Hey everyone! We had lots of fun at our latest DataFinch Virtual Academy workshop with Linda LeBlanc. There was a lot of great information, so in case you missed it, we’ve summed up our top 10 takeaways from the experience here.
1. Choices about the procedures used to track behavior over time are pivotal because direct-observation data impact other important decisions. Applied behavior analysis should never be practiced without the collection of meaningful data.
2. We have lots of options for measuring behavior and each measure has strengths and weaknesses that make them more or less suited for specific applied circumstances.
3. The questions in the selection model focus on several important considerations in the order that logically governs selection of a measure: a) specific characteristics of the behavior, b) personnel resources and constraints, c) important dimensions of behavior, and d) the nature of behavior as a free or restricted operant.
4. Use continuous measures whenever possible because they eliminate the error associated with estimate-based discontinuous measurement.
5. Momentary time sampling produces estimates that more closely approximate continuous measures than partial interval recording as long as the interval is SHORT!
6. When there is a restricted opportunity for the behavior to occur (e.g., can only be noncompliant if you have just
been given a task), collect a continuous measure (e.g., frequency, duration), but use a denominator that reflects
the restricted observation window in which the behavior can occur in the conversion to a rate or percentage duration measure.
7. If you choose duration, latency, or magnitude measures, you can also derive event frequency as long as you record each event separately.
8. Collect data on other measures such as 1) appropriate behaviors, 2) implementation behaviors, and collateral
behaviors along with your primary measure of problem behavior. These other measures can help you assess the robustness of your treatment effects.
9. Graph different topographies of problem behavior separately. This helps you detect separate functions or differential responsiveness to treatment components for separate topographies.
10. Keep calm and collect meaningful data!