A Framework for Unifying Formal and Empirical Analysis

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.Empirical observation, in the absence of a theoretical base, is at best descriptive. It tells one what happened, but not why it has the pattern one perceives. Theoretical analysis, in the absence of empirical testing, has a framework more noteworthy for its logical or mathematical elegance than for its utility in generating insights into the real world. The first exercise has been described as "data dredging," the second as building "elegant models of irrelevant universes." My purpose is to try to understand what I believe to be a problem of major importance. This understanding cannot be achieved merely by observation, for their comments and assistance. 1 The discussion focuses on formal analysis and applied statistical analysis. Formal analysis refers to deductive modeling that includes a theorem and proof presentation or computational modeling requiring simulation. Applied statistical analysis involves data analysis using statistical tools. We use the terms analysis and modeling interchangeably.In addition, the linkage of formal and applied statistical analysis-the form of methodological unification described in this articlepossesses important attributes that aid in falsification and, ultimately, scientific cumulation. Formal models, for example, force clarity stract symbols. Real insight can be gained only by their combination.

Transdisciplinary Sustainability Science at Higher Education Institutions: Science Policy Tools for Incremental Institutional Change

“…For example, the consortium on qualitative research methods holds an annual intensive seminar on qualitative and multi-method research. The US National Science Foundation has supported methodological training programs for the social sciences, including month-long courses such as the empirical implications of theoretical models (EITM) program, which offer training in how to combine multiple quantitative methods within a single research program [56,57]. Opportunities to develop more specialized qualitative research skills include the summer school in methods and techniques offered by the European Consortium for Political Research, and, in the United States, the Inter-University Consortium for Political and Social Research.…”

EITM: An assessment with an application to economic voting

“…This particular analogue allowed for a particular dynamic prediction he linked with data. 15 See Granato (2005) and Granato et al (2010aGranato et al ( , 2010bGranato et al ( , 2011 for a description and examples of the EITM framework. 16 In political science, for example, a student of democracy might focus on choice (and decision making): how do demographic and attitudinal variables drive individual selection over political parties.…”

“…Instead, the focus is on the size of the t-statistic and whether one can get "significant" results. 22 EITM Framework examples are found in Granato (2005), Granato, Lo, and Wong (2010a, 2010b. 23 On this matter, Robert Solow (1956: 65) maintains:…”

Qualitative investigation of theoretical models: the value of process tracing

“…The broad goal of empirically evaluating a model is often conflated with using statistics to evaluate a model's covariational predictions (e.g., Morton 1999;Granato et al 2012). …”

“…Little and Pepinsky (2016) and Svolik (forthcoming) revisit the topic. For examples of the latter, see Morton (1999), Clarke and Primo (2012), and Granato et al (2012). 2 We focus on international relations and comparative politics because qualitative evidence has been far less prominent in formal theoretical research on American politics.…”