Posted by
Rich Ulrich on
Jan 05, 2014; 10:35pm
URL: http://spssx-discussion.165.s1.nabble.com/Factor-analysis-tp5723780p5723782.html
[no original message seen yet.]
I would restate Art's initial paragraph -
If these are items that are suppose to make up scales,
then you *ought* to be using PFA, not PCA. So: What
do the variables measure?
Also - You will not see structure if there are too many
variables for the number of cases. (And remember that any
case with a missing value will be dropped, by default.)
Ten times the number of variables is usually enough cases,
but that multiplier depends thoroughly on the typical
correlations between variables that will make up a factor.
So you could hint at the r's, too, if you want fuller advice.
- I always look at the univariate data first, to see that
the coding is what I expected ... no weird values, no non-varying
variables.
--
Rich Ulrich
________________________________
> Date: Sun, 5 Jan 2014 14:10:51 -0800
> From:
[hidden email]
> Subject: Re: Factor analysis
> To:
[hidden email]
>
> What is the goal of your factor analysis?
> Are you trying to create summative scales?
>
> Are you interested in accounting for the variance that is common to the
> variables or are you interested in accounting for the unique variance
> also?
>
> Did you use parallel analysis to determine the number of factors to
> retain? If not how did you decide how many factors to retain?
>
> What constitutes a case in you data? how many cases do you have?
>
> How many variables were input to the PCA?
>
> How many factors did you retain?
>
> Did you use varimax rotation? if not, how did you choose the rotation method?
>
>
> Art Kendall
> Social Research Consultants
>
> On 1/5/2014 4:50 PM, Promises [via SPSSX Discussion] wrote:
> 1. What do I do if the Principal Components Aanalysis factors (clusters
> of variables) do not produce common underlying themes that can be
> explained as a real world construct? The variables are more or less all
> over the place...
>
> 2. Could I remove variables/items prior to conducting PCA, if for
> example I find that they're outliers, following descriptive analysis?
>
>
> I will be grateful for any guidance you can offer. Thanks in advance.
>
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