Single-case experimental designs (SCEDs) are interrupted time-series designs that have recently gained recognition as being able to provide a strong basis for establishing intervention effect. Typically, SCED data are short time series and autocorrelated, which renders maximum likelihood and parametric analyses inadequate for data analysis, respectively. Although Bayesian methods overcome these challenges, most practitioners do not use Bayesian estimation because of: (a) its steep learning curve, (b) lack of Bayesian training, and (c) tack of knowledge of Bayesian software solutions. This study demonstrates two Bayesian interrupted time-series models using freeware programs R and JAGS. Practitioners could modify these codes and run them for their own data by changing the values in the codes where indicated. Providing practitioners with such tools to facilitate their analysis is one way to improve methodological rigor in applied research.
