Date: Mon, 23 Aug 2010 10:09:40 +0800
Reply-To: Eins Bernardo <einsbernardo@yahoo.com.ph>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: Eins Bernardo <einsbernardo@yahoo.com.ph>
Subject: Re: Advantages of cluster analysis over factor analysis?
In-Reply-To: <4C71710C.9080905@umich.edu>
Content-Type: multipart/alternative;
Dear Steve et al,
you wrote:
(even though it is usually better to select your best raw variables
for CA, unless the FA reveals highly reliable and uni-dimensional
factors that include everything you want to know about subgroups)
In FA, when can a unidimensional factor be considered reliable factor? Are you referring to a high cronbach alpha?
Eins
--- On Sun, 8/22/10, Steve Peck <link@umich.edu> wrote:
From: Steve Peck <link@umich.edu>
Subject: Re: Advantages of cluster analysis over factor analysis?
To: SPSSX-L@LISTSERV.UGA.EDU
Date: Sunday, 22 August, 2010, 6:48 PM
it seems to me that...
"FA" and "CA" address completely different
questions; that is,
FA assesses the relations among *variables* -- aka "variable-centered"
analysis -- and finds fewer vars aka "factors" that explain the
relations among the raw variables.
CA assesses the relations among *objects* (e.g., people) -- aka
"person-centered" analysis -- and finds relatively homogeneous sugroups
of objects (defined by similar patterns of values on the given cluster
variables).
In practice, people generally first use FA to find a reduced number of
variables and then CA to find subgroups based on this reduced number of
variables/factors.
(even though it is usually better to select your best raw variables
for CA, unless the FA reveals highly reliable and uni-dimensional
factors that include everything you want to know about subgroups)
Any reviewer who suggests using FA instead of CA does not understand CA.
On 8/20/2010 11:47 AM, Tanya wrote:
Hi,
I submitted a paper where I had used a cluster analysis (Ward's method and
k-means) to structure a questionnaire. However, this was rejected by one
reviewer who did not find my approach convincing (factor analysis is more
common indeed). The journal addresses practitioners, therefore I preferred
the clear-cut 7-cluster solution of the cluster analysis to the 17-factor
result from the cluster analysis with strong crossloadings (the
questionnaire concerns a certain subgroup of students, therefore this might
probably be expected; the many mini factors consisting of two or three items
only suck, though, and eliminating them might shorten the questionnaire
significantly ...).
So, are there any arguments about the statistical advantages of CA over FA
which might help convince the reviewer? :)
Thanks in advance
Tanya
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--
Stephen C. Peck
Research Investigator
Achievement Research Lab
Research Center for Group Dynamics
Institute for Social Research
University of Michigan
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link@umich.edu
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