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Date:         Tue, 1 May 2001 16:32:38 -0400
Reply-To:     "Edgar F. Johns" <efjmoj@MEDIAONE.NET>
Sender:       "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From:         "Edgar F. Johns" <efjmoj@MEDIAONE.NET>
Subject:      Re: Factor Analysis
Comments: To: "Parise, Carol A." <PariseC@SUTTERHEALTH.ORG>
In-Reply-To:  <0D17115C1823D311800100805FE68B1B02002AC6@gnsc8mx.sutterhealth.org>

Carol Parise asks "Does this mean that when you are trying to develop subscales from a questionnaire that FA would be more appropriate than PCA even though the results are very similar?"

That's the way I like to think about it.

In my own experience, the two solutions yield similar results, too. Notwithstanding that, I do believe that it's the purpose that determines the "preferred" solution to use. So, if I don't want to make any statements about the structure of the underlying data (e.g., when I'm doing a regression analysis and I have too many predictor variables, I suspect multicollinearity in my predictor variables), then I would use PCA. On the other hand, if I want to try to define and interpret "factors" (i.e., constructs) underlying my variables, then I'll go with some method of FA (principal axes, maximum likelihood) AND probably use an oblique method of rotation (PROMAX).

Hope this helps. Edgar _____ Edgar F. Johns <EdgarJohns@obik.com> Obik, LLC 2906 River Meadow Circle Canton, MI 48188 Tel. 734.495.1292, Fax 734.495.1981 http://www.obik.com [snip]


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