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01-12-2010 | ORIGINAL PAPER

Explicit Evidence on the Import of Implicit Attitudes: The IAT and Immigration Policy Judgments

Author: Efrén O. Pérez

Published in: Political Behavior | Issue 4/2010

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Abstract

The implicit association test (IAT) is increasingly used to detect automatic attitudes. Yet a fundamental question remains about this measure: How well can it predict individual judgments? Though studies find that IAT scores shape individual evaluations, these inquiries do not account for an array of well-validated, theoretically relevant variables, thus raising the challenge of omitted variable bias. For scholars using the IAT, the risk here is one of misattributing to implicit attitudes what can be better explained by alternate and rigorous self-reports of explicit constructs. This paper examines the IAT’s performance in the context of U.S. immigration politics. Using a representative web survey of adults, I demonstrate the IAT effectively captures implicit attitude toward Latino immigrants. Critically, I then show these attitudes substantively mold individual preferences for illegal and legal immigration policy, net of political ideology, socio-economic concerns, and well-established measures of intolerance toward immigrants, such as authoritarianism and ethnocentrism. Combined, these results suggest the IAT measures attitudes that are non-redundant and potent predictors of individual political judgments.

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There is a large family of implicit attitude measures (e.g., Fazio et al. 1995; Burdein et al. 2006; Kam 2007). The IAT is only one of them. That being said, it is one of the more popular and extensively used by researchers (e.g., Greenwald et al. 2009), as well as one of the more controversial (Fazio and Olson 2003). For these reasons, it is the focus of this paper.

To say that implicit attitudes are well-crystallized is not the same thing as saying that they are not malleable. Indeed, several researchers have examined the influence of contextual features on IAT responses, such as the type of information one is exposed to prior to completion of the IAT (e.g., Dasgupta and Greenwald 2001; Lowery et al. 2001; Richeson and Nussbaum 2004). In this way, implicit attitudes are similar to explicit attitudes insofar as both can, and sometimes do, respond to features in one’s immediate decision environments.

The order of these blocks is counterbalanced across participants (Nosek et al. 2005). Individuals also complete shorter practice trials to acclimate them to the measure. See Greenwald et al. (1998) for details on the IAT described above.

This dissociation varies by domain (see Nosek et al. 2002; Nosek 2005).

Indeed, in concurrent experimental work (Pérez 2010), I find that implicit attitude toward Latino immigrants heightens opposition to immigration, even if one directs people’s attention to non-Latino immigrants.

The latest figures from the Department of Homeland Security’s Office of Immigration Statistics reveal that in 2006, 65% of illegal immigrants came from three Latin American countries: Mexico, El Salvador, and Guatemala. Those same three countries contributed only 18% of the total flow of legal immigrants to the U.S. during 2006.

It may be reasonably argued that (H1) is not a hypothesis, but rather a descriptive observation. Yet I describe the claim that individuals possess an implicit attitude toward Latino immigrants as a hypothesis because it is a claim that can be falsified. From a measurement perspective, a latent implicit attitude is theorized to “cause” individuals’ responses on the IAT, net of measurement error (e.g., Brown 2007). If, indeed, those responses reflect an implicit attitude, responses on the matched task should be faster than those on the mismatched task. In contrast, if individuals do not possess this implicit attitude, they should score at comparable speeds across both tasks. Furthermore, it is important to consider that while the IAT has been around since 1998, this version of it did not exist until now; which is to say, there is a very real possibility that it fails to measure its intended construct. And if it fails, one logical conclusion would be that implicit attitudes toward Latino immigrants do not exist. Thus, for these reasons, I believe it is appropriate to describe hypothesis 1 as an actual hypothesis rather than a descriptive statement.

Twenty-two (22) Asian American and seven (7) Hispanic subjects also completed the study. They are generally excluded from the analysis to ensure comparability between the pilot study and the web survey, which focused on non-Hispanic Whites. Nevertheless, I do use the data for non-White subjects to provide evidence for the IAT’s validity as an indicator of individual attitude. I explicitly discuss this evidence in the results section. The substantive findings from the pilot study remain unchanged if non-White subjects are included.

