Hi,
I'm just getting started with Realcom Impute and I wonder if you could help me please. My model includes one variable with missing values and 6 (including dependent var) with no missing vals. Prior to running multiple imputation, it looks ok in MlwiN; i.e. parameter estimates and SEs look reasonable. Once I run multiple imputation in Realcom Impute, the SEs for the variables with no missing values become really huge and the parameter estimate and SE for the variable with imputed values are both 0.000. I think it maybe that I'm doing something simple incorrectly; i.e. when I click imputation -> start analysis; things look ok, values look reasonable, but they are in blue. Once I press start or more, PE and SE for variable with imputed values become zero and SEs for other variables get very very large. Is there something else I should do at this point rather than clicking start/more?
Many thanks,
Lorraine
Large SEs after imputation
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Re: Large SEs after imputation
Clicking:
imputation -> start analysis
is the correct way to perform the analysis on your set of imputed data. It doesn't turn green because the results from several model fits have been combined and therefore don't technically match the last remembered iteration. If you press "start" then MLwiN will attempt to fit the model using the data that contains the missing values, hence the strange values that you were seeing. For an example of a complete imputation analysis see:
http://www.bris.ac.uk/cmm/software/real ... tation.pdf
imputation -> start analysis
is the correct way to perform the analysis on your set of imputed data. It doesn't turn green because the results from several model fits have been combined and therefore don't technically match the last remembered iteration. If you press "start" then MLwiN will attempt to fit the model using the data that contains the missing values, hence the strange values that you were seeing. For an example of a complete imputation analysis see:
http://www.bris.ac.uk/cmm/software/real ... tation.pdf
Re: Large SEs after imputation
great, thanks.