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st: RE: tobit?


From   "Nick Cox" <n.j.cox@durham.ac.uk>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: RE: tobit?
Date   Tue, 12 Aug 2008 14:06:19 +0100

BMI I take to be body-mass index. SES is perhaps socio-economic status,
but I have no idea how it is measured. 

It's always helpful to have in mind that most people on Statalist will
not work in your own field and may not know whatever jargon is standard
to you. 

Generalising your question somewhat to a search for some smooth(BMI |
SES), possibly a linear function of SES, it is standard that at most any
assumption of normality is about conditional distributions for BMI given
SES, not marginal distributions. Also, a transformation does not have to
"work" perfectly (whatever that means) to be useful. 

Note that ln and log10 are going to have exactly the same effect on the
shape of a distribution, as they differ by only a multiplicative
constant. 

-tobit- is for censored responses; I do not see any indication here that
that is your situation. 

If I had your data I would want to try out various smooths before
working out the best modelling method. Fractional polynomials are
particularly well supported in Stata. 

Nick
n.j.cox@durham.ac.uk 

Mona Mowafi

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?


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