Dancing the Sample Size Limbo With Mixed Models: How Low Can You Go?

Whereas general sample size guidelines have been suggested when estimating multilevel models, they are only generalizable to a relatively limited number of data conditions and model structures, both of which are not very feasible for the applied researcher. In an effort to expand the understanding of two-level multilevel models under less than ideal conditions, Monte Carlo methods, through SAS/IML, were used to examine model convergence rates, parameter point estimates (statistical bias), parameter interval estimates (confidence interval accuracy and precision), and both Type I error control and statistical power of tests associated with the fixed effects, from linear two-level models estimated with PROC MIXED.

Provided by: University of South Carolina Topic: Mobility Date Added: Mar 2010 Format: PDF

Download Now

Find By Topic