Generalized Linear Models

Gill, Jeff. “Political Science Is A Data Science.” Journal of Politics (Forthcoming).

Gill, Jeff, and Kumail Wasif. “Bayesian Analyses of Political Decision Making.” In Oxford Encyclopedia of Political Decision Making, edited by Dave Redlawsk, Forthcoming. Publisher's Version

Gill, Jeff. “Measuring Constituency Ideology Using Bayesian Universal Kriging.” State Politics & Policy Quarterly (2020). Abstract

sppq2020.pdf

sppqappendices.pdf

In this article, we develop and make available measures of public ideology in 2010 for the 50 American states, 435 congressional districts, and state legislative districts. We do this using the geospatial statistical technique of Bayesian universal kriging, which uses the locations of survey respondents, as well as population covariate values, to predict ideology for simulated citizens in districts across the country. In doing this, we improve on past research that uses the kriging technique for forecasting public opinion by incorporating Alaska and Hawaii, making the important distinction between ZIP codes and ZIP Code Tabulation Areas, and introducing more precise data from the 2010 Census. We show that our estimates of ideology at the state,
congressional district, and state legislative district levels appropriately predict the ideology of legislators elected from these districts, serving as an external validity check.

JEFF GILL

Distinguished Professor,

Department of Government
Department of Mathematics & Statistics,

Founding Director, Center for Data Science
Member, Center for Neuroscience and Behavior

Generalized Linear Models

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