Phantom Effects in School Composition Research: Consequences of Failure to Control Biases Due to Measurement Error in Traditional Multilevel Models

Autor/inn/en	Televantou, Ioulia; Marsh, Herbert W.; Kyriakides, Leonidas; Nagengast, Benjamin; Fletcher, John; Malmberg, Lars-Erik
Titel	Phantom Effects in School Composition Research: Consequences of Failure to Control Biases Due to Measurement Error in Traditional Multilevel Models
Quelle	In: School Effectiveness and School Improvement, 26 (2015) 1, S.75-101 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0924-3453
DOI	10.1080/09243453.2013.871302
Schlagwörter	Hierarchical Linear Modeling; Statistical Bias; Error of Measurement; Educational Research; Structural Equation Models; Sampling; Elementary School Students; Academic Achievement; School Demography; School Effectiveness; Foreign Countries; Cyprus; United Kingdom (England) + Suchen Sie Ihr Suchwort? Messfehler; Bildungsforschung; Pädagogische Forschung; Schulleistung; Schulbesuchsrate; Schuleffizienz; Ausland; Zypern
Abstract	The main objective of this study was to quantify the impact of failing to account for measurement error on school compositional effects. Multilevel structural equation models were incorporated to control for measurement error and/or sampling error. Study 1, a large sample of English primary students in Years 1 and 4, revealed a significantly negative compositional effect associated with school-average achievement that became more negative after controlling for measurement error. Study 2, a large study of Cypriot primary students in Year 4, showed a small, positive but statistically significant compositional effect that became non-significant after controlling for measurement error. Our findings have important methodological, substantive, and theoretical implications for on-going debates on the school compositional effects on students' outcomes, because nearly all previous research has been based on traditional approaches to multilevel models, which are positively biased, due to the failure to control for measurement error. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01