For my undergrad dissertation I'm assessing the unidimensionality of a 75-item questionnaire using PCA and CFA (because a secondary analysis requires this). The total sample is 1040 participants, which I have split into two groups to do the analyses separately.
After looking at the scree plot it's not looking very unidimensional, as it suggested extracting 18 factors, and the first factor accounts for just 18% of the variance. I want to run a CFA on this first factor anyway, so that I can report the fit statistics, even though I suspect they'll be poor. After running a different extraction method to the scree plot (Very Simple Structure), a two-factor model was proposed, and a PCA with oblique rotation reveals two factors which makes sense conceptually, and account for 24% of the variance, so I will run this as a comparison.
The one-factor model consists of 34 items and the two-factor model consists of 52 items (if I take loadings above 0.4)
My question is:
- In my CFA sample, do I include ALL 75 questions, even if many of them didn't load onto either factor? Or would I include only the 34 items for the one-factor CFA, and the 52 items for the two factor CFA?
My understanding is that because I am not trying to produce a unidimensional model (e.g. by removing items), but rather am trying to test if the existing 75-item questionnaire (which is already in use in clinical samples) is unidimensional, I would keep all 75 factors in both the one-factor and two-factor CFA. Is that correct?
I realise that (a) EFA is probably better than PCA for this task and (b) I will get poor fitting models in each, but I've already written all the codes for this and I am prepared for discussing this outcome in my project.
Below is the code for my CFA
one.factor.model <- 'Factor1 =~ Q14 + Q27 + Q11 + Q29 + Q65 + Q72 + Q62 + Q28 + Q30 + Q38 + Q6 (etc) Factor1 ~~ 1*Factor1' one.factor.fit <- cfa(one.factor.model, data= data) summary(one.factor.fit, fit.measures=TRUE)