Category Archives: Implicit Attitudes

Reexamining Cunningham, Preacher, and Banaji’s Multi-Method Model of Racism Measures

Article:
William A. Cunningham, Kristopher J. Preacher, and Mahzarin R. Banaji. (2001).
Implicit Attitude Measures: Consistency, Stability, and Convergent Validity, Psychological Science, 12(2), 163-170.

Abstract:
In recent years, several techniques have been developed to measure implicit social cognition. Despite their increased use, little attention has been devoted to their reliability and validity. This article undertakes a direct assessment of the interitem consistency, stability, and convergent validity of some implicit attitude measures. Attitudes toward blacks and whites were measured on four separate occasions, each 2 weeks apart, using three relatively implicit measures (response window evaluative priming, the Implicit Association Test, and the response-window Implicit Association Test) and one explicit measure (Modern Racism Scale). After correcting for interitem inconsistency with latent variable analyses, we found that (a) stability indices improved and (b) implicit measures were substantially correlated with each other, forming a single latent factor. The psychometric properties of response-latency implicit measures have greater integrity than recently suggested.

Critique of Original Article

This article has been cited 362 times (Web of Science, January 2017).  It still is one of the most rigorous evaluations of the psychometric properties of the race Implicit Association Test (IAT).  As noted in the abstract, the strength of the study is the use of several implicit measures and the repeated measurement of attitudes on four separate occasions.  This design makes it possible to separate several variance components in the race IAT.  First, it is possible to examine how much variance is explained by causal factors that are stable over time and shared by implicit and explicit attitude measures.  Second, it is possible to measure the amount of variance that is unique to the IAT.  As this component is not shared with other implicit measures, this variance can be attributed to systematic measurement error that is stable over time.  A third variance component is variance that is shared only with other implicit measures and that is stable over time. This variance component could reflect stable implicit racial attitudes.  Finally, it is possible to identify occasion specific variance in attitudes.  This component would reveal systematic changes in implicit attitudes.

The original article presents a structural equation model that makes it possible to identify some of these variance components.  However, the model is not ideal for this purpose and the authors do not test some of these variance components.  For example, the model does not include any occasion specific variation in attitudes.  This could be because attitudes do not vary over the one-month interval of the study, or it could mean that the model failed to specify this variance component.

This reanalysis also challenges the claim by the original authors that they provided evidence for a dissociation of implicit and explicit attitudes.  “We found a dissociation between implicit and explicit measures of race attitude: Participants simultaneously self-reported nonprejudiced explicit attitudes toward black Americans while showing an implicit difficulty in associating black with positive attributes” (p. 169). The main problem is that the design does not allow to make this claim because the study included only a single explicit racism measure.  Consequently, it is impossible to determine whether unique variance in the explicit measure reflects systematic measurement in explicit attitude measures (social desirable responding, acquiescence response styles) or whether this variance reflects consciously accessible attitudes that are distinct from implicit attitudes.  In this regard, the authors claim that “a single-factor solution does not fit the data” (p. 170) is inconsistent with their own structural equation model that shows a single second-order factor that explains the covariance among the three implicit measures and the explicit measure.

The authors caution that a single IAT measure is not very reliable, but their statement about reliability is vague. “Our analyses of implicit attitude measures suggest that the degree of measurement error in response-latency measures can be substantial; estimates of Cronbach’s alpha indicated that, on average, more than 30% of the variance associated with the measurements was random error.” (p. 160).  More than 30% random measurement error leaves a rather large range of reliability estimates ranging from 0% to 70%.   The respective parameter estimates for the IAT in Figure 4 are .53^2 = .28, .65^2 = .42, .74^2 = .55, and .38^2 = .14.  These reliability estimates vary considerably due to the small sample size, but the loading of the first IAT would suggest that only 19% of the variance in a single IAT is reliable. As reliablity is the upper limit for validity, it would imply that no more than 20% of the variance in a single IAT captures variation in implicit racial attitudes.

The authors caution readers about the use of a single IAT to measure implicit attitudes. “When using latency-based measures as indices of individual differences, it may be essential to employ analytic techniques, such as covariance structure modeling, that can separate measurement error from a measure of individual differences. Without such analyses, estimates of relationships involving implicit measures may produce misleading null results” (p. 169).  However, the authors fail to mention that the low reliability of a single IAT also has important implications for the use of the IAT for the assessment of implicit prejudice.  Given this low estimate of validity, users of the Harvard website that provides information about individual’s performance on the IAT should be warned that the feedback is neither reliable nor valid by conventional standards for psychological tests.

Reanalysis of Published Correlation Matrix

The Table below reproduces the correlation matrix. The standard deviations in the last row are rescaled to avoid rounding problems. This has no effect on the results.

