I’m new to brms and especially interested in the Probit models for the analysis of Likert items and Likert scales data. I went through previous threads but could not find a clear answer to the following question.
I would like to interpret population-level effects: point estimates of b coefficients and their credible intervals. I understand that the values reported by the analysis are standard-deviation units, but I’m straggling to figure out: (i) what these standard deviations represent and (ii) if there is an easy way to transform these estimates back to the original scale.
For example, consider the analysis of people opinions about funding stem-cell research (in the tutorial of Burkner and Vuorre). For the first analysis presented in the tutorial (rating ~ 1 + belief), the estimates of the two b coefficients are -0.24, 95% CI [-0.43, -0.06] and 0.31, 95% CI [0.13, 0.50], respectively. However, I am not sure which standard deviation these numbers refer to. How could we transform these values into the units of the original scale, where ratings range from 1 to 4?
I would like to communicate such results to an audience of non-experts and provide some clear intuition. I know that the conditional_effects function allows for showing credible intervals in the original scale for each individual group. How can we do the same for contrasts, e.g., difference in ratings between liberals and moderates? Is there something wrong in this logic that I miss?