MAXIMUM MARGINAL LIKELIHOOD ESTIMATION FOR SEMIPARAMETRIC ITEM ANALYSIS
MAXIMUM MARGINAL LIKELIHOOD ESTIMATION FOR SEMIPARAMETRIC ITEM ANALYSIS
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DOI：
10.1007/bf02294480
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发表时间：
1991-09-01
期刊：
影响因子：
3
通讯作者：
WINSBERG, S
中科院分区：
文献类型：
--
作者：
RAMSAY, JO;WINSBERG, S
项目特征曲线（ICC）定义了能力和选择测试项目的特定选项的概率之间的关系，可以通过使用多项式回归样条来估计。 这些提供了一个更灵活的家庭的功能比三参数的逻辑家庭。 样条ICC的估计是通过最大化β先验分布上的积分能力所形成的边缘似然来描述的。 一些模拟结果比较这种方法与能力和项目参数的联合估计。
The item characteristic curve (ICC), defining the relation between ability and the probability of choosing a particular option for a test item, can be estimated by using polynomial regression splines. These provide a more flexible family of functions than is given by the three-parameter logistic family. The estimation of spline ICCs is described by maximizing the marginal likelihood formed by integrating ability over a beta prior distribution. Some simulation results compare this approach with the joint estimation of ability and item parameters.