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.

The Implicit Association Test (IAT) is one of the most commonly used measures for the implicit assessment of preferences and attitudes. Nevertheless, the meaning of the IAT effect remains unclear. Since the IAT is based on the speed and accuracy with which different stimuli are categorized in the category to which they belong, the sense of the IAT measure is strongly related to the functioning of the stimuli used to represent the categories. Thus, a model providing a fine-grained analysis at both respondents and stimuli levels would help in clarifying and interpreting the IAT measure. By applying Linear Mixed Effects Models to IAT accuracy and time responses, this study aims to present a modeling framework for IAT data resulting in such a detailed analysis at both respondents and stimuli levels. The proposed models provide a detailed picture of the contribution of each stimulus to the IAT effect, allowing for the identification of malfunctioning stimuli that can be eliminated or substituted to obtain better performing IATs. Additionally, the detailed information on respondents’ performance yields a better understanding of the classic measure of the IAT effect (i.e., the D score). Implications of the results and future research directions, also implying different implicit procedures, are discussed

The advent of implicit measures opened the access to processes of which people might not be completely aware but that can still influence their attitudes, preferences, and behaviors towards different objects. Among the existing implicit measures, the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) is one of the most studied and used. The descriptive literature review presented in this work was aimed at providing an overview of how the IAT has been used from the year of its first introduction until current days. Specifically, the main fields of application of the IAT, the specific topics for which it has been used, and its concurrent use with other implicit measures have been highlighted and described. When possible, information on the samples on which the studies were carried out are reported. Results indicate an on-going growth of the IAT in a constantly wider range of topics. The ability of the IAT to overcome self-presentation biases and to access the implicit aspects of attitudes have been particularly exploited for investigating biases towards different out-groups, especially in sensitive contexts.