Nick Cox asks:
> Why do you need a composite measure? It is often a good way
> of blurring important distinctions. If in fact these measures
> are highly related, then one will serve as well as any other.
> If, as seems a little more likely, they measure rather
> different things, it is not clear that any composite measure
> will add much to looking separately at your different responses.
Survey data often have lots of (presumed to be random) measurement
error, in which case comparing individual items leads to interpretation
of noise; scaling them gives a more reliable measure of the underlying
construct.
Don't know and missing are a big issue, with various solutions (omit
casewise; omit pairwise; impute), each of which has the usual caveats.
Nick WInter
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