Re: st: interpreting probit estimates

[Richard Williams <Richard.A.Williams.5@ND.edu>]

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.


-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
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