Re: st: Svy: ordinal regression assumptions

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

At 12:34 PM 5/29/2008, Simon, Alan (CDC/CCHIS/NCHS) wrote:
> Hi all,
>
> I have a complex sample design survey (svy), and would like to use an
> ordinal regression.  Does anyone know how to test the assumption of
> proportional odds that is made with ordinal regression if I also have a
> complex sample design.  Without complex sample design, it seems easy
> enough to use the brant test, but this doesn't seem to work after
> ordinal regression in svy.  Any suggestions?
There are various ways to do this with gologit2, which can be 
downloaded from SSC.  gologit2 also lets you estimate partial 
proportional odds models, which can produce more parsimonious models.  Example:

. webuse nhanes2f

. gologit2 health female black, svy

Generalized Ordered Logit Estimates

Number of strata   =        31                 Number of obs      =      10335
Number of PSUs     =        62                 Population size    =  116997257
                                               Design df          =         31
                                               F(   8,     24)    =      27.06
                                               Prob > F           =     0.0000

------------------------------------------------------------------------------
             |             Linearized
      health |      Coef.   Std. Err.      t    P>|t|     [95% Conf. Interval]
-------------+----------------------------------------------------------------
poor         |
      female |   .1451402    .098885     1.47   0.152     -.056537    .3468174
       black |  -.9556602   .1435276    -6.66   0.000    -1.248387   -.6629337
       _cons |    3.06297    .114295    26.80   0.000     2.829864    3.296076
-------------+----------------------------------------------------------------
fair         |
      female |  -.1731967   .0663503    -2.61   0.014    -.3085191   -.0378743
       black |  -.8136862   .0911644    -8.93   0.000    -.9996173   -.6277551
       _cons |   1.773817   .0700574    25.32   0.000     1.630934      1.9167
-------------+----------------------------------------------------------------
average      |
      female |  -.1726385   .0576992    -2.99   0.005    -.2903167   -.0549602
       black |  -.8834612   .0709884   -12.45   0.000    -1.028243   -.7386795
       _cons |   .3752842   .0436688     8.59   0.000     .2862212    .4643472
-------------+----------------------------------------------------------------
good         |
      female |  -.2333665   .0614472    -3.80   0.001    -.3586889    -.108044
       black |  -.7486971   .1310825    -5.71   0.000    -1.016042   -.4813526
       _cons |  -.7903677    .042234   -18.71   0.000    -.8765045    -.704231
------------------------------------------------------------------------------

. test [#1 = #2 = #3 =#4]

Adjusted Wald test

 ( 1)  [poor]female - [fair]female = 0
 ( 2)  [poor]black - [fair]black = 0
 ( 3)  [poor]female - [average]female = 0
 ( 4)  [poor]black - [average]black = 0
 ( 5)  [poor]female - [good]female = 0
 ( 6)  [poor]black - [good]black = 0

       F(  6,    26) =    6.65
            Prob > F =    0.0002

. test [#1 = #2 = #3 =#4]:female

Adjusted Wald test

 ( 1)  [poor]female - [fair]female = 0
 ( 2)  [poor]female - [average]female = 0
 ( 3)  [poor]female - [good]female = 0

       F(  3,    29) =    7.19
            Prob > F =    0.0009

. test [#1 = #2 = #3 =#4]:black

Adjusted Wald test

 ( 1)  [poor]black - [fair]black = 0
 ( 2)  [poor]black - [average]black = 0
 ( 3)  [poor]black - [good]black = 0

       F(  3,    29) =    0.89
            Prob > F =    0.4579

The proportional odds assumption is violated in this case (but only 
for the female variable, not black).

For more, see the gologit2 support page at

http://www.nd.edu/~rwilliam/gologit2/index.html


-------------------------------------------
Richard Williams, Notre Dame Dept of Sociology
OFFICE: (574)631-6668, (574)631-6463
HOME:   (574)289-5227
EMAIL:  Richard.A.Williams.5@ND.Edu
WWW:    http://www.nd.edu/~rwilliam

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