Methods Workshop

Department of Political Science Columbia University

Grad student led series introducing various applied statistical and formal methods and topics in statistical computing. Our primary constituency is comprised of PhD students and faculty in the political science department at Columbia. All CU-affiliated graduate students and faculty are also welcome. Individuals not currently affiliated with CU who wish to attend should contact the coordinators in advance.

2018-2019 Coordinators:
Simone Paci
Jose Gomez


© 2019. All rights reserved.

2018 - 2019 Academic Year

The workshop will meet on Fridays 3:30-5:00pm in Room 707 International Affairs Building, except where otherwise noted below.

Date
(Schedule Change)		 Topic Presenter
28 Sep 2018
(3:30pm, IAB 1201) 		Formal Models and Lab Experiments Giovanna Invernizzi
05 Oct 2018 Intro to Python Jeff Jacobs
19 Oct 2018 Web Scraping in R Erin York
2 Nov 2018 Working with Interview Data Colleen Wood
9 Nov 2018 Introduction to GIS in R - Volume 1 Merlin Noël Heidemanns
30 Nov 2018
(3:30pm, IAB 1201) 		RDDs: Theory and Practice Pablo Argonte
8 Feb 2019 Sentiment Analysis Jeff Jacobs
22 Feb 2019 Bayesian Process Tracing Theo Milonopoulos
1 Mar 2019 Regular Expressions Julian Gerez
8 Mar 2019 Conceptualizing Democracy Charles Battaglini
15 Mar 2019 The BIQQ Framework Simone Paci
5 Apr 2019 SurveyCTO Dylan Groves
24 Apr 2019 Introduction to GIS in R - Volume 2 Merlin Noël Heidemanns
3 May 2019 Design, Implementation and Analysis of Conjoint experiments Anja Kilibarda and Julia Rubio

Workshop Materials

List Experiments

list experiments statistics R
15 Nov 2013

Presenter: Alex Coppock, Alissa Stollwerk, Trish Kirkland, Dane Thorley

Install R packages:

  • arm, list

Readings:

  • Glynn, Adam N. 2013. “What can we learn with statistical truth serum? Design and analysis of the list experiment.” Public Opinion Quarterly. 77; 159-172.
  • Blair, Graeme and Kosuke Imai. 2012. “Statistical Analysis of List Experiments.” Political Analysis. 20: 47-77.

Intro to Sensitivity Analysis

sensitivity analysis statistics R
01 Nov 2013

Presenters: Alex Coppock, Albert Fang

Install R packages:

  • rbounds

Selected Readings:

Note: first three papers are short.

  • Imbens, Guido. 2003. “Sensitivity to Exogeneity Assumptions in Program Evaluation.” The American Economic Review. [download from CU Libraries]
  • Rosenbaum, Paul R. and Donald B. Rubin. 1983. “Assessing Sensitivity to an Unobserved Binary Covariate in an Observational Study with Binary Outcome.” JRSS, Series B. 45(2): 212-218. [download from CU Libraries]
  • Rosenbaum, Paul R. 2005. “Sensitivity Analysis in Observational Studies.” in Everitt, Brian S. and David C. Howell, eds. Encyclopedia of Statistics in Behavioral Science. Vol 4. pp. 1809-1814. [paper]
  • Harada, Masataka. 2013. “Generalized Sensitivity Analysis and Application to Quasi-Experiments.” Working Paper: New York University. [paper] [Stata ado files]

Other useful references:

  • Rosenbaum, Paul R. 2010. Design of Observational Studies. Springer-Verlag.
  • Rosenbaum, Paul R. 2002. Observational Studies. Springer.

