Interval Data Collection: Which Type Should You Use?
Interval data collection is one of the most practical ways to track behavior in clinical and educational settings, especially when continuous recording isn't an option. The type you choose directly affects the accuracy of your data.
What is interval data collection?
Interval data collection is a method of recording if a behavior occurs during set periods, rather than tracking every instance.
Instead of counting each occurrence, you split your observation window into equal intervals and note whether the behavior happened during each interval.
It's widely used in clinical settings, classrooms, and group sessions where continuous tracking just isn't practical.
Using interval data collection in ABA
In Applied Behavior Analysis (ABA), interval data collection is a key way to measure and track behavior.
It's one of the main discontinuous measurement methods in ABA. “Discontinuous” means that it samples behavior rather than capturing all of it.
There are three common types of interval-based recording: partial-interval recording, whole-interval recording, and momentary time sampling. Each method records behavior differently, and using the wrong method for the situation can produce misleading data.
3 common types of interval data collection
1. Partial-interval recording
Partial-interval recording marks an interval as "yes" if the behavior occurs at any point during that interval, even for 1 second.
Say you're using 10-second intervals during a session. If a learner engages in vocal stereotypy for just 2 seconds, that interval gets marked. The behavior doesn't need to last for the full interval, it just needs to happen.
This is why partial-interval recording can often show a behavior as happening more than it really does. It gives you an estimate, not an exact count – and the longer your intervals, the bigger that gap gets.
If your goal is to reduce a behavior, an overestimate can still be useful. If the data shows a behavior in 60% of intervals, and it drops to 20%, that trend tells you something meaningful, even without an exact count.
Partial-interval recording usually works best for challenging behaviors you want to reduce, for behaviors that happen often, and when knowing that a behavior happened matters more than knowing exactly how many times.
2. Whole-interval recording
Whole-interval recording only marks an interval as "yes" if the behavior occurs for the entire interval.
If a learner is on-task for 8/10 seconds, it doesn't count. The behavior has to continue for the full interval to be recorded.
This means whole-interval recording often shows a behavior happening less than it does. But that works in your favor when your goal is to increase a behavior.
If you're trying to build sustained engagement with a task, any upward trend in the data reflects real, lasting progress rather than a brief improvement that happened to get counted.
Whole-interval recording works best for on-task behavior, sustained engagement, and any behavior you're trying to increase. Still, its tendency to underestimate means small gains might not show up in the data right away.
3. Momentary time sampling
Momentary time sampling records if the behavior is happening at the exact moment each interval ends, not during the interval.
If you're using 5-minute intervals, you check at the 5-minute mark, the 10-minute mark, and so on. What happens in between doesn't get recorded. You're not checking the whole time, just when the timer goes off.
Studies have found that momentary time sampling produces more accurate estimates than partial-interval recording under some conditions, especially when intervals are short.
It works best for high-frequency or stable behaviors, group settings where you're watching multiple learners, and sessions where you're actively running the therapy.
It's not the right choice for behaviors that are brief or don't happen very often, since you could miss them at each checkpoint.
How to collect interval data: A step-by-step guide
Once you've chosen your method and defined your target behavior, the general process is similar across all three methods.
1. Define the target behavior
Write a clear definition that any Registered Behavior Technician (RBT) on your team could follow without having to make a judgment call.
For example, "out-of-seat behavior" is too vague, but "learner's bottom is not in contact with the chair’s seat" is specific enough for all RBTs to understand.
2. Choose your interval length
Research shows that shorter intervals produce less measurement error than longer ones.
Interval lengths vary widely, depending on the setting and the target behavior. Generally, the shorter the interval, the more accurate your data. Still, intervals under 9 seconds may be hard to manage in a live session without the right tech support.
3. Set a timer
Use an audio cue like a beep or vibration so you're not watching the clock. This keeps your attention on the learner.
4. Record at each interval
For partial-interval recording, mark whether the behavior occurred at any point.
For whole-interval recording, mark only if the behavior lasted for the full interval.
For momentary time sampling, check and record only when the timer goes off.
5. Calculate the percentage
Divide the number of intervals marked "yes" by the total number of intervals, then multiply by 100. This gives you a percentage you can track across sessions.
