| Publisher | SAS Institute | ||
|---|---|---|---|
| Format | 76.2KB PDF | Date added | 14 May 2007 |
| Topics | Knowledge and Data Management, Programming Languages, Data Mining - Analysis | ||
| Downloads | 47 | ||
Accommodating hierarchical or nested data has historically been problematic for researchers. General linear mixed models have the ability to account for nested data overcoming some of the limitations of the more traditional models. These advantages have made mixed modeling an increasingly popular statistical method, but questions still remain regarding the integrity of the model (e.g., power and sample size, the impact of measurement error, and the effect of multicollinearity). Programming was created to address these issues through simulation. This paper presents a SAS simulation program that creates samples of multilevel data and allows specification of sample size, number of regressors, reliability of regressors, and correlation between regressors.
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