Web Scraping in R and Python

web scraping computational methods R Python
18 Oct 2013

Presenters: Andy Guess, Sung Eun Kim

Install:

  • R packages: RCurl and XML
  • Python and relevant packages: See installation instructions handout [pdf]

Readings:

  • None

Survey Experiments

survey experiments statistics R
04 Oct 2013

Presenters: Sarah Khan, Dane Thorley, Lauren Young

Install:

  • R package: endorse (implements the statistical model proposed by Bullock, Imai & Shapiro 2011 to analyze endorsement experiments)

Key Readings:

  • Jason Barabas and Jennifer Jerit. Are survey experiments externally valid? American Political Science Review, 104(2), May 2010. [paper]
  • Jens Hainmueller, Daniel J. Hopkins, Teppei Yamamoto. 2013. “Causal Inference in Conjoint Analysis: Understanding Multidimensional Choices Via Stated Preference Experiments.” MIT Political Science Department Research Paper No. 2013-4. [paper]
  • Brian J. Gaines, James H. Kuklinski, and Paul J. Quirk. The logic of the survey experiment reexamined. Political Analysis, 15:1-20, 2007. [paper]
  • Diana C. Mutz. Population-Based Survey Experiments. Princeton, NJ: Princeton University Press, 2011.

Key Applied Papers:

  • Will Bullock, Kosuke Imai, and Jake Shapiro. Statistical analysis of endorsement experiments: Measuring support for militant groups in pakistan. Political Analysis, 19(4):363{384, 2011. [paper] [replication data]
  • Jason Lyall, Graeme Blair, and Kosuke Imai. Explaining support for combatants during wartime: A survey experiment in Afghanistan. American Political Science Review, forthcoming. [paper] [replication data]
  • Graeme Blair, C. Christine Fair, Neil Malhotra, and Jake Shapiro. Poverty and support for militant politics: Evidence from pakistan. American Journal of Political Science, 57(1):30-48, 2013. [paper] [replication data]
  • James M. Glaser and Timothy J. Ryan. Changing Minds, If Not Hearts. Philadelphia: University of Pennsylvania Press, 2013. Herbert H. Hyman and Paul B. Sheatsley. The current status of american public opinion. In The teaching of contemporary affairs. 1950.
  • Ilyana Kuziemko, Michael I. Norton, Emmanuel Saez, and Stefanie Stantcheva. How elastic are preferences for redistribution? evidence from randomized survey experiments. NBER Working Paper, (18865), March 2013.
  • Evan Lieberman, Daniel Posner, and Lily Tsai. Does information lead to more active citizenship? Evidence from an education intervention in rural Kenya. 2013.
  • Michael Tomz and Jessica Weeks. An experimental investigation of the democratic peace.

Regression Discontinuity

regression discontinuity statistics R
27 Sep 2013

Presenters: Albert Fang, Gabriella Sacramone-Lutz

Install:

  • R packages: rdd, AER, lmtest

Selected Background Papers:

  • Lee, David S. and Thomas Lemieux. 2010. “Regression Discontinuity Designs in Economics.” Journal of Economic Literature. 48: 281-355. [pdf; gated - access via CU Libraries]
  • Imbens, Guido and Karthik Kalyanaraman. 2012. “Optimal Bandwidth Choice for the Regression Discontinuity Estimator.” Review of Economic Studies. doi:10.1093/restud/rdr043 [pdf] [replication files]
  • McCrary, Justin. 2008. “Manipulation of the Running Variable in the Regression Discontinuity Design: A Density Test.” Journal of Econometrics. 142(2). [pdf] [replication files]

Selected Applied Papers:

  • Caughey, Devin and Jasjeet S. Sekhon. 2011. “Elections and the Regression Discontinuity Design: Lessons from Close U.S. House Races, 1942-2008.” Political Analysis. 19:385-408. doi:10.1093/pan/mpr032 [pdf]
  • Eggers, Andrew C., Olle Folke, Anthony Fowler, Jens Hainmueller, Andrew B. Hall, James M. Snyder, Jr. 2013. “On the Validity of the Regression Discontinuity Design for Estimating Electoral Effects: New Evidence from Over 40,000 Close Races.” Working Paper. [pdf]
  • Erikson, Robert S. and Rocio Titiunik. 2013. “Using Regression Discontinuity to Uncover the Personal Incumbency Advantage.” Working Paper. [pdf]