- Thread starter Martin Marko
- Start date
- Tags polychoric. assumption.

the degree in which said distribution deviates from normal will result in more and more biased estimates of the polychoric correlation.

Moreover, this assumption of a latent continuous variable can never be verified in practice

if it would actual influence the results if it was not true.

It was always stressed to me that it was impossible to every know for sure a latent variable existed.

What about in cases where the binary variable really is the result of transforming a continuous response. Something like

Obese = 1 if BMI > 30, 0 otherwise

Clearly each observation came from looking at a person's BMI and then just categorizing it.

Or if it had a specific distribution. Certain elements can suggest it, so can SEM, but that is not proof it exists. But that may simply be a quibble.

now, if you believe in a latent variable like the ones you and i work with (which is something you just simply need to make a leap of faith towards) then yes, you can test a lot of things about that latent variable to see wehther your assumptions are tenable or not. but you ahve to *believe* in it first.

What about in cases where the binary variable really is the result of transforming a continuous response. Something like

Obese = 1 if BMI > 30, 0 otherwise

Clearly each observation came from looking at a person's BMI and then just categorizing it.

Obese = 1 if BMI > 30, 0 otherwise

Clearly each observation came from looking at a person's BMI and then just categorizing it.

Pretty much every statistical text I ever read includes somewhere the statement that statistics can not prove causality. Hume's black swans is one element of this, but there are many others. Some argue that experiments where you deliberately manipulate levels can prove causality, but that assumes of course that you can generalize from the research setting to the larger environment -which may or may not be true.