I want to see how 7 measures of text correction behaviour (time spent correcting the text, number of keystrokes, etc.) relate to each other. The measures are correlated. I ran a PCA to see how the measures projected onto PC1 and PC2, which avoided the overlap of running separate two-way correlation tests between the measures.
I was asked why not using t-SNE, since the relationship between some of the measures might be non-linear.
I can see how allowing for non-linearity would improve this, but I wonder if there is any good reason to use PCA in this case and not t-SNE? I'm not interested in clustering the texts according to their relationship to the measures, but rather in the relationship between the measures themselves.
(I guess EFA could also a better/another approach, but that's a different discussion.) Compared to other methods, there are few posts on here about t-SNE, so the question seems worth asking.