http://spssx-discussion.165.s1.nabble.com/Principal-components-analysis-regression-tp5727080p5727087.html
I'm pretty sure that the point of Mike's (2) is that an N of 50 is far too
small for a PCA or PFA on 31 items. The rule of thumb says, 10 or 20
times as many cases as items. Smaller N's work when there are higher
correlations and evident structure than what you are apt to see with items
scored as 0,1,2. - So - Your PCA should pretty thoroughly unreliable.
I might try a PFA, assuming common factors; and if there are some factors that
come out of varimax rotation which also have face validity, I would score up those
factors from the high loadings. Every variable not in one of those factors would
be preserved to look at separately. The whole set of analyses, with too many
variables for the N, should be regarded as largely exploratory.
ANOTHER ISSUE.
I would expect that several states would show heterogeneity in the outcome
(graduation rate) between cities, or cities vs. rural in; that situation (if it exists)
would imply that using an average figure is not a very good idea if you want to
explain those rates. Similarly: Is "State control over X" homogeneous within
states, or is that also problematic?
--
Rich Ulrich
Date: Tue, 26 Aug 2014 10:41:41 -0700
From:
[hidden email]Subject: Re: Principal components analysis + regression?
To:
[hidden email]1) I have a survey that consisted of 31 questions, administered to each of the 50 states.
2) I'm not even sure what this is asking. My data is as follows
| |
State control over X |
State control over Y |
State control over Z |
| Alabama |
1 |
2 |
3 |
| Alaska |
2 |
2 |
3 |
| Arizona |
2 |
2 |
3 |
In SPSS I Analyze -> Dimension Reduction -> Factor -> Variables where Variables were "State control over X", "State control over Y", "State control over Z", and so on with a total of 31 variables.
3) I'll recode it the other way.
4) I'll look at simple Pearson r between my dependent variable and the *all* of the component scores.
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Re: Principal components analysis + regression?
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