Posted by
Garry Gelade on
Aug 17, 2011; 7:46am
URL: http://spssx-discussion.165.s1.nabble.com/basic-que-Factor-Analysis-tp4706531p4707204.html
Mehul
A few suggestions:
1) The maximum number of variables you can analyse to give a robust solution
depends on the number of cases (subjects) and partly on how clear the factor
structure is. A common suggestion is at least 10 cases per variable, though
5 can work fine in some situations.
2) If you choose Analyze Correlation Matrix on the Extraction submenu, the
standardization will be done automatically.
3) Your 93rd variable may be problematic. Eg if it has many missing values,
and you are using the default Listwise (on the Options submenu) it will
reduce the number of cases available for analysis. Or it may simply be a few
more iterations are needed. Try increasing the number of iterations on the
Rotation submenu to say 100. If the solution still doesn't converge, it is
likely your extra variable is causing the problem.
4) Yes you can cluster on the factors, provided your factor solution "makes
sense" (i.e. is substantively interpretable).
HTH
Garry Gelade
-----Original Message-----
From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of
Mehul Pajwani
Sent: 17 August 2011 03:17
To:
[hidden email]
Subject: basic que-Factor Analysis
Hi,
I am trying to run (for the first time) factor analysis in SPSS.18 and
would appreciate any feedback and recommendations from seasoned SPSS
users and statisticians on the following
I started with 93 continuous variables (5-point, 6-point and 7-point
rating scale type questions) and trying to reach to a reasonable
factor solution. Here are key questions I am struggling with.
1) I started with 93 variables. Are these too many variables to start
with? Is there any guideline about (minimum and maximum) number of
variables?
2) Though all variables I am using are continuous (rating scale) they
are of different scale points and types (e.g. 5-point ratings where
1=Strongly Disagree and 5=Strongly Agree; 6-point scale where 1=Will
not influence at all and 6=Will strongly influence etc.). I
understand that this will have very different variance and therefore
can possible affect the results. Having said that do I need to
standardize these variables or factors analysis will automatically
take care of it?
3) I selected an option for Rotated Component Matrix but it failed to
produce that with the message "Rotation failed to converge in 25
iterations. (Convergence = .001)." Surprisingly, I ran the factor
analysis with the same data couple days before (with 92 variables, I
guess) and it did produce Rotated Component Matrix. Any thoughts on
why it is showing that error and how to handle this?
4) My ultimate goal is to identify broader dimensions based on factor
analysis and then use these composite variables for the segmentation
by doing cluster analysis. Is this the right approach for the
segmentation?
I would appreciate any help on this.
Regards,
Mehul
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