The multilevel model (MLM) is easily parameterized to handle partially clustered data (see, for example, Baldwin, Bauer, Stice, & Rohde, 2011). The current study evaluated the performance of this model under various departures from underlying assumptions, including assumptions of normally distributed and homoscedastic Level 1 residuals. Two estimating models – one assuming homoscedasticity, the other allowing for the estimation of unique Level 1 variance components – were compared. Results from a Monte Carlo simulation suggest that the MLM for partially clustered data yields consistently unbiased parameter estimates, except for an underestimation of the Level 2 variance component under heteroscedastic generating conditions. However, this negative parameter bias desisted when the MLM allowed for Level 1 heteroscedasticity. Standard errors for variance component estimates at both levels were underestimated in the presence of nonnormally distributed Level 1 residuals. A discussion of results, as well as suggestions for future research, is provided. / text
| Identifer | oai:union.ndltd.org:UTEXAS/oai:repositories.lib.utexas.edu:2152/22630 |
| Date | 11 December 2013 |
| Creators | Talley, Anna Elizabeth |
| Source Sets | University of Texas |
| Language | en_US |
| Detected Language | English |
| Format | application/pdf |
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