I have a dataset where participants responded to questions that have 2 different response scales: one response scale was 1-5 (see attached data: variable = connection_artist_general and connection_audience_general) and the other was 1-7 (see attached data: variable = connection_artist_circles and connection_audience_circles). I fit a model with brm and I visualized with conditional_effects().

I would like to know if there is any way to **separate out the two types of items in the model** so that I could make two different plots, one visualizing the conditional effects of the items that had a scale 1-5 and the other visualizing the conditional effects of the items that had a scale 1-7.

If that is not an option, do you think it is reasonable to normalize the 1-5 scale onto a 1-7 scale?

Also, I am under the impression that this does not matter for the brms modelling, but do these response scales matter meaning I should normalize and re-run my models?

```
# First, read data attached in this post, then:
data$value<-factor(data$value, ordered= TRUE)
data$Participant<-factor(data$Participant)
data$allowed_vote<-factor(data$allowed_vote, levels = c("0", "1"),labels = c("No", "Yes"))
# selected adjacent category model with category-specific effects because LOOIC was lowest and because it makes sense theoretically
fit_G2<-brm(
formula = value ~ 1 + allowed_vote + (1|Participant) + (cs(1)|variable),
data = data,
family = acat("probit")
)
conditional_effects(fit_G2, "allowed_vote", categorical = TRUE)
```

- Operating System: Windows 10
- brms Version: 2.13.5

Thank you so much for any and all help!

SampleData.csv (5.6 KB)