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

Marta Desimoni,Antonella Costanzo

doi:10.1186/s40536-017-0048-4

Abstract

The 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.

Highlights

Learning outcomes are considered positive indicators towards future economic social and cultural opportunities of a number of countries (Woessmann 2004)
Standard errors in parenthesis computed with non-clustered bootstrap estimation
The information about whether the estimated quantile regression (QR) effects of predictors are significantly different across the conditional distribution of performances can be gathered from the equivalence test results reported in the last column

Summary

Introduction

Learning outcomes are considered positive indicators towards future economic social and cultural opportunities of a number of countries (Woessmann 2004). Over the last decades, studies facing inequality issues in educational outcomes using cognitive achievement tests and variables from large-scale assessment data have increased. 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) carried out by the National Institute for the Evaluation of Education System (INVALSI). Gender differences and the impact of students’ socioeconomic conditions on learning achievement have been largely explored by international comparative studies, such as those carried out by the International Association of the Evaluation of Educational Achievement (IEA), the Organization for the Economic Cooperation and Development (OECD), and national large-scale assessments, e.g. the National Assessment of Educational Progress (NAEP). Feingold (1995) pointed out that analyzing gender disparities by examining differences at the population mean is certainly useful but can lead to misleading conclusions when distribution of performance differs between males and females. Halpern et al (2007) in their review on gender differences suggest that the magnitude of gender gap in educational achievement might depend on which portion of the ability distribution is investigated

Objectives

Methods

Results

Conclusion