Undergraduate Courses

Psyc 317: Quantitative Methods

The course covers the application of statistical principles to psychological research problems, including an introduction to the principles of experimental design.

Graduate Course

Psyc 728/828: Regression/Correlational Design

The course covers regression analysis with an emphasis on the general linear model (GLM). Topics include partial correlations, regression diagnostics, curvilinear relationships, categorical and continuous interactions (moderation), and an intro to patch analysis (mediation). Course is presented in the context of correlational designs and survey research.

​Psyc 745/845: Psychometric Theory

The course introduces classical test theory, including test reliability definitions and equations, standard error of measurement, and related statistics. Additional topics include scaling, test validity, and item statistics useful in test construction, and norms commonly used in educational and psychological tests. Other topics covered are generalizability theory (G-theory) and factor analysis. Item Response Theory (IRT) is introduced if time permits.

​Psyc 746/846: Structural Equation Modeling

This course covers the topics of linear structural equation modeling and focuses on estimation, measurement models, confirmatory and hierarchical factor analysis, structural equations, longitudinal models, multisample analyses, and mean structures.

​Psyc 747/847: Multivariate Methods for the Behavioral/Social Sciences

The course introduces several models for multivariate data typically applied in the behavioral/social sciences. Topics covered are regression in matrix algebra, MANOVA, Profile Analysis/Repeated Measures MANOVA, MANCOVA, discriminant analysis, principal components, and exploratory factor analysis (EFA).

​Psyc 795/895: Categorical Methods for the Behavioral/Social Sciences

The course introduces several models for the analysis of categorical response data. Topics covered are contingency tables and models for binary, multi-category, count, and correlated clustered data. The generalized linear model is also introduced

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