Using the General Diagnostic Model to Measure Learning and Change in a Longitudinal Large-Scale Assessment. Research Report. ETS RR-09-28

Autor/inn/en	von Davier, Matthias; Xu, Xueli; Carstensen, Claus H.
Institution	Educational Testing Service
Titel	Using the General Diagnostic Model to Measure Learning and Change in a Longitudinal Large-Scale Assessment. Research Report. ETS RR-09-28
Quelle	(2009), (26 Seiten) PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
Schlagwörter	Models; Item Response Theory; Longitudinal Studies; Measurement; Comparative Analysis; Learning; Change; Statistical Distributions; Goodness of Fit; Data Analysis; Mathematics Tests; Foreign Countries; Germany; Program for International Student Assessment + Suchen Sie Ihr Suchwort? Analogiemodell; Item-Response-Theorie; Longitudinal study; Longitudinal method; Longitudinal methods; Längsschnittuntersuchung; Messverfahren; Lernen; Wandel; Wahrscheinlichkeitsverteilung; Auswertung; Ausland; Deutschland
Abstract	A general diagnostic model was used to specify and compare two multidimensional item-response-theory (MIRT) models for longitudinal data: (a) a model that handles repeated measurements as multiple, correlated variables over time (Andersen, 1985) and (b) a model that assumes one common variable over time and additional orthogonal variables that quantify the change (Embretson, 1991). Using MIRT-model ability distributions that we allowed to vary across subpopulations defined by type of school, we also compared (a) a model with a single two-dimensional ability distribution to (b) extensions of the Andersen and Embretson approaches, assuming multiple populations. In addition, we specified a hierarchical-mixture distribution variant of the (Andersen and Embretson) MIRT models and compared it to all four of the above alternatives. These four types of models are growth-mixture models that allow for variation of the mixing proportions across clusters in a hierarchically organized sample. To illustrate the models presented in this paper, we applied the models to the PISA-I-Plus data for assessing learning and change across multiple subpopulations. The results indicate that (a) the Embretson-type model with multiple-group assumptions fits the data better than the other models investigated, and (b) the higher performing group shows larger improvement at Time Point 2 than the lower performing group. (Contains 8 tables and 1 figure.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10