Best research practices for using the Implicit Association Test

This is the final version. Available from Springer via the DOI in this record. Interest in unintended discrimination that can result from implicit attitudes and stereotypes (implicit biases) has stimulated many research investigations. Much of this research has used the Implicit Association Test (IAT) to measure association strengths that are presumed to underlie implicit biases. It had been more than a decade since the last published treatment of recommended best practices for research using IAT measures. After an initial draft by the first author, and continuing through three subsequent drafts, the 22 authors and 14 commenters contributed extensively to refining the selection and description of recommendation-worthy research practices. Individual judgments of agreement or disagreement were provided by 29 of the 36 authors and commenters. Of the 21 recommended practices for conducting research with IAT measures presented in this article, all but two were endorsed by 90% or more of those who felt knowledgeable enough to express agreement or disagreement; only 4% of the totality of judgments expressed disagreement. For two practices that were retained despite more than two judgments of disagreement (four for one, five for the other), the bases for those disagreements are described in presenting the recommendations. The article additionally provides recommendations for how to report procedures of IAT measures in empirical articles.Economic and Social Research Council (ESRC

Brand Suicide? Memory and Liking of Negative Brand Names

Negative brand names are surprisingly common in the marketplace (e.g., Poison perfume; Hell pizza, and Monster energy drink), yet their effects on consumer behavior are currently unknown. Three studies investigated the effects of negative brand name valence on brand name memory and liking of a branded product. Study 1 demonstrates that relative to nonnegative brand names, negative brand names and their associated logos are better recognised. Studies 2 and 3 demonstrate that negative valence of a brand name tends to have a detrimental influence on product evaluation with evaluations worsening as negative valence increases. However, evaluation is also dependent on brand name arousal, with high arousal brand names resulting in more positive evaluations, such that moderately negative brand names are equally as attractive as some non-negative brand names. Study 3 shows evidence for affective habituation, whereby the effects of negative valence reduce with repeated exposures to some classes of negative brand name

Sensitivity to Varying Gains and Losses: The Role of Self-Discrepancies and Event Framing

Three studies psychophysically measured people's discrimination among different sizes of monetary net gains or net losses. Participants imagined either gains or nonlosses (i.e., net gains) or losses or nongains (i.e., net losses). Participants discriminated more when the identical event was framed as the presence (gains and losses) versus the absence (nonlosses and nongains) of an outcome, presumably because the latter is harder to represent. Discrimination was enhanced when the motivational features of the imagined event were either both the same as or both different from a person's self-discrepancy. Discrimination was reduced when only one of the motivational features was different. A model of excitations, inhibitions, and disinhibitions between mental representation is suggested to account for these findings

Distinguishing Constructs from Variables in Designing Research

Current research practices often conflate theoretical constructs and explanatory hypotheses with va riables and predicted effects, to the detriment of research progress. This has led to the use of procedu res such as manipulation checks, mediation analysis, and boundary conditions predicated on the i dea that matching constructs to variables is necessary to validate that a theory corresponds to an effect . An alternative perspective, Inference to the Best Explanation (IBE), calls for designing researc h to exploit the power of distinguishing constructs from variables, hypotheses from predict i ons, and theory from effects. IBE calls for stating hypotheses (Hs) about construct-to- constru ct relationships and, separately, the predictions (Ps) about variable-to-variable effects that are explai n ed by the hypotheses. In addition, articles should include disparate effects, a single explanation coverin g all studies, and a discussion of the use of the research in specific problem contexts. The applic a tion of IBE is illustrated with research investigating when judgments are based on a feeling about t h e ease of information retrieval versus the information content itself