There's not a consensus regarding the nature, form, and effectiveness of single parameter tests for variance components. With that caution in mind, you can examine the confidence interval to see if it contains 0--to get a rough idea regarding the rejection of the null. A better method is to evaluate/compare models using the deviance statistic along with changes in ICC as you systematically build and evaluate your models.
Scott Millis
--- On Wed, 8/5/09, Martin kavao <[email protected]> wrote:
> From: Martin kavao <[email protected]>
> Subject: st: How to get the P values of the random effects after running an xtmixed command
> To: [email protected]
> Date: Wednesday, August 5, 2009, 11:14 AM
> I am doing a multilevel analysis
> using xtmixed command and I need to report
> the significance of the random component in the final
> models i.e. the p
> values. Anyone has an idea how to compute it after running
> the xtmixed
> command
>
> thanks
>
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