Parenting Styles

Parenting styles reflect variations in the attitudes and practices of parents, and comprise discrete parenting behaviors. “Parenting style” refers to a cluster of parental practices that produce relatively stable and identifiable patterns in child adjustment outcomes. Parental authority is a key concept within parenting styles research, and refers to the type and extent of discipline used by parents in carrying out parental authority (i.e., behavioral control versus psychological control). Research on parenting evolved out of an increased interest in leadership styles, and particularly authoritarian personality, that emerged following World War II. The dimensions underlying leadership styles could be applied to parenting, and several researchers were simultaneously engaged in examining the dimensions underlying parenting behaviors. It was not until Diana Baumrind introduced her parenting styles typology in 1966 that research on parenting styles coalesced. Baumrind’s typology borrowed the terms authoritarian and permissive parenting from the leadership literature, and introduced the concept of authoritative parenting to the parenting research lexicon. Since its introduction, the parenting styles typology has expanded to specify two underlying dimensions of parenting that combine in various ways to result in four rather than three original types, as well as to identify a series of subtypes reflecting moderate levels of the two dimensions. This change in the typology increased its external validity, and led to an interest in examining the applicability of the typology in diverse socioeconomic, racial/ethnic, and cultural groups. The typology has also been used to understand a relatively new trend in parenting, commonly referred to as helicopter parenting, and to examine its consequences for children at a variety of developmental stages, particularly during emergent adulthood. One consequence of parenting styles that has received a lot of research attention is narcissism. Baumrind’s typology continues to serve as the classic nomenclature within parenting styles research, and has dominated the parent-child research agenda, appearing in some capacity in most parenting studies to this day.

Employee Stress and Well-Being

This article provides a general overview of overarching topics in the literature on stress and well-being. Broadly stated, the study of employee stress and well-being is concerned with the impact of work on the mental and physical health of employees. Topics covered in this overview include stress theories, commonly studied job stressors, strains associated with those stressors, moderators, and mediators of the stressor-strain relationship, as well as interventions to address job-related stress in the workplace. In addition to general overviews of these topics (and subtopics), brief annotations of research articles for each topic are included.

Turning Points in the Assessment and Clinical Applications of Individual Differences in Attachment

Attachment may have reached a turning point in which two sometimes incompatible approaches to individual differences in attachment are being compared. The outcome could influence future work in attachment. This article focuses on individual differences because applications of attachment are predicated on the consequences of individual differences for adaptation and well-being. The issue is which model is better suited to future research and clinical applications. Both models augment the original Ainsworth ABC model that everyone agrees is not sufficiently differentiated to cover the range of human behavior. The two models are “ABC+D” (the model that has disorganization (D) as its fourth category) and “DMM” (the model that expands the A and C categories from two subcategories each to a total of eight each, plus A/C combinations). The current disagreement has two sources: (a) the increasing acceptance outside the United States of the DMM, particularly in clinical and forensic settings, while the US remains largely unaware of the DMM; and (b) the recent announcement by prominent ABC+D researchers from Europe and the US that D is not a suitable category for clinical or forensic use. ABC+D researchers have not proposed an alternative, and some US funding sources and courts eschew attachment altogether, believing the theory itself lacks validity and utility, thus weakening attachment’s potential to inform clinical research and decision-making. This article proposes DMM as a viable alternative to both ABC+D and psychiatric diagnoses and examines the development and contributions of each model for the purpose of creating a model of individual differences in attachment that is scientifically robust, open to change as new evidence becomes available, and applicable to troubled individuals and families. Notably, attachment theory has engendered controversy from its beginnings. When John Bowlby offered attachment as a universal human characteristic that promoted species and individual survival, he was criticized by others in his field. When Mary Ainsworth identified the ABC categories of individual differences in attachment, her ideas were attacked from outside attachment theory. While the “first generation” issue around the existence of attachment has largely died away, a second generation of attachment theorists is disputing the nature of individual differences, their focus on individuals (ABC+D) or interpersonal systems (DMM Family Functional Formulations), and their relevance to clinical work. The ABC+D and DMM models that expand Ainsworth’s work were developed by two of her students, Mary Main (ABC+D) and Patricia Crittenden (DMM). They and their colleagues have worked separately for half a century producing two large and sometimes discrepant bodies of work, which have now become the topic of open debate. This bibliography focuses on the conceptual and empirical bases for that exchange. Part I outlines the roots of the ABC+D and DMM models, together with comparative validating information. Part II presents the central research findings on individual differences in attachment from four decades of research with each model. The authors wish to thank Robbie Duschinsky, Udita Iyengar, and Andrea Landini for their helpful comments on this bibliography.

