This might be trivial, but I'm used to HLM7 software output and now I'm switching to Stata (`xtmixed`

).

To give an example imagine I have students (level-1) nested within schools (level-2).

Running an empty model, in HLM, I can easily see the variance component associated to each level, to see how much variation is at level-1 and how much is at level-2.

Starting from that, I also calculate the intra-class correlation coefficient.

Now I see the variance components, in the `xtmixed`

output, these are reported as the standard deviation estimates of the intercept `sd(_cons)`

and of the residuals `sd(Residual)`

.

How do I calculate the associated p-value to see if there is significance?

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#### Best Answer

Concerning the display of the results, specify the option `variance`

if you prefer variances over standard deviations.

Concerning the significance, you can run an OLS of the dependent variable on all independent variables with exception of the level 2 identifier (i.e. schools), using the command `regress`

. Store the estimates you obtain through `estimates store`

[name1].

Then estimate your multilevel model using `xtmixed`

and again store the estimates by `estimates store`

[name2].

The difference between these models is the random intercept you allowed in the multilevel estimation but not in the OLS estimation; hence testing whether the unconstrained model performs better is equivalent to testing significance of the random intercept. `lrtest`

[name1] [name2]`, force`

will do this for you. You will need to specify the `force`

option; otherwise Stata deems the test invalid.

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