Quantitative Methods Reading Group

Fortnightly group for PhD students

Please note that due to the COVID-19 outbreak, the Reading Group sessions expected to take place in Summer Term have not yet been scheduled. Further details will be provided on due course.

The Quantitative Methods Reading Group is a fortnightly gathering of PhD students.

At each session, one of the attendees will lead the discussion of an article which they found influential for their field or respective PhD topic. These articles can be reviews of existing methods, a newly developed statistical analysis, or a substantive paper using advanced methodology.

For PhD students in later stages of their PhD, this is an excellent opportunity to present their own work, provided that it fits the criteria outlined above. Nevertheless, the presenter does not need to be an expert of the particular field/method, novel and sometimes puzzling applications and approaches can be also proposed for discussion. The topic and discipline of the articles will vary each week, articles can touch upon quantitative applications regarding Big Data, text analysis, causal inference, structural equation modelling, longitudinal analysis etc. 

Fortnightly meetings take place at Columbia House in Seminar room 8.13 (COL8.13) on Tuesdays between 12:30 - 13:30. Complimentary sandwiches are provided for each event. If this piqued your interest please get in touch with Christian Mueller ( who will put you on the Reading Group’s mailing list.

2019/20 reading list

Lent Term

28th January

Elwert, F., & Pfeffer, F. T. (2019). The Future Strikes Back: Using Future Treatments to Detect and Reduce Hidden Bias. Sociological Methods & Research. doi: 10.1177/0049124119875958

11th February

De Vries, E., Schoonvelde, M., & Schumacher, G. (2018). No Longer Lost in Translation: Evidence that Google Translate Works for Comparative Bag-of-Words Text Applications. Political Analysis, 26(4), 417–430. doi: 10.1017/pan.2018.26

3rd March

Saris, W. E., & Revilla, M. (2015). Correction for Measurement Errors in Survey Research: Necessary and Possible. Social Indicators Research, 127(3), 1005–1020. doi: 10.1007/s11205-015-1002-x

17th March

Imai, K., & Kim, I. S. (2019). When Should We Use Unit Fixed Effects Regression Models for Causal Inference with Longitudinal Data? American Journal of Political Science, 63(2), 467–490. doi: 10.1111/ajps.12417

31st March


Michaelmas Term

8th October

Horiuchi, Y., Smith, D. M., & Yamamoto, T. (2018). Measuring Voters’ Multidimensional Policy Preferences with Conjoint Analysis: Application to Japan’s 2014 Election. Political Analysis, 26(2), 190–209. doi: 10.1017/pan.2018.2

22nd October

Starns, J. J., Cataldo, A. M., Rotello, C. M., Annis, J., Aschenbrenner, A., Bröder, A., … Wilson, J. (2019). Assessing Theoretical Conclusions With Blinded Inference to Investigate a Potential Inference Crisis. Advances in Methods and Practices in Psychological Science. doi: 10.1177/2515245919869583

12th November

Revilla, M., Ochoa, C., & Loewe, G. (2016). Using Passive Data From a Meter to Complement Survey Data in Order to Study Online Behavior. Social Science Computer Review, 35(4), 521–536. doi: 10.1177/0894439316638457.

26th November

De Benedictis-Kessner, J. (2018). Off-Cycle and Out of Office: Election Timing and the Incumbency Advantage. The Journal of Politics, 80(1), 119–132. doi: 10.1086/694396.

10th December

Leszczensky, L., & Wolbring, T. (2019). How to Deal With Reverse Causality Using Panel Data? Recommendations for Researchers Based on a Simulation Study. Sociological Methods & Research, OnlineFirst. doi: 10.1177/0049124119882473.

2018/19 reading list

Cranmer, S. J., Leifeld, P., McClurg, S. D., & Rolfe, M. (2017). Navigating the Range of Statistical Tools for Inferential Network Analysis. American Journal of Political Science, 61(1), 237–251.


Wiklund, J., & Shepherd, D. A. (2011). Where to from here? EO-as-experimentation, failure, and distribution of outcomes. Entrepreneurship: Theory and Practice, 35(5), 925–946.

