Power of a randomization test in a single case multiple baseline AB design

A randomization test can be used to statistically test hypotheses in multiple baseline designs to complement the commonly used visual inspection analysis. A crossed factor simulation study was performed to investigate the power of a randomization test in an multiple baseline design. The results show...

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Published inPloS one Vol. 15; no. 2; p. e0228355
Main Authors Bouwmeester, Samantha, Jongerling, Joran
Format Journal Article
LanguageEnglish
Published United States Public Library of Science 06.02.2020
Public Library of Science (PLoS)
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Abstract A randomization test can be used to statistically test hypotheses in multiple baseline designs to complement the commonly used visual inspection analysis. A crossed factor simulation study was performed to investigate the power of a randomization test in an multiple baseline design. The results show that the degree of autocorrelation of the observations, the number of participants, the effect size, the overlap of possible start moments of the intervention between participants, the ratio of the number of measurements in the baseline- and intervention phase, a gradually emerging effect, and the number of measurements had strong main effects on the power. The two-way interactions between number of participants and effect size, and between the number of measurements and the number of start moments of the intervention also had a large effect. An online tool was developed to calculate the power of a multiple baseline design given several design characteristics.
AbstractList A randomization test can be used to statistically test hypotheses in multiple baseline designs to complement the commonly used visual inspection analysis. A crossed factor simulation study was performed to investigate the power of a randomization test in an multiple baseline design. The results show that the degree of autocorrelation of the observations, the number of participants, the effect size, the overlap of possible start moments of the intervention between participants, the ratio of the number of measurements in the baseline- and intervention phase, a gradually emerging effect, and the number of measurements had strong main effects on the power. The two-way interactions between number of participants and effect size, and between the number of measurements and the number of start moments of the intervention also had a large effect. An online tool was developed to calculate the power of a multiple baseline design given several design characteristics.A randomization test can be used to statistically test hypotheses in multiple baseline designs to complement the commonly used visual inspection analysis. A crossed factor simulation study was performed to investigate the power of a randomization test in an multiple baseline design. The results show that the degree of autocorrelation of the observations, the number of participants, the effect size, the overlap of possible start moments of the intervention between participants, the ratio of the number of measurements in the baseline- and intervention phase, a gradually emerging effect, and the number of measurements had strong main effects on the power. The two-way interactions between number of participants and effect size, and between the number of measurements and the number of start moments of the intervention also had a large effect. An online tool was developed to calculate the power of a multiple baseline design given several design characteristics.
A randomization test can be used to statistically test hypotheses in multiple baseline designs to complement the commonly used visual inspection analysis. A crossed factor simulation study was performed to investigate the power of a randomization test in an multiple baseline design. The results show that the degree of autocorrelation of the observations, the number of participants, the effect size, the overlap of possible start moments of the intervention between participants, the ratio of the number of measurements in the baseline- and intervention phase, a gradually emerging effect, and the number of measurements had strong main effects on the power. The two-way interactions between number of participants and effect size, and between the number of measurements and the number of start moments of the intervention also had a large effect. An online tool was developed to calculate the power of a multiple baseline design given several design characteristics.
Audience Academic
Author Bouwmeester, Samantha
Jongerling, Joran
AuthorAffiliation Politecnico di Torino, ITALY
Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands
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BackLink https://www.ncbi.nlm.nih.gov/pubmed/32027683$$D View this record in MEDLINE/PubMed
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Snippet A randomization test can be used to statistically test hypotheses in multiple baseline designs to complement the commonly used visual inspection analysis. A...
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StartPage e0228355
SubjectTerms Analysis
Biometry
Computer Simulation
Cross-Over Studies
Design
Engineering and Technology
Humans
Inspection
Intervention
Methods
Normal distribution
Observation
Physical Examination
Physical Sciences
Power (Philosophy)
Psychological research
Random Allocation
Randomization
Research and Analysis Methods
Research design
Research Design - standards
Research Design - statistics & numerical data
Researchers
Sample Size
Single subject research
Single-Case Studies as Topic - methods
Single-Case Studies as Topic - statistics & numerical data
Statistical analysis
Studies
Time series
Validity
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Title Power of a randomization test in a single case multiple baseline AB design
URI https://www.ncbi.nlm.nih.gov/pubmed/32027683
https://www.proquest.com/docview/2352045200
https://www.proquest.com/docview/2352641804
https://pubmed.ncbi.nlm.nih.gov/PMC7004358
https://doaj.org/article/368bbd0a77ca4ce7bf9b34bc54796ea5
http://dx.doi.org/10.1371/journal.pone.0228355
Volume 15
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