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The CLASSI-N Method for the Study of Sequential Processes

Published online by Cambridge University Press:  01 January 2025

Eva Vande Gaer ,
Eva Ceulemans ,
Iven Van Mechelen  and
Peter Kuppens
Eva Vande Gaer*
Affiliation:
Katholieke Universiteit Leuven
Eva Ceulemans
Affiliation:
Katholieke Universiteit Leuven
Iven Van Mechelen
Affiliation:
Katholieke Universiteit Leuven
Peter Kuppens
Affiliation:
Katholieke Universiteit Leuven
*
Requests for reprints should be sent to Eva Vande Gaer, Methodology of Educational Sciences Research Group and Research Group of Quantitative Psychology and Individual Differences, Katholieke Universiteit Leuven, Andreas Vesaliusstraat 2, P.O. Box 3762, 3000 Leuven, Belgium. E-mail: eva.vandegaer@ppw.kuleuven.be
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Abstract

In many psychological research domains stimulus-response profiles are explained by conjecturing a sequential process in which some variables mediate between stimuli and responses. Charting sequential processes is often a complex task because (1) many possible mediating variables may exist, and (2) interindividual differences may occur in the relationship between these mediating variables and the response. Recently, Ceulemans and Van Mechelen (Psychometrika 73(1):107–124, 2008) addressed these challenges by developing the CLASSI model. A major drawback of CLASSI is that it requires information about the same set of stimuli for all participants (i.e., crossed data), whereas recently a number of data gathering techniques have been proposed in which the set of stimuli differs across participants, yielding nested data. Therefore we present the CLASSI-N model, which extends the CLASSI model to nested data. A simulated annealing algorithm is proposed. The results of a simulation study are discussed as well as an application to data concerning depression.

Keywords

sequential processesCLASSIindividual differencesbinary dataclusterwise regressionclustering
Type
Article
Information
Psychometrika , Volume 77 , Issue 1 , January 2012 , pp. 85 - 105
Copyright
Copyright © 2011 The Psychometric Society

