Rotations for factor analysis

[statsgirl]

Hi,

I have a data set of 3784 participants (no missing data) who answered 16 General Human papillomavirus (a disease) knowledge items.  As an example (HPV is rare) These items were asked as true, false and I don't know. I want to examine how many factors should be retained. I conducted an exploratory factor analysis (see below).

My results indicate 1 factor (by looking at eigenvalues >1, and the scree plot). This is great, as we were hoping for unidemniolnality of this scale

I am wondering if I should try a rotation ? I believe a rotation is not appropriate since I have 1 factor

Thanks,

Samara

proc

factor data=WORK.A method=principal nfactor=16 heywood scree;

var Q28A Q28B Q28C Q28D Q28E Q28F Q28G Q28H Q28I Q28J Q28K Q28L Q28M Q28N Q28O Q28P;

run

[PaigeMiller]

I am wondering if I should try a rotation ?

What benefits do you think you will obtain from a rotation?

Normally, rotation of factors is performed to improve the interpretability of the factors. Is that a problem here? if not, then I would say don't bother to perform any rotation.

[Tommy1201]

I am not a statistician, but according to the literature, I think the best way to determine the number of factors or principal components for analysis is to conduct the parallel analysis..

[Tommy1201]

Use google to find paper "Determining the Number of Factors to Retain in EFA: an easy-to-use computer program for carrying out Parallel Analysis"

And, from this web-site you can download SAS macro for parallel analysis....Programs for Number of Components

[PaigeMiller]

... except the question was not about how to find the proper number of components ...

[Tommy1201]

There is something strange to me in this....It seems that she wants to examine (at first) how many FA or PC to retain in model...then, suddenly she said that model with one extracted PC is acceptable..??