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A Framework for Unifying Formal and Empirical Analysis

Authors


  • Versions of this article have been presented at the 2004 EITM Summer Program (Duke University), the 2005 annual meeting of the Southern Political Science Association (New Orleans, Louisiana), the 2005 annual meeting of the Canadian Political Science Association (London, Ontario), Ohio State University, Penn State University, the University of Texas at Austin, the 2006 EITM Summer Program (University of Michigan), and the 2007 and 2008 Essex Summer School in Social Science Data Analysis and Collection Program (University of Essex, Colchester, UK). We thank Chris Achen, Jim Alt, Mary Bange, Harold Clarke, Eva Coffey, Michelle Costanzo, John Freeman, Mark Jones, Skip Lupia, Elinor Ostrom, David Primo, Frank Scioli, Shalendra Sharma, and Miao Wang for their comments and assistance.

Jim Granato is the Director of the Hobby Center for Public Policy and Professor of Political Science, University of Houston, 104 Heyne Building, Houston, TX 77204-5021 (jgranato@uh.edu). Melody Lo is Associate Professor of Economics, University of Texas at San Antonio, College of Business, Department of Economics, San Antonio, TX 78249-0631 (melody.lo@utsa.edu). M. C. Sunny Wong is Associate Professor of Economics, University of San Francisco, 2130 Fulton Street, San Francisco, CA 94117-1080 (mwong11@usfca.edu).

Abstract

An important disconnect exists between the current use of formal modeling and applied statistical analysis. In general, a lack of linkage between the two can produce statistically significant parameters of ambiguous origin that, in turn, fail to assist in falsifying theories and hypotheses. To address this scientific challenge, a framework for unification is proposed. Methodological unification leverages the mutually reinforcing properties of formal and applied statistical analysis to produce greater transparency in relating theory to test. This framework for methodological unification, or what has been referred to as the empirical implications of theoretical models (EITM), includes (1) connecting behavioral (formal) and applied statistical concepts, (2) developing behavioral (formal) and applied statistical analogues of these concepts, and (3) linking and evaluating the behavioral (formal) and applied statistical analogues. The elements of this EITM framework are illustrated with examples from voting behavior, macroeconomic policy and outcomes, and political turnout.

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