Whenever analysis of data is made through SPSS, we get rotated component Matrix. How do we understand the value that we get and what could be its significance.
Thanks to all my colleagues- Arezoo Bijani, Esraa T.Al Aazwee, Aparna Sathya Murthy, Anand Raghvendra Rao and Javed Ali
Rotated Component Matrix
Figure 1. Rotated component matrix
Rotated component matrix with variables in the rows and components in the columns
The rotated component matrix helps you to determine what the components represent. The first component is most highly correlated with Price in thousands and Horsepower. Price in thousands is a better representative, however, because it is less correlated with the other two components. The second component is most highly correlated with Length. The third component is most highly correlated with Vehicle type. This suggests that you can focus on Price in thousands, Length, and Vehicle type in further analyses, but you can do even better by saving component scores.
https://www.ibm.com/support/knowledgecenter/en/SSLVMB_sub/spss/tutorials/fac_cars_rotcompmtrx_01.html
Rotated Component Matrix
The rotated component matrix, sometimes referred to as the loadings, is the key output of principal components analysis. It contains estimates of the correlations between each of the variables and the estimated components.
Please see the link below:
http://www.surveyanalysis.org/wiki/Category:Principal_Components_Analysis
As far as I understand , with PCA we take the first or maximum value or "principle component" , taking correlated and transforming them to form linearly uncorrelated components.
Mathematically , these eigen values give data variance (or co-variance or correlation all statistical terms) , this measure is RCM (given by the SPSS)
Agree with views shared Aparna, Esraa and Bijani
Understand it is an established process of analysis, it all depends on the applicability
Thanks to all my colleagues- Arezoo Bijani, Esraa T.Al Aazwee, Aparna Sathya Murthy, Anand Raghvendra Rao and Javed Ali
tIn addition to the above,The rotated component matrix helps you to determine what the components represent.
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