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Tools for analyzing multiple imputed datasets

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Author Info

  • John B. Carlin

    (Murdoch Children's Research Institute and University of Melbourne Department of Paediatrics)

  • Ning Li

    (Murdoch Children's Research Institute and University of Melbourne Department of Paediatrics)

  • Philip Greenwood

    (Murdoch Children's Research Institute and University of Melbourne Department of Paediatrics)

  • Carolyn Coffey

    (Murdoch Children's Research Institute and University of Melbourne Department of Paediatrics)

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Abstract

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. Copyright 2003 by StataCorp LP.

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Bibliographic Info

Article provided by StataCorp LP in its journal Stata Journal.

Volume (Year): 3 (2003)
Issue (Month): 3 (September)
Pages: 226-244

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Handle: RePEc:tsj:stataj:v:3:y:2003:i:3:p:226-244

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Related research

Keywords: missing data; multiple imputation; RubinÕs rule of combination; overall estimates;

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Citations

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Cited by:
  1. Patrick Royston, 2004. "Multiple imputation of missing values," Stata Journal, StataCorp LP, vol. 4(3), pages 227-241, September.
  2. John Bound & Michael Lovenheim & Sarah Turner, 2009. "Why Have College Completion Rates Declined? An Analysis of Changing Student Preparation and Collegiate Resources," NBER Working Papers 15566, National Bureau of Economic Research, Inc.
  3. Johannes Geyer, 2011. "The Effect of Health and Employment Risks on Precautionary Savings," SOEPpapers on Multidisciplinary Panel Data Research 408, DIW Berlin, The German Socio-Economic Panel (SOEP).
  4. Yang, Jue & Managi, Shunsuke & Sato, Masayuki, 2013. "The effect of institutional quality on national wealth: An examination using multiple imputation method," MPRA Paper 55436, University Library of Munich, Germany, revised 08 Apr 2014.
  5. Gabriele Beissel Durrant, 2009. "Imputation Methods for Handling Item-Nonresponse in the Social Sciences: A Methodological Review," Working Papers id:2007, eSocialSciences.
  6. Patrick Royston, 2005. "Multiple imputation of missing values: update," Stata Journal, StataCorp LP, vol. 5(2), pages 188-201, June.

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