Based on what I was taught and the little factor analysis I've done (that is, I'm far from expert, too).
Jim's suggestion to limit the factors to the number you expect based on theoretical rationale is a good way to go. The scree plot is useful to examine, too. If you don't have experience with examining them, you are looking for the point where the plot shifts from a downward trajectory to a more or less horizontal trajectory. The point of the shift is the number of factors for which you reliably estimate a greater proportion of the variance. The best book, IMO, on all this is: Tabachnick and Fidell's Using Multivariate Statistics. It is probably in your university library. It has excellent examples for all major statistical packages, has sample results sections, explains necessary assumptions, etc. http://www.pearsonhighered.com/educator/product/Using-Multivariate-Statistics-Plus-MySearchLab-with-eText-Access-Card-Package/9780205885664.page Paul On Jun 18, 2013, at 4:38 PM, Jim Clark wrote: > Hi > > Following simulation in spss shows the effect of dichotomizing variables on > factor analysis. With the original continuous variables, factor picks up the > correct 3 factor solution automatically most of the time. Dichotomizing, > produces more factors most of the time, with last some not that > interpretable. Asking for 3 factors with the dichotomous data, recovers the > correct structure. So one thing to try might be to specify how many factors > you think there are in the data [/criteria = factor(#)] and see if the > results are interpretable? > > Jim > > input program. > loop o = 1 to 200. > end case. > end loop. > end file. > end input program. > list. > > compute z1 = rv.norm(0,1). > compute z2 = rv.norm(0,1). > compute z3 = rv.norm(0,1). > do repeat a = a1 to a8. > compute a = .707*z1 + .707*rv.norm(0,1). > end repeat. > do repeat b = b1 to b8. > compute b = .707*z2 + .707*rv.norm(0,1). > end repeat. > do repeat c = c1 to c8. > compute c = .707*z3 + .707*rv.norm(0,1). > end repeat. > > factor /vari = a1 to c8. > > rank a1 to c8 /ntiles(2). > > factor /vari = na1 to nc8. > > factor /vari = na1 to nc8 /crit fact(3). > > > > 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=13441.4e79e96ebb5671bdb50111f18f263003&n=T&l=tips&o=26141 > or send a blank email to > leave-26141-13441.4e79e96ebb5671bdb50111f18f263...@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=26144 or send a blank email to leave-26144-13090.68da6e6e5325aa33287ff385b70df...@fsulist.frostburg.edu
