Solved – Significance of variance components in Stata output

Question

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?

Answer 1

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.