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Andersson, Björn; Xin, Tao – Educational and Psychological Measurement, 2018
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…
Descriptors: Item Response Theory, Test Reliability, Test Items, Scores
Mahmud, Jumailiyah; Sutikno, Muzayanah; Naga, Dali S. – Educational Research and Reviews, 2016
The aim of this study is to determine variance difference between maximum likelihood and expected A posteriori estimation methods viewed from number of test items of aptitude test. The variance presents an accuracy generated by both maximum likelihood and Bayes estimation methods. The test consists of three subtests, each with 40 multiple-choice…
Descriptors: Maximum Likelihood Statistics, Computation, Item Response Theory, Test Items
Wang, Zhen; Yao, Lihua – ETS Research Report Series, 2013
The current study used simulated data to investigate the properties of a newly proposed method (Yao's rater model) for modeling rater severity and its distribution under different conditions. Our study examined the effects of rater severity, distributions of rater severity, the difference between item response theory (IRT) models with rater effect…
Descriptors: Test Format, Test Items, Responses, Computation
Galindo-Garre, Francisca; Vermunt, Jeroen K. – Psychometrika, 2004
This paper presents a row-column (RC) association model in which the estimated row and column scores are forced to be in agreement with a priori specified ordering. Two efficient algorithms for finding the order-restricted maximum likelihood (ML) estimates are proposed and their reliability under different degrees of association is investigated by…
Descriptors: Mathematics, Test Reliability, Computation, Testing
Enders, Craig K. – Educational and Psychological Measurement, 2004
A method for incorporating maximum likelihood (ML) estimation into reliability analyses with item-level missing data is outlined. An ML estimate of the covariance matrix is first obtained using the expectation maximization (EM) algorithm, and coefficient alpha is subsequently computed using standard formulae. A simulation study demonstrated that…
Descriptors: Intervals, Simulation, Test Reliability, Computation

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