qualitative-coding 1.4.0

Qualitative coding tools for computer scientists

qc is a free, open-source command-line-based tool for qualitative data analysis designed to support computational thinking. In addition to making the qualitative data analysis process more efficient, computational thinking can contribute to the richness of subjective interpretation. The typical workflow in qualitative research is an iterative cycle of "notice things," "think about things," and "collect things" (seidel, 1998). qc provides computational affordances for each of these practices, including the ability to integrate manual coding with automated coding, a tree-based hierarchy of codes stored in a YAML file, allowing versioning of thematic analysis, and a powerful query interface for viewing code statistics and snippets of coded documents.

Qualitative data analysis, in its various forms, is a core methodology for qualitative, mixed methods, and some quantitative research in the social sciences. Although there are a variety of well-known commercial QDA software packages such as NVivo, Dedoose, Atlas.TI, and MaxQDA, they are generally designed to protect users from complexity rather than providing affordances for engaging with complexity via algorithms and data structures. The central design hypothesis of qc is that a closer partnership between the researcher and the computational tool can enhance the quality of QDA. qc adopts the "unix philosophy" (McIlroy, 1978) of building tools which do one thing well while being composable into flexible workflows, and the values of "plain-text social science" (Healy, 2020), emphasizing reproducability, transparency, and collaborative open science.

qc was used in a prior paper and the author's doctoral dissertation; qc is currently a core tool supporting a large NSF-funded Delphi study involving multiple interviews with forty participant experts, open coding with over a thousand distinct codes, four separate coders, and several custom machine learning tools supporting the research team with clustering and synthesizing emergent themes. qc is a free, open-source command-line-based tool for qualitative data analysis designed to support computational thinking. In addition to making qualitative data analysis process more efficient, computational thinking can contribute to the richness of subjective interpretation. Although numerous powerful software packages exist for qualitative data analysis, they are generally designed to protect users from complexity rather than providing affordances for engaging with complexity via algorithms and data structures.

Installation

qc is distributed via the Python Package Index (PYPI), and can be installed on any POSIX system (Linux, Unix, Mac OS, or Windows Subsystem for Linux) which has Python 3.9 or higher installed. If you want to install qc globally on your system, the cleanest approaach is to use pipx.

pipx install qualitative-coding

If your research project is already contained within a Python package and you want to install qc as a local dependency, simply add qualitative-coding to pyproject.toml or requirements.txt.

qc relies on Pandoc for converting between file formats, so make sure that is installed as well. qc uses a text editor for coding; you should install Visual Studio Code, the default editor, unless you prefer a different editor such as emacs or vim.

Usage

Please see the package documentation for details on the design of qc, a vignette illustrating its usage, and full documentation of qc's commands.

Acknowledgements

Partial support for development of qc was provided by UB's Digital Studio Scholarship Network. Logo design by Blessed Mhungu.