Crowdsourcing the Implicit Association Test: Limitations and Best Practices

Abstract

Although the use of crowdsourced online panels for behavioral data collection is commonplace in media and advertising research, only recently have software advancements made it possible for researchers to easily collect implicit measures online. Motivated by the recent decline in MTurk data quality and a dearth of literature examining the use of Implicit Association Tests with crowdsourced samples, we investigate cross-sectional data from eight IAT studies conducted using various samples (Mturk, online undergraduate students, and undergraduate behavioral labs). We document relative rates of participant inattention, non-naivety, and lack of motivation between crowdsourced and traditional samples and demonstrate the ramifications of these threats to the reliability and validity of IAT results. Finally, we build on these insights to outline best practices for crowdsourcing implicit measures in advertising and media research.