Stata itself can help you in these queries.
. search variance components
yields, among other pointers,
[XT] xtmixed . . . . . . . . . . Multilevel mixed-effects linear regression
(help xtmixed)
SJ-6-1 st0095 . . . . . . . . . . . Estimating variance components in Stata
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . Y. Marchenko
Q1/06 SJ 6(1):1--21 (no commands)
describes using xtmixed to estimate variance components
in linear models
SJ-4-4 st0077 . . CIs for the variance comp. of random-effects linear models
(help xtvc if installed) . . . . . . . . . . M. Bottai and N. Orsini
Q4/04 SJ 4(4):429--435
confidence intervals for the variance components of
random-effects linear regression models.
In addition, check out -gllamm-.
Nick
[email protected]
Michael Crain
> This methodolgy is different from ANOVA.
>
> Under a variance components approach, the variance of each of
> the factors is
> independently generated by a random process. Once the
> variance of a factor
> has been estimated, it remains fixed for the remainder of the
> analysis. The
> variance components approach uses zero means and constant but unknown
> variances for all the factors. (Only the error term meets
> this criteria
> under ANOVA.) Therefore, each factor can be disentangled
> from the other
> factors to explain each one independent of the others. The
> substance of
> this approach is to explain that the differences among the
> factor variances
> are natural and independent of the other effects. The
> ultimate result of
> this analysis is expressed as a measure of relative
> importance of each
> factor. ANOVA allows us to test statistical significance but
> does not allow
> one to find relative importance.
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