Strengthening the procedural fidelity research‐to‐practice loop in animal behavior

Kodak et al. (2022) looked at every paper that measured procedural fidelity in animal labs. They asked: how do researchers pick fidelity metrics and crunch the numbers?

The team wrote a story-style review. They compared how animal and human studies track whether staff run procedures the same way each time.

The same data can say "high fidelity" or "low fidelity" just by changing the math. Picking the metric after you see the graph can hide mistakes.

Animal labs share the same trap. The paper shows a loop: better metrics → clearer data → better practice → back to better metrics.

Manolov et al. (2022) also warn about post-hoc picks. They give a flowchart to choose effect measures before data collection. Kodak adds: do the same for fidelity metrics.

Ninci (2023) tells analysts to guard internal validity when they eye-ball graphs. Kodak widens the lens to the staff side—did the tech even do the protocol right?

Young (2019) offers a Monte Carlo app for p-values. Kodak nods: any stat tool is only as good as the fidelity numbers you feed it.

Before your next session, pick one fidelity formula and stick to it. Write the rule in your protocol sheet. Share the sheet with your RBT. When you graph the child's data later, you will trust both the behavior line and the fidelity line. No more "maybe we drifted" doubts.