Significant Predictors of Mathematical Literacy for Top-Tiered Countries/Economies, Canada, and the United States on PISA 2012: Case for the Sparse Regression Model

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Autor/in	Brow, Mark V.
Titel	Significant Predictors of Mathematical Literacy for Top-Tiered Countries/Economies, Canada, and the United States on PISA 2012: Case for the Sparse Regression Model
Quelle	In: British Journal of Educational Psychology, 89 (2019) 4, S.726-749 (24 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	ORCID (Brow, Mark V.)
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0007-0998
DOI	10.1111/bjep.12254
Schlagwörter	Predictor Variables; Numeracy; Foreign Countries; Comparative Education; Achievement Tests; Secondary School Students; International Assessment; Mathematics Achievement; Self Efficacy; Truancy; Educational Quality; Difficulty Level; Student Attitudes; Canada; United States; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Prädiktor; Rechenkompetenz; Ausland; Vergleichende Erziehungswissenschaft; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Sekundarschüler; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Self-efficacy; Selbstwirksamkeit; Schulabsentismus; Schulschwänzen; Schulverweigerung; Quality of education; Bildungsqualität; Schwierigkeitsgrad; Schülerverhalten; Kanada; USA
Abstract	Background: National ranking from the triennial Programme of International Student Assessment (PISA) often serves as a barometer of national performance and human capital. Though excessive student- and school-level covariates (n > 700) may prove intractable for traditional least-squares estimate procedures, shrinkage methods may be more suitable for subset selection. Aims: With a focus on the United States, this paper proposes sparse regression for PISA 2012 to discover salient student- and school-level predictor variables for mathematical literacy achievement. Sample: The sparse regression analysis was conducted on 10 top-tiered OECD countries/economies, Canada, and the United States in mathematical literacy on the 2012 PISA. Two- and three-level hierarchical regression analyses were performed on Canadian and US students (N = 26,522) along with five of the ten top-tiered countries/economies (N = 58,385). Methods: Using the 'least absolute shrinkage and selection operator' (LASSO) technique, the study (1) identified salient predictor variables of mathematical literacy performance for the top-tiered countries/economies, Canada, and the United States and (2) used these salient variables to perform two- and three-level hierarchical regression on data from Canada and the United States along with five top-tiered countries/economies. Weights and replicates were used to account for complex sample design. A weighted, two-level confirmatory factor analysis was performed to identify latent constructs. Missing data were handled through multiple imputation. Results: Separate two-level hierarchical models accounted for 32-35% student-level and 58-70% school-level variance in Canada and the United States, respectively; three-level models accounted for 33% of level-one variance, 62-65% level-two variance, and 13-44% of level-three variance for the US/Canada and US/Canada/top-tiered students, respectively. Following top-tiered countries/economies, Canadian students had high levels of self-efficacy, were more likely to encounter advanced concepts in class, were less activity/small group-centred, and were more likely to consider truancy a learning hindrance. Factor analyses revealed a positive relation with rigour and class organization (teacher-centred) for top-tiered countries and Canada, though not for the United States. For all countries, there was a strong relation between rigour and self-beliefs. Conclusion: Compared to top performers, a less rigorous curriculum, coupled with class and school factors, may explain lag in US performance. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01