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.
1 maintainer
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