Abstract
A relative assessment of implicit biases is limited because it produces a combined summary evaluation of two attitudinal beliefs while concealing the biases driving this evaluation. Similar limitations occur for relative explicit measures. Here, we will discuss the benefits and weaknesses of using relative versus absolute (individual/separate) assessments of implicit and explicit attitudes. The Implicit Association Test (IAT) will be the focal implicit measure discussed, and we will present a new perspective challenging the evidence that the IAT can only be utilized to measure relative, not absolute, implicit attitudes. Modeling techniques (i.e., Quad models) that can determine the separate biases behind the relative summary evaluation will also be considered. Accurately utilizing absolute implicit bias scores will enable academia and industry to answer more complex research questions. For implicit social cognition to maintain and expand its usefulness, we encourage researchers to further test and refine the measurement of absolute implicit biases.
Highlights
A relative assessment of implicit biases is limited because it produces a combined summary evaluation of two attitudinal beliefs while concealing the biases driving this evaluation
Despite contributing important advancements in academia and industry, we argue that the Implicit Association Test (IAT), and implicit measures in general, will become less relevant and useful due to the need to tackle more complex research questions
Multinomial processing trees (MPTs) have been applied to a wide variety of implicit measures with the Quad model (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005) and the ReAL model (Meissner & Rothermund, 2013) previously being applied to IAT data
Summary
Multinomial processing trees (MPTs: Batchelder & Riefer, 1999) are one prominent class of methods to separate attitudes into their component evaluations. MPTs have been applied to a wide variety of implicit measures (for reviews, see Erdfelder et al, 2009, and Hütter & Klauer, 2016) with the Quad model (Conrey, Sherman, Gawronski, Hugenberg, & Groom, 2005) and the ReAL model (Meissner & Rothermund, 2013) previously being applied to IAT data. Comparable to standard reaction time or error rate calculations, future research should test the impact of the contrasting category when using MPTs. Quad modeling has shown that the effects identified by relative IAT scores mainly reflect differences in control-oriented processes (i.e., an individuals’ ability to overcome a bias) rather than being solely due to attitude evaluations (e.g., Gonsalkorale, Sherman, & Klauer, 2009, 2014). Future research would benefit from testing when, why, and for whom the Quad or ReAL model parameters converge or diverge with other absolute implicit attitude estimates such as the DD-scores
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