Thanks for your advice Gary.
Mark
On Tue, Sep 18, 2018 at 3:57 AM Gary King <king(a)harvard.edu> wrote:
Hi Mark, a better practice would be to put the transformed variable in Amelia, get out
the best possible imputations you can, do your analysis, and then transform results to
your quantity of interest. Clarify or Zelig style analyses might help with that. Best of
luck with your research,
Gary
--
Gary King - Albert J. Weatherhead III University Professor - Director, IQSS - Harvard
University
GaryKing.org - King(a)Harvard.edu - @KingGary - 617-500-7570 - Assistant: 617-495-9271
On Sat, Sep 15, 2018 at 4:33 PM Mark Seeto <markseeto(a)gmail.com> wrote:
>
> Dear Amelia group,
>
> Suppose my data set has a variable v that I want to include as a
> predictor variable in a regression model. Supoose that some
> transformation of v, for example, sqrt(v) or log(50 - v), looks more
> normally distributed than v does. However, to keep the interpretation
> of the model simpler, I want to include v itself as a predictor
> variable, not a transformation of v.
>
> What I had been doing previously was to use the "sqrts" or
"logs"
> argument of amelia(), and then use v (not the transformed v) in the
> model. Or if a different transformation was required, I would create
> the transformed variable then impute (with v as an idvar) then
> back-transform, and use the back-transformed v in the model.
>
> Is this considered poor practice because I was using the transformed v
> for imputation but using v itself in the regression model? If it is,
> would I be better off simply imputing without using any transformation
> of v, assuming that v is the variable I want to include in the
> regression model?
>
> Thanks for any advice, and thanks to the Amelia team for all their work.
>
> Mark
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