Dear Stata Users

`I would appreciate your guidance on the following topic regarding multiple
``imputation (MI) and z-standardisation. I am currently learning MI using the
``excellent stata help resources, but have an issue I can't find much support
``for.
`

`I have a small dataset of 54 subjects, 4 of whom have missing data on a
``variable which measures social capital in their neighbourhood, let's call
``this variable "sc". It is a continuous variable with an approximate normal
``distribution. I wish to use this variable in the substantive analysis
``(eventually, a cox regression) as a predictor, using MI to estimate missing
``values. The best way to include this in such an analysis is as a
``z-standardised variable with a mean of 0 and sd of 1, to make parameter
``estimates more interpretable.
`

`I have followed the MI commands and can obtain MI estimates for sc. My
``question is as follows:
`

`I am unclear how/when/if to perform z-transformation on the multiply
``imputed data. I have considered two options:
`

`1. Prior to MI, generate "zsc" using the "egen zsc=std(sc)" command and
``then run the appropriate MI commands, including "mi impute" on "zsc" to
``obtain direct estimates of the missing zsc values under an MI scenario.
`

`2. Estimate missing values of "sc" using "mi impute" and then transform the
``variable after imputation using the command "mi passive: egen zsc=std(sc)".
``(An aside, I am assuming here that this is the correct way to specify
``"zsc", as it is a function of "sc"; your input would be welcome).
`

`Either way, when I check the summary distribution of zsc for the Mth
``imputation ("mi xeq 0 1 20: summ zsc"), I do not quite get back the zsc
``variable with a mean of 0 & sd of 1, obviously, as the imputed values are
``just that, though the summaries for each imputative are reasonably close to
``this value (i.e. mean~-.03, sd~.99).
`
So my questions are really:

`A. Can I still use the zsc variable in my substantive analysis and make the
``assumption it still has a mean of 0 / sd of 1?
`
B. Is either method (1 vs 2) preferable?

`C. Is there another, preferable, way of achieving z-standardisation
``before/after MI?
`

`D. Should I be using z-standardisations at all with MI?
``
``Many thanks in advance for your help with this matter.
``
``Best wishes
``
``James
``*
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