Comparing Multiple-Group Multinomial Log-Linear Models for Multidimensional Skill Distributions in the General Diagnostic Model. Research Report. ETS RR-08-35

Autor/inn/en	Xu, Xueli; von Davier, Matthias
Titel	Comparing Multiple-Group Multinomial Log-Linear Models for Multidimensional Skill Distributions in the General Diagnostic Model. Research Report. ETS RR-08-35
Quelle	In: ETS Research Report Series, (2008), (18 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	2330-8516
Schlagwörter	Comparative Analysis; Models; Computation; National Competency Tests; Least Squares Statistics; Grade 4; Grade 8; Reading Tests; National Assessment of Educational Progress + Suchen Sie Ihr Suchwort? Analogiemodell; School year 04; 4. Schuljahr; Schuljahr 04; School year 08; 8. Schuljahr; Schuljahr 08; Lesetest
Abstract	The general diagnostic model (GDM) utilizes located latent classes for modeling a multidimensional proficiency variable. In this paper, the GDM is extended by employing a log-linear model for multiple populations that assumes constraints on parameters across multiple groups. This constrained model is compared to log-linear models that assume separate sets of parameters to fit the distribution of latent variables in each group of a multiple-group model. Estimation of these constrained log-linear models using iterative weighted least squares (IWLS) methods is outlined and an application to NAEP data exemplifies the differences between constrained and unconstrained models in the presence of larger numbers of group-specific proficiency distributions. The use of log-linear models for the latent skill space distributions using constraints across populations allows for efficient computations in models that include many proficiency distributions. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01