Re: R and R square over .90? for regression with imputed dataset

Posted by Kylie on Dec 19, 2012; 6:37am
URL: http://spssx-discussion.165.s1.nabble.com/R-and-R-square-over-90-for-regression-with-imputed-dataset-tp5717033p5717044.html

Hi Rich,

 

Could you please expand on your comment? I have always been under the impression that one should include the outcome in an imputation model to ensure that all relevant relationships are accounted for, and that excluding the outcome could/would introduce bias (see, for example [1]). Are you aware of scenarios in which that isn’t the case?

 

I know that there is debate about whether to include cases that have had the outcome imputed in the final analysis (multiple imputation then deletion, [2]), but that appears to be a separate issue to what you describe.

 

Thanks,

Kylie.

 

[1] Moons KGM, Donders RART, Stijen T, Harrell FE. (2006) Using the outcome for imputation of missing predictor values was preferred. J of Clinical Epidemiology 59: 1092-1101.

[2] von Hippel PT. (2007) Regression with missing Ys: An improved strategy for analysing multiply imputed data. Sociological Methodology 37(1): 83-117.

 

 

 

From: SPSSX(r) Discussion [mailto:[hidden email]] On Behalf Of Rich Ulrich
Sent: Wednesday, 19 December 2012 4:38 PM
To: [hidden email]
Subject: Re: R and R square over .90? for regression with imputed dataset

 

It is a no-no to use your criterion variable for imputing values for
your predictors.  Probably because, that could account for this sort
of result.

--
Rich Ulrich

> Date: Tue, 18 Dec 2012 19:51:51 -0800
> From: [hidden email]
> Subject: R and R square over .90? for regression with imputed dataset
> To: [hidden email]
>
> Hello,
>
> I was running regressions with the imputed variables, and I got R and R2
> over .90. Is this ever possible? The highest R2 I had with original dataset
> was over .50.
>
> I used SPSS Multiple Imputation and Missing Value Analysis functions by
> following all steps suggested by the IBM SPSS guide. The original dataset
> had a significant amount of missing values (about 10-40%). Some variables
> imputed are at the scale level as well as the individual scale items. I
> spoke to my advisor and she, too, is skeptical about this result.
>
> Any suggestions would be appreciated. Thanks much.
>
> ...