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Course Description

(Cross-listed w/DLS 276). Students will be introduced to multivariate statistics, with a special emphasis on methods for studying change and effects of context. Topics will include general linear hypothesis testing, logistic regression, multilevel models, cluster analysis, principal component analysis, exploratory data analysis and structural equation modeling. The focus of the course will be on using the computer to analyze real data by using the statistical techniques introduced through lectures, interpreting the results and writing about the findings. Students should have a good background in multiple regression analysis, including the use and interpretation of dummy variables and interactions. Prerequisite: Graduate students only. 2 semesters of statistics and data analysis methods.

Affiliated With:

  • School of Arts & Sciences