Hi Annette...a few points (not sure these will help, but maybe...). (1) Kaiser criterion is notorious for factor over-extraction. So if you used the default in SPSS, which is the Kaiser criterion (eigenvalues >1), you might have ended up with a lot of uninterpretable factors. If you haven't already, I'd look at the scree plot of the eigenvalues as a first start; if you want to get fancier later, you can do a parallel analysis to test the number of dimensions statistically. Based on these results, you might want to try a factor solution with far fewer factors. (2) Technically, PCA isn't factor analysis; it examines the full variance among all of the dimensions rather than just the shared variance. It might not make much of a difference (usually it doesn't), but I'd instead do a PFA (principal factors analysis), which is technically factor analysis. (3) You might want to (and some purists would say you should) use tetrachoric rs rather than phi coefficients (equivalents of Pearson rs with a dichotomous item format), which estimate the Pearson r between the underlying dimensions (assuming that they are approximately normally distributed at a latent level).
EFA experts on the list (I'm definitely not an expert) may have more to add; but point #1 will probably be the most important to start with. Odds are that you have an overextracted solution....of course, one has to remember that EFA is, well, exploratory, so the finds would later need to be confirmed in another data set, ideally through CFA. ...Scott Scott O. Lilienfeld, Ph.D. Professor Department of Psychology, Room 473 Emory University 36 Eagle Row Atlanta, Georgia 30322 [email protected]; 404-727-1125 The Master in the Art of Living makes little distinction between his work and his play, his labor and his leisure, his mind and his body, his education and his recreation, his love and his intellectual passions. He hardly knows which is which. He simply pursues his vision of excellence in whatever he does, leaving others to decide whether he is working or playing. To him - he is always doing both. - Zen Buddhist text (slightly modified) -----Original Message----- From: Annette Taylor [mailto:[email protected]] Sent: Tuesday, June 18, 2013 3:32 PM To: Teaching in the Psychological Sciences (TIPS) Subject: [tips] factor analysis I am coming to the statistical well one more time. Sigh. Other than what I can figure out from SPSS with my colleague, we are at a loss on what we can do with factor analysis--we understand the basic premises. The problem is how to carry it out with SPSS. Or perhaps we have done it correctly and there truly is nothing here :( We had hoped to find some factors and had a couple of possible ways we thought the items might cluster together. We have a data set with over 200 participants and a questionnaire with 23 items. The items were coded as 0 = incorrect response and 1 = correct response in a 2-choice forced-choice format. We entered the 0,1 data set for these participants into a factor analysis using principal components analysis with a varimax rotation method with Kaiser normalization that gives what we understand to be an "orthogonal" analysis. We have 10 factors for the 23 items, the largest has 5 items, then there a bunch of 3, 2, 1 item factors :( We repeated this with a principal components analysis with a quatrimax rotation with Kaiser normalization which gave us what we think is a "correlated" analysis. Except for the precise component values the factors were 100% exactly the same. Unfortunately, the factors seem weird to us and not at all what we might have predicted in our scenario. Does someone with more factor analysis knowledge have some suggestions for us? Thanks in advance! Annette Annette Kujawski Taylor, Ph. D. Professor, Psychological Sciences University of San Diego 5998 Alcala Park San Diego, CA 92110 [email protected] --- You are currently subscribed to tips as: [email protected]. To unsubscribe click here: http://fsulist.frostburg.edu/u?id=13509.d0999cebc8f4ed4eb54d5317367e9b2f&n=T&l=tips&o=26137 or send a blank email to leave-26137-13509.d0999cebc8f4ed4eb54d5317367e9...@fsulist.frostburg.edu ________________________________ This e-mail message (including any attachments) is for the sole use of the intended recipient(s) and may contain confidential and privileged information. If the reader of this message is not the intended recipient, you are hereby notified that any dissemination, distribution or copying of this message (including any attachments) is strictly prohibited. If you have received this message in error, please contact the sender by reply e-mail message and destroy all copies of the original message (including attachments). --- You are currently subscribed to tips as: [email protected]. To unsubscribe click here: http://fsulist.frostburg.edu/u?id=13090.68da6e6e5325aa33287ff385b70df5d5&n=T&l=tips&o=26138 or send a blank email to leave-26138-13090.68da6e6e5325aa33287ff385b70df...@fsulist.frostburg.edu
