Implicit Social Cognition

Greenwald, Anthony G.; Lai, Calvin K.

doi:10.1146/annurev-psych-010419-050837

Cited by 242 publications

(237 citation statements)

References 115 publications

(79 reference statements)

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“…Measures of internal consistency (such as split-half correlations or Cronbach's alpha) estimate the proportion of variance on a single testing occasion that is consistently measured. For the IAT, internal consistency has been found to average r = .80 in the meta-analysis of 257 studies located by Greenwald and Lai (2020). The difference between the Although meta-analytic estimates of internal consistency and test-retest reliability are not available specifically for the parallel self-report measures used in the meta-analyzed studies, these can be approximately estimated, respectively, as r ≈ .90 and r ≈ .80.…”

Section: Sources Of Variance In Iat and Self-report Measuresmentioning

confidence: 99%

“…In contrast, implicit measures do not use self-report and do not require the subject to know the nature of the construct being assessed, which might be an attitude, a stereotype, BIT META-ANALYSIS 6 an identity, or self-esteem. A recent treatment comparing the two types of measures is available in Greenwald and Lai (2020).…”

Section: Implicit and Explicit Measuresmentioning

confidence: 99%

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Meta-Analytic Use of Balanced Identity Theory to Validate the Implicit Association Test

Cvencek

Meltzoff

Maddox

et al. 2020

Pers Soc Psychol Bull

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This meta-analysis evaluated theoretical predictions from balanced identity theory (BIT) and evaluated the validity of zero points of Implicit Association Test (IAT) and self-report measures used to test these predictions. Twenty-one researchers contributed individual subject data from 36 experiments (total N = 12,773) that used both explicit and implicit measures of the social–cognitive constructs. The meta-analysis confirmed predictions of BIT’s balance–congruity principle and simultaneously validated interpretation of the IAT’s zero point as indicating absence of preference between two attitude objects. Statistical power afforded by the sample size enabled the first confirmations of balance–congruity predictions with self-report measures. Beyond these empirical results, the meta-analysis introduced a within-study statistical test of the balance–congruity principle, finding that it had greater efficiency than the previous best method. The meta-analysis’s full data set has been publicly archived to enable further studies of interrelations among attitudes, stereotypes, and identities.

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Section: Sources Of Variance In Iat and Self-report Measuresmentioning

confidence: 99%

Section: Implicit and Explicit Measuresmentioning

confidence: 99%

Meta-Analytic Use of Balanced Identity Theory to Validate the Implicit Association Test

Cvencek

Meltzoff

Maddox

et al. 2020

Pers Soc Psychol Bull

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“…Measurement accuracy can be assessed via estimates of test–retest reliability. Recent meta-analytic work puts the test–retest reliability ( r ) of the IAT at .5 (Greenwald & Lai, 2020); however, there is evidence that the race IAT (as opposed to, say, a self-esteem IAT) may be less reliable than this. Gawronski and colleagues (2017) reported 10 test–retest correlations for the race IAT for which the weighted mean was .38.…”

Section: A Parsimonious Alternative: Measurement Error and Aggregationmentioning

confidence: 99%

“…The internal consistency of implicit-bias tests is indeed greater than their test–retest reliability. A recent meta-analytic estimate put the overall internal consistency of the IAT at r = .80 (Greenwald & Lai, 2020), and although some evidence suggests—as discussed above—that the psychometric properties of the race IAT may not be quite as strong as those of other variants of the IAT, there is little doubt that the tests exhibit higher internal consistency than test–retest reliability; the meta-analysis authors concluded that test–retest reliability “is generally smaller than [internal consistency]; . .…”

Section: The Counterargumentsmentioning

confidence: 99%

The Bias of Individuals (in Crowds): Why Implicit Bias Is Probably a Noisily Measured Individual-Level Construct

Connor

Evers

2020

Perspect Psychol Sci

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Payne, Vuletich, and Lundberg’s bias-of-crowds model proposes that a number of empirical puzzles can be resolved by conceptualizing implicit bias as a feature of situations rather than a feature of individuals. In the present article we argue against this model and propose that, given the existing evidence, implicit bias is best understood as an individual-level construct measured with substantial error. First, using real and simulated data, we show how each of Payne and colleagues’ proposed puzzles can be explained as being the result of measurement error and its reduction via aggregation. Second, we discuss why the authors’ counterarguments against this explanation have been unconvincing. Finally, we test a hypothesis derived from the bias-of-crowds model about the effect of an individually targeted “implicit-bias-based expulsion program” within universities and show the model to lack empirical support. We conclude by considering the implications of conceptualizing implicit bias as a noisily measured individual-level construct for ongoing implicit-bias research. All data and code are available at https://osf.io/tj8u6/ .

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“…Of the 18 interventions, nine were effective at immediately changing D-scores toward neutrality. 1 By implicit measures, we are referring to the large class of social cognition measures that assess associative constructs indirectly without requiring participants to actively bring to mind a target association (Forscher, Lai et al, 2019;Greenwald & Lai, 2020). 2 On the IAT, compatible trials are the trials that are theoretically more in line with pre-existing biases (e.g., pairing White with Good and Black with Bad on the Race IAT) and the incompatible trials are the trials that are theoretically more in conflict with pre-existing biases (e.g., pairing White with Bad and Black with Good on the Race IAT).…”

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confidence: 99%

A diffusion model approach for understanding the impact of 17 interventions on the race Implicit Association Test

Röhner¹,

Lai²

2020

Preprint

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Performance on implicit measures reflects construct-specific and non-construct-specific processes. This creates an interpretive issue for understanding interventions to change implicit measures: change in performance could reflect changes in the constructs-of-interest or changes in other mental processes. We re-analyzed data from six studies (N = 23,342) to examine the process-level effects of 17 interventions and one sham intervention to change race Implicit Association Test (IAT) performance. Diffusion models decompose overall IAT performance (D-scores) into construct-specific (ease of decision-making), and non-construct-specific processes (speed-accuracy tradeoffs, non-decision-related processes like motor execution). Interventions that effectively reduced D-scores changed ease of decision-making on compatible and incompatible trials. They also eliminated differences in speed-accuracy tradeoffs between compatible and incompatible trials. Non-decision-related processes were impacted by two interventions only. There was little evidence that interventions had any long-term effects. These findings highlight the value of diffusion modeling for understanding the mechanisms by which interventions affect implicit measure performance.

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Implicit Social Cognition

Cited by 242 publications

References 115 publications

Meta-Analytic Use of Balanced Identity Theory to Validate the Implicit Association Test

Meta-Analytic Use of Balanced Identity Theory to Validate the Implicit Association Test

The Bias of Individuals (in Crowds): Why Implicit Bias Is Probably a Noisily Measured Individual-Level Construct

A diffusion model approach for understanding the impact of 17 interventions on the race Implicit Association Test

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