Linear Models

Semesters Taught
Spring 2013, 2015, 2017

The purpose of this course is to familiarize students with and broaden students' knowledge of data analytic approaches using the general linear model. The course begins with a review of correlation and regression with a single predictor. It continues with discussions of multiple regression, standardized regression, robust regression, moderators, and regression with categorical predictors (i.e. using regression in experimental designs), and logistic regression. Building on this knowledge base the course covers path analysis, structural equation models, and multi-level modeling. If time permits the course will also introduce latent growth curve modeling.

The course consists of approximately 50% lecture on these topics and 50% practice on these topics using the R statistical environment. Prior knowledge of R will certainly help but is not required.

Syllabus (spring 2015)

Previous Syllabi

Syllabus (spring 2013)