Hi @yaniva đź‘‹
Yes, these can be a bit tricky to interpret because they are not on the (possibly) numerical scale of confidence. You are quite right that they are in the latent scale, but since you used a logit link function, they are on the logit scale. The only difference between those two is that the assumed logistic latent variable has “fatter tails”:
For practical interpretation, this difference doesn’t matter. In either case, it does make sense to talk about the coefficients, and you shouldn’t do any standardization (both link functions already assume a mean = 0 and dispersion = 1).
So what does e.g. x1 = 0.1 mean? You can think that a one unit increase in x1 shifts the entire latent distribution 0.1 units to the right. As a consequence, the probabilities with which responses fall to one or the other side of each of the thresholds shift such that greater response values become more likely.
Does that make any sense? If you’re familiar with the standard normal or logistic distribution, probably yes. But consumers of these analyses often are not. In those cases, you might consider calculating probabilities for the different response categories e.g. when x1 = 0 and when x1 = 1. You could then report e.g. that
“probability(response = 4 “really confident” given x1 = 0)” = 0.5 95%CI=[0.4, 0.6], and “probability(response = 4 “really confident” given x1 = 1)” = 0.55 [0.45, 0.65], and the difference was 0.05 [0.025, 0.075] (made up numbers!) That is, participants were on average 0.05% likely to use the highest response category when x1 was 1 versus when it was 0. Overall, the model indicated that the (assumed to be logistic-distributed) latent variable “confidence” increased by 0.1 units as a function of x1.
Does that help?