Dear Statlistserv members,
I am having trouble interpreting the results of a principle factor
analysis I am conducting. The command and results are shown below.
Several things puzzle me about the results table. Why are some
eigenvalues < 0? Why are some of the proportions <0? Why are most of
the cumulative values >1. I suspect the answer to one of these
questions applies to all three. My understanding of factor analysis is
that I would interpret the results table as retaining all factor with
an eigen value >1 because they explain more of the variance than the
original variable and that the set of retained factors explains the
"cumulative" percent of the variance in the dataset. I thought that all
the variance (100%) would be explained by all the factors, but that a
subset of those factors would therefor only explain less than 100%. In
my case, I would retain factor 1 and by itself it would explain 133% of
the variance, which does not make much sense to me. When I run a
principle component analysis on the same data, I get a two component
solution explaining 52% of the variance. That result table is more
similar to what I have seen elsewhere, but I am puzzled as to why there
seems to be such a difference between procedures on the same data (and
the single factor solution of the pfa also makes more theoretical sense
as this point)
I am not a statistician but would like to understand in general terms
what is happening with the factor command and how to interpret its
results. I have spoken with two statisticians I work with and they are
surprised to see eigen values<0 and cumulative values >1, but they are
not STATA users. Maybe we are misinterpreting the results or maybe I am
doing something wrong with the software. If the results were not valid,
I would have expected STATA to give me some sort of error message
rather than an aberrant result.
Thank you very much for your help.
FACTOR ANALYSIS WITH PRINCIPLE FACTOR EXTRACTION
factor att2r att3r att9r att20r att22 att23, mineigen(1)
--------------------------------------------------------------------------
Factor | Eigenvalue Difference Proportion Cumulative
-------------+------------------------------------------------------------
Factor1 | 1.34388 1.21292 1.3335 1.3335
Factor2 | 0.13096 0.14728 0.1300 1.4635
Factor3 | -0.01632 0.04961 -0.0162 1.4473
Factor4 | -0.06593 0.09743 -0.0654 1.3819
Factor5 | -0.16336 0.05812 -0.1621 1.2198
Factor6 | -0.22148 . -0.2198 1.0000
--------------------------------------------------------------------------
LR test: independent vs. saturated: chi2(15) = 304.22
Prob>chi2 = 0.0000
PRINCIPLE COMPONENT ANALYSIS
quietly pca att2r att3r att9r att20r att22 att23, mineigen(1)
rotate
--------------------------------------------------------------------------
Component | Variance Difference Proportion Cumulative
-------------+------------------------------------------------------------
Comp1 | 2.05242 .95265 0.3421 0.3421
Comp2 | 1.09977 . 0.1833 0.5254
--------------------------------------------------------------------------
Jean-Gael "JG" Collomb
PhD candidate
School of Natural Resources and Environment / School of Forest
Resources and Conservation
University of Florida
[email protected]
[email protected]
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