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Machine learning to analyze single‐case graphs: A comparison to visual inspection
Author(s) -
Lanovaz Marc J.,
Hranchuk Kieva
Publication year - 2021
Publication title -
journal of applied behavior analysis
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.1
H-Index - 76
eISSN - 1938-3703
pISSN - 0021-8855
DOI - 10.1002/jaba.863
Subject(s) - visual inspection , machine learning , artificial intelligence , reliability (semiconductor) , contrast (vision) , computer science , graph , word error rate , statistics , power (physics) , mathematics , theoretical computer science , physics , quantum mechanics
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.