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政大機構典藏 > 商學院 > 統計學系 > 期刊論文 > Item 140.119/72222

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題名:	Online Calibration Via Variable Length Computerized Adaptive Testing
作者:	張源俊 Chang, Yuan-chin I.;Lu, Hung-Yi
貢獻者:	統計系
日期:	2009-07
上傳時間:	2014-12-23 15:18:51 (UTC+8)
摘要:	Item calibration is an essential issue in modern item response theory based psychological or educational testing. Due to the popularity of computerized adaptive testing, methods to efficiently calibrate new items have become more important than that in the time when paper and pencil test administration is the norm. There are many calibration processes being proposed and discussed from both theoretical and practical perspectives. Among them, the online calibration may be one of the most cost effective processes. In this paper, under a variable length computerized adaptive testing scenario, we integrate the methods of adaptive design, sequential estimation, and measurement error models to solve online item calibration problems. The proposed sequential estimate of item parameters is shown to be strongly consistent and asymptotically normally distributed with a prechosen accuracy. Numerical results show that the proposed method is very promising in terms of both estimation accuracy and efficiency. The results of using calibrated items to estimate the latent trait levels are also reported.
關聯:	Psychometrika,75(1), 140-157
資料類型:	article
顯示於類別:	[統計學系] 期刊論文

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檔案	描述	大小	格式	瀏覽次數
140-157.pdf		508Kb	Adobe PDF2	1054	檢視/開啟

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