Computerized Adaptive Testing

One article about MAT

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Multidimensional adaptive testing using the weighted likelihood estimation.
by Tseng, Fen-Lan, Ph.D., University of Pittsburgh, 2000, 156 pages; AAT 9998631
Advisor: Hsu, Tse-chi
School: University of Pittsburgh
School Location: United States -- Pennsylvania
Index terms(keywords): Multidimensional adaptive testing, Weighted likelihood estimation, Item response theory, Estimation
Source: DAI-A 61/12, p. 4746, Jun 2001
Source type: DISSERTATION
Subjects: Educational evaluation, Educational psychology, Psychological tests
Publication Number: AAT 9998631
ISBN: 0493077332
Document URL: http://proquest.umi.com/pqdweb?did=727870381&sid=18&Fmt=2&clientId=43390&RQT=309&VName=PQD
ProQuest document ID: 727870381
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Abstract (Document Summary)

This study extended Warm's (1989) weighted likelihood estimation (WLE) to a multidimensional computerized adaptive test (MCAT) setting. WLE was compared with the maximum likelihood estimation (MLE), expected a posteriori (EAP), and maximum a posteriori (MAP) using a three-dimensional 3PL IRT model under a variety of computerized adaptive testing conditions. The dependent variables included bias, standard error of ability estimates (SE), square root of mean square error (RMSE), and test information. The independent variables were ability estimation methods, intercorrelation levels between dimensions, multidimensional structures, and ability combinations. Simulation results were presented in terms of descriptive statistics, such as figures and tables. In addition, inferential procedures were used to analyze bias by conceptualizing this Monte Carlo study as a statistical sampling experiment.

The results of this study indicate that WLE and the other three estimation methods yield significantly more accurate ability estimates under an approximate simple test structure with one dominant dimension and several secondary dimensions. All four estimation methods, especially WLE, yield very large SEs when a three equally dominant multidimensional structure was employed. Consistent with previous findings based on unidimensional IRT model, MLE and WLE are less biased in the extreme of the ability scale; MLE and WLE yield larger SEs than the Bayesian methods; test information-based SEs underestimate actual SEs for both MLE and WLE in MCAT situations, especially at shorter test lengths; WLE reduced the bias of MLE under the approximate simple structure; test information-based SEs underestimates the actual SEs of MLE and WLE estimators in the MCAT conditions, similar to the findings of Warm (1989) in the unidimensional case.

The results from the MCAT simulations did show some advantages of WLE in reducing the bias of MLE under the approximate simple structure with a fixed test length of 50 items, which was consistent with the previous research findings based on different unidimensional models. It is clear from the current results that all four methods perform very poorly when the multidimensional structures with multiple dominant factors were employed. More research efforts are urged to investigate systematically how different multidimensional structures affect the accuracy and reliability of ability estimation. Based on the simulated results in this study, there is no significant effect found on the ability estimation from the intercorrelation between dimensions.

posted by Shunkai Fu @ 6:11 PM