ArticleJournal of applied behavior analysis2021
Machine learning to analyze single-case graphs: A comparison to visual inspection.
Article in Journal of applied behavior analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed, 30 citations in OpenAlex.
- Machine learning to detect schedules using spatiotemporal data of behavior: A proof of concept.Journal of the experimental analysis of behavior · 2025Article
- Generalized Linear Mixed Effects Modeling (GLMM) of Functional Analysis Graphical Construction Elements on Visual Analysis.Perspectives on behavior science · 2024Article
- The Promises and Possibilities of Artificial Intelligence in the Delivery of Behavior Analytic Services.Behavior analysis in practice · 2024Review
- Starting the Conversation Around the Ethical Use of Artificial Intelligence in Applied Behavior Analysis.Behavior analysis in practice · 2024Review
- Lexicon-Based Sentiment Analysis in Behavioral Research.Perspectives on behavior science · 2024Article
- Does the choice of a linear trend-assessment technique matter in the context of single-case data?Behavior research methods · 2023Article
- Baseline Variability Affects N-of-1 Intervention Effect: Simulation and Field Studies.Journal of personalized medicine · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.