While it is true that, as far as the U.S. census is concerned, Latino immigrants can and do self-identify themselves as White in racial terms (e.g., Hattham 2007), it is likely that to non-Hispanics, the former category is generally not perceived as White (e.g., Devos and Banaji 2005).

A practice block precedes each key block. These practice blocks yield information similar to the key blocks, and thus, are used to compute IAT scores in line with previous research (Greenwald et al. 2003). The full sequence of the IAT for this paper is detailed in Appendix 1.

This is likely to be true if, for instance, attitudes toward all immigrant groups are similar and equally predictive of immigration policy preferences—as is suggested by some research on ethnocentric attitudes toward immigrants (e.g., Sniderman et al. 2000). Inasmuch as this is true, a comparison between Latino and White immigrants should return small differences in IAT sorting times, which cuts against a possible relationship between the IAT and immigration policy preferences. I thank an anonymous reviewer for this line of reasoning.

Gender is measured with a dummy variable, where female is the baseline category. Age is assessed through age in years. Education is assessed through a 6-point scale, ranging from no high school to post-graduate education. Income was gauged via a 14-point scale running from less than $10,000 to $150,000 or more.

These studies find that the effects of these demographic controls can sometimes depend on the type of policy being assessed and the year the survey data used were collected. This mixed pattern of evidence thus prevents one from making firm predictions about the effects of these demographic controls. However, since other scholars have deemed these demographic controls important enough to control, I do as well. For instance, Citrin et al.’s (1997) analysis of 1994 ANES data show that older age and higher levels of education both decreased opposition to immigration, though only the latter achieved statistical significance. In turn, being female and having higher levels of income were both associated with higher levels of opposition to immigration, though only income achieved significance. Scheve and Slaugher’s (2001) analysis of the same data for the years 1992, 1994, and 1996 find that education has a consistently negative and reliable effect on opposition to immigration across these years, but the other demographic controls do not. It is important to point out, however, that if these demographic controls are excluded, the substantive conclusions of the pending analysis remain virtually unchanged.

In their pioneering study, Sniderman et al. (2004) find that items such as these are “double-barreled” when they directly mention immigrants. That is, they confound one’s attitude about immigrants with one’s attitude toward a specific social or economic domain. Before fielding this study, I anticipated administering at least two measures of attitude toward immigrants: (1) ethnocentrism items and (2) the IAT. What I needed, then, was a set of items that more directly assessed people’s concerns about crime, jobs, schools, and culture without confounding these concerns with attitude toward immigrants.

The anticipated IAT effects emerge both in the raw and transformed data.

This effect is large, but not atypical. Nosek et al. (2005), for instance, uncover very large IAT D scores for several IATs, including those involving “young people” and “old people” as contrast categories (IAT D = 1.35).

In other words, how do we know that the IAT is capturing negative attitude toward Latino immigrants, rather than knowledge that society holds Latino immigrants in lower regard than White immigrants and Asian immigrants? For compelling evidence against this interpretation, see Nosek and Hansen (2008).

In the Latino immigrant-White immigrant IAT, the difference in means between Hispanic and non-Hispanic White subjects is significant at the 1% level (one-tailed). In the Latino immigrant-Asian immigrant IAT, the difference in means between Asian and non-Hispanic White subjects is significant at the 5% level (one-tailed).

Each recoded from 0 to 1.

These correlations are disattenuated for measurement error. The correlation between IAT scores and authoritarianism is significant at p < .10. The correlation between IAT scores and ethnocentrism is significant at p < .05. In concurrent work (Pérez 2010), I show that a measurement model where indicators of authoritarianism, ethnocentrism, and implicit attitude measure their intended construct yields an excellent fit (CFI = .92; TLI = .93; RMSEA = .07), whereas a model where these indicators are modeled as measures of the same underlying trait produces a poor fit (CFI = .12; TLI = .28; RMSEA = .24).

