2023 Spring

The Methodology Working Group meets this Spring from 2-3:30pm on Mondays in McNeil 367 (the Sociology Conference Room).

01/30/2023: Using UPenn SAS High Performance Computing/Slurm Scheduler and PSCStat for Computationally Intensive Research
Presenter: Xi Song

02/09/2023: Introduction to the PSID
Presenter: Paula Fomby
**NOTE: This presentation will take place on Thursday, February 9th from 2 – 3:30pm in McNeil 367

02/13/2023: Introduction to Supervised Machine Learning (Python Code) (Data)
Presenter: Rebecca Johnson (Assistant Professor of Public Policy, Georgetown University)

02/20/2023: Causal mediation analysis
Presenter: Nick Graetz

02/27/2023: Polynomial Regression with Response Surface Analysis
Presenter: Kuo Zhao

03/20/2023: Matching Methods
Presenter: Alexander Adames

03/27/2023: Regression Discontinuity Design
Presenter: Sneha Mani

04/03/2023 Introduction to the LIFE-M Database
Presenter: Paul Mohnen

04/10/2023:  Embracing Essential Discourse in Educational Policy about Causal Inferences from Observational Studies: Towards Pragmatic Social Science
Presenter: Ken Frank (Michigan State University)
Co-sponsored with the Education and Inequality Working Group

04/17/2023: Semantic Network Analysis
Presenter: Alejandra Regla-Vargas

04/24/2023: Computational Demography in R (tentative)
Presenter: Michael Lachanski (tentative)

05/01/2023: Shift-share instruments (tentative)
Presenter: Michael Lachanski (tentative)

2022 Fall

The Methodology Working Group workshops are held on Wednesdays from 10:30-11:30 am.

09/21/2022: An Introduction to Neural Networks
Presenters: Bhuv Jain and Dimitrios Tanoglidis
Resources: &

10/12/2022: FSRDC Data Types & Becoming a Special Sworn Status Researcher
Presenters: Joe Ballegeer (U.S. Census Bureau) and Jeff Lin (U.S. Census Bureau)

10/19/2022: Nonstable Population Relations and Applications in Aging, Labor, Health, Immigration (R Code)
Presenter: Michael Lachanski

10/26/2022: Kinship Estimation Using the DemoKin Package in R (R Code)
Presenters: Hal Caswell (University of Amsterdam) and Kai Feng

Data: DemoKin Package on GitHub

11/02/2022: Modern Regression Discontinuity Designs (R Code) (Data)
Presenter: Michael Lachanski


11/09/2022: Double/De-biased Machine Learning for Causal Inference (R Code

Presenter: Cheney Yu


11/16/2022: Marginal Structural Models (R Code) (Data)
Presenter: Sukie Yang

11/30/2022: Word Embedding Using Occupation Data (Python Code)
Presenter: Wenhao Jiang (NYU)


12/07/2022: Dyadic Sequence Analysis (R Code)
Presenter: Allison Dunatchik


2022 Summer

08/30/2022: Computer Vision in the Social Sciences
Presenter: Doron Shiffer-Sebba

06/21/2022: Estimation Philosophy
Presenters: Michael Lachanski & Richard Patti
Lundberg, Ian, Rebecca Johnson, and Brandon M. Stewart. 2021.”What Is Your Estimand? Defining the Target Quantity Connects Statistical Evidence to Theory.” American Sociological Review 86(3):532-65. 

06/10/2022: GitHub for Research Reproducibility and Transparency
Presenter: Hunter York

06/07/2022: Spatial Bayesian Regression (R Code) (Data)
Presenters: Treva Tam & Eugenio Paglino
Morris, Mitzi, Katherine Wheeler-Martin, Dan Simpson, Stephen J. Mooney, Andrew Gelman, and Charles DiMaggio. 2019. “Bayesian Hierarchical Spatial Models: Implementing the Besag York Mollié Model in Stan.” Spatial and Spatio-temporal Epidemiology 31:100301. [Alternative Access via Research Gate]
Book: Blangiardo, Marta, and Michela Cameletti. 2015. Spatial and Spatio-Temporal Bayesian Models with R-INLA. Chichester, England: Wiley. [Penn Libraries Online Access]

05/24/2022: Difference-in-Differences (Introduction) (R Code)
Presenter: Alexander Adames
Paper:  Angrist, Joshua D. and Jörn-Steffen Pischke. 2009. “Chapter 5: Parallel Worlds: Fixed Effects, Difference-in Differences, and Panel Data Angrist.” Pp. 221-246 in Mostly Harmless Econometrics: An Empiricist’s Companion. Princeton. NJ: Princeton University Press. [Penn Libraries Online Access]


05/29/2020: Data Visualization Mini-Hackathon