A Bayesian Hierarchical Selection Model for Academic Growth with Missing Data

Autor/in	Allen, Jeff
Titel	A Bayesian Hierarchical Selection Model for Academic Growth with Missing Data
Quelle	In: Applied Measurement in Education, 30 (2017) 2, S.147-162 (16 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
DOI	10.1080/08957347.2017.1283318
Schlagwörter	Achievement Gains; Academic Achievement; Growth Models; Scores; Bayesian Statistics; Regression (Statistics); Data; Longitudinal Studies; Comparative Analysis; School Effectiveness; Grade 9; Grade 10; Grade 11; High School Students; Hierarchical Linear Modeling; Goodness of Fit + Suchen Sie Ihr Suchwort? Achievement gain; Leistungssteigerung; Schulleistung; Regression; Regressionsanalyse; Daten; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Schuleffizienz; School year 09; 9. Schuljahr; Schuljahr 09; School year 11; 11. Schuljahr; Schuljahr 11; High school; High schools; Student; Students; Oberschule; Schüler; Schülerin; Studentin
Abstract	Using a sample of schools testing annually in grades 9-11 with a vertically linked series of assessments, a latent growth curve model is used to model test scores with student intercepts and slopes nested within school. Missed assessments can occur because of student mobility, student dropout, absenteeism, and other reasons. Missing data indicators are modeled using logistic regression, with grade 9 and potentially unobserved growth scores used as covariates. Under a hierarchical selection model, estimates of school effects on academic growth and missingness are obtained. The results from the selection model are compared to a model that ignores the missing data process. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01