Dear Colleagues:
We have ratings of skill demands on many dimensions (let's just say
it's a lot) for approximately 50 jobs. We have three hypotheses we'd
like to test against each other. First, that all the jobs have the
same skill demands (i.e., all the jobs are the same). Second, that
there are really three groups of jobs, where the jobs in each group
are more similar than different on these skill demands. These three
groups are identified a priori. Third, that it's better to identify
job groupings empirically than a priori.
To begin, we will probably reduce the y vector by using something like
principal components or factor analysis. This we are quite clear
about.
Next, we're thinking that we can basically get R-squares for each
hypothesis that we can compare.
1) For the R-square that all jobs are the same, we will use coefficient alpha.
2) For the three-group models, we were hoping to use an R-square (or
pseudo R-square) from MANOVA.
However, Stata's MANOVA doesn't report R-square, but does give
Hotelling's T-square... This has led me to consider whether we should
try to compare F-statistics across these three models...
Any ideas?
Thanks,
Larry
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