Please use this identifier to cite or link to this item: http://hdl.handle.net/10419/206685
Authors: 
Elff, Martin
Heisig, Jan Paul
Schaeffer, Merlin
Shikano, Susumu
Year of Publication: 
2020
Citation: 
[Journal:] British Journal of Political Science [ISSN:] 1469-2112 [Publisher:] Cambridge University Press [Place:] Cambridge [Year:] 2020 [Issue:] FirstView Articles
Abstract: 
Quantitative comparative social scientists have long worried about the performance of multilevel models when the number of upper-level units is small. Adding to these concerns, an influential Monte Carlo study by Stegmueller (2013) suggests that standard maximum-likelihood (ML) methods yield biased point estimates and severely anti-conservative inference with few upper-level units. In this article, the authors seek to rectify this negative assessment. First, they show that ML estimators of coefficients are unbiased in linear multilevel models. The apparent bias in coefficient estimates found by Stegmueller can be attributed to Monte Carlo Error and a flaw in the design of his simulation study. Secondly, they demonstrate how inferential problems can be overcome by using restricted ML estimators for variance parameters and a t-distribution with appropriate degrees of freedom for statistical inference. Thus, accurate multilevel analysis is possible within the framework that most practitioners are familiar with, even if there are only a few upper-level units.
Subjects: 
multilevel analysis
cross-national comparison
comparative politics
methodology
statistical inference
maximum likelihood
Persistent Identifier of the first edition: 
Additional Information: 
Accepted for publication: Feb. 2019
Creative Commons License: 
https://creativecommons.org/licenses/by-nc-sa/4.0/
Document Type: 
Article
Document Version: 
Published Version






Items in EconStor are protected by copyright, with all rights reserved, unless otherwise indicated.