Background: The existence of a multiplier, compositional or social context effect is debated extensively in the literature on school effectiveness and also relates to the wider issue of equity in educational outcomes. However, comparatively little attention has been given to whether or not the association between student achievement and school socio-economic composition may vary across the achievement distribution. Furthermore, with limited exception, comparatively little use has been made of unconditional quantile modelling approaches in the education literature. Methods: This paper uses Irish data from the Programme for International Student Assessment 2018 and employs ordinary least squares regression and unconditional quantile regression empirical approaches to examine the association between school socio-economic composition and achievement. Reading and mathematics achievement are used as outcome variables and models control for a rich set of school and student characteristics. Results: Findings from the ordinary least squares regression show that, on average, there is a significant negative relationship between school socio-economic disadvantage and student achievement in reading and mathematics having controlled from a range of individual and school-level variables. From a distributional perspective, unconditional quantile regression results show variation in the strength of the relationship between school socio-economic disadvantage and student achievement, particularly in reading, with a stronger association at the lower end of the achievement distribution. Findings illustrate the need to give nuanced consideration to how students with varying levels of achievement may experience a socio-economically disadvantaged context at school. Our findings also draw attention to the benefit of examining variation in the association between achievement and explanatory variables across the achievement distribution and underscore the importance of moving beyond an exclusive focus on the mean of the distribution. Finally, we emphasise the importance of drawing population-level inferences when using the unconditional quantile regression method.