Reference : Methodological Issues in Value-Added Modeling: An International Review from 26 Countries

	Document type :	Scientific journals : Article
	Discipline(s) :	Social & behavioral sciences, psychology : Education & instruction
	To cite this reference:	http://hdl.handle.net/10993/39893

Title :	Methodological Issues in Value-Added Modeling: An International Review from 26 Countries
Language :	English
Author, co-author :	Levy, Jessica [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Luxembourg Centre for Educational Testing (LUCET) >]
	Brunner, Martin [University of Potsdam]
	Keller, Ulrich [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Luxembourg Centre for Educational Testing (LUCET) >]
	Fischbach, Antoine [University of Luxembourg > Faculty of Language and Literature, Humanities, Arts and Education (FLSHASE) > Luxembourg Centre for Educational Testing (LUCET) >]
Publication date :	Aug-2019
Journal title :	Educational Assessment, Evaluation and Accountability
Publisher :	Springer
Volume :	31
Issue/season :	3
Pages :	257-287
Peer reviewed :	Yes
Audience :	International
ISSN :	1874-8597
e-ISSN :	1874-8600
City :	Norwell
Country :	Germany
Keywords :	[en] Value-added modeling ; Literature review ; Primary and secondary education ; Teacher effectiveness ; School effectiveness
Abstract :	[en] Value-added (VA) modeling can be used to quantify teacher and school effectiveness by estimating the effect of pedagogical actions on students’ achievement. It is gaining increasing importance in educational evaluation, teacher accountability, and high-stakes decisions. We analyzed 370 empirical studies on VA modeling, focusing on modeling and methodological issues to identify key factors for improvement. The studies stemmed from 26 countries (68% from the USA). Most studies applied linear regression or multilevel models. Most studies (i.e., 85%) included prior achievement as a covariate, but only 2% included noncognitive predictors of achievement (e.g., personality or affective student variables). Fifty-five percent of the studies did not apply statistical adjustments (e.g., shrinkage) to increase precision in effectiveness estimates, and 88% included no model diagnostics. We conclude that research on VA modeling can be significantly enhanced regarding the inclusion of covariates, model adjustment and diagnostics, and the clarity and transparency of reporting.
Permalink :	http://hdl.handle.net/10993/39893
DOI :	10.1007/s11092-019-09303-w
Other URL :	http://link.springer.com/article/10.1007/s11092-019-09303-w
FnR project :	FnR ; FNR10921377 > Adelheid Hu > CALIDIE > Capitalising on Linguistic Diversity in Education > 15/01/2017 > 14/07/2023 > 2016

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