http://spssx-discussion.165.s1.nabble.com/Re-Principal-Component-Analysis-in-Different-Measurement-Units-tp5726162p5726168.html
If he was analyzing correlations, I should hope that the algorithm for
computing r is robust enough that there would be no excuse for the
correlations to come out different. So, the likely explanation is some
unnoted change in data. Look at all the means and r's.
If he was analyzing variances - which must be considered a mistake -
there is no reason for loadings of the two scores to have much
resemblance across analyses. And the "number of factors extracted"
is not meaningfully related to a cutoff of 1.0, if that was used. When
PCA is employed on correlations, the 1.0 represents the amount of
variance to be explained for each variable, and "less than one" says
that the factor is worth less than a single variable and thus might be
ignored for subsequent rotation... assuming you are working from a
theory about important latent factors.
--
Rich Ulrich
Date: Tue, 20 May 2014 12:44:22 -0500
From:
[hidden email]Subject: Re: Principal Component Analysis in Different Measurement Units
To:
[hidden email]
My friend performed Principal
Component Analysis using SPSS 13.0 and got different results using two
different data. The first data set is the raw data: 85(proportion of urban
population), $80354(GDP per capita); the second data set is the adjusted data: 0.85(the
proportion of the urban population), $80.354 thousand (GDP per capita). Actually,
the second data set is different from the first data set only in measurement
units. For the first data, my friend got four main components, for the second data
set, he got five components. Could you advise what might be the reason for the difference?
Thanks for your help.
Han Chen