Accuracy and Variability of Item Parameter Estimates from Marginal Maximum a Posteriori Estimation and Bayesian Inference via Gibbs Samplers

Autor/in	Wu, Yi-Fang
Titel	Accuracy and Variability of Item Parameter Estimates from Marginal Maximum a Posteriori Estimation and Bayesian Inference via Gibbs Samplers
Quelle	(2015), (350 Seiten) PDF als Volltext Verfügbarkeit Ph.D. Dissertation, The University of Iowa
Sprache	englisch
Dokumenttyp	gedruckt; online; Monographie
ISBN	978-0-3552-4333-8
Schlagwörter	Hochschulschrift; Dissertation; Item Response Theory; Test Items; Accuracy; Computation; Markov Processes; Monte Carlo Methods; Sampling; Test Format; Test Length; Sample Size; Difficulty Level; Guessing (Tests); Comparative Analysis + Suchen Sie Ihr Suchwort? Thesis; Dissertations; Academic thesis; Item-Response-Theorie; Test content; Testaufgabe; Markowscher Prozess; Monte-Carlo-Methode; Testentwicklung; Schwierigkeitsgrad; Erraten
Abstract	Item response theory (IRT) uses a family of statistical models for estimating stable characteristics of items and examinees and defining how these characteristics interact in describing item and test performance. With a focus on the three-parameter logistic IRT (Birnbaum, 1968; Lord, 1980) model, the current study examines the accuracy and variability of the item parameter estimates from the marginal maximum a posteriori estimation via an expectation-maximization algorithm (MMAP/EM) and the Markov chain Monte Carlo Gibbs sampling (MCMC/GS) approach. In the study, the various factors which have an impact on the accuracy and variability of the item parameter estimates are discussed, and then further evaluated through a large scale simulation. The factors of interest include the composition and length of tests, the distribution of underlying latent traits, the size of samples, and the prior distributions of discrimination, difficulty, and pseudo-guessing parameters. The results of the two estimation methods are compared to determine the lower limit--in terms of test length, sample size, test characteristics, and prior distributions of item parameters--at which the methods can satisfactorily recover item parameters and efficiently function in reality. For practitioners, the results help to define limits on the appropriate use of the BILOG-MG (which implements MMAP/EM) and also, to assist in deciding the utility of OpenBUGS (which carries out MCMC/GS) for item parameter estimation in practice. [The dissertation citations contained here are published with the permission of ProQuest LLC. Further reproduction is prohibited without permission. Copies of dissertations may be obtained by Telephone (800) 1-800-521-0600. Web page: http://www.proquest.com/en-US/products/dissertations/individuals.shtml.] (As Provided).
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Update	2020/1/01