In contrast to most scientific research that goes largely unrecognized by the general public, the concept of implicit bias broke through into the public sphere. This success comes with the challenge that academic nuances and clearly stated limitations often get lost in translation. Moreover, given ongoing scientific debates about what implicit bias is and how to measure it, perhaps the phenomenon got out into public consciousness before scientists have fully understood it.

In the study of racial prejudice in America, symbolic racism (and its close cousin, racial resentment) has been especially successful at predicting evaluations of race-related policies, evaluations of African-American politicians, voting behavior, and much more. This paper tests a proposal made by the theory of symbolic racism about the origin of racial prejudice: that symbolic racism is a blend of anti-Black affect and the perception that Black people violate traditional American values. Analyzed using a new approach that more fully meets the conceptualization of value-violation beliefs than in past research, data from college students and from a representative national sample of Americans disconfirmed the blend hypothesis. Instead, the data are consistent with a mediational chain: beliefs that Black people violate traditional values mediate the effect of anti-Black affect on responses to symbolic racism items, which, in turn, shape people’s attitudes toward racial policies. Thus, the previously suggested “blending” of proposed ingredients appears to be mediational rather than interactive or synergistic. These findings cast new light on the origins of symbolic racism.

The assessment of racial attitudes remains central to social science research, yet researchers differ widely in how they are measured. There is an ongoing debate over whether it is possible to assess racial attitudes, directly leading some researchers to develop measures of new racism such as modern racism and others to abandon the explicit assessment of racial negativity altogether in favor of implicit measures. Nonetheless, explicit measures of racial negativity remain pervasive in social and political psychological research. But unlike implicit attitudes, there is no consensus on the best way in which to measure them. In this chapter, we document current diversity in the measurement of explicit racial attitudes and demonstrate that component scale items can be divided empirically into three distinct concepts. Not all three concepts clearly reflect racial animosity, however. We map these three concepts onto racial resentment, a widely used measure of new racism, to demonstrate its questionable status as a measure of racial negativity. We conclude by suggesting the adoption of overt racism measures in psychological race-related research and urge for greater uniformity in the assessment of explicit racial attitudes.

Scholars have long recognized that successful prediction of behavior on the basis of explicit attitudes depends on the correspondence between the attitude measure and the focal behavior. Fishbein and Ajzen (2010) argued that behaviors vary in terms of their action, target, context, and time, and that the prediction of specific behaviors is greatly enhanced when explicit attitude measures reflect these features of the to-be-predicted behavior. We argue that the same principle applies in the case of predicting behavior from implicit attitudes, and we review relevant evidence relating to each of Fishbein and Ajzen’s parameters. Special attention is paid to the target parameter, given increasing awareness of the intersectional nature of bias. A global race bias may not extend equally to all members of a particular racial identity, and cross-cutting factors such as gender, age, or sexuality may qualify the extent to which global measures of race bias predict discriminatory behavior toward particular individuals.

Eight methodological issues relevant to improving the quality of research on implicit attitudes are considered. These include (1) formulating and implementing strong psychometric models for implicit attitude measures, (2) using modern theories of explicit attitudes as a base from which to test key propositions about implicit attitudes, (3) using sound psychometric practices to assess explicit attitudes, (4) addressing the problem of endogeneity, (5) addressing ecological fallacies when pursuing aggregate analyses of implicit attitudes, (6) evaluating the magnitude of effect sizes, (7) using structural equation modeling in implicit attitude research, and (8) using proper moderation analysis and incremental explained variance analysis in meta-analyses. The central elements of each problem are described and recommendations for addressing them are provided.

How do implicit and explicit racial attitudes compare in their ability to predict political attitudes and behaviors? Data from existing studies suggest that implicit measures may be less relevant than explicit ones for predicting vote choice. This chapter replicates that result using data from 2008 and 2012 and considers whether the dominance of explicit measures in this domain can be attributed to the fact that voting is a highly considered action, wherein individuals may have taken steps to mitigate their own biases. To assess this, we use nationally representative panel survey data to examine whether the relative dominance of explicit measures over the Affect Misattribution Procedure was similarly true across the campaign season and for alternative outcomes that may have encouraged less cognitive control than voting. Results indicate that explicit measures were more predictive for the vast majority of political outcomes. This raises questions about the added value of considering implicit measures in addition to explicit ones when measuring political attitudes and behaviors.

Implicit measures were introduced to explain phenomena that are characterized by a gap between self-reported attitudes and behavior. Recent meta-analyses revealed, however, that implicit measures have only limited predictive validity that goes beyond self-reports. We identify possible reasons for this failure: (a) A lack of validity that is due to the influence of extraneous processes, (b) a focus on evaluation instead of motivation, (c) a focus on associations instead of propositional beliefs, and (d) a focus on global instead of context-dependent attitudes. Recent developments in the field of implicit measures addressed these problems: (a) Statistical process models increase the internal validity of implicit measures, (b) implicit measures of wanting have the potential to predict behavior better than implicit measures of liking, (c) new paradigms provide measures of automatically activated attitudes for propositions that have an unambiguous interpretation, and (d) assessment of context-dependent beliefs is better suited to predict specific behaviors. Incorporating these developments into research on implicit bias will help to realize the initial expectations of describing, explaining, and predicting behavior in many situations.

While schema theory motivated the original measures of automatic cognitive associations between constructs in memory, researchers soon modified these to explore a different domain: implicit attitudes about social groups that elude standard self-reports. As the so-called implicit attitude revolution gained steam, the original measurement goal got much less attention, especially in political science. We believe the schema concept – automatic cognitive associations between features of an attitude object – continues to hold great value for political psychology. We offer a retrofit of the popular implicit association test (IAT), one more efficient than many lexical tasks, to tap these associations in surveys. The new technique captures the degree to which citizens link ideas about ostensibly group-neutral policies to specific social categories. We use this measurement strategy to explore the psychological mechanisms underlying group centrism in politics, an effort that has been largely abandoned due to measurement difficulties. Results from four studies offer practical suggestions about the application of implicit measures for capturing the automatic ways people link groups to important political objects. We conclude by discussing the broader promise of implicit measurement of group schemas, not just implicit affect, for political psychology.

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).