Grouping: The gallery I linked is an almost unfiltered assortment of all the graphs I generated, but I eventually ignored the ones where some cohorts were very small. Even in the case of motivation vs. education, where I already grouped the originally five levels into two, the result (that people who hadn’t visited a university were more easily motivated for or curious about EA) was not “significant” (or what the proper term is, Bayes factor of 0.5).
That was a grouping of demographic levels, though. Is what you’re suggesting closer to this or this one?
Bootstrapping: My university course and my textbook only touch on that in the context of things they wish they had had the time to cover… Do you mean that I could use bootstrapping to determine the variance of the individual measures or of the rank of the items? The first seems doable to me, the latter more tricky.
Grouping: The gallery I linked is an almost unfiltered assortment of all the graphs I generated, but I eventually ignored the ones where some cohorts were very small. Even in the case of motivation vs. education, where I already grouped the originally five levels into two, the result (that people who hadn’t visited a university were more easily motivated for or curious about EA) was not “significant” (or what the proper term is, Bayes factor of 0.5).
That was a grouping of demographic levels, though. Is what you’re suggesting closer to this or this one?
Bootstrapping: My university course and my textbook only touch on that in the context of things they wish they had had the time to cover… Do you mean that I could use bootstrapping to determine the variance of the individual measures or of the rank of the items? The first seems doable to me, the latter more tricky.