--- Mona Mowafi <[email protected]> wrote:
> I have a dataset in which I am evaluating the effect of SES on BMI
> and BMI is heavily skewed toward obesity (i.e. over 50% of the sample
> >30 BMI). I preferred to run a linear regression so as to use the
> full range of data, but the outcome distribution violates normality
> assumption and I've tried ln, log10, and sqrt transformations, none
> of which work.
>
> Is it appropriate to use tobit for modeling BMI in this instance? If
> not, any suggestions?
No, -tobit- is not appropriate, it is intended for censored data. Say
your data was collected in the following way: everyone with BMI less
then 25 is given the value 25, and all others where given their actual
observed BMI. In this case you would know who is not overweight, but
you would not know their BMI. So this model is only useful in very
specialized situations.
Martin already suggested -gladder-. You should be careful however that
the assumption behind -regress- is not that BMI is normally
distributed, but that the residuals are normally distributed. The
residuals can easily be normal when the BMI is non-normal and vice
versa.
-- Maarten
-----------------------------------------
Maarten L. Buis
Department of Social Research Methodology
Vrije Universiteit Amsterdam
Boelelaan 1081
1081 HV Amsterdam
The Netherlands
visiting address:
Buitenveldertselaan 3 (Metropolitan), room Z434
+31 20 5986715
http://home.fsw.vu.nl/m.buis/
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