Hi Hannah,
I used Paul Kline's 'Easy Guide to Factor Analysis' in my PhD research and
it did exactly what it said on the tin. I don't recall if it dealt
directly with questionnaires and item selection, but may help you
understand your factors, or maybe direct you to another type of factor
analysis rotation (i.e. orthogonal vs oblique).
If your purpose is to design a questionnaire that measures only one thing,
then crossloadings can be seen as problematic - however, if the items that
load on the second factor make sense together, you could well be tapping
into two different domains. But if they don't make sense it could be
unwanted noise. There are techniques of item analysis you can use that
will tell you whether each item is contributing meaningfully to your
total(s), so this might be worthwhile, as it could give you a rationale
for excluding some problematic items.
The other point I'd make though is to not be too hard on your results -
some say factor analysis is more art than science - even well-established
and simple measures like the 16 Bond-Lader Visual Analogue Scales have
cross-loadings of a similar magnitude to yours. I'll forward their paper
to you for reference (rather than to the group).
Hope that helps,
Brian
> I'm hoping for some advice on how to treat items that crossload in an
> exploratory factor analysis.
>
> For my PhD research I'm running a factor analysis to refine a
> questionnaire I've created. I have several items that crossload onto a
> second factor (at above .3 on both factors) and I'm not sure what to do
> with these. The text books I've read don't say whether I delete them,
> ignore them ...... Can anyone advise me on how to treat these?
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> I tried just deleting these items and re-running the factor analysis
> without them but this then found another cross loading item. Removing this
> item too gets rid of all cross loading items and creates a solution that
> makes sense of the data. However this slightly reduces the overall
> Cronbach's alpha to .78 (and for each of the five factors). The amount of
> variance explained stays roughly the same as in the original solution
> though.
>
> Any advice or a push in the right direction (a text boo
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