Panel data, which consist of information gathered from the same individuals or units at several different points in time, are commonly used in the social sciences to test theories of individual and social change. This book provides an overview of models that are appropriate for the analysis of panel data, focusing specifically on the area where panels offer major advantages over cross-sectional research designs: the analysis of causal interrelationships among variables. Without "painting" panel data as a cure all for the problems of causal inference in nonexperimental research, the author shows how panel data offer multiple ways of strengthening the causal inference process. In addition, he shows how to estimate models that contain a variety of lag specifications, reciprocal effects, and imperfectly measured variables. Appropriate for readers who are familiar with multiple regression analysis and causal modeling, this book will offer readers the highlights of developments in this technique from diverse disciplines to analytic traditions.
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Steven E. Finkel is the Daniel Wallace Professor of political science at the University of Pittsburgh. His areas of expertise include comparative political behavior, democratization, public opinion, and quantitative methods. He is the author of Causal Analysis With Panel Data (SAGE, 1995) as well as numerous articles on political participation, voting behavior, and civic education in new and established democracies. He holds a PhD in political science from the State University of New York at Stony Brook and has taught previously at the University of Virginia, Arizona State University, the Hertie School of Governance (Berlin), the University of Gothenburg (Sweden), and the University of Copenhagen (Denmark). His work has been supported by the National Science Foundation, the National Endowment for Democracy, the Deutscher Akademischer Austauschdienst (DAAD), and the United States Agency for International Development.
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