All is Data: Applying Methods Triangulation within Grounded Theory for the Research on Quality Assurance in Higher Education

Introduction: A common assertion in grounded theory (GT), that ‘all is data’, highlights the value of diverse sources when generating theory through the GT methodology. This study investigates the interplay of quality assurance, innovation, and performance in small higher education institutions (HEIs), employing GT enriched by methods triangulation to understand how these domains interact.

Goals and Methods: The aim is to demonstrate how methods triangulation significantly strengthens data analysis and theory generation. GT was operationalized through constant comparison - data incidents to incidents, codes to codes, codes to categories, categories to categories - across 21 institutional cases. Qualitative data included in-depth interviews and external quality assurance reports. Quantitative evidence drew from online databases and audited financial reports. Evolving sampling strategies; convenience, purposive and theoretical, guided the study toward saturation, producing a rich, multilayered substantive model.

Results: The analysis built from the strength of the two diverse forms of data to yield a model capturing the contextual conditions of small and micro HEIs, the structural and resource challenges they face, and the adaptive strategies they deploy to leverage strengths for sustainability and growth. To arrive at this, the quantitative data was first applied to place the various HEI’s into four quadrants of a Performance – Quality Assurance based taxonomy, with the rich qualitative data then being used to extract deep insights on each quadrant of this taxonomy.

Conclusion: Findings contribute a grounded theoretical account of how small HEIs negotiate the interdependence of quality assurance, innovation, and performance. Beyond practical insights for institutional leaders and policymakers, the study illustrates how methods triangulation enhances the robustness of GT in exploring the competitiveness of higher education organizations. It also confirms the common assertion that qualitative and quantitative data can be fruitfully combined to build upon complementary aspects of the same phenomenon.