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Analyze the Strengths and Weaknesses of the IAT as a Research Tool

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The Implicit Association Test (IAT) measures the strength of associations between concepts (e.g., black people, gay people) and evaluations (e.g., good, bad) or stereotypes (e.g., athletic, clumsy)

The main idea is that making a response is easier when closely related items share the same response key. We would say that one has an implicit preference for straight people relative to gay people if they are faster to complete the task when Straight People + Good / Gay People + Bad are paired together compared to when Gay People + Good / Straight People + Bad are paired together.You would receive feedback saying you have an implicit preference for flowers compared to insects if you respond faster when Flowers + Good / Insects + Bad are paired together compared to when Insects + Good / Flowers + Bad are paired together. The labels ‘slight’, ‘moderate’ and ‘strong’ reflect the strength of the implicit preference based on how much faster you respond to Flowers + Good / Insects + Bad versus Insects + Good / Flowers + Bad.

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Another reason IAT critics think that the Web site shouldn't provide feedback is because the measure is quite sensitive to the social context in which it's taken: In fact, people's scores often change from one test to another. "It's not as malleable as mood and not as reliable as a personality trait," agrees Nosek. "It's in between the two--a blend of both a trait and a state characteristic." Russell Fazio, PhD, a social psychologist at Ohio State University, describes the IAT as "noisy." There's no way to determine whether it's measuring unconscious attitudes or simply associations picked up from the environment, he says. "The bottom line is that it has a potential to be a remarkably powerful tool," he says. "But as traditionally implemented, it really has problems." He's developed his own version of the measure that he thinks homes in on whether associations are true unconscious attitudes or not. It's called the personalized IAT. Instead of pairing categories like race or gender with such general terms as "good" and "bad," Fazio pairs them with "I like" and "I don't like." Studies comparing the personalized IAT with the traditional IAT indicate that it provides a more valid assessment of people's attitudes, says Fazio

While Nosek and Greenwald say they agree that the IAT measures external influences and not just personally held attitudes, they add that it's a reflection of reality, not a problem with the test. "In my view," says Nosek, "implicit associations are the sum total of everyday associations." Premature publicity? In the end, the relationship between IAT scores and the real world will determine its value. In fact, to prove the test's worth, the test's developers have recently completed a meta-analysis of the IAT's predictive power, which is in press. "We found that in the domain of intergroup discrimination--race, age, sexual orientation--the IAT does better than self-report at predicting behavior," says Greenwald. Northwestern University social psychologist Alice Eagly, PhD, thinks the meta-analysis shows that the IAT provides modest predictions of behavior. "The IAT adds something," she says, "but it's not a direct line to the unconscious." Given these findings, the IAT is not yet ready for use in applied settings such as courtrooms, critics say. But the hype and public promotion of the measure have garnered the attention of many legal scholars who have begun to use the research to bolster workplace and other types of discrimination cases, says Mitchell. In fact, Greenwald and others have discussed concepts of implicit social cognition in court. In one case of workplace discrimination, Greenwald described the literature on implicit bias in rebuttal to a defense expert who, says Greenwald, had inaccurately described the research. However, he and his colleagues have made it clear that they do not support the use of specific IAT results in court either to help select a jury or screen witnesses for implicit bias. Mitchell thinks any discussion of implicit bias in the courtroom goes too far based on the state of the science. "The idea that we have associations that may be primed by the stimuli on the IAT sounds perfectly plausible," says Mitchell. "What those associations mean and what they indicate is an open question. To equate it with automatic preferences for different social groups is much less plausible."

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We believe that consumer psychology should build on this movement and that more research should investigate implicit consumer social cognition: that is, the automatic consumer affective processes and cognitions that exist outside of conscious awareness or control. However, measurement issues have often constrained our understanding of implicit effects. Although researchers agree that “valid measurement is the sine qua non of science” (Peter, 1979, p. 6), a lack of satisfactory implicit measurement tools has led consumer psychologists to depend a lot on explicit measures. For example, research on incidental ad exposure (Janiszewski, 1993; Shapiro, 1999) assumes that preferences are “formed independently of conscious processing” (Janiszewski, 1988, p. 200). At the same time, however, this research relies on self-report measures of attitude toward the ad (Aad), recognition, and familiarity

In sum, whether it be to circumvent the willingness or awareness issue, the availability of valid implicit measures has become a theoretical imperative. In other words, implicit measures are not “explicit measures without bias’ and they do not always assess constructs identical to those assessed by explicit measures. Although related, the two types of measures stem from different information processing streams and appear linked to activations in different regions of the brain.

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In summary, we strongly argue not to take the validity of implicit measures like the IAT for granted. Instead, we should take into account the complexity of these measures, especially when it comes to the predictive value for real-life behavior. As outlined in the current review, the past 20 years of research have provided us with a number of good reasons for why the IAT and its derivatives did not succeed in closing the attitude-behavior gap, and enriched our toolbox with promising, sophisticated improvements. Future research will benefit from harnessing the power of such a more differentiated view on implicit measures.

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Janiszewski, C. (1993). Preattentive mere exposure effects. Journal of Consumer Research, 20, 376–392.

Janiszewski, C. (1988). Preconscious processing effects: The independence of attitude formation and conscious thought. Journal of Consumer Research, 15, 199–209.

Nosek, B. A., Banaji, M. R., & Greenwald, A. G. (2002b). Math = male, me = female, therefore math doesn’t equal me. Journal of Personality and Social Psychology, 83, 44–59.

Peter, J. P. (1979). Reliability: A review of psychometric basics and recent marketing practices. Journal of Marketing Research, 16, 6–17.

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