Predicting drunk driving using a variant of the implicit association test

Drunk driving is one of the primary causes of road traffic injuries and fatalities. A possible approach to reduce drunk driving rates is to identify which individuals are at risk of such behavior and establish targeted prevention. Simply asking individuals about drunk driving in real-world contexts would be problematic because of potential deception. The use of implicit measures such as the Implicit Association Test (IAT) could overcome this problem because they are less controllable than self-reports and thus less susceptible to deception. However, previous studies have shown poor predictive utility of implicit measures for drunk driving behavior. The current studies aimed to test the predictive utility of a variant of the IAT designed to assess beliefs about past driving under the influence (the P-DUI-IAT).

Study 1 (N = 216) tested whether the P-DUI-IAT could predict self-reported prior drunk driving and future likelihood of drunk driving. We also examined incremental predictive validity of the P-DUI-IAT for these outcomes. Study 2 (N = 159) examined whether results from Study 1 were reproducible.

In both studies, results showed that the P-DUI-IAT discriminated well between participants who had engaged in drunk driving and participants who had not. The P-DUI-IAT also showed independent and incremental predictive validity for past drunk driving and future likelihood of drunk driving.

These studies provided initial evidence for the predictive utility of the P-DUI-IAT for drunk driving.

The P-DUI-IAT is a promising tool for identifying which individuals are at risk of drunk driving. The application of this measure could especially be valuable for identifying young novice drivers at risk for drunk driving-related accidents.