| Registro completo |
| Provedor de dados: |
AgEcon
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| País: |
United States
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| Título: |
Tools for analyzing multiple imputed datasets
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| Autores: |
Carlin, John B.
Li, Ning
Greenwood, Philip
Coffey, Carolyn
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| Data: |
2011-09-30
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| Ano: |
2003
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| Palavras-chave: |
Missing data
Multiple imputation
Rubin's rule of combination
Overall estimates
Research Methods/ Statistical Methods
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| Resumo: |
The method of multiple imputation (MI) is used increasingly for analyzing datasets with missing observations. Two sets of tasks are required in order to implement the method: (a) generating multiple complete datasets in which missing values have been imputed by simulating from an appropriate probability distribution and (b) analyzing the multiple imputed datasets and combining complete data inferences from them to form an overall inference for parameters of interest. An increasing number of software tools are available for task (a), although this is difficult to automate, because the method of imputation should depend on the context and available covariate data. When the quantity of missing data is not great, the sensitivity of results to the imputation model may be relatively low. In this context, software tools that enable task (b) to be performed with similar ease to the analysis of a single dataset should facilitate the wider use of multiple imputation. Such tools need not only to implement techniques for inference from multiple imputed datasets but also to allow standard manipulations such as transformation and recoding of variables. In this article, we describe a set of Stata commands that we have developed for manipulating and analyzing multiple datasets.
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| Tipo: |
Journal Article
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| Idioma: |
Inglês
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| Identificador: |
st0042
http://purl.umn.edu/116087
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| Relação: |
Stata Journal>Volume 3, Number 3, 3rd Quarter 2003
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| Formato: |
19
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