Using Structural Equation Modeling to Examine Group Differences in Assessment Booklet Designs with Sparse Data

Autor/inn/en	Willse, John T.; Goodman, Joshua T.; Allen, Nancy; Klaric, John
Titel	Using Structural Equation Modeling to Examine Group Differences in Assessment Booklet Designs with Sparse Data
Quelle	In: Applied Measurement in Education, 21 (2008) 3, S.253-272 (20 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0895-7347
Schlagwörter	Structural Equation Models; Simulation; Item Response Theory; Factor Analysis; Effect Size; Sample Size; International Adult Literacy Survey + Suchen Sie Ihr Suchwort? Simulation program; Simulationsprogramm; Item-Response-Theorie; Faktorenanalyse
Abstract	The current research demonstrates the effectiveness of using structural equation modeling (SEM) for the investigation of subgroup differences with sparse data designs where not every student takes every item. Simulations were conducted that reflected missing data structures like those encountered in large survey assessment programs (e.g., National Assessment of Educational Progress). A maximum likelihood method of estimation was implemented that allowed all data to be used without performing any imputation. A multiple indicators multiple causes (MIMIC) model was used to examine group differences. There was no detriment to the estimation of the MIMIC model parameters under sparse data design conditions when compared to the design without missing data. The overall size of samples had more influence on the variability of estimates than did the data design. (Contains 3 figures, 1 footnote and 8 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10