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# Re: st: Wald test in Random Coefficient Model

 From Shikha Sinha To statalist@hsphsun2.harvard.edu Subject Re: st: Wald test in Random Coefficient Model Date Thu, 24 Feb 2011 16:44:37 -0500

```Thank you very much for your response. It is WHO "Health Behaviour in
school-aged children " data.

May I also ask you the interpretation of "LR test vs. logistic
regression:     chi2(3) =  2132.55   Prob > chi2 = 0.0000". in the
following output.

------------------------------------------------------------------------------
Random-effects Parameters  |   Estimate   Std. Err.     [95% Conf. Interval]
-----------------------------+------------------------------------------------
wp5: Unstructured            |
var(drel) |    .037479   .0209765      .0125136    .1122521
var(_cons) |   .1952712   .0515963      .1163391    .3277558
cov(drel,_cons) |  -.0052752   .0251633     -.0545943    .0440439
------------------------------------------------------------------------------
LR test vs. logistic regression:     chi2(3) =  2132.55   Prob > chi2 = 0.0000

Thanks,
Shikha

On Thu, Feb 24, 2011 at 4:19 PM, Joerg Luedicke
<joerg.luedicke@gmail.com> wrote:
>> My question is How do test the significance of randome coefficient
>> variance at country level? I saw a few papers using wald statistics.
>> Could you suggest how to get the walt statistic from the above output?
>>  Is it this - Wald chi2(11)      =   2324.18?
>
> I don't know what kind of Wald "test" those others are using. If that
> is not clear from the paper you may bug the authors about that.
> Anyway, the one that you mentioned (Wald chi2(11)      =   2324.18) is
> no such "test" (this one is just what is sometimes referred to as an
> "omnibus" test, basically telling you that at least one coefficient
> from your model differs significantly from zero...).  However, what
> you could do to check if the variation of the effect across country is
> sort of considerable is to do a likelihood ratio test in which you
> compare the model fit with and without the random coefficient (-help
> lrtest-). If the model fits better in the case where variation of the
> coefficient is allowed across countries it might make sense to further
> investigate that. One thing that is definitely helpful in this context
> is a caterpillar plot in which you plot the effect for each country
> including confidence bounds (and e.g. order it from smallest to
> biggest). From that you can immediately see to what extent the
> coefficient varies across countries.
>
> May I ask you what data that is? Is this some ISSP stuff?
>
> HTH,
>
> J.
>
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```