A Statistical Method for Empirical Testing of Competing Theories

Empirical testing of competing theories lies at the heart of social science research. We demonstrate that a well-known class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated...

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Bibliographic Details
Published in:American journal of political science Vol. 56; no. 1; pp. 218 - 236
Main Authors: Imai, Kosuke, Tingley, Dustin
Format: Journal Article
Language:English
Published: Malden, USA Blackwell Publishing Inc 01.01.2012
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Blackwell Publishing Ltd
Subjects:
AJPS WORKSHOPS
Economic models
Empirical research
Estimation theory
Modeling
Observational research
Parametric models
Political science
Political theory
Probabilities
Probability
Regression analysis
Simulation
Social Class
Social research
Social Science Research
Social sciences
Statistical analysis
Statistical methods
Statistical models
Statistics
Test theory
Tests
Theory
Trade policy
ISSN:0092-5853, 1540-5907
Online Access:Get full text
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Summary:Empirical testing of competing theories lies at the heart of social science research. We demonstrate that a well-known class of statistical models, called finite mixture models, provides an effective way of rival theory testing. In the proposed framework, each observation is assumed to be generated either from a statistical model implied by one of the competing theories or more generally from a weighted combination of multiple statistical models under consideration. Researchers can then estimate the probability that a specific observation is consistent with each rival theory. By modeling this probability with covariates, one can also explore the conditions under which a particular theory applies. We discuss a principled way to identify a list of observations that are statistically significantly consistent with each theory and propose measures of the overall performance of each competing theory. We illustrate the relative advantages of our method over existing methods through empirical and simulation studies.
Bibliography:istex:DDBFFDA66294FCB22C277101F8E7DA915956A82E
ark:/67375/WNG-4H1DQQCP-D
ArticleID:AJPS555
The replication code and data archive for this article are available at
http://scholar.harvard.edu/dtingley
kimai@princeton.edu
http://imai.princeton.edu
http://hdl.handle.net/1902.1/16378
We thank Mike Hiscox and Todd Allee for kindly sharing their data. Thanks to Will Bullock, Christina Davis, Marty Gilens, Michael Hiscox, Simon Jackman, Evan Lieberman, Helen Milner, Grigo Pop‐Eleches, Brandon Stewart, Teppei Yamamoto, Carlos Velasco Rivera, Jaquilyn Waddell Boie, Robert Walker, and seminar participants at Harvard University, Princeton University, the University of California, Berkeley, and the University of Chicago, Harris School for helpful suggestions. We also thank the editor and the four anonymous reviewers for extensive comments that have significantly improved this article. Imai acknowledges the financial support from the National Science Foundation (SES–0918968).
Kosuke Imai is Assistant Professor, Department of Politics, Princeton University, Corwin Hall 036, Princeton, NJ 08544
dtingley@gov.harvard.edu
Dustin Tingley is Assistant Professor, Department of Government, Harvard University, 1737 Cambridge St., Cambridge, MA 02138
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ISSN:0092-5853
1540-5907
DOI:10.1111/j.1540-5907.2011.00555.x