# Re: st: gllamm output

 From Timothy.Mak@iop.kcl.ac.uk To statalist@hsphsun2.harvard.edu Subject Re: st: gllamm output Date Mon, 12 Jun 2006 09:27:47 +0100

```Hi Dari,

But the answer to the second question I think is obvious. You were running
a binary logistic model. Variance at level 1 is estimated directly from p,
so p_hat = logit-1(b_hat). and variance = p_hat(1-p_hat).

As for the third question, I don't know how your V1-V8 relate to your
interaction variables, but you wouldn't expect equal results I guess
simply because the gllamm model has a random effect and the probit
hasn't,.

Sorry if I have misunderstood.

Tim

Dari Sylvester <dsylvester@pacific.edu>
Sent by: owner-statalist@hsphsun2.harvard.edu
11/06/2006 21:17
statalist@hsphsun2.harvard.edu

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Subject
st: gllamm output

I used the gllamm command for a multilevel analysis that estimates the
dichotomous outcome of volunteering or not volunteering based on
individual characteristics (level 1) and the number of volunteer
organizations in the county in which one lives (level 2).
There are explanatory variables at the level of individuals from V1
through V8.
I have no particular equation to estimate for the county level.
I ran the following:

gllamm DV V1 V2 V3 V4 V5 V6 V7 V8, i(count) family(bin) link(logit)

**Where DV is the dependent variable, V1-V8 are explanatory variables
for the individual level, COUNT is the count of organizations in the
county level.  **

number of level 1 units = 19395
number of level 2 units = 137

Condition Number = 481.48689

gllamm model

log likelihood = -8947.353

------------------------------------------------------------------------------
DV      |      Coef.   Std. Err.      z    P>|z|     [95% Conf.
Interval]
-------------+----------------------------------------------------------------
V1       |   .0052359   .0003777    13.86   0.000     .0044956
.0059763
V2          |   .6474295   .0736646     8.79   0.000     .5030495
.7918094
V3       |   1.544726   .0851626    18.14   0.000      1.37781
1.711642
V4    |   .2398346   .0198328    12.09   0.000      .200963
.2787061
V5     |   .4001724   .0403935     9.91   0.000     .3210025
.4793423
V6      |    .386209   .0383842    10.06   0.000     .3109772
.4614407
V7      |   .6155165   .0775643     7.94   0.000     .4634934
.7675397
V8        |   3.722145   .0636431    58.48   0.000     3.597407
3.846883
_cons |   -5.60294   .1000996   -55.97   0.000    -5.799131
-5.406748

Variances and covariances of random effects
-----------------------------------------------------------------------

***level 2 (count)

var(1): .01434088 (.00613062)

Questions:

1. I'm not sure why the condition number is so high - leads me to
believe I've made an error in how I've set up the gllamm model.
2. Why isn't level 1 variance reported?
3. How does the interaction between levels (i.e. the individual nested
in numbers of county organizations) differ in this multilevel estimation
from running a probit with interaction terms added for each of the
individual level variables of interest multiplied by the number of
county organizations?  In other words, running:

probit DV V1 V2 V3 V1*COUNT V2*COUNT V3*COUNT

Thank you kindly,
D. Sylvester

Dari E. Sylvester
Assistant Professor, Political Science
Senior Fellow, Jacoby Center for Public Service and Civic Leadership
University of the Pacific
Stockton, CA 95211
Phone: (209) 946-2007
Fax:     (209) 946-2318
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```