Large Sample Confidence Intervals for Item Response Theory Reliability Coefficients

Autor/inn/en	Andersson, Björn; Xin, Tao
Titel	Large Sample Confidence Intervals for Item Response Theory Reliability Coefficients
Quelle	In: Educational and Psychological Measurement, 78 (2018) 1, S.32-45 (14 Seiten)Infoseite zur Zeitschrift PDF als Volltext Verfügbarkeit
Sprache	englisch
Dokumenttyp	gedruckt; online; Zeitschriftenaufsatz
ISSN	0013-1644
DOI	10.1177/0013164417713570
Schlagwörter	Item Response Theory; Test Reliability; Test Items; Scores; Computation; Statistical Analysis; Equations (Mathematics); Simulation; Maximum Likelihood Statistics; Monte Carlo Methods + Suchen Sie Ihr Suchwort? Item-Response-Theorie; Testreliabilität; Test content; Testaufgabe; Statistische Analyse; Equations; Mathematics; Gleichungslehre; Simulation program; Simulationsprogramm; Monte-Carlo-Methode
Abstract	In applications of item response theory (IRT), an estimate of the reliability of the ability estimates or sum scores is often reported. However, analytical expressions for the standard errors of the estimators of the reliability coefficients are not available in the literature and therefore the variability associated with the estimated reliability is typically not reported. In this study, the asymptotic variances of the IRT marginal and test reliability coefficient estimators are derived for dichotomous and polytomous IRT models assuming an underlying asymptotically normally distributed item parameter estimator. The results are used to construct confidence intervals for the reliability coefficients. Simulations are presented which show that the confidence intervals for the test reliability coefficient have good coverage properties in finite samples under a variety of settings with the generalized partial credit model and the three-parameter logistic model. Meanwhile, it is shown that the estimator of the marginal reliability coefficient has finite sample bias resulting in confidence intervals that do not attain the nominal level for small sample sizes but that the bias tends to zero as the sample size increases. (As Provided).
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Erfasst von	ERIC (Education Resources Information Center), Washington, DC
Update	2020/1/01