文章基本信息

标题：Machine learning to analyze single‐case graphs: A comparison to visual inspection
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作者：Marc J. Lanovaz ; Kieva Hranchuk
期刊名称：Journal of Applied Behavior Analysis
印刷版ISSN：0021-8855
电子版ISSN：1938-3703
出版年度：2021
卷号：54
期号：4
页码：1541-1552
DOI：10.1002/jaba.863
语种：English
出版社：University of Kansas - Dept Human Development
摘要：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.