What do fakers actually do to fake the IAT? An investigation of faking strategies under different faking conditions

Röhner, Jessica; Schröder–Abé, Michela; Schütz, Astrid

doi:10.1016/j.jrp.2013.02.009

Cited by 76 publications

(154 citation statements)

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“…The standard deviations in the faking condition were considerably larger than other conditions by 27% in Study 1, 44% in Study 2, 36% in Study 3, and 40% in Study 4 making it easy to detect compared to "real" interventions that presumably altered the activation or expression of implicit preferences (see Table 2). In this context, actual interventions shifting activation and expression of implicit preferences could outperform task manipulation through faking, and the latter is detectable (as "not real") through increases in variability (Cvencek et al, 2010;Röhner, Schröder-Abé, & Schütz, 2013). This way, mechanism research can focus on the particular combinations that are effective rather than the inefficiently examining all possible combinations with maximal experimental control.…”

Section: Notable Features Of Successful Interventionsmentioning

confidence: 99%

Reducing implicit racial preferences: I. A comparative investigation of 17 interventions.

Lai¹,

Marini

Lehr

et al. 2014

Journal of Experimental Psychology: General

531

482

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Many methods for reducing implicit prejudice have been identified, but little is known about their relative effectiveness. We held a research contest to experimentally compare interventions for reducing the expression of implicit racial prejudice. Teams submitted seventeen interventions that were tested an average of 3.70 times each in four studies (total N = 17,021), with rules for revising interventions between studies. Eight of seventeen interventions were effective at reducing implicit preferences for Whites compared to Blacks, particularly ones that provided experience with counterstereotypical exemplars, used evaluative conditioning methods, and provided strategies to override biases. The other nine interventions were ineffective, particularly ones that engaged participants with others' perspectives, asked participants to consider egalitarian values, or induced a positive emotion. The most potent interventions were ones that invoked high self-involvement or linked Black people with positivity and White people with negativity. No intervention consistently reduced explicit racial preferences. Furthermore, intervention effectiveness only weakly extended to implicit preferences for Asians and Hispanics. Abstract = 160 words Keywords = attitudes, racial prejudice, implicit social cognition, malleability, Implicit Association Test September 9, 2016 update: We updated this manuscript to fix several minor reporting errors that we have learned about since the publication of the manuscript in August 2014. For a summary of these updates, please see pages 67 and 68. Authors' note:This project was supported by a gift from Project Implicit. Lai and Hawkins are consultants and Nosek is an officer of Project Implicit, Inc., a non-profit organization that includes in its mission "To develop and deliver methods for investigating and applying phenomena of implicit social cognition, including especially phenomena of implicit bias based on age, race, gender or other factors. Thoughts and feelings outside of conscious awareness shape social perception, judgment and action (Bargh, 1999;Devine, 1989;Greenwald & Banaji, 1995). Nowhere has this idea been more explored than in studies of racial prejudice in which people report egalitarian racial attitudes, but also implicitly prefer Whites compared to Blacks (Devine, 1989;Dovidio, Kawakami, Johnson, Johnson, & Howard, 1997;Fazio, Jackson, Dunton, & Williams, 1995;. These studies have been influential because implicit racial preferences predict behaviors such as negative interracial contact (McConnell & Leibold, 2001), biases in medical decision-making (Green et al., 2007), and hiring discrimination (Rooth, 2010).From the hundreds of studies conducted, we can conclude that implicit preferences (1) are related to, but distinct from, explicit preferences (Greenwald & Banaji, 1995;, (2) are constructed through different mechanisms than explicit preferences (De Houwer, Teige-Mocigemba, Spruyt, & Moors, 2009;Ranganath & Nosek, 2008;Ratliff & Nosek, 2011;Rydell & McConnell, 2006), ...

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Section: Notable Features Of Successful Interventionsmentioning

confidence: 99%

Reducing implicit racial preferences: I. A comparative investigation of 17 interventions.

