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Preprints are early versions of research articles that have not been peer reviewed. They should not be regarded as conclusive and should not be reported in news media as established information.
Mixing the implicit: A Linear Mixed-Effects Models approach for a Rasch analysis of the Implicit Association Test and the Single Category Implicit Association Test
preprintposted on 2021-09-10, 16:03 authored by Ottavia M. EpifaniaOttavia M. Epifania, Pasquale Anselmi, Egidio Robusto
The indirect investigation of psychological constructs has become prominent in social sciences thanks to the so-called implicit measures. Different implicit measures can be administered concurrently to the same respondents for obtaining detailed and multifaceted information on the constructs of interest. In this study, a Rasch analysis of accuracy and time responses of two commonly used implicit measures is presented. The focus in on the concurrent administration of the Implicit Association Test (IAT; Greenwald et al., 1998) and the Single Category IAT (SC-IAT; Karpinski & Steinman, 2006). Linear Mixed-Effects Models are used to address the within– and between–measures sources of variability and to obtain a Rasch parametrization of the data. By disentangling the respondent’s contribution from the stimulus contribution to the observed responses, these models allow for delving deeper on the functioning of the IAT and the SC-IAT, as well as for grasping a better understanding of the processes driving a behavioral decision. Implications of the results for social sciences and future research directions are discussed.
Declaration of conflicts of interestThe authors have no conflict of interests to disclose.
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