Ordered Beta Regression: A Parsimonious, Well-Fitting Model for Survey Sliders and Visual Analog Scales

I propose a new model, ordered beta regression, for data collected from human subjects using slider scales/visual analog scales with lower and upper bounds. This model employs the cutpoint technique popularized by ordered logit to simultaneously estimate the probability that the outcome is at the upper bound, lower bound, or any continuous number in between. This model is contrasted with existing approaches, including ordinary least squares (OLS) regression and the zero-one-inflated beta regression (ZOIB) model. Simulation evidence shows that the proposed model, relative to existing approaches, estimates effects with more accuracy while capturing the full uncertainty in the distribution. Furthermore, an analysis of data on U.S. public opinion towards college professors reveals that the proposed model is better able to combine variation across continuous and degenerate responses. The model can be fit with the R package brms.