What is done with missing data? Does the missingness mechanism matter? Is it a good idea to just use the
default options in the major statistical packages? Even some highly trained statisticians do this, so can the
non-statistician analysing their own data cope with some of the better techniques for handling missing
data? This paper shows how the mean and standard deviation are affected by different methods of
imputation, given different missingness mechanisms. Better options than the standard default options are
available in the major statistical software, offering the chance to 'do the right thing' to the statistical and
non-statistical community alike.
Scheffer, J. (2002), Dealing with missing data, Research Letters in the Information and Mathematical Sciences, 3, 153-160