Validity, Reliability, IOA, Procedural Integrity, and Dosage

Accuracy, Reliability, and Validity

Three measurement-quality concepts anchor this section. Accuracy is the extent to which observed values match the true value of the behavior (often established by an independent calibrated standard or a thorough "true" count). Reliability is the consistency of measurement: the same behavior measured repeatedly yields the same value. Validity asks whether you measured the right thing, the behavior and dimension that answer the question.

These are independent. A bathroom scale that always reads 5 pounds high is reliable but inaccurate. Observers can be highly reliable yet invalid if they share a flawed definition, measuring accidental contact as "aggression" identically every time. The exam's recurring lesson: high agreement never proves validity. Always confirm the measure captures the intended response class and dimension before trusting consistent data.

Interobserver Agreement (IOA): Types and a Worked Calculation

Interobserver agreement (IOA) is the degree to which two independent observers report the same values for the same events. It is a reliability index, not a validity index. The method must match the measurement system:

Worked example (mean count-per-interval). Two observers count a behavior across five 1-minute intervals:

Mean count-per-interval IOA is approximately 86%: average the five interval percentages, using unrounded ratios in the calculation. Exact count-per-interval IOA uses identical counts instead: only intervals 1 and 3 match, so (2 / 5) x 100 = 40%. Total count IOA compares the session totals of 20 and 19, giving (19 / 20) x 100 = 95%. These three methods answer different agreement questions. Mean count-per-interval can expose disagreements hidden by session totals, but its score is not necessarily lower than total count IOA for every dataset. Joint zero counts count as exact agreement and contribute 100% to the mean method. Common IOA benchmarks depend on the measurement protocol; no score alone proves accuracy or validity.

Procedural Integrity, Treatment Fidelity, and Dosage

Data quality is more than the dependent-variable line on a graph. Procedural integrity (treatment fidelity / treatment integrity) measures whether the independent variable, the intervention, was implemented as written. Low integrity threatens internal validity: if outcomes are flat but the plan was not run correctly, you cannot conclude the plan failed. Dosage measures the amount of exposure: minutes, sessions, opportunities, or trials delivered.

Use this decision chain when interpreting disappointing data:

• Observers disagree -> check the definition, training, scoring rules, and IOA method first.
• Data do not match the clinical question -> check validity of the measure and dimension.
• Intervention data are flat -> check procedural integrity and dosage before rejecting the plan.
• Change appears only on some days -> check setting events, schedule, observer coverage, and representativeness.

The defensible rule: if outcomes are poor but integrity is low, improve implementation and collect more data; if integrity and dosage are adequate and outcomes are still poor, modification is warranted. Do not let a strong IOA value distract from validity or integrity; observers can agree perfectly on the wrong response class while the intervention was never delivered as designed.

Threats to Accuracy and Choosing the Right IOA Method

Several predictable threats degrade accuracy and reliability, and the exam expects you to name and prevent them. Observer drift is the gradual, unintentional change in how an observer applies a definition over time, two observers trained together slowly diverge. Observer reactivity occurs when being observed changes the observer's scoring (e.g., scoring more carefully when a supervisor is present).

Observer bias / expectancy is scoring that is nudged toward an anticipated result, which is why observers should be blind to phase or condition when feasible. Poorly designed datasheets and complex definitions also lower accuracy. The standard safeguards are clear definitions, thorough training, periodic recalibration, and routine IOA checks across the study, not just at the start.

Matching the IOA method to the data is itself a high-yield skill. The wrong method can inflate agreement and hide real disagreement:

For interval data with very low-rate behavior, occurrence-only IOA (agreement only on intervals where at least one observer scored an occurrence) prevents agreement inflation from a long run of jointly empty intervals; for very high-rate behavior, nonoccurrence IOA does the parallel job.

Recognizing when total count IOA overstates agreement, and when occurrence/nonoccurrence agreement is the honest index, is exactly the nuance Domain C items probe. The unifying principle: report the agreement statistic that is most stringent and most informative for the measurement system you actually used, then interpret it as a check on reliability, never as evidence of validity.