
After presenting a brief rundown of 20th-century psychology movements, Keirsey and Bates encourage you to take the 70-question "Keirsey Temperament Sorter," a sort of mini-Myers-Briggs test that places you in 1 of 16 personality types. Like the Myers-Briggs system, this test sorts your personality into groups of extraversion/introversion (E/I), sensation/intuition (S/N), thinking/feeling (T/F), and perceiving/judging (P/J). Unlike the Myers-Briggs system, Please Understand Me also presents four easy-to-remember temperament types--Dionysian (freedom first), Epimethean (wants to be useful), Promethean (desires power), and Apollonian (searches for self)--that underlie the 16 possible personalities identified by the test. The book then delves into a detailed analysis of each type, with sections on mates, children, and leaders. An appendix paints portraits of the 16 possible personality types.
A book would be better than an online test because the point should be to learn about all the other personality types, and how they interact, rather than understand your own.
http://www.amazon.com/Please-Understand-Me-Character-Tempera...
I generally do the same thing when I'm learning something, and I have to fully understand a concept and then attack the concrete applications of the concept. But I've also learned how to go the other way when I need to because much of the technical writing I encounter is written for people who need a lot of examples but don't follow abstract concepts. So I've internalized building up the abstract concept from the concrete examples.
I believe it is both. You can go against your brain's coding with some extra work; somewhat like an adapter, I suppose. Your brain just still is coded a certain way natively.
> or example, what you're describing is basically typical of how Keirsey describes concrete versus abstract reasoning in Please Understand Me. And it could be kind of alienating to be the one abstract reasoner in a group of concrete reasoners and if you believe his statistics it's kind of likely that you'll find yourself in that situation a lot because you gravitate toward the abstract rather than the concrete/operational aspects of a concept.
This is very interesting. I've never heard of concrete/abstract reasoners before, but that does sound similar to what I've described. Thank you for the book.
> much of the technical writing I encounter is written for people who need a lot of examples but don't follow abstract concepts.
Yes! I love using examples to illustrate applications of an abstract concept, but I always explain the concept first. When the concept isn't explained first, I am sad. :(
> So I've internalized building up the abstract concept from the concrete examples.
I believe you may be better at it than I :)
EDIT1: Also B5 just names 5 axes instead of MB naming (4*2) groups which always bugged me. Beyond that, because B5 does not discretize, it puts the measurement error center stage. Emphasizing measurement error is a major positive for something already so vague.
EDIT2: Also, while situation, context, how well you slept, or whatever absolutely matter, I believe this vagueness underwrites much of the "MB is pseudoscience" hostility. To some, it's so vague as to be meaningless jibber-jabber. To others, it's specific enough to be just on this side of the "science" line. Even if you think it is science, science can clearly be misapplied by those who don't understand it, and this "science" probably is..a lot (as bemoaned by the article).
As the author @dyno-might points out in fledgling steps to harden the ideas, there could be an intermediate 'x' value for each of the 4 axes. He does not say how far out from the mean one has to be "to not be in 'x'". Far enough out for each of the 4 and, boom, 99% of people are XXXX. At that point, the tests become only outlier detectors (again modulated by other, unmeasured variables) discriminating only 1% of people. Note 0.317 is also about outside 1 standard deviation for a normal curve and assuming independence, for simplicity of the example, 0.317*4 = .01. At 1.25 SDs that goes to 0.002. (Not random values - @dyno-might's distributions looked a little platykurtic.) So, it might easily be the test is only very informative (i.e. maybe predictive) for a tiny fraction of people - so not exactly unscientific, but also not exactly very useful if "over-concluding" is likely for 99% to 99.9% of the "modal" test takers. Textual profiles (or book chapters!) can take that over-concluding from just binning up a few notches.
