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Diagnostic task selection for strategy classification in judgment and decision making

  • Marc Jekel
  • Susann Fiedler
  • Andreas Glockner
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    One major statistical and methodological challenge in Judgment and Decision Making research is the reliable identification of individual decision strategies by selection of diagnostic tasks, that is, tasks for which predictions of the strategies differ sufficiently. The more strategies are considered, and the larger the number of dependent measures simultaneously taken into account in strategy classification (e.g., choices, decision time, confidence ratings; Glockner, 2009), the more complex the selection of the most diagnostic tasks becomes. We suggest the Euclidian Diagnostic Task Selection (EDTS) method as a standardized solution for the problem. According to EDTS, experimental tasks are selected that maximize the average difference between strategy predictions for any multidimensional prediction space. In a comprehensive model recovery simulation, we evaluate and quantify the influence of diagnostic task selection on identification rates in strategy classification. Strategy classification with EDTS shows superior performance in comparison to less diagnostic task selection algorithms such as representative sampling. The advantage of EDTS is particularly large if only few dependent measures are considered. We also provide an easy-to-use function in the free software package R that allows generating predictions for the most commonly considered strategies for a specified set of tasks and evaluating the diagnosticity of those tasks via EDTS; thus, to apply EDTS, no prior programming knowledge is necessary.

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

    Volume (Year): 6 (2011)
    Issue (Month): 8 (December)
    Pages: 782-799

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    Handle: RePEc:jdm:journl:v:6:y:2011:i:8:p:782-799
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    1. Natalia Karelaia & Robin Hogarth, 2007. "Determinants of linear judgment: A meta-analysis of lens model studies," Economics Working Papers 1007, Department of Economics and Business, Universitat Pompeu Fabra.
    2. Andreas Glöckner & Tilmann Betsch, 2008. "Multiple-Reason Decision Making Based on Automatic Processing," Working Paper Series of the Max Planck Institute for Research on Collective Goods 2008_12, Max Planck Institute for Research on Collective Goods.
    3. Andreas Glöckner & Tilmann Betsch, 2008. "Modeling Option and Strategy Choices with Connectionist Networks: Towards an Integrative Model of Automatic and Deliberate Decision Making," Working Paper Series of the Max Planck Institute for Research on Collective Goods 2008_02, Max Planck Institute for Research on Collective Goods.
    4. Klaus Fiedler, 2010. "How to study cognitive decision algorithms: The case of the priority heuristic," Judgment and Decision Making, Society for Judgment and Decision Making, vol. 5(1), pages 21-32, February.
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