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Instructor Class Description

Time Schedule:

Ann Bostrom
PB AF 528
Seattle Campus

Quantitative Analysis II

Second quarter of a two-quarter sequence aimed at helping students become informed users and critical consumers of research and statistical analysis. Combines material on research design and data collection methods with tools for multivariate analysis. The multivariate analysis methods include correlation and an introduction to multivariate regression. Prerequisite: PB AF 527.

Class description

This course is the second of the two quarter Evans School sequence in Quantitative Analysis. The sequence aims to help you become an informed user and critical consumer of research and statistical analyses. This course introduces multivariate statistical models in the context of policy and management research, with a focus on multivariate regression analysis. The course begins with linear regression modeling, and includes more advanced regression techniques, with attention to the limitations and potential problems associated with using regression models and alternative models. In-class exercises, homework problems, additional readings and tests are used in the course to promote your first-hand experience and appreciation of regression analysis.

Student learning goals

Understand how to do policy analysis using multivariate regression analysis.

Be aware of the difference between correlation and causation, and criteria for assessing causality.

Select appropriate univariate, bivariate, or multivariate analytic techniques to answer a given policy or management question.

Understand the mechanics, assumptions, and interpretation of regression models for policy or management questions, how to use regression models for both prediction and hypothesis testing, and the assumptions behind and possible "fixes" for problems with models.

Produce a useful multivariate empirical analysis for a non-statistician, including clear data presentation and the graphical display of data.

Recognize how policy analysis, program evaluation, and performance measurement employ research methods and statistical techniques. Be exposed to nonlinear models and understand their purposes.

General method of instruction

Recommended preparation

Class assignments and grading

The course requirements include seven homework sets, one in-class exam, and a final data analysis exam. The purpose of the exam is to help diagnose your progress in learning the mechanics and interpretation of regression. The data analysis exam will be a take-home exam that will provide an opportunity for you to consolidate your learning about regression models, apply what you have learned to a policy context, and practice communicating your results to a nontechnical audience.

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.
Last Update by Ellen Weinstein
Date: 04/13/2011