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Re: st: interpreting probit estimates


From   Richard Williams <[email protected]>
To   [email protected], "[email protected]" <[email protected]>
Subject   Re: st: interpreting probit estimates
Date   Wed, 15 Feb 2006 14:51:52 -0500

At 02:02 PM 2/15/2006, Jonathan A. Schwabish wrote:
This may or may not be a Stata question. I am trying
to convert probit estimates to the following
interpretation: "A standard deviation increase in the
[independent variable] increases the [dependent
variable] by x% (or x standard deviations)."

The -listcoef- command is very useful but for
interpretation purposes, is only applicable to the
logit command (log odds). Does anyone know of a Stata
command, or just a way to modify probit results, to
fit this type of interpretation?
-listcoef- works fine after both logit and probit, and I would say that the interpretation is the same, with the main difference being that the distribution of the underlying latent variable Y* is different. So, for example,

. use "http://www.indiana.edu/~jslsoc/stata/spex_data/ordwarm2.dta";
(77 & 89 General Social Survey)

. quietly probit warmlt2 yr89 male white age ed prst

. listcoef

probit (N=2293): Unstandardized and Standardized Estimates

Observed SD: .33585294
Latent SD: 1.0785709

-------------------------------------------------------------------------------
warmlt2 | b z P>|z| bStdX bStdY bStdXY SDofX
-------------+-----------------------------------------------------------------
yr89 | -0.51003 -6.517 0.000 -0.2498 -0.4729 -0.2316 0.4897
male | 0.16937 2.439 0.015 0.0845 0.1570 0.0783 0.4989
white | 0.28195 2.382 0.017 0.0928 0.2614 0.0860 0.3290
age | 0.00920 4.223 0.000 0.1544 0.0085 0.1432 16.7790
ed | -0.05786 -4.207 0.000 -0.1829 -0.0536 -0.1696 3.1608
prst | 0.00066 0.221 0.825 0.0095 0.0006 0.0088 14.4923
-------------------------------------------------------------------------------

For your purposes you focus on the column bStdXY. This would tell you, for example, that a 1 standard deviation increase in age results in a .1432 standard deviation increase in the latent variable Y*. (In this case that happens to mean that older people are less supportive of mothers working.) (Incidentally, if I was going to use any of the standardizations, I would probably use bStdY, i.e. Y* standardized while X is not standardized.)

Long and Freese's new book (available from Stata Press) is highly recommended.


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Richard Williams, Notre Dame Dept of Sociology
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