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http://bura.brunel.ac.uk/handle/2438/22148
Title: | Comparing the Bayesian Unknown Change-Point Model and Simulation Modeling Analysis to Analyze Single Case Experimental Designs |
Authors: | Natesan Batley, P Nandakumar, R Palka, J Shrestha, P |
Keywords: | Bayesian;Markov chain Monte Carlo;single case designs;simulation Modeling Analysis;small samples |
Issue Date: | 15-Jan-2021 |
Publisher: | Frontiers Media |
Citation: | Natesan Batley P, et al. (2021) 'Comparing the Bayesian Unknown Change-Point Model and Simulation Modeling Analysis to Analyze Single Case Experimental Designs', Frontiers in Psychology, 11, 617047, pp. 1 - 11. doi: 10.3389/fpsyg.2020.617047. |
Abstract: | Copyright © 2021 Natesan Batley, Nandakumar, Palka and Shrestha. 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 four 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. |
Description: | Data Availability Statement: The original contributions presented in the study are included in the article/supplementary material (https://github.com/prathiba-stat/BUCP), further inquiries can be directed to the corresponding author. |
URI: | https://bura.brunel.ac.uk/handle/2438/22148 |
ISSN: | 1664-1078 |
Other Identifiers: | ORCID iD: Prathiba Natesan Batley https://orcid.org/0000-0002-5137-792X 617047 |
Appears in Collections: | Dept of Life Sciences Research Papers |
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