Implicit measures were initially assumed to assess stable individual differences, but other perspectives posit that they reflect context-dependent processes. This pre-registered research investigates whether the processes contributing to responses on the race Implicit Association Test are temporally stable and reliably measured using multinomial processing tree modeling. We applied two models-the Quad model and the Process Dissociation Procedure-to six datasets (N = 2,036), each collected over two occasions, examined the within-measurement reliability and between-measurement stability of model parameters, and meta-analyzed the results. Parameters reflecting accuracy-oriented processes demonstrate adequate stability and reliability, which suggests these processes are relatively stable within individuals. Parameters reflecting evaluative associations demonstrate poor stability but modest reliability, which suggests that associations are either context-dependent or stable but noisily measured. These findings suggest that processes contributing to racial bias on implicit measures differ in temporal stability, which has practical implications for predicting behavior using the Implicit Association Test.

Recent advances in large-scale data collection have created new opportunities for psychological scientists who study intergroup bias. By leveraging big data, researchers can aggregate individual measures of intergroup bias into regional estimates to predict outcomes of consequence. This small-but-growing area of study has already impacted the field with well-powered research identifying relationships between regional intergroup biases and societally-important, ecologically-valid outcomes. In this chapter, we summarize existing regional intergroup bias research and review relevant theoretical perspectives. Next, we present new and recent evidence that cannot be explained by existing theory, and offer a new perspective on regional intergroup bias that highlights aggregation as changing its’ qualitative nature relative to individual intergroup bias. We conclude with a discussion of some of the important challenges that regional intergroup bias research will need to address in moving forward, focusing on issues of prediction and causality; constructs, measures, and data sources; and levels of analysis