dc.contributor.author | Lanovaz, Marc | |
dc.contributor.author | Hranchuk, Kieva | |
dc.date.accessioned | 2021-07-27T11:38:50Z | |
dc.date.available | NO_RESTRICTION | fr |
dc.date.available | 2021-07-27T11:38:50Z | |
dc.date.issued | 2021-07-15 | |
dc.identifier.uri | http://hdl.handle.net/1866/25350 | |
dc.publisher | Wiley | fr |
dc.rights | Ce document est mis à disposition selon les termes de la Licence Creative Commons
Paternité 4.0 International. / This work is licensed under a Creative Commons Attribution 4.0
International License. | |
dc.rights.uri | https://creativecommons.org/licenses/by/4.0/deed.fr | |
dc.title | Machine learning to analyze single-case graphs : a comparison
to visual inspection | fr |
dc.type | Article | fr |
dc.contributor.affiliation | Université de Montréal. Faculté des arts et des sciences. École de psychoéducation | fr |
dc.identifier.doi | 10.1002/jaba.863 | |
dcterms.abstract | Behavior analysts commonly use visual inspection to analyze single-case graphs, but studies on its reliability have produced mixed results. To examine this issue, we compared the Type I error rate and power of visual inspection with a novel approach—machine learning. Five expert visual raters analyzed 1,024 simulated AB graphs, which differed on number of points per phase, autocorrelation, trend, variability, and effect size. The ratings were compared to those obtained by the conservative dual-criteria method and two models derived from machine learning. On average, visual raters agreed with each other on only 75% of graphs. In contrast, both models derived from machine learning showed the best balance between Type I error rate and power while producing more consistent results across different graph characteristics. The results suggest that machine learning may support researchers and practitioners in making fewer errors when analyzing single-case graphs, but replications remain necessary. | fr |
dcterms.isPartOf | urn:ISSN:1938-3703 | fr |
dcterms.language | eng | fr |
dcterms.relation | https://github.com/labrl/machine-learning-for-single-case-designs | fr |
UdeM.ReferenceFournieParDeposant | Lanovaz, M. J., & Hranchuk, K. (2021). Machine learning to analyze single-case to analyze single-case graphs: A comparison to visual analysis. Journal of Applied Behavior Analysis. https://doi.org/10.1002/jaba.863 | fr |
UdeM.VersionRioxx | Version publiée / Version of Record | fr |
oaire.citationTitle | Journal of applied behavior analysis | fr |