSI, which was explicitly designed for this task. In particular, the key principle of CLASSI consists of reducing the S, M, and R nodes of a sequential process to a few mutually exclusive types and inducing an S-M and an M-R person typology from the data, with the S-M person types being characterized in terms of if S type then M type rules and the M-R person types in terms of if M type then R type rules. As such, the S-M and M-R person types and their associated if–then rules represent the important individual differences in the S-M and M-R links of the sequential process under study. An algorithm to fit the CLASSI model is described and evaluated in a simulation study. An application of CLASSI to data from the behavioral domain of anger and sadness is discussed. Finally, we relate CLASSI to other methods and discuss possible extensions.

Keywords

sequential processindividual differencesmultiway clusteringclassificationsimulated annealing
Type
Theory and Methods
Information
Psychometrika , Volume 73 , Issue 1 , March 2008 , pp. 107 - 124
Copyright
Copyright © 2007 The Psychometric Society

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Footnotes

The first author is a post-doctoral fellow of the Fund for Scientific Research—Flanders (Belgium). The research reported in this paper was partially supported by the Research Council of K.U. Leuven (GOA/05/04).

References

Aarts, E.H., & Lenstra, J.K. (1997). Local search in combinatorial optimization, Chichester: Wiley.Google Scholar
Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50, 179–211.CrossRefGoogle Scholar
Brusco, M.J. (2001). A simulated annealing heuristic for unidimensional and multidimensional (city-block) scaling of symmetric proximity matrices. Journal of Classification, 18, 3–33.CrossRefGoogle Scholar
Ceulemans, E., & Kiers, H.A.L. (2006). Selecting among three-mode principal component models of different types and complexities: A numerical convex hull-based method. British Journal of Mathematical and Statistical Psychology, 59, 133–150.CrossRefGoogle ScholarPubMed
Ceulemans, E., Van Mechelen, I., & Kuppens, P. (2004). Adapting the formal to the substantive: Constrained Tucker3-Hiclas. Journal of Classification, 21, 19–50.CrossRefGoogle Scholar
Ceulemans, E., Van Mechelen, I., & Leenen, I. (in press). The local minima problem in hierarchical classes analysis: An evaluation of a simulated annealing algorithm and various multistart procedures. Psychometrika.Google Scholar
Frijda, N.H., Kuipers, P., & ter Schure, E. (1989). Relations among emotion, appraisal, and emotional action readiness. Journal of Personality and Social Psychology, 57, 212–228.CrossRefGoogle Scholar
Mischel, W., & Shoda, Y. (1995). A cognitive-affective system-theory of personality—Reconceptualizing situations, dispositions, dynamics, and invariance in personality structure. Psychological Review, 102, 246–268.CrossRefGoogle ScholarPubMed
Mischel, W., & Shoda, Y. (1998). Reconciling processing dynamics and personality dispositions. Annual Review of Personality, 49, 229–258.Google ScholarPubMed
Murillo, A., Vera, J.F., & Heiser, W.J. (2005). A permutation-translation simulated annealing algorithm for l 1 and l 2 unidimensional scaling. Journal of Classification, 22, 119–138.CrossRefGoogle Scholar
Schepers, J., Van Mechelen, I., & Ceulemans, E. (2006). Three-mode partitioning. Computational Statistics and Data Analysis, 51, 1623–1642.CrossRefGoogle Scholar
Schepers, J., Ceulemans, E., & Van Mechelen, I. (2007). Selecting among multi-mode partitioning models of different complexities: A comparison of four model selection criteria. Manuscript submitted for publication.Google Scholar
Scherer, K.R. (2001). The nature and study of appraisal: A review of the issues. In Scherer, K.R., Schorr, A., & Johnstone, T. (Eds.), Appraisal processes in emotion: Theory, methods, research (pp. 369–392). New York: Oxford University Press.CrossRefGoogle Scholar
Smilde, A.K., & Kiers, H.A.L. (1999). Multiway covariates regression models. Journal of Chemometrics, 13, 31–48.3.0.CO;2-P>CrossRefGoogle Scholar
Trejos, J., & Castillo, W. (2000). Simulated annealing optimization for two-mode partitioning. In Gaul, W. & Decker, R. (Eds.), Classification and information at the turn of the millennium (pp. 135–142). Heidelberg: Springer.Google Scholar
Van Mechelen, I., & De Boeck, P. (1989). Implicit taxonomy in psychiatric diagnosis: A case study. Journal of Social and Clinical Psychology, 8, 276–287.CrossRefGoogle Scholar
Van Rosmalen, J., Groenen, P.J.F., Trejos, J., & Castillo, W. (2007). Global optimization strategies for two-mode clustering. Manuscript submitted for publication.Google Scholar
Vansteelandt, K., & Van Mechelen, I. (2006). Individual differences in anger and sadness: In pursuit of active situational features and psychological processes. Journal of Personality, 74, 871–910.CrossRefGoogle ScholarPubMed