Mediation, Moderation and Multivariate Methods

Catalog description

This course introduces students to advanced statistical techniques for examining complex relationships among variables in the social, behavioral, and health sciences. Topics include mediation and moderation analysis using regression frameworks, path analysis, and conditional process modeling. Students will also explore multivariate methods such as MANOVA, discriminant analysis, principal component analysis (PCA), factor analysis, cluster analysis, and structural equation modeling. Emphasis is placed on the conceptual understanding, implementation, and interpretation of these methods using statistical software. Real-world examples and datasets are used to develop applied skills in modeling indirect effects, interaction effects, and multidimensional data structures. This course is ideal for students planning to conduct empirical research involving complex data relationships.