11.5: Reliability in Unobtrusive Research
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Define stability and describe strategies for overcoming problems of stability.
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Define reproducibility and describe strategies for overcoming problems of reproducibility.
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Define accuracy and describe strategies for overcoming problems of accuracy.
Reliability in Unobtrusive Research
This final section of the chapter investigates a few particularities related to reliability in unobtrusive research projects that warrant our attention. These particularities have to do with how and by whom the coding of data occurs. As methodologist Klaus Krippendorff (2022) explains, issues of stability, reproducibility, and accuracy all speak to the unique problems—and opportunities—with establishing reliability in unobtrusive research projects.
Stability refers to the tendency for coders to consistently re-code the same data in the same way over a period of time (Krippendorff, 2022). If stability is a problem, it will reveal itself when the same person codes the same content at different times and comes up with different results. Coding is said to be stable when the same content has been coded multiple times by the same person with the same result each time. If you discover problems of instability in your coding procedures, it is possible that your coding rules are ambiguous and need to be clarified. Ambiguities in the text itself might also contribute to problems of stability. While you cannot alter your original textual data sources, simply being aware of possible ambiguities in the data as you code may help reduce the likelihood of problems with stability. It is also possible that problems with stability may result from a simple coding error, such as inadvertently jotting a 1 instead of a 10 on your code sheet.
Reproducibility, sometimes referred to as intercoder reliability, is the tendency for a group of coders to classify category membership in the same way (Krippendorff, 2022). In qualitative research, an explicit intercoder reliability assessment can yield numerous benefits, including improving the systematicity, communicability, and transparency of the coding process (O'Connor & Joffe, 2020). Cognitive differences among the individuals coding data may result in problems with reproducibility, as could ambiguous coding instructions. Random coding errors might also cause problems. One way of overcoming problems of reproducibility is to have coders work together. For instance, a research team might share the responsibility for coding data by conducting their coding at the same time in the same room. Resolving ambiguities together through dialogue ensures that the team develops a shared understanding of how to code various bits of data, which ultimately helps convince diverse audiences of the trustworthiness of the final analysis (O'Connor & Joffe, 2020).
Finally, accuracy refers to the extent to which the classification of text corresponds to a standard or norm statistically (Columbia Public Health, 2023; Krippendorff, 2022). This presumes that a standard coding strategy has already been established for whatever text you are analyzing. It may not be the case that official standards have been set, but perusing the prior literature for the collective wisdom on coding in your particular area is time well spent. Scholarship focused on similar data or coding procedures will no doubt help you to clarify and improve your own coding procedures.
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Stability: Can become an issue in an unobtrusive research project when the results of coding by the same person vary across different time periods.
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Reproducibility: Has to do with multiple coders’ results being the same for the same text (also known as intercoder reliability).
- Accuracy: Refers to the extent to which one’s coding procedures correspond to some preexisting standard.