Hi, Francisco.

I hope I have understood your question. If so, I think the solution may be simple.

By default SPSS retains factors with eigenvalues greater than 1.

You can modify this option. You can select the option "extract a number of factors" and include as many factors as variables you have to make the reduction.

I just tested with a solution having 8 variables that were reduced to two factors. Now I can see the matrix components with 8 variables.

Greetings

Hi, Francisco.

I hope I have understood your question. If so, I think the solution may be simple.

By default SPSS retains factors with eigenvalues greater than 1.

You can modify this option. You can select the option "extract a number of factors" and include as many factors as variables you have to make the reduction.

I just tested with a solution having 8 variables that were reduced to two factors. Now I can see the matrix components with 8 variables.

Greetings

Thanks Ruben, indeed this works but the loadings in the component matrix are different from the loadings when only factors whose eigenvalues >= 1 are retained. I would like to see the loadings corresponding to the eigenvalues >=1 criteria but not only for the principal components but also for the other components. Thanks in advance =)

P.D. I'm using varimax rotation. Without any rotated solution the loadings are the same (extract all factors or only factors with eigenvalues >=1) but if the solution is rotated, then they change.

first of all, you have to make sure that you are going to conduct Factor Analysis or Principal Component Analysis. Cause two reduction base process differ from each other in theory.