Lineups

Lineups are conducted in the course of police investigations when a crime has been witnessed by one or more people. A lineup typically consists of a person whom the police believe committed the crime (i.e., the suspect) and some number of people who are known to be innocent of the crime (i.e., fillers). When the police have developed a suspect, they show witnesses a lineup to test whether they will claim that the suspect is the person who committed the crime (i.e., the perpetrator). If so, the witness is said to have made a positive identification of the suspect. What is not clear, at least in real-world investigations, is whether that identification is correct, because sometimes suspects are guilty and sometimes they are innocent. Since the late 1970s, psychologists have conducted experiments to find lineup procedures that decrease the likelihood that witnesses will mistakenly identify innocent suspects. These experiments are typically conducted in laboratory settings in which researchers expose participants to a simulated crime, often on videotape. After the participant-witnesses have viewed the crime, they are asked to attempt an identification from a lineup. In the laboratory, researchers can vary whether the perpetrator appears in that lineup. When the perpetrator is present in the lineup (i.e., a target-present lineup), the witness can identify the suspect (a correct identification), identify a filler, or say that the perpetrator is not there (an incorrect rejection of the lineup). When the perpetrator is not present (i.e., a target-absent lineup), the witness can make a mistaken identification of the suspect, identify a filler, or correctly reject the lineup. Using this method, researchers have identified lineup procedures that decrease mistaken identifications, which are the leading cause of wrongful convictions among those who have been exonerated by DNA tests conducted after trial. This article contains sections describing comprehensive General Overviews of research on lineups, research demonstrating that Live Lineups Are Equivalent to Photo Lineups, and Policy Recommendations and Best Practice Guidelines. The remaining sections describe many of these policy recommendations, including how Lineups Are Superior to Showups, having an Evidence-Based Suspicion for placing a suspect in a lineup, unbiased Lineup Composition, Double-Blind Administration, proper Lineup Instructions, collecting witnesses’ Confidence Statements in the accuracy of their identification immediately after the initial identification, Video Recording Identification Procedures, and avoiding Repeated Lineups. An additional section addresses special issues that need to be considered when Conducting Lineups with Children.

Kurtosis

In statistics, kurtosis is a measure of the probability distribution of a random variable or a vector of random variables. As mean measures the centrality and variance measures the spreadness of a probability distribution, kurtosis measures the tailedness of the distribution. Kurtosis for a univariate distribution was first introduced by Karl Pearson in 1905. Kurtosis, together with skewness, is widely used to quantify the non-normality—the deviation from a normal distribution—of a distribution. In psychology, kurtosis has often been studied in the field of quantitative psychology to evaluate its effects on psychometric models.

Wilhelm Wundt

Wilhelm Maximilian Wundt (b. 1832―d. 1920) was a central figure in German culture between the second half of the 19th century and the first two decades of the 20th century. Coming from a medical and neurophysiological background with a PhD in medicine, Wundt shifted his interest toward psychological and philosophical questions, becoming full professor of philosophy: first, at the University of Zurich in 1874; then, at the University of Leipzig in 1875. In the early 21st century, he is known worldwide as one of the founders of scientific psychology. In Leipzig, he founded in 1879 the Psychological Laboratory, which later became the first psychological institute in the world. Moreover, he founded the first journal for experimental psychology, which he called Philosophische Studien (Philosophical Studies), later Psychologische Studien (Psychological Studies). In so doing, he created the first international training center for psychologists, attracting to Leipzig students from all over the world. Wundt had a significant impact upon the development of scientific psychology in many countries, not least in the United States, where his former students founded psychological laboratories inspired by the Leipzig model. Apart from his contributions to psychology, Wundt also developed a philosophical system that is crucial to understanding his psychological program and methodology, but which has not received due attention among psychologists. Wundt’s writings have been published in different, mostly enlarged editions throughout his career. The great majority of these volumes have not yet been translated into English, and the same holds true for much of the relevant research literature.

