Beyond the mean estimate: a quantile regression analysis of inequalities in educational outcomes using INVALSI survey data

Abstract

BackgroundLearning outcomes are considered positive indicators towards future economic social and cultural opportunities of a number of countries (Woessmann 2004). Therefore, over the last decades, studies facing inequality issues in educational outcomes using cognitive achievement tests and variables from large-scale assessment data have increased. From a methodological point of view, the traditional approach used to explore the relationship between explicative variables and students' performance is based on average effects within a classical linear regression setup (OECD 2012). Undoubtedly, estimates on average will yield straightforward interpretations but will represent only a part of the information concerning the complex and nuanced nature of the relation between predictors and conditional performance distribution. Essentially, the main concern for policy purposes might be not only to assess if the relevant variables carry an impact or not, but AbstractThe number of studies addressing issues of inequality in educational outcomes using cognitive achievement tests and variables from large-scale assessment data has increased. Here the value of using a quantile regression approach is compared with a classical regression analysis approach to study the relationships between educational outcomes and likely predictor variables. Italian primary school data from INVALSI large-scale assessments were analyzed using both quantile and standard regression approaches. Mathematics and reading scores were regressed on students' characteristics and geographical variables selected for their theoretical and policy relevance. The results demonstrated that, in Italy, the role of gender and immigrant status varied across the entire conditional distribution of students' performance. Analogous results emerged pertaining to the difference in students' performance across Italian geographic areas. These findings suggest that quantile regression analysis is a useful tool to explore the determinants and mechanisms of inequality in educational outcomes. A proper interpretation of quantile estimates may enable teachers to identify effective learning activities and help policymakers to develop tailored programs that increase equity in education. Costanzo and Desimoni Large-scale Assess Educ (2017) -scale Assess Educ (2017) 5:14 also to investigate if and how they are associated with greater or lower variation in educational outcomes. METHODOLOGYPage 2 of 25 Costanzo and Desimoni LargeConsistently, some studies have been recently enhanced by using models which extend the viewpoint on the whole conditional distribution of performance representing the different levels of students' attainment (Fryer and Levitt 2010;Davino et al. 2013), i.e. the quantile regression model (Koenker and Basset 1978). In the present study, the differential impact of variables related to inequalities in educational outcomes will be assessed through the quantile regression (QR) approach using data from the Italian Annual Survey on Educational Achievement (SNV)...

Economic Pressure and Self-Efficacy as Independent Predictors of Academic Grades and Career Indecision for Southern European Middle School Students: A Confirming Study

“…This finding for Isles contrasts with generally significant relationships between SES and PISA scores and, importantly, with our results even if it referred only for science subject. With regarding math and reading skills, the socio-economic background was found significantly affecting results of INVALSI (National Institute for the Evaluation of Education Systems) test in primary school and, additionally, with significantly regional significant differences among Northern and Southern Italy (Costanzo and Desimoni, 2017). If social origins explain most of inequality in academic outcomes for Italian students (Pensiero et al, 2019), particularly in Southern Italy, regional and disparities should be explained not only by environmental factors but also in terms of efficacy of teaching and management (Argentin et al, 2017).…”

Trends in gender gaps: using 20 years of evidence from TIMSS

“…Recently, some research used quantile regression analysis (Davino et al 2014) as another way of differentiating inequalities along ability distributions. For example, Costanzo and Desimoni (2017) found varying gender inequalities for the different quantiles of the mathematics and reading scores distributions, using data from an Italian study of second and fifths grade students.…”

Spatial variations of school-level determinants of reading achievement in Italy

“…From a policy perspective, it is important to understand when the gap first shows up and identify the geographical critical areas. The results of the quantile regression analysis on mathematics and reading performance in Italy performed by Costanzo and Desimoni (2017) suggest the necessity analyse the role of the gender in a more complex framework than the traditional regression model. Another variable widely associated with inequalities in educational outcomes is the immigrant status (Azzolini et al 2012;Schnell and Azzolini 2015).…”

“…Hippe et al (2018) exploited the regional distribution of skills in Italy and Spain, studying the extent of the regional inequalities in PISA 2015 using descriptive statistics and estimating several regression models. Costanzo and Desimoni (2017) explored inequalities in education using a quantile regression approach applied to primary school data from INVALSI largescale assessments; mathematics and reading scores were regressed on students' characteristics and geographical variables.…”