Consider a gaussian trait with high levels stigmatized. From a careless observer’s perspective, +1s will be rare (somewhat hiding), +2s extremely rare (thoroughly hiding), +3s nonexistant (very hiding *and* rare to begin with) but +4s unable to hide and in fact talked about incessantly on the news / clickbait / juicy rumor mill. Which looks like there are two populations, a large left-skewed one and an entirely distinct but much smaller one. The trait at [-3,+1] and at +4 may not even look that qualitatively similar! So our observer uses a categorical model.
It’s the wrong model. The trait is gaussian. That’s the scenario.
So we have a category for people who are high in our trait with neither natural place nor social consensus on where draw a border line. This will be bad for all discourse on the subject.
Law of Extremity does some weirder stuff...
Consider a gaussian trait with high levels stigmatized. From a careless observer’s perspective, +1s will be rare (somewhat hiding), +2s extremely rare (thoroughly hiding), +3s nonexistant (very hiding *and* rare to begin with) but +4s unable to hide and in fact talked about incessantly on the news / clickbait / juicy rumor mill. Which looks like there are two populations, a large left-skewed one and an entirely distinct but much smaller one. The trait at [-3,+1] and at +4 may not even look that qualitatively similar! So our observer uses a categorical model.
It’s the wrong model. The trait is gaussian. That’s the scenario.
So we have a category for people who are high in our trait with neither natural place nor social consensus on where draw a border line. This will be bad for all discourse on the subject.
What sort of model do you have in mind here, a liability-threshold one with a deviancy variable + bad-luck variable?
Could you give a real-world example of this (or a place where you suspect this may be happening)?