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The CLASSI-N Method for the Study of Sequential Processes

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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.

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References

  • Aarts, E., Korst, J., & Michiels, W. (2005). Simulated annealing. In E.K. Burke & G. Kendall (Eds.), Search methodologies (pp. 187–210). New York: Springer.

    Chapter  Google Scholar 

  • Barrett, L.F., & Barrett, D.J. (2001). An introduction to computerized experience sampling in psychology. Social Science Computer Review, 19(2), 175–185.

    Article  Google 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.

    Article  Google 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.

    Google 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.

    Article  Google 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.

    Article  Google Scholar 

  • Csikszentmihalyi, M., & Larsen, R. (1987). Validity and reliability of the experience-sampling method. Journal of Nervous and Mental Disease, 175(9), 526–536.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • de Jong, S., & Kiers, H.A.L. (1992). Principal covariates regression. Part I. Theory. Chemometrics and Intelligent Laboratory Systems, 14(1–3), 155–164.

    Article  Google Scholar 

  • DeSarbo, W.S., Oliver, R.L., & Rangaswamy, A. (1989). A simulated annealing methodology for clusterwise linear regression. Psychometrika, 54(4), 707–736.

    Article  Google Scholar 

  • Dryden, W., & David, D. (2008). Rational emotive behaviour therapy: current status. Journal of Cognitive Psychotherapy: An International Quarterly, 22(3), 195–209.

    Article  Google 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.

    Article  PubMed  Google Scholar 

  • Gross, J.J. (2001). Emotion regulation in adulthood: timing is everything. Current Directions in Psychological Science, 10(6), 214–219.

    Article  Google Scholar 

  • Gross, J.J. (2008). Emotion regulation. In M. Lewis, J. Haviland-Jones, & L. Barrett (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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • Hubert, L., & Arabie, P. (1985). Comparing partitions. Journal of Classification, 2(1), 193–218.

    Article  Google 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  Google 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.

    Article  PubMed  Google Scholar 

  • Leenen, I., & Van Mechelen, I. (2001). An evaluation of two algorithms for hierarchical classes analysis. Journal of Classification, 18(1), 57–80.

    Article  Google Scholar 

  • Li, T. (2005). A general model for clustering binary data. In KDD’05: Proceedings of the eleventh ACM SIGKDD international conference on knowledge discovery in data mining (pp. 188–197). New York: ACM.

    Chapter  Google Scholar 

  • Mischel, W., & Shoda, Y. (1998). Reconciling processing dynamics and personality dispositions. Annual Review of Psychology, 49(1), 229–258.

    Article  PubMed  Google Scholar 

  • Nolen-Hoeksema, S., Wisco, B.E., & Lyubomirsky, S. (2008). Rethinking rumination. Perspectives on Psychological Science, 3(5), 400–424.

    Article  Google Scholar 

  • 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.

    Article  PubMed  Google Scholar 

  • 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.

    Article  Google 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.

    Article  PubMed  Google Scholar 

  • Roseman, I.J., & Smith, C.A. (2001). Appraisal theory: overview, assumptions, varieties, controversies. In K.R. Scherer, A. Schorr, & T. Johnstone (Eds.), Appraisal processes in emotion: theory, methods, research (pp. 3–19). London: Oxford University Press.

    Google Scholar 

  • Scherer, K.R. (1999). Appraisal theory. In T. Dalgleish & M. Power (Eds.), Handbook of cognition and emotion (pp. 637–663). Chichester: Wiley.

    Google 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.

    Article  PubMed  Google Scholar 

  • Steinley, D. (2004). Properties of the Hubert-Arabie adjusted Rand index. Psychological Methods, 9(3), 386–396.

    Article  PubMed  Google Scholar 

  • Trejos, J., & Castillo, W. (2000). Simulated annealing optimization for two-mode partitioning. In W. Gaul & R. Decker (Eds.), Classification and information at the turn of the millenium (pp. 135–142). Heidelberg: Springer.

    Chapter  Google Scholar 

  • Van Mechelen, I., & Hennes, K. (2009). The appraisal basis of anger occurence and intensity revisited. Cognition and Emotion, 23(7), 1373–1388.

    Article  Google 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.

    Article  Google Scholar 

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Correspondence to Eva Vande Gaer.

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Vande Gaer, E., Ceulemans, E., Van Mechelen, I. et al. The CLASSI-N Method for the Study of Sequential Processes. Psychometrika 77, 85–105 (2012). https://doi.org/10.1007/s11336-011-9235-3

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  • DOI: https://doi.org/10.1007/s11336-011-9235-3

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