ABSTRACT
In this chapter we critically evaluate the ability of IAT scores to explain real world racial inequality. This is especially difficult to do because there are so many conceptual, psychometric, and validity issues related to the IAT, which we review before tracking the main question of accounting for gaps. That review indicates that the IAT is not a clean measure of either implicit biases or of associations. Nonetheless, several meta-analyses have shown that IAT scores predict discrimination to at least a modest extent. To address its ability to account for racial gaps, we work backward. Given the size of some gaps, how much of that is likely to be explained by IAT scores? Although we do not answer the question quantitatively, we present a series of heuristic models intended to provide increased clarity about the likely range of possibilities. Alternative explanations for gaps are briefly reviewed, and our models assume that IAT scores can only explain what is left over, which is likely to be only a modest portion of those gaps. We conclude by arguing that, despite its limitations, the IAT should not be abandoned, but that, even after 20 years, much more research is needed to fully understand what the IAT measures and explains.
