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The effects of model specification e...

Jiao, Hong.

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The effects of model specification error in Item Response Theory-based computerized classification test using sequential probability ratio test.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	The effects of model specification error in Item Response Theory-based computerized classification test using sequential probability ratio test./
作者:	Jiao, Hong.
面頁冊數:	152 p.
附註:	Source: Dissertation Abstracts International, Volume: 64-02, Section: A, page: 0478.
Contained By:	Dissertation Abstracts International64-02A.
標題:	Education, Tests and Measurements. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3081443

The effects of model specification error in Item Response Theory-based computerized classification test using sequential probability ratio test.
Jiao, Hong.

The effects of model specification error in Item Response Theory-based computerized classification test using sequential probability ratio test. - 152 p.

Source: Dissertation Abstracts International, Volume: 64-02, Section: A, page: 0478.

Thesis (Ph.D.)--The Florida State University, 2003.

This study investigated the effects of model specification error on classification accuracy, error rates, and average test length in Item Response Theory (IRT) based computerized classification test (CCT) using sequential probability ratio test (SPRT) in making binary decisions from examinees' dichotomous responses.Subjects--Topical Terms:

1017589
Education, Tests and Measurements.

The effects of model specification error in Item Response Theory-based computerized classification test using sequential probability ratio test.
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245 1 0 $a The effects of model specification error in Item Response Theory-based computerized classification test using sequential probability ratio test.
300 $a 152 p.
500 $a Source: Dissertation Abstracts International, Volume: 64-02, Section: A, page: 0478.
500 $a Major Professor: Akihito Kamata.
502 $a Thesis (Ph.D.)--The Florida State University, 2003.
520 $a This study investigated the effects of model specification error on classification accuracy, error rates, and average test length in Item Response Theory (IRT) based computerized classification test (CCT) using sequential probability ratio test (SPRT) in making binary decisions from examinees' dichotomous responses.
520 $a This study consisted of three sub-studies. In each sub-study, one of the three unidimensional dichotomous IRT models, the 1-parameter logistic (IPL), the 2-parameter logistic (2PL), and the 3-parameter logistic (3PL) model was set as the true model and the other two models were treated as the misfit models. Item pool composition, test length, and stratum depth were manipulated to simulate different test conditions. To ensure the validity of the study results, the true model based CCTs using the true and the recalibrated item parameters were compared first to study the effect of estimation error in item parameters in CCTs. Then, the true model and the misfit model based CCTs were compared to accomplish the research goal,
520 $a The results indicated that estimation error in item parameters did not affect classification results based on CCTs using SPRT. The effect of model specification error depended on the true model, the misfit model, and the item pool composition. When the IPL or the 2PL IRT model was the true model, the use of another IRT model had little impact on the CCT results. When the 3PL IRT model was the true model, the use of the 1PL model raised the false positive error rates. The influence of using the 2PL instead of the 3PL model depended on the item pool composition. When the item discrimination parameters varied greatly from uniformity of one, the use of the 2PL IRT model raised the false negative error rates to above the nominal level. In the simulated test conditions with test length and item exposure constraints, using a misfit model in CCTs most often affected the average test length. Its effects on error rates and classification accuracy were negligible. It was concluded that in CCTs using SPRT, IRT model selection and evaluation is indispensable
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790 $a 0071
791 $a Ph.D.
792 $a 2003
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