Running multilevel models in MLwiN from within Stata: runmlwin
AbstractMultilevel analysis is the statistical modeling of hierarchical and nonhierarchical clustered data. These data structures are common in social and medical sciences. Stata provides the xtmixed, xtmelogit, and xtmepoisson commands for fitting multilevel models, but these are only relevant for univariate continuous, binary, and count response variables, respectively. A much wider range of multilevel models can be fit using the user-written gllamm command, but gllamm can be computationally slow for large datasets or when there are many random effects. Many Stata users therefore turn to specialist multilevel modeling packages such as MLwiN for fast fitting of a wide range of complex multilevel models. MLwiN includes the following features: fitting of multilevel models for n-level hierarchical and nonhierarchical data structures; fast fitting via classical and Bayesian methods; fitting of multilevel models for continuous, binary, ordered categorical, unordered categorical, and count data; fitting of multilevel multivariate response models, spatial models, measurement error models, multiple-imputation models, and multilevel factor models; interactive model equation windows and graph windows for model exploration; and availability that is free to academics in the United Kingdom. In this presentation, we will introduce the runmlwin command to fit multilevel models in MLwiN from within Stata and to return estimation results to the Stata environment. We shall demonstrate runmlwin in action with several example multilevel analyses in which we fit models and use Stata's standard postestimation commands such as predict and test to calculate predictions, perform hypothesis tests, and produce publication-quality graphics.
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Bibliographic InfoPaper provided by Stata Users Group in its series United Kingdom Stata Users' Group Meetings 2011 with number 19.
Date of creation: 26 Sep 2011
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This paper has been announced in the following NEP Reports:
- NEP-ALL-2011-10-09 (All new papers)
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