Psychology researchers often desire to share their scientific expertise and translate academic scholarship to inform pressing social issues and public debates. Although they have much to contribute, unfortunately, most have never been trained in how to work with non-academic partners and communicate research to non-academic audiences. The overarching goal of this course is to provide students with opportunities to develop skills and gain practice in public engagement and outreach.

The hierarchical linear model provides a conceptual framework and a flexible set of analytic tools to study a variety of educational, social and developmental processes. One set of applications focuses on data in which persons are clustered within social contexts such as couples, families, classrooms, schools, or neighborhoods. A second set of applications concerns individual growth or change over time.

We will learn an inferential approach known as Bayesian statistics. Bayesian methods promote a more thoughtful approach to science. Bayesian statistics address the question that scientists need to answer: Based on the information I have, what can I conclude and how certain can I be that my conclusion is correct? This course will provide a tutorial introduction to Bayesian data analysis. We'll start by covering the basics of probability and Bayes Theorem and then explore how these concepts can be applied to answer research questions.

Not available at this time

Not available at this time

Credit, variable; may be repeated for additional credit.

Practice in application of psychological techniques to clinical settings and counseling. Taught with staffs of cooperating institutions and agencies. Credit, variable; may be repeated for additional credit.

Practice in application of psychological techniques to clinical settings and counseling. Taught with staffs of cooperating institutions and agencies. Credit, variable; may be repeated for additional credit.