![]() Researchers will often change, lump together, split, or re-organize codes as they analyze their data. Some software packages allow the researcher to visualize codes and the relationships between them in new and innovative ways however, a cursory review of the literature suggests that qualitative researchers often use very basic features some even use software such as Endnote on Microsoft Word or even analog systems such as paper or sticky notes. Perhaps more importantly, software allows easy visualization and analysis of where codes co-occur (i.e., where multiple codes have been applied to the same snippets of text), and other linking activities that help researchers identify and specify themes in the data. Using software for QDA allows researchers to nest codes, then begin to see the number of instances in which a particular code has been applied. This approach is common toward the end of the process in fields such as organization studies, management, and other disciplines and is prominent as an approach from the beginning in fields with experimental or clinical roots (e.g., psychology). Sometimes the QDA process involves looking for instances that demonstrate some concept, mechanism, or theory from the academic literature on the subject. Later rounds involve conflating codes that might mean the same thing, relating codes to one another (often by documenting their meanings in a similar way to software documentation), and eliminating codes that no longer make sense. In another round, she might get even more specific with codes such as “conversation_package_nomoney” if a participant discussed not having money to create a package in R. For instance, if the researcher is coding observation notes and senses that conversations between two individuals will be relevant to the research questions, she might tag the instances in which the two individuals speak with the code “conversation.” In the next round of coding, she might classify what the participants discuss with a finer tag, like “conversation_package” if they were talking about creating packages in R. The researcher starts with open coding, meaning that she is free to tag snippets of text with whatever descriptions she deems appropriate. Qualitative data analysis (QDA) processes, particularly those developed by Corbin and Strauss (2014), Miles, Huberman, and Saldana (2013), and Glaser and Strauss (2017), can be thought of as layering interpretation onto the text. Textual qualitative data refers to text from interview transcripts, observation notes, memos, jottings and primary source/archival documents. The motivation stems from the need for a free, open source option for analyzing textual qualitative data.
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