Student background determinants of reading achievement in Italy. A quantile regression analysis

Giambona, Francesca; Porcu, Mariano

doi:10.1016/j.ijedudev.2015.07.005

Cited by 33 publications

(29 citation statements)

References 53 publications

(49 reference statements)

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“…Also, the gender disparities in reading were not uniform across the range of reading scores, with larger differences at the extreme tails of the distribution in second grade and in the lower tail of the distribution in fifth grade. Overall, these results confirm that gender gaps vary across the ability distribution, as observed in previous studies exploring gender differences using a quantile regression approach (Penner and Paret 2008;Robinson and Lubiensky 2011;Giambona and Porcu 2015;Contini et al 2016) and in Baye and Monseur (2016) meta-analysis on databases from IEA and PISA international surveys. Findings on the fifth-graders confirm that the male advantage in mathematics is more substantial at the upper tail of the ability distribution (e.g.…”

Section: Discussionsupporting

confidence: 88%

“…Referring to the literature relative to correlates of reading and math achievements (Baye and Monseur 2016;Gursakal et al 2016;Giambona and Porcu 2015), as well as the information about students' achievement estimates two group of variables have been considered from the INVALSI dataset.…”

Section: Students' Characteristics and Geographical Variablesmentioning

confidence: 99%

“…Also, the evidence of disparities where males outperform females is concentrated among high achievers. As for reading achievement, by using data from OECD-PISA 2009 survey on 15 year-old Italian students, Giambona and Porcu (2015) found that the gender gap is more pronounced at the lower tail of the conditional distribution of performances.…”

Section: Introductionmentioning

confidence: 99%

“…Regarding the Italian context, the previously mentioned Giambona and Porcu (2015) analysis of OECD-PISA 2009 survey data on 15-year-old Italian students shed light on whether and how relevant predictors, i.e. individual and family background variables, the school program and the school geographic location are significantly associated with students' performance through the estimation of a quantile model exploring the effects of the covariates at different levels of reading achievement.…”

Section: Introductionmentioning

confidence: 99%

“…It is also worthy of notice that most of the existing studies following the QR approach refer to secondary school students' data (e.g. Gursakal et al 2016;Giambona and Porcu 2015). Instead, less is known about the differential impact of learning-related factors on children's performance in primary school, excepting some studies about gender gap in early grades (e.g.…”

Section: Introductionmentioning

confidence: 99%

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

Costanzo

Desimoni

2017

Large-scale Assess Educ

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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)...

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Section: Discussionsupporting

confidence: 88%

Section: Students' Characteristics and Geographical Variablesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

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

Costanzo

Desimoni

2017

Large-scale Assess Educ

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Gender Effect at the Beginning of Higher Education Careers in STEM Studies: Does Female Recover Better Than Male?

D’Agostino

Ghellini

Lombardi

2022

Methodology for Multilevel Modeling in Educational Research

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Studying the relationship between anxiety and school achievement: evidence from PISA data

2021

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Using the Programme for International Student Assessment (PISA) 2015 data for Italy, this paper offers a complete overview of the relationship between test anxiety and school performance by studying how anxiety affects the performance of students along the overall conditional distribution of mathematics, literature and science scores. We aim to indirectly measure whether higher goals increase test anxiety, starting from the hypothesis that high-skilled students generally set themselves high goals. We use an M-quantile regression approach that allows us to take into account the hierarchical structure and sampling weights of the PISA data. There is evidence of a negative and statistically significant relationship between test anxiety and school performance. The size of the estimated association is greater at the upper tail of the distribution of each score than at the lower tail. Therefore, our results suggest that high-performing students are more affected than low-performing students by emotional reactions to tests and school-work anxiety.

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Student background determinants of reading achievement in Italy. A quantile regression analysis

Cited by 33 publications

References 53 publications

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

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

Gender Effect at the Beginning of Higher Education Careers in STEM Studies: Does Female Recover Better Than Male?

Studying the relationship between anxiety and school achievement: evidence from PISA data

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