1
.80   1
.78 .82  1
.76 .77 .86   1
.21 .15 .15 .14   1
.13 .14 .10 .08 .31  1
.16 .26 .23 .20 .42 .50 1
.14 .17 .16 .13 .16 .33 .17 1
.20 .16 .19 .26 .33 .11 .23 .07 1
.26 .29 .18 .19 .20 .27 .36 .29 .26   1
.35 .33 .34 .25 .28 .29 .34 .33 .36 .39   1
.19 .17 .08 .07 .12 .25 .30 .14 .01 .17 .24 1
.00 .11 .07 .04 .27 .18 .19 .02 .03 .01 .02 .07 1
.16 .08 .04 .08 .26 .27 .24 .22 .14 .32 .32 .17 .13 1
.12 .01 .02 .07 .13 .19 .18 .00 .02 .00 .11 .04 .17 .30 1
.33 .18 .26 .31 .14 .24 .31 .15 .22 .20 .27 .04 .01 .48 .42 1

SD 0.84 0.82 0.88 0.86 2.2066 1.2951 1.0130 0.9076 1.2 1.0 1.1 1.0 0.7 0.8 0.8 0.9

1-4 = Modern Racism Scale (1-4); 5-8 Implicit Association Test (1-4);  9-12 = Response Window IAT (1-4);  13-16 Response Window Evaluative Priming (1-4)

newmodel

Fitting the data to the original model reproduced the original results.  I then fitted the data to a model with a single attitude factor (see Figure 1).  The model also allowed for measure-specific variances.  An initial model showed no significant measure-specific variances for the two versions of the IAT .  Hence, these method factors were not included in the final model.  To control for variance that is clearly consciously accessible, I modeled the relationship between the explicit factor and the attitude factor as a causal path from the explicit factor to the attitude factor.  This path should not be interpreted as a causal relationship in this case. Rather the path can be used to estimate how much of the variance in the attitude factor is explained by consciously accessible information that influences the explicit measure.  In this model, the residual variance is variation that is shared among implicit measures, but not with the explicit measure.

The model had good fit to the data.  I then imposed constraints on factor loadings.  The constrained model had better fit than the unconstrained model (delta AIC = 4.60, delta BIC = 43.53).  The main finding is that the standard IAT had a loading of .55 on the attitude factor.  The indirect path from the implicit attitude factor to a single IAT measure is only slightly smaller, .55*.92 = .51.  The 95%CI for this parameter ranged from .41 to .60.  The upper bound of the 95%CI would imply that at most 36% of the variance in a single IAT reflects implicit racial attitudes.  However, it is important to note that the model in Figure 1 assumes that the Modern Racism Scale is a perfectly valid measure of consciously accessible attitudes. Any systematic measurement error in the Modern Racism Scale would reduce the amount of variance in the attitude factor that reflects unconscious factors.  Again, the lack of multiple explicit measures makes it impossible to separate systematic measurement error from valid variance in explicit measures.  Thus, the amount of variance in a single IAT that reflects unconscious racial attitudes can range from 0 to 36%.

How Variable are Implicit Racial Attitudes?

The design repeated measurement of implicit attitudes on four occasions.  If recent experiences influence implicit attitudes, we would expect that implicit measures of attitudes on the same occasion are more highly correlated with each other than implicit measures taken on different occasions.  Given the low validity of implicit attitude measures, I examined this question with constrained parameters. By estimating a single parameter, the model has more power to reveal a consistent relationship between implicit measures that were obtained during the same testing session.  Neither the two IATs, nor the IAT and the evaluative priming task (EP) showed significant occasion-specific variance.  Although this finding may be due to low power to detect occasion specific variation, this finding suggests that most of the variance in an IAT is due to stable variation and random measurement error.

Conclusion

Cunningham et al. (2001) conducted a rigorous psychometric study of the Implicit Association Test.  The original article reported results that could be reproduced.  The authors correctly interpret their results as evidence that a single IAT has low reliability. However, they falsely imply that their results provide evidence that the IAT and other implicit measures are valid measures of an implicit form of racism that is not consciously accessible.  My new analysis shows that their results are consistent with this hypothesis, if one assumes that the Modern Racism Scale is a perfectly valid measure of consciously accessible racial attitudes.  Under this assumption, about 25% (95%CI 16-36) of the variance in a single IAT would reflect implicit attitudes.  However, it is rather unlikely that the Modern Racism Scale is a perfect measure of explicit racial attitudes, and the amount of variance in performance on the IAT that reflects unconscious racism is likely to be smaller. Another important finding that was implicit, but not explicitly mentioned, in the original model is that there is no evidence for situation-specific variation in implicit attitudes. At least over the one-month period of the study, racial attitudes remained stable and did not vary as a function of naturally occurring events that might influence racial attitudes (e.g., positive or negative intergroup contact).  This finding may explain why experimental manipulations of implicit attitudes also often produce very small effects (Joy Gaba & Nosek, 2010).

