Measurement Error in Multilevel Models of School and Classroom Environments: Implications for Reliability, Precision, and Prediction. CRESST Report 828

Autor/in	Schweig, Jonathan
Institution	National Center for Research on Evaluation, Standards, and Student Testing
Titel	Measurement Error in Multilevel Models of School and Classroom Environments: Implications for Reliability, Precision, and Prediction. CRESST Report 828
Quelle	(2013), (44 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Zusatzinformation	Weitere Informationen
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Error of Measurement; Hierarchical Linear Modeling; Educational Environment; Classroom Environment; Generalizability Theory; Comparative Analysis; Surveys; Reliability; Elementary Secondary Education; California; North Carolina + Suchen Sie Ihr Suchwort? Messfehler; Lernumgebung; Pädagogische Umwelt; Schulumwelt; Klassenklima; Unterrichtsklima; Survey; Umfrage; Befragung; Reliabilität; Kalifornien
Abstract	Measuring school and classroom environments has become central in a nation-wide effort to develop comprehensive programs that measure teacher quality and teacher effectiveness. Formulating successful programs necessitates accurate and reliable methods for measuring these environmental variables. This paper uses a generalizability theory framework to compare and contrast four widely used approaches for accounting for measurement error in school and classroom level variables. Then, this paper uses two empirical examples to demonstrate how each of these approaches lead to different conclusions about measurement precision, and influences the conclusions about relationships between the environmental variables and policy-relevant outcomes. Additionally, this paper shows how one widely used model may misrepresent the structure of the data in many survey administration scenarios. The following are appended: (1) A: provides detail on the models used to estimate variance components and syntax for estimating these models in MPlus and in the nlme package in R; (2) B: provides syntax (R and MPlus) for estimating contextual effects models for Designs A, B, and C; (3) C: provides detail on the Expected Mean Squares (EMS) basis for the variance components in Designs B, C, and E; and (4) D: provides detail on the equivalence of the group-mean reliabilities for Design B and Design E. (As Provided).
Anmerkungen	National Center for Research on Evaluation, Standards, and Student Testing (CRESST). 300 Charles E Young Drive N, GSE&IS Building 3rd Floor, Mailbox 951522, Los Angeles, CA 90095-1522. Tel: 310-206-1532; Fax: 310-825-3883; Web site: http://www.cresst.org
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01