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References

Aarts, E., Korst, J., Michiels, W. (2005). Simulated annealing. In Burke, E.K., Kendall, G. (Eds.), Search methodologies (pp. 187–210). New York: Springer.CrossRefGoogle Scholar
Barrett, L.F., Barrett, D.J. (2001). An introduction to computerized experience sampling in psychology. Social Science Computer Review, 19(2), 175–185.CrossRefGoogle Scholar
Bigné, J.E., Mattila, A.S., Andreu, L. (2008). The impact of experiential consumption cognitions and emotions on behavioral intentions. Journal of Services Marketing, 22(4), 303–315.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(1), 3–33.CrossRefGoogle Scholar
Ceulemans, E., Van Mechelen, I. (2008). CLASSI: a classification model for the study of sequential processes and individual differences therein. Psychometrika, 73(1), 107–124.CrossRefGoogle Scholar
Chaturvedi, A., Carroll, J.D. (1994). An alternating combinatorial optimization approach to fitting the INDCLUS and generalized INDCLUS models. Journal of Classification, 11(2), 155–170.CrossRefGoogle Scholar
Csikszentmihalyi, M., Larsen, R. (1987). Validity and reliability of the experience-sampling method. Journal of Nervous and Mental Disease, 175(9), 526–536.CrossRefGoogle ScholarPubMed
Davidson, R.J., Jackson, D.C., Kalin, N.H. (2000). Emotion plasticity, context, and regulation: perspectives from affective neuroscience. Psychological Bulletin, 126(6), 890–909.CrossRefGoogle ScholarPubMed
de Jong, S., Kiers, H.A.L. (1992). Principal covariates regression. Part I. Theory. Chemometrics and Intelligent Laboratory Systems, 14 1–3155–164.CrossRefGoogle Scholar
DeSarbo, W.S., Oliver, R.L., Rangaswamy, A. (1989). A simulated annealing methodology for clusterwise linear regression. Psychometrika, 54(4), 707–736.CrossRefGoogle Scholar
Dryden, W., David, D. (2008). Rational emotive behaviour therapy: current status. Journal of Cognitive Psychotherapy: An International Quarterly, 22(3), 195–209.CrossRefGoogle Scholar
Gross, J.J. (1998). Antecedent- and response-focused emotion regulation: divergent consequences for experience, expression, and physiology. Journal of Personality and Social Psychology, 74, 224–237.CrossRefGoogle ScholarPubMed
Gross, J.J. (2001). Emotion regulation in adulthood: timing is everything. Current Directions in Psychological Science, 10(6), 214–219.CrossRefGoogle Scholar
Gross, J.J. (2008). Emotion regulation. In Lewis, M., Haviland-Jones, J., Barrett, L. (Eds.), Handbook of emotions (pp. 497–512). New York: Guilford.Google Scholar
Gross, J.J., John, O.P. (2003). Individual differences in two emotion regulation processes: implications for affect relationships and well-being. Journal of Personality and Social Psychology, 85(2), 348–362.CrossRefGoogle ScholarPubMed
Haggard, E.A. (1958). Intraclass correlation and the analysis of variance, Troy: Dryden.Google Scholar
Hartigan, J.A. (1975). Clustering algorithms, New York: Wiley.Google Scholar
Hemenover, S.H., Augustine, A.A., Shulman, T., Tran, T., Barlett, C.P. (2008). Individual differences in negative affect repair. Emotion, 8(4), 468–478.CrossRefGoogle ScholarPubMed
Hubert, L., Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218.CrossRefGoogle Scholar
Kirk, R.E. (1995). Experimental design: procedures for the behavioral sciences, Belmont: Brooks and Cole.Google Scholar
Kirkpatrick, S., Gelatt, C.D., Vecchi, M.P. (1983). Optimization by simulated annealing. Science, 220(4598), 671–680.CrossRefGoogle ScholarPubMed
Knuth, E.J., Stephens, A.C., McNeil, N.M., Alibali, M.W. (2006). Does understanding the equal sign matter? Evidence from solving equations. Journal for Research in Mathematics Education, 37(4), 297–312.Google Scholar
Koole, S.L. (2009). The psychology of emotion regulation: an integrative review. Cognition and Emotion, 23(1), 4–41.CrossRefGoogle Scholar
Kuppens, P., Van Mechelen, I., Rijmen, F. (2008). Towards disentangling sources of individual differences in appraisal and anger. Journal of Personality, 76(4), 969–1000.CrossRefGoogle Scholar
Leenen, I., Van Mechelen, I. (2001). An evaluation of two algorithms for hierarchical classes analysis. Journal of Classification, 18(1), 57–80.CrossRefGoogle Scholar
Li, T. (2005). A general model for clustering binary data. KDD’05: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining (pp. 188–197). New York: ACM.CrossRefGoogle Scholar
Mischel, W., Shoda, Y. (1998). Reconciling processing dynamics and personality dispositions. Annual Review of Psychology, 49(1), 229–258.CrossRefGoogle ScholarPubMed
Nolen-Hoeksema, S., Wisco, B.E., Lyubomirsky, S. (2008). Rethinking rumination. Perspectives on Psychological Science, 3(5), 400–424.CrossRefGoogle ScholarPubMed
O’Brien, T.B., DeLongis, A. (1996). The interactional context of problem-, emotion-, and relationship-focused coping: the role of the big five personality factors. Journal of Personality, 64(4), 775–813.CrossRefGoogle ScholarPubMed
Radloff, L.S. (2006). The CES-D scale: a self-report depression scale for research in the general population. Applied Psychological Measurement, 1, 385–401.CrossRefGoogle Scholar
Robinson, N.S., Garber, J., Hilsman, R. (1995). Cognitions and stress: direct and moderating effects on depressive versus externalizing symptoms during the junior high school transition. Journal of Abnormal Psychology, 104(3), 453–463.CrossRefGoogle ScholarPubMed
Roseman, I.J., Smith, C.A. (2001). Appraisal theory: overview, assumptions, varieties, controversies. In Scherer, K.R., Schorr, A., Johnstone, T. (Eds.), Appraisal processes in emotion: theory, methods, research (pp. 3–19). London: Oxford University Press.CrossRefGoogle Scholar
Scherer, K.R. (1999). Appraisal theory. In Dalgleish, T., Power, M. (Eds.), Handbook of cognition and emotion (pp. 637–663). Chichester: Wiley.CrossRefGoogle Scholar
Shafer, A.B. (2006). Meta-analysis of the factor structures of four depression questionnaires: Beck, CES-D, Hamilton, and Zung. Journal of Clinical Psychology, 62, 123–146.CrossRefGoogle ScholarPubMed
Steinley, D. (2004). Properties of the Hubert-Arabie adjusted Rand index. Psychological Methods, 9(3), 386–396.CrossRefGoogle ScholarPubMed
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 millenium (pp. 135–142). Heidelberg: Springer.Google Scholar
Van Mechelen, I., Hennes, K. (2009). The appraisal basis of anger occurence and intensity revisited. Cognition and Emotion, 23(7), 1373–1388.CrossRefGoogle Scholar
Wilderjans, T.F., Ceulemans, E., Van Mechelen, I. (2008). The CHIC model: a global model for coupled binary data. Psychometrika, 73(4), 729–751.CrossRefGoogle Scholar