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The Cognitive-Miser Response Model: Testing for Intuitive and Deliberate Reasoning

Published online by Cambridge University Press:  01 January 2025

Ulf Böckenholt
Ulf Böckenholt*
Affiliation:
Northwestern University
*
Requests for reprints should be sent to Ulf Böckenholt, Kellogg School of Management, Northwestern University, 2001 Sheridan Road, Evanston, IL 60208, USA. E-mail: u-bockenholt@kellogg.northwestern.edu
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Abstract

In a number of psychological studies, answers to reasoning vignettes have been shown to result from both intuitive and deliberate response processes. This paper utilizes a psychometric model to separate these two response tendencies. An experimental application shows that the proposed model facilitates the analysis of dual-process item responses and the assessment of individual-difference factors, as well as conditions that favor one response tendency over another one.

Keywords

local item dependenciesmultiple-choice itemsnominal response modelmarginal maximum likelihood estimation
Type
Original Paper
Information
Psychometrika , Volume 77 , Issue 2 , April 2012 , pp. 388 - 399
Copyright
Copyright © 2012 The Psychometric Society

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References

Alter, A., Oppenheimer, D., Epley, N., Eyre, R. (2007). Overcoming intuition: Metacognitive difficulty activates analytic reasoning. Journal of Experimental Psychology: General, 136, 569–576CrossRefGoogle ScholarPubMed
Bock, R.D. (1972). Estimating item parameters and latent ability when responses are scored in two or more nominal categories. Psychometrika, 37, 29–51CrossRefGoogle Scholar
Bock, R.D., Aitkin, M. (1981). Marginal maximum likelihood estimation of item parameters: Application of an EM algorithm. Psychometrika, 46, 443–459CrossRefGoogle Scholar
Bodenhausen, G.V. (1990). Stereotypes as judgmental heuristics: Evidence of circadian variations in discrimination. Psychological Science, 1, 319–322CrossRefGoogle Scholar
De Boeck, P., Wilson, M. (2004). Explanatory item response models, New York: SpringerCrossRefGoogle Scholar
De Neys, W. (2006). Dual processing in reasoning. Psychological Science, 17, 428–433CrossRefGoogle ScholarPubMed
Evans, J.S.B.T. (2008). Dual-processing accounts of reasoning, judgment and social cognition. Annual Review of Psychology, 59, 255–278CrossRefGoogle ScholarPubMed
Frederick, S. (2005). Cognitive reflection and decision making. Journal of Economic Perspectives, 19, 25–42CrossRefGoogle Scholar
Gill, P., Murray, W., Wright, M. (1981). Practical optimization, London: Academic PressGoogle Scholar
Horne, J., Ostberg, O. (1976). A self-assessment questionnaire to determine morningness–eveningness in human circadian rhythms. International Journal of Chronobiology, 4, 97–110Google ScholarPubMed
Kahneman, D., Frederick, S. (2002). Representativeness revisited: Attribute substitution in intuitive judgment. In Gilovich, T., Griffin, D., Kahneman, D. Heuristics and biases: The psychology of intuitive judgments, Cambridge: Cambridge University Press 4981CrossRefGoogle Scholar
Kahneman, D., Frederick, S. (2005). A model of heuristic judgment. In Holyoak, K.J., Morrison, R.G. The Cambridge handbook of thinking and reasoning, Cambridge: Cambridge University Press 267293Google Scholar
Logan, G.D., Schachar, R.J., Tannock, R. (1997). Impulsivity and inhibitory control. Psychological Science, 8, 60–64CrossRefGoogle Scholar
Orlando, M., Thissen, D. (2000). New item fit indices for dichotomous item response theory models. Applied Psychological Measurement, 24, 50–64CrossRefGoogle Scholar
Rasch, G. (1960). Probabilistic models for some intelligence and attainment tests, Copenhagen: Danish Institute for Educational ResearchGoogle Scholar
Stanovich, K.E. (2009). What intelligence tests miss: The psychology of rational thought, New Haven: Yale University PressGoogle Scholar
Stanovich, K.E., West, R.F. (2008). On the relative independence of thinking biases and cognitive ability. Journal of Personality and Social Psychology, 94, 672–695CrossRefGoogle ScholarPubMed
Sternberg, R. (2000). Handbook of intelligence, Cambridge: Cambridge University PressCrossRefGoogle Scholar
Strack, F., Martin, L.L., Schwarz, N. (1988). Priming and communication: The social determinants of information use in judgments of life satisfaction. European Journal of Social Psychology, 8, 429–442CrossRefGoogle Scholar
Suh, Y., Bolt, D.M. (2010). Nested logit models for multiple-choice item response data. Psychometrika, 75, 454–473CrossRefGoogle Scholar
Thissen, D., Steinberg, L. (1984). A response model for multiple choice items. Psychometrika, 49, 501–519CrossRefGoogle Scholar
Thurstone, L.L. (1927). The nature of intelligence, New York: HarcourtGoogle Scholar
Tutz, F. (1997). Sequential models for ordered responses. In van der Linden, W.J., Hambleton, R.K. Handbook of modern item response theory, New York: Springer 139152CrossRefGoogle Scholar
Verhelst, N.D., Glass, C.A.W., de Vries, H.H. (1997). A steps model to analyze partial credit. In van der Linden, W.J., Hambleton, R.K. Handbook of modern item response theory, New York: Springer 123138CrossRefGoogle Scholar