# Mulitlevel model with ordinal data

The response items in the dataset is inherently categorical and ordinal (Likert-scale items) in nature. After having run variables of each level to see how they will run independently, I just tried to combine the levels only to get the following error:

Error in runHelper(model, frontendStart, intervals, silent, suppressWarnings, :

MxExpectationRAM: Ordinal indicators are not supported in multilevel models

It hadn't occurred to me that this could be a problem, and it puts me in somewhat of a predicament about how to proceed. Searching through some old forum threads, I did find a [thread that discusses this topic](https://openmx.ssri.psu.edu/node/4536), with a [comment suggestion using `mxFitFunctionAlgebra()` and `mxEvaluateOnGrid()`](https://openmx.ssri.psu.edu/comment/8418#comment-8418). However, I would not know how to proceed with that.

Has there any more developments on this issue? If there's no direct solution, I would need to consider workarounds or alternative strategies. The simplest alternative for the ordinal level 2 and 3 variables would be to model them as continuous. However, this does not seem like a good idea for variables which do not have any semblance of a normal distribution.

***Any suggestions how to proceed, or any code examples or similar that addresses this issue, would be appreciated.***

## No Solution

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In reply to No Solution by mhunter

## Two-level WLS solution

Another solution would be to be to model level two and three variables as continuous. However, I am uncertain how this might affect model fit and estimation. What would the major drawbacks and pitfalls to such an approach be?

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## MCMC Bayes

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In reply to MCMC Bayes by AdminRobK

## MCMC Bayesian

I guess I didn't specify in the OP, but I am already down to using WLS estimation if that changes the outlook somewhat.

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