Lai¹,

Marini

Lehr

et al. 2014

Journal of Experimental Psychology: General

531

482

View full text Add to dashboard Cite

show abstract

“…To convey an impression of the advantage from analyzing IAT results with diffusion model analyses in addition to the D measure, we represent some results from our current study that was examined to investigate the effectiveness of diffusion model analyses with fast-dm in order to analyze and interpret non-faked and faked IAT results (for further information see Röhner and Ewers, 2016; see also Röhner et al, 2013). In Table 1 the impact of faking on the traditional IAT effect (i.e., D measure) and on IAT effects from diffusion model analyses (i.e., IAT v , IAT a , and IAT t0 ) is represented in the form of between and within post hoc comparisons from a total of four repeatedmeasures ANOVAs.…”

Section: Resultsmentioning

confidence: 99%

“…You used experimental software to collect your IAT data under either standard instructions or faking instructions (for further information see Röhner et al, 2013) via computer, you saved the IAT output data (e.g., as *.dat-files), and you downloaded and installed fast-dm on your computer. If you have not already downloaded and installed fast-dm, start with step 0.…”

Section: Before You Can Start: Some Prerequisitesmentioning

confidence: 99%

“…The latter might be of particular concern among researchers as not only has the fakeability of the IAT been well documented by many studies (Fiedler & Bluemke, 2005;Röhner et al, 2011Röhner et al, , 2013, but also, the IAT is often used to measure associations with socially stigmatized or sensitive topics (e.g., pedophilia, racism, stereotypes, and sexism; Greenwald et al, 1998;Agerström & Rooth, 2011;Banse, Schmidt, & Clarbour, 2010;Banse, Seise, & Zerbes, 2001;Carlsson & Björklund, 2010;Gray, Brown, MacCulloch, Smith, & Snowden, 2005;Greenwald & Banaji, 1995;Latu et al, 2011) which might increase the motivation to fake. Thus, a possible and welcome alternative to analyzing and interpreting the results of the IAT effect came from diffusion model analyses that can be applied to analyze the results of fast binary decision tasks, including the IAT (e.g., Klauer, Voss, Schmitz, & Teige-Mocigemba, 2007;Ratcliff, 1978Ratcliff, , 2014van Ravenzwaaij, van der Maas, & Wagenmakers, 2011;A.…”

Section: Introductionmentioning

confidence: 99%

“…Information about erroneous responses is not considered in the parameter estimation. As faking on the IAT sometimes includes the manipulation of errors (Röhner et al, 2013), errors are a relevant source of information. Therefore, although EZ will work for analyzing IAT results in general, it might be problematic to use it for faked IAT data (at least when fakers manipulate their errors).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

How to analyze (faked) Implicit Association Test data by applying diffusion model analyses with the fast-dm software: A companion to Röhner & Ewers (2016)

Röhner¹,

Ewers²

2016

TQMP

Self Cite

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The Implicit Association Test (IAT) is a popular and frequently used measure in research on implicit associations. However, an important drawback of the traditional computation of IAT results with the so-called D measure is that the D measure may verifiably include more than just indications of the implicit associations that should be measured. It can also be contaminated by faking and other sources of variance. The D measure does not differentiate between different sources of variance. With the help of diffusion model analyses, IAT results can be analyzed and interpreted in a more detailed manner because three separable IAT effects (i.e., IAT v , IAT a , and IAT t0 ) can be computed from the parameters from diffusion model analyses. These effects have been assumed to separate faking-and construct-specific variance from each other. Thus, a possible advantage of using diffusion model analyses instead of the traditional IAT effect is that less contaminated and more interpretable IAT effects are produced (i.e., IAT v , which captures the construct-related variance; IAT a and IAT t0 , which capture the faking-specific variance). This paper was written to demonstrate how to use the software fast-dm to compute these three newly developed IAT effects and to describe how to interpret them.

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Indirect Measures of Deviant Sexual Interest

Bartels

Gray

Snowden

2016

The Wiley Handbook on the Theories, Assessment and Treatment of Sexual Offending

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Latency‐ and accuracy‐based indirect measures have become of increasing interest to researchers and clinicians who work with sexual offenders. Most notably, they have been regarded as a useful tool for measuring deviant sexual interests, as they are thought to circumvent many of the issues associated with existing assessment tools. Thus, a number of tasks have been adapted indirectly assess deviant sexual interest and an increasing body of research detailing their utility and psychometric properties has amassed. This chapter examines this literature, providing a comprehensive overview that it is hoped will prove useful for both clinicians and researchers. Specifically, details on how each measure works, their underlying rationale, their validity and reliability, and their limitations are discussed. Suggestions for future research are also offered throughout the chapter.

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What do fakers actually do to fake the IAT? An investigation of faking strategies under different faking conditions

Cited by 76 publications

References 14 publications

Reducing implicit racial preferences: I. A comparative investigation of 17 interventions.

Reducing implicit racial preferences: I. A comparative investigation of 17 interventions.

How to analyze (faked) Implicit Association Test data by applying diffusion model analyses with the fast-dm software: A companion to Röhner & Ewers (2016)

Indirect Measures of Deviant Sexual Interest

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