# What to do in PCA when one variable has similar values in several principal component eigenvectors?

I'm performing a principal component analysis (PCA) using some economic variables of a region. I have six variables and I want to reduce them to two principal components. Most of the variables have a larger value in either one of the first two principal components' eigenvectors. For example, they either have a large value in the first principal component [eigenvector] and near zero in the second, or vice versa.

However, I have one variable that has almost the same value in the eigenvectors of the first and the second principal components.

Does this tell me anything about this variable? Should I keep it or should I remove it from the analysis?

• I have one variable that has almost the same value... Eigenvector entry is the cosine of the angle between the component and the variable axes in space. Should I keep it or... We don't know the aims and nuances of your particular analysis. What makes you think you have to get rid of such a variable? – ttnphns Jun 26 '15 at 9:11