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Spring 2013 - 63096 - PA397C - Advanced Empirical Methods for Policy Analysis

Quantitative Methods for Social Policy Analysis

Instructor(s): Wong, Pat
Unique Number: 63096
Day & Time: T 9:00 am -12:00 pm
Room: SRh 3.221/212
Waitlist Information:For LBJ Students: UT Waitlist Information
Course Overview

In addition to the Introduction to Empirical Methods course in the common core, MPAff students are required to take another three-hour course in quantitative analysis, selected from among a set of courses focusing on the application of quantitative theory and techniques to policy analysis. Topics offered vary from year to year but include econometrics, demographic techniques, systems analysis, simulation modeling, and quantitative indicator methods. As the second course in the two-course MPAff quantitative sequence, this course is intended to provide students with an in-depth understanding and hands-on experience with a specific quantitative method useful in policy analysis. This course is usually taken during the second semester of the first year.

Section Description

Course Objective: AEM is the second course in the empirical methods sequence in the MPAff core curriculum. This section is an extension of the statistical inference portion of IEM.

This section is structured around research issues in social policy analysis and program evaluation, but no background in social policy is needed. We will use empirical studies in social policy research merely as the context to work towards the actual learning objective. That objective is to develop conceptual logic as well as practical skills in statistical analysis of policy issues. The logic and skills learned in this course is of course applicable to other policy areas as well.

The substantive content of the course is about 50 percent on understanding regression analysis—we will review concepts learned in IEM before moving on to advanced issues; and 50 percent on practical knowledge about research design, national data files, and hands-on data-analytic skills using Stata as statistical software.

Learning Experience:While “textbook” type reading suggestions will be offered by the instructor, course materials are organized mostly around actual empirical research articles. Class members will be asked to read carefully and analytically one empirical article each week on average. The purpose is to help develop the thought process in formulating and implementing analytic research.

The second major component of this course is an independent research project—team-based or individual-based depending on class size—which each student will start working on from the beginning of the semester. This research project will require (1) the formulation of an empirical research problem, (2) the use of a data set to analyze the problem, (3) the completion of a research brief for interpretation, and (4) a class presentation to teach class members the analytic issues involved. This is the “hands-on” part of the learning experience.

In addition to the two primary learning components above, the course will also include two individual-based, open-book, four-hour exercises on two designated Fridays, one around the fifth or sixth week and the other around the tenth or eleventh week of the semester.

Expectations: Successful completion of IEM at the LBJ School or its equivalent is a prerequisite. In particular, it is good understanding in the statistical analysis (not decision optimization) segment of IEM which is important for this course. Background in social policy is not necessary. Interest in thinking about the analytic research process will be very helpful. Class members can vote, on a majoritarian basis, whether to take notes in class or to refrain from note-taking in exchange for weekly notes from the instructor.

Tentative Organization of Topics

Wk 1      Conceptual Review 1: Research design and inferential logic

Wk 2      Conceptual Review 2: OLS Regression model and use of social data

Wk 3      Conceptual Review 3: Assumption violation in OLS Regression

Wk 4      Research Design: Level of data aggregation and targeting problem

Wk 5      Research Design: Social experiment, regression discontinuity, propensity scores

Wk 6      Limited-Criteria Models: Logic and use of logit, probit, and tobit models

Wk 7      Advanced issues in regression analysis

Wk 8      Social Indicators: Measurement of social phenomena

Wk 9      Integrative applications: Analysis of empirical research articles

Wk 10    Integrative applications: Analysis of empirical research articles

Wk 11    Revisiting Basic Issues in Statistical Logic

Wk 12    Project Presentations

Wk 13    Project Presentations

Wk 14    Project Presentations

(One empirical article each week from Week 4 to Week 8, and two  in Weeks 9 and 10.)