I have multiple time series variables in my data, all with missing values, and I have been experimenting with different lags, leads, polys or splines to find the best "fit" for my dependent variable. However, the treatment that provides the best imputation for one variable performs poorly for other variables. So, is it reasonable to use imputed datasets from different Amelia runs for further analysis, or should a single imputation be selected that does OK for all of the variables, since the variables will be further analyzed together?

 

For example, if I have variables temperature, salinity and growth. Temp points are imputed best using splines, but do bad using poly=1, while growth does best using poly=1 and bad with splines. Salinity does best w/ poly=3.

 

Would you do separate runs adjusting these settings, extract the imputed values for the variables that perform well for each setting, and then combine the variables into a new data set?

 

Thank you for your input

 

Erin Graham

 

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