Procedures and Accuracy of Discontinuous Measurement of Problem Behavior in Common Practice of Applied Behavior Analysis
Cut your observation intervals to three minutes or less and choose momentary time sampling for truer behavior charts.
01Research in Context
What this study did
LeBlanc et al. (2020) looked at how long your observation intervals should be. They wanted to know what gives the most accurate picture of problem behavior.
They compared short intervals (three minutes or less) with longer ones. They also checked if momentary time sampling beats partial or whole-interval recording.
What they found
Shorter wins. Intervals of three minutes or less gave cleaner data.
Momentary time sampling came out on top. It tracked the real behavior curve better than the other discontinuous methods.
How this fits with other research
Springer et al. (1981) told us to drop discontinuous methods altogether. LeBlanc answers that critique with hard numbers: keep the slices tiny and MTS works fine.
Grauerholz-Fisher et al. (2019) took the same short-interval MTS idea and showed it can also flag staff inactivity or safety hazards in a busy clinic. The rule travels beyond problem-behavior counts.
Laugeson et al. (2014) ring a similar alarm: the way you score can fake a treatment win. LeBlanc adds that the size of your interval is another hidden knob that can hide or exaggerate effects.
Why it matters
Next time you open the stopwatch, set it for three minutes or less and use momentary time sampling. You will get data that look more like the real behavior and you will make safer decisions about treatment or fading. No extra gear, just tighter slices.
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02At a glance
03Original abstract
Discontinuous measurement involves dividing an observation into intervals and recording whether a behavior occurred during some or all of each interval (i.e., interval recording) or at the exact time of observation (i.e., momentary time sampling; MTS). Collecting discontinuous data is often easier for observers than collecting continuous data, but it also produces more measurement error. Smaller intervals (e.g., 5 s, 10 s, 15 s) tend to produce less error but may not be used in everyday practice. This study examined the most common intervals used by a large sample of data collectors and evaluated the effect of these intervals on measurement error. The most commonly used intervals fell between 2 and 5 min. We then analyzed over 800 sessions to evaluate the correspondence between continuous and discontinuous data at each commonly used interval. Intervals of 3 min or less produced the greatest correspondence, and MTS outperformed interval recording.
Behavior Analysis in Practice, 2020 · doi:10.1007/s40617-019-00361-6