English, Article, Journal or magazine article edition: Multiple imputations for missing data in lifecourse studies Bianca L. De Stavola

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Bookmark: http://trove.nla.gov.au/version/176186
Physical Description
  • preprint
Language
  • English

Edition details

Title
  • Multiple imputations for missing data in lifecourse studies
Author
  • Bianca L. De Stavola
Physical Description
  • preprint
Notes
  • Missing imputation (MI) is a method to deal with missing at random (MAR) data. It is a Monte Carlo procedure where missing values are replaced by several (usually less than 10) simulated versions. It consists of three steps (Shafer, 1999): i. generation of the imputed values for the missing data; ii. analysis of each imputed data set where missing observations are replaced by imputed ones; iii. combination of the results from all imputed data sets. The procedure is easily implemented in Stata for univariate normally distributed missing variables. Extensions to the case of multivariate normal variables - often encountered in life course epidemiology - will be discussed.
  • RePEc:boc:usug03:08
Language
  • English
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