How Do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added

Autor/inn/en	Dieterle, Steven; Guarino, Cassandra M.; Reckase, Mark D.; Wooldridge, Jeffrey M.
Titel	How Do Principals Assign Students to Teachers? Finding Evidence in Administrative Data and the Implications for Value Added
Quelle	In: Journal of Policy Analysis and Management, 34 (2015) 1, S.32-58 (27 Seiten) PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0276-8739
DOI	10.1002/pam.21781
Schlagwörter	Principals; Student Placement; Teacher Effectiveness; Bias; Teacher Evaluation; Scores; Classification; Computation; Elementary Secondary Education + Suchen Sie Ihr Suchwort? Principal; Schulleiter; Schülerpraktikum; Effectiveness of teaching; Instructional effectiveness; Lehrerleistung; Unterrichtserfolg; Teacher appraisal; Lehrerbeurteilung; Classification system; Klassifikation; Klassifikationssystem
Abstract	The federal government's Race to the Top competition has promoted the adoption of test-based value-added measures (VAMs) of performance as a component of teacher evaluations throughout many states, but the validity of these measures has been controversial among researchers and widely contested by teachers' unions. A key concern is the extent to which nonrandom sorting of students to teachers may bias the results and lead to a misclassification of teachers as high or low performing. In light of potential for bias, it is important to assess the extent to which evidence of sorting can be found in the large administrative data sets used for VAM estimation. Using a large longitudinal data set from an anonymous state, we find evidence that a nontrivial amount of sorting exists--particularly sorting based on prior test scores--and that the extent of sorting varies considerably across schools, a fact obscured by the types of aggregate sorting indices developed in prior research. We also find that VAM estimation is sensitive to the presence of nonrandom sorting. There is less agreement across estimation approaches regarding a particular teacher's rank in the distribution of estimated effectiveness when schools engage in sorting. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2022/4/11