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ABSTRACT
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.
KEYWORDS
Content analysis; inter-coder analysis; interviews; methods; natural resources
INTRODUCTION
There are many instances in rural agricultural lands management where researchers seek to answer questions such as how a phenomenon is perceived by individuals or reported through policy, plans, and media accounts, why people behave the way they do, and what contextual elements contribute to perceptions and behavior. In cases such as these - how, why, and what questions - qualitative methodologies such as interviews, focus groups, observations, and content analysis are appropriate approaches to data collection (Creswell 2013). Analysis of qualitative data requires the researcher to interpret the meaning of research participants' words and actions, as well as text found in reports and publications. There are extensive discussions surrounding validity, reliability, and trustworthiness of qualitative research (e.g. Prokopy 2011) - how can researchers assure readers that their conclusions are not subjective or biased in some way? Some qualitative researchers question whether data validity standards to indicate rigor can be incorporated into qualitative data analysis, while also allowing for the creativity and nuance of qualitative methodologies and the voices of the researched and researcher (Whittemore, Chase, and Mandle 2001). As a response to such debates, scholars have offered a variety of techniques qualitative researchers can use to help ensure trustworthiness of their data. Such techniques include data triangulation, checking for negative evidence or...