Upon successful completion of the course, students will be able to describe and identify appropriate multivariate statistical techniques that address a variety of research questions. Students will also be able to evaluate and judge the appropriateness of the use of various analytic techniques presented in the scientific literature. Most importantly, students who successfully complete the course will be able to describe the theoretical underpinnings of general and generalized linear models, report and interpret results of these analyses, and be able to choose and utilize appropriate statistical techniques in their subsequent research.
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This course provides an overview to multivariate analytic methods, and represents the foundation of the statistical knowledge and skills required for subsequent statistical courses. Multivariate statistical techniques are the basic tools for experimental and non-experimental research in the psychological and educational sciences. These techniques examine linear (and curvilinear) relationships between at least two independent variables (predictors) and one or more dependent variables (outcomes). This course begins with a brief review of the basic principles of hypothesis testing, research design, and bivariate analysis. Numerous multivariate statistical techniques are then elaborated in terms of the types of research questions they address. These techniques include, but are not limited to: linear and logistic regression, hierarchical loglinear analysis, repeated measures analyses of variance and covariance, and discriminant function analysis. This course provides students with the fundamental tools needed for their statistical toolbox.
Teaching methods: Lectures and computer-laboratory.
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