Estimation of Teacher Value Added in the Presence of Missing Data

Autor/inn/en	Gao, Niu; Semykina, Anastasia
Titel	Estimation of Teacher Value Added in the Presence of Missing Data
Quelle	In: Journal of Research on Educational Effectiveness, 14 (2021) 3, S.676-695 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1934-5747
DOI	10.1080/19345747.2021.1894518
Schlagwörter	Value Added Models; Elementary School Teachers; Computation; Scores; Academic Achievement; Data; Bias; Productivity; Teacher Effectiveness; Grade 4; Grade 5; North Carolina + Suchen Sie Ihr Suchwort? Elementary school; Teacher; Teachers; Grundschule; Volksschule; Lehrer; Lehrerin; Lehrende; Schulleistung; Daten; Produktivität; Effectiveness of teaching; Instructional effectiveness; Lehrerleistung; Unterrichtserfolg; School year 04; 4. Schuljahr; Schuljahr 04; School year 05; 5. Schuljahr; Schuljahr 05
Abstract	Inappropriate treatment of missing data may introduce bias into the value-added estimation. We consider a commonly used value-added model (VAM), which includes the past student test score as a covariate. We formulate a joint model of student achievement and missing data, in which the probability of observing a test score depends on observing the test score in the previous grade. We develop a correction procedure that removes the bias in VAM estimates due to data missing not at random and use it to estimate the value-added of elementary school teachers in North Carolina. We find that errors in VAM and missing data equations are uncorrelated, and corrected estimates of teacher productivity are very similar to their ordinary least squares counterparts. Thus, missingness does not cause bias in our data. We also perform simulations to identify scenarios where a correction may be necessary. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2024/1/01