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.
Highlights
Behavior analysts commonly use visual inspection to analyze single-case graphs, but studies on its reliability have produced mixed results
The two methods of analyses with the highest accuracy were those derived from machine learning—stochastic gradient descent (.77) and support vector classifier (.81)
The expert with highest power (Expert A) had the highest Type I error rate. These patterns underline a challenge faced by both visual raters and structured methods of analysis: When an analysis is made more stringent to reduce Type I error rates, this manipulation tends to produce an increase in Type II error rates, which reduces power
Summary
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. Visual raters agreed with each other on only 75% of graphs 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. When using single-case designs, researchers and practitioners must analyze the resulting graphs to determine whether a functional relation exists between the independent variable and the behavior of interest.
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