Annual Review of Psychology - Volume 60, 2009

Cultural competency practices have been widely adopted in the mental health field because of the disparities in the quality of services delivered to ethnic minority groups. In this review, we examine the meaning of cultural competency, positions that have been taken in favor of and against it, and the guidelines for its practice in the mental health field. Empirical research that tests the benefits of cultural competency is discussed.

This review presents a practical summary of the missing data literature, including a sketch of missing data theory and descriptions of normal-model multiple imputation (MI) and maximum likelihood methods. Practical missing data analysis issues are discussed, most notably the inclusion of auxiliary variables for improving power and reducing bias. Solutions are given for missing data challenges such as handling longitudinal, categorical, and clustered data with normal-model MI; including interactions in the missing data model; and handling large numbers of variables. The discussion of attrition and nonignorable missingness emphasizes the need for longitudinal diagnostics and for reducing the uncertainty about the missing data mechanism under attrition. Strategies suggested for reducing attrition bias include using auxiliary variables, collecting follow-up data on a sample of those initially missing, and collecting data on intent to drop out. Suggestions are given for moving forward with research on missing data and attrition.

This review considers a common question in data analysis: What is the most useful way to analyze longitudinal repeated measures data? We discuss some contemporary forms of structural equation models (SEMs) based on the inclusion of latent variables. The specific goals of this review are to clarify basic SEM definitions, consider relations to classical models, focus on testable features of the new models, and provide recent references to more complete presentations. A broader goal is to illustrate why so many researchers are enthusiastic about the SEM approach to data analysis. We first outline some classic problems in longitudinal data analysis, consider definitions of differences and changes, and raise issues about measurement errors. We then present several classic SEMs based on the inclusion of invariant common factors and explain why these are so important. This leads to newer SEMs based on latent change scores, and we explain why these are useful.

Most experiments are done in laboratories. However, there is also a theory and practice of field experimentation. It has had its successes and failures over the past four decades but is now increasingly used for answering causal questions. This is true for both randomized and—perhaps more surprisingly—nonrandomized experiments. In this article, we review the history of the use of field experiments, discuss some of the reasons for their current renaissance, and focus the bulk of the article on the particular technical developments that have made this renaissance possible across four kinds of widely used experimental and quasi-experimental designs—randomized experiments, regression discontinuity designs in which those units above a cutoff get one treatment and those below get another, short interrupted time series, and nonrandomized experiments using a nonequivalent comparison group. We focus this review on some of the key technical developments addressing problems that previously stymied accurate effect estimation, the solution of which opens the way for accurate estimation of effects under the often difficult conditions of field implementation—the estimation of treatment effects under partial treatment implementation, the prevention and analysis of attrition, analysis of nested designs, new analytic developments for both regression discontinuity designs and short interrupted time series, and propensity score analysis. We also cover the key empirical evidence showing the conditions under which some nonrandomized experiments may be able to approximate results from randomized experiments.

In this article, we review theoretical and empirical advances in research on romantic relationships between age 10 and the early twenties. First, we describe key themes in this area of research. Next, we briefly characterize the most influential theoretical formulations and distinctive methodological issues. We then describe research findings regarding pertinent social and developmental processes. We summarize the extensive findings on relationships with parents and peers as a context for romantic relationships. Finally, we characterize the growing evidence that adolescent romantic relationships are significant for individual adjustment and development, and we note promising directions for further research.

There is a convergence between cognitive models of imitation, constructs derived from social psychology studies on mimicry and empathy, and recent empirical findings from the neurosciences. The ideomotor framework of human actions assumes a common representational format for action and perception that facilitates imitation. Furthermore, the associative sequence learning model of imitation proposes that experience-based Hebbian learning forms links between sensory processing of the actions of others and motor plans. Social psychology studies have demonstrated that imitation and mimicry are pervasive, automatic, and facilitate empathy. Neuroscience investigations have demonstrated physiological mechanisms of mirroring at single-cell and neural-system levels that support the cognitive and social psychology constructs. Why were these neural mechanisms selected, and what is their adaptive advantage? Neural mirroring solves the “problem of other minds” (how we can access and understand the minds of others) and makes intersubjectivity possible, thus facilitating social behavior.

Consistent with the relative recency of research on workplace aggression and the considerable media attention given to high-profile incidents, numerous myths about the nature of workplace aggression have emerged. In this review, we examine these myths from an evidence-based perspective, bringing greater clarity to our understanding of the predictors of workplace aggression. We conclude by pointing to the need for more research focusing on construct validity and prevention issues as well as for methodologies that minimize the likelihood of mono-method bias and that strengthen the ability to make causal inferences.

Social cognition in humans is distinguished by psychological processes that allow us to make inferences about what is going on inside other people—their intentions, feelings, and thoughts. Some of these processes likely account for aspects of human social behavior that are unique, such as our culture and civilization. Most schemes divide social information processing into those processes that are relatively automatic and driven by the stimuli, versus those that are more deliberative and controlled, and sensitive to context and strategy. These distinctions are reflected in the neural structures that underlie social cognition, where there is a recent wealth of data primarily from functional neuroimaging. Here I provide a broad survey of the key abilities, processes, and ways in which to relate these to data from cognitive neuroscience.

This article reviews research on workplace victimization, which we define as acts of aggression perpetrated by one or more members of an organization that cause psychological, emotional, or physical harm to their intended target. We compare several types of victimizing behaviors that have been introduced into the organizational psychology literature to illustrate differences and similarities among them. We then review studies looking at who is likely to become a victim of aggression. Predictors include personality, demographic, behavioral, structural, and organizational variables. We also review research on coping strategies for victimization, which include problem-focused and emotion-focused strategies. We conclude with a summary of challenges for victimization research. These include addressing the proliferation of constructs and terms into the literature, attempting to clarify inconclusive findings, and using theory to guide the selection of study variables.