Quant Reading header-Students_9369

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 online on Zoom on Tuesdays between 16:00 - 17:00 (BST). If this piqued your interest please get in touch with Thiago Oliveira (T.Rodrigues-Oliveira@lse.ac.uk) who will add you on the Reading Group’s mailing list.

2020/21 reading list

Summer Term

19th May

Machine learning for social science

Discussant: Dr Yan Wang

Reading: Gimmer, Justin, Margaret Roberts, & Brandon Stewart. 2021. ‘Machine Learning for Social Science: An Agnostic Approach.’ Annual Review of Political Science 24. https://doi.org/10.1146/annurev-polisci-053119-015921  

Extra reading: Keith, Katherine, David Jensen, & Brendan O-Connor. 2020. ‘Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates.’ ArXiv preprint arXiv:2005.00649. https://arxiv.org/abs/2005.00649

Lent Term

2nd February

Discussion on intersectional bias in hate speech and abusive language datasets

Discussant: Sarah Jewett

Reading: Kim, Jae Yeon, Carlos Ortiz, Sarah Nam, Sarah Santiago, & Vivek Datta. 2020. ‘Intersectional Bias in Hate Speech and Abusive Language Datasets’. ArXiv 2005.05921https://arxiv.org/abs/2005.05921

16th February

How to measure social desirability bias? Taboos, Trump and COVID-19

Discussant: Katharina Lawall

Reading: Coppock, Alexander. "Did Shy Trump Supporters Bias the 2016 Polls? Evidence from a Nationally-representative List Experiment" Statistics, Politics and Policy, vol. 8, no. 1, 2017, pp. 29-40. https://doi.org/10.1515/spp-2016-0005

2nd March

Conjoint experiments

Discussant: Noam Titelman

Reading: Bansak, Kirk, Jens Hainmueller, Daniel Hopkins, and Teppei Yamamoto. 2019. ‘Beyond the breaking point: Survey satisficing in conjoint experiments.’ Political Science Research and Methods 9 (1): 53-71. https://doi.org/10.1017/psrm.2019.13

Extra reading: Hainmueller, Jens, Dominik Hangartner, and Teppei Yamamoto. 2015. ‘Validating vignette and conjoint survey experiments against real-world behavior.’ Proceedings of the National Academy of Sciences of the United States 112 (8): 2395-2400. https://doi.org/10.1073/pnas.1416587112 - Bridges, John FP, A brett Hauber, Deborah Marshall, Andrew Lloyd, Lisa A Prosser, Dean A Regier, F Reed Johnson, Josephine Mauskopf. 2011. ‘Conjoint analysis applications in health — a checklist: a report of the ISPOR Good Research Practices for Conjoint Analysis Task Force.’ Value in Health 14 (4), 403-413. http://doi.org/10.1016/j.jval.2010.11.013

16th March

Causal inference using front-door estimators

Discussant: Thiago R. Oliveira

Reading: Glynn, Adam N. & Konstantin Kashin. 2017. Front‐Door Difference‐in‐Differences Estimators. American Journal of Political Science 61 (4): 989-1002. https://doi.org/10.1111/ajps.12311

30th March (this event has been postponed to Summer Term, date TBC)

Machine learning for social science

Discussant: Yan Wang

Reading: Gimmer, Justin, Margaret Roberts, & Brandon Stewart. 2021. ‘Machine Learning for Social Science: An Agnostic Approach.’ Annual Review of Political Science 24. https://doi.org/10.1146/annurev-polisci-053119-015921  

Extra reading: Keith, Katherine, David Jensen, & Brendan O-Connor. 2020. ‘Text and Causal Inference: A Review of Using Text to Remove Confounding from Causal Estimates.’ ArXiv preprint arXiv:2005.00649. https://arxiv.org/abs/2005.00649

Michaelmas Term

20th October

Causal inference using panel data?

Discussant: Thiago Oliveira

Reading: 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 Science63(2), 467–490. doi: 10.1111/ajps.12417

Extra reading: Imai, K., & Kim, I. S. (2020). On the Use of Two-way Fixed Effects Regression Models for Causal Inference with Panel Data. Manuscript available at: http://web.mit.edu/insong/www/pdf/FEmatch-twoway.pdf

3rd November

Election night! Predicting election outcomes

Discussant: Denise Baron

Reading: Gelman, A. & Silver, N. (2010). What Do We Know at 7 PM on Election Night? Mathematics Magazine, 83: 258-266.

An evaluation of 2016 Election Polls in the US

 What Pollsters Have Changed Since 2016 — And What Still Worries Them About 2020

Exit Polls Can Be Misleading — Especially This Year

17th November

The use of the causal inference framework to deal with nonprobability surveys

Discussant: Oriol Bosch-Jover

Reading: Mercer, A.; Kreuter, F.; Keeter, S.; & Stuart, E. (2017). Theory and practice in nonprobability surveys: parallels between causal inference and survey inference. Public Opinion Quarterly, 81(1): 250-271. https://doi.org/10.1093/poq/nfw060  

Extra reading: Cornesse, C. et al. (2020). A review of conceptual approaches and empirical evidence on probability and nonprobability sample survey research. Journal of Survey Statistics and Methodology, 8(1): 4-26. https://doi.org/10.1093/jssam/smz041

Kohler, U.; Kreuter, F.; & Stuart, E. (2019). Nonprobability sampling and causal analysis. Annual Review of Statistics and Its Application, 6: 149-172. https://doi.org/10.1146/annurev-statistics-030718-104951

8th December

Estimating political ideology using Twitter data

Discussant: Yuanmo He

Reading: Barberá, Pablo. 2015. ‘Birds of the Same Feather Tweet Together: Bayesian Ideal Point Estimation Using Twitter Data’. Political Analysis 23(1):76–91. doi: 10.1093/pan/mpu011. (Yuanmo is also suggesting we read the methods section of this paper, as the method provides faster estimation on the same model) Barberá, Pablo, John T. Jost, Jonathan Nagler, Joshua A. Tucker, and Richard Bonneau. 2015. ‘Tweeting From Left to Right: Is Online Political Communication More Than an Echo Chamber?’ Psychological Science 26(10):1531–42. doi: 10.1177/0956797615594620.

Extra reading: Bond, Robert, and Solomon Messing. 2015. ‘Quantifying Social Media’s Political Space: Estimating Ideology from Publicly Revealed Preferences on Facebook’. American Political Science Review 109(1):62–78. doi: 10.1017/S0003055414000525. Goldberg, Amir. 2011. ‘Mapping Shared Understandings Using Relational Class Analysis: The Case of the Cultural Omnivore Reexamined’. American Journal of Sociology 116(5):1397–1436. doi: 10.1086/657976.

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 Analysis26(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 Research127(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 Science63(2), 467–490. doi: 10.1111/ajps.12417

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 Analysis26(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 Politics80(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. 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.