Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions
Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and sev...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Frontiers Media S.A.
2019-11-01
|
| Series: | Frontiers in Psychology |
| Subjects: | |
| Online Access: | https://www.frontiersin.org/article/10.3389/fpsyg.2019.02483/full |
| _version_ | 1811325271735795712 |
|---|---|
| author | Franziska Meissner Laura Anne Grigutsch Nicolas Koranyi Florian Müller Klaus Rothermund |
| author_facet | Franziska Meissner Laura Anne Grigutsch Nicolas Koranyi Florian Müller Klaus Rothermund |
| author_sort | Franziska Meissner |
| collection | DOAJ |
| description | Two decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so. |
| first_indexed | 2024-04-13T14:30:13Z |
| format | Article |
| id | doaj.art-14e699afa66b48d5b5766255ace353d2 |
| institution | Directory Open Access Journal |
| issn | 1664-1078 |
| language | English |
| last_indexed | 2024-04-13T14:30:13Z |
| publishDate | 2019-11-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Psychology |
| spelling | doaj.art-14e699afa66b48d5b5766255ace353d22022-12-22T02:43:12ZengFrontiers Media S.A.Frontiers in Psychology1664-10782019-11-011010.3389/fpsyg.2019.02483476878Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated SolutionsFranziska Meissner0Laura Anne Grigutsch1Nicolas Koranyi2Florian Müller3Klaus Rothermund4General Psychology II, Institute of Psychology, Friedrich Schiller University Jena, Jena, GermanyGeneral Psychology II, Institute of Psychology, Friedrich Schiller University Jena, Jena, GermanyGeneral Psychology II, Institute of Psychology, Friedrich Schiller University Jena, Jena, GermanyDepartment for the Psychology of Human Movement and Sport, Institute for Sports Science, Friedrich Schiller University Jena, Jena, GermanyGeneral Psychology II, Institute of Psychology, Friedrich Schiller University Jena, Jena, GermanyTwo decades ago, the introduction of the Implicit Association Test (IAT) sparked enthusiastic reactions. With implicit measures like the IAT, researchers hoped to finally be able to bridge the gap between self-reported attitudes on one hand and behavior on the other. Twenty years of research and several meta-analyses later, however, we have to conclude that neither the IAT nor its derivatives have fulfilled these expectations. Their predictive value for behavioral criteria is weak and their incremental validity over and above self-report measures is negligible. In our review, we present an overview of explanations for these unsatisfactory findings and delineate promising ways forward. Over the years, several reasons for the IAT’s weak predictive validity have been proposed. They point to four potentially problematic features: First, the IAT is by no means a pure measure of individual differences in associations but suffers from extraneous influences like recoding. Hence, the predictive validity of IAT-scores should not be confused with the predictive validity of associations. Second, with the IAT, we usually aim to measure evaluation (“liking”) instead of motivation (“wanting”). Yet, behavior might be determined much more often by the latter than the former. Third, the IAT focuses on measuring associations instead of propositional beliefs and thus taps into a construct that might be too unspecific to account for behavior. Finally, studies on predictive validity are often characterized by a mismatch between predictor and criterion (e.g., while behavior is highly context-specific, the IAT usually takes into account neither the situation nor the domain). Recent research, however, also revealed advances addressing each of these problems, namely (1) procedural and analytical advances to control for recoding in the IAT, (2) measurement procedures to assess implicit wanting, (3) measurement procedures to assess implicit beliefs, and (4) approaches to increase the fit between implicit measures and behavioral criteria (e.g., by incorporating contextual information). Implicit measures like the IAT hold an enormous potential. In order to allow them to fulfill this potential, however, we have to refine our understanding of these measures, and we should incorporate recent conceptual and methodological advancements. This review provides specific recommendations on how to do so.https://www.frontiersin.org/article/10.3389/fpsyg.2019.02483/fullimplicit measurespredictive validityIATattitude-behavior gapmultinomial processing tree modelswanting vs. liking |
| spellingShingle | Franziska Meissner Laura Anne Grigutsch Nicolas Koranyi Florian Müller Klaus Rothermund Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions Frontiers in Psychology implicit measures predictive validity IAT attitude-behavior gap multinomial processing tree models wanting vs. liking |
| title | Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions |
| title_full | Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions |
| title_fullStr | Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions |
| title_full_unstemmed | Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions |
| title_short | Predicting Behavior With Implicit Measures: Disillusioning Findings, Reasonable Explanations, and Sophisticated Solutions |
| title_sort | predicting behavior with implicit measures disillusioning findings reasonable explanations and sophisticated solutions |
| topic | implicit measures predictive validity IAT attitude-behavior gap multinomial processing tree models wanting vs. liking |
| url | https://www.frontiersin.org/article/10.3389/fpsyg.2019.02483/full |
| work_keys_str_mv | AT franziskameissner predictingbehaviorwithimplicitmeasuresdisillusioningfindingsreasonableexplanationsandsophisticatedsolutions AT lauraannegrigutsch predictingbehaviorwithimplicitmeasuresdisillusioningfindingsreasonableexplanationsandsophisticatedsolutions AT nicolaskoranyi predictingbehaviorwithimplicitmeasuresdisillusioningfindingsreasonableexplanationsandsophisticatedsolutions AT florianmuller predictingbehaviorwithimplicitmeasuresdisillusioningfindingsreasonableexplanationsandsophisticatedsolutions AT klausrothermund predictingbehaviorwithimplicitmeasuresdisillusioningfindingsreasonableexplanationsandsophisticatedsolutions |