Personality and Legislative Politics: The Big Five Trait Dimensions Among U.S. State Legislators

The impact of personality traits on people's attitudes and behaviors is widely recognized, yet systematic attention to personality in large‐N research on elected officials has been rare. Among psychologists, five‐factor frameworks that focus on openness to experience, conscientiousness, extraversion, agreeableness, and emotional stability have gained tremendous prominence in the past two decades. Applications of these frameworks to the study of mass political behavior have been highly fruitful, but corresponding applications in the study of legislators have been rare. In an effort to assess the utility of a Big Five approach in the study of legislative politics, this article addresses three questions: whether elected officials will be willing to provide personality self‐assessments, whether any data they do provide will exhibit meaningful variance, and whether the Big Five trait dimensions will correspond with patterns in respondents' attitudes and behaviors. These questions are addressed using data from members of the state legislatures in Arizona, Connecticut, and Maine. Results provide considerable grounds for optimism regarding the likely utility of more extensive applications of the Big Five in research on elected officials.     

Athlete identity and athlete satisfaction: The nonconformity of exclusivity

The goal of this study was to determine if there was a relationship between athletic identity, a relatively stable individual difference, and athlete satisfaction. A second goal was to evaluate the structure of athletic identity to examine potential differences between facets. Analyzing data from 175 NCAA student athletes, results indicated that use of overall athletic identity hid meaningful relationships between this self-identity concept and level of athlete satisfaction. When examined at the facet level, results indicated that athletic social identity and negative affectivity were positively related to athlete satisfaction while the exclusivity facet of athletic identity was negatively related to athlete satisfaction.     

Profiles of Emotion Socialization Across Development and Longitudinal Associations with Youth Psychopathology

Research on Child and Adolescent Psychopathology - Although there is relative consensus in the literature regarding associations between certain emotion socialization (ES) strategies and youth...     

Jandu Yani U (for all families): Tailored Triple P Training and Support Promote Empowerment in Remote Aboriginal Communities

Journal of Child and Family Studies - To enhance the accessibility and acceptability of evidence-based parenting programs in Indigenous communities, there is a need to build a confident and skilled...     

Hierarchical Approximate Bayesian Computation

Approximate Bayesian computation (ABC) is a powerful technique for estimating the posterior distribution of a model’s parameters. It is especially important when the model to be fit has no explicit likelihood function, which happens for computational (or simulation-based) models such as those that are popular in cognitive neuroscience and other areas in psychology. However, ABC is usually applied only to models with few parameters. Extending ABC to hierarchical models has been difficult because high-dimensional hierarchical models add computational complexity that conventional ABC cannot accommodate. In this paper, we summarize some current approaches for performing hierarchical ABC and introduce a new algorithm called Gibbs ABC. This new algorithm incorporates well-known Bayesian techniques to improve the accuracy and efficiency of the ABC approach for estimation of hierarchical models. We then use the Gibbs ABC algorithm to estimate the parameters of two models of signal detection, one with and one without a tractable likelihood function.