drawnorm will do it. Suppose you wanted 100 observations of each, with
a specified mean and correlation structure. You could use:
matrix means = (10, 30)
matrix stdev = (5, 9)
matrix cor = (1.0, 0.4 \ 0.4 1.0)
drawnorm measure1 measure2, n(100) m(means) corr(cor) sds(stdev)
This will sample 100 observations on measure1 and measure2 with means
(approximately) 10 and 30, a correlation of approximately 0.4, and
standard deviations of 5 and 9, respectively.
There is an important difference between corr2data and
drawnorm. With corr2data, the simulated data set will have EXACTLY
the correlations, means, and standard deviations you specify. It is
like having the entire population in your data set. With drawnorm,
you are drawing a random sample from a population with the population
parameters you specify. Hence, because of sampling variability, the
correlations etc. in your simulated sample will differ somewhat from
the parameters you have specified.