PUAF 610

Quantitative Methods in Policy Analysis

Description

This course explores the use of statistics to shed light on policy issues. Through a variety of examples, we discuss how to translate policy questions into forms that can be analyzed quantitatively, assemble data and carry out computations, and interpret and present the results. Special attention is given to the validity of underlying assumptions and the detection of faulty analysis. The goal is not to make students into number crunchers, but to make them more informed consumers—and alert critics—of quantitative analyses. Along the way, students become proficient in using Microsoft Excel, which is an excellent general-purpose tool. The use of specific statistical techniques is unavoidable, but the larger goal is to develop certain patterns of thought and to gain a deeper appreciation for the strengths and limitations of quantitative analysis as one way of understanding policy issues and choices.

Problem sets and other important class documents and data are available from the class website here: http://www.puaf.umd.edu/puaf610/

Meetings

Monday

9:15 -11:55 am

Friday

Sec 201

Monday

Wednesday

7:00 -  9:30 pm

Sec 301

Wednesday

1:30 -  4:00 pm

Discussion sections are optional, to help with problem sets and review concepts introduced in the lectures. Students may attend any (or all!) lecture or discussion section, regardless of registration, so long as seats are available. (You should, however, take the midterm exam with the section in which you are registered.) Students should be aware that this semester's academic calendar forces the Wednesday section to be two weeks ahead of the Monday sections for most of the semester. The Monday afternoon discussion will track with the Wednesday subject matter, whereas the other sections will track with the Wednesday material. All meetings are in 1207 Van Munching Hall.

Grading

The course grade is determined as follows: weekly problem sets, 25%; midterm exam, 25%, final exam, 45%; mini-project, 5% (with the option of up to 2 additional mini-projects for extra credit) . Problem sets are self-graded, but it is essential that you complete and understand every assignment. We encourage students to work in small groups (2-4 people). Each student should bring a printed copy of of the problem set to class to be self-graded and handed in. Exam questions will be similar in nature to those on the problem sets, and must be answered without access to notes (except for one sheet of paper); see previous exams for examples. Exam retakes and incompletes will be granted only in exceptional circumstances (e.g., illness, or death in your family).

Textbook

Neil A. Weiss, Introductory Statistics, 7 th Ed. (Pearson Education, 2005) is available at both bookstores, and from on-line sources for less. This textbook is highly recommended, but not required.

Software

Instructors