Sriram and Greenwald (2009) introduced a Brief version of the Implicit Association Test (BIAT). The present research identified analytical best practices for overall psychometric performance of the BIAT. In 7 studies and multiple replications, we investigated analytic practices with several evaluation criteria: sensitivity to detecting known effects and group differences, internal consistency, relations with implicit measures of the same topic, relations with explicit measures of the same topic and other criterion variables, and resistance to an extraneous influence of average response time. Two data transformation algorithms, G and D, outperformed other approaches. This replicates and extends the strong prior performance of D compared to conventional analytic techniques (Greenwald, Nosek, & Banaji, 2003), and introduces G as a strong analytic method with minimal data treatment. We conclude with recommended analytic practices for standard use of the BIAT.