  This paper experiments with designing a semi-automated qualitative data
analysis (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. The
refined TM algorithm split the text into various sizes based on a manually
revised dictionary. This lemmatisation approach may reflect the context of the
text better than uniformly tokenising the text into one single size. TM results
were used for initial coding. Code repacking was guided by association measures
and external data to implement a general inductive QDA approach. The
information retrieved by TM and QDA was depicted in subgraphs for comparisons.
The analyses were completed in 6-7 days. Both algorithms retrieved contextually
consistent and relevant information. However, the QDA algorithm retrieved more
specific information than TM alone. The QDA algorithm does not strictly comply
with the convention of TM or of QDA, but becomes a more efficient, systematic
and transparent text analysis approach than a conventional QDA approach.
Scaling up QDA to reliably discover knowledge from text was exactly the
research purpose. This paper also sheds light on understanding the relations
between information technologies, theory and methodologies.
