ncku-talk2016-01-07

NCKU Math Colloquium / RCTS Seminar

DATE 2016-01-07　15:10-16:00

PLACE 數學館3174教室

SPEAKER 張育瑋博士（密西根大學統計系）

TITLE A Speeded Item Response Model: Leave the Harder till Later

ABSTRACT Abstract: Item response theory models are statistical models commonly used in analyzing data from large-scale testings or questionnaires. In this talk, we will first give a brief introduction of item response models so that everyone could have basic understandings. Next, the proposed speeded item response model will be introduced. We consider the situation where examinees may retain the harder items to a later test period in a time limit test. With such a strategy, examinees may not finish answering some of the harder items within the allocated time. In the proposed model, we try to describe such a mechanism by incorporating a speeded-effect term into the two-parameter logistic item response model. A Bayesian estimation procedure of the current model using Markov chain Monte Carlo is presented, and its performance over the two-parameter logistic item response model in a speeded test is demonstrated through simulations. The methodology is applied to physics examination data of the Department Required Test for college entrance in Taiwan for illustration.

NCKU Math Colloquium / RCTS Seminar

DATE	2016-01-07　15:10-16:00

PLACE	數學館3174教室

SPEAKER	張育瑋博士（密西根大學統計系）

TITLE	A Speeded Item Response Model: Leave the Harder till Later

ABSTRACT	Abstract: Item response theory models are statistical models commonly used in analyzing data from large-scale testings or questionnaires. In this talk, we will first give a brief introduction of item response models so that everyone could have basic understandings. Next, the proposed speeded item response model will be introduced. We consider the situation where examinees may retain the harder items to a later test period in a time limit test. With such a strategy, examinees may not finish answering some of the harder items within the allocated time. In the proposed model, we try to describe such a mechanism by incorporating a speeded-effect term into the two-parameter logistic item response model. A Bayesian estimation procedure of the current model using Markov chain Monte Carlo is presented, and its performance over the two-parameter logistic item response model in a speeded test is demonstrated through simulations. The methodology is applied to physics examination data of the Department Required Test for college entrance in Taiwan for illustration.