Background: A single-case design focuses on individual performance and measures the causal relationships between variables (Kazdin, 2019). This experimental design enables researchers to measure the learning behaviors of individual participants over time across phases and assess the effectiveness of an instructional strategy in improving or…

Cluster sampling is common in survey practice, and the corresponding inference has been predominantly design-based. We develop a Bayesian framework for cluster sampling and account for the design effect in the outcome modeling. We consider a two-stage cluster sampling design where the clusters are first selected with probability proportional to…

Single-case design is a repeated-measures research approach for the study of the effect of an intervention, and its importance is increasingly being recognized in education and psychology. We propose a Bayesian approach for estimating intervention effects in SCD. A Bayesian inference does not rely on large sample theories and thus is particularly…

Although immediacy is one of the necessary criteria to show strong evidence of a causal relation in SCDs, no inferential statistical tool is currently used to demonstrate it. We propose a Bayesian unknown change-point model to investigate and quantify immediacy in SCD analysis. Unlike visual analysis that considers only 3-5 observations in…

Lecoutre, Lecoutre, and Poitevineau (2010) have provided sophisticated grounding for "p[subscript rep]." Computing it precisely appears, fortunately, no more difficult than doing so approximately. Their analysis will help move predictive inference into the mainstream. Iverson, Wagenmakers, and Lee (2010) have also validated…

P. R. Killeen's (2005a) probability of replication ("p[subscript rep]") of an experimental result is the fiducial Bayesian predictive probability of finding a same-sign effect in a replication of an experiment. "p[subscript rep]" is now routinely reported in "Psychological Science" and has also begun to appear in…