[R-sig-ME] Is it ok to use lmer() for an ordered categorical (5 levels) response variable?
F@rr@r@D@v|d @end|ng |rom ep@@gov
Thu Mar 7 14:49:42 CET 2019
The simple addition has an interpretation of "number of positive indicators." Before applying a more refined approach, I wonder if there is a quick check that some of the indicators might be handled differently, e.g., do some load much differently on the first PC (I should check my PC terminology).
Or course, this then raises whether there is a refined PC-like approach for binary responses.
I wonder if this might lead to a small adjustment that would be easy to explain.
"Hessian condition I obtained with such model is > 5.0e+06. I.e. assumption is violated (simplifying my initial full model did not help at all)."
Forgive me please if this is a special operation for ordinal modeling; however, the Hessian conditions I can think of would be a measure of ill-conditioning, which might suggest whether a model is overfitted.
There is now at least one whole book on longitudinal discrete data.
In America, BTW, Hedera would be bad.
From: R-sig-mixed-models <r-sig-mixed-models-bounces using r-project.org> On Behalf Of Stuart Luppescu
Sent: Wednesday, March 06, 2019 9:03 PM
To: r-sig-mixed-models using r-project.org
Subject: Re: [R-sig-ME] Is it ok to use lmer() for an ordered categorical (5 levels) response variable?
On Wed, 2019-03-06 at 16:49 +0000, Pierce, Steven wrote:
> Many researchers refer to a survey instrument as a scale, refer to the
> sum of the items from such an instrument as a scale score, then go on
> to use such scores in their research. That's the sense in which I am
> using the term scale score. It's obviously a looser, more informal
> usage than you prefer but that doesn’t mean I'm wrong. It's an
> empirical fact that lots of people use the term the way I did.
I'm with Harold Doran on this one (not for the first time by any means). Just because a lot of people do it doesn't have anything to do whether it's a good method or not. When I was in graduate school a long time ago we learned that the numerical codes associated with the response categories for survey data are category labels, nominal data, not numeric data. And as such it is not appropriate to do arithmetic (such as calculating sums and means) on them.
Chief Psychometrician (ret.)
UChicago Consortium on School Research
lupp using uchicago.edu
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