Enhancing Replicability and Reproducibility in Observational Coding Research: A Tutorial in R

Replicating observational‐coding studies is notoriously difficult: coder variance attenuates effect sizes, protocols can be opaque, and raw text/video/audio cannot always be shared. This tutorial presents a three-step, open science workflow that links design decisions to reproducibility outcomes, assuming interchangeable observers and simple mean rating scores. Step 1 shows how (under the appropriate assumptions) the Spearman-Brown Prophecy and attenuated correlation formulas translate coder reliability into sample size targets; in an illustrative example, we illustrate that with two coders and N ≈ 158 participants, researchers retain 80% power to detect a correlation of r = .30. Step 2 provides a six step loop from training coders to calculating inter-rater reliability. This protocol emphasizes the importance of creating agreement matrices and running periodic inter-rater reliability checks; using simulated data, we demonstrate how off diagonal errors pinpoint coder drift. Step 3 addresses coder positionality and ethical data sharing, offering a decision tree that maps media sensitivity and participant consent onto data repository options. Annotated code, simulated data, and a brief consent template are hosted on the OSF repository for this tutorial. Adopting this pipeline enables researchers to plan, monitor, and disseminate observational work in a way that is both transparent and statistically robust.