Abstract

We demonstrate the power of Bayesian approach to modeling data with ordered outcome variable and illustrate it using data on the effect of the imposed online teaching during the Covid-19 pandemic on students with special needs. It is known that even frequentist ordinal modeling provides better theoretical interpretation and inference for such data than models which treat the ordered (Likert) outcome variable as metric. We argue that Bayesian approach adds flexibility in specifying the ordered models and provides goodness-of-fit measures to compare alternative models such as cumulative model and adjacent-category model with equal and unequal variances across levels of a covariate. Data for illustration come from about 6000 students who responded to a questionnaire on online teaching at Stockholm University during the Spring term 2021. Preliminary results using the Bayesian Regression Models using 'Stan’ (brms) R-Package show that students with special needs were adversely affected by the imposed online teaching.  Among other things, the students felt less engaged intellectually, had lower confidence, felt examination instructions were unclear and spent more hours per week than their counterparts with no special needs.