Political Data in 1996

European Journal of Political Research -     

Political Data in 1997

European Journal of Political Research -     

Political Data in 1999

European Journal of Political Research -     

Political Data in 1998

European Journal of Political Research -     

Data Analysis and Data Visualization as Active Learning in Political Science

While data analysis and the related skills of data management and data visualization are important skills for undergraduates in the field of political science, the process of learning these skills can also be used to develop critical thinking, encourage active and collaborative learning, and to apply knowledge gained in the classroom. Drawing on our experiences using data work in upper-level courses in International Relations and American Politics, we discuss how data work and quantitative analysis can be incorporated into subject-based (i.e., nonmethods specific) courses, and how it can also enhance critical reasoning skills. An evaluation of this approach using direct and indirect assessment is included.     

Estimating Dynamic Panel Data Models in Political Science

Panel data are a very valuable resource for finding empiricalsolutions to political science puzzles. Yet numerous publishedstudies in political science that use panel data to estimatemodels with dynamics have failed to take into account importantestimation issues, which calls into question the inferenceswe can make from these analyses. The failure to account explicitlyfor unobserved individual effects in dynamic panel data inducesbias and inconsistency in cross-sectional estimators. The purposeof this paper is to review dynamic panel data estimators thateliminate these problems. I first show how the problems withcross-sectional estimators arise in dynamic models for paneldata. I then show how to correct for these problems using generalizedmethod of moments estimators. Finally, I demonstrate the usefulnessof these methods with replications of analyses in the debateover the dynamics of party identification.     

Tree‐Based Models for Political Science Data

Political scientists often find themselves analyzing data sets with a large number of observations, a large number of variables, or both. Yet, traditional statistical techniques fail to take full advantage of the opportunities inherent in “big data,” as they are too rigid to recover nonlinearities and do not facilitate the easy exploration of interactions in high‐dimensional data sets. In this article, we introduce a family of tree‐based nonparametric techniques that may, in some circumstances, be more appropriate than traditional methods for confronting these data challenges. In particular, tree models are very effective for detecting nonlinearities and interactions, even in data sets with many (potentially irrelevant) covariates. We introduce the basic logic of tree‐based models, provide an overview of the most prominent methods in the literature, and conduct three analyses that illustrate how the methods can be implemented while highlighting both their advantages and limitations.     

Spatial Econometric Models of Cross-Sectional Interdependence in Political Science Panel and Time-Series-Cross-Section Data

Jude C. Hays Department of Political Science, University of Illinois at Urbana-Champaign, Urbana-Champaign, IL 61801 e-mail: jchays{at}uiuc.edu e-mail: franzese{at}umich.edu (corresponding author) In this paper, we demonstrate the econometric consequences ofdifferent specification and estimation choices in the analysisof spatially interdependent data and show how to calculate andpresent spatial effect estimates substantively. We considerfour common estimators—nonspatial OLS, spatial OLS, spatial2SLS, and spatial ML. We examine analytically the respectiveomitted-variable and simultaneity biases of nonspatial OLS andspatial OLS in the simplest case and then evaluate the performanceof all four estimators in bias, efficiency, and SE accuracyterms under more realistic conditions using Monte Carlo experiments.We provide empirical illustration, showing how to calculateand present spatial effect estimates effectively, using dataon European governments' active labor market expenditures. Ourmain conclusions are that spatial OLS, despite its simultaneity,performs acceptably under low-to-moderate interdependence strengthand reasonable sample dimensions. Spatial 2SLS or spatial MLmay be advised for other conditions, but, unless interdependenceis truly absent or minuscule, any of the spatial estimatorsunambiguously, and often dramatically, dominates on all threecriteria the nonspatial OLS commonly used currently in empiricalwork in political science. Authors' note: This research was supported in part by NationalScience Foundation grant no. 0318045. We thank Chris Achen,Jim Alt, Kenichi Ariga, Neal Beck, Jake Bowers, Kerwin Charles,Bryce Corrigan, Tom Cusack, David Darmofal, Jakob de Haan, JohnDinardo, Zach Elkins, John Freeman, Fabrizio Gilardi, KristianGleditsch, Mark Hallerberg, John Jackson, Aya Kachi, JonathanKatz, Mark Kayser, Achim Kemmerling, Gary King, Hasan Kirmanoglu,James Kuklinski, Tse-Min Lin, Xiaobo Lu, Walter Mebane, CovadongaMeseguer, Michael Peress, Thomas Pluemper, Dennis Quinn, MeganReif, Frances Rosenbluth, Ken Scheve, Phil Schrodt, Beth Simmons,Duane Swank, Wendy Tam Cho, Craig Volden, Michael Ward, andGregory J. Wawro for useful comments on this and/or other workin our broader project on spatial econometric models in politicalscience. Bryce Corrigan, Aya Kachi, and Xiaobo Lu each providedexcellent research assistance and Kristian Gleditsch, Mark Hallerberg,and Duane Swank also generously shared data. We alone are responsiblefor any errors.