Using statistical process control to make data-based clinical decisions.
Draw control-chart guardrails on your daily behavior graph to spot intervention trouble before it crashes.
01Research in Context
What this study did
English et al. (1995) wrote a how-to paper. They showed BCBAs how to borrow factory-style control charts. The charts watch behavior data the same way Toyota watches defects.
The authors set three simple rules. If a data point lands outside three standard deviations, stop. If two of three points break two-sigma, stop. If eight points hug one side of the mean, stop.
What they found
The paper does not give new client data. It gives a decision map. The map tells you when to tweak an intervention before the whole skill falls apart.
Think of it like a car dashboard. The chart lights up early so you fix the engine before it dies on the highway.
How this fits with other research
Emerson et al. (2023) checked 1,800 single-case graphs. Most baseline data are not normal. A et al. assumed they were. Use the control rules, but expect more false alarms if your data skew hard.
Manolov et al. (2017) built free R code that draws these same control lines for you. You no longer need graph paper and a calculator. Their package updates the 1995 rules for 2020s speed.
Morris et al. (2022) warn that dirty data kill any chart. Train your RBTs first, then trust the lines. Without clean measurement, the prettiest control chart is still garbage.
Why it matters
You can start Monday. Pick one priority behavior. Plot the last ten sessions. Add the three-sigma lines by hand or with the free R package from Manolov et al. (2017). When the next point lands outside, pause the session and troubleshoot. You will catch problems days earlier than eye-balling trends.
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02At a glance
03Original abstract
Applied behavior analysis is based on an investigation of variability due to interrelationships among antecedents, behavior, and consequences. This permits testable hypotheses about the causes of behavior as well as for the course of treatment to be evaluated empirically. Such information provides corrective feedback for making data-based clinical decisions. This paper considers how a different approach to the analysis of variability based on the writings of Walter Shewart and W. Edwards Deming in the area of industrial quality control helps to achieve similar objectives. Statistical process control (SPC) was developed to implement a process of continual product improvement while achieving compliance with production standards and other requirements for promoting customer satisfaction. SPC involves the use of simple statistical tools, such as histograms and control charts, as well as problem-solving techniques, such as flow charts, cause-and-effect diagrams, and Pareto charts, to implement Deming's management philosophy. These data-analytic procedures can be incorporated into a human service organization to help to achieve its stated objectives in a manner that leads to continuous improvement in the functioning of the clients who are its customers. Examples are provided to illustrate how SPC procedures can be used to analyze behavioral data. Issues related to the application of these tools for making data-based clinical decisions and for creating an organizational climate that promotes their routine use in applied settings are also considered.
Journal of applied behavior analysis, 1995 · doi:10.1901/jaba.1995.28-349