A Statistical Method for Empirical Testing of Competing Theories
Bibliographic Data
| ID | 6232017 |
|---|---|
| Authors | Katsushi Imai (0000-0002-2748-1022, Princeton University), Kosuke Imai, Dustin Tingley (0000-0002-2216-6293, Harvard University Press) |
| Year | 2012 |
| Volume | 56 |
| Issue | 1 |
| Pages | 218-236 |
| Publication date | 2012-01-01 |
| Peer Reviewed | Yes |
| Open Access | Yes |
| Type | ARTICLE |
| Venue | American Journal of Political Science (JOURNAL) |
| Journal identifiers | ISSN: 0092-5853 • E-ISSN: 1540-5907 |
| Publisher | Wiley (PUBLISHER • GB) |
| DOI | 10.1111/j.1540-5907.2011.00555.x |
| OpenAlex | W2113102853 |
| Language | EN |
| Citations received | 31 |
| References cited | 60 |
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
Class (philosophy · Covariate · Econometrics · Empirical research · Statistical hypothesis testing · Statistical model · Statistical theory · Statistics · Artificial Intelligence · Computer Science · Data Analysis with R · Electoral Systems and Political Participation · Mathematics · Statistical Methods and Inference
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| Unique citing works | 31 |
|---|---|
| Citations per year | 2,07 |
| Citation span | 2011 - 2026 (16) |
| Citation velocity | current |
| Highly cited | No |
| Citation types | Neutral: 31 |