Tools for analyzing multiple imputed datasets

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dc.creatorCarlin, John B.
dc.creatorLi, Ning
dc.creatorGreenwood, Philip
dc.creatorCoffey, Carolyn
dc.date2017-04-01T14:09:11Z
dc.date.accessioned2026-07-09T05:47:23Z
dc.descriptionThe 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.
dc.identifierOther:st0042
dc.identifierdoi:10.22004/ag.econ.116087
dc.identifierhttps://ageconsearch.umn.edu/record/116087/files/sjart_st0042.pdf
dc.identifierhttp://ageconsearch.umn.edu/record/116087
dc.identifier.urihttp://hdl.handle.net/123456789/568391
dc.languageeng
dc.publisher
dc.sourcehttp://ageconsearch.umn.edu/record/116087
dc.titleTools for analyzing multiple imputed datasets
dc.typeText

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