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Re: st: gllamm output


From   "Dari Sylvester" <dsylvester@pacific.edu>
To   <statalist@hsphsun2.harvard.edu>
Subject   Re: st: gllamm output
Date   Tue, 13 Jun 2006 08:55:21 -0700

Christian,

Thanks so much.  I really appreciate your help.

Best,
Dari

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
>>> christian.ganser@soziologie.uni-muenchen.de 06/13/06 6:49 AM >>>
Dari,

if you just want a random intercept and count as an independent 
variable, type

gllamm volrel2 schmooze socialtrust civpartr group coll married healthr
relatendr count, i(countyid) family(bin) link(logit)

I'm sorry I can't tell much about the condition number, but to me 584 
seems to be very high, indeed. However, the gllamm-manual states: "From 
our experience so far, large condition numbers do not necessarily imply 
poor identification; however, it is unlikely that a low condition number

is obtained when the model is not identified." Perhaps someone else on 
the list can tell you more about this...

Dari Sylvester wrote:
> Christian,
>
> Thank you.  Yes - you are correct that county id should be the level 2
> identifier.  If I want to have "count" as a level 2 independent
> variable, would I have the following:
>
> eqs count:  cons
> gllamm volrel2 schmooze socialtrust civpartr group coll married
healthr
> relatendr, i(countyid) family(bin) link(logit) 
>
> When I ran that, it seemed to converge, but with a condition number of
> 584!  If I am correct, that's not necessarily a good sign.
>
> Thanks for your help, Christian.
>
> Best,
> Dari
> 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
>   
>>>> christian.ganser@soziologie.uni-muenchen.de 06/12/06 1:33 AM >>>
>>>>         
> Only answering questions 1 and 2.
>
> Are you sure you don't want to fit the following:
>
> gllamm DV V1 V2 V3 V4 V5 V6 V7 V8 count, i(YOUR COUNTY-ID) family(bin)
> link(logit) 
>
> The number of volunteer organizations in the county is an independent 
> variable, isn't it? You used it as the county-identifier, so your
model 
> doesn't make much sense, I think.
>
> Question 2: Using the latent-response formulation of the logistic 
> regression model, y*i=b0+b1x1i+...+ei, it is assumed that ei has a 
> logistic distribution with mean zero and variance pi^2/3=3.29.
>
> Dari Sylvester wrote:
>   
>> 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.  **
>>  
>> I received the following output:
>>  
>> 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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