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: https://github.com/dtanoglidis/ML_Edu_Demos/blob/main/UPenn_Methodology_DL.ipynb & https://github.com/dtanoglidis/ML_Edu_Demos

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
Papers:

• • Caswell, Hal. 2019. The Formal Demography of Kinship: A Matrix Formulation. Demographic Research 41(24): 679-712.
  • Caswell, Hal. 2020. The Formal Demography of Kinship II: Multistate Models, Parity, and Sibship. Demographic Research 42(38): 1097-1144
  • Caswell, Hal. and Xi Song. 2021. The Formal Demography of Kinship III. Kinship Dynamics with Time-Varying Demographic Rates. Demographic Research 45(16):517-546.
  • Caswell, Hal. 2022. The Formal Demography of Kinship IV: Two-Sex Models. bioRxiv.

Data: DemoKin Package on GitHub

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

Papers:

• • Cattaneo, M. D., & Titiunik, R. (2022). Regression discontinuity designs. Annual Review of Economics, 14, 821-851.
  • Cattaneo, M. D., Keele, L., & Titiunik, R. (2021). Covariate Adjustment in regression discontinuity designs. arXiv preprint arXiv:2110.08410.
  • Cattaneo, M. D., Idrobo, N., & Titiunik, R. (2019). A practical introduction to regression discontinuity designs: Foundations. Cambridge University Press.

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

Presenter: Cheney Yu

Papers:

• • Chernozhukov, Victor, Denis Chetverikov, Mert Demirer, Esther Duflo, Christian Hansen, Whitney Newey, and James Robins. 2018. “Double / Debiased Machine Learning for Treatment and Structural Parameters,” Econometrics Journal 21(1):1-68.
  • Felderer, B., Kueck, J., & Spindler, M. (2022). Using Double Machine Learning to Understand Nonresponse in the Recruitment of a Mixed-Mode Online Panel. Social Science Computer Review, 08944393221095194.
  • Robinson, Peter. 1988. “Root-N-Consistent Semiparametric Regression,” Econometrica 56(4):931-954.

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)

Papers:

• • Kozlowski, A. C., Taddy, M., & Evans, J. A. (2019). The geometry of culture: Analyzing the meanings of class through word embeddings. American Sociological Review, 84(5), 905-949.
  • Singular Value Decomposition Tutorial

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

Papers:

• • Liao, T,  Bolano, D., Brzinsky-Fay, C., Cornwell, B., Fasang, A. E., Helske, S., Piccarreta, R., Raab, M., Ritschard, G., Struffolino, E., Studer, M. (2022) Sequence analysis: Its past, present, and future. Social Science Research.
  • Liao, T. (2021) Using Sequence Analysis to Quantify How Strongly Life Courses Are Linked. Sociological Science

2022 Summer

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

• • Torres, Michelle and Francisco Cantú. 2021. “Learning to See: Convolutional Neural Networks for the Analysis of Social Science Data.” Political Analysis 30(1): 113-131.
  • Goldstein, Yoav, Nicolas M. Legewie, and Doron Shiffer-Sebba. 2022. “3D Social Research: Analysis of Social Interaction Using Computer Vision.” Sociological Methods and Research, Conditionally Accepted.

06/21/2022: Estimation Philosophy
Presenters: Michael Lachanski & Richard Patti
Paper: 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
Paper: 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]

2020

05/29/2020: Data Visualization Mini-Hackathon