Implicit Attitude Measures: Consistency, Stability, and Convergent Validity

Cunningham, William A.; Preacher, Kristopher J.; Banaji, Mahzarin R.

doi:10.1111/1467-9280.00328

Cited by 799 publications

(728 citation statements)

References 27 publications

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“…However, this interpretation requires caution. Given that correlations between implicit measures and effect sizes can be attenuated by measurement error (e.g., method-related variance, internal consistency; Bar-Anan & Nosek, 2014;Baugh, 2002;Cunningham, Preacher, & Banaji, 2001) and that our studies may have been underpowered, future research is needed to provide additional evidence for the possibility that, even for young children, these measures tap into different constructs (e.g., BarAnan & Nosek, 2014;Gawronski & De Houwer, 2014;.…”

Section: Discussionmentioning

confidence: 99%

Assessing Children’s Implicit Attitudes Using the Affect Misattribution Procedure

Williams

Steele

Lipman

2016

Journal of Cognition and Development

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Section: Discussionmentioning

confidence: 99%

Assessing Children’s Implicit Attitudes Using the Affect Misattribution Procedure

Williams

Steele

Lipman

2016

Journal of Cognition and Development

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“…However, there is a psychometric caveat here that precludes drawing firm theoretical conclusions-the reliability of measurement. Specifically, affective priming measures have been repeatedly criticized for their low reliability (e.g., Cunningham, Preacher, & Banaji, 2001), and these differences may account for large portions of this moderator effect. Note, however, that an interpretation in terms of reliability faces some difficulties in explaining the gap between the magnitude of EC effects for self-report as compared with IAT measures because internal consistencies of the IAT typically are around .80 (e.g., Hofmann, Gawronski, Gschwendner, Le, & Schmitt, 2005) and thus similar to those commonly obtained for self-reports.…”

Section: What Are the Processes Underlying Evaluative Conditioning?mentioning

confidence: 99%

Evaluative conditioning in humans: A meta-analysis.

Hofmann

Houwer

Perugini

et al. 2010

Psychological Bulletin

827

755

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This article presents a meta-analysis of research on evaluative conditioning (EC), defined as a change in the liking of a stimulus (conditioned stimulus; CS) that results from pairing that stimulus with other positive or negative stimuli (unconditioned stimulus; US). Across a total of 214 studies included in the main sample, the mean EC effect was d ϭ .52, with a 95% confidence interval of .466 -.582. As estimated from a random-effects model, about 70% of the variance in effect sizes were attributable to true systematic variation rather than sampling error. Moderator analyses were conducted to partially explain this variation, both as a function of concrete aspects of the procedural implementation and as a function of the abstract aspects of the relation between CS and US. Among a range of other findings, EC effects were stronger for high than for low contingency awareness, for supraliminal than for subliminal US presentation, for postacquisition than for postextinction effects, and for self-report than for implicit measures. These findings are discussed with regard to the procedural boundary conditions of EC and theoretical accounts about the mental processes underlying EC.Keywords: evaluative conditioning, affective learning, attitude learning, associative learning, propositional learning One of the most influential ideas in psychology is that human behavior is, to a large extent, governed by likes and dislikes (Allport, 1935;Martin & Levey, 1978). For instance, people prefer the company of people they like and try to avoid those they do not like; people buy and consume products they like rather than those they dislike; and they vote for and support politicians and ideas that they find sympathetic rather than repelling. Furthermore, preferences influence attention, memory, and judgments and form the basis of our emotional life (Fox, 2009). Given the pervasive impact of preferences on behavior, it is vital for our discipline to understand how preferences are formed and how they can be influenced. Although some likes and dislikes may be genetically determined (Poulton & Menzies, 2002), the vast majority of our preferences are learned rather than innate (Rozin & Millman, 1987). But precisely how humans acquire their likes and dislikes continues to be the subject of vigorous debate (Rozin, 1982;De Houwer, Thomas, & Baeyens, 2001).The present article provides a meta-analysis of research on one possible manner in which likes and dislikes can be learned: evaluative conditioning (EC), which may be best defined as an effect that is attributed to a particular core procedure. Specifically, EC refers to a change in the valence of a stimulus (the effect) that is due to the pairing of that stimulus with another positive or negative stimulus (the procedure) (De Houwer, 2007a;De Houwer et al., 2001). The first stimulus is often referred to as the conditioned stimulus (CS), and the second stimulus is often referred to as the unconditioned stimulus (US). Typically, a CS becomes more positive when it has been paired with a posit...

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“…Cunningham et al (2001), for example, found that the correlation between the IAT (Greenwald et al, 1998) and affective priming (Fazio et al, 1995) is only moderate, even when measurement error is controlled. This finding may indicate that the nature of the associations assessed with these tasks have only partial overlap (e.g., Olson & Fazio, 2003).…”

Section: Implications and Future Directionsmentioning

confidence: 99%

Separating multiple processes in implicit social cognition: The quad-model of implicit task performance

Conrey¹,

Sherman²,

Gawronski³

2005

PsycEXTRA Dataset

158

327

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The authors argue that implicit measures of social cognition do not reflect only automatic processes but rather the joint contributions of multiple, qualitatively different processes. The quadruple process model proposed and tested in the present article quantitatively disentangles the influences of 4 distinct processes on implicit task performance: the likelihood that automatic bias is activated by a stimulus; that a correct response can be determined; that automatic bias is overcome; and that, in the absence of other information, a guessing bias drives responses. The stochastic and construct validity of the model is confirmed in 5 studies. The model is shown to provide a more nuanced and detailed understanding of the interplay of multiple processes in implicit task performance, including implicit measures of attitudes, prejudice, and stereotyping.

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Implicit Attitude Measures: Consistency, Stability, and Convergent Validity

Abstract: Abstract-

Cited by 799 publications

References 27 publications

Assessing Children’s Implicit Attitudes Using the Affect Misattribution Procedure

Assessing Children’s Implicit Attitudes Using the Affect Misattribution Procedure

Evaluative conditioning in humans: A meta-analysis.

Separating multiple processes in implicit social cognition: The quad-model of implicit task performance

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