Hi I've generally had good luck with the defaults, which perhaps is what you have used. A couple of things to explore (sorry not more specific help).
Google "factor analysis dichotomous data" and you will find info on some issues that arise with binary data and factor analysis. Could be contributing to your problems. Google "confirmatory factor analysis" (or perhaps with spss) to determine whether you can try to check the expected pattern in the data. Might be more positive? Unless it is because of one of the settings, not obvious to me that you should be getting factors with single items. Default for extracting factors is eigenvalue > 1, which normally only happens when multiple items load on a common factor. There is a literature, however, suggesting that more factors should be extracted when you anticipate singletons (just one item measuring a factor) in your items. Hope this helps. Take care Jim Jim Clark Professor & Chair of Psychology U Winnipeg Room 4L41A 204-786-9757 204-774-4134 Fax ________________________________________ From: Annette Taylor [[email protected]] Sent: June-18-13 2:32 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=13251.645f86b5cec4da0a56ffea7a891720c9&n=T&l=tips&o=26137 or send a blank email to leave-26137-13251.645f86b5cec4da0a56ffea7a89172...@fsulist.frostburg.edu --- 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=26139 or send a blank email to leave-26139-13090.68da6e6e5325aa33287ff385b70df...@fsulist.frostburg.edu
