Data Analysis II

Overview Goals

Overview This is the second course in the 3-course graduate statistics sequence taught in the University of Oregon Department of Psychology. In this course, material on complex analyses of variance and multiple regression is covered in the context of the general linear model. The courses in this sequence are entitled “Data Analysis” and not statistics to reflect the course philosophy. These courses are designed to assist the student in learning how and when to appropriately apply a wide range of statistical methods. They are not designed to provide the student with in-depth knowledge of the underlying statistical theory or to simply provide students with “cook-book” instructions for performing statistical analyses. Rather, the emphasis is on understanding and appropriate application. Goals By the end of the course, students should have at their command a variety of statistical tools that they can use to analyze data. They should know how to design, analyze, and interpret the results of experiments with any combination of crossed or nested fixed and random factors. Students should be able to model a variety of relations between multiple categorical or continuous predictors and a single continuous criterion variable and handle a variety of common problems including autocorrelated errors, heteroscedasticity, and missing values.