Cardiovascular measures for social and behavioral research have been historically popular because they are often non-invasive, inexpensive, and capture the dynamic nature of cardiac physiology. Among adults, many measures are static, like height – they do not change over time – but importantly, cardiovascular measures change moment-to-moment. For example, measuring the heart rate is easy and valuable for documenting different health conditions and can be predictive of overall longevity and disease. Electronic medical records provide access to retrospective high-quality cardiac measures; plus, now that consumer wearable devices are ubiquitous, it is even easier to prospectively collect cardiovascular measures that are continuous and automatically obtained. Thus, cardiovascular measures are important metrics of overall health, and their dynamic nature is important to capture with both established and novel scientific instruments. This chapter will focus on physiological measures that validate psychometric data, describe types of cardiovascular measures of health, and present future directions of cardiovascular measures in research.

In this chapter, we discuss the definitions of power and how to interpret power in Null Hypothesis Significance Testing. Next, the main determinants of power are outlined, including the sample size, effect size (and variability), α, and the type of statistical test. Each influence on power is demonstrated with example studies on statistics education and data literacy. Different types of power analyses, planning for sample sizes and sensitivity, are illustrated using power tables, popular programs, simulation, and accuracy in parameter estimation. Last, the limitations of power – especially what it does not tell you and what you should not do – are outlined to warn you about the potential misuses of power analyses. Suggestions on appropriate power planning are provided at the end of the chapter.

Electroencephalography (EEG) and its measures, such as event-related brain potentials (ERPs) and time-frequency analysis (TFA), are powerful tools for investigating cognitive and behavioral processes in humans and therefore are increasingly attracting attention in the social and behavioral sciences. This chapter has been written for readers who are interested in getting involved in EEG research or who may already have some experience and wish to expand their toolbox of EEG methods. It aims to address both needs by providing a brief overview of human electrophysiology, with new users in mind, followed by a discussion of common challenges and typical applications. We conclude by describing current trends and potential for future developments.

In adolescence, an important challenge for parents is to keep track of their adolescents’ behaviors and to create conditions in which adolescents disclose relevant information about themselves. According to Self-Determination Theory (SDT), dynamics of autonomy play a central role in both the effectiveness of parental monitoring and adolescents’ willingness to disclose toward parents. This chapter provides a review of SDT-based studies on parental monitoring and adolescent disclosure. This research begins to show that, whereas autonomy-supportive communication increases the potential benefits associated with parental monitoring, controlling communication of monitoring is rather counterproductive. Further, adolescents disclose more often toward parents and do so more willingly when parents are perceived as autonomy supportive (rather than controlling). In conversations about unfamiliar topics, adolescents additionally benefit from parental support for competence (i.e. guidance). Studies also highlight adolescents’ agency in the dynamics of monitoring and disclosure. Implications for practice and directions for future research are discussed.

This chapter offers a broad review of why replication is important to science by considering all aspects of the construct from definition to publication. The chapter introduces critical considerations about how to discuss replication given the complexity of its meaning as well as the challenges in conducting and interpreting it. Additionally, the chapter describes why replications are critical for any single construct as well as their contribution to generalizability across scientific disciplines. By conducting high-quality replications before and after effects are published, researchers can remain confident in their contributions to science.

Electrodermal activity (EDA) is a conductance measure that can be used to assess the sympathetic nervous system arousal and for the diagnosis of stress, pain, sleepiness, seizure prediction, neuropathies, depression, and other states. EDA has potential for ambulatory research applications, as it can be collected using wearable devices, but motion artifacts are an issue. While EDA was discovered in 1879 by Vigouroux, the signal was traditionally observed in most of the studies as the mean value of the signal in response to a given stimulus, which provides static information but does not account for time-varying dynamics of the signal. The new technologies for EDA collection and the development of novel and robust signal processing algorithms have increased the interest in EDA for many new and emerging fields, including affective computing, seizure prediction, and pain monitoring. We aim to summarize the characteristics of EDA, describe current and future applications, and outline challenges when using EDA.

