2016
DOI: 10.20982/tqmp.12.3.p220
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How to analyze (faked) Implicit Association Test data by applying diffusion model analyses with the fast-dm software: A companion to Röhner & Ewers (2016)

Abstract: 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 diffus… Show more

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Cited by 11 publications
(11 citation statements)
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“…Consequently, diffusion models have been applied to a variety of decision tasks such as recognition memory (e.g., Spaniol et al, 2006) and perceptual decisionmaking (e.g., Germar et al, 2014) to name only two here. For detailed introductions to diffusion modeling and tutorials, see, for example, Ratcliff (2014), Röhner and Ewers (2016a), (Voss, Nagler and Lerche, 2013), or Wagenmakers et al (2007). To understand decision-making, diffusion models are comprised of three main parts: v (ease of decision-making), a (response caution), and t 0 (non-decision-related processes) that are reflected in diffusionmodel-based IAT effects (Table 1).…”
Section: Diffusion Model Analysesmentioning
confidence: 99%
“…Consequently, diffusion models have been applied to a variety of decision tasks such as recognition memory (e.g., Spaniol et al, 2006) and perceptual decisionmaking (e.g., Germar et al, 2014) to name only two here. For detailed introductions to diffusion modeling and tutorials, see, for example, Ratcliff (2014), Röhner and Ewers (2016a), (Voss, Nagler and Lerche, 2013), or Wagenmakers et al (2007). To understand decision-making, diffusion models are comprised of three main parts: v (ease of decision-making), a (response caution), and t 0 (non-decision-related processes) that are reflected in diffusionmodel-based IAT effects (Table 1).…”
Section: Diffusion Model Analysesmentioning
confidence: 99%
“…For detailed introductions to diffusion modeling and tutorials, see e.g. Ratcliff (2014), Röhner and Ewers (2016a), , or Wagenmakers et al (2007). To understand decision-making, diffusion models are comprised of three main parts: v (ease of decision-making), a (response caution), and t0 (non-decisionrelated processes), that are reflected in diffusion-model-based IAT effects (Table 1).…”
Section: Diffusion Model Analysesmentioning
confidence: 99%
“…In terms of changing ease of decision-making, the effective interventions ranked from most to least effective by meta-analytic effect size were: vivid counterstereotypic scenario, using interventions had small effect sizes (d's = -0.32 to -0.14). Faking may have been more effective than the rest at changing response caution because the faking manipulation involved the deliberate and intentional choice to adapt speed and accuracy (Röhner & Ewers, 2016).…”
Section: Insights Into Intervention Effectivenessmentioning
confidence: 99%
“…Recent research has highlighted the idea that diffusion model analyses (Klauer, Voss, Schmitz, & Teige-Mocigemba, 2007;Röhner & Ewers, 2016b) or the quad model (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005) are useful for decomposing the processes underlying the IAT effect in a more detailed manner. If you want to add diffusion-model-based IAT effects to the traditional IAT effects, the tutorial by Röhner and Ewers (2016a) as well as the paper by Röhner and Thoss (2018) can provide additional information and a guide for how to do this.…”
Section: Are There Alternatives To Computing Iat Effects?mentioning
confidence: 99%