st: [Why does Stata drop an additional category?]

[Marcello Pagano <pagano@hsph.harvard.edu>]

I am posting this for Yaojun.  I have left off the SPSS output which is 
a Word file.  Anyone want it, please contact Yajun (y.li.3@bham.ac.uk) 
or me, directly.

m.p.

Dear all,

I would like to ask you for advice. I am doing a logit on unemployment
and my independent variables are:

religion	1=Protestant; 2=Catholic; 3=None
cath		1=Catholic; 0=Other
educ5		1=Degree; 2=Prof below degree; 3=A Levels; 4=O Levels;
5=None
e5c		-2=None; -1=O Level; 0=A Levels; 1=Prof below degree;
2=Degree

gen	cath=religion==2 if religion<=3
char 	religion[omit]	1 	/*	Protestants	=base*/
char 	educ5[omit]		5 	/*	None/Prim	=base*/

I wish to use the main effects of religion and educ5, and the
interaction effects for cath*e5c.

The first four of the following commands all end in _Ieduc5_1 dropped
due to collinearity; the last gives the coefficient for this category
but the model statistics seems strange: no constant and no comparability
with the output in SPSS for the same data (the syntax and the results
are attached).

xi: logistic unemployed  i.religion i.educ5 i.cath*e5c 	
xi: logistic unemployed  i.religion i.educ5 i.cath*e5c,coef	
xi: logit    unemployed  i.religion i.educ5 i.cath*e5c, or	
xi: logit    unemployed  i.religion i.educ5 i.cath*e5c	
xi: logit    unemployed  i.religion i.educ5 i.cath*e5c,nocons or

I would be most grateful if anyone could explain to me why in the first
four commands the first category of education (_Ieduc5_1) is dropped (I
used both Stata 7 and Stata 8); and how to obtain an output similar to
that in SPSS. The results for models 2-4 are not presented because they
are very similar to results from model 1.

Many thanks.
Yours sincerely
Yaojun Li

************************************************************************
****
*Stata outputs for model 1:
. xi: logistic unemployed  i.religion i.educ5 i.cath*e5c
/*_Ieduc5_1 dropped*/
i.religion        _Ireligion_1-3      (naturally coded; _Ireligion_1
omitted)
i.educ5           _Ieduc5_1-5         (naturally coded; _Ieduc5_5
omitted)
i.cath            _Icath_0-1          (naturally coded; _Icath_0
omitted)
i.cath*e5c        _IcatXe5c_#         (coded as above)

note: _Ieduc5_1 dropped due to collinearity
note: _Icath_1 dropped due to collinearity

Logistic regression                               Number of obs   =
1936
                                                  LR chi2(7)      =
186.93
                                                  Prob > chi2     =
0.0000
Log likelihood = -934.26692                       Pseudo R2       =
0.0909

------------------------------------------------------------------------
------
  unemployed | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------
------
_Ireligion_2 |   3.150993   .5743082     6.30   0.000     2.204475
4.503909
_Ireligion_3 |   2.095747   .6119331     2.53   0.011     1.182492
3.714322
   _Ieduc5_2 |   2.469711   1.182263     1.89   0.059     .9664331
6.311325
   _Ieduc5_3 |   2.554673    .851014     2.82   0.005     1.329789
4.907813
   _Ieduc5_4 |   .8599691   .2137221    -0.61   0.544     .5283719
1.399671
         e5c |   .4881296   .0769878    -4.55   0.000     .3583303
.6649465
 _IcatXe5c_1 |   1.079315   .1140581     0.72   0.470     .8773971
1.3277
------------------------------------------------------------------------
------

(results from models 2-4 omitted)

*Stata output for model 5:
. xi: logit    unemployed  i.religion i.educ5 i.cath*e5c,nocons or
i.religion        _Ireligion_1-3      (naturally coded; _Ireligion_1
omitted)
i.educ5           _Ieduc5_1-5         (naturally coded; _Ieduc5_5
omitted)
i.cath            _Icath_0-1          (naturally coded; _Icath_0
omitted)
i.cath*e5c        _IcatXe5c_#         (coded as above)

note: _Icath_1 dropped due to collinearity
Iteration 0:   log likelihood = -1341.9329
Iteration 1:   log likelihood = -956.23273
Iteration 2:   log likelihood = -936.45777
Iteration 3:   log likelihood = -934.40179
Iteration 4:   log likelihood = -934.26857
Iteration 5:   log likelihood = -934.26692

Logit estimates                                   Number of obs   =
1936
                                                  LR chi2(8)      =
.
Log likelihood = -934.26692                       Prob > chi2     =
.

------------------------------------------------------------------------
------
  unemployed | Odds Ratio   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------
------
_Ireligion_2 |   3.150993   .5742976     6.30   0.000     2.204489
4.503879
_Ireligion_3 |   2.095747   .6119294     2.53   0.011     1.182496
3.714309
   _Ieduc5_1 |   .0037776   .0023571    -8.94   0.000      .001112
.0128333
   _Ieduc5_2 |   .0376322   .0088729   -13.91   0.000     .0237062
.0597387
   _Ieduc5_3 |    .157016   .0276982   -10.50   0.000     .1111192
.2218702
   _Ieduc5_4 |      .2132   .0434898    -7.58   0.000       .14294
.3179952
         e5c |   1.968932   .1016632    13.12   0.000     1.779427
2.17862
 _IcatXe5c_1 |   1.079315   .1140558     0.72   0.470     .8774008
1.327695
------------------------------------------------------------------------
----




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