Researchers in cognitive psychology have proposed that there are two distinct cognitive systems or dual processes underlying reasoning: automatic (implicit) processing and effortful (explicit) processing. Multiple measures have since been developed to capture implicit attitudes. However, do these new measures truly capture implicit attitudes? And can these implicit measures be used interchangeably? To answer this question, we investigated the differences between two of the most popular implicit attitudes measures used in the study of political behavior, the Implicit Association Test (IAT) and the Affect Misattribution Procedure (AMP). We examined data from an original survey experiment investigating gender attitudes and a nationally representative survey that measured racial attitudes. We found that it is important to consider implicit measures alongside explicit measures, as they are not redundant measures. However, when implicit attitudes are measured with the IAT, our inferences are more consistent with predictions of dual process accounts. Moreover, the IAT picks up out-group bias in a way that the AMP does not. The two studies point to the presence of significant differences between different types of implicit measures, and a need to reconsider how implicit attitudes are measured.

In this chapter we identify scientific gaps research to date regarding the ability of IAT scores to explain real world racial gaps. We use the term “IAT scores” rather than “implicit bias” because, as we show: (1) Implicit bias has no consensual scientific definition; (2) A definition offered by Greenwald (2017) is shown to be logically incoherent and empirically unjustified; (3) Exactly what the IAT measures remains unclear. Nonetheless, meta-analyses have shown that IAT scores predict discrimination to a modest extent. Alternative explanations for gaps are briefly reviewed, highlighting that IAT scores offer only one of many possible such explanations. We then present a series of heuristic models that assume that IAT scores can only explain what is left over, after accounting for other explanations of gaps. This review concludes that IAT scores probably explain a modest portion of those gaps. Even if the IAT captures implicit biases, and those implicit biases were completely eliminated, the extent to which racial gaps would be reduced is minimal. We conclude by arguing that, despite its limitations, the IAT should not be abandoned, but that, even after twenty years, much more research is needed to fully understand what the IAT measures and explains.

There are widespread assumptions that implicit group bias leads to biased behavior. This chapter summarizes existing evidence on the link between implicit group bias and biased behavior, with an analysis of the strength of that evidence for causality. Our review leads to the conclusion that although there is substantial evidence that implicit group bias is related to biased behavior, claims about causality are not currently supported. With plausible alternative explanations for observed associations, as well as the possibility of reverse causation, scientists and policy makers need to be careful about claims made and actions taken to address discrimination, based on the assumption that implicit bias is the problem.

There are many reasons why the implicit bias construct took root in everyday conversation, but one of them is that millions of people have experienced the most commonly used measure of implicit bias – the Implicit Association Test (IAT; Greenwald, McGhee, & Schwartz, 1998) at the Project Implicit websites. Project Implicit is a non-profit organization and international research collaboration between behavioral scientists interested in implicit social cognition. The organization’s primary public contribution is its education websites (https://implicit.harvard.edu) where more than twenty-eight million IATs have been completed. This chapter provides an overview of Project Implicit and the contributions and challenges of more than twenty years of internet-based data collection on implicit attitudes and stereotypes. The first section describes Project Implicit’s history and organizational structure; next, some of the key insights gleaned from the data collected at the Project Implicit websites are reviewed. These include assessment of the pervasiveness and correlates of implicit bias, comparisons across time and by geographic area, and reactions to learning about one’s own implicit bias. Finally, we reflect on some of the challenges of being uniquely situated between academic researchers and the general public, and describe how changing scientific knowledge has changed scientific communication about implicit bias.

The theatre-audience method is an indirect approach to ‘measuring’ implicit language attitudes by means of a social-psychological experiment, designed in an attempt to escape the ‘artificial’ laboratory-like contexts of the typical matched- and verbal-guise studies. The method works as follows: Over a number of evenings, an invitation to answer a questionnaire is given in different languages or varieties over the loudspeaker system in a theatre, and the varying level of cooperation is measured as the ratio of answered questionnaires to sold tickets – and interpreted in terms of variation in attitudes towards the languages or varieties used in the loudspeaker announcements. This chapter discusses the strengths of this method (e.g. data collection in a ‘natural’ context) and its limitations (e.g. the fact that people in the audience are not the same from evening to evening), and it explains the key practical issues of planning and research design (e.g. considerations regarding the choice of weekdays in order to secure comparability across evenings). Moreover, the chapter explains how to conduct statistical analyses of the data. A case study of attitudes towards varieties of Danish illustrates the main points made in the chapter.

As the use of survey experiments has spread throughout political science, experimental designs have grown increasingly complex. Yet, most survey experiments rest on a basic protocol by which treatments are delivered with textual vignettes and the effects of these interventions are then measured using self-reports of political attitudes or behaviors. We outline several design innovations that allow researchers to move beyond self-reports by directly embedding politically-relevant behaviors into survey experiments. As described in this chapter, these innovations enable experimentalists to strengthen the power of their treatments while enhancing the validity of their measures of treatment effects. We document these advances with illustrations drawn from a wide range of studies focusing on exposure to news reports, party polarization, racial prejudice, and physiological arousal.