The use of mixed modeling to evaluate the impact of treatment integrity on learning
Catch the first teaching error fast—stacked mistakes early in learning sink acquisition fastest.
01Research in Context
What this study did
Bottini et al. (2020) used mixed modeling to see how teaching errors change learning speed.
They compared single errors, stacked errors, and no errors during early teaching trials.
The study looked at which error patterns hurt learning the most.
What they found
Learning got worse when errors were stacked together.
One error at a time slowed learning less than many errors at once.
Early commission errors had the biggest negative impact on new skills.
How this fits with other research
Nuta et al. (2021) extends this work into homes. Parents kept high integrity while running multi-part plans, showing real-world control is possible.
Bartle et al. (2026) builds on the warning: they show adding non-examples to training videos prevents the same stacked errors Bottini flagged.
Bergmann et al. (2023) explains why we miss these errors. Global checklists hide small mistakes; only part-by-part scoring catches what Bottini modeled.
Why it matters
You can protect acquisition by catching single errors early. Switch from wide Likert scales to item-by-item data sheets. Stop the session after the first error and re-teach, before a second error stacks on top. This small habit keeps teaching efficient and prevents the steep learning drop Bottini’s model predicts.
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02At a glance
03Original abstract
Treatment integrity, or the degree to which an intervention is implemented as intended, is a critical feature of skill acquisition tasks. Single‐case design consistently demonstrates that low treatment integrity slows or inhibits learning, but the relative impact of different types of instructional errors, or the presence of multiple errors, is less clear. The present study utilized a multilevel modeling approach to evaluate the impact of type of error (omission versus omission and commission) and error component (reinforcer delivery versus feedback) on learning. Findings revealed that learning outcomes worsened based on the type of error and as the complexity of errors increased; more specifically, participants performed better when only a single type of error occurred and when only a single error component was manipulated. Additionally, individual characteristics contributed to learning outcomes, highlighting the use of multilevel modeling as a helpful tool to supplement single‐case design. The differential impact of integrity errors on learning may be due to timing of errors (i.e., commission errors more likely to occur early in learning) or how errors affect reinforcement schedule versus discriminative control.
Behavioral Interventions, 2020 · doi:10.1002/bin.1718