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Missing Values

One major problem of any analysis of data is caused by missing values. The resulting, partially empty data matrices are hard to interpret and should be avoided whenever possible. However, several methods exist to deal with missing values.

Possibilities to deal with missing values:


The results of a model or analysis should always be checked with and without the missing data. If they are markedly different you should try to find some explanation for this. More information on that topic is available in the book on missing data by Rubin .

Be sure to always mark imputed data as such. Otherwise you may confuse it with real data later on.
 


Last Update: 2006-Jšn-17