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Precise models deserve precise measures: A methodological dissection

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  • Benjamin E. Hilbig
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    Abstract

    The recognition heuristic (RH) --- which predicts non-compensatory reliance on recognition in comparative judgments --- has attracted much research and some disagreement, at times. Most studies have dealt with whether or under which conditions the RH is truly used in paired-comparisons. However, even though the RH is a precise descriptive model, there has been less attention concerning the precision of the methods applied to measure RH-use. In the current work, I provide an overview of different measures of RH-use tailored to the paradigm of natural recognition which has emerged as a preferred way of studying the RH. The measures are compared with respect to different criteria --- with particular emphasis on how well they uncover true use of the RH. To this end, both simulations and a re-analysis of empirical data are presented. The results indicate that the adherence rate --- which has been pervasively applied to measure RH-use --- is a severely biased measure. As an alternative, a recently developed formal measurement model emerges as the recommended candidate for assessment of RH-use.

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    Bibliographic Info

    Article provided by Society for Judgment and Decision Making in its journal Judgment and Decision Making.

    Volume (Year): 5 (2010)
    Issue (Month): 4 (July)
    Pages: 272-284

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    Handle: RePEc:jdm:journl:v:5:y:2010:i:4:p:272-284

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    Related research

    Keywords: recognition heuristic; methodology; simulation; adherence rate; signal detection theory; multinomial processing tree model.;

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    Cited by:
    1. Rudiger F. Pohl, 2011. "On the use of recognition in inferential decision making: An overview of the debate," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(5), pages 423-438, July.
    2. Konstantinos V. Katsikopoulos & Cherng-Horng (Dan) Lan, 2011. "Herbert Simon’s spell on judgment and decision making," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 722-732, December.
    3. Clintin P. Davis-Stober & Nicholas Brown, 2011. "A shift in strategy or "error"? Strategy classification over multiple stochastic specifications," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 800-813, December.
    4. Thorsten Pachur, 2011. "The limited value of precise tests of the recognition heuristic," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(5), pages 413-422, July.
    5. Andreas Glockner & Tilmann Betsch, 2011. "The Empirical content of theories in judgment and decision making: Shortcomings and remedies," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 711-721, December.
    6. Morten Moshagen & Benjamin E. Hilbig, 2011. "Methodological notes on model comparisons and strategy classification: A falsificationist proposition," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(8), pages 814-820, December.
    7. Julian N. Marewski & Rudiger F. Pohl & Oliver Vitouch, 2011. "Recognition-based judgments and decisions: What we have learned (so far)," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 6(5), pages 359-380, July.

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