Partial-interval measurement bias arises because any occurrence makes the whole interval positive. Brief responses spread across many intervals can produce a high sampled percentage despite little total duration. Interval length and response pattern shape the difference. Report the sampled rule and coverage, and compare with continuous count or duration when the clinical decision needs exact magnitude.

Inspect response distribution

The same total duration can appear in one interval or across many intervals.

Show interval length

A new interval size changes the possible percentage and its bias pattern.

Pair with continuous data

Use count or duration on a representative sample when magnitude matters.

Avoid correction formulas without support

Describe the sampling limits instead of claiming one universal adjustment.

Explain where the bias comes from

Partial-interval recording converts every valid interval into a yes or no score for any occurrence. Multiple responses inside one interval still produce one positive score. A very brief occurrence can produce the same score as responding that occupies most of the interval. The resulting percentage therefore describes the distribution of positive intervals under that procedure.

Interval length and response pattern shape the difference between the sampled percentage and a continuous measure. Longer intervals create more opportunity for at least one occurrence. High-rate or widely distributed responding can produce many positive intervals even when total duration differs across observations. These features should be part of the interpretation, not hidden in a footnote.

Inspect the raw interval pattern

Keep the sequence of positive, negative, missed, and invalid intervals. Look for clustering around activities, people, transitions, observer changes, or access conditions. Two sessions can both equal 50% while showing very different patterns, such as alternating positives and negatives in one session and a single concentrated block in another.

Report planned and valid coverage with the percentage. Missing observation during the most difficult part of a routine can affect the meaning of the sample. Review when intervals were missed and what was happening before accepting the observed subset as representative.

Use a direct comparison when the decision requires it

If treatment or safety decisions depend on rate or duration, collect a feasible continuous comparison during representative samples. Pair the interval percentage with count, total duration, or another direct measure chosen by the qualified clinician. Do not apply a universal adjustment factor to turn sampled intervals into an invented exact value.

When the procedure, length, setting, or exposure changes, mark a new phase or method version. A trend across incompatible sampling rules can make procedural change look like client change. Preserve the original data and document why any new method better answers the current question.

Tie the interpretation to one decision

The partial-interval bias comparison for Juno identifies the response definition, unit, interval or event boundary, planned exposure, valid denominator, missing-state rule, calculation, responsible qualified role, and review date before data collection.

Worked example: compare sampled and direct measures

Juno responds for one second in each of five one-minute intervals. Partial-interval recording is 5 of 5, or 100%. Total duration is 5 of 300 seconds, or 1.7%. Both calculations are correct for their definitions, yet they answer very different questions.

Review coverage and measurement meaning

For Juno, report planned, observed, valid, positive, negative, missed, invalid, and outside-exposure units as applicable. Preserve raw records, timestamps, interval length, procedure version, and rounding. A sampled percentage retains the limits of the selected procedure.

Keep access available during measurement

During the partial-interval bias interpretation review for Juno, keep AAC, interpreters, mobility, food, water, bathroom use, prescribed care, health support, rest, relationships, and emergency help available. Record access or health changes and use accessible communication to ask whether the outcome matters.

Use evidence within its stated scope

For Juno, the BACB ethics hub and CASP public summary provide professional context. The BCBA Test Content Outline covers continuous and discontinuous procedures, temporal dimensions, product measures, validity, reliability, and representative measurement as examination content.

For the partial-interval bias interpretation question, Pritchard and colleagues and Wirth and colleagues examine accuracy and decision implications of discontinuous measurement procedures. Lobo and colleagues supply single-case measurement and analysis context. ASHA says AAC users should always have access to their communication tools or devices. These sources support careful method selection without creating a universal interval length, correction factor, or clinical threshold.

Document the clinical review

Review the partial-interval bias comparison with Juno and the responsible clinician. Record the procedure, raw data, coverage, calculation, limitations, selected action, owner, next sample, and review date. Reopen the method when the response pattern, access, setting, interval, exposure, system, or decision changes.

Clinician review checklist

  • Does the report define partial-interval percentage as a sampled occurrence measure?
  • Are interval length, response pattern, planned coverage, and valid coverage visible?
  • Has the reviewer inspected the raw sequence and setting conditions?
  • Are missed intervals evaluated for possible concentration in important periods?
  • Is a continuous comparison available when rate or duration drives the decision?
  • Are method changes marked before trends are interpreted?
  • Is client feedback included in deciding whether the measured change matters?

Related resources

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