```Date: Thu, 31 Mar 2005 21:24:28 -0500 Reply-To: Art@DrKendall.org Sender: "SPSSX(r) Discussion" From: Art Kendall Organization: Social Research Consultants Subject: Re: PCA versus FA Comments: To: Jeff Stuewig In-Reply-To: <5.1.1.6.0.20050331195827.00b146d8@mail.gmu.edu> Content-type: text/plain; charset=ISO-8859-1; format=flowed One way in which these two forms of factor analysis Principal Components and Principal Axis differ is how much of the total variance of the items they try to account for. Items are considered to be compose of 3 parts, common variance, unique variance (i.e., specific to the items), and random error variance. In PC you try to account for both the common and unique variance. (Leave ones in the diagonal.) In PAF you are only interested in the common construct underlying a set of items. (So you put some estimate of communality on the diagonal, ususally the squared multiple correlation with the set of other items.) I suggest you google to find out how structures are viualized in structural equations modelling. Art Art@DrKendall.org Social Research Consultants University Park, MD USA (301) 864-5570 Jeff Stuewig wrote: > I've been having difficulty explaining Principal Component > Analyses to > some students. If I understand it correctly (and that is a big if), PCA > assumes no error in measurement. So would it be correct if I was drawing > this relationship that the arrows from the observed variables will point > toward the different components? While in Factor Analyses (principal > axis) > the arrows go from the latent constructs (factors) to the observed > variables? Thanks for any help. > > Jeff > > ```

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