Local Dependence Diagnostics in IRT Modeling of Binary Data

Autor/inn/en	Liu, Yang; Maydeu-Olivares, Alberto
Titel	Local Dependence Diagnostics in IRT Modeling of Binary Data
Quelle	In: Educational and Psychological Measurement, 73 (2013) 2, S.254-274 (21 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164412453841
Schlagwörter	Item Response Theory; Statistical Analysis; Models; Goodness of Fit; Comparative Analysis; College Entrance Examinations; Error of Measurement; Law School Admission Test + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Statistische Analyse; Analogiemodell; Aufnahmeprüfung; Messfehler; Law school; Assessment; Admission criteria; Admission procedures; Rechtswissenschaft; Fachbereich; Eignungsprüfung; Eignungstest; Hochschulzulassung
Abstract	Local dependence (LD) for binary IRT models can be diagnosed using Chen and Thissen's bivariate X[superscript 2] statistic and the score test statistics proposed by Glas and Suarez-Falcon, and Liu and Thissen. Alternatively, LD can be assessed using general purpose statistics such as bivariate residuals or Maydeu-Olivares and Joe's M[subscript r] statistic. The authors introduce a new general statistic for assessing the source of model misfit, R[subscript 2], and compare its performance to the above statistics using a simulation study. Results suggest that the bivariate and trivariate X[superscript 2] statistics have unacceptable Type I error rates. As for the remaining statistics, if their computation involves the information matrix (bivariate residuals and score tests), they show good power; if not (M[subscript r] and R[subscript 2]), they lack power. Of course, the performance of the bivariate residuals and score tests depends on how the information matrix is approximated. (Contains 9 tables.) (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2017/4/10