School socio-economic context and student achievement in Ireland: an unconditional quantile regression analysis using PISA 2018 data

© 2023. The authors. This document is made available under the CC-BY 4.0 license http://creativecommons.org/licenses/by /4.0/ This document is the published version of a Published Work that appeared in final form in Large-scale Assessments in Education. To access the final edited and published work see https://doi.org/10.1186/s40536-023-00171-xBackground: The existence of a multiplier, compositional or social context efect is debated extensively in the literature on school efectiveness 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 signifcant 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 fndings also draw attention to the beneft 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

Equity in mathematics and science outcomes: characteristics associated with high and low achievement on PISA 2006 in Ireland

Equity in education is a key concern internationally; however, it is rare that this issue is examined separately for low- and high-achieving students and concurrently across different subject domains. This study examines student and school background characteristics associated with low and high achievement in mathematics and science on the Programme for International Student Assessment. Based on the results of a multilevel multinomial model of achievement for each domain, findings indicate that a greater number of the variables examined are associated with low rather than high achievement. At student level, home language, intention to leave school early, socioeconomic status, grade level, cultural capital, and books in the home are significantly associated with achievement in mathematics and science. At school level, only school average socioeconomic status is statistically significant in the models. Significant gender differences are found in the distribution of high and low achievers, which vary across the domains. In mathematics, females are more likely to be low achievers while males are more likely to be high achievers. In science, gender interacts with early school-leaving intent whereas males intending to leave school early are more likely to be in the low-achieving group than females intending to leave early. Conclusions emphasise the need for targeting resources aimed at promoting equity in outcomes at student level as well as at school level. Future work may extend the current analyses by incorporating domain-specific variables or examining cross-country differences

An empirical investigation of the association between musical aptitude and foreign language aptitude

THESIS 7803Given the joint ubiquity of music and language, and pre-theoretic similarities between the two, it is relevant to consider the relationship between musical ability and linguistic ability, specifically in relation to second language acquisition. The specific question of musical aptitude and its relationship to foreign language aptitude is the central focus of this thesis. In Chapter 2, the dissertation reviews classical and recent research on individual differences, in particular those individual differences which are known to have a major impact on the second language learning process. This review reveals a complex relationship between language aptitude, intelligence and working memory. Chapter 3 examines the relationship between music and other cognitive abilities, focussing primarily on the relationship between music and language ability. This suggests that further analysis of the music-language relationship is indeed justified. From the analysis of past research reported in Chapters 2 and 3, open questions emerge about two important issues: the extent to which music and language aptitude are related, and the extent to which that relationship is mediated by general intelligence. Empirical investigations are carried out to investigate these issues quantitatively and qualitatively

School socio-economic context and student achievement in Ireland: an unconditional quantile regression analysis using PISA 2018 data

Background: The existence of a multiplier, compositional or social context efect is debated extensively in the literature on school efectiveness 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 signifcant negative relationship between school socio-economic disadvan?tage 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 fndings also draw attention to the beneft 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. </p