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.
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.
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.