Abstract
Analyses of learning often rely on coded data. One important aspect of coding is establishing reliability. Previous research has shown that the common approach for establishing coding reliability is seriously flawed in that it produces unacceptably high Type I error rates. This paper focuses on testing whether or not these error rates correspond to specific reliability metrics or a larger methodological problem. Our results show that the method for establishing reliability is not metric specific, and we suggest the adoption of new practices to control Type I error rates associated with establishing coding reliability.
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Eagan, B., Brohinsky, J., Wang, J., & Shaffer, D. W. (2020). Testing the reliability of inter-rater reliability. In ACM International Conference Proceeding Series (pp. 454–461). Association for Computing Machinery. https://doi.org/10.1145/3375462.3375508
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