TY - JOUR
T1 - Comparing the Bayesian Unknown Change-Point Model and Simulation Modeling Analysis to Analyze Single Case Experimental Designs
AU - Natesan Batley, Prathiba
AU - Nandakumar, Ratna
AU - Palka, Jayme M.
AU - Shrestha, Pragya
N1 - Publisher Copyright:
© Copyright © 2021 Natesan Batley, Nandakumar, Palka and Shrestha.
PY - 2021/1/15
Y1 - 2021/1/15
N2 - 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.
AB - 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.
KW - Bayesian
KW - Markov Chain Monte Carlo Method
KW - interrupted time series analysis
KW - single case design
KW - single case experimental designs
KW - statistical simulation model
UR - http://www.scopus.com/inward/record.url?scp=85100312832&partnerID=8YFLogxK
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U2 - 10.3389/fpsyg.2020.617047
DO - 10.3389/fpsyg.2020.617047
M3 - Article
C2 - 33519641
AN - SCOPUS:85100312832
SN - 1664-1078
VL - 11
JO - Frontiers in Psychology
JF - Frontiers in Psychology
M1 - 617047
ER -