Comparing the Bayesian Unknown Change-Point Model and Simulation Modeling Analysis to Analyze Single Case Experimental Designs

Frontiers in Psychology

Prathiba Natesan Batley¹  Jayme M. Palka²  Ratna Nandakumar³  Pragya Shrestha³ 

[1] College of Health and Life Sciences, Brunel University London, Uxbridge, United Kingdom;Department of Educational Psychology, University of North Texas, Denton, TX, United States;School of Education, University of Delaware, Newark, DE, United States;

关键词: single case design;    Bayesian;    Markov Chain Monte Carlo Method;    statistical simulation model;    interrupted time series analysis;    single case experimental designs;   

DOI  :  10.3389/fpsyg.2020.617047

来源: Frontiers

【 摘 要 】

Recently, there has been an increased interest in developing statistical methodologies for analyzing single case experimental design (SCED) data to supplement visual analysis. Some of these are simulation-driven such as Bayesian methods because Bayesian methods can compensate for small sample sizes, which is a main challenge of SCEDs. Two simulation-driven approaches: Bayesian unknown change-point model (BUCP) and simulation modeling analysis (SMA) were compared in the present study for three real datasets that exhibit “clear” immediacy, “unclear” immediacy, and delayed effects. Although SMA estimates can be used to answer some aspects of functional relationship between the independent and the outcome variables, they cannot address immediacy or provide an effect size estimate that considers autocorrelation as required by the What Works Clearinghouse (WWC) Standards. BUCP overcomes these drawbacks of SMA. In final analysis, it is recommended that both visual and statistical analyses be conducted for a thorough analysis of SCEDs.

【 授权许可】

CC BY