In previous sections, we saw that implicit measures of prejudice were not consistent predictors of behavior, a conclusion in line with meta-analyses documenting relatively weak associations between implicit measures and behavior (Greenwald et al., 2009, 2015; Kurdi et al., 2018; Oswald et al., 2015). If we take implicit bias scores too literally, this can lead to the labeling of some people as prejudiced when they do not manifest prejudiced behavior. Some observers wonder whether such mismatch instances are the results of base rate knowledge.

Over the last several years, the study of implicit bias has taken the world by storm. Implicit bias was even mentioned by the then candidate, Hillary Clinton, in a presidential debate in 2016. She went on to claim that implicit bias can have deadly consequences when Black men encounter law enforcement (for example, see Correll et al., 2002; Correll et al., 2007; Eberhardt et al., 2004). The controversy over police shootings of Black men and women has only intensified as evidenced by public outcry over the murder of George Floyd on May 25, 2020 and increasing public support for the “Black Lives Matter” movement and its calls for liberty, justice, and freedom (Cohn & Quealy, 2020). These current events are but one reason why the study of implicit bias has so captivated the attention of the larger public: reducing it seems to have the potential to solve real-world problems. One idea is that if police officers were made aware of their implicit bias or participated in training workshops to reduce implicit bias, then perhaps fewer Black people would end up dead, arrested, or disproportionately sentenced to receive the death penalty (Baumgartner et al., 2014; Eberhardt, 2020).

This chapter traces the development of the concept of “symbolic racism,” now more commonly known as “racial resentment,” using explicit measures, unlike the implicit biases featured in other chapters. It was first introduced in a survey about the 1969 Los Angeles mayoral election, as a new form of white racial prejudice, more common and more politically powerful than the “old-fashioned racism” of the prior century, especially in white suburbs and outside the old South. I begin with the historical context of the time, as influenced by national events, the local political situation, and my personal background and that of my principal collaborators. I closely examine the original research as it appeared over the next decade, which seems to have focused more on rejecting the role of traditional racial prejudice than on fully developing the idea of a new racism. The growing clarification of the conceptualization and measurement of the new racisms over the next two decades is described. The case is made for its great, and increasing, utility for understanding the politics of the white mass public over the last half-century. I describe the main critiques of this research and our rejoinders and comment on the acrimony of these controversies.

The last two decades have been marked by excitement for measuring implicit attitudes and implicit biases, as well as optimism that new technologies have made this possible.  Despite considerable attention, this movement is marked by weak measures.  Current implicit measures do not have the psychometric properties needed to meet the standards required for psychological assessment or necessary for reliable criterion prediction. Some of the creativity that defines this approach has also introduced measures with unusual properties that constrain their applications and limit interpretations.  We illustrate these problems by summarizing our research using the Implicit Association Test (IAT) as a case study to reveal the challenges these measures face. We consider such issues as reliability, validity, model misspecification, sources of both random and systematic method variance, as well as unusual and arbitrary properties of the IAT’s metric and scoring algorithm. We then review and critique four new interpretations of the IAT that have been advanced to defend the measure and its properties. We conclude that the IAT is not a viable measure of individual differences in biases or attitudes. Efforts to prove otherwise have diverted resources and attention, limiting progress in the scientific study of racism and bias.

In April 2018, Starbucks closed all of its branches in the US and required some 175,000 employees to participate in a four-hour training session on implicit bias. Although this was undoubtedly well-meaning, the devoting of substantial resources to such an effort seems wisest if empirical evidence indicates that such training is effective. But in fact, the majority of evaluations of attempts to change implicit bias have shown no lasting effects (Forscher et al., 2019).

In the last two decades, neuroscience studies have suggested that various psychological phenomena are produced by predictive processes in the brain. When considered together, these studies form a coherent, neurobiologically inspired program for guiding psychological research on a variety of topics, including implicit attitudes and their relation to behaviors.

The explosion of attention to measuring and understanding implicit bias has been influential inside and outside the academy. The purpose of this chapter is to balance the conversation about how to unpack and understand implicit bias, with an exploration of what we know about Whites’ explicit bias, and how surveys and other data can be used to measure it. This chapter begins with a review of survey-based data on White racial attitudes that reveal complex trends and patterns, with some topics showing changes for the better, but others showing persistent negative or stagnant trends. Drawing on examples using a variety of methodological tools, including (1) traditional survey questions; (2) survey-based mode/question wording experiments; (3) open-ended questions embedded in surveys; and (4) in-depth interviews, I illustrate what explicit racial biases can look like, and how they might be consequential. I argue that a full understanding of intergroup relations requires sophisticated methods and theories surrounding both explicit and implicit biases, how they function separately and in combination, and their causes and consequences.

This chapter reviews research on a contemporary form of prejudice – aversive racism – and considers the important role of implicit bias in the subtle expressions of discrimination associated with aversive racism. Aversive racism characterizes the racial attitudes of a substantial portion of well-intentioned people who genuinely endorse egalitarian values and believe that they are not prejudiced but at the same time possess automatically activated, often nonconscious, negative feelings and beliefs about members of another group. Our focus in this chapter is on the bias of White Americans toward Black Americans, but we also discuss relevant findings in other intergroup contexts. We emphasize the importance of considering, jointly, both explicit and implicit biases for understanding subtle, and potentially unintentional, expressions of discrimination. The chapter concludes by discussing how research on aversive racism and implicit bias has been mutually informative and suggests specific promising directions for future work.

The attentive public widely believes a false proposition, namely, that the race Implicit Association Test (“IAT”) measures unconscious bias within individuals that causes discriminatory behavior. We document how prominent social psychologists created this misconception and the field helped perpetuate it for years, while skeptics were portrayed as a small group of non-experts with questionable motives. When a group highly values a goal and leaders of the group reward commitment to that goal while marginalizing dissent, the group will often go too far before it realizes that it has gone too far. To avoid the sort of groupthink that produced the mismatch between what science now knows about the race IAT and what the public believes, social psychologists need to self-consciously embrace skepticism when evaluating claims consistent with their beliefs and values, and governing bodies need to put in place mechanisms that ensure that official pronouncements on policy issues, such as white papers and amicus briefs, are the product of rigorous and balanced reviews of the scientific evidence and its limitations.

The implicit revolution seems to have arrived with the declaration that “explicit measures are informed by and (possibly) rendered invalid by unconscious cognition.” What is the view from survey research, which has relied on explicit methodology for over a century, and whose methods have extended to the political domain in ways that have changed the landscape of politics in the United States and beyond? One survey researcher weighs in. The overwhelming evidence points to the continuing power of explicit measures to predict voting and behavior. Whether implicit measures can do the same, especially beyond what explicit measures can do, is far more ambiguous. The analysis further raises doubts, as others before have done, as to what exactly implicit measures measure, and particularly questions the co-opting among implicit researchers the word “attitude” when such measures instead represent associations. The conclusion: Keep your torches at home. There is no revolution.

Mediation analysis practices in social and personality psychology would benefit from the integration of practices from statistical mediation analysis, which is currently commonly implemented in social and personality psychology, and causal mediation analysis, which is not frequently used in psychology. In this chapter, I briefly describe each method on its own, then provide recommendations for how to integrate practices from each method to simultaneously evaluate statistical inference and causal inference as part of a single analysis. At the end of the chapter, I describe additional areas of recent development in mediation analysis that that social and personality psychologists should also consider adopting I order to improve the quality of inference in their mediation analysis: latent variables and longitudinal models. Ultimately, this chapter is meant to be a kind introduction to causal inference in the context of mediation with very practical recommendations for how one can implement these practices in one’s own research.