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 th...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
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Cambridge University Press
2010-07-01
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| Series: | Judgment and Decision Making |
| Subjects: | |
| Online Access: | https://www.cambridge.org/core/product/identifier/S193029750000351X/type/journal_article |
| _version_ | 1797697985398702080 |
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| author | Benjamin E. Hilbig Julian N. Marewski Rüdiger F. Pohl Oliver Vitouch |
| author_facet | Benjamin E. Hilbig Julian N. Marewski Rüdiger F. Pohl Oliver Vitouch |
| author_sort | Benjamin E. Hilbig |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T03:47:42Z |
| format | Article |
| id | doaj.art-a602c7194d5e4b5cbfb6c4de4ca51c70 |
| institution | Directory Open Access Journal |
| issn | 1930-2975 |
| language | English |
| last_indexed | 2024-03-12T03:47:42Z |
| publishDate | 2010-07-01 |
| publisher | Cambridge University Press |
| record_format | Article |
| series | Judgment and Decision Making |
| spelling | doaj.art-a602c7194d5e4b5cbfb6c4de4ca51c702023-09-03T12:43:19ZengCambridge University PressJudgment and Decision Making1930-29752010-07-01527228410.1017/S193029750000351XPrecise models deserve precise measures: A methodological dissectionBenjamin E. Hilbig0Julian N. MarewskiRüdiger F. PohlOliver VitouchUniversity of Mannheim and Max Planck Institute for Research on Collective GoodsThe 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.https://www.cambridge.org/core/product/identifier/S193029750000351X/type/journal_articlerecognition heuristicmethodologysimulationadherence ratesignal detection theorymultinomial processing tree model |
| spellingShingle | Benjamin E. Hilbig Julian N. Marewski Rüdiger F. Pohl Oliver Vitouch Precise models deserve precise measures: A methodological dissection Judgment and Decision Making recognition heuristic methodology simulation adherence rate signal detection theory multinomial processing tree model |
| title | Precise models deserve precise measures: A methodological dissection |
| title_full | Precise models deserve precise measures: A methodological dissection |
| title_fullStr | Precise models deserve precise measures: A methodological dissection |
| title_full_unstemmed | Precise models deserve precise measures: A methodological dissection |
| title_short | Precise models deserve precise measures: A methodological dissection |
| title_sort | precise models deserve precise measures a methodological dissection |
| topic | recognition heuristic methodology simulation adherence rate signal detection theory multinomial processing tree model |
| url | https://www.cambridge.org/core/product/identifier/S193029750000351X/type/journal_article |
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