Enhancing the Implicit Association Test: A Four-Step Model to Find Appropriate Stimuli

Gerhard Brenner (), Monika Koller () and Peter Walla ()
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Gerhard Brenner: University of Applied Sciences
Monika Koller: WU Vienna
Peter Walla: Webster Vienna Private University

A chapter in Information Systems and Neuroscience, 2019, pp 111-118 from Springer

Abstract: Abstract The Implicit Association Test (IAT) is a promising tool to assess implicit attitudes. Next to neuroscientific methods applied within the field of NeuroIS the IAT helps to overcome limits of traditional approaches, such as self-report studies. Introduced 20 years ago, it has been developed further within subsequent years. However, hardly any attention has been paid to optimize the stimuli sets. This is unfortunate, as if the time span participants need to decode the stimuli varies across the IAT, or if the subjects do not understand the stimuli equally, reaction times can be biased. As an IAT includes 120 measuring points per subject such biases might potentiate across all participants. The results might be biased and neither the researchers nor the participants would recognize such confounding effects. Thus, we focus on the time span between stimulus onset and response and develop a four-step model to create an optimized stimuli set including (1) brainstorming, (2) forming & performing (i.e. pretesting), (3) backward-brainstorming and (4) informing & interviewing.

Keywords: Implicit Association test; Implicit measures; Indirect measures (search for similar items in EconPapers)
Date: 2019
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-030-01087-4_13

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DOI: 10.1007/978-3-030-01087-4_13

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