Visual and Statistical Methods to Calculate Intercoder Reliability for Time-Resolved Observational Research

摘要

While calculating intercoder reliability (ICR) is straightforward for text-based data, such as for interview transcript excerpts, determining ICR for naturalistic observational video data is much more complex. To date, there have been few methods proposed in literature that are robust enough to handle complexities such as the occurrence of simultaneous event complexity and partial agreement by the raters. This is especially important with the emergence of high-resolution video data, which collects nearly continuous or continuous observational data in naturalistic settings. In this paper, we present three approaches to calculating ICR. First, we present the technical approach to clean and compare two coders’ results such that traditional metrics of ICR (e.g., Cohen’s k, Krippendorff’s a, Scott’s P) can be calculated, methods previously unarticulated in literature. However, these calculations are intensive, requiring significant data manipulation. As an alternative, this paper also proposes two novel methods to calculate ICR by algorithmically comparing visual representations of each coders’ results. To demonstrate efficacy of the approaches, we employ all three methods on data from two separate ongoing research contexts using observational data. We find that the visual methods perform as well as the traditional measures of ICR and offer significant reduction in the work required to calculate ICR, with an added advantage of allowing the researcher to set thresholds for acceptable agreement in lag time. These methods may transform the consideration of ICR in other studies across disciplines that employ observational data.