Precise Models Deserve Precise Measures: A Methodological Dissection

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.

並列關鍵字

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

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