Glen Barnett <[EMAIL PROTECTED]> wrote in message
9rndu1$gqq$[EMAIL PROTECTED]">news:9rndu1$gqq$[EMAIL PROTECTED]...
> I'd probably suggest not trying to group the data and do a chi-squared
measure
> of association (you're throwing away the ordering, where most of the
information
> will be), except perhaps just as an exploratory technique that's fast.
Actually, one of the approaches where the chi-square is split into orthogonal
components, and you pick the components relevant to you (a bit like testing
a contrast in ANOVA) to test, so you're not spreading your power over
alternatives you don't want power in anyway might be a reasonable idea,
since that can work quite well. I think Rayner and Best's book has some of it,
but I believe they have done more on that since. (It relates to the Neyman
and Barton Smooth tests, which can be shown to partition the chi-square
statistic, but that's not the only way to partition it.)
But you're still probably better off using some model for the continuous
data if you have something appropriate.
Glen
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