My talks at the University of Chicago this Thursday and Friday

Political Economy Workshop (12:30pm, Thurs 23 May 2019, Room 1022 of Harris Public Policy (Keller Center) 1307 E 60th Street):

Political Science and the Replication Crisis

We’ve heard a lot about the replication crisis in science (silly studies about ESP, evolutionary psychology, miraculous life hacks, etc.), how it happened (p-values, forking paths), and proposed remedies both procedural (preregistration, publishing of replications) and statistical (replacing hypothesis testing with multilevel modeling and decision analysis). But also of interest are the theories, implicit or explicit, associated with unreplicated or unreplicable work in medicine, psychology, economics, policy analysis, and political science: a model of the social and biological world driven by hidden influences, a perspective which we argue is both oversimplified and needlessly complex. When applied to political behavior, these theories seem to be associated with a cynical view of human nature that lends itself to anti-democratic attitudes. Fortunately, the research that is said to support this view has been misunderstood.

Some recommended reading:

[2015] Disagreements about the strength of evidence

[2015] The connection between varying treatment effects and the crisis of unreplicable research: A Bayesian perspective

[2016] The mythical swing voter.

[2018] Some experiments are just too noisy to tell us much of anything at all: Political science edition

Quantitative Methods Committee and QMSA (10:30am, Fri 24 May 2019, 5757 S. University in Saieh Hall (lower Level) Room 021):

Multilevel Modeling as a Way of Life

The three challenges of statistical inference are: (1) generalizing from sample to population, (2) generalizing from control to treatment group, and (3) generalizing from observed measurements to the underlying constructs of interest. Multilevel modeling is central to all of these tasks, in ways that you might not realize. We illustrate with several examples in social science and public health.

Some recommended reading:

[2004] Treatment effects in before-after data

[2012] Why we (usually) don’t have to worry about multiple comparisons

[2013] Deep interactions with MRP: Election turnout and voting patterns among small electoral subgroups

[2018] Bayesian aggregation of average data: An application in drug development