Integrating thematic analysis with cluster analysis of unstructured interview datasets: an evaluative case study of an inquiry into values and approaches to learning mathematics

ABSTRACT

A novel approach to integrating Cluster Analysis (CA) within qualitative inquiry is presented, grounded in a large, unstructured dataset from open and rather unstructured interviews. This dataset was previously subjected to typical (theory sensitive) thematic analyses. Transformed into quantitative binary matrix structures, the CA offers robustness and transparency as it systematically exhausts the whole dataset in a replicable procedure. However, then the transformation becomes bi-directional, as resulting clusters provoke new qualitative interpretations and even further quantitative analyses. This approach led to theoretically interpretable results that significantly extended previous understandings of relations between ‘values’ and ‘learning approach’ relating to mathematics learner identity. This integrated methodology is evaluated for its significance to the substantive field, but is discussed more widely for social science research drawing on such interview datasets in general.

KEYWORDS:

• Cluster analysis
• mixed method
• integrative design
• unstructured interviews
• biographical interviews
• identity
• mathematics learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

This work was supported by ESRC TLRP.