If everything looks fine, that's when you check the data

It is tempting to treat a smooth session as proof that nothing needs attention, but a calm session is exactly when reviewing the data pays off. When the child is engaged and the technician is running targets well, there is finally room to look closely at skill-acquisition and behavior data rather than spending the hour managing a crisis. Data functions like a record of progress that is easy to miss in the moment, especially in short or hectic sessions. Reviewing it during a good session catches trends early, while there is still time to act on them before the next assessment, team meeting, or family update. Checking the data is also a legitimate purpose to state out loud, so the technician understands the observation is about the programming, not about catching them in an error.

A practical way to review every target is to read its trend and respond accordingly. An upward trend needs no change — the program is working, so acknowledge the technician's work and move on. A downward trend is a signal to investigate whether the difficulty is too high, the materials are not motivating, or the procedure is being run differently than intended, and the technician's observations often point straight to the cause. A flat trend is not automatically a problem, because some skills simply need more time, but a trend that stays flat for an extended stretch is worth examining rather than waiting on indefinitely. When targets are mastered, the supervisor updates the relevant assessment grid right away — whether that is the VB-MAPP, the ABLLS, the PEAK, or another tool — because doing it in the moment saves substantial time later and makes family updates smoother.

The review is not just maintenance; it is where the next supervision goal comes from. When skills are mastered, opening new targets keeps the program moving and can set the stage for behavioral cusps — skills that unlock many new learning opportunities. Making these calls from the data, together with the technician, models data-based decision-making rather than reacting to impressions of how a session seemed to go. A supervisor might note that a target has been flat for several days and then ask the technician whether lowering the criterion for one phase or changing the materials better fits what they have seen, turning the data into a shared decision. Reviewing data systematically and acting on what the trends show is a core feature of effective, accountable practice (Cooper, Heron, & Heward, 2020), and building it into routine supervision keeps progress visible and intentional (Sellers, Valentino, & LeBlanc, 2016).

References

Cooper, J. O., Heron, T. E., & Heward, W. L. (2020). Applied behavior analysis (3rd ed.). Pearson.

Sellers, T. P., Valentino, A. L., & LeBlanc, L. A. (2016). Recommended practices for individual supervision of aspiring behavior analysts. Behavior Analysis in Practice, 9(4), 274–286. https://doi.org/10.1007/s40617-016-0110-7