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

TL;DR

This study develops a semi-automated qualitative data analysis method combining text-mining and coding to analyze small-N transcripts efficiently, revealing more specific insights than traditional approaches.

Contribution

It introduces a novel semi-automated QDA algorithm that integrates text-mining with manual coding, improving efficiency and specificity in small sample qualitative analysis.

Findings

QDA retrieved more specific information than TM alone

The combined approach was completed in 6-7 days

The method enhances transparency and systematic analysis

Abstract

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.