Data Sharing in Psychology

Narrowly defined, data sharing is the practice of making scientific research data available to other researchers. However, the term is often used to include a variety of open-science practices, including making data, methodology (e.g., coding scheme), analytic syntax, and other research materials available to other researchers, as well as the reuse of those resources by others. There are multiple avenues for data sharing, for example data repositories (either subscription-based or free) or direct request to the researcher. Data sharing is a fairly common practice in the life and earth sciences. Excepting a handful of longitudinal projects, psychology lacks this robust historical precedent for sharing data. In fact, in the not-so-distant past, institutional review boards typically required that data be destroyed after a preset period in order to protect participants’ privacy—and some still do. And many researchers still do not take the first step—modifying their informed consent procedures to include explicit consent to share. Although still not frequent, data sharing in psychology is becoming more common. In part, this trend is being driven by the requirements set by publications and funding agencies. For publications, data sharing is intrinsic to transparency and replication of study findings. For funders, data sharing ensures greater return on investment—that expensive and time-consuming primary data collection does not wind up sitting on a dusty shelf, but rather can be reused for secondary data analysis to answer new questions. In psychology as in other fields, technological improvements in storage capacity and computing power have also facilitated data sharing and reuse. While many psychologists are still concerned that data sharing will result in being “scooped” or found in error, there is increasing recognition of the benefits of data sharing. First, data repositories ensure that data are archived, and that the burden of preservation does not fall on the researcher or the researcher’s institution. Sharing also increases the pace of scientific progress, as researchers can build on each other’s work. For example, researchers can learn how other experts approached measurement or coding of a given outcome. In replication studies, inconsistent findings can point to contextual variations in the construct under study, rather than researcher error. And in a field where null findings are often difficult to publish, sharing allows these data to be included in meta-analyses across studies to examine broader impacts. Most importantly, data sharing enhances transparency, a key ingredient in the scientific process.

Moral Conviction

Moral conviction refers to the perception that one’s feelings about a given attitude object are based on one’s beliefs about right and wrong. Holding an attitude with moral conviction means that a person has attached moral significance to it. Some people hold an attitude based on their likes and dislikes, or their preferences. Other attitudes may be based more on norms and conventions in a society, such as what the law dictates or what close others believe. Still other attitudes are based on people’s beliefs about right and wrong, and thus are attitudes held with moral conviction. Unlike some of the other dominant ways of conceptualizing morality in moral psychology, research on moral conviction takes a bottom-up approach. Instead of assuming certain issues are moral, individuals are asked to evaluate different attitude objects and issues based on their beliefs about right and wrong with questions like, “To what extent is your position on X connected to your beliefs about fundamental right and wrong?” Attitudes held with strong moral conviction, also called “moral mandates,” have a number of important characteristics and consequences that set them apart from other strong, but nonmoral, attitudes. When an attitude is based on one’s sense of right and wrong, it is perceived to be more of an objective fact (e.g., it is the correct and factual position to have) that should be universally held. Morally convicted attitudes have a stronger emotional intensity than equally strong but nonmoral attitudes. These attitudes are more likely to have a motivational component to them, so people act in favor of their moral attitudes because they provide justification for the action and are seen as obligations. Moral convictions can also provide an internal guide for behavior, independent of authority or group influence (i.e., authority independence). Moral mandates have a variety of consequences, which can be seen in either a normatively positive or negative light. Moral conviction is, for example, associated with increased political engagement and volunteerism (generally seen as normative goods), but also predicts increased intolerance and unwillingness to compromise with those who do not share one’s moral point of view (generally seen as normative bads).

