Tutorial: Applying Machine Learning in Behavioral Research
You can start predicting parent engagement today using the same free machine-learning scripts you use for graphing.
01Research in Context
What this study did
Turgeon et al. (2020) wrote a how-to guide. They show BCBAs every click needed to run machine-learning tools on small data sets.
The authors use a parent web-training project as the worked example. No new experiment—just the recipe.
What they found
The paper does not report new results. It simply proves you can open free software today and predict which parents will finish your online modules.
How this fits with other research
Cox et al. (2025) took the next step. They fed live operant data into a Q-learning model and hit 95% accuracy guessing the next response. Their study extends the 2020 tutorial into real-time prediction.
Becraft et al. (2020) offers a sister guide. Instead of machine learning, they walk readers through multilevel meta-analysis of single-case AB data. Both papers lower the tech wall for practitioners.
Li et al. (2018) also teach advanced stats, but they favor Bayesian models. The trio—Turgeon, Becraft, Li—form a toolbox: pick random forest, multilevel, or Bayesian to match your question.
Why it matters
You no longer need a data-science partner. Download the free code, swap in your variables, and flag families at risk of dropout before the first Zoom session. One hour of setup can save weeks of chasing no-shows.
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Join Free →Open the tutorial’s GitHub link, run the random-forest script on last quarter’s parent login data, and see who gets a low engagement score.
02At a glance
03Original abstract
Machine-learning algorithms hold promise for revolutionizing how educators and clinicians make decisions. However, researchers in behavior analysis have been slow to adopt this methodology to further develop their understanding of human behavior and improve the application of the science to problems of applied significance. One potential explanation for the scarcity of research is that machine learning is not typically taught as part of training programs in behavior analysis. This tutorial aims to address this barrier by promoting increased research using machine learning in behavior analysis. We present how to apply the random forest, support vector machine, stochastic gradient descent, and k-nearest neighbors algorithms on a small dataset to better identify parents of children with autism who would benefit from a behavior analytic interactive web training. These step-by-step applications should allow researchers to implement machine-learning algorithms with novel research questions and datasets.
Perspectives on Behavior Science, 2020 · doi:10.1007/s40614-020-00270-y