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        "author": "Chris Proctor",
        "author_email": "pypi.org@accounts.chrisproctor.net",
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            "License :: OSI Approved :: GNU Affero General Public License v3",
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        "description": "# Qualitative Coding\n\nQualitative coding for computer scientists. \n\nQualitative coding is a form of feature extraction in which text (or images,\nvideo, etc.) is tagged with features of interest. Sometimes the codebook is\ndefined ahead of time, other times it emerges through multiple rounds of coding.\nFor more on how and why to use qualitative coding, see Emerson, Fretz, and\nShaw's *Writing Ethnographic Fieldnotes* or Shaffer's *Quantitative\nEthnography*.\n\nMost of the tools available for qualitative coding and subsequent analysis were\ndesigned for non-programmers. They are GUI-based, proprietary, and don't expose \nthe data in well-structured ways. Concepts from computer science, such as trees,\nsorting, and filtering, could also be applied to qualitative coding analysis if\nthe interface supported it. \n\nQualitative Coding, or `qc`, was designed to address these issues. I have used\n`qc` as a primary coding tool in a [SIGCSE\npaper](http://chrisproctor.net/publications/proctor_2019_defining_cs.html) on\nhow K-12 schools define and design computer science courses. The impetus for\npackaging and releasing a stable version was my own dissertation work. \n\n## Limitations\n\n- Due to its nature as a command-line program, `qc` is only well-suited to coding textual data. \n- `qc` uses line numbers as a fundamental unit. Therefore, it requires text files in your corpus to be \n  hard-wrapped at 80 characters. The `init` task will handle this for you. \n- Currently, the only interface for actually doing the coding is a split-screen\n  in vim, with the corpus text on one side and comma-separated codes adjacent. This works well \n  for me, but might not work well for you. I have other ideas in the pipeline,\n  but they won't be around soon.\n\n# Installation\n\n    pip install qualitative-coding\n\n# Setup \n\n- All the source files you want to code should be in a directory (possibly\n  nested). \n- Choose a working directory. Run `qc init`. This will create `settings.yaml`.\n- In `settings.yaml`, update `corpus_dir` with the directory holding your source\n  files. This may be relative to `settings.yaml` or absolute. Similarly, specify\n  directories for `codes_dir` `logs_dir`, `memos_dir`, and the YAML file where you want\n  to store your codebook. Unless you're particular, the default settings are fine. \n- Run `qc init --prepare-corpus --prepare-codes --coder yourname`. This will\n  hard-wrap all the text in your corpus at 80 characters and create blank coding\n  files. \n\n# Usage\n\n## Workflow\n\n`qc` is designed to give you a powerful terminal-based interface. The general\nworkflow is to use `code` to apply qualitative codes to your text files. As you\ngo, you will start to have ideas about the meanings and organization of your\ncodes. Use `memo` to capture these. \n\nOnce you finish a round of coding, it's time to reorganize your codes. Use\n`codebook` to refresh the codebook based on new coding. Use `stats` to see the\ndistribution of your codes. If you want to move codes into a tree, make these\nchanges directly in the codebook's YAML. If you realize you have redundant\ncodes, use `rename`. \n\nAfter you finish coding, you may want to use your codes for analysis. Tools \nare provided for viewing statistics and examples of codes, with many options for\nselecting and filtering. \nThe `--coder` argument supports keeping track of multiple coders on a project,\nand there are options to filter on coder where relevant. More analytical tools, such\nas correlations (on multiple units of analysis) and inter-rater reliability are\ncoming. \n\n## Tutorial\n\nCreate a new directory somewhere. We will create a virtual environment, intstall\n`qc`, and download some sample text from Wikipedia. \n\n    $ python3 -m venv env\n    $ source env/bin/activate\n    $ pip install qualitative-coding\n    $ qc init\n    $ qc init\n    $ curl -o corpus/what_is_coding.txt \"https://en.wikipedia.org/w/index.php?title=Coding_%28social_sciences%29&action=raw\"\n    $ qc init --prepare-corpus --prepare-codes --coder chris\n\nWhy run `qc init` three times? The first time creates a prepopulated\n`settings.py` file. You could then change any settings. The second time \nreads `settings.py` and creates the specified files and directories. \nAnd the third run, with the flags, processes the corpus file and creates a\ncorresponding coding file.\n\nNow we're ready to start coding. This next command will open a split-window vim session. \nAdd comma-separated codes to the blank file on the right. I usually page-up (control+u) \nand page-down (control+d) each file to keep their line numbers synchronized. Once you've \nadded some codes, we can analyze and refine them.