Fitting multilevel models with ordinal outcomes: performance of alternative specifications and methods of estimation.

ABSTRACT: Previous research has compared methods of estimation for fitting multilevel models to binary data, but there are reasons to believe that the results will not always generalize to the ordinal case. This article thus evaluates (a) whether and when fitting multilevel linear models to ordinal outcome data is justified and (b) which estimator to employ when instead fitting multilevel cumulative logit models to ordinal data, maximum likelihood (ML), or penalized quasi-likelihood (PQL). ML and PQL are compared across variations in sample size, magnitude of variance components, number of outcome categories, and distribution shape. Fitting a multilevel linear model to ordinal outcomes is shown to be inferior in virtually all circumstances. PQL performance improves markedly with the number of ordina

SUBMITTER: Bauer DJ 

PROVIDER: S-EPMC3252624 | biostudies-literature | 2011 Dec

REPOSITORIES: biostudies-literature