RASCH GONE MIXED: A MIXED MODEL APPROACH TO THE IMPLICIT ASSOCIATION TEST

Despite the Implicit Association Test (IAT) is widely used for the implicit assessment of attitudes, the meaning of its effect remains unclear. Literature on the IAT has already highlighted the importance of the stimuli characteristics in influencing the meaning and the validity of the IAT measure. A model providing in-depth information at both respondents and stimuli levels can help in clarifying the meaning of the IAT measure. A modeling framework based on Linear Mixed Effects Models for a fine-grained analysis at both the respondent and the stimulus levels is presented. 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. The information on respondents allows for a better interpretation of the IAT effect. Implications of the results and future research directions are discussed