When can Ordinal Variables be treated as Interval Variables?

fog37 said:

TL;DR Summary: Understanding when ok to treat ordinal variables treated as interval variables

What justifies that?

Dale said:

People did it previously in published papers. Doing it currently doesn’t sink a paper.

It isn’t a great justification. As you say, there are a variety of opinions on the topic. Including ones that are supportive of the practice.

So it will continue to be done for the time being. Most reviewers are statistically unsophisticated, and ordinal methods are less familiar and often less powerful.

fog37 said:

TL;DR Summary: Understanding when ok to treat ordinal variables treated as interval variables

Hello,

Ordinal variables (see Likert scale) can be labelled using numbers and ranked by those numbers. However, the difference between category 2 and category 3 may not be exactly be the same as the difference between category 4 and 5. That said, I noticed that in social science ordinal variables are sometimes approximately treated as if they were numerical predictors if the ordinal variable has many levels...Is that a correct approach? What justifies that? I did some reading and found a variety of opinions on the topic...

Thanks

statdad said:

binning continuous data can be a very bad thing to do and you're losing information: care hast to be take even in the best of situations. Imagine a data set that is actually bimodal (or multimodal): a histogram with too few bins probably won't detect it.

Dale said:

Binning can also produce a bimodal discrete distribution where the underlying continuous distribution is not bimodal.

However, very often with psychological data you are working with latent variables so you have no choice but to do an unknown binning on the unobservable latent scale.

WWGD said:

There are methods too, for Latent variables, that assume observed data originate from a continuous,
iirc (wolg) normal variables. let me see if I can find refs.