LINEup aims to better understand existing practices and trends on educational inequalities by adopting a mixed research approach to exploit existing longitudinal data and carry out case studies in high and low-achieving schools in order to understand which compensatory interventions are effective in a specific context.
When investigating educational inequalities, longitudinal data are particularly relevant as they consider time an important variable to study dynamic concepts such as achievement and attainment of students’ learning outcomes. By collecting information from the same group of people over time, they provide an opportunity to understand how people develop and why inequalities emerge. They help identify trends and understand how different aspects of individual lives interact with each other to affect outcomes.
However, given the multidimensional nature of educational inequality, to be able to implement effective compensatory policies, it is important to go beyond factors that research has already shown having a significant effect on academic performance but cannot be changed, as for instance students’ socio-economic background. In this context, students’ engagement with school is one of the strongest predictors of academic outcomes and a malleable phenomenon that schools can foster by positively influencing students’ learning outcomes.
These are the hypothesis guiding the LINEup research which takes a nomothetic and idiographic approach to the research by putting data into context and combining an analysis of longitudinal data (quantitative approach aiming at generalising results and identifying trends) with qualitative research to better consider and understand the qualities of a specific situation in different contexts (countries, regions, and schools).
This will be done in all participating countries by adopting a common analytical scheme which applies the same research questions within each country’s educational context, and fosters a soft comparative approach to the qualitative research but elaborating on the conclusions derived from a rigorous comparison of quantitative longitudinal data.
