A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects

Autor/inn/en	Karl, Andrew T.; Yang, Yan; Lohr, Sharon L.
Titel	A Correlated Random Effects Model for Nonignorable Missing Data in Value-Added Assessment of Teacher Effects
Quelle	In: Journal of Educational and Behavioral Statistics, 38 (2013) 6, S.577-603 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998613494819
Schlagwörter	Evaluation Methods; Teacher Effectiveness; Academic Achievement; Achievement Gains; Responses; Correlation; Calculus; College Mathematics; Elementary Schools; Hierarchical Linear Modeling; Data Analysis; Scores; Statistical Analysis; Equations (Mathematics); Statistical Distributions; Effect Size; Models; Teacher Influence + Suchen Sie Ihr Suchwort? Effectiveness of teaching; Instructional effectiveness; Lehrerleistung; Unterrichtserfolg; Schulleistung; Achievement gain; Leistungssteigerung; Korrelation; Analysis; Differenzialrechnung; Infinitesimalrechnung; Integralrechnung; Elementary school; Grundschule; Volksschule; Auswertung; Statistische Analyse; Equations; Mathematics; Gleichungslehre; Wahrscheinlichkeitsverteilung; Analogiemodell
Abstract	Value-added models have been widely used to assess the contributions of individual teachers and schools to students' academic growth based on longitudinal student achievement outcomes. There is concern, however, that ignoring the presence of missing values, which are common in longitudinal studies, can bias teachers' value-added scores. In this article, a flexible correlated random effects model is developed that jointly models the student responses and the student missing data indicators. Both the student responses and the missing data mechanism depend on latent teacher effects as well as latent student effects, and the correlation between the sets of random effects adjusts teachers' value-added scores for informative missing data. The methods are illustrated with data from calculus classes at a large public university and with data from an elementary school district. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10