\n\n    $ qc code chris -f\n    $ qc codebook\n    $ qc list\n    - a_priori\n    - analysis\n    - coding_process\n    - computers\n    - errors\n    - grounded_coding\n    - themes\n\nNow that we have coded our corpus (consisting of a single document), we should\nthink about whether these codes have any structure. All data in `qc` is stored\nin flat files, so you can easily modify it by hand. Re-organize some of your\ncodes in `codebook.yaml`. When you finish, run `codebook` again. It will go\nthrough your corpus and add any missing codes. \n\n    $ qc list\n    - analysis\n    - coding_process\n        - a_priori\n        - grounded_coding\n    - computers\n    - errors\n    - themes\n\nI decided to group a priori coding and grounded coding together under coding\nprocess. Let's see some statistics on the codes:\n\n    $ qc stats\n    Code                  Count\n    ------------------  -------\n    analysis                  2\n    coding_process            7\n    .  a_priori               2\n    .  grounded_coding        2\n    computers                 2\n    errors                    1\n    themes                    2\n\n`stats` has lots of useful filtering and formatting options. For example, `qc\nstats --files wiki --depth 1 --min 10 --format latex` would only consider files\nhaving \"wiki\" in the filename. Within these files, it would show only\ntop-level categories of codes having at least ten instances, and would output a\ntable suitable for inclusion in a LaTeX document. Use `--help` on any command to\nsee available options.\n\nNext, we might want to see examples of what we have coded. \n\n    $ qc code analysis\n    Showing results for codes:  analysis\n\n    what_is_coding.txt (2)\n    ================================================================================\n\n    [0:3]\n    In the [[social science|social sciences]], '''coding''' is an analytical process | analysis\n    in which data, in both [[quantitative research|quantitative]] form (such as      | \n    [[questionnaire]]s results) or [[qualitative research|qualitative]] form (such   | \n\n    [52:57]\n    process of selecting core thematic categories present in several documents to    | \n    discover common patterns and relations.<ref>Grbich, Carol. (2013). \"Qualitative  | \n    Data Analysis\" (2nd ed.). The Flinders University of South Australia: SAGE       | analysis\n    Publications Ltd.</ref>                                                          | \n                                                                                     | \n\nAgain, there are lots of options for filtering and viewing your coding. At some\npoint, you will probably want to revise your codes. You can easily rename a\ncode, or collapse codes together, with the `remane` command. This updates your \ncodebook as well as in all your code files.\n\n    $ qc rename grounded_coding grounded\n\nAt this point, you are starting to realize some of the deeper themes running\nthrough your corpus. Capturing these in an \"integrative memo\" is an important\npart of qualitative coding. `memo` will open a preformatted document for you in vim. \n\n    $ qc memo chris --message \"Thoughts on coding process\"\n\nCongratulations! You have finished the first round of coding. Before you move\non, this would be an excellent time to check your files into version control.\nI hope you find `qc` to be powerful and efficient; it's worked for me!\n\n-Chris Proctor\n\n## Commands\n\nUse `--help` for a full list of available options for each command.\n\n### init\nInitializes a new coding project, as described above.\n\n    $ qc init\n\n### check\nChecks that all required files and directories are in place. \n\n    $ qc check\n\n### code\nOpens a split-screen vim window with a corpus file and the corresponding code\nfile. The name of the coder is a required positional argument. \nUse `--pattern` to glob-match the corpus file you want to code. If\nmultiple are matched, you will be prompted to choose. The `--first-without-codes` option is\nparticularly useful for coding the next uncoded text.\n\n    $ qc code chris -f\n\n### codebook (cb)\nScans through all the code files and adds new codes to the codebook. \n\n    $ qc codebook\n\n### list (ls)\nLists all the codes currently in use. By default, lists them as a tree. The `--expanded` option \nwill instead flatten the list of codes, and list each as something like `subjects:math:algebra`.\n\n    $ qc list --expanded\n\n### rename\nGoes through all the code files and replaces one code with another. Removes the old code from the codebook.\n\n    $ qc rename funy funny\n\n### find\nDisplays all occurences of the provided code(s). With the `--recursive` option, also includes child\ncodes in the codebook's tree of codes. Note that a code may appear multiple times in the codebook; in this case, \nthe `--recursive` option will search for all children of all instances. When you want to grab text for a quotation,\nuse the `--textonly` option. The `--files` option lets you filter which corpus files to search.\n\n    $ qc find math science art --recursive\n\n### stats\nDisplays frequency of usage for each code. Note that counts include all usages of children.\nList code names to show only certain codes. Filter code results with \n`--depth`, `--max`, and `--min`. Use the `--expanded` option to show the full name of each code, rather than the \ntree representation. Arguments to `--format` may be any supported by [tabulate](https://bitbucket.org/astanin/python-tabulate).\nThe `--files` option lets you filter which corpus files to use in computing stats.\n\n    $ qc stats curriculum math algebra --depth 1\n\n\n",
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