The IAT framework suggests modest correlations with explicit measures, higher in less controversial domains.
Across domains (self-concept, attitude, stereotype IATs), meta-analyses report IAT/explicit-self-report correla-
tions averaging .24 ( Hofmann et al., 2005 ) and .37 ( Nosek, 2005 ). Higher correlations emerge when concepts are
strong and subjectively important, bipolar rather than unipolar, individually distinctive, and introspectively
accessible ( Nosek, 2005 ).
Thus the IAT is especially suited to intergroup bias and other socially sensitive topics that do not necessarily
yield reliable self-report. Explicit self-reports are well suited to uncontroversial political and consumer concepts,
presumably topics that do not raise social anxieties. When participants are indeed willing to report their racial
prejudices explicitly, implicit measures correlate with them ( Wittenbrink, Judd, & Park, 1997 ). Correlations
increase for spontaneous self-reports and conceptually similar implicit measures. Overall, the IAT has particular
utility for racial attitudes that people do not often report explicitly.
Divergent/Discriminant
From the outset, the IAT has aimed to differentiate itself from rival concepts. For example, racial ingroup pref-
erence is not a function of sheer unfamiliarity with outgroup names and faces ( Dasgupta, McGhee, Greenwald, &
Banaji, 2000 ). Similarly, according to critics, the IAT might mix personal and cultural beliefs ( Arkes & Tetlock,
2004; Karpinski & Hilton, 2001; Kihlstrom, 2004 ;Olson & Fazio, 2004 ;Uhlmann, Brescoll, & Paluck, 2006 ).
However, because attitudes do partially originate in culture, even when people disagree with the culture, the IAT
should detect the difference between attitudes and cultural influences ( Banaji, Nosek, & Greenwald, 2004;
Lowery, Hardin, & Sinclair, 2001 ).
Comparing to other indirect measures, the IAT as a measure requires conscious pairing and sometimes aware-
ness of the evaluative association among the categories involved, but the IAT does not as much involve control
over response latencies ( Dasgupta et al., 2000 ). Other priming techniques (below) do not necessarily include even
awareness of the stimuli.
As noted, Black Americans on average show less White favoritism and relatively more Black favoritism (or at least
neutrality) than do participants from other racial /C0ethnic groups, whereas Whites show the most White favoritism of
all racial groups (e.g., Nosek et al., 2007 ). Whites’ ingroup preference (and relative Black outgroup disfavor) is less-
ened after diversity training ( Rudman, Ashmore, & Gary, 2001 ). The IAT also picks up Asians’ degrees of Asian ver-
sus European-American self-reported identify and immersion ( Greenwald et al., 1998 ).
Some critics suggest the IAT is not automatic or unconscious, and its creators do not insist that it is purely so.
Nevertheless, this debate has sparked research on how flexible IAT responses really are; if it is malleable, how
implicit is it? Most relevantly here, Whites who practice associating self with Blacks then diminish their IAT racial
bias ( Phills, Kawakami, Tabi, Nadolny, & Inzlicht, 2011 ; see also Devine, Plant, Amodio, Harmon-Jones, & Vance,
2002). IAT race-prejudice scores decrease with a variety of relevant experiences: exposure to positive minority and
negative majority role models ( Dasgupta & Greenwald, 2001 ; but see Joy-Gaba & Nosek, 2010 ), diversity training
(Rudman et al., 2001 ), counter-stereotypic images ( Wittenbrink, Judd, & Park, 2001b ), context ( Mitchell, Nosek, &
Banaji, 2003 ), and social influence ( Lowery et al., 2001 ). Also, race-IAT data depend on whether people are aware that
it assesses racism, potentially increasing self-defeating control efforts by well-intentioned respondents, as well as
picking up the biases of automatically bigoted ones ( Frantz, Cuddy, Burnett, Ray, & Hart, 2004; Siegel, Dougherty, &
Huber, 2012 ). However, faking is potentially detectable (gender and nationality IATs: Cvencek et al., 2010 ).
Construct/Factor Analytic
The IAT often simplifies bias down to a bipolar positive /C0negative evaluative association, though other associa-
tions (e.g., women and humanities; men and math/science) sometimes appear. No commentators imply that the
associations are not evaluative, though their origin (personal or cultural) is disputed (see above).
Criterion/Predictive
According to a meta-analysis, the IAT predicts related interpersonal affect, social judgment, and behavior, act-
ing like an attitude ( Greenwald, Poehlman, Uhlmann, & Banaji, 2009 ). It also correlates with physiological
response, including a palpable sense of the ease of certain (e.g., prejudiced) pairings over others ( Ashburn-Nardo
et al., 2001 ), allowing a teachable moment in seminars. The IAT’s predictive validity for behavior often fares bet-
ter than explicit self-reports, especially on interracial responses and other socially sensitive issues. The IAT, like
other indirect measures (see below) tends to predict interpersonal nonverbal behaviors, such as observers’ molar
impressions of friendliness, smiling, speech hesitation, speech errors, and talking time ( McConnell & Leibold,
2001 ). On a similar nonverbal level, Whites’ implicit associations predict their own early detection of threatening695 OVERVIEW OF THE MEASURES
VI. SOCIOCULTURAL INTERACTION AND CONFLICT