Essex Summer School in
Social Science Data Analysis

Chris Adolph

Chris Adolph is an assistant professor of political science, an adjunct professor of statistics, and a core faculty member in the Center for Statistics and the Social Sciences at the University of Washington, Seattle. He is the author of Bankers, Bureaucrats, and Central Bank Politics (Cambridge University Press, 2013), and has published in a variety of social science journals including the American Political Science Review and Political Analysis. His research interests lie in comparative political economy and quantitative methods and include the politics of monetary policy, bureaucratic and elite careers, the visual display of quantitative information, the analysis of rank data and compositional data, and health politics and policy.

2I Panel Data Analysis for Comparative Research

Chris Adolph, University of Washington–Seattle
22 July - 2 August (two week course / 35 hrs)

Detailed Course Outline [PDF]

Course Content

This course provides a survey of regression models for time series (TS) and time series cross-section (TSCS) data, with an emphasis on modeling dynamics and panel structures. After a review of the theory and estimation of linear regression and maximum likelihood, we will cover the following topics: modeling time series dynamics using ARIMA models, lagged dependent variables, and distributed lags; cointegration and error correction models; modeling cross-sectional variation using fixed and random effects; coping with panel heteroskedasticity, and presentation and interpretation of TS and TSCS models. Time permitting, we will cover advanced topics based on student interest, which in past years have included TSCS models for binary, ordered, or categorical data, multiple imputation for missing data in panel datasets, and linkages between panel data and hierarchical linear models.

Course Objectives

For many political science subfields, including political economy, international relations, and comparative politics, panel data are ubiquitous, and training in TSCS analysis essential for quantitative research. Participants will gain an introductory understanding of the theory behind TSCS models and a working understanding of how to estimate, select, and interpret these models. Emphasis is placed on the development of both conceptual understanding and the ability to apply tools learned in the class using a variety of packages available for the R statistical language.

Course Prerequisites

Students should enter the course with a solid understanding of first year statistics as taught in a standard political science doctoral program, an interest in data with either a time series or time series cross-sectional (panel) data structure, and either exposure to, or willingness to try, the R statistical package. Specifically, students should be familiar with basic data structures and computing, elementary matrix algebra, and the basic theory and application of the linear regression model.

Required Reading

Reading will be provided; however, students unfamiliar with R will benefit from reading this book ahead of the class.

Alain F. Zuur, Elena N. Ieno, and Erik H.W.G. Meesters. 2009. A Beginners Guide to R. Springer-Verlag.

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