Using SAS to conduct nonparametric residual bootstrap multilevel modeling with a small number of groups


Wang, JC; Carpenter, JR; Kepler, MA; (2006) Using SAS to conduct nonparametric residual bootstrap multilevel modeling with a small number of groups. Computer methods and programs in biomedicine, 82 (2). pp. 130-143. ISSN 0169-2607 DOI: https://doi.org/10.1016/j.cmpb.2006.02.006

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Abstract

In multilevel modeling, researchers often encounter data with a relatively small number of units at the higher levels. As a result, of this and/or non-normality of the residuals, model parameter estimates, particularly the variance components and standard errors of parameter estimates at the group level, may be biased, thus the corresponding statistical inferences may not be trustworthy. This problem can be addressed by using bootstrap methods to estimate the standard errors of the parameter estimates for significance testing. This study illustrates how to use statistical analysis system (SAS) to conduct nonparametric residual bootstrap multilevel modeling. Specific SAS programs for such modeling are provided. (c) 2006 Elsevier Ireland Ltd. All rights reserved.

Item Type: Article
Keywords: SAS proc mixed, SAS macro, multilevel modeling, bootstrap, Issues
Faculty and Department: Faculty of Epidemiology and Population Health > Dept of Medical Statistics
PubMed ID: 16569459
Web of Science ID: 237906800006
URI: http://researchonline.lshtm.ac.uk/id/eprint/11733

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