Traditionally, interrater reliability (IRR) is determined for easily defined events, such as deciding within which category a piece of qualitative data falls. However, for time-resolved or time-dependent observational data and other nontraditional data, complications arise due to the complexity of the data being interpreted and analyzed. In this paper, we present two promising new methods for calculating IRR based on visual representations of analyzed time-resolved data. We compare the IRR calculated using these two visual methods with five of the most common statistical measures for calculating IRR, finding excellent agreement between our new methods and existing statistical formulae. This methods development is exemplified using data for our ongoing research, in which we are working to analyze time-resolved engineering writing data recorded through screen capture technology. The process of developing methods of interrater reliability for our context can also be applied to other researchers who seek to analyze nontraditional data, such as those collected during eye-tracking, screen capture, or observational studies.
|Original language||English (US)|
|Journal||ASEE Annual Conference and Exposition, Conference Proceedings|
|State||Published - Jun 15 2019|
|Event||126th ASEE Annual Conference and Exposition: Charged Up for the Next 125 Years, ASEE 2019 - Tampa, United States|
Duration: Jun 15 2019 → Jun 19 2019
All Science Journal Classification (ASJC) codes