Apologies for cross-posting.
Dear all,
A slightly different issu ...
I'm analyzing some qualitative data from a research project about NGOs
and have data for about 40 NGOs and their advocacy activities. The NGOs
vary along at least 15 dimensions which I can see (size, staffing,
income, understanding of advocacy, approach to advocacy, age etc.)
I've been analyzing the data using Atlas (which is great), but I'm
wondering about using principal components analysis to help me to
identify or think about which of the dimensions of variance are most
significant.
Does anyone have any comments on, experience with, examples of, or
references on using principal components analysis with qualitative data?
I know it's a slightly odd thing to do and am aware of the problems of
quantifying qualitative data, but am just wondering about using PCA as a
tool for this purpose.
Any ideas?
cheers,
alan
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Dr. Alan C. Hudson (Research Fellow in International Political Economy)
Government and Politics Discipline
Faculty of Social Sciences
The Open University
Walton Hall
Milton Keynes
MK7 6AA
United Kingdom
Tel: + 44 (0) 1223 358354 (Cambridge office + answerphone)
Tel: + 44 (0) 1908 654441 (Milton Keynes office - direct line)
Fax: + 44 (0) 1908 654488
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