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 ]
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Office hours on Wed @ 2-4 PM in 124 Mt. Auburn St., Room 2310
[ Open, no signup needed ] -
Office hours on Thurs @ 3-4 PM in 124 Mt. Auburn St., Room 2310 or via Zoom
[ Please sign-up for an appointment ]
Jonathan Borck [ email ] [ Section A ]
Teddy Svoronos [ email ] [ Section B ]
Teaching Fellows
Ben Berger [ email ]
Guillermo Palacios [ email ]
Course Assistants
Gaby Aboulafia [ email ]
Stuti Ginodia [ email ]
Isaac Kim [ email ]
Kendrick McDonald [ email ]
Morgan Pratt [ email ]
Emma Winiski [ email ]
Jamie Wu [ email ]
Schedule [ Section C ]
Lectures: Tuesdays and Thursdays @ 1:30 PM - 2:45 PM in Wexner 330
Review sessions:
- Session 1: Fridays @ 12:00 PM - 1:15 PM in Littauer 140
- Session 2: Firdays @ 3:00 PM – 4:15 PM in Wexner 436
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 one of 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. Please let us know ways to improve the effectiveness of the course for you personally or for other students. You may use our (anonymous) comment box to let us know which aspects of the class are going well and which could be improved.
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, Sep 1: Introduction Handout ] [
- Tuesday, Sep 6: Data manipulation and visualization Handout ] [ R code ] [
- Thursday, Sep 8: Describing data: Statistical summaries Handout ] [ R code ] [
- Tuesday, Sep 13: Introduction to probability Handout ] [ R code ] [
- Thursday, Sep 15: Bayes' Rule Handout ] [ R code ] [
- Tuesday, Sep 20: Application: Public Pensions in Mexico Handout ] [
- Thursday, Sep 22: Discrete probability distributions Handout ] [ R code ] [
- Tuesday, Sep 27: Continuous probability distributions Handout ] [ R code ] [
- Thursday, Sep 29: Introduction to decision analysis Handout ] [
- Tuesday, Oct 4: Reviewing R Handout ] [ R code ] [
- Thursday, Oct 6: Thinking probabilistically about the world Handout ] [
- Tuesday, Oct 11: Final exercise Handout ] [
- Thursday, Oct 13: Introduction to statistical inference
- Tuesday, Oct 18: Bias and variance Handout ] [ R code ] [
- Thursday, Oct 20: Confidence intervals Handout ] [ R code ] [
- Tuesday, Oct 25: Estimating differences Handout ] [ R code ] [
- Thursday, Oct 27: Statistical significance Handout ] [ R code ] [
- Tuesday, Nov 1: Statistical significance and its limitations Handout ] [ R code ] [
- Thursday, Nov 3: Critical assessment of evidence Handout ] [
- Tuesday, Nov 8: Sampling and survey design Handout ] [
- Thursday, Nov 10: Application: Oregon Health Study Handout ] [
- Tuesday, Nov 15: Introduction to qualitative methods Handout ] [
- Thursday, Nov 17: Application: synthesizing evidence Handout ] [
- Tuesday, Nov 22: Looking back and looking ahead Handout ] [
- Tuesday, Nov 29: Final exercise presentations I
- Thursday, Dec 1: Final exercise presentations II