  A notable advance in the psychometrics of implicit bias has been the advent of multinomial (or formal process) models, which identify distinct processes contributing to performance on implicit measures. For example, elderly people tend to show greater bias on the race-evaluation IAT compared with younger people, but this may be due to their having stronger preferences for whites or having weaker control over their biased responding (Nosek et al. 2011). Multinomial models, like the Quadruple Process Model (Conrey et al. 2005), are used to tease apart these possibilities. The Quad model identifies four distinct processes that contribute to responses: (1) the automatic activation of an association; (2) the subject’s ability to determine a correct response (i.e., a response that reflects one’s subjective assessment of truth); (3) the ability to override automatic associations; and (4) general response biases (e.g., favoring right-handed responses). Multinomial modeling has made clear that implicit measures are not “process pure,” i.e., they do not tap into a single unified psychological process.