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An interrupted time-series design can be illustrated by tracking the number of student absences per week in a course. In this scenario, the 'interruption' is the instructor beginning to publicly take daily attendance. If the multiple measurements show a consistently high number of absences before this treatment, followed by an immediate and sustained drop afterward, the treatment is deemed effective. However, if the long-term trend of absences remains virtually unchanged on average after the intervention, it suggests the treatment failed to produce a meaningful effect, even if minor weekly fluctuations exist.

Example of an Interrupted Time-Series Design: Student Absences

Another practical example of an interrupted time-series design with nonequivalent groups involves assessing whether taking daily attendance reduces student absences. A researcher could track absences over the semester in one course section where the intervention is introduced (the treatment group), and simultaneously track absences in another section where attendance is not taken (the nonequivalent control group). Superior evidence for the intervention's effectiveness is established if the treatment group shows a consistently high number of absences before the policy change followed by a sustained decrease afterward, while the control group's absences remain consistently high throughout the entire term.

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