Here is a link to the manuscript, data, and MPLUS scripts for reproducibility. https://osf.io/mu7e6/
Greenwald et al. (1998) proposed that the IAT measures individual differences in implicit social cognition. This claim requires evidence of construct validity. I review the evidence and show that there is insufficient evidence for this claim. Most important, I show that few studies were able to test discriminant validity of the IAT as a measure of implicit constructs. I examine discriminant validity in several multi-method studies and find no or weak evidence for discriminant validity. I also show that validity of the IAT as a measure of attitudes varies across constructs. Validity of the self-esteem IAT is low, but estimates vary across studies. About 20% of the variance in the race IAT reflects racial preferences. The highest validity is obtained for measuring political orientation with the IAT (64% valid variance). Most of this valid variance stems from a distinction between individuals with opposing attitudes, while reaction times contribute less than 10% of variance in the prediction of explicit attitude measures. In all domains, explicit measures are more valid than the IAT, but the IAT can be used as a measure of sensitive attitudes to reduce measurement error by using a multi-method measurement model.
Keywords: Personality, Individual Differences, Social Cognition, Measurement, Construct Validity, Convergent Validity, Discriminant Validity, Structural Equation Modeling
Despite its popularity, relatively little is known about the construct validity of the IAT.
As Cronbach (1989) pointed out, construct validation is better examined by independent experts than by authors of a test because “colleagues are especially able to refine the interpretation, as they compensate for blind spots and capitalize on their own distinctive experience” (p. 163).
It is of utmost importance to determine how much of the variance in IAT scores is valid variance and how much of the variance is due to measurement error, especially when IAT scores are used to provide individualized feedback.
There is also no consensus in the literature whether the IAT measures something different from explicit measures.
In conclusion, while there is general consensus to make a distinction between explicit measures and implicit measures, it is not clear what the IAT measures
To complicate matters further, the validity of the IAT may vary across attitude objects. After all the IAT is a method, just like Likert scales are a method, and it is impossible to say that a method is valid (Cronbach, 1971).
At present, relatively little is known about the contribution of these three parameters to observed correlations in hundreds of mono-method studies.
A Critical Review of Greenwald et al.’s (1998) Original Article
In conclusion, the seminal IAT article introduced the IAT as a measure of implicit constructs that cannot be measured with explicit measures, but it did not really test this dual-attitude model.
Construct Validity in 2007
In conclusion, the 2007 review of construct validity revealed major psychometric challenges for the construct validity of the IAT, which explains why some researchers have concluded that the IAT cannot be used to measure individual differences (Payne et al., 2017). It also revealed that most studies were mono-method studies that could not examine convergent and discriminant validity
Cunningham, Preacher and Banaji (2001)
Another noteworthy finding is that a single factor accounted for correlations among all measures on the same occasion and across measurement occasions. This finding shows that there were no true changes in racial attitudes over the course of this two-month study. This finding is important because Cunningham et al.’s (2001) study is often cited as evidence that implicit attitudes are highly unstable and malleable (e.g., Payne et al., 2017). This interpretation is based on the failure to distinguish random measurement error and true change in the construct that is being measured (Anusic & Schimmack, 2016). While Cunningham et al.’s (2001) results suggest that the IAT is a highly unreliable measure, the results also suggest that the racial attitudes that are measured with the race IAT are highly stable over periods of weeks or months.
Bar-Anan & Vianello, 2018
this large study of construct validity also provides little evidence for the original claim that the IAT measures a new construct that cannot be measured with explicit measures, and confirms the estimate from Cunningham et al. (2001) that about 20% of the variance in IAT scores reflects variance in racial attitudes.
Greenwald et al. (2009)
“When entered after the self-report measures, the two implicit measures incrementally explained 2.1% of vote intention variance, p=.001, and when political conservativism was also included in the model, “the pair of implicit measures incrementally predicted only 0.6% of voting intention variance, p = .05.” (Greenwald et al., 2009, p. 247).
I tried to reproduce these results with the published correlation matrix and failed to do so. I contacted Anthony Greenwald, who provided the raw data, but I was unable to recreate the sample size of N = 1,057. Instead I obtained a similar sample size of N = 1,035. Performing the analysis on this sample also produced non-significant results (IAT: b = -.003, se = .044, t = .070, p = .944; AMP: b = -.014, se = .042, t = 0.344, p = .731). Thus, there is no evidence for incremental predictive validity in this study.
With N = 540,723 respondents, sampling error is very small, σ = .002, and parameter estimates can be interpreted as true scores in the population of Project Implicit visitors. A comparison of the factor loadings shows that explicit ratings are more valid than IAT scores. The factor loading of the race IAT on the attitude factor once more suggests that about 20% of the variance in IAT scores reflects racial attitudes
Falk, Heine, Zhang, and Hsu (2015)
Most important, the self-esteem IAT and the other implicit measures have low and non-significant loadings on the self-esteem factor.
