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Christopher Zorn

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University of Ljubljana, University of St.Gallen
Pennsylvania State University

Course location

University of Ljubljana, University of St.Gallen

Home university

Pennsylvania State University
Chris_Zorn
Christopher Zorn is the Liberal Arts Research Professor of Political Science, Professor of Sociology and Crime, Law, and Justice (by courtesy), and Affiliate Professor of Law at Pennsylvania State University. He holds a Ph.D. in political science from Ohio State University (1997) and a B.A. in political science and philosophy from Truman State University (1991). His research focuses on judicial politics and on statistics for the social and behavioral sciences. His work has appeared in the American Political Science Review, the American Journal of Political Science, the Journal of Politics, the Journal of Law, Economics and Organization, Political Analysis, and numerous other journals. Professor Zorn is also the recipient of eight grants from the NSF, as well as numerous other fellowships and awards.

Courses taught by this instructor

Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Next course

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Course

Description

Instructor

Level

B = Basic
M = Intermediate
A = Advanced

Location

Next course

Analyzing Panel Data

Analysts increasingly find themselves presented with data that vary both over cross-sectional units and across time. Such panel data provides unique and valuable opportunities to address substantive questions in the economic, social, and behavioral sciences. This course will begin with a discussion of the relevant dimensions of variation in such data, and discuss some of the challenges and opportunities that such data provide. It will then progress to linear models for one-way unit effects (fixed, between, and random), models for complex panel error structures, dynamic panel models, nonlinear models for discrete dependent variables, and models that leverage panel data to make causal inferences in observational contexts. Students will learn the statistical theory behind the various models, details about estimation and inference, and techniques for the visualization and substantive interpretation of their statistical results. Students will also develop statistical software skills for fitting and interpreting the models in question, and will use the models in both simulated and real data applications. Students will leave the course with a thorough understanding of both the theoretical and practical aspects of conducting analyses of panel data.
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A

2023

2023

Analyzing Panel Data

Analysts increasingly find themselves presented with data that vary both over cross-sectional units and across time. Such panel data provides unique and valuable opportunities to address substantive questions in the economic, social, and behavioral sciences. This course will begin with a discussion of the relevant dimensions of variation in such data, and discuss some of the challenges and opportunities that such data provide. It will then progress to linear models for one-way unit effects (fixed, between, and random), models for complex panel error structures, dynamic panel models, nonlinear models for discrete dependent variables, and models that leverage panel data to make causal inferences in observational contexts. Students will learn the statistical theory behind the various models, details about estimation and inference, and techniques for the visualization and substantive interpretation of their statistical results. Students will also develop statistical software skills for fitting and interpreting the models in question, and will use the models in both simulated and real data applications. Students will leave the course with a thorough understanding of both the theoretical and practical aspects of conducting analyses of panel data.
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Regression for Publishing

This course builds directly upon the foundations laid in Regression II, with a focus on successfully applying linear and generalized linear regression models. After a brief review of the linear regression model, the course addresses a series of practical issues in the application of such models: presentation and discussion of results (including tabular, graphical, and textual modes of presentation); fitting, presentation, and interpretation of two- and three-way multiplicative interaction terms; model specification for dealing with nonlinearities in covariate effects; and post-estimation diagnostics, including specification and sensitivity testing. The course then moves to a discussion of generalized linear models, including logistic, probit, and Poisson regression, as well as textual, tabular, and graphical methods for presentation and discussion of such models. The course concludes with a “participants’ choice” session, where we will discuss specific issues and concerns raised by students’ own research projects and agendas.
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A

Regression for Publishing

This course builds directly upon the foundations laid in Regression II, with a focus on successfully applying linear and generalized linear regression models. After a brief review of the linear regression model, the course addresses a series of practical issues in the application of such models: presentation and discussion of results (including tabular, graphical, and textual modes of presentation); fitting, presentation, and interpretation of two- and three-way multiplicative interaction terms; model specification for dealing with nonlinearities in covariate effects; and post-estimation diagnostics, including specification and sensitivity testing. The course then moves to a discussion of generalized linear models, including logistic, probit, and Poisson regression, as well as textual, tabular, and graphical methods for presentation and discussion of such models. The course concludes with a “participants’ choice” session, where we will discuss specific issues and concerns raised by students’ own research projects and agendas.
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