The 3.29 appears to be pi^2/3 which is the standard deviation of a
standard logistic distribution.
Tony
Peter A. Lachenbruch
Department of Public Health
Oregon State University
Corvallis, OR 97330
Phone: 541-737-3832
FAX: 541-737-4001
-----Original Message-----
From: [email protected]
[mailto:[email protected]] On Behalf Of Richard
Williams
Sent: Wednesday, February 20, 2008 2:17 PM
To: [email protected]; [email protected]
Subject: Re: st: Differences in regression slopes
At 12:14 PM 2/20/2008, E. Paul Wileyto wrote:
>Responses so far have sent you this way and that. Just look up
>-test- in STATA help.
>
>To get to the point of using -test- for your purpose, you would need
>to specify a model that has group-specific slopes, or combine two
>regressions, one for each group, using -suest-.
>
>Paul
Without going into all the gory details, in logit and probit models
such comparisons have much the same problem as you have in OLS if you
try to compare standardized coefficients across groups. In OLS, the
problem with comparing standardized coefficients is that, unless the
means and standard deviations of variables are the same across
populations, the variables will get standardized differently across
populations (e.g. in one population the variable gets divided by 3
while in the other it gets divided by 4) so the coefficients are not
comparable.
In logit and probit models, the coefficients are inherently
standardized, albeit in a different way. In order to identify the
coefficients, in a logit model, the residual variance is typically
fixed at pi^2/3, or about 3.29. In probit, the residual variance is
typically fixed at one. BUT, if residual variability differs across
populations, the coefficients in the two populations get standardized
differently and hence are not directly comparable.
For a much more detailed and probably clearer discussion, see
Allison, Paul. 1999. "Comparing Logit and Probit Coefficients Across
Groups." Sociological Methods and Research 28(2): 186-208.
Incidentally, a little exercise I use to help my students see
this: Run a logit model. Then run Long and Freese's -fitstat-
command. The error variance will be reported as 3.29. Now, add some
variables to the model. Or, if you prefer, drop some variables. Or,
just use entirely different variables. No matter what you do, the
error variance is always 3.29. It is very different from the way we
are used to seeing things in OLS.
-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME: (574)289-5227
EMAIL: [email protected]
WWW: http://www.nd.edu/~rwilliam
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