A Model for Teacher Effects from Longitudinal Data without Assuming Vertical Scaling

Autor/inn/en	Mariano, Louis T.; McCaffrey, Daniel F.; Lockwood, J. R.
Titel	A Model for Teacher Effects from Longitudinal Data without Assuming Vertical Scaling
Quelle	In: Journal of Educational and Behavioral Statistics, 35 (2010) 3, S.253-279 (27 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1076-9986
DOI	10.3102/1076998609346967
Schlagwörter	Persistence; Academic Achievement; Data Analysis; Teacher Influence; Longitudinal Studies; Computation; Mathematics Achievement; Scaling; Achievement Tests; Scores; Mathematics Skills; Models; Bayesian Statistics; Elementary Schools + Suchen Sie Ihr Suchwort? Ausdauer; Schulleistung; Auswertung; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Scale construction; Skalenkonstruktion; Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Mathematics ability; Analogiemodell; Elementary school; Grundschule; Volksschule
Abstract	There is an increasing interest in using longitudinal measures of student achievement to estimate individual teacher effects. Current multivariate models assume each teacher has a single effect on student outcomes that persists undiminished to all future test administrations (complete persistence [CP]) or can diminish with time but remains perfectly correlated (variable persistence [VP]). However, when state assessments do not use a vertical scale or the evolution of the mix of topics present across a sequence of vertically aligned assessments changes as students advance in school, these assumptions of persistence may not be consistent with the achievement data. We develop the "generalized persistence" (GP) model, a Bayesian multivariate model for estimating teacher effects that accommodates longitudinal data that are not vertically scaled by allowing less than perfect correlation of a teacher's effects across test administrations. We illustrate the model using mathematics assessment data. (Contains 3 tables, 3 figures and 6 notes.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10