Sociology Statistics Reading Group

The Sociology Statistics Reading Group is an approximately bi-weekly group to discuss interesting statistical methods papers drawn from a wide range of literatures.  Each meeting we choose a paper which all members of the group read.  A single discussant then walks through the material and we have a broader group discussion.  

If you are at Princeton and want to join: please email our coordinators for this semester Rebecca Johnson (raj2 at princeton.edu) and Simone Zhang (sxz at princeton.edu).

Please note that the presentations below often draw figures from the original papers and sometimes from presentations graciously provided by the paper authors. For some exemplars of presentations see the Causal Forests presentation by Ian Lundberg or Sensitivity Analysis by Rebecca Johnson.

Year 3: 2018-2019
(Coordinators: Rebecca Johnson & Simone Zhang)

Year 2: 2017-2018
(Coordinator: Ian Lundberg)

May 3, 2018: Gradient Boosting
Efron and Hastie Computer Age Statistical Inference. 17.2-17.4
Presenter: Jeremy Cohen (Presentation)

April 19, 2018: Stochastic Actor Oriented Models
Block et al (2018) Social Networks. Change we can believe in: Comparing longitidunal network models on consistency, interpretability and predictive power.
Presenter: Daniel Karell (Presentation)

March 29, 2018: Machine Learning and Personalized Policy
Bansak et al (2018) Science Improving refugee integration through data-driven algorithmic assignment
Presenter: Hannah Postel (Presentation)

March 8, 2018: Scale-Free Networks
Barabási (2015) Network Science Chapter 4: The Scale-Free Property
Broido and Clauset (2018) "Scale-free networks are rare"
Presenter: Cambria Naslund (Presentation)

March 1, 2018: LASSO and Causal Inference
Belloni, Chernozhukov and Hansen (2014) "High-Dimensional Methods and Inference on Structural and Treatment Effects." Journal of Economic Perspectives.
Hastie, Tibshirani and Friedman (2009) Elements of Statistical Learning Selections from Chapter 3 on the LASSO
Presenter: Daniela Urbina Julio (Presentation)

February 8, 2018: Mixture of Regressions
Imai and Tingley (2012) "A Statistical Method for Empirical Testing of Competing Theories." American Journal of Political Science.
Presenter: Ryan Parsons (Presentation)

December 14, 2017: High-Dimensional Interactions
Egami and Imai (2017) "Causal Interaction in Factorial Experiments: Application to Conjoint Analysis."
Presenter: Belén Unzueta 

November 9, 2017: Sensitivity Analysis
Ding and Vanderweele (2016) "Sensitivity Analysis Without Assumptions." Epidemiology.
Presenter: Chris Felton (Presentation)

October 26, 2017: Exponential Random Graph Models
Robins, Pattison, Kalish and Lusher (2007) "An introduction to exponential random graph (p*) models for social networks." Social Networks.
Presenter: Janet Xu (Presentation)

October 12, 2017: Causal Forests: A Tutorial in High-Dimensional Causal Inference
Athey and Imbens (2016) "Recursive paritioning for heterogenous causal effects" Proceedings of the National Academy of Sciences.
Presenter: Ian Lundberg (Presentation)

September 28, 2017: Problems with p-values
Simmons, Nelson and Simonsohn (2011) "False-positive psychology: Undisclosed flexibility in data collection and analysis allows presenting anything as significant." Psychological Science.
Greenland et al. (2016) "Statistical tests, P values, confidence intervals, and power: a guide to misinterpretationsEuropean Journal of Epidemiology.
Presenter: Xinyi Duan

September 14, 2017: Predictive Social Science
Collins (1984) "Statistics versus WordsSociological Theory.
Hofman, Sharma and Watts (2017) "Prediction and explanation in social systemsScience.
Cranmer and Desmarais (2017) "What can we learn from predictive modeling?Political Analysis.
Presenter: Alex Kindel (Presentation)

Year 1: 2016-2017
(Coordinator: Clark Bernier)

