Grain Size and Parameter Recovery with TIMSS and the General Diagnostic Model

Autor/inn/en	Skaggs, Gary; Wilkins, Jesse L. M.; Hein, Serge F.
Titel	Grain Size and Parameter Recovery with TIMSS and the General Diagnostic Model
Quelle	In: International Journal of Testing, 16 (2016) 4, S.310-330 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1530-5058
DOI	10.1080/15305058.2016.1145683
Schlagwörter	Achievement Tests; Foreign Countries; Elementary Secondary Education; Science Achievement; Mathematics Tests; Mathematics Achievement; Science Tests; International Assessment; Sample Size; Item Response Theory; Mathematical Models; Bayesian Statistics; Goodness of Fit; Grade 8; Statistical Distributions; Matrices; Statistical Bias; Sampling; Statistical Inference; Trends in International Mathematics and Science Study + Suchen Sie Ihr Suchwort? Achievement test; Achievement; Testing; Test; Tests; Leistungsbeurteilung; Leistungsüberprüfung; Leistung; Testdurchführung; Testen; Ausland; Mathmatics sikills; Mathmatics achievement; Mathematical ability; Mathematische Kompetenz; Item-Response-Theorie; Mathematical model; Mathematisches Modell; School year 08; 8. Schuljahr; Schuljahr 08; Wahrscheinlichkeitsverteilung; Matrizenrechnung; Inferential statistics; Schließende Statistik
Abstract	The purpose of this study was to explore the degree of grain size of the attributes and the sample sizes that can support accurate parameter recovery with the General Diagnostic Model (GDM) for a large-scale international assessment. In this resampling study, bootstrap samples were obtained from the 2003 Grade 8 TIMSS in Mathematics at varying sample sizes from 500 to 4000 and grain sizes of the attributes from a unidimensional model to one with ten attributes. The results showed that the eight-attribute model was the one most consistently identified as best fitting. Parameter estimation for more than ten attributes and samples less than 500 failed. Furthermore, the precision of item parameter recovery decreased as the number of attributes measured by an item increased and sample size decreased. On the other hand, the distributions of latent classes were relatively stable across all models and sample sizes. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01