Adequate measurement of psychological phenomena is a fundamental aspect of theory construction and validation. Forming composite scales from individual items has a long and honored tradition, although, for predictive purposes, the power of using individual items should be considered. We outline several fundamental steps in the scale construction process, including (1) choosing between prediction and explanation; (2) specifying the construct(s) to measure; (3) choosing items thought to measure these constructs; (4) administering the items; (5) examining the structure and properties of composites of items (scales); (6) forming, scoring, and examining the scales; and (7) validating the resulting scales.

US Latinx adolescents strongly endorse familism, a salient cultural value characterized by close family relationships, interdependence between family members, and the prioritization of family over self. Cultural values, like familism, can serve as cultural scripts that inform behaviors, such as Latinx adolescents’ routine and self-disclosure. In this chapter, we examine routine and self-disclosure and/or domains of disclosure to parents among US Latinx youth while attending to parent and youth gender. Further, we explore associations between familism values and Latinx adolescents’ routine and self-disclosure and/or domains of disclosure to parents and siblings. Based on this literature review, we identify limitations of the current literature. We also recommend future research directions, for example, examining how associations differ based on involvement in US mainstream culture, exploring Latinx youth’s disclosure to extended family members, and investigating Latinx cultural values beyond familism.

Without doubt the validity of scientific theories and their usability for solving societal, economic, ecological, and health-related problems are contingent on the existence of robust and replicable empirical findings. However, a review of the recently published replication literature portrays a rather pessimistic picture of the replicability of even very prominent empirical results, as is evident both from large-scale meta-analytic replication projects and from distinct attempts to replicate selected examples of well-established key findings of personality and social psychology. The present chapter offers a twofold explanation for this undesirable state of affairs. On one hand, the widespread evidence on replication failures reflects to a considerable extent the neglect of logical and methodological standards in replication studies, which sorely ignore such essential issues as manipulation checks, reliability control, regressive shrinkage, and intricacies of multi-causality. On the other hand, however, the community of behavioral scientists must blame themselves for an intrinsic weakness of their corporate identity and their incentive and publication system, namely the tendency to mistake sexy and unexpected findings for original insights and neglect of the assets of cumulative science and theoretical constraints.

Smartphones and social media have considerably transformed adolescents’ media engagement. Adolescents consume, create, and share media content anywhere, anytime, and with anyone, often beyond parents’ oversight. Parents try to keep track of their adolescents’ media use by employing control, surveillance, and solicitation. This chapter explores the prevalence and predictors of such monitoring strategies, and their effectiveness in managing adolescents’ media use and shaping the potential consequences of adolescents’ media use for their mental health. In addition, the chapter discusses parents’ use of digital media for monitoring adolescents’ nonmedia activities, such as the use of location-tracking applications. Overall, evidence regarding the prevalence, predictors, and effectiveness of parental media monitoring is limited and inconclusive. The chapter underscores the need for refining conceptualizations of media monitoring. Moreover, it highlights the importance of understanding the effectiveness of media monitoring within an ever-evolving digital world.

This chapter is a personal account of choices and dilemmas I have faced when conducting case studies. From the very start of my career as a researcher, I have been attracted to getting my hands dirty and conducting case studies in the field. Case studies are not a straightforward research strategy, however. There are multiple choices and dilemmas on the way and there is no “right way” of doing them. Nevertheless, my long career as a case researcher has given me the courage to compile some reflections and recommendations. Throughout the chapter, I draw on some of the most prominent case researchers in the field and present their various approaches to conducting case studies. To give life to the text, I have included examples and illustrations from my own work, being as honest and open as I can about my choices and the difficulties and ethical dilemmas I met on my way.

Survey research is a method commonly used to understand what members of a population think, feel, and do. This chapter uses the total survey error perspective and the fitness for use perspective to explore how biasing and variable errors occur in surveys. Coverage error and sample frames, nonprobability samples and web panels, sampling error, nonresponse rates and nonresponse bias, and sources of measurement error are discussed. Different pretesting methods and modes of data collection commonly used in surveys are described. The chapter concludes that survey research is a tool that social psychologists may use to improve the generalizability of studies, to evaluate how different populations react to different experimental conditions, and to understand patterns in outcomes that may vary over time, place, or people.

This chapter provides an overview on the use and validity of student samples in the behavioral and social sciences. In some instances, data collected from students can be of limited value or even inappropriate; however, in other cases, this approach provides useful data. I offer three general ways to evaluate the use of student samples. First, consider the research design. Descriptive studies that rely on students to draw inferences about the overall population are likely problematic. Second, statistical controls such as multivariate analyses that adjust for other factors may reduce some of the biases that may be introduced through sampling. Third, consider the theorized mechanism – a clear theoretical mechanism that does not vary based on the demographics of the sample allows us to put more faith in constrained samples. Despite these approaches, and regardless of our methods, statistics, and theoretical mechanism, we should be cautious with generalizability claims.

This chapter considers parental monitoring behaviors through the lens of Communication Privacy Management theory (CPM; Petronio, 2002). This chapter details the personal, relational, and cultural factors that guide changes in family privacy boundaries during adolescence, drawing parallels with other prominent theories of social development. Youth can interpret both overt (parental solicitation and control) and covert (“snooping”) monitoring as invasive of privacy; these subjective invasion perceptions are intricately associated with adolescent’s attempts to manage their personal information and maintain desired levels of privacy, but prior research is inconsistent regarding the presence, directionality, and valence of effects. Cultural factors can potentially explain this heterogeneity, including independent versus interdependent orientations toward self-construal, horizontal versus vertical orientations toward privacy control, and power distance in family relationships. Future research should examine parental motivations for intrusive monitoring, the accuracy of youth reports about such practices, and how families should handle information uncovered through parental invasions.

Self-report measures are questions that are answered by respondents about themselves. They are essential to researchers and policy-makers; they provide a direct window for researchers and policy-makers to learn what people know, what they do, and how they think about an issue, a person, or an event. This chapter begins with an overview of how people go about answering survey questions. To answer a survey question, respondents must first understand what the question asks. Next, they retrieve relevant information required by the question and integrate it into an estimate or a judgment. Then, they map the estimate or the judgment to one of the response options provided to them. At each stage of this survey response process, respondents could run into problems that would negatively impact the accuracy and completeness of their answers. Lastly, the chapter discusses how the context in which a survey item is asked and the mode of data collection affect self-report measures. The chapter concludes with recommendations on how to improve the quality of self-report measures.

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.