Generalized Linear Models

Citation:

Gill, Jeff. “Generalized Linear Models.” In Encyclopedia of Social Science Research Methods. Sage, 2004.

Notes:

Reprints: Postscript, PDF.

Publisher's Version

Last updated on 06/03/2020

Contact

American University
(202) 885-6275
4400 Massachusetts Avenue, NW
Washington, DC 20016
jgill@american.edu

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Recent Publications

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

gillsppq2020.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.

Gill, Jeff, and Simon Heuberger. “Bayesian Modeling and Inference: A Postmodern Perspective.” In SAGE Handbook of Research Methods in Political Science and International Relations, edited by Luigi Curini and Robert Franzese. Sage, 2020. Publisher's Version

Bayesian Modeling and Inference 2020.pdf

The attached PDF is the correct version - please note that the publisher's version does not show the correct formulas.

Gill, Jeff, and Michelle Torres. Generalized Linear Models: A Unified Approach. Second Ed. Thousand Oaks, CA: Sage, 2019. Publisher's Version Abstract

Generalized Linear Models: A Unified Approach provides an introduction to and overview of GLMs, with each chapter carefully laying the groundwork for the next. The Second Edition provides examples using real data from multiple fields in the social sciences such as psychology, education, economics, and political science, including data on voting intentions in the 2016 U.S. Republican presidential primaries. The Second Edition also strengthens material on the exponential family form, including a new discussion on multinomial distribution; adds more information on how to interpret results and make inferences in the chapter on estimation procedures; and has a new section on extensions to generalized linear models.

Software scripts, supporting documentation, data for the examples, and some extended mathematical derivations are available on the authors’ websites as well as through the \texttt{R} package \texttt{GLMpack}. All links are available at www.sagepub.com/gill2e.

JEFF GILL

Distinguished Professor,

Department of Government
Department of Mathematics & Statistics,

Founding Director, Center for Data Science
Member, Center for Behavioral Neuroscience

Generalized Linear Models

Citation:

Notes:

Contact

Publications by Type:

Recent Publications

564f00644b4c4c0124c207bc10176a44

JEFF GILL

Distinguished Professor,

Department of GovernmentDepartment of Mathematics & Statistics,

Founding Director, Center for Data ScienceMember, Center for Behavioral Neuroscience

Citation:

Notes:

Contact

Publications by Type:

Recent Publications

564f00644b4c4c0124c207bc10176a44

Department of Government
Department of Mathematics & Statistics,

Founding Director, Center for Data Science
Member, Center for Behavioral Neuroscience