Courses I currently teach
Introduction to Quantitative Social Science (first course in graduate methods sequence)
Fall 2022 SyllabusComparative Politics Field Seminar, Part 2 (Methods focus)
Spring 2023 SyllabusSocial Science Inquiry, Part 3 (Climate change focus)
Spring 2023 SyllabusPast teaching
Research design and methods (Oxford MT 2019)
My slides for both lecturesIntermediate causal inference (Oxford Spring School, 2019)
Causal Inference (Oxford HT 2019)
- Syllabus
- Week 1: Potential outcomes framework and experiments
- Week 2-3: Covariate adjustment
- Week 4: IV
- Week 5: RDD
- Week 6-7: Diff-in-diff and panel
- Week 8: Robustness tests, subgroup analysis, placebo tests
Research design and methods (Oxford MT 2018)
My slides for both lecturesSummer Institute (Oxford 2018)
US politics (Oxford HT 2018)
Slides for Weeks 5 and 6, HT 2018
Causal Inference (Oxford HT 2018)
- Week 1: Potential outcomes framework and experiments
- Weeks 2 and 3: Regression and matching
- Week 4: Instrumental variables
- Week 5: Regression discontinuity design
- Week 6: Diff-in-diff
- Week 7: Panel regression (generalizing diff-in-diff)
- Week 8: Treatment effect heterogeneity
Research Design and Methods (Oxford MT 2018)
Slides for Weeks 5 and 6, MT 2017
Political Analysis II (Oxford MT 2018)
Slides for Week 1 lecture, MT 2017
Summer Institute (Oxford 2017)
Content analysis (Oxford TT 2017)
- Slides for week 1
- Worksheet 1
- Worksheet 1 solution code
- Slides for week 2
- Worksheet 2
- Worksheet 2 solution code
Q-Step Data Day for Schools (Oxford 2017)
Panel data (Oxford TT, 2016)
- Syllabus
- Slides for week 1
- Worksheet for week 1
- Slides for week 2
- Slides for week 3 (after finishing week 2)
- Worksheet for week 3
Content analysis (Oxford TT, 2016)
- Syllabus
- Slides for week 1
- Worksheet for week 1
- Slides for week 2
- Worksheet for week 2
- R code: introduction to regular expressions
Oxford Spring School: Computerized Text Analysis (2016)
Lectures
- Slides for "Turning text into data" (pdf)
- Slides for "Statistical models" (pdf)
- Slides for "Scaling models" (pdf)
- Slides for classification and clustering (pdf)
Code
- R code for "Turning text into data" (and version from class)
- R code (wordfish) for "Scaling models"
- R code for specificities and correspondence factor analysis
- R code for classification and clustering
Data
- Candidate biographies from the Times Guide to the House of Commons, 1950-1970 (zipped CSV file, 1.2M)
- Candidate biographies from the Times Guide to the House of Commons, 1950 and Con-Lab-Lib only (zipped CSV file, 215k)
- Candidate biographies from the Times Guide to the House of Commons, 1950 and Con-Lab-Lib only (CSV file, 699k)
- Term document matrix for Federalist Papers -- stop words only (CSV file, 78k)
Syllabi
- Money in Politics (Yale undergrad seminar, spring 2011)
- Political science and public policy (LSE MPA lecture course, more or less the way I taught it in spring 2014)
- Theory of voting (Oxford graduate seminar, taught Hilary term 2015)
Lectures
Here are slides (and sometimes accompanying assignments) for some lectures I've given while teaching at the LSE and Oxford.
Political science and public policy, January 2016
- Electoral representation
- Collective action
- Lobbying and regulation of influence
- Social movements and revolution
- Media, internet, and politics
- Politics of migration
- Does democracy work?
Research design for postgrads, November 2015 (in which I told students about the "credibility revolution" and asked what it means for their research and the discipline)
- Research design (two weeks of a course on research design for our Comparative Government and European Politics student)
Panel data, April/May 2015 (this ended up being mostly about diff-in-diff, with some extensions)
Content analysis, April/May 2015 (the first two lectures in a four-week course)
- Content analysis, week 1 (mostly about dictionary methods, regular expressions, the need for good questions) [worksheet]
- Content analysis, week 2 (mostly about clustering and topic models) [worksheet]
Political Analysis (QStep Part 1), January-March 2015 (four lectures in an eight-lecture course)
- QStep1, week 2 (research design)
- QStep1, week 6 (bivariate analysis)
- QStep1, week 7 (multivariate analysis)
- QStep1, week 8 (inference, p-values, standard errors)
Politics of the USA, October 2015 (two lectures in a sixteen lecture course)
Teaching datasets and lab assignments
Lab assignments for Political Analysis (QStep Part 1), January-March 2015 (these were developed by a team including Andreas Murr, Spyros Kosmidis, Elias Dinas, Andrea Ruggeri, and me)