Quantitative Analysis and Empirical Methods
This MPP core course introduces students to a range of analytic tools commonly used to inform public policy issues. Key content falls in the areas of descriptive statistics, probability theory, decision analysis, statistical inference, and qualitative approaches, with an emphasis on the ways in which they are applied to practical policy questions. The course also provides students with an introduction to the statistical programming language R, a powerful tool to analyze quantitative data.
Teaching team
Instructors
Sharad Goel [ email ] [ Section C ]
- Office hours on Weds @ 2-4 PM in 124 Mt. Auburn St., Room 2310 or via Zoom
[ Please sign-up for an appointment ] - Office hours on Thurs @ 3-4 PM in 124 Mt. Auburn St., Room 2310
[ Open, no signup needed ]
Charles Taylor [ email ] [ Section D ]
Teaching Fellows
Michelle Li [ email ]
Course Assistants
Manvika Gupta [ email ]
Madeline Shiley [ email ]
Luke Shields [ email ]
Huw Spencer [ email ]
Schedule [ Section C ]
Lectures: Tuesdays and Thursdays @ 1:30 PM - 2:45 PM in Rubenstein 306 Review sessions: Tuesdays @ 4:30 - 5:45 in Starr Aud (Belfer 200)
On-time attendance at lectures is required. Our aim is to create a collaborative and supportive learning environment. One of the best ways to learn the course material is to engage with the lectures by asking questions. If you are unable to attend class on a given day, please submit an excused absence request via the link on the Canvas page.
Attendance at the review sessions is highly encouraged, and you should plan to attend the sessions each week. These sessions are critical to understanding the week’s material and being able to complete the week’s problem set.
Inclusivity
It is our intent that all students, regardless of their backgrounds or perspectives, be well served by this course, that students’ learning needs be addressed both in and out of class, and that the diversity that students bring to this class be viewed as a resource, strength, and benefit. We aim to present materials and conduct activities in ways that are respectful of this diversity. Your suggestions are encouraged and appreciated.
Evaluation
Our main goal in this course is to help you learn concepts, develop skills, and ultimately change the way you think about the world. To achieve this, we expect you to exhibit the highest professional and ethical standards for every activity you undertake in the course.
The class grade will be based on the following criteria:
- Pre-class Exercises [ PCEs ]: 10%
- Class participation and engagement: 10%
- Problem Sets: 20%
- Midterm Exam: 20%
- Final Exam: 25%
- Final Exercise: 15%
[ Tentative ] Syllabus
- Thursday, Aug 31: Welcome & Introduction Handout ] [
- Tuesday, Sep 5: Intro to R Handout ] [ R code ] [
- Thursday, Sep 7: Grammar of data analysis - I Handout ] [ R code ] [
- Tuesday, Sep 12: Grammar of data analysis - II Handout ] [ R code ] [
- Thursday, Sep 14: Visualizing data Handout ] [
- Tuesday, Sep 19: The grammar of graphics Handout ] [ R code ] [
- Thursday, Sep 21: Selecting the right statistic Handout ] [
- Tuesday, Sep 26: Cause and effect Handout ] [
- Thursday, Sep 28: Strategies for causal inference Handout ] [
- Tuesday, Oct 3: Intro to probability Handout ] [
- Thursday, Oct 5: Bayes' rule Handout ] [
- Tuesday, Oct 10: Application: public pensions in Mexico Handout ] [
- Thursday, Oct 12: Thinking statistically about the world Handout ] [
- Tuesday, Oct 17: RStudio
- Thursday, Oct 19: Expected value Handout ] [
- Tuesday, Oct 24: Decision analysis Handout ] [
- Thursday, Oct 26: Intro to statistical inference Handout ] [
- Tuesday, Oct 31: Confidence intervals Handout ] [
- Thursday, Nov 2: Statistical significance Handout ] [
- Tuesday, Nov 7: Limits of statistical significance Handout ] [
- Thursday, Nov 9: Sampling and survey design Handout ] [
- Tuesday, Nov 14: Application: Oregon Health Study Handout ] [
- Thursday, Nov 16: Intro to qualitative research Handout ] [
- Tuesday, Nov 24: Looking back and looking ahead Handout ] [
- Tuesday, Nov 28: Final exercise presentations I
- Thursday, Nov 30: Final exercise presentations II