The Effects of Q Matrix Misspecification on Item and Model Fit

Autor/inn/en	Sünbül, Seçil Ömür; Asire, Semih
Titel	The Effects of Q Matrix Misspecification on Item and Model Fit
Quelle	In: International Journal of Progressive Education, 14 (2018) 5, S.144-154 (11 Seiten)Infoseite zur Zeitschrift PDF als Volltext kostenfreie Datei Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	1554-5210
Schlagwörter	Clinical Diagnosis; Cognitive Ability; Problem Solving; Models; Q Methodology; Measurement Techniques; Test Items; Goodness of Fit; Matrices + Suchen Sie Ihr Suchwort? Denkfähigkeit; Problemlösen; Analogiemodell; Messtechnik; Test content; Testaufgabe; Matrizenrechnung
Abstract	In this study it was aimed to evaluate the effects of various factors such as sample sizes, percentage of misfit items in the test and item quality (item discrimination) on item and model fit in case of misspecification of Q matrix. Data were generated in accordance with DINA model. Q matrix was specified for 4 attributes and 15 items. While data were generated, sample sizes as 1000, 2000, 4000, s and g parameters as low and high discrimination index were manipulated. Three different misspecified Q matrix (overspecified, underspecified and mixed) was developed considering the percentage of misfit items (20% and 40%). In the study, S-X2 was used as item fit statistics. Furthermore absolute (abs(fcor), max(X2)) and relative (-2 log-likelihood, Akaike's information criterion (AIC) and Bayesian information criterion (BIC)) model fit statistics were used. Investigating the results obtained from this simulation study, it was concluded that S-X2 was sufficient to detect misfit items. When the percentage of misfit items in the test was much or Q matrix was both under-specified and overspecified, the correct detection of both abs(fcor) and max(X2) statistics was approximately 1 or 1. In addition, the correct detection rates of both statistics was high under other conditions, too. AIC and BIC were successful to detect model misfit in the cases where the Q matrix under-specified, whereas, they were failed detect model misfit for other cases. It can be said that the performance of BIC was mostly better than other relative model fit statistics to detect model misfit. (As Provided).
Anmerkungen	International Association of Educators. Egitim Fakultesi Dekanligi, Canakkale Onsekiz Mart University, Canakkale 17100, Turkey. e-mail: secretary@inased.org; Web site: http://www.inased.org/ijpe.htm
Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01