Beyond the Mean Estimate: A Quantile Regression Analysis of Inequalities in Educational Outcomes Using INVALSI Survey Data

Autor/inn/en	Costanzo, Antonella; Desimoni, Marta
Titel	Beyond the Mean Estimate: A Quantile Regression Analysis of Inequalities in Educational Outcomes Using INVALSI Survey Data
Quelle	In: Large-scale Assessments in Education, 5 (2017), Artikel 14 (25 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2196-0739
DOI	10.1186/s40536-017-0048-4
Schlagwörter	Achievement Tests; Measurement; Regression (Statistics); Correlation; Predictor Variables; Elementary School Students; Scores; Mathematics Achievement; Reading Achievement; Student Characteristics; Geographic Regions; Gender Differences; Immigrants; Performance; Foreign Countries; Questionnaires; Statistical Analysis; Grade 2; Grade 5; Italy + Suchen Sie Ihr Suchwort? Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Messverfahren; Regression; Regressionsanalyse; Korrelation; Prädiktor; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Leseleistung; Geschlechterkonflikt; Immigrant; Immigrantin; Immigranten; Ausland; Fragebogen; Statistische Analyse; School year 02; 2. Schuljahr; Schuljahr 02; School year 05; 5. Schuljahr; Schuljahr 05; Italien
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. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01