Qualitative research methods contend with debates surrounding subjectivity and bias. Researchers use a variety of techniques to help ensure data trustworthiness. One such technique is to involve multiple coders in data analysis. The deliberative nature of codebook development among multiple coders produces rich data analysis that may not otherwise be achieved with a single (or even two) researcher(s). In this manuscript, we make a plea for researchers and journals to include data analysis procedures and descriptions in published literature. In addition, we illustrate minimal reporting of qualitative data analysis processes through a synthesis of 21 years of agricultural best management practice adoption literature. We present two rural agricultural case studies on multi-coder team codebook development and intercoder reliability processes specific to interviews, focus groups, and content analysis. Overall, we argue that multi-coder teams can improve data quality, and reporting data analysis procedures can mitigate implications of subjectivity in qualitative methods.
Church, Sarah, Michael Dunn, and Linda Prokopy. 2019. "Benefits to Qualitative Data Quality with Multiple Coders: Two Case Studies in Multi-coder Data Analysis." Journal of Rural Social Sciences, 34(1): Article 2. Available at: https://egrove.olemiss.edu/jrss/vol34/iss1/2