# Poor models from PCA and CFA

For my undergraduate dissertation I'm analysing a 75-item self-report questionnaire filled out by 1140 autistic participants (randomly split into two groups. I have split the sample and am carrying out a PCA on one sample, and a CFA on the other. Previous Rasch analyses have indicated that this questionnaire is meant to be unidimensional, but my one-factor model is coming back with terrible fit indices. I've tried multiple different models by now, including a 2-factor, 3-factor, 4-factor model (always using oblimin rotation, always removing items with cross-loadings, but varying different cut-offs/ inclusion criteria- for example removing items which don't correlate highly with other items).

None of these models have returned with good fits. I'm okay with this, I know it's not good to keep running the analysis until I get something, and I know there are many issues with the exploratory nature of this analysis and the very simple tests I'm using.

But I'm wondering if I'm doing something wrong, or if there's something obviously wrong that will cost me marks on my dissertation. Is this an appropriate analysis for a 75-item questionnaire? Or should I be trying to do something with parcelling or correlated error in SEM?

The basic code I'm using is below.

Thank you

one.factor.model <-   'Factor1 =~ Q11 + Q29 + Q14 + Q65 + Q72 + Q52 + Q68 + Q55 + Q2 + Q5 + Q28 + Q62 + Q22 + Q75'
one.factor.fit <- cfa(one.factor.one, data= half2)
summary(one.factor.fit, fit.measures=TRUE)