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 in | PloS one Vol. 15; no. 2; p. e0228355 |
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Main Authors | , |
Format | Journal Article |
Language | English |
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06.02.2020
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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. |
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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 |
AuthorAffiliation_xml | – name: Department of Psychology, Education and Child Studies, Erasmus University Rotterdam, Rotterdam, The Netherlands – name: Politecnico di Torino, ITALY |
Author_xml | – sequence: 1 givenname: Samantha orcidid: 0000-0003-1771-0546 surname: Bouwmeester fullname: Bouwmeester, Samantha – sequence: 2 givenname: Joran surname: Jongerling fullname: Jongerling, Joran |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/32027683$$D View this record in MEDLINE/PubMed |
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Copyright | COPYRIGHT 2020 Public Library of Science 2020 Bouwmeester, Jongerling. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License. 2020 Bouwmeester, Jongerling 2020 Bouwmeester, Jongerling |
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Title | Power of a randomization test in a single case multiple baseline AB design |
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