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st: RE: mean, mode or median for missing values


From   "Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>
To   "'statalist@hsphsun2.harvard.edu'" <statalist@hsphsun2.harvard.edu>
Subject   st: RE: mean, mode or median for missing values
Date   Wed, 3 Feb 2010 12:35:39 -0500

>My variable is measured by likert scales, and there are a few
missing values. I am thinking of using mean, mode or median to
substitute the missing values. Which is better given the ordinal
nature of the measurement?<

None of the above. Univariate location imputations (mean, median or mode) seriously distorts relationships with the rest of the dataset and also pushes the estimate of scale downwards. If you have ordinal data like that, it is often the case that using MI and simply treating your discrete data as interval works well enough (you may need to do some transformation, e.g., logging or square rooting counts). If you need things to be back in the discrete scaling, simply round off fractions to the nearest integer and/or truncate to push things back into the sample space. It's not perfect but it's a lot better than univariate mean imputation. 



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