ABA Discontinuous Measurement: What It Is and How It Works

Discontinuous measurement in Applied Behavior Analysis (ABA) means you don’t record every single example of a behavior. Here’s how to do it by observing behavior in set time intervals or at planned moments.

What is discontinuous measurement in ABA?

Discontinuous measurement is a way to capture behavior in snapshots instead of continuously.

Examples of discontinuous measurement include:

• Partial-interval recording: Marking “yes” if the behavior happens at any time during the interval.
• Whole-interval recording: Only marking “yes” if the behavior lasts throughout the entire interval.
• Momentary time sampling (MTS): Checking for the behavior only at the end of each interval and recording if it’s happening then.

Breaking sessions into smaller parts makes data collection easier, especially in real-world settings like classrooms.

Why use discontinuous measurement in ABA?

Discontinuous measurement doesn’t catch every behavior, but it’s useful when you need a faster way to collect data. Use it when you need a data-collection method that’s:

• More practical in real settings: It’s easier to collect interval data than to watch behavior constantly.
• Less burden: Observers can attend to other responsibilities between observation intervals rather than watching continuously.
• Flexible: You can choose the interval length that works for your needs and adjust it based on how the behavior changes.

There are some situations where discontinuous measurement is especially helpful for ABA clinicians:

• Limited staff or resources: If you can’t watch behavior all the time, MTS or interval recording is a practical way to sample behavior.
• High-rate behaviors: When a behavior happens often, like vocal stereotypy, partial-interval recording lets you see if it occurs without counting every single instance.
• Long observation periods: For long activities, like group work or classroom tasks, MTS helps estimate behavior without tiring the observer.
• Hard-to-track behaviors: MTS is useful for motor stereotypy, like hand-flapping, when therapists must collect data on multiple individuals or behaviors at the same time.

What are the downsides of discontinuous measurement?

Discontinuous measurement is useful in many cases, but there are also drawbacks to keep in mind:

• Potential for error: Discontinuous methods can lead to data mistakes. Short intervals improve accuracy, while long intervals can give a misleading picture of the behavior.
• Biased estimates: Partial-interval recording can make a behavior look more frequent than it is because it counts any brief occurrence. Whole-interval recording can make it look less frequent because the behavior has to last the whole interval.
• Snapshot only: You don’t see everything. MTS checks the behavior only at set moments, so you may miss short but important actions.

Because of these limits, clinicians often mix different discontinuous methods or combine them with continuous measurement. This gives the data you need without overworking your staff.

The evidence from research

Research shows that well-planned discontinuous measurement can be accurate enough to guide clinical decisions and can be easier to use than continuous measurement.

One study comparing MTS and partial-interval recording found that discontinuous methods often led to the same treatment decisions as continuous measures.

Another study looking at many sessions showed that when shorter sampling intervals (three minutes or less) were used, discontinuous data matched continuous data more closely, with MTS doing especially well.

How to use discontinuous measurement in ABA

There are a few things you can do to get the most out of discontinuous measurement:

• Train your observers: Make sure everyone is consistent and understands what “yes” means for each interval.
• Choose interval length carefully: Use shorter intervals for more accuracy, but balance this with what works for your team.
• Use timers or software: Use a timer or an app to mark interval boundaries so you don’t lose track.
• Test it out: Use your chosen discontinuous method for a few sessions to see if it works well and gives accurate results.
• Check and adjust: Review your data often. If intervals are too long or not accurate, make them shorter or try a different method.

When not to use discontinuous measurement in ABA

Discontinuous measurement isn’t necessarily the right choice for every session. You might want to skip it when:

• You need precise or complete data (e.g., when doing a functional analysis to find out why a behavior happens).
• Behavior is rare, and missing any single instance matters.
• You have enough staff and capacity to do continuous recording reliably.
• The behavior is brief but important, or if even small occurrences matter.

Why discontinuous measurement works for clinicians

Discontinuous measurement gives you a snapshot of sessions without tracking every moment.

