Hi Mouna -
I wouldn't necessarily agree that the "best result" is the mean pre-imputation value for each variable. Part of the reason for doing imputation is because attrition, non-response, etc. may have introduced biases to the sample. As such, the imputation process creates a variety of estimates that will vary around that pre-imputation mean. When you later conduct your analyses on the imputed data, the analyses will account for this variation - it's an important
piece of information.
To compare your pre-imputation mean to post-imputation means is important - post-imputation values that are very different from pre-imputation values suggest you may need to rethink your imputation model - but as long as there are no "red flags" - I would suggest letting the imputation results stand as they are.
-Alicia
On Thu, Sep 18, 2014 at 8:09 AM, mouna kessentini
<kessentini_mouna@yahoo.fr> wrote:
Dear professors,
I have performed multiple imputation for panel data with Amelia II. After 10 iterations, I noticed that the imputed data concerning for example my variable « X1 » is better after the imputation number 4. However, the imputed data concerning the variable « X2 » is better after the imputation number 2.
Is it possible to compare the imputed data for every variable in each iteration and retain the best result knowing that the comparison criteria for the best result is the mean value for
each variable before and after imputation? Thank you in advance for your answer.
Best regards,
Mouna kessentini
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Alicia Doyle Lynch, Ph.D.
Boston College, Lynch School of Education
Department of Developmental and Educational Psychology
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