Kim, Y., & Muthén, B. O. (2009). Two-Part Factor Mixture Modeling: Application to an Aggressive Behavior Measurement Instrument. Structural Equation Modeling: A Multidisciplinary Journal, 16(4), 602–624.

Voelkle, M. C., Gische, C., Driver, C. C., & Lindenberger, U. (2019). The Role of Time in the Quest for Understanding Psychological Mechanisms. Multivariate Behavioral Research.

Rozenas, A. (2017). Detecting Election Fraud from Irregularities in Vote-Share Distributions. Political Analysis, 25(1), 41–56;

Berk, R., Heidari, H., Jabbari, S., Kearns, M., & Roth, A. (2018). Fairness in Criminal Justice Risk Assessments: The State of the Art. Sociological Methods and Research, OnlineFirst, 1–42.

Lazer, D., & Radford, J. (2017). Data Ex Machina: Introduction to Big Data. Annual Review Of Sociology, 43(1), 19–39.

Callaway, B., & Sant’Anna, P. H. C. (2018). Difference-in-Differences With Multiple Time Periods and an Application on the Minimum Wage and Employment. Arxiv.

Bianconcini, S., & Bollen, K. A. (2018). The Latent Variable-Autoregressive Latent Trajectory Model: A General Framework for Longitudinal Data Analysis. Structural Equation Modeling, 25(5), 791–808.

Berry, C.R. & Fowler A. (2018). Leadership or Luck? Randomization Inference for Leader Effects. Working paper.

Buchanan, A. L., Hudgens, M. G., Cole, S. R., Mollan, K. R., Sax, P. E., Daar, E. S., … Mugavero, M. J. (2018). Generalizing evidence from randomized trials using inverse probability of sampling weights. Journal of the Royal Statistical Society. Series A: Statistics in Society, Early View.

Marquardt, K. L., & Pemstein, D. (2018). IRT Models for Expert-Coded Panel Data. Political Analysis, FirstView.

2017/18 reading list

An, W., Winship, C. (2017). Causal Inference in Panel Data With Application to Estimating Race-of-Interviewer Effects in the General Social Survey. Sociological Methods & Research, 46(1), 68-102.

Bell, A. & Jones, K. (2015). Explaining Fixed Effects: Random Effects Modeling of Time-Series Cross-Sectional and Panel Data. Political Science Research and Methods, 3, 133-153.

Kim, I. S., Londregan, J., & Ratkovic, M. (2018). Estimating Spatial Preferences from Votes and Text. Political Analysis, 26(2), 210–229.

King, G., Lam, P., & Roberts, M. E. (2017). Computer-Assisted Keyword and Document Set Discovery from Unstructured Text. American Journal of Political Science, 61, 1–18.

Klasnja, M., Titunik, R. (2017). The Incumbency Curse: Weak Parties, Term Limits, and Unfulfilled Accountability. American Political Science Review, 111(1), 129-148.

Na, C., Loughran, T.A. & Paternoster, R. (2015). On the Importance of Treatment Effect Heterogeneity in Experimentally-Evaluated Criminal Justice Interventions. Journal of Quantitative Criminology, 31, 289-310.

Nosek, B.A. et al. (2015). Promoting an open research culture. Science, 348(6242):1422-1425. 

Roberts, M. E., Stewart, B. M., Tingley, D., Lucas, C., Leder-Luis, J., Gadarian, S. K., Rand, D. G. (2014). Structural Topic Models for Open-Ended Survey Responses. American Journal of Political Science, 58(4), 1064–1082.

Rosenfeld, B., Imai, K., & Shapiro, J. N. (2016). An empirical validation study of popular survey methodologies for sensitive questions. American Journal of Political Science, 60(3), 783-802.

Wang, X. & Wang, Y. (2018). "'Say Goodbye to the Good Old Days': Anti-corruption, Uncertainty and Firm Behaviors in China". Working paper.

Watanabe, K. (2017). Measuring news bias: Russia’s official news agency ITAR-TASS’ coverage of the Ukraine crisis. European Journal of Communication, 32, 224-241.

Zubizarreta, J.R., Keele, L. (2017). Optimal Multilevel Matching in Clustered Observational Studies: A Case Study of the Effectiveness of Private Schools Under a Large-Scale Voucher System, Journal of the American Statistical Association, 112:518, 547-560.