I'm coming in at a different slant from what I have seen posted
on this thread (in sci.stat.edu).
On Thu, 29 Mar 2001 20:30:59 +0200, "H.Goudriaan"
<[EMAIL PROTECTED]> wrote:
...
> I have 2 questionnaires assessing (physical and emotional) health of
> heart patients. The 1st measures present state and it's assessed before
> treatment and a couple of months after treatment, so that difference
> scores can be calculated. The 2nd questionnaire is assessed after
> treatment only, and asks respondents how much they have changed on every
> aspect (same aspects as the first questionnaire) since just before
> treatment.
> Respondents received both questionnaires. Now I would like to
> investigate the convergent validity of the two domains assessed with
> both questionnaire versions. Is there a standard, straightforward way of
> doing this? Someone advised me to do a factoranalysis (PCA) (on the
> baseline items, the serially measured change scores and the
> retrosepctively assessed change scores) and then compare the
> factorloadings (I assume after rotation? (Varimax?)). I haven't got a
> good feeling about this method for two reasons:
> - my questionnaire items are measured on 5- and 7-point Likert scales,
> so they're not measured on an interval level and consequently not
> (bivariate) normally distributed;
[ snip, about factor loading.]
If items were really Likert, they would be close enough to normal.
But there is no way (that comes to mind) that you should have labels
for "Change" that are Likert: Likert range is "completely disagree"
... "completely agree" and responses describe attitudes. You can
claim to have Likert-type labels, if you do have a symmetrical set.
That is more likely to apply to your Present-Status reports, than to
Changes. At any rate -- despite the fact that I have never found
clean definitions on this -- having a summed score is not enough
to qualify a scale as Likert.
Thus, you *may* be well-advised, if someone has advised you so,
to treat your responses as 'categories' -- at least, until you do the
dual-scaling or other item analyses that will justify regarding them
as "interval." For someone experienced in drawing up scales, or
if you were picking up items from much-used tests, that would
not be a problem; but people are apt to make mistakes if they
haven't seen those mistakes well-illustrated.
What is your question about individual items? Are some, perhaps,
grossly inappropriate? Or, too rarely marked? If 11 are intended for
a "physical factor", there *should* emerge a factor or principal
component to reflect it. Ditto, for emotional. Any items that don't
load are duds (that would be my guess). Or do you imagine 2 strong
factors? Again -- whatever happens should not come as much
surprise if you've done this sort of thing before.
IF the items are done in strict-parallel, it seems unnecessary and
obfuscatory to omit a comparison of responses, item by item.
--
Rich Ulrich, [EMAIL PROTECTED]
http://www.pitt.edu/~wpilib/index.html
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