Re: st: gllamm output

[Christian Ganser <christian.ganser@soziologie.uni-muenchen.de>]

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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