Received: from norn.mailbase.ac.uk (norn.ncl.ac.uk) by sghms.ac.uk (SGHMSV1.0) ID AA07761; Sat, 10 Feb 96 06:30:58 GMT Received: by norn.mailbase.ac.uk id (8.6.12/ for mailbase.ac.uk); Sat, 10 Feb 1996 06:16:23 GMT Received: from pacificu.edu by norn.mailbase.ac.uk id (8.6.12/ for mailbase.ac.uk) with SMTP; Sat, 10 Feb 1996 06:16:17 GMT Received: by pacificu.edu (4.1/SMI-SVR4) id AA04736; Fri, 9 Feb 96 22:18:49 PST Date: Fri, 9 Feb 1996 22:18:49 -0800 (PST) From: "Tim A. Connor" To: PCP Subject: Grid analysis Message-Id: Mime-Version: 1.0 Content-Type: TEXT/PLAIN; charset=US-ASCII X-List: pcp@mailbase.ac.uk X-Unsub: To leave, send text 'leave pcp' to mailbase@mailbase.ac.uk Reply-To: pcp@mailbase.ac.uk Sender: pcp-request@mailbase.ac.uk Precedence: list X-UIDL: 823934225.000 X-PMFLAGS: 34078848 I've been playing around with a grid I administered to a client a couple of months ago. It's been useful therapeutically, but being something of a novice to this I've been trying out different things just to see what I can squeeze out of it. It's a 9x9 grid; the elements are the self in various situations. I'm not sure how much information one would need to give useful feedback, so I'll err on the side of too much. Raw grid (elements in columns): 2 1 -2 2 2 -2 2 2 1 2 -2 -2 2 2 0 -1 2 -1 2 -2 -2 2 2 0 -1 2 -1 2 -2 -2 2 2 1 -2 2 1 2 -2 -1 2 2 1 -2 2 -1 1 -2 -1 2 2 0 -2 2 -1 2 -1 -1 2 2 0 1 1 2 2 -1 -2 1 1 1 -2 2 1 1 -2 -2 1 0 -1 2 2 -1 Correlation matrix (from OMNIGRID): C1 C2 C3 C4 C5 C6 C7 C8 C9 C1 .55 .49 .37 .27 .35 .69 .34 .7 C2 .62 .9 .8 .94 .73 .81 .66 C3 .37 .23 .41 .44 .35 .83 C4 .97 .89 .76 .95 .42 C5 .83 .75 .92 .31 C6 .59 .78 .47 C7 .64 .62 C8 .35 PCA (from NCSS): Component 1: Value 6.031759 Percent 67.01959 Asociated Eigenvector 1 Construct 1 .2502107 2 Construct 2 .3931041 3 Construct 3 .2475273 4 Construct 4 .3807792 5 Construct 5 .3535509 6 Construct 6 .3583258 7 Construct 7 .3446303 8 Construct 8 .3537053 9 Construct 9 .2816513 Component 2: Value 1.660841 Percent 18.4538 Associated Eigenvector 1 Construct 1 -.4332807 2 Construct 2 .005710154 3 Construct 3 -.4751942 4 Construct 4 .267817 5 Construct 5 .3580443 6 Construct 6 .1899857 7 Construct 7 -.08576919 8 Construct 8 .2850366 9 Construct 9 -.511671 The first two components account for 85% of the variance, so I won't take up bandwidth with the others. My main question is, is the first component interpretable as it stands? All the constructs load positively, none very heavily, and all within a fairly narrow range. NCSS doesn't do rotation, so I may have just hit the limits of my not-terribly-sophisticated software. Another question is how the subjects-to-variables ratio affects PCA with grids. All the grids I've seen violate the rule of thumb that the STV ratio should be >5; this one, in addition, has fewer than 100 observations. Does this limit the value of PCA with smaller grids? All suggestions are welcome. As I said, I'm fairly new to this, so there may be (almost certainly are) questions I haven't thought to ask. Thanks, Tim Connor, M.S. Pacific University School of Professional Psychology -- End --