Multilevel Analysis with Few Clusters: Improving Likelihood-based Methods to Provide Unbiased Estimates and Accurate Inference

Martin Elff, Jan Paul Heisig, Merlin Schaeffer and Susumu Shikano

EconStor Open Access Articles and Book Chapters, 2021, vol. 51, issue 1, 412-426

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.

Keywords: multilevel analysis; cross-national comparison; comparative politics; methodology; statistical inference; maximum likelihood (search for similar items in EconPapers)
Date: 2021
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Citations: View citations in EconPapers (7)

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Persistent link: https://EconPapers.repec.org/RePEc:zbw:espost:206685

DOI: 10.1017/S0007123419000097

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