When to use interval data collection (and when not to)
Interval data collection is a discontinuous measurement method, which means it samples behavior rather than recording all of it. This works well in some situations, but it’s the wrong choice for others.
Use interval recording when:
- The behavior happens too fast or too often to count individually.
- The behavior doesn't have a clear start and end.
- You need to collect data while running the session.
- You're tracking behavior across a group.
Avoid interval recording when:
- The behavior doesn’t happen very often,as you’ll risk missing it at each interval or checkpoint.
- You need an exact count or duration for clinical or insurance documentation.
- The behavior is brief but clinically significant, since partial-interval recording can't tell you how long it lasted.
When exact counts matter, frequency or rate recording is more appropriate. When duration matters, duration recording will give you more useful data.
Common mistakes with interval data collection
Even experienced teams can run into problems with interval recording. Here are some common errors to watch out for.
Using intervals that are too long
The longer your interval, the more measurement error you can introduce. A 5-minute partial-interval window will overestimate a behavior far more than a 10-second one. Keep intervals as short as your session allows.
Inconsistent observation periods
If you collect data during different parts of the session from day to day, trends might reflect when you measured, rather than changes in behavior. Pick an observation window and stick to it.
Vague behavior definitions
If your team isn't sure what counts as the behavior, your data won't be consistent. The operational definition should be clear enough that everyone recognizes it the same way.
Using the wrong type for your goal
It's worth double-checking that the method matches what you're trying to achieve. Using whole-interval recording to track a behavior you want to reduce or partial-interval recording for one you want to increase could make your data harder to interpret and progress harder to see.
Make interval data collection easier with Passage Health
Choosing the right interval recording method is only part of the challenge. Collecting it consistently across a team of busy RBTs without losing accuracy is where it gets hard.
Passage Health builds interval data collection into your everyday clinical workflow. With Passage Health, your team can:
- Record interval data in real time: RBTs mark each interval directly in the app during sessions. Data syncs on the go, so there are no paper sheets to transcribe later.
- Set behavior definitions once: Board Certified Behavior Analysts (BCBAs) create definitions in the platform that every team member can access. Everyone records the same behavior the same way, keeping data consistent across staff and sessions.
- Track trends across therapists and settings: See how data compares among different therapists and environments, so you can spot patterns and make informed decisions quickly.
- Feed data directly into treatment reports: Interval data flows directly into progress graphs, session notes, and reports, cutting out manual data entry.
Book a demo to see how Passage Health can make interval data collection simpler for your entire team.
Frequently asked questions
What is interval data collection?
Interval data collection in ABA is a way to record if a behavior occurs during set periods, rather than tracking every instance. It's most useful for behaviors that happen too often or too fast to count individually.
What are the three types of interval recording?
Three common types of interval recording are partial-interval recording, whole-interval recording, and momentary time sampling. Each measures behavior differently, so the right choice depends on what you're tracking, the context of the session and learner, and whether you're working to increase or decrease a behavior.
When should I use partial-interval vs. whole-interval recording?
In general, use partial-interval recording when your goal is to decrease a behavior, and whole-interval recording when your goal is to increase a behavior, though there are exceptions. When matching the method to your goal, it’s also important to consider the type of behavior and the context.
Does interval recording give you exact behavior counts?
No, interval recording gives you a percentage of intervals in which a behavior occurred, not an exact count. If you need precise frequency or duration data for clinical or insurance documentation, frequency recording or duration recording are better choices.
How do I calculate the results of interval recording?
Divide the number of intervals where the behavior was recorded by the total number of intervals, then multiply by 100. If a behavior was marked in 12 out of 20 intervals, for example, it occurred in 60% of your observation period.
References
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/
King, S., Rodgers, D., & Enders, O. (2025). Partial-interval recording and estimates of duration in meta-analyses: Insights from self-monitoring research. Journal of Behavior Education. Retrieved from https://link.springer.com/article/10.1007/s10864-025-09595-7
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://pmc.ncbi.nlm.nih.gov/articles/PMC7314895/
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/
Wirth, O., Slaven, J., Taylor, M. A., et al. (2014). Interval sampling methods and measurement error: A computer simulation. Journal of Applied Behavior Analysis in Practice, 47(1), 83-100. Retrieved from https://pmc.ncbi.nlm.nih.gov/articles/PMC4580971/
