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
Abstract
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.