Date: Mon, 14 Feb 2005 20:27:05 +0000
Reply-To: David Hitchin <d.h.hitchin@sussex.ac.uk>
Sender: "SPSSX(r) Discussion" <SPSSX-L@LISTSERV.UGA.EDU>
From: David Hitchin <d.h.hitchin@sussex.ac.uk>
Subject: Re: help factor analysis
In-Reply-To: <20050214172521.4638.qmail@web53502.mail.yahoo.com>
Content-Type: text/plain
It often happens. The default setting just does not allow SPSS to do
enough work to get the solution. Just increase the default maximum
number of iterations (which is specified at the bottom of the "Factor
Rotation" panel to something more realistic, such as 100. It doesn't
take much longer to run, and this is generally enough to get you the
solution.
Many people like to create "summated rating scales", i.e. they get a
rating score by adding together variables which have similar factor
loadings, in other words rather than using a formula for factor scores
which uses fractional weights, the weights are either 0 (the item does
not belong to the subscale) or 1(it does). Its quite a lot of work
building such scales, and rather than using factor analysis it may be
better to use the "reliability" procedure to repeatedly chip out the
subsets that you need.
Of course for people scoring tests by hand, summated rating scales are
easy, but they can't create proper factor scores by hand as there is
too much complicated work. The surprise is, that if this is well done,
the rating scales correlate pretty highly with the factor scores. In
other words, you don't lose much by using 0/1 weightings rather than
coefficients to several decimal places.
> I am told that the correlation coefficient matrix that rotations are
> based on in SPSS are not ideal for ordinal data? if not what
> alternatives are available?
>
If there is a problem, it is in the factoring and the calculation of
statistical tests, and not in the rotations.
You have to face the fact that ordinal data (in a sense) contains less
information than equal-interval data, and once you have lost (or failed
to collect) information, then you can't recover it by doing more
analysis.
Perhaps your advisers are thinking of rank correlations as input to the
factor analysis rather than the usual Pearson product-moment
correlations. However, the Spearman method is equivalent to ranking
your data and then proceeding in the usual way, while the Kendall
method (if I remember rightly) is not guaranteed to produce a positive-
definite matrix.
You wrote:
> I
> want to use promax results but what I would like to do first is to
> compare it against Varimax and Oblimin rotations. I want to try to
> extract different factors and try different rotations and and report
> the results that makes the most sense but I am getting this error in
> most cases.
Bear in mind that Varimax tries to give you the clearest orthogonal
solution to your problem, i.e. the factor scores are uncorrelated.
However, people who think about your data may think in terms of
correlated constructs, and it may be easier to use factor scores that
are aligned with the way that people think - even if these are not
independent of each other.
If you are to use promax or oblimin solutions, that implies that you
are already expecting a certain kind of factor structure - you know
where you want the coefficients to be large and where you want them to
be small. In this case you have a hypothesis to test, that a certain
pattern will be present in the factor matrix. You won't find this
structure in the most effective way by using ordinary factor analysis
followed by rotations, and nor will this test such a hypothesis.
For this you need a structural equation modelling routine, such as
Lisrel, EQS,Amos or something similar. In this you specify the expected
structure of your data, and the computer performs a version of factor
analysis which seeks the optimum fit to your hypothesis, and tests the
significance of the fit.
David Hitchin
Quoting Mpundu Mukanga <engineeringresearch3000@yahoo.com>:
> I have likert type data...trying to perfom factor analysis. When I
> specify the number of factors I am getting the error as shown below.
> Any syntax and pointers would greatly help. What is happening is
> here? what am I doing wrong? what does that say about the data? Any
> reasons why rotation is not converging? I maybe wrong here but I
> want to use promax results but what I would like to do first is to
> compare it against Varimax and Oblimin rotations. I want to try to
> extract different factors and try different rotations and and report
> the results that makes the most sense but I am getting this error in
> most cases.
>
>
> Secondly is it a recommended practice to sum up or average the scales
> scores? what i would like to do is create scales for each factor.
>
> I am told that the correlation coefficient matrix that rotations are
> based on in SPSS are not ideal for ordinal data? if not what
> alternatives are available?
>
> Thanks for help
>
>
> Rotated Component Matrix(a)
>
>
>
> a Rotation failed to converge in 25 iterations. (Convergence =
> .000).
>
> "Rotated Component Matrix(a)
>
>
>
>
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