Although frequentist estimators can effectively fit ordinal confirmatory factor analysis (CFA) models, their assumptions are difficult to establish and estimation problems may prohibit their use at times. Consequently, researchers may want to also look to Bayesian analysis to fit their ordinal models. Bayesian methods offer researchers an effective means of estimating, testing, and interpreting ordinal CFA models. Unfortunately, there are few applied resources on the subject. The purpose of this article is to provide researchers with an introduction to the essential concepts, practice recommendations, and process of fitting ordinal CFA models using Bayesian analysis. Mplus 7.4 and data from the Pittsburg Common Cold Study 3 are used to example how researchers can set up their Bayesian models, conduct diagnostic checks, and interpret the results. This article also highlights the benefits and challenges of Bayesian ordinal CFA modeling.