Data Collection and Analysis for Quality Assurance

This chapter explores the pivotal role of data collection and analysis in fostering effective internal quality assurance (IQA) systems within higher education institutions. It emphasizes the strategic selection of Key Performance Indicators (KPIs) as the foundation for evidence-based quality assurance, highlighting criteria such as clarity, measurability, and alignment with institutional goals. The chapter details robust data collection methods, combining quantitative tools (e.g., surveys, institutional records) and qualitative approaches (e.g., interviews, focus groups) to ensure comprehensive and valid insights. It addresses the importance of data security and privacy, advocating for compliance with regulations like GDPR and proactive measures such as encryption and staff training. Advanced analytical techniques, including big data analytics, machine learning, and qualitative methods, are examined to transform raw data into actionable insights for institutional improvement. The chapter also underscores the critical role of external stakeholders-such as employers, alumni, and accreditation bodies-in enhancing the relevance and impact of QA processes. By integrating practical strategies, ethical considerations, and stakeholder collaboration, the chapter provides a roadmap for higher education institutions to build data-driven, transparent, and impactful IQA systems that promote accountability and continuous improvement.