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This paper experiments with designing a semi-automated qualitative dataanalysis (QDA) algorithm to analyse 20 transcripts by using freeware.Text-mining (TM) and QDA were guided by frequency and association measures,because these statistics remain robust when the sample size is small. Therefined TM algorithm split the text into various sizes based on a manuallyrevised dictionary. This lemmatisation approach may reflect the context of thetext better than uniformly tokenising the text into one single size. TM resultswere used for initial coding. Code repacking was guided by association measuresand external data to implement a general inductive QDA approach. Theinformation retrieved by TM and QDA was depicted in subgraphs for comparisons.The analyses were completed in 6-7 days. Both algorithms retrieved contextuallyconsistent and relevant information. However, the QDA algorithm retrieved morespecific information than TM alone. The QDA algorithm does not strictly complywith the convention of TM or of QDA, but becomes a more efficient, systematicand transparent text analysis approach than a conventional QDA approach.Scaling up QDA to reliably discover knowledge from text was exactly theresearch purpose. This paper also sheds light on understanding the relationsbetween information technologies, theory and methodologies.

An experiment exploring the theoretical and methodological challenges in developing a semi-automated approach to analysis of small-N qualitative data