Bar-Anan & Vianello (2018)
Thus, low validity contributes considerably to low observed correlations between IAT scores and explicit self-esteem measures.
Bar-Anan & Vianello (2018) – Political Orientation
More important, the factor loading of the IAT on the implicit factor is much higher than for self-esteem or racial attitudes, suggesting over 50% of the variance in political orientation IAT scores is valid variance, π = .79, σ = .016. The loading of the self-report on the explicit ratings was also higher, π = .90, σ = .010
Variation of Implicit – Explicit Correlations Across Domains
This suggests that the IAT is good in classifying individuals into opposing groups, but it has low validity of individual differences in the strength of attitudes.
What Do IATs Measure?
The present results suggest that measurement error alone is often sufficient to explain these low correlations. Thus, there is little empirical support for the claim that the IAT measures implicit attitudes that are not accessible to introspection and that cannot be measured with self-report measures.
For 21 years the lack of discriminant validity has been overlooked because psychologists often fail to take measurement error into account and do not clearly distinguish between measures and constructs.
In the future, researchers need to be more careful when they make claims about constructs based on a single measure like the IAT because measurement error can produce misleading results.
Researchers should avoid terms like implicit attitude or implicit preferences that make claims about constructs simply because attitudes were measured with an implicit measure
Recently, Greenwald and Banaji (2017) also expressed concerns about their earlier assumption that IAT scores reflect unconscious processes. “Even though the present authors find themselves occasionally lapsing to use implicit and explicit as if they had conceptual meaning, they strongly endorse the empirical understanding of the implicit– explicit distinction” (p. 862).
How Well Does the IAT Measure What it Measures?
Studies with the IAT can be divided into applied studies (A-studies) and basic studies (B-studies). B-studies employ the IAT to study basic psychological processes. In contrast, A-studies use the IAT as a measure of individual differences. Whereas B-studies contribute to the understanding of the IAT, A-studies require that IAT scores have construct validity. Thus, B-studies should provide quantitative information about the psychometric properties for researchers who are conducting A-studies. Unfortunately, 21 years of B-studies have failed to do so. For example, after an exhaustive review of the IAT literature, de Houwer et al. (2009) conclude that “IAT effects are reliable enough to be used as a measure of individual differences” (p. 363). This conclusion is not helpful for the use of the IAT in A-studies because (a) no quantitative information about reliability is given, and (b) reliability is necessary but not sufficient for validity. Height can be measured reliably, but it is not a valid measure of happiness.
This article provides the first quantitative information about validity of three IATs. The evidence suggests that the self-esteem IAT has no clear evidence of construct validity (Falk et al., 2015). The race-IAT has about 20% valid variance and even less valid variance in studies that focus on attitudes of members from a single group. The political orientation IAT has over 40% valid variance, but most of this variance is explained by group-differences and overlaps with explicit measures of political orientation. Although validity of the IAT needs to be examined on a case by case basis, the results suggest that the IAT has limited utility as a measurement method in A-studies. It is either invalid or the construct can be measured more easily with direct ratings.
Implications for the Use of IAT scores in Personality Assessment
I suggest to replace the reliability coefficient with the validity coefficient. For example, if we assume that 20% of the variance in scores on the race IAT is valid variance, the 95%CI for IAT scores from Project Implicit (Axt, 2018), using the D-scoring method, with a mean of .30 and a standard deviation of.46 ranges from -.51 to 1.11. Thus, participants who score at the mean level could have an extreme pro-White bias (Cohen’s d = 1.11/.46 = 2.41), but also an extreme pro-Black Bias (Cohen’s d = -.51/.46 = -1.10). Thus, it seems problematic to provide individuals with feedback that their IAT score may reveal something about their attitudes that is more valid than their beliefs.
Social psychologists have always distrusted self-report, especially for the measurement of sensitive topics like prejudice. Many attempts were made to measure attitudes and other constructs with indirect methods. The IAT was a major breakthrough because it has relatively high reliability compared to other methods. Thus, creating the IAT was a major achievement that should not be underestimated because the IAT lacks construct validity as a measure of implicit constructs. Even creating an indirect measure of attitudes is a formidable feat. However, in the early 1990s, social psychologists were enthralled by work in cognitive psychology that demonstrated unconscious or uncontrollable processes (Greenwald & Banaji, 1995). Implicit measures were based on this work and it seemed reasonable to assume that they might provide a window into the unconscious (Banaji & Greenwald, 2013). However, the processes that are involved in the measurement of attitudes with implicit measures are not the personality characteristics that are being measured. There is nothing implicit about being a Republican or Democrat, gay or straight, or having low self-esteem. Conflating implicit processes in the measurement of attitudes with implicit personality constructs has created a lot of confusion. It is time to end this confusion. The IAT is an implicit measure of attitudes with varying validity. It is not a window into people’s unconscious feelings, cognitions, or attitudes.