This is more of a conceptual/methodological question than
"how to do it in R", so anyone who cares to reply might
want to do it off list.
I have censored rank order data .. electors have been asked
to rank the 4 most important issues out of a list of 20. For each
individual we therefore have a vector of 20 measurements 1..5,
where 1..4 are ranks and 5 = not ranked/less important than the
nominated 4.
I would like to be able to use the full information in the ranking,
not just rely on first preferences. Nor do I want to average ranks,
which appears to be common practice.
I would like to make statements of the sort 'I am at least 80%
confident that the most important issue is "B" '.
The immediate thought is some sort of simulation/bootstrapping,
which should be straightforward enough if I use just the first rank to
denote "most important"... but that seems to ignore the information
contained in the lower ranks.
My next thought is that I should attempt some form of ordination ..
some unidimensional scaling using perhaps a 1 dimensional
cmdscale solution .. this rests on the ability to build a suitable
distance matrix, which I think is possible. Or maybe a form of
Thurstone scaling. If I wrapped this in the function called by boot ..
a unique ordination solution for each sample draw, mapped to 1="B
has highest value"/ 0 otherwise .. then perhaps I might be on the
right track. (I guess I would have to do something about scale
flip/flop, indeterminacy).
If this floundering around makes any sense to anyone .. perhaps
someone who has worked with such data .. I'd appreciate some
feedback.
Regards
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