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Re: st: svy, subpop ( ) with version 10
Conditional logistic regression will not produce predictions that can
be compared to those of ordinary "fixed effects" or random effects
logistic regression. This was my answer to your original query.
However it appears that prediction is not your primary aim. So,
conditional logistic regression (use -clogit-) sounds like the
correct approach.
Good luck!
-Steven
On Jun 6, 2008, at 10:46 PM, Leda Inga wrote:
Unfortunately I don't have cluster specific variables. I'm worried
that you say fixed-effects logit will not solve the problem. I've read
the reference manual and it says that clogit ( which is the same as fe
logit) can be used for matched-case controles studies. In my data
every individual in a certain cluster for which the dependet variable
is zero would be a control for the rest (for which the dependent is
one). It also says that it doesn't matter if each cluster has a
different number of observations.
Finally, in the book of Wooldridge of panel data (p 496) it says that
an unobserved effect framework can be used for cluster samples. The
author cites the work of Geronimus and Korenman (1992) where they "use
sister pairs to determine the effects of teenage motherhood on
subsequente economic outcomes". They use a fixed-effects approach when
the outcome is binary.
In my model the most important omitted variables correlated with the
regressors are geographical access and cultural barriers.
Please, I would like to be confirmed if my approach is really
incorrect.
2008/6/6 Steven Samuels <[email protected]>:
If you want a 3-level or higher model with estimated variances at
each
level, then your only recourse is -gllamm-. You would have to
compute your
own weighted predictions.
In any case, -xtlogit- will not solve your problem; its "fixed-
effects"
option makes sense only for longitudinal data.
-svylogit- will give you the standard goodness of fit statistics;
will
permit you to model multiple stages of sampling, but will not provide
estimators of variance for each level. You can include cluster-
specific
covariates and test the interaction of cluster-covariates and
individual
covariates.
-Steven
On Jun 6, 2008, at 9:29 PM, Leda Inga wrote:
I'm still trying to find if there's a good measure of fit for a
fixed-effect logit model. As I answered in the last email, I'm
really
interesed in getting one.
2008/6/6 Steven Samuels <[email protected]>:
The numbers of PSU and strata (before subsetting) are different.
Therefore
either the -svyset- commands or the data sets differ. By the
way, how
did
you resolve your cluster problem?
Steven
On Jun 6, 2008, at 8:04 PM, Leda Inga wrote:
Hi,
I'm running a logit using svy and found a difference in the
coefficients and pvalues reported by version 9 and 10 of Stata. I
thought that it could be because of the observations that each one
takes into account. As you can see below the version 10
considers 4178
observations but there are actually 8471 and the results are
the same
as using the "if" after svy. Can anybody explain this?
The command I used for both versions was:
svy, subpop(urbano): logit PS n_hijos educ seguro V157 NSE rural
_ISREGION_3 _ISREGION_5 CPci CPci2 labor_prol
VERSION 10:
Survey: Logistic regression
Number of strata = 24 Number of
obs =
4178
Number of PSUs = 451 Population size =
2920.9792
Subpop. no. of obs = 4178
Subpop.
size
= 2920.9792
Design df
= 427
F( 10,
418) = 30.43
Prob > F
= 0.0000
------------------------------------------------------------------
------------
| Linearized
PS | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
n_hijos | -.0627022 .0233953 -2.68 0.008 -.1086865
-.0167179
educ | .0995025 .0176722 5.63 0.000 .0647671
.1342379
seguro | .2691795 .1430446 1.88 0.061 -.0119797
.5503387
V157 | .2184025 .0961237 2.27 0.024 .0294679
.4073371
NSE | .5893834 .0859681 6.86 0.000 .4204101
.7583568
_ISREGION_3 | -.8503491 .2073588 -4.10 0.000 -1.25792
-.4427781
_ISREGION_5 | -.7629466 .2529318 -3.02 0.003 -1.260093
-.2658003
CPci | .298412 .0547975 5.45 0.000 .1907057
.4061184
CPci2 | -.0109094 .0043379 -2.51 0.012 -.0194356
-.0023832
labor_prol | .415821 .1035528 4.02 0.000 .2122843
.6193578
_cons | -2.961981 .3026012 -9.79 0.000 -3.556754
-2.367207
------------------------------------------------------------------
------------
Note: 25 strata omitted because they contain no subpopulation
members.
VERSION 9:
Survey: Logistic regression
Number of strata = 49 Number of
obs =
8471
Number of PSUs = 1118 Population size =
6948.092
Subpop.
no.
of obs = 4212
Subpop.
size
= 2945.3116
Design df
= 1069
F( 10,
1060) = 31.19
Prob > F
= 0.0000
------------------------------------------------------------------
------------
| Linearized
PS | Coef. Std. Err. t P>|t| [95% Conf.
Interval]
-------------
+----------------------------------------------------------------
n_hijos | -.0624413 .023198 -2.69 0.007 -.10796
-.0169225
educ | .100353 .0178213 5.63 0.000 .0653842
.1353218
seguro | .2789113 .1415214 1.97 0.049 .0012202
.5566025
V157 | .2192173 .0962575 2.28 0.023 .0303421
.4080924
NSE | .5896208 .0851999 6.92 0.000 .4224427
.7567989
_ISREGION_3 | -.8230625 .2058183 -4.00 0.000 -1.226916
-.4192088
_ISREGION_5 | -.7412179 .2491071 -2.98 0.003 -1.230012
-.2524236
CPci | .2921813 .04976 5.87 0.000 .194543
.3898195
CPci2 | -.0103967 .0037064 -2.81 0.005 -.0176694
-.003124
labor_prol | .4267068 .1025795 4.16 0.000 .2254268
.6279867
_cons | -2.97932 .293572 -10.15 0.000 -3.555362
-2.403277
------------------------------------------------------------------
------------
Thanks.
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