A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education

A primer on gathering and analysing multi-level quantitative evidence for differential student outcomes in higher education

A significant challenge currently facing the higher education sector is how to address differential student outcomes in terms of attainment and continuation gaps at various stages of students’ transitions. Worryingly, there appears to be a ‘deficit’ discourse among some university staff in which differential outcomes are perceived to be due to student deficiencies. This may be exacerbated by institutional analyses placing an over-emphasis on the presence of the gaps rather than the causes. The purpose of this primer is to provide advice about how institutions can carry out far more nuanced analyses of their institutional data without requiring specialist software or expertise. Drawing on a multi-level framework for explaining differential outcomes, we begin with guidance for gathering quantitative data on explanatory factors for attainment and continuation gaps, largely by linking sources of internal data that have not previously been connected. Using illustrative examples, we then provide tutorials for how to model explanatory factors employing IBM SPSS Statistics (IBM Corp., Armonk, NY, USA) to perform and interpret regression and meta-regression analyses of individual- and group-level (aggregated) student data, combined with data on micro- and meso-level factors. We propose that university staff with strategic responsibilities could use these approaches with their institutional data, and the findings could then inform the design of context-specific interventions that focus on changing practices associated with gaps. In doing so, institutions could enhance the evidence-base, raise awareness, and further ‘embed the agenda’ when it comes to understanding potential reasons for differential student outcomes during educational transitions.