In most social psychological studies, researchers conduct analyses that treat participants as a random effect. This means that inferential statistics about the effects of manipulated variables address the question whether one can generalize effects from the sample of participants included in the research to other participants that might have been used. In many research domains, experiments actually involve multiple random variables (e.g., stimuli or items to which participants respond, experimental accomplices, interacting partners, groups). If analyses in these studies treat participants as the only random factor, then conclusions cannot be generalized to other stimuli, items, accomplices, partners, or groups. What are required are mixed models that allow multiple random factors. For studies with single experimental manipulations, we consider alternative designs with multiple random factors, analytic models, and power considerations. Additionally, we discuss how random factors that vary between studies, rather than within them, may induce effect size heterogeneity, with implications for power and the conduct of replication studies.

This chapter highlights the transactional nature of associations between parent behaviors and adolescent information management with a focus on the role of interpersonal emotion dynamics. We argue that timing is an important, yet understudied, aspect of this transactional process. We further focus on how parental empathy is a key way in which parents might encourage adolescent disclosure. We conclude with some directions for future research, including greater attention to cultural values in parenting and information management, and highlight some implications of research in this area.

Not all empirical investigations require fresh data collection. Abundant sources of data, amenable for systematic evaluation, already exist in the form of archival data. Meaning may be extracted from a variety of data sources surrounding us (primary archival data – e.g., obituaries, blogs, yearbooks) or from data previously collected by other researchers (secondary archival data – e.g., computerized archives). Examples of both primary and secondary archival data are provided, with an emphasis on qualitative sources. Common techniques for analyzing qualitative data are then described, and a suggested set of steps for proceeding with a study employing archival data is detailed. The chapter ends with a list of important qualitative secondary archival data sets.

As people migrate to digital environments they produce an enormous amount of data, such as images, videos, data from mobile sensors, text, and usage logs. These digital footprints documenting people’s spontaneous behaviors in natural environments are a gold mine for social scientists, offering novel insights; more diversity; and more reliable, replicable, and ecologically valid results.

Adolescents’ ability to access health care depends on sharing accurate information about concerns, needs, and conditions. Parents and other adults serve as both resources and gatekeepers in adolescents’ ability to access and manage care. Understanding information sharing between adolescents and parents, adolescents and providers, and parents and providers is thus critical. This chapter distinguishes between adolescents’ routine and self-disclosure of information. The former refers to sharing information required for the partner to perform their role. The latter refers to voluntarily sharing more information than required. Because the roles of parent and provider are distinct relative to the adolescent, disclosure decisions can conflict. These differences are discussed in the context of communication privacy management theory and the literature on legitimacy of authority. A framework for understanding information sharing processes is developed that considers stage of care, type of care, stigma/privacy associated with the condition, and the age of the adolescent.

Various areas in psychology are interested in whether specific processes underlying judgments and behavior operate in an automatic or nonautomatic fashion. In social psychology, valuable insights can be gained from evidence on whether and how judgments and behavior under suboptimal processing conditions differ from judgments and behavior under optimal processing conditions. In personality psychology, valuable insights can be gained from individual differences in behavioral tendencies under optimal and suboptimal processing conditions. The current chapter provides a method-focused overview of different features of automaticity (e.g., unintentionality, efficiency, uncontrollability, unconsciousness), how these features can be studied empirically, and pragmatic issues in research on automaticity. Expanding on this overview, the chapter describes the procedures of extant implicit measures and the value of implicit measures for studying automatic processes in judgments and behavior. The chapter concludes with a discussion of pragmatic issues in research using implicit measures.

In this chapter we review advanced psychometric methods for examining the validity of self-report measures of attitudes, beliefs, personality style, and other social psychological and personality constructs that rely on introspection. The methods include confirmatory-factor analysis to examine whether measurements can be interpreted as meaningful continua, and measurement invariance analysis to examine whether items are answered the same way in different groups of people. We illustrate the methods using a measure of individual differences in openness to political pluralism, which includes four conceptual facets. To understand how the facets relate to the overall dimension of openness to political pluralism, we compare a second-order factor model and a bifactor model. We also check to see whether the psychometric patterns of item responses are the same for males and females. These psychometric methods can both document the quality of obtained measurements and inform theorists about nuances of their constructs.