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sbbds if you found high correlations consistent with the structure of socionics, wouldn't that be empirical proof of the model?
Like if you were testing temperament, and you found that your measurements for extrovert / introvert, static / dynamic and rational / irrational were all independent (no correlation) but in combinations of all three, there were only 4 main types rather than the 8 possible, that seems like validation of the temperament relationship. If you got 8 types, that is proof at least one of those dichotomies does not act how it theoretically should. You wouldn't know which one was bad or even if they were all bad, but you could preform a similar experiment with a different small group. Assuming at least some of the small groups tested worked, you could use those to figure out the good dichotomies, and through process of elimination, figure out the bad.
In a real test though, I doubt it would be clear cut. A lot of the dichotomies might work a little, but also be wrong a lot of the time. You would need statistics to rank the traits and figure out which are worth keeping and which need to be changed or discarded.
Also like I mentioned in that long post, you could make dichotomies out of higher order concepts, like Model A function information. You need to know the structure of socionics to combine these concepts and look for theoretic correlation, and you would need statistics to weight and measure the integrity of the system.
If the result was a lot of high correlation consistent with the theory of socionics, wouldn't that be proof of the model? The model predicted certain correlations that were empirically demonstrated. However, if there were no correlations, the structure would totally collapse, proving Model A wrong or at least that application wrong. If no application could be found that fit the rules set up by Model A, then you have proven the model is useless. This is the kind if scientific test I need help developing.