Social Science Quantitative Methods Reading Group

The University of Kansas Social Science Quantitative Methods Reading Group meets monthly to discuss quantitative research methods in the social sciences. Topics will vary and may include causal inference, longitudinal analysis, quantitative methods using big data, structural equation modeling, and text analysis.

Coffee and snacks are provided courtesy of the Psychology Department.

September 29th, 20234:00 PMKansas UnionAlderson AuditoriumDifference-in-Differences
October 27th, 20234:00 PMKansas UnionCentennial RoomMeasurement Validation
December 1st, 20234:00 PMKansas UnionKansas RoomDecomposition Models
January 26th, 20244:00 PMKansas UnionAlderson AuditoriumTime Series Statistics
February 23rd, 20244:00 PMKansas UnionAlderson AuditoriumModel Interpretation
March 29th, 20244:00 PMKansas UnionAlderson AuditoriumNatural Experiments
April 19th, 20244:00 PMKansas UnionAlderson AuditoriumComputational Modeling of Spatio-Temporal Processes

Helpful links for upcoming reading groups. 

March, 2024

1) Dobkin LM, Gould H, Barar RE, Ferrari M, Weiss EI, Foster DG. Implementing a prospective study of women seeking abortion in the United States: understanding and overcoming barriers to recruitment. Women's Health Issues. 2014 Jan-Feb;24(1):e115-23. doi: 10.1016/j.whi.2013.10.004.

2) Miller, Sarah, Laura R. Wherry, and Diana Greene Foster. 2023. "The Economic Consequences of Being Denied an Abortion." American Economic Journal: Economic Policy, 15 (1): 394-437.

3) Ingo E. Isphording, Marc Lipfert, Nico Pestel. Does re-opening schools contribute to the spread of SARS-CoV-2? Evidence from staggered summer breaks in Germany, Journal of Public Economics, Volume 198, 2021.

4) Fetzer T, Graeber T. Measuring the scientific effectiveness of contact tracing: Evidence from a natural experiment. Proc Natl Acad Sci U S A. 2021 Aug 17;118(33):e2100814118. doi: 10.1073/pnas.2100814118.

5) Barnett ML, Olenksi AR, Jena AB. Opioid Prescribing by Emergency Physicians and Risk of Long-Term Use. N Engl J Med. 2017 May 11;376(19):1896. doi: 10.1056/NEJMc1703338. PMID: 28489999.

February, 2024

1) Lundberg and Lee (2017). A unified approach to interpreting model predictions. 31st Conference on Neural Information Processing Systems.

2) Stenwig, E., Salvi, G., Rossi, P. S., & Skjærvold, N. K. (2022). Comparative analysis of explainable machine learning prediction models for hospital mortality. BMC Medical Research Methodology, 22(1), 1-14.

3) Link to webpage that explains the R package and methods implemented in Lundberg and Lee's 2017 paper: SHAPVIZ

Helpful links for past reading groups. 

January, 2024

1) Webb, Linn, and Lego (2019). A Bounds Approach to Inference Using the Long Run Multiplier. Political Analysis 27:281-301.

2) Webb, Linn, and Lebo (2020). Beyond the Unit Root Question: Uncertainty and Inference. American Journal of Political Science 64(2): 275-292. 

3) Slides, Stata Code, R Code, and Data

November/December, 2023

1) Fan, W. and L. Luo (2020). Understanding Trends in the Concentration of Infant Mortality among Disadvantaged White and Black Mothers in the U.S. 1983-2013. Demography 57:919-1005. 

2) Prickett and Augustine, J (2021). Trends in Mothers Parenting Time by Education and Work 2003 - 2017. Demography 58(3): 1065-91. 

October, 2023

1) Flake, J. K., & Fried, E. I. (2020). Measurement schmeasurement: Questionable measurement practices and how to avoid them. Advances in Methods and Practices in Psychological Science, 3(4), 456–465.

2) Cizek, G. J. (2016). Validating test score meaning and defending test score use: Different aims, different methods. Assessment in Education: Principles, Policy & Practice, 23(2), 212–225.

September, 2023

Introduction to Difference-in-Differences (CFDR Workshop)

Paper on Difference-in-Differences (Wooldridge, 2021)



Mailing List

Use the link below to add your name to the email list for the University of Kansas Social Science Quantitative Methods Reading Group. This list will be used for updates about workshops and reading sessions organized by the Economic, Political Science, Psychology, and Sociology departments in coordination with the Kansas Data Science Consortium (KDSC).