Preparation and Analyses of Implicit Attitude Measures: Challenges, Pitfalls, and Recommendations (original) (raw)
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Understanding and using the implicit association test: I. An improved scoring algorithm
Journal of personality and …, 2003
In reporting Implicit Association Test (IAT) results, researchers have most often used scoring conventions described in the first publication of the IAT ). Demonstration IATs available on the Internet have produced large data sets that were used in the current article to evaluate alternative scoring procedures. Candidate new algorithms were examined in terms of their (a) correlations with parallel self-report measures, (b) resistance to an artifact associated with speed of responding, (c) internal consistency, (d) sensitivity to known influences on IAT measures, and (e) resistance to known procedural influences. The best-performing measure incorporates data from the IAT's practice trials, uses a metric that is calibrated by each respondent's latency variability, and includes a latency penalty for errors. This new algorithm strongly outperforms the earlier (conventional) procedure.
Detecting attitude change with the implicit association test
Les Cahiers De Recherche, 2008
The Implicit Association Test and its variants have become pervasive measures of attitudes in a variety of domains and contexts. In two experiments, we provide evidence that a recent variant, the Personalized IAT developed by Olson and Fazio (2004) may more accurately detect changes in personal attitudes than the conventional Traditional IAT devised by Greenwald, McGhee, and Schwartz (1998). Our findings suggest that the Personalized IAT may be more sensitive to detecting attitude changes than the Traditional IAT because it is less affected by extrapersonal associations (i. e. salient associations not contributing to personal evaluations of the object). More generally, this research suggests that for attitude domains characterized by potentially strong extrapersonal associations, using the Personalized and Traditional IATs may provide researchers with complementary insights about knowledge structures.
Journal of Personality and Social Psychology, 2013
We introduce the ReAL model for the Implicit Association Test (IAT), a multinomial processing tree model that allows one to mathematically separate the contributions of attitude-based evaluative associations and recoding processes in a specific IAT. The ReAL model explains the observed pattern of erroneous and correct responses in the IAT via 3 underlying processes: Recoding of target and attribute categories into a binary representation in the compatible block (Re), evaluative associations of the target categories (A), and label-based identification of the response that is assigned to the respective nominal category (L). In 7 validation studies, using an adaptive response deadline procedure in order to increase the amount of erroneous responses in the IAT, we demonstrated that the ReAL model fits IAT data and that the model parameters vary independently in response to corresponding experimental manipulations. Further studies yielded evidence for the specific predictive validity of the model parameters in the domain of consumer behavior. The ReAL model allows one to disentangle different sources of IAT effects where global effect measures based on response times lead to equivocal interpretations. Possible applications and implications for future IAT research are discussed.
2010
Schwartz, 1998) can be contaminated by associations that do not contribute to one’s evaluation of an attitude object and thus do not become activated when one encounters the object but that are nevertheless available in memory. The authors propose a variant of the IAT that reduces the contamination of these “extrapersonal associations. ” Consistent with the notion that the traditional version of the IAT is affected by society’s negative portrayal of minority groups, the “personalized ” IAT revealed relatively less racial prejudice among Whites in Experiments 1 and 2. In Experiments 3 and 4, the personalized IAT correlated more strongly with explicit measures of attitudes and behavioral intentions than did the traditional IAT. The feasibility of disentangling personal and extrapersonal associations is discussed. Implicit measures have enjoyed widespread use in social psychology in recent years. The Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) has become a part...
Journal of Personality and Social Psychology, 2004
- can be contaminated by associations that do not contribute to one's evaluation of an attitude object and thus do not become activated when one encounters the object but that are nevertheless available in memory. The authors propose a variant of the IAT that reduces the contamination of these "extrapersonal associations." Consistent with the notion that the traditional version of the IAT is affected by society's negative portrayal of minority groups, the "personalized" IAT revealed relatively less racial prejudice among Whites in Experiments 1 and 2. In Experiments 3 and 4, the personalized IAT correlated more strongly with explicit measures of attitudes and behavioral intentions than did the traditional IAT. The feasibility of disentangling personal and extrapersonal associations is discussed. Implicit measures have enjoyed widespread use in social psychology in recent years. The Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) has become a particularly popular implicit lens for viewing such social phenomena as prej
Decoding the implicit association test: Im-plications for criterion prediction
2006
The implicit association test (IAT) is believed to measure implicit evaluations by assessing reaction times on two cognitive tasks, often termed ''compatible'' and ''incompatible'' tasks. A common rationale for studying the IAT is that it might improve our prediction and understanding of meaningful psychological criteria. To date, however, no clear psychometric theory has been advanced for this measure. We examine the theory, methods and analytic strategies surrounding the IAT in the context of criterion prediction to determine measurement and causal models a researcher embraces (knowingly or unknowingly) by using the test. Our analyses reveal that the IAT revolves around interpretation of two distinct relative constructs, one at the conceptual level and one at the observed level. We show that interest in relative implicit evaluations at the conceptual level imposes a causal model that is restrictive in form. We then examine measurement models of the IAT and show how computing a difference score at the observed level may lack empirical justification. These issues are highlighted in a study replicating an effect established in the literature (Study 1). We then introduce a new variant of the IAT and use it to evaluate the reasonableness of traditional IAT methods (Study 2).
ASSESSING IMPLICIT COGNITIONS WITH A PAPER FORMAT IMPLICIT ASSOCIATION TEST
2008
The Implicit Association Test (IAT; ) is a frequently used measure of implicit cognitions that is typically administered on computers. This chapter reports development of an IAT that can be administered on paper. First, it describes a suggested analytic procedure for paper IAT data. Next, two studies measuring implicit racial preferences are reported that suggest that the paperformat IAT elicits similar but somewhat weaker mean effects than the computer-format IAT, and shows test-retest reliability comparable to the computer-format IAT. The paper format IAT may be more sensitive to the type of stimuli used in the task. It performed better with all-verbal stimuli compared with pictures of faces. Use of the paper-format IAT with verbal stimuli may be a useful supplement to computerized data collections, or a viable approach when computer data collection is not feasible.
Understanding and using the Implicit Association Test: II. Method variables and construct validity
Personality and Social …, 2005
The Implicit Association Test (IAT) assesses relative strengths of four associations involving two pairs of contrasted concepts (e.g., male-female and family-career). In four studies, analyses of data from 11 Web IATs, averaging 12,000 respondents per data set, supported the following conclusions: (a) sorting IAT trials into subsets does not yield conceptually distinct measures; (b) valid IAT measures can be produced using as few as two items to represent each concept; (c) there are conditions for which the administration order of IAT and self-report measures does not alter psychometric properties of either measure; and (d) a known extraneous effect of IAT task block order was sharply reduced by using extra practice trials. Together, these analyses provide additional construct validation for the IAT and suggest practical guidelines to users of the IAT.