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RE: st: hierarchical logistic regression command


From   Maria Niarchou <m.niarchou@hotmail.com>
To   <statalist@hsphsun2.harvard.edu>
Subject   RE: st: hierarchical logistic regression command
Date   Thu, 16 Feb 2012 13:30:28 +0200

Hi Maarten,

thank you for your e-mail. I am sorry I didn't mention this, the hireg program was written by Paul H. Bern (Syracuse University phbern@syr.edu) so I think we are talking about the same one. 

I am currently using this command:
stepwise, pe(.2) hierarchical: logistic 

which seems to work. Do you think it is a correct way of doing hierarchical logistic regression? 
I only have 2 independend variables that I add to the model.

Thanks again,
Maria 

----------------------------------------
> Date: Thu, 16 Feb 2012 12:18:22 +0100
> Subject: Re: st: hierarchical logistic regression command
> From: maartenlbuis@gmail.com
> To: statalist@hsphsun2.harvard.edu
>
> 2012/2/16 Maria Niarchou :
> >> I would like to perform a hierarchical logistic regression analysis in which
> >> independent variables are entered in blocks. 'Hireg' doesn't seem to work with categorical outcomes.
> >> Could you please let me know if there is an alternative command to do this?
>
> -hireg- is a user written program, so per the Statalist FAQ you must
> tell us where you got it from. The purpose of that rule is not to make
> your life hard, but to make sure that all of us are talking about the
> same program. There are often different versions of user written
> programs floating around in cyber space, if you do not tell us which
> version you are referring to than it can easily happen that we are
> talking about different versions and you will get advise that does not
> help you.
>
> Anyhow, it is good news that -hireg- (I assume you got it from SSC)
> does not work with logistic regression, because that is not a good
> idea with non-linear models like -logit-. A lot of the nice properties
> of these comparisons of models with different sets of independent
> variables critically depend on the linearity (in parameters) assumed
> in -regress-, and these nice properties do not generalize to
> non-linear models. For explanations see:
>
> M.L. Buis (2010) "Direct and indirect effects in a logit model", The
> Stata Journal, 10(1), pp. 11-29.
>
> Williams, R. (2009) "Using heterogenous choice models to compare logit
> and probit coefficients across groups", Sociological Methods &
> Research 37: 531–559.
>
> Hope this helps,
> Maarten
>
> --------------------------
> Maarten L. Buis
> Institut fuer Soziologie
> Universitaet Tuebingen
> Wilhelmstrasse 36
> 72074 Tuebingen
> Germany
>
>
> http://www.maartenbuis.nl
> --------------------------
>
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