On Fri, 30 Mar 2001 [EMAIL PROTECTED] wrote:
> Donald Burrill writes:
>
> > On Thu, 29 Mar 2001, H.Goudriaan wrote in part:
> >
> > > - my questionnaire items are measured on 5- and 7-point Likert scales,
> >
> > > and consequently not (bivariate) normally distributed;
> >
> > Real data hardly ever is. Do you need it to be? Usually the
> > question of interest is whether it's close enough to be an adequate
> > approximation for guv'mint work.
>
> Ok, I understand and agree. But isn't it a bit naive to think that a
> group of variables with 5 categories may result in a good factor
> analysis (or whatever other parametric analyses)?
I frankly don't see the relevance of naivete to the question at
hand. It isn't, one gathers, as though you had any choice in the matter:
either in the number of points on each item scale (since this is all, as
you told Dennis, an existing scale) nor in the bivariate distribution of
the two constructs in which (one gathers) you are interested. (And you
haven't said why you think you want these two constructs to be bivariate
normal -- rather than, say, linearly related and unimodal. Nor, for that
matter, have you indicated whether you have examined the bivariate
distribution in question and actually found it to depart worrisomely from
a reasonable distribution.)
You also replied to Dennis that you have 16 items, 11 of which
are alleged to measure one construct and 5 measure another. That sounds
to me like two variables, one with a potential range of 11 to 55 and the
other with a potential range of 5 to 25 (for the 5-point scales; where
you have 7-point scales the potential range will be somewhat wider). I
should think that your interest would then lie in the validity of these
two variables, not in the individual items that contribute to them;
unless you want to do an item analysis of one kind or another.
You write also, "with 5 categories". If you insist that the item
responses must be treated as _categories_, rather than ordered points on
a scale, then you ought, one would think, to be applying the methods of
dual scaling (also known as correspondence analysis). Or, if you allow
that the responses are ordered, use the variation of dual scaling that
applies to ordered categories. (All this for dealing with data at the
item level, of course.)
You haven't explicitly said (that I recall), but you seem to be
unwilling to treat the item responses as of approximately interval
scale. Why not? Do you have evidence that the scale intervals are
grossly unequal? (That seems to me unlikely.) Or are the distributions
of responses for some items so peculiar as to generate serious doubt
about the intervals? (If so, you might wish to convert any such item to
a series of 0/1 categories -- which brings us back to dual scaling.)
-- DFB.
------------------------------------------------------------------------
Donald F. Burrill [EMAIL PROTECTED]
348 Hyde Hall, Plymouth State College, [EMAIL PROTECTED]
MSC #29, Plymouth, NH 03264 603-535-2597
184 Nashua Road, Bedford, NH 03110 603-471-7128
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