# Analysing survey data: hierarchical ordinal/IRT or beta-binomial models?

• Operating System: Win 10.0.17134
• brms Version: 2.9.0

Greetings, I’m looking for advice on how to proceed with a current analytical challenge within the brms framework…

Background: analysing data from a single survey given to different sampling cohorts (`sample`, n = 8) in which all participants (`subject`, n = 500) answered the same question set under two fictional scenarios (`scenario`). So, `subject` is nested within `sample`, and `scenario` is fully-crossed within `subject`. The question set contained 32 statements, each rated on the same 7pt ordinal scale (1-7, strongly disagree to strongly agree, although only verbal labels were displayed to participants). Most of the statements are indicators of five outcomes scales of interest (number of indicators ranges from 3-6 for a given scale), and the remaining nine statements comprise another “scale” that, for simplicity, is being scored via averaging a person’s responses across the items and treated as a standardised (z-score) continuous predictor (`pred_1`). Of central interest is the effect of `scenario` and `pred_1` on a given outcome, and whether or not the effect of `pred_1` varies across `scenario`. All outcomes are correlated with each other.

Current Approach: So far, and following Bürkner & Vuorre (2019), I’ve used separate multilevel ordinal regression models to predict scores for a each outcome (`resp`) :

`resp ~ scenario * pred_1 + (1|item) + (1|subject) + (1|sample)`,
`family = cumulative("logit")`

`prior <- prior("normal(0, 3)", class = "sd", group = "sample") + prior("normal(0, 3)", class = "sd", group = "subject") + prior("normal(0, 3)", class = "sd", group = "item")`

While not ideal, since outcomes are correlated, this may ultimately suffice. However, I have two questions about such an approach. First, does the model syntax look correct? And second, is it possible to produce a scatterplot for a given model showing:

1. predicted values of the latent outcome variable on the y-axis,
2. predicted values of the continuous predictor, `pred_1`, on the x-axis,
3. colour the points by `scenario`,
then
4. overlay lines representing the predicted interaction of `scenario*pred_1`?

Using `marginal_effects()` shows the probability of responses on the observed metric (1-7 scale), which isn’t quite what I’m looking for.

Alternative Approach: Since the outcomes are correlated, might it make more sense to use a custom beta-binomial distribution to estimate all outcomes, as sum-scores, simultaneously? An example of this approach can be found here, although I’ve having difficulty mapping my situation to the model syntax.

Any insight, and/or additional approaches, would be greatly appreciated!

1 Like

The univariate models look sensible to my understanding. What not combine them into a multivariate model and make the group-level effects correlated across responses? This is explained in more detail in the brms_multivariate vignette.

With regard to question number 2, you may want to try `dpars = "mu"` in marginal_effects and fitted. Perhaps this is what you are looking for.

I would be against the beta-binomial approach as you are likely to loose a lot of information.

1 Like

Thanks for the response, @paul.buerkner. I’ll try the multivariate version of the cumulative model later tonight. I automatically discounted this as even the univariates take a while to run. (I did explore separate beta binomial models for my outcomes but the fit was rather poor, so I’ll stick with the ordinal approach).

It’s not clear to me what you mean by using `dpars = "mu"` with marginal_effects and fitted to get latent outcome scores. Is there documentation on this somewhere? I read your Bayesian IRT paper but couldn’t find a means to extract/utilise latent trait scores from the models. Maybe I overlooked something… thanks again!

The dpars argument is documented in fitted.brmsfit and helps you to extract predictions of certain distributional parameters. The main parameter in all models is always called mu and it seems you want to get predictions for it.