Section C-8 asks that you’re able to evaluate the validity and reliability of measurement procedures. That’s hard to do if you get those two terms mixed up, and even harder if we throw in a third term: accuracy– another marker of trustworthy measurement. Let’s discriminate between these terms once and for all.

Validity is the most important one. When we say a measurement or a measurement procedure is valid, we mean that it is meaningful. That can only be if we measure the right thing– behavior. Measuring the intended dimension of that behavior or, the intended timing, using a procedure that paints a meaningful picture of that behavior. If we use time sampling, is the data an over or under estimation? And if so, it’s not meaningful, and paints a misleading picture of the behavior. So first and foremost, you have to measure the right thing. Another hint about validity, it’s not a quantity, it’s a statement of fact: “I measured the right thing”.

Now, you have to prove that your measurement is reliable. To prove reliability, you’re going to need actual numbers. How do you do that? Well, you repeat the measurement in exactly the same way, at the exact same time. And if you yield the same results each time, you have reliable measurement. Think of it as measurement you can count on, like a reliable person who you can depend on to always be consistent.

And finally, accuracy. Another quantity-based item. And though not mentioned in the task list, it’s important and belongs here with validity and reliability. Accuracy means that, whatever you measured, you measured it right. You counted correctly and what you counted (that’s your observed value) matches the true value. You counted five fingers and sure enough, one-two-three-four-five– accurate.

So, the takeaways here: if you don’t have validity, then reliability and accuracy are moot. And if you are not able to yield an accuracy or a reliability measure, then you can focus on either the accuracy or reliability.

Related Content

Validity

Measurement is trustworthy when the measurement that produces data is applicable and specific to the target behavior of interest and the relevant dimension of that behavior; in other words, you…

Reliability

Measurement is trustworthy when the degree to which repeating a measurement procedure under the same conditions produces the same result.

Observer drift

When observers collecting data have a shift/drift in how they interpret the operational definition of the target behavior that affects the validity of their data.

Observer reactivity

When an observer’s data collection or performance is influenced by the awareness that they are being monitored and evaluated.

Measurement artifact

Data that represents an unwarranted and misleading picture of behavior because of how it was measured, typically a product of time-sampling, limiting measurement scales, or poorly scheduled measurement periods.

Measurement bias

When an observer’s measurement is influenced by an expectation or belief they hold rather than what actually occurred.

Interobserver agreement (IOA)

A measurement of the degree to which two or more observers report the same values when observing and collecting data for the same behaviors/events.

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Reactivity

Refers to the changes in an individual’s behavior due to the presence of an observer.

Accuracy

The degree to which what was measured (observed value) is representative of what actually occurred (true value); in other words, the data was measured correctly.

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