I also modeled these items as indicators of the same underlying construct. This model yielded fit indices that suggest a poor model fit, as well as several large residuals.

Let’s say, for instance, that scores on the IAT reflect one’s knowledge that society devalues Latino immigrants relative to White immigrants, but that one does not necessarily endorse this view (e.g., Arkes and Tetlock 2004; Olson and Fazio 2004; Karpinski and Hilton 2001). Here, the individual could score high on the IAT in spite of not endorsing the attitude it presumably captures. That the same individuals who score high on the IAT also support stricter immigration policies is more consistent with the view of implicit attitudes as individually endorsed attitudes, since they help predict one’s personal immigration policy preferences (Nosek and Hansen 2008; Ashburn-Nardo et al. 2003).

An astute reader may recall that Brader et al. (2008) discover that threatening communications activate anxiety only when such information cues a Latino immigrant. This would suggest a positive interaction term between one’s implicit attitude and level of socio-economic concern. Though my continuous measure of socio-economic concerns is not directly comparable to the threat manipulation in Brader et al. (2008), I nevertheless find that the anticipated interaction term is in the expected positive direction across both domains of immigration, though significant only in the legal immigration policy analysis (β = .798, p < .05 one-tailed). One reason for this discrepancy is the fact that the current analysis distinguishes between illegal and legal immigration policy preferences. The resulting pattern is thus consistent with the claims of Brader et al. (2008) as well as with this paper’s view that illegal and legal immigration preferences are empirically distinct.

The finding that gender might have differential effects depending on the domain of immigration is a novel finding worthy of further inquiry, especially since this survey is more recent than previous surveys addressing attitudes toward immigration (e.g., Citrin et al. 1997).

A key consideration is whether the coefficients from both regressions are reliably different from each other. Thus, I estimated a baseline model where both latent factors were simultaneously regressed on the same battery of predictors from Tables 3 and 4. I then re-ran alternate models with single coefficients constrained to equality. This series of nested models enable one to gauge the degree to which such parameter constraints deteriorate the fit of the model, as captured by statistically significant changes in χ² (e.g., Kline 2005; Bollen 1989). Significant changes in this statistic imply that a set of coefficients is reliably different from each other. In this analysis, these include: conservatism (Δχ² = 10.34, p < .01); ethnocentrism (Δχ² = 7.83, p < .01); socio-economic concerns (Δχ² = 2.70, p < .10); and gender (Δχ² = 14.91, p < .01). The effects of implicit attitude were not reliably different (Δχ² = .69, p < .41), a pattern which underscores the persistent effects of this construct across varied immigration policy domains.

Critically, these results are substantively the same if one utilizes the more traditional meditational approach employed by Baron and Kenny (1986). Here, for mediation to occur, an independent variable should influence the dependent variable as well as the proposed mediator. Furthermore, when the effects of the independent variable and mediator on the dependent variable are simultaneously controlled, the effect of the mediated variable should be reduced. I followed this approach using OLS for each proposed mediator and replicated the above results, as indicated by the statistical significance of the Sobel test (z) for most proposed mediators: conservatism (z = 2.53, p < .01); socio-economic concerns (z = 1.67, p < .09); authoritarianism (z = 1.50, p < .13); ethnocentrism (z = 1.71, p < .09); and explicit-Latino immigrant (z = 3.02, p < .01)(all tests two-tailed). Importantly, while most of these mediators channel some of the effect of implicit attitudes on immigration policy judgments, the direct effect of implicit attitudes remains positive, robust, and statistically significant, thereby corroborating the above findings.

I thank an anonymous reviewer for this suggestion.

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Title: Explicit Evidence on the Import of Implicit Attitudes: The IAT and Immigration Policy Judgments
Author: Efrén O. Pérez
Publication date: 01-12-2010
Publisher: Springer US
Published in: Political Behavior / Issue 4/2010
Print ISSN: 0190-9320
Electronic ISSN: 1573-6687
DOI: https://doi.org/10.1007/s11109-010-9115-z

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