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Interdisciplinary Seminar in Quantitative Methods Archive 2017

About the workshops

The goal of the Interdisciplinary Seminar in Quantitative Methods is to provide an interdisciplinary environment where researchers can present and discuss cutting-edge research in quantitative methodology. The talks are aimed at a broad audience, with emphasis on conceptual rather than technical issues. The research presented is varied, ranging from new methodological developments to applied empirical papers that use methodology in an innovative way. We welcome speakers and audiences from all disciplines and fields, including the social, natural, biomedical, and behavioral sciences.

2017-2018 Series

Historical Record Linkage: An Overview of Methods and Their Performance

July 5, 2017: Martha Bailey, Economics, University of Michigan


Saving Science from Itself: Rethinking the Value of Research in an Era of Electronic Expertise

September 6, 2017: Arthur Lupia, Political Science, University of Michigan


Targeted Undersmoothing

September 20, 2017: Christian B. Hansen, Booth School of Business, The University of Chicago


Identification and Estimation of Spillover Effects in Randomized Experiments

October 4, 2017: Gonzalo Vazquez-Bare, Economics, University of Michigan


Text Preprocessing for Unsupervised Learning: Why It Matters, When It Misleads, and What to Do about It

October 25, 2017: Arthur Spirling, Politics and Center for Data Science, New York University


iFusion: Individualized Fusion Learning

November 15, 2017: Regina Liu, Statistics & Biostatistics, Rutgers University


Mate Pursuit and the Structure of Online Dating Market

December 6, 2017: Elizabeth Bruch, Sociology and Complex Systems, University of Michigan


Out of Bounds? Testing for Long Run Relationships under Uncertainty Over Univariate Dynamics

February 21, 2018: Suzanna Linn, Political Science, Pennsylvania State University


Measuring Attentiveness on Self-Administered Surveys

March 7, 2018: Adam Berinsky, Political Science, Massachusetts Institute of Technology


Clustering Analysis Through Integrating Diverse, High Dimensional and Noisy Data Sets

April 4, 2018: Hongyu Zhao, Biostatistics, Statistics, and Genetics, Yale University


Methods for Using Selection on Observed Variables to Address Selection on Unobserved Variables

April 25, 2018: Chris Taber, Economics, University of Wisconsin