One surprising finding was that the IAT showed no systematic measurement error in this model. This would imply that repeated measures of the IAT could be used to measure racial attitudes with high validity.  Unfortunately, most studies with the IAT rely on a single testing situation and ignore that most of the variance in a single IAT is measurement error.  To improve research on racial attitudes and prejudice, social psychologists should use multiple explicit and implicit measures and use structural equation models to examine which variance components of a measurement model of racial attitudes predict actual behavior.

Validity of the Implicit Association Test as a Measure of Implicit Attitudes

This blog post reports the results of an analysis of correlations among 4 explicit and 3 implicit attitude measures published by Ranganath, Tucker, and Nosek (2008).

Original article:
Kate A. Ranganath, Colin Tucker Smith, & Brian A. Nosek (2008). Distinguishing automatic and controlled components of attitudes from direct and indirect measurement methods. Journal of Experimental Social Psychology 44 (2008) 386–396; doi:10.1016/j.jesp.2006.12.008

Abstract
Distinct automatic and controlled processes are presumed to influence social evaluation. Most empirical approaches examine automatic processes using indirect methods, and controlled processes using direct methods. We distinguished processes from measurement methods to test whether a process distinction is more useful than a measurement distinction for taxonomies of attitudes. Results from two studies suggest that automatic components of attitudes can be measured directly. Direct measures of automatic attitudes were reports of gut reactions (Study 1) and behavioral performance in a speeded self-report task (Study 2). Confirmatory factor analyses comparing two factor models revealed better fits when self-reports of gut reactions and speeded self-reports shared a factor with automatic measures versus sharing a factor with controlled self-report measures. Thus, distinguishing attitudes by the processes they are presumed to measure (automatic versus controlled) is more meaningful than distinguishing based on the directness of measurement.

Description of Original Study

Study 1 measured relative attitudes towards heterosexuals and homosexuals with seven measures; four explicit measures and three reaction time tasks. Specifically, the four explicit measures were

Actual = Participants were asked to report their “actual feelings” towards gay and straight people when given enough time for full consideration on a scale ranging from 1=very negative to 8 = very positive.

Gut = Participants were asked to report their “gut reaction” towards gay and straight people when given enough time for full consideration on a scale ranging from 1=very negative to 8 = very positive.

Time0 and Time5: A second explicit rating task assessed an “attitude timeline”. Participants reported their attitudes toward the two groups at multiple time points: (1) instant reaction, (2) reaction a split-second later, (3) reaction after 1 s, (4) reaction after 5 s, and (5) reaction when given enough time to think fully. Only the first (Time0) and the last (Time5) rating were included in the model.

The three reaction time measures were the Implicit Association Test (IAT), the Go-NoGo Association Test (GNAT), and a Four-Category Sorting Paired Features Task (SPF). All three measures use differences in response times to measure attitudes.

Table A1 in the Appendix reported the correlations among the seven tasks.

IAT 1
GNAT .36 1
SPF .26 .18 1
GUT .23 .33 .12 1
Actual .16 .31 .01 .65 1
Time0 .19 .31 .16 .85 .50 1
Time5 .01 .24 .01 .54 .81 .50 1

The authors tested a variety of structural equation models. The best fitting model, preferred by the authors, was a model with three correlated latent factors. “In this three-factor model, self-reported gut feelings (GutFeeling, Instant Feeling) comprised their own attitude factor distinct from a factor comprised of the indirect, automatic measures (IAT, GNAT, SPF) and from a factor comprised of the direct, controlled measures (Actual Feeling, Fully Considered Feeling). The data were an excellent fit (chi^2(12) = 10.8).

The authors then state “while self-reported gut feelings were more similar to the indirect measures than to the other self-reported attitude measures, there was some unique variance in self-reported gut feelings that was distinct from both.” (p. 391) and they go on to speculate that “one possibility is that these reports are a self-theory that has some but not complete correspondence with automatic evaluations” (p. 391). The also consider the possibility that “measures like the IAT, GNAT, and SPF partly assess automatic evaluations that are “experienced” and amenable to introspective report, and partly evaluations that are not” (p. 391). But they favor the hypothesis that “self-report of ‘gut feelings’ is a meaningful account of some components of automatic evaluation” (p. 391). The interpret these results as strong support for their “contention that a taxonomy of attitudes by measurement features is not as effective as one that distinguishes by presumed component processes” (p. 391). The conclusion reiterates this point. “The present studies suggest that attitudes have distinct but related automatic and controlled factors contributing to social evaluation and that parsing attitudes by underlying processes is superior to parsing attitude measures by measurement features” (p. 393). Surprisingly, the author do not mention the three-factor model in the Discussion and rather claim support for a two-factor model that distinguishes processes rather than measures (explicit vs. implicit). “In both studies, model comparison using confimatory factor analysis showed the data were better fit to a two-factor model distinguishing automatic and controlled components of attitudes than to a model distinguishing attitudes by whether they were measured directly or indirectly” (p. 393). The authors then suggest that some explicit measures (ratings of gut reactions) can measure automatic attitudes. “These findings suggest that direct measures can be devised to capture automatic components of attitudes despite suggestions that indirect measures are essential for such assessments” (p. 393).

