On the Estimation of Hierarchical Latent Linear Models for Large Scale Assessments. Research Report. ETS RR-06-37

Autor/inn/en	Deping, Li; Oranje, Andreas
Titel	On the Estimation of Hierarchical Latent Linear Models for Large Scale Assessments. Research Report. ETS RR-06-37
Quelle	In: ETS Research Report Series, (2006), (39 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2330-8516
Schlagwörter	Hierarchical Linear Modeling; Computation; Measurement; Regression (Statistics); Mathematics; Error of Measurement; Comparative Analysis; National Competency Tests; Markov Processes; Monte Carlo Methods; Maximum Likelihood Statistics; National Assessment of Educational Progress + Suchen Sie Ihr Suchwort? Messverfahren; Regression; Regressionsanalyse; Mathematik; Messfehler; Markowscher Prozess; Monte-Carlo-Methode
Abstract	A hierarchical latent regression model is suggested to estimate nested and nonnested relationships in complex samples such as found in the National Assessment of Educational Progress (NAEP). The proposed model aims at improving both parameters and variance estimates via a two-level hierarchical linear model. This model falls naturally within the set of models used in most large scale surveys, in that all of them are special cases of the hierarchical latent regression model. The model parameter estimates are obtained via the expectation-maximization (EM) algorithm. An example with NAEP data is presented and results of parameter estimation and standard errors are compared with results from operational procedures of NAEP. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01