A Typology of State Financial Aid Grant Programs Using Latent Class Analysis

Research in Higher Education - State financial aid grant programs are commonly categorized as either need-based, merit-based, or both, but their initial eligibility requirements include many more...     

A New Approach to Quality Assurance for Higher Education

This article describes the Engineering Professors' Conference (EPC) approach to quality assurance for Higher Education Institutions, which is currently the only formalized alternative to British Standard 5750 'Quality Systems'. The EPC approach is based on principles of quality assurance and Total Quality Management which have been developed in industry. These ideas are briefly outlined, and an opinion expressed on the interpretation and application of BSS7S0 for HE. The EPC model is explained, illustrated with example elements taken from the EPC Specification and Guidelines for Quality Assurance in Higher Education. A top-down approach to the development of an institutional quality system is described, based on a quality policy statement. Areas of activity which have an important impact on the quality of education are identified, emphasising the quality assurance requirements of academic departments. Supporting and administrative quality system requirements are also described, as these also play an important role in the quality of education. Formal quality procedures and documentation in key areas are proposed.     

Learning from participants in field based research

This paper takes a critically reflective look at field research done in the early career of an academic and in so doing uncovers the dilemmas of a novice researcher that are rarely acknowledged in texts that address qualitative methods. It addresses issues of power in research associations surrounding different paradigms and the ways in which the study changed as boundaries were increasingly blurred. It also examines lessons learned about interviewing and challenges the notion of an un‐analytical informant as being necessary to quality and depth of data.