Modeling and visualizing ordinal data with different response scales

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I found a possible solution to my plotting issue at this post: Split conditional_effects plot in facets according to the value of the categorical DV

However I am still curious about the effect these different scale ranges may have on the models. Should I be building separate models for the separate response scales?

Additionally, I also realized that the code I provided results in many divergent transitions. Here is the new code that works for me using a cumulative model instead:

prior<-get_prior(formula = value ~ 1 + allowed_vote + (1|Participant) + (1|variable),
data = data,
family = cumulative("probit"))

fit_G1 <- brm(
formula = value ~ 1 + allowed_vote + (1|Participant) + (1|variable),
data = data,
family = cumulative("probit"),
prior = prior,
save_all_pars = TRUE,
control = list(adapt_delta = 0.95)
)

fit_G3<-brm(
  formula = bf(value ~ 1 + allowed_vote + (1|Participant) + (1|variable))+
    lf(disc ~ 0 + allowed_vote, cmc = FALSE),
  data = long_Agency_SC,
  family = cumulative("probit"),
  save_all_pars = TRUE,
  prior = prior_unequalVar,
  control = list(adapt_delta = 0.85)
)
  • Disclaimer: please feel free to correct any incorrect assumptions I am making as I am definitely not an expert in Bayesian statistics.

Thank you so much!

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