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.     

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.