This approach works well in ABA because you can:

• Balance accuracy and resources: Get useful behavior data without overloading your staff.
• Watch behavior in realistic settings: Collect data during long sessions or when you can’t observe continuously, like in classrooms.
• Make smart treatment choices: Use interval or MTS data to see patterns and check if your interventions are working.
• Balance accuracy and resources: Get useful behavior data without the demands on staff time needed for continuous observation.

Key takeaways on ABA discontinuous measurement

• Discontinuous measurement records behavior in intervals or at planned moments.
• It works for measuring high-rate, long-duration, or hard-to-watch behaviors.
• It takes less time and effort than continuous measurement, but it can over- or under-estimate behavior if intervals aren’t set right.
• Using different discontinuous methods together, or mixing them with continuous measurement, helps balance accuracy and practicality.
• To use it well: train your team, choose interval lengths carefully, use timers or software, try it out first, and review your data often.

Make discontinuous measurement easier with Passage Health

Discontinuous measurement allows you to track behavior, but recording intervals by hand can still be slow and tiring.

Passage Health makes it easier for you with:

• Built-in timing tools: Use timers or prompts to mark the start and end of intervals automatically.
• Pre-set interval templates: Set up your partial, whole, or MTS intervals once, and reuse them easily across sessions.
• Instant data logging: Record interval data in real time using Passage Health’s mobile or web app.
• Automated summaries: Turn your data into charts showing the percent of intervals with the behavior, trends over time, and comparisons across intervention phases.
• Flexible reporting: Export your interval data or connect it to your ABA notes (SOAP, DAP, or custom notes) to see behavior changes and how well your interventions are working.

Passage Health can build discontinuous measurement right into your workflow.

Book a demo to see how your team can spend less time on data collection and more time helping clients.

Frequently asked questions

What does discontinuous measurement mean?

Discontinuous measurement is when observers record behavior only at set time intervals or specific moments, instead of tracking every single instance.

What are the common types of discontinuous measurement?

The main types of discontinuous measurement are partial-interval recording, whole-interval recording, and MTS.

Why use discontinuous measurement instead of continuous measurement?

It’s usually easier and uses fewer resources. Discontinuous methods still provide useful data, especially when continuous tracking isn’t possible.

Is discontinuous measurement accurate?

Yes. If you pick the right interval length, data from discontinuous methods compares favorably with continuous data.

Can software help with ABA discontinuous measurement?

Yes. Tools like Passage Health let you set up interval templates, mark behavior at each interval, and see charts and reports right away.

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References

Cummings, A. R., & Carr, J. E. (2009). Evaluating progress in behavioral programs for children with autism spectrum disorders via continuous and discontinuous measurement. Journal of Applied Behavior Analysis, 42(1), 57-71. Retrieved from https://pubmed.ncbi.nlm.nih.gov/19721730/

Devine, S. L., Rapp, J. T., Testa, J. R., et al. (2011). Detecting changes in simulated events using partial-interval recording and momentary time sampling III: Evaluating sensitivity as a function of session length. Behavioral Interventions, 26, 103-124. Retrieved from https://onlinelibrary.wiley.com/doi/10.1002/bin.328

Fiske, K., & Delmolino, L. (2012). Use of discontinuous methods of data collection in behavioral intervention: Guidelines for practitioners. Behavior Analysis in Practice, 5(2), 77-81. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC3592492/

LeBlanc, L. A., Lund, C., Kooken, C., et al. (2019). Procedures and accuracy of discontinuous measurement of problem behavior in common practice of applied behavior analysis. Behavior Analysis in Practice, 13(2), 411-420. Retrieved from https://pubmed.ncbi.nlm.nih.gov/32642397/

LeBlanc, L. A., Raetz, P. B., Sellers, T. P., et al. (2015). A proposed model for selecting measurement procedures for the assessment and treatment of problem behavior. Behavior Analysis in Practice, 9(1), 77-83. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC4788644/

Meany-Daboul, M. G., Roscoe, E. M., Bourret, J. C., et al. (2007). A comparison of momentary time sampling and partial-interval recording for evaluating functional relations. Journal of Applied Behavior Analysis, 40(3), 501-514. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC1986695/