Great oaks from giant acorns grow: How causal-impact judgments depend on the strength of a cause
Causal impact is maximal when weak causes have strong effects. Do people understand this logic when they assess causal impact? In four experiments, participants judged the causal impact of strong or weak dietary treatments leading to strong or weak health effects in fictitious health studies. Rather...
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| Hauptverfasser: | , , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2011
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| In: |
European journal of social psychology
Year: 2011, Jahrgang: 41, Heft: 2, Pages: 162-172 |
| ISSN: | 1099-0992 |
| DOI: | 10.1002/ejsp.750 |
| Online-Zugang: | Verlag, lizenzpflichtig, Volltext: https://doi.org/10.1002/ejsp.750 Verlag, lizenzpflichtig, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1002/ejsp.750 
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| Verfasserangaben: | Klaus Fiedler, Peter Freytag and Christian Unkelbach |
| Zusammenfassung: | Causal impact is maximal when weak causes have strong effects. Do people understand this logic when they assess causal impact? In four experiments, participants judged the causal impact of strong or weak dietary treatments leading to strong or weak health effects in fictitious health studies. Rather than following the ratio of effect strength to treatment strength, judgments were influenced by three aspects of the detectability of a cause-effect relation. First, because detectability depends on the effect being strong more than on the cause being subtle, causal judgments were mainly determined by effect strength, whereas the strength of the causal treatment necessary to induce an effect was often neglected. Second, if causal input was not ignored, judgments increased when the maximal covariation between a strong causal treatment and a strong effect rendered the causal link most detectable. Or, third, causal judgments increased when a plausible causal schema facilitated detection. Consistent with sampling models of judgment and decision making, causal-impact ratings were driven by an uncritical assessment of a detectable difference in a study sample. However, ratings were insensitive to the logical implications of the underlying causal treatment that was necessary to induce a detectable effect. |
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| Beschreibung: | Published online 14 April 2010 Gesehen am 09.05.2022 |
| Beschreibung: | Online Resource |
| ISSN: | 1099-0992 |
| DOI: | 10.1002/ejsp.750 |