May 4, 2017: Weights in Survey Experiments
Franco, Malhotra, Simonovits, Zigerell (2017) "Developing Standards for Post-Stratification Weighting in Population-Based Survey ExperimentsJournal of Experimental Political Science.
Miratrix, Sekhon, Theororidis and Campos (2017) "Worth Weighting? How to Think About and Use Sample Weights in Survey Experiments"
Presenter: Janet Xu (Presentation)

April 19, 2017: Mixed Factor Analysis
Quinn (2004) "Bayesian factor Analysis for Mixed Ordinal and Continuous ResponsesPolitical Analysis.
Presenter Ryan Parsons (Presentation)

March 30, 2017: Fixed Effects
Imai and Kim (2016) "When Should We Use Linear Fixed Effects Regression Models for Causal Inference With Longitudinal Data?"
Presenter Jeremy Cohen (Presentation)

March 16, 2017: Ecological inference
King, Rosen and Tanner (2004) "Information in Ecological Inference: An Introduction" in  Ecological Inference: New Methodological Strategies.
Imai and Khanna (2016) "Improving Ecological Inference by Predicting Individual Ethnicity from Voter Registration RecordsPolitical Analysis.
(Optional) King, Rosen, Tanner and Wagner "Ordinary Economic Voting Behavior in the Extraordinary Election of Adolf HitlerThe Journal of Economic History.
Presenter Simone Zhang (Presentation)
*Special thanks to Gary King for sharing slides used in this section.

March 2, 2017: Conjoint Analysis
Hainmueller, Hopkins and Yamamoto (2014) "Causal Inference in Conjoint Analysis: Understanding Multi-Dimensional Choices Via Stated Preference ExperimentsPolitical Analysis.
Presenter Xinyi Duan (Presentation)

February 16, 2017: Mixed Membership Stochastic Blockmodels
Airoldi, Blei, Fienberg and Xing (2008) "Mixed Membership Stochastic BlockmodelsJournal of Machine Learning Research.
Presenter: Herissa Lamothe (Presentation)

December 1, 2016: Cross-Validation
Ward, Greenhill and Bakke (2010) "The perils of policy by p-value: Predicting civil conflictsJournal of Peace Research.
Selections on Cross Validation from Andrew Ng and Hastie, Tibshirani and Friedman (2009).
Presenter: Alex Kindel (Presentation)

November 10, 2016: Front-door Adjustment Strategies
Glynn and Kashin (2014) "Front-door Versus Back-door Adjustment with Unmeasured Confounding: Bias Formulas for Front-door and Hybrid Adjustments"
Presenter: Ethan Fosse (Presentation)

October 27, 2016: Sensitivity Analysis
Blackwell (2014) "A Selection Bias Approach to Sensitivity Analysis for Causal EffectsAmerican Journal of Political Science.
and
Morgan and Winship (2015) "Chapter 12: Distributional Assumptions, Set Identification, and Sensitivity Analysis" in Counterfactuals and Causal Inference: Methods and Principles for Social Research, Second Edition.
Presenter: Rebecca Johnson (Presentation)

October 13, 2016: Mediation
Imai, Keele, Tingley, Yamamoto (2011) "Unpacking the black box of causality: Learning about causal mechanisms from experimental and observational studiesAmerican Political Science Review.
and
Acharya, Blackwell and Sen (2016) "Explaining Causal Findings Without Bias: Detecting and Assessing Direct EffectsAmerican Political Science Review.
Presenter: Ian Lundberg
*Special thanks to Dustin Tingley and Matt Blackwell for providing us with slides for this session.

September 29, 2016: Interactions
Hainmueller, Mummolo, Xu (2016) "How Much Should We Trust Estimates from Multiplicative Interaction Models? Simple Tools to Improve Empirical Practice"
Presenter: Jason Windawi (Presentation)
*Special Thanks to Yiqing Xu for supplying slides on which much of the presentation is based.

September 22, 2016: Colliders
Elwert and Winship (2014) "Endogenous selection bias: The problem of conditioning on a collider variableAnnual Review of Sociology
Presenter: Han Zhang (Presentation)