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Published Online:https://doi.org/10.1027/1015-5759.24.1.27

Abstract. This study investigated the responses of N = 1,789 participants to a set of 12 Likert-type items for the assessment of personal need for structure (PNS). Mixture-distribution Rasch models were used to analyze the homogeneity of the response format across items and the homogeneity of the item parameters and category parameters across persons. Model selection yielded a two-class rating scale model as the favorite model. This model contains the assumptions that the Likert response scale is used in a constant way for all items but that the item or category parameters differ between two latent subpopulations. The parameter estimates revealed large differences in the threshold parameters for the response categories between the two subpopulations. While the larger subpopulation showed a tendency to avoid extreme response categories, the smaller subpopulation used the whole range of the response scale. The different response styles identified by the mixture-distribution Rasch analysis were validated by significantly higher Extraversion scores for participants in the smaller subpopulation that showed more extreme and impulsive rating behavior. The results confirmed that PNS reflects quantitative interindividual differences, and they also showed that the total score of the 12 PNS items forms a combination of the latent PNS trait and response style.

References

  • Andrich, D. (1978). A rating formulation for ordered response categories. Psychometrika, 43, 561– 573 First citation in articleCrossrefGoogle Scholar

  • Andrich, D. (1982). An extension of the Rasch model for ratings providing both location and dispersion parameters. Psychometrika, 47, 105– 113 First citation in articleCrossrefGoogle Scholar

  • Borkenau, P. , Ostendorf, F. (1993). NEO-Fünf-Faktoren Inventar (NEO-FFI) . [NEO-Five-Factor Inventory (NEO-FFI)]. Göttingen: Hogrefe First citation in articleGoogle Scholar

  • Eid, M. , Zickar, M.J. (2007). Detecting response styles and faking in personality and organizational assessments by mixed Rasch models. In M. von Davier & C.H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models. Extensions and applications (pp. 255-270). New York: Springer Science + Business Media First citation in articleGoogle Scholar

  • Fischer, G.H. , Molenaar, I.W. Eds. (1995). Rasch models. Foundations, recent developments, and applications . New York: Springer-Verlag First citation in articleGoogle Scholar

  • Machunsky, M. , Meiser, T. (2006). Personal need for structure als differenzialpsychologisches Konstrukt in der Sozialpsychologie: Psychometrische Analyse und Validierung einer deutschsprachigen PNS-Skala. [Personal need for structure as a construct of dispositional differences in social psychology: Psychometric analysis and validation of a German PNS scale] Zeitschrift für Sozialpsychologie, 37, 87– 97 First citation in articleLinkGoogle Scholar

  • Masters, G.N. (1982). A Rasch model for partial credit scoring. Psychometrika, 47, 149– 174 First citation in articleCrossrefGoogle Scholar

  • McLachlan, G. , Peel, D. (2000). Finite mixture models . New York: Wiley First citation in articleGoogle Scholar

  • Mislevy, R.J. , Verhelst, N. (1990). Modeling item responses when different subjects employ different solution strategies. Psychometrika, 55, 195– 215 First citation in articleCrossrefGoogle Scholar

  • Moskowitz, G.B. (1993). Individual differences in social categorization: The influence of personal need for structure on spontaneous trait inferences. Journal of Personality and Social Psychology, 65, 132– 142 First citation in articleCrossrefGoogle Scholar

  • Neuberg, S.L. , Newsom, J.T. (1993). Personal need for structure: Individual differences in the desire for simple structure. Journal of Personality and Social Psychology, 65, 113– 131 First citation in articleCrossrefGoogle Scholar

  • Perreault, S. , Bourhis, R.Y. (1999). Ethnocentrism, social identification, and discrimination. Personality and Social Psychology Bulletin, 25, 92– 103 First citation in articleCrossrefGoogle Scholar

  • Rasch, G. (1968). An individualistic approach to item analysis. In P.F. Lazarsfeld & N.W. Henry (Eds.), Readings in mathematical social science (pp. 89-107). Cambridge: MIT Press First citation in articleGoogle Scholar

