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Instructor Class Description

Time Schedule:

Adrian Dobra
STAT 560
Seattle Campus

Hierarchical Modeling for the Social Sciences

Explores ways in which data are hierarchically organized, such as voters nested within electoral districts that are in turn nested within states. Provides a basic theoretical understanding and practical knowledge of models for clustered data and a set of tools to help make accurate inferences. Prerequisite: SOC 504, SOC 505, SOC 506 or equivalent; recommended: CS&SS 505, CS&SS 506 or equivalent. Offered: jointly with CS&SS 560/SOC 560.

Class description

This is an applied data analysis course that focuses on hierarchical linear models. It explores basic statistical models such as linear and logistic regression, generalized linear models before moving on to introducing their multilevel extensions. Key issues related to simulation methods, causal inference, analysis of variance, sample size calculations, model selection and missing-data imputation will also be covered in depth. Although the underlying theory behind hierarchical models is certainly important, the numerous examples we will discuss during the lectures will be crucial. At the end of this course the students should be proficient at analyzing hierarchical data and fully understand all the statistical issues involved.

Student learning goals

General method of instruction

Recommended preparation

Class assignments and grading

The homework will count as 70% of your grade. There will be a final class project that counts for the rest of your grade. There will be no midterm or final exams.

The information above is intended to be helpful in choosing courses. Because the instructor may further develop his/her plans for this course, its characteristics are subject to change without notice. In most cases, the official course syllabus will be distributed on the first day of class.
Additional Information
Last Update by Adrian Dobra
Date: 01/02/2007