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.

This chapter provides a categorization of mathematical and computational models, and discusses the purposes they serve and criteria for evaluating models. Models considered include statistical models, descriptive models, measurement models, structural models, baseline models, and models that provide theoretical accounts at different levels of theoretical analysis. Models serve to provide concise summaries of data, to provide theoretical accounts of data, to discriminate between competing theoretical accounts, and to provide measures of latent psychological variables and upper and lower baselines against which to contrast observed behavior. Criteria for evaluating models comprise goodness of fit in relation to model flexibility, consistency across applications, competitiveness, psychological validation, and generativity. Three social psychological models exemplify these issues, a Bayesian marginal model of pseudocontingencies, a source-monitoring model of illusory correlations, and the dynamic interactive model of person construal.

In this chapter we focus on associations between intrusive parenting, the parent–adolescent relationship, and adolescents’ information management strategies. Theoretically, parenting that threatens adolescents’ autonomy leads to suboptimal adolescent adjustment. We discuss when overprotective parenting, psychological control, behavioral control, and helicopter parenting may be intrusive and how they are associated with the parent–adolescent relationship and adolescent information management. We also consider parental intrusiveness and adolescents’ information management in two specific contexts, namely in relation to adolescents’ sexuality and media use. We suggest that an intrusive parenting environment is not the optimal way to promote healthy adolescent information management.

Social and personality psychologists have conducted surveys and experiments online for nearly twenty-five years. Researchers have used the Internet to ask questions about a wide range of topics, including racial bias, personality development, and attitude change. The frequency of conducting internet research has increased over time and understanding how to conduct online research has become a critical skill for psychologists. This chapter provides a general introduction to conducting survey and experimental research online. We outline how researchers can host and program internet studies, as well as their options for recruiting participant samples. We also cover important issues that researchers should consider about data quality, representativeness, generalizability, and upholding ethical standards. Throughout the chapter we discuss practices and guidelines that we view as optimal at the current time, and direct readers to additional literature that can further inform their thinking.

The process of questionnaire design has been done intuitively by investigators for decades despite a large literature being available to guide the process to yield maximally reliable and valid measurement tools. This chapter offers two conceptual frameworks involving (1) the cognitive processes involved in answering questions optimally, and (2) conversational conventions that govern everyday communication. We use these frameworks to explain a range of empirical evidence documenting the impact of question manipulations on responses. Topics covered include open vs. closed questions, rating vs. ranking, rating scale length and scale point labels, acquiescence response bias, multiple select questions, response order effects, treatment of non-substantive response options, social desirability response bias, question wording and order, questionnaire length, and considerations for internet surveys. In all, we provide a set of best practices that should be useful to all researchers.

This chapter presents a broad overview of the measurement of hormones, spanning from their collection in different biospecimens and the assay of hormones across laboratory strategies to a brief overview of statistical treatment and analysis that extracts the hormone of interest. We organize each section into a description of measurement tools followed by an agnostic analysis of the tools for their strengths, weaknesses, prospects, and pitfalls. We do not view any single approach as “best” or “optimal.” This view is commensurate with the production and cellular conversion of hormones – adaptive physiological processes that are not “best” or “optimal” but rather constantly changing biobehavioral markers that shift according to the demands of the environment. Measuring the hormone is just the beginning of exploring the multifaceted ways that hormones can inform health, development, morbidity, and mortality.

Over-time, repeated measures, or longitudinal data are terms referring to repeated measurements of the same variables within the same unit (e.g., person, family, team, company). Longitudinal data come from many sources, including self-reports, behaviors, observations, and physiology. Researchers collect repeated measures for a variety of reasons, such as wanting to model change in a process over time or wanting to increase measurement reliability. Whatever the reason for data collection, longitudinal methods pose unique challenges and opportunities. This chapter has three main goals: (1) to help researchers consider design decisions when developing a longitudinal study, (2) to describe the different decisions researchers have to make when analyzing longitudinal data, and (3) to consider the unique properties of longitudinal designs that researchers should be aware of when designing and analyzing longitudinal studies. We aim to provide a comprehensive overview of the major issues that researchers should consider, and we also point to more extensive resources.

This chapter challenges the traditional unidirectional view of parental monitoring by presenting a novel theoretical dynamic process model of parent–adolescent communication in which parents and adolescents causally influence each other. A review of empirical studies highlights that adolescents are active agents who strategically manage information from their parents. However, few studies have subjected the frequently hypothesized bidirectional processes to more rigorous within-family tests. Six studies with yearly intervals suggest that parent–adolescent communication about adolescent activities is bidirectionally related to adolescent outcomes. A handful of daily diary studies suggest that adolescents disclose more on days when there is more parental monitoring and when the quality of the relationship is better. What remains to be empirically determined is how real-time and everyday family functioning may explain the development of adolescent functioning. The chapter concludes with a discussion of four potential open questions for future research on transactional monitoring processes.