Re: st: linear probability model (LPM)

[David Airey <david.airey@Vanderbilt.Edu>]

.

I seem to have missed this thread, but this post suggests it was a  
little heated or somebody is feeling a little punchy.

Quite a few pages at the UCLA ATS Stata site describe interpretation  
of the logit/probit models in terms of predicted probabilities. That  
seems to also be the basis for a package from that group (Mitchell) on  
interpreting main effects and interactions in logit models (vibl),  
through graphics of the predicted probabilities. So even when using  
the logit models, thinking in terms of probabilities seems worthwhile,  
and doesn't require a linear probability model (with constraints to  
keep the predictions between 0 and 1). The Spost package and  
literature is also available to help sensibly and intelligently  
interpret logit and other categorical dependent variable outcome models.

-Dave


On Jan 15, 2008, at 9:00 AM, Nick Cox wrote:


> Well, Maarten is sensible, intelligent and a non-statistician...
> Perhaps if you a non-statistician you need to be very sensible
> and very intelligent to understand odds ratios...
>
> Nick
>
> Paul Seed
>
> My experience is that even the most
> sensible & intelligent & non-statistician
> is likely to be defeated by odds ratios.
> Time & again, I have known respected colleagues
> present them as though they are risk ratios,
> & they seem quite surprised when I point out the
> differences.
>
> ...
>
> Maarten buis
>
> > I disagree, both measures are perfectly understandable. With odds
> > ratios you are representing chance by at odds instead of probability.
> > Both are easy to understand: odds give you the expected number of
> > successes for every failure, while probability gives you the expected
> > proportion of successes. With odds ratios groups are compared by
> > calculating the ratio: e.g. the odds of success for men is 10% higher
> > than the odds of succes for women. With risk difference you are
> > comparing the groups by computing differences.
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