Bayesian Statistical Methods in Psychology

The use of Bayesian statistics within psychology is on the rise, and this trajectory will likely continue to accelerate in the coming years. There are many different reasons why a researcher may want to implement Bayesian methodology. First, there are cases where models are too “complex” for traditional (frequentist) methods to handle. Second, Bayesian methods are sometimes preferred if only small samples are available, since the use of priors can improve estimation accuracy with minimal data. Third, the researcher may prefer to include background information in the estimation process, and this can be done via the priors. Finally, Bayesian methods produce results that are rich with detail and can be more informative about the population parameters. Specifically, information surrounding the entire posterior distribution is provided through Bayesian estimation, as opposed to a point estimate obtained through traditional (frequentist) methods. All of these reasons make Bayesian methods attractive to the psychological sciences. This bibliography begins with a section on General Overviews, which presents works that provide general introductions to Bayesian methods. A subsection within this overview section covers Papers Introducing Bayesian Methods to Subfields in Psychology, and a second subsection includes Resources for Particular Model Types Popular in Psychological Research. Next, some of the more comprehensive Bayesian Textbooks are presented, and this is followed by a treatment of the Philosophy that underlies Bayesian statistics. The next section is Markov Chain Monte Carlo and Samplers. One of the most common tools for Bayesian estimation is the Markov chain Monte Carlo (MCMC) algorithm. MCMC is used to construct chains through samplers, and these chains represent draws from the posterior. A subsection on Convergence is included here to highlight the importance of assessing Markov chain convergence. This is followed by a section on Prior Distributions, which includes subsections on Expert Elicitation of Priors and the Data-Prior Conflict. A section on Software Resources is presented, which covers some of the main software programs implementing Bayesian statistical modeling. Finally, a section on Model Assessment and Fit is presented. Each of these sections and subsections were selected to highlight an understanding of Bayesian statistics, the role it plays in psychology, and proper implementation.

Measurement in Health Psychology Research and Practice

Health psychology is the investigation of psychological and behavioral processes in health, illness, and health care. It investigates the behavior of people who are healthy or ill as well as practitioners and policymakers delivering or making decisions that influence health care. Measures may be used in studies aiming to elucidate and understand processes to advance theory and evidence, or to gain greater understanding or even intervening to improve health and health-care outcomes. A wide range of research designs are used including observational, intensive longitudinal (or ecological momentary assessment [EMA]), experimental laboratory, pre-post event or intervention, and randomized controlled trials. Frequently investigations measure a target behavior or psychological state and examine (a) whether it can be predicted or influenced by antecedents (events, environments, cognitions, interventions) and (b) whether it predicts or influences consequences, especially health/illness outcomes. Health psychology shares measurement issues that are challenging for psychology in general, including establishing reliability and validity, scaling mental processes, and reactivity of measures. Additionally, self-report measures are frequently used to assess symptoms such as pain or reports of health behaviors such as smoking, and these assessments may be used to assess a clinical condition and to guide clinical decisions. They may be validated against an objective measure, but the objective measure may fail to represent the full range, context, or occurrence of the target construct and can therefore only give an indication rather than a solid assessment of validity of the self-report. Measures are often used to measure change over time, especially change associated with an intervention or a changing clinical state, and therefore need to be repeatable while retaining meaning; sensitivity to change may be more important than test-retest reliability. Longitudinal measurement has been aided by the use of digital mobile technologies, including wearables and automatic measurement of physiological processes. Additionally, physiological functioning, especially autonomic, immune, and cardiac, are commonly measured. Data collected routinely by health-care organizations such as attendance at appointments or health outcomes may be useful, but these data may be incomplete or only give indirect evidence of the target behaviors or outcomes. Finally, the context of measurement may determine what is possible: ill patients could be burdened by a long questionnaire; health-care professionals may have limited time and opportunity to respond to assessments and measures may need to be adapted for a clinical environment. The following sections address measures of key psychological constructs, health-related behaviors, and responses to health, illness, and health care.