title: Bayesian estimates of autocorrelations in single-case designs.
abstract: Researchers in the single-case design tradition have debated the size and importance of the observed autocorrelations in those designs. All of the past estimates of the autocorrelation in that literature have taken the observed autocorrelation estimates as the data to be used in the debate. However, estimates of the autocorrelation are subject to great sampling error when the design has a small number of time points, as is typically the situation in single-case designs. Thus, a given observed autocorrelation may greatly over- or underestimate the corresponding population parameter. This article presents Bayesian estimates of the autocorrelation that greatly reduce the role of sampling error, as compared to past estimators. Simpler empirical Bayes estimates are presented first, in order to illustrate the fundamental notions of autocorrelation sampling error and shrinkage, followed by fully Bayesian estimates, and the difference between the two is explained. Scripts to do the analyses are available as supplemental materials. The analyses are illustrated using two examples from the single-case design literature. Bayesian estimation warrants wider use, not only in debates about the size of autocorrelations, but also in statistical methods that require an independent estimate of the autocorrelation to analyze the data.
authors.affiliation: School of Social Sciences, Humanities and Arts, University of California, Merced, 5200 North Lake Rd, Merced, CA 95343, USA. wshadish@ucmerced.edu
authors.forename: William R
authors.initials: WR
authors.lastname: Shadish
authors.forename: David M
authors.initials: DM
authors.lastname: Rindskopf
authors.forename: Larry V
authors.initials: LV
authors.lastname: Hedges
authors.forename: Kristynn J
authors.initials: KJ
authors.lastname: Sullivan
mesh_terms: D001499:Bayes Theorem; D003627:Data Interpretation, Statistical; D006801:Humans; D015233:Models, Statistical; D012044:Regression Analysis; D012107:Research Design; D018401:Sample Size; D015983:Selection Bias