Literary_Festival_bookshop_1366x1024_16-9_sRGBe

Quantitative Methods Reading Group

Fortnightly group for PhD students

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 (C.Mueller@lse.ac.uk) who will put you on the Reading Group’s mailing list.

2019/20 reading list

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

TBC

26th November

TBC

10th December

Usami, S., Murayama, K., & Hamaker, E. L. (2019). A unified framework of longitudinal models to examine reciprocal relations. Psychological Methods, 24(5), 637–657. doi: 10.1037/met0000210 

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. https://doi.org/10.1111/ajps.12263

 

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. https://doi.org/10.1111/j.1540-6520.2011.00454.x

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. https://doi.org/10.1080/10705510903203516

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. https://doi.org/10.1080/00273171.2018.1496813

Rozenas, A. (2017). Detecting Election Fraud from Irregularities in Vote-Share Distributions. Political Analysis, 25(1), 41–56;https://doi.org/10.1017/pan.2016.9

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. https://doi.org/10.1177/0049124118782533

Lazer, D., & Radford, J. (2017). Data Ex Machina: Introduction to Big Data. Annual Review Of Sociology, 43(1), 19–39. https://doi.org/10.1146/annurev-soc-060116-053457

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. https://doi.org/10.2139/ssrn.3148250

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. https://doi.org/10.1080/10705511.2018.1426467

Berry, C.R. & Fowler A. (2018). Leadership or Luck? Randomization Inference for Leader Effects. Working paper. https://www.dropbox.com/s/c3chnh7jntcqd1w/BerryFowler_Leaders.pdf

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. http://doi.org/10.1111/rssa.12357

Marquardt, K. L., & Pemstein, D. (2018). IRT Models for Expert-Coded Panel Data. Political Analysis, FirstView. http://doi.org/10.2139/ssrn.2897442

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.