Dados Bibliográficos

AUTOR(ES) L. Brown , K. Zhang , L. Zhao , E. George , Richard Berk , Andreas Buja , Emil Pitkin
AFILIAÇÃO(ÕES) Department of Statistics, University of Pennsylvania, Philadelphia, PA, USA
ANO 2014
TIPO Artigo
PERIÓDICO Sociological Methods and Research
ISSN 0049-1241
E-ISSN 1552-8294
EDITORA SAGE Publications
DOI 10.1177/0049124114526375
ADICIONADO EM 2025-08-18
MD5 9da1239af2e67a4fbbc06f1520361acd

Resumo

There are over three decades of largely unrebutted criticism of regression analysis as practiced in the social sciences. Yet, regression analysis broadly construed remains for many the method of choice for characterizing conditional relationships. One possible explanation is that the existing alternatives sometimes can be seen by researchers as unsatisfying. In this article, we provide a different formulation. We allow the regression model to be incorrect and consider what can be learned nevertheless. To this end, the search for a correct model is abandoned. We offer instead a rigorous way to learn from regression approximations. These approximations, not 'the truth,' are the estimation targets. There exist estimators that are asymptotically unbiased and standard errors that are asymptotically correct even when there are important specification errors. Both can be obtained easily from popular statistical packages.

Ferramentas