Understanding and Using the Implicit Association Test: I. An Improved Scoring Algorithm

In reporting Implicit Association Test (IAT) results, researchers have most often used scoring conventions described in the first publication of the IAT (A. G. Greenwald, D. E. McGhee, & J. L. K. Schwartz, 1998). Demonstration IATs available on the Internet have produced large data sets that were used in the current article to evaluate alternative scoring procedures. Candidate new algorithms were examined in terms of their (a) correlations with parallel self-report measures, (b) resistance to an artifact associated with speed of responding, (c) internal consistency, (d) sensitivity to known influences on IAT measures, and (e) resistance to known procedural influences. The best-performing measure incorporates data from the IATʹs practice trials, uses a metric that is calibrated by each respondentʹs latency variability, and includes a latency penalty for errors. This new algorithm strongly outperforms the earlier (conventional) procedure.