Principal components are weighted composites of the observed variable,
which is why they are properly referred to as components not factors. Factor
analysis estimates the proportion of common factor variance and attempts to
factor this common variance, ignoring the specific and error variance. Principal
components are likely to combine specific factor variance and error variance
into the components. Principal components are useful as data reduction but not
for understanding the structure of the data.
Dr. Paul R. Swank,
Professor and Director of Research
Children's Learning Institute
University of Texas Health Science Center-Houston
From: SPSSX(r) Discussion
[mailto:[hidden email]] On Behalf Of Jims More
Sent: Friday, April 10, 2009 4:46 AM
To: [hidden email]
Subject: EFA vs PCA
Can someone help expound the difference between EFA and
PCA?
|
The
new Internet Explorer 8 optimised for Yahoo!7: Faster, Safer, Easier. Get it now..
Locked
