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I would categorize these broadly into (1) вЂњbarely maintained,вЂќ older style Windows software (some of it even available for free or as open source software), (2) adjacent or look-alike applications that on closer inspection donвЂ™t really offer the same thing, and (3) web-based applications. There are also many other, minor players. The three main contenders are in a features вЂњarms raceвЂќ with each other, regularly introducing new capabilities that fewer and fewer users actually need. These are software packages that need to be installed on a researcherвЂ™s computer (PC or Mac). The most common specialized QDA software suites are Atlas.ti, MAXQDA, and NVivo. There is only a handful of vendors that compete in this market segment, which is small and mostly populated with users who demand a lot but want to pay as little as possible (students, academics). While it may occasionally be handy for political reasons to be able to make claims about the вЂњstatistical validityвЂќ of oneвЂ™s research, coding cannot and will not somehow magically convert an ethnographic dataset into quantitative research.Д¬odingвЂ™s other dirty secret is how expensive the specialized software is that is used in the process. Coding also risks creating the impression, particularly in the hands of less experienced practitioners, that it somehow enables insights that are вЂњstatisticallyвЂќ valid because they have been mined from the entire data set using a rigorous process.

This is because it is hard, focused work and therefore costly in terms of time (which, in the context of business ethnography, translates to money). Coding increases contextual data retrieval, speeds up the analysis phase of a project, and enables easy comparisons across large data sets.Д¬oding in qualitative data analysis also has its вЂњdirty secrets.вЂќ For one thing, it is widely discussed but not nearly as universally practiced. Textual material in its original form may be unwieldy and, as a result, somewhat difficult to work with (вЂњвЂ¦where did I see that quote againвЂ¦?вЂќ).

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For example, one might conduct several interviews, transcribe them, and then code them to examine what themes emerge from the whole set of interviews. This technique allows researchers to develop category sets-arranged, perhaps, into different themes-which span different qualitative data. These tags, and the data they label, can then be used to underpin analysis and interpretation. Across the social sciences, including business ethnography, the so-called вЂњbest practiceвЂќ for qualitative data analysis is to use coding, the process of categorizing data by applying tags to portions of it.

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