impute missing values in correlated variables: transcan?

At the risk of stirring up a hornet's nest , I'd suggest that
means are dangerous in such applications.  A nice paper
on combining ratings is:  Gilbert Bassett and Joseph  Persky,
Rating Skating,  JASA, 1994,  1075-1079.

Here is the abstract, which seems to capture what the article
says:

"Among judged sports, figure skating uses a unique method of
median ranks for determining placement. This system responds
positively to increased marks by each judge and follows majority
rule when a majority of judges agree on a skater's rank. It is
demonstrated that this is the only aggregation system possessing
these two properties. Median ranks provide strong safeguards
against manipulation by a minority of judges. These positive
features do not require the sacrifice of efficiency in
controlling measurement error. In a Monte Carlo study, the median
rank system consistently outperforms alternatives when judges'
marks are significantly skewed toward an upper limit."

I think this is irrelevant.  We are using ratings, not rankings.

(And there was a small error in my original post.  The disturbing
effect of missing data at the high or low end would be on the
slope rather than the intercept or mean.)

Jon

Jonathan Baron, Professor of Psychology, University of Pennsylvania
Home page: http://www.sas.upenn.edu/~baron