The individual-level precision of implicit measures

Implicit measures are used extensively in psychological science. One fundamental goal of these measures is to provide information diagnostic of an individual's attitudes or beliefs. After 25 years of research, this goal has not been achieved. We argue that this is because psychologists have not yet even quantified the individual-level precision of implicit measures, much less calibrated them to it. In this paper, we examine the individual-level precision of six different implicit measures across three different attitude domains (race, politics, and self-esteem) using a very large open dataset. Despite some variation, we find that there is substantial room for improvement for the precision of implicit measures as measures of individual attitudes. We recommend that researchers who wish to make theoretical inferences about individuals directly quantify individual-level precision to calibrate their tasks appropriately, both in the context of implicit measures and with tasks in psychological science more broadly.