[EMAIL PROTECTED] wrote:
> Would any of you know a rule of thumb for selecting the proper (of
> optimal) number of factors to be extracted from a factor analysis.
> Also, how many variables can there be in such factor (is two variable
> in one factor not enough?).
While the Kaiser criterion may be popular there is little evidence to
suggest that it works well. If you are considering a limited number
of possible solutions (say 1 to 6 factors), then you might consider
calculating the RMSEA fit index for each of the possibilities.
RMSEA = sqrt ( (Model Chi-Square - Model Degrees of Freedom) /
(Model Degrees of Freedom*(N - 1)) )
RMSEA values of .05 or less indicate close fit to the observed
correlation matrix and higher values indicate the model is not
adequate. Choose the model with the number of factors which has the
lowest RMSEA.
For a discussion of these issues, I recommend this recent paper on
Exploratory Factor Analysis:
Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J.
(1999). Evaluating the use of exploratory factor analysis in
psychological research. Psychological Methods, 3, 272-299.
Hope this helps,
Chuck
----------------------------------------------
Chuck Cleland
Institute for the Study of Child Development
UMDNJ-Robert Wood Johnson Medical School
97 Paterson Street
New Brunswick, NJ 08903
phone: (732) 235-7699
fax: (732) 235-6189
http://www2.umdnj.edu/iscdweb/
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