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A meta-analysis of procedures to change implicit measures
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Abstract
s| < .30). Most studies focused on producing short-term changes with brief, single-session manipulations. Procedures that associate sets of concepts, invoke goals or motivations, or tax mental resources changed implicit measures the most, whereas procedures that induced threat, affirmation, or specific moods/emotions changed implicit measures the least. Bias tests suggested that implicit effects could be inflated relative to their true population values. Procedures changed explicit measures less consistently and to a smaller degree than implicit measures and generally produced trivial changes in behavior. Finally, changes in implicit measures did not mediate changes in explicit measures or behavior. Our findings suggest that changes in implicit measures are possible, but those changes do not necessarily translate into changes in explicit measures or behavior. (PsycINFO Database Record (c) 2019 APA, all rights reserved).
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Imagining intergroup contact reduces implicit prejudice
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The Roots of Stereotype Threat: When Automatic Associations Disrupt Girls' Math Performance
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Reducing Anti‐Fat Prejudice in Preservice Health Students: A Randomized Trial
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Experiencing ownership over a dark-skinned body reduces implicit racial bias
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Media-based Strategies to Reduce Racial Stereotypes Activated by News Stories
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A Threat in the Computer: The Race Implicit Association Test as a Stereotype Threat Experience
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Experimental manipulation of attentional biases in heavy drinkers: do the effects generalise?
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Interracial Roommate Relationships
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High self-esteem buffers negative feedback: Once more with feeling
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Divergent effects of activating thoughts of God on self-regulation.
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What goes well with physics? Measuring and altering the image of science
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Eliminating automatic racial bias: Making race non-diagnostic for responses to criminal suspects
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Implicit self-esteem compensation: Automatic threat defense
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Flagged first, then by citations
2 of the 464 references couldn’t be matched to a known paper.
Research works printed but not linked: 42
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Cited by
Sorted by citations
Reducing Prejudice and Discrimination
(2013) · Psychology Press eBooks · doi:10.4324/9781410605634 · Cited by 1,133
Prejudice Reduction: Progress and Challenges
Paluck, E. L., Porat, R., Clark, C. S., & Green, D. P. (2021) · Annual Review of Psychology · doi:10.1146/annurev-psych-071620-030619 · Cited by 686
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Gender bias in academia: A lifetime problem that needs solutions
Llorens, A., Tzovara, A., Bellier, L., Bhaya-Grossman, I., Bidet‐Caulet, A., Chang, W. K., Cross, Z. R., Dominguez-Faus, R., Flinker, A., Fonken, Y. M., et al. (2021) · Neuron · doi:10.1016/j.neuron.2021.06.002 · Cited by 427
Metadata
- Authors
- Forscher, P. S., Lai, C. K., Axt, J. R., Ebersole, C. R., Herman, M., Devine, P. G., & Nosek, B. A.
- ORCID
- Venue
- Journal of Personality and Social Psychology
- Published
- 2019-09
- Volume
- 117
- Issue
- 3
- Pages
- 522-559
- ISSN
- 0022-3514 (ISSN-L)
- 1939-1315
- PMC
- PMC6687518
- OpenAlex
- W2779953302
- Cited by
- 700
- References
- 464
- Citations by year
- 2026: 96 · 2025: 86 · 2024: 121 · 2023: 106 · 2022: 98 · 2021: 91 · 2020: 71 · 2019: 19
- FWCI
- 18.10
- Type
- journal article
- Language
- en
- Open access
- hybrid
- doi.org
- Locations
- Journal of Personality and Social Psychology (publishedVersion, other-oa)
- PubMed (publishedVersion)
- PubMed Central (acceptedVersion)
- Journal of the Arkansas Academy of Science (submittedVersion, cc-by-nd)
- Funders
- National Institutes of Health (5R01GM111002-02)
- Topics
- Social Sciences › Sociology and Political Science › Social and Intergroup Psychology
- Social Sciences › Psychology › Applied Psychology › Psychological Testing and Assessment
- Social Sciences › Psychology › Social Psychology › Deception detection and forensic psychology
- Keywords
- implicit measures, explicit measures, implicit bias, mood induction, behavior change
- MeSH terms
- Network Meta-Analysis, Humans, Psychological Tests, Psychology, Social, Social Perception
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
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