Investigating immediacy in multiple-phase-change single-case experimental designs using a Bayesian unknown change-points model.

Immediacy is one of the necessary criteria to show strong evidence of treatment effect in single-case experimental designs (SCEDs). However, with the exception of Natesan and Hedges (2017), no inferential statistical tool has been used to demonstrate or quantify it until now. We investigate and quantify immediacy by treating the change points between the baseline and treatment phases as unknown.

Bayesian Prior Choice in IRT Estimation Using MCMC and Variational Bayes.

This study investigated the impact of three prior distributions: matched, standard vague, and hierarchical in Bayesian estimation parameter recovery in two and one parameter models. Two Bayesian estimation methods were utilized: Markov chain Monte Carlo (MCMC) and the relatively new, Variational Bayesian (VB). Conditional (CML) and Marginal Maximum Likelihood (MML) estimates were used as baseline methods for comparison.