This course is an introduction to the new field of network psychometrics, with an emphasis on exploratory graph analysis (EGA). Students will have a hands-on experience on how to use the EGAnet package for R for implementing all techniques related to EGA, from quantum information theory and network science methods to dynamic exploratory graph analysis and more.

Course covers the foundations of psychology & statistical techniques used in behavioral science, in particular foundations of traditional statistical testing, R programming, modern statistical testing using bootstrapping & resampling, & very basic introduction to Factor Analysis & applications of information theory. The course has 3 lecture hours & 2 lab hours that teaches computational aspects of the course in R. Basic training in R required.

Includes Chi-square tests for contingency tables, correlation, multiple regression, analysis of variance of one-way and factorial designs including repeated measures experiments, and analysis of covariance. Extension work with SPSS and MANOVA computer routines. Prerequisite: PSYC 7710 or equivalent.

This course will provide students exposure to current knowledge in the area of affect, mood, and emotion, including research on models of emotion and emotion regulation, historical and developmental perspectives on emotion, and prominent measures and methods used to advance understanding of affective sciences. This will include basic research on affect and healthy emotional functioning, along with research on psychopathology and mood disorders.

This course will provide students exposure to current knowledge in the area of developmental aspects of behavior, including transitions, growth, and development across an individual's life, along with prominent methods used to advance understanding of developmental processes. This will include basic research on both typical and atypical development across the lifespan.

This course has a two-fold purpose. First, it is designed to provide a working introduction to the ethical issues, principles, and techniques of psychotherapy and supervision. Second, will explore psychology's history and development and then investigate the role of History and Systems on our current psychotherapy.

This course will provide clinical psychology doctoral students exposure to current knowledge and challenging issues in ethical decision-making and practices tied to being a clinical scientist. The course will focus on clinical science roles as a researcher, educator (e.g., teacher, mentor), and member of the broader field.

This course is designed to provide students with a context for supervision and consultation through sharing with each other their experiences with various clients, providing peer supervision to each other (with the instructor in the room), receiving immediate feedback from the instructor regarding their supervision of each other, and receiving feedback from each other, at the end of the course.

Introduces major statistical methods used for the data analysis of multiple measures. Includes elementary matrix algebra, multivariate regression (canonical correlation; multivariate analysis of variance and covariance; and discriminant analysis and classification), correlational methods (principal components and exploratory and confirmatory factor analysis), and the analysis of multivariate contingency tables using log-linear models. Emphasizes concepts, issues, and examples over mathematical derivations. Prerequisite: PSYC 7710-7720 or equivalent.

Analyzes current theory and research in social and personality developments from infancy through adolescence. Prerequisite: Instructor permission.