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st: Wald Chi-Square in Logistic with Cluster Option


From   "Indro, Daniel" <dci1@gv.psu.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   st: Wald Chi-Square in Logistic with Cluster Option
Date   Fri, 10 Mar 2006 17:12:21 -0500

I ran a logistic regression with a cluster option.  In one model, the
results showed a Wald Chi-Square in the order of 100,000.  When I ran a
different model (by adding additional independent variables), I got a
much smaller Wald Chi-Square (in the order of 30,000 or 2,000 depending
on the additional independent variable being added).  I have seen a
paper reporting a Wald Chi-Square as high as 30,000 in a logistic
regression with a robust option, but haven't been able to locate any
information about why I got such a high Wald Chi-Square.  Could someone
explain if my results are normal or if I have done something wrong?

Below is the partial output for three models.  The three models are
specified as follows:

Model 1: y is a function of 23 x's (call them x1 - x23)
Model 2: y is a function of x1 - x23 in model 1 plus x24
Model 3: y is a function of x1 - x23 in model 1 plus x25

Thanks.
Daniel Indro
 

---Model 1---
Iteration 0:   log pseudolikelihood = -1413.5696
Iteration 1:   log pseudolikelihood = -1348.5214
Iteration 2:   log pseudolikelihood = -1313.6178
Iteration 3:   log pseudolikelihood = -1302.8919
Iteration 4:   log pseudolikelihood = -1301.6519
Iteration 5:   log pseudolikelihood = -1301.6301
Iteration 6:   log pseudolikelihood = -1301.6301

Logit estimates                                   Number of obs   =
11309
                                                  Wald chi2(23)   =
113167.68
                                                  Prob > chi2     =
0.0000
Log pseudolikelihood = -1301.6301                 Pseudo R2       =
0.0792

                           (standard errors adjusted for clustering on
compid)


---Model 2---
Iteration 0:   log pseudolikelihood = -1413.5696
Iteration 1:   log pseudolikelihood =  -1258.636
Iteration 2:   log pseudolikelihood = -1226.6914
Iteration 3:   log pseudolikelihood = -1220.7719
Iteration 4:   log pseudolikelihood = -1219.9377
Iteration 5:   log pseudolikelihood = -1219.9183
Iteration 6:   log pseudolikelihood = -1219.9183

Logit estimates                                   Number of obs   =
11309
                                                  Wald chi2(24)   =
32637.80
                                                  Prob > chi2     =
0.0000
Log pseudolikelihood = -1219.9183                 Pseudo R2       =
0.1370

                           (standard errors adjusted for clustering on
compid)


---Model 3---
Iteration 0:   log pseudolikelihood = -1413.5696
Iteration 1:   log pseudolikelihood = -1386.0868
Iteration 2:   log pseudolikelihood = -1299.8042
Iteration 3:   log pseudolikelihood =   -1288.04
Iteration 4:   log pseudolikelihood = -1286.8147
Iteration 5:   log pseudolikelihood = -1286.7925
Iteration 6:   log pseudolikelihood = -1286.7925

Logit estimates                                   Number of obs   =
11309
                                                  Wald chi2(24)   =
1954.11
                                                  Prob > chi2     =
0.0000
Log pseudolikelihood = -1286.7925                 Pseudo R2       =
0.0897

                           (standard errors adjusted for clustering on
compid)




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