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From |
"Nick Cox" <n.j.cox@durham.ac.uk> |

To |
<statalist@hsphsun2.harvard.edu> |

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

Date |
Wed, 3 Feb 2010 17:48:46 -0000 |

There is another take, orthogonal to several good points. You could impute in all sorts of different ways and then compare the results with each other and with those from the incomplete data. The ideal outcome is clearly that you might reach the same conclusion, so none of this matters. I can't tell you what to conclude if you get very different results except that the problem of missing data inhibits firm conclusions. For "likert" read "Likert", passim. Nick n.j.cox@durham.ac.uk Verkuilen, Jay >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. * * For searches and help try: * http://www.stata.com/help.cgi?search * http://www.stata.com/support/statalist/faq * http://www.ats.ucla.edu/stat/stata/

**Follow-Ups**:**st: RE: RE: RE: mean, mode or median for missing values***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

**References**:**st: mean, mode or median for missing values***From:*Jet <lsj555@gmail.com>

**st: RE: mean, mode or median for missing values***From:*"Verkuilen, Jay" <JVerkuilen@gc.cuny.edu>

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