  • Rasch, G. (1980). Probabilistic models for some intelligence and attainment tests . Chicago: University of Chicago Press (Original published 1960, Copenhagen: Danish Institute of Educational Research) First citation in articleGoogle Scholar

  • Rost, J. (1988). Measuring attitudes with a threshold model drawing on a traditional scaling concept. Applied Psychological Measurement, 12, 397– 409 First citation in articleCrossrefGoogle Scholar

  • Rost, J. (1990). Rasch models in latent classes: An integration of two approaches to item analysis. Applied Psychological Measurement, 14, 271– 282 First citation in articleCrossrefGoogle Scholar

  • Rost, J. (1991). A logistic mixture distribution model for polychotomous item responses. British Journal of Mathematical and Statistical Psychology, 44, 75– 92 First citation in articleCrossrefGoogle Scholar

  • Rost, J. , Carstensen, C.H. , von Davier, M. (1997). Applying the mixed Rasch model to personality questionnaires. In J. Rost & R. Langeheine (Eds.), Applications of latent trait and latent class models in the social sciences (pp. 324-332). Münster: Waxmann First citation in articleGoogle Scholar

  • Rost, J. , Carstensen, C.H. , von Davier, M. (1999). Sind die Big Five Rasch-skalierbar? Eine Reanalyse der NEO-FFI-Normierungsdaten. [Are the Big Five Rasch-scalable? A reanalysis of the NEO-FFI norm data] Diagnostica, 45, 119– 127 First citation in articleLinkGoogle Scholar

  • Schaller, M. , Boyd, C. , Yohannes, J. , O'Brien, M. (1995). The prejudiced personality revisited: Personal need for structure and formation of erroneous group stereotypes. Journal of Personality and Social Psychology, 68, 544– 555 First citation in articleCrossrefGoogle Scholar

  • Schwarz, G. (1978). Estimating the dimension of a model. Annals of Statistics, 6, 461– 464 First citation in articleCrossrefGoogle Scholar

  • Thompson, E.P. , Roman, R.J. , Moskowitz, G.B. , Chaiken, S. , Bargh, J.A. (1994). Accuracy motivation attenuates covert priming: The systematic reprocessing of social information. Journal of Personality and Social Psychology, 66, 474– 489 First citation in articleCrossrefGoogle Scholar

  • Thompson, M.M. , Naccarato, M.E. , Parker, K.C.H. , Moskowitz, G.B. (2001). The personal need for structure and personal fear of invalidity measures: Historical perspectives, current applications, and future directions. In G.B. Moskowitz (Ed.), Cognitive social psychology: The Princeton symposium on the legacy and future of social cognition (pp. 19-39). Mahwah, NJ: Erlbaum First citation in articleGoogle Scholar

  • von Davier, M. (1997). Bootstrapping goodness-of-fit statistics for sparse categorical data - Results of a Monte-Carlo study. Methods of Psychological Research, 2, 29– 48 First citation in articleGoogle Scholar

  • von Davier, M. (2001). WINMIRA 2001 - A Windows program for analyses with the Rasch model, with the latent class analysis, and with the mixed Rasch model . Software distributed by Assessment Systems Corporation, USA, and Science Plus Group, Groningen, The Netherlands First citation in articleGoogle Scholar

  • von Davier, M. , Carstensen, C.H. Eds. (in press). Multivariate and mixture distribution Rasch models. Extensions and applications . New York: Springer Science + Business Media First citation in articleGoogle Scholar

  • von Davier, M. , Rost, J. (1995). Polytomous mixed Rasch models. In G.H. Fischer & I.W. Molenaar (Eds.), Rasch models. Foundations, recent developments, and applications (pp. 371- 379). New York: Springer-Verlag First citation in articleGoogle Scholar

  • von Davier, M. , Yamamoto, K. (2007). Mixture distribution and hybrid Rasch models. In M. von Davier & C.H. Carstensen (Eds.), Multivariate and mixture distribution Rasch models. Extensions and applications (pp. 99-115). New York: Springer-Verlag First citation in articleGoogle Scholar