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Hello and good day to you. I am using Principle Component Analysis (PCA) with Varimax rotation to analyze variables on my research.

There are 20 variables and 6 components were extracted. From the result, I noticed that the variables were strongly loaded on one component.

And I attached the Rotated component Matrix which I got from the PCA analysis.

As you can see from the picture there were 10 different variables which put into the first component

I don't know where did I makes mistakes of where the fault is coming from, can anybody please give me a suggestion? Thank You.

Rotating Component Matrix

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    $\begingroup$ Why is that a mistake? $\endgroup$
    – rapaio
    Sep 28, 2019 at 15:25
  • $\begingroup$ What is your goal in using PCA? And are your variables measured without error? $\endgroup$
    – rolando2
    Sep 28, 2019 at 20:36
  • $\begingroup$ Thats my question, is it a problem when many factors were group into only one strong dimension. My goal using PCA was for grouping the 20 factors into different component dimensions $\endgroup$ Sep 29, 2019 at 15:31

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There's no indication that there's any mistake here. PCA is a dimension reduction technique, so it will find orthogonal vectors. If you have many correlated variables, it's not surprising that many of them will be strongly loaded onto a single dimension.

It might be worth your time to read our most-upvoted thread on CV, which has many great explanations of what PCA is and how it works.

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