New Analysis 

The main problem with this article is that the author never report parameter estimates for the model. Depending on the pattern of correlations among the three factors and factor loadings, the interpretation of the results can change. I first tried to fit the three-factor model to the covariance matrix (setting variances to 1) to the published correlation matrix. MPLUS7.1 showed some problems with negative residual variance for Actual. Also the model had one less degree of freedom than the published model. However, fixing the residual variance of actual did not solve the problem. I then proceeded to fit my own model. The model is essentially the same model as the three-factor model with the exception that I modeled the correlation among the three-latent factor with a single higher-order factor. This factor represents variation in common causes that influences all attitude measures. The problem of negative variance in the actual measure was solved by allowing for an extra correlation between the actual and gut ratings. As seen in the correlation table, these two explicit measures correlated more highly with each other (r = .65) than the corresponding T0 and T5 measures (rs = .54, .50). As in the original article, model fit was good (see Figure). Figure 1 shows for the first time the parameter estimates of the model.

attitude-multi-method

 

The loadings of the explicit measures on the primary latent factors are above .80. For single item measures, this implies that these ratings are essentially measuring the same construct with some random error. Thus, the latent factors can be interpreted as explicit ratings of affective responses immediately or after some reflection. The loadings of these two factors on the higher order factor show that reflective and immediate responses are strongly influenced by the common factor. This is not surprising. Reflection may alter the immediate response somewhat, but it is unlikely to reverse or dramatically change the response a few seconds later. Interestingly, the immediate response has a higher loading on the attitude factor, although in this small sample the differences in loadings is not significant (chi^2(1) = 0.22. The third primary factor represents the shared variance among the three reaction time measures. It also loads on the general attitude factor, but the loading is weaker than the loading for the explicit measures. The parameter estimates suggest that about 25% of the variance is explained by the common attitude (.51^2) and 75% is unique to the reaction time measures. This variance component can be interpreted as unique variance in implicit measures. The factor loadings of the three reaction time measures are also relevant. The loading of the IAT suggests that only 28% (.53^2) of the observed variance in the IAT reflects the effect of causal factors that influence reaction time measures of attitudes. As some of this variance is also shared with explicit measures, only 21% ((.86*.53)^2) of the variance in the IAT represents the variance in the implicit attitude factor This has important implications for the use of the IAT to examine potential effects of implicit attitudes on behavior. Even if implicit attitudes had a strong effect on a behavior (r = .5), the correlation between IAT scores and the behavior only would be r = .86*.53*.5 = .23. A sample size of N = 146 participants would be needed to have 80% power to provide significant evidence for such a relationship (p < .05, two-tailed). Given a more modest effect of attitudes on behavior, r = .86*.53*.30 = .14, the sample size would need to be larger (N = 398). As many studies of implicit attitudes and behavior used smaller samples, we would expect many non-significant results, unless non-significant results remain unreported and published results report inflated effect sizes. One solution to the problem of low power in studies of implicit attitudes would be the use of multiple implicit attitude measures. This study suggests that a battery of different reaction time tasks can be used to remove random and task specific measurement error. Such a multi-method approach to the measurement of implicit attitudes is highly recommended for future studies because it would also help to interpret results of studies in which implicit attitudes do not influence behavior. If a set of implicit measures show convergent validity, this finding would indicate that implicit attitudes did not influence the behavior. In contrast, a null-result with a single implicit measure may simply show that the measure failed to measure implicit attitudes.

Conclusion

This article reported some interesting data, but failed to report the actual results. This analysis of the data showed that explicit measures are highly correlated with each other and show discriminant validity from implicit, reaction time measures. The new analysis also made it possible to estimate the amount of variance in the Implicit Association Test that reflects variance that is not shared with explicit measures but shared with other implicit measures. The estimate of 20% suggests that most of the variance in the IAT is due to factors other than implicit attitudes and that the test cannot be used to diagnose individuals. Whether the 20% of variance that is uniquely shared with other implicit measures reflects unconscious attitudes or method variance that is common to reaction time tasks remains unclear. The model also implies that predictive validity of a single IAT for prejudice behaviors is expected to be small to moderate (r < .30), which means large samples are needed to study the effect of implicit attitudes on behavior.