APPLYING METHODS TRIANGULATION TO RESEARCH QUALITY ASSURANCE IN HIGHER EDUCATION
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Grounded theory (GT) is widely used in qualitative research to generate theory through constant comparison, memoing, theoretical sampling and category development. However, less attention has been given to how methods triangulation can be operationalised within GT without displacing qualitative analysis as the central source of conceptual development. This article demonstrates how methods triangulation can strengthen qualitative GT analysis. It shows how different data slices can support constant comparison, expose dissonance, guide theoretical sampling, stimulate abductive reasoning, and contribute to category saturation and theory generation. The study was conducted in the context of small higher education institutions (HEIs) and examined the relationship between quality assurance, innovation and performance. Twenty-one in-depth interviews formed the analytical engine of the research and were analysed through iterative coding, memoing and constant comparison. Additional sources, including external quality assurance reports, accreditation data, and audited financial reports were incorporated as comparative indicators within the GT process. These sources were not treated as independent mixed-methods strands, but as analytically useful data slices that informed and refined the sampling strategy, category development, and theoretical saturation. The triangulated process enabled the development of a four-quadrant taxonomy linking quality assurance readiness, institutional performance and innovation activity. The taxonomy was then used to return to the qualitative data, compare institutional groups and deepen the emerging grounded theory explanation. The article contributes to the qualitative research methodology by showing how methods triangulation can be embedded within GT as an analytical rather than merely a validating strategy. It maintains the qualitative interviews as the central engine of theory generation while other qualitative, quantitative, and quantised data sources act as prompts for comparison, dissonance and abductive interpretation.
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