I used ATLAS.ti to do the word count. I also did the training from ATLAS.ti, where they demonstrated how to use word lists to cut out certain words and focus on meaningful data. The original word count consisted of 650 words. Utilizing a word list with words such as “a,” and “because” brought the count to 241. The software also allows the removal of certain parts of speech, such as numerals or particles. However, this feature did not reveal a difference for this data set.

Sinkovics and Alfodi (2012) stated that it had been known for a while that such software was never meant to replace the researcher's particular abilities in analyzing and interpreting complicated data. I found this to be true with CAQDAS in this class. The software can present the data set in an organized fashion, but it still requires manual coding. In the ATLAS.ti training, the instructor stated that the AI coding feature in ATLAS.ti is better suited as a complement to manual coding. For describing qualitative procedures and rating quality, there is no standard "boilerplate" (Pratt, 2009). Qualitative findings lack a "magic number" of interviews/observations to answer questions (Pratt, 2009). The writers only provide their analysis of the data, not any raw data leading to how the researchers transitioned from their data to their interpretations needs to be better supported by evidence (Pratt, 2009). This means stripping away too many words in the word count analysis can lose raw data and relying more on researcher interpretations.

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