A Statistical Method for Empirical Testing of Competing Theories

Kosuke Imai and Dustin Tingley

American Journal of Political Science, 2012, vol. 56, issue 1, 218-236

Abstract: 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.

Date: 2012
References: Add references at CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://doi.org/10.1111/j.1540-5907.2011.00555.x

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:wly:amposc:v:56:y:2012:i:1:p:218-236

Access Statistics for this article

More articles in American Journal of Political Science from John Wiley & Sons
Bibliographic data for series maintained by Wiley Content Delivery ().