NotesFAQContact Us
Search Tips
Peer reviewed Peer reviewed
Direct linkDirect link
ERIC Number: EJ1137636
Record Type: Journal
Publication Date: 2017-Jan
Pages: 38
Abstractor: As Provided
ISSN: ISSN-0049-1241
Model Uncertainty and Robustness: A Computational Framework for Multimodel Analysis
Young, Cristobal; Holsteen, Katherine
Sociological Methods & Research, v46 n1 p3-40 Jan 2017
Model uncertainty is pervasive in social science. A key question is how robust empirical results are to sensible changes in model specification. We present a new approach and applied statistical software for computational multimodel analysis. Our approach proceeds in two steps: First, we estimate the modeling distribution of estimates across all combinations of possible controls as well as specified functional form issues, variable definitions, standard error calculations, and estimation commands. This allows analysts to present their core, preferred estimate in the context of a distribution of plausible estimates. Second, we develop a model influence analysis showing how each model ingredient affects the coefficient of interest. This shows which model assumptions, if any, are critical to obtaining an empirical result. We demonstrate the architecture and interpretation of multimodel analysis using data on the union wage premium, gender dynamics in mortgage lending, and tax flight migration among U.S. states. These illustrate how initial results can be strongly robust to alternative model specifications or remarkably dependent on a knife-edge specification.
SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail:; Web site:
Publication Type: Journal Articles; Reports - Research
Education Level: N/A
Audience: N/A
Language: English
Sponsor: N/A
Authoring Institution: N/A
Grant or Contract Numbers: N/A