Posted by
Ian Martin-2 on
URL: http://spssx-discussion.165.s1.nabble.com/Re-Principal-Component-Analysis-in-Different-Measurement-Units-tp5726162p5726166.html
Maybe he used VCV matrix input instead of correlation matrix?
Ian
On May 20, 2014, at 2:08 PM, "Maguin, Eugene" <
[hidden email]> wrote:
> Hard to see that 85 is the proportion of urban population. I wonder if the problem might be the large ratios between pairs of variables. For example 80354 is roughly 1000 times larger than 85. The ratio of variances will be roughly 10E6. Perhaps significant digits are being lost in the eigenvalue solution. I suggest that your friend adjust the data so that the scale of variables is approximately equal. 85, 80.354 or .85, .80354.
>
> Gene Maguin
>
> From: SPSSX(r) Discussion [mailto:
[hidden email]] On Behalf Of Han Chen
> Sent: Tuesday, May 20, 2014 1:44 PM
> To:
[hidden email]
> Subject: Re: Principal Component Analysis in Different Measurement Units
>
> 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
>
>
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