项目反应理论等级反应模型项目信息量

IRT Information Function of Polytomously Scored Items under the Graded Response Model

信息函数作为项目反应理论中的一个重要概念,在进行项目和测验分析的工作中,以及在指导测验编制的工作中,有着非常重要的应用价值。信息函数的应用在计算机化自适应测验中更是重中之重,也受到最大关注。然而,关于多级记分项目信息函数特性的研究还比较少。该研究模拟了被试特质水平参数数据和项目参数数据,其中被试特质水平参数生成了121个被试特质水平参数点,项目参数生成了4批不同区分度参数数据,每批数据有126个不同难度等级参数组合模式的项目,每个项目有5个难度等级。通过数据分析后发现,等级反应模型项目提供最大信息量所对应的被试特质水平,是与该项目几个相互临近的难度等级组相适应,既不是只与其中一个难度等级对应,也不一定是与所有难度等级对应。该研究称这种规律为"临近难度等级占优"。

Computerized adaptive testing （CAT） is one of the ultimate areas in the field of item response theory （IRT）. Many high stake tests, such as GRE and TOEFL, have their CAT versions. Item selection strategy is the core content of CAT. And item information function （IIF） always is the important index of item selection. Although item information of dichotomously scored items has been extensively studied, item information of polytomously scored items receives much less attention. However, due to the advantages inbred in Computerized adaptive testing （CAT） with polytomously scored items, it gains more and more attention now. But the item selection strategies implemented under such situations are not systematically proved to be efficient. Many researchers use the degree of closeness between trait level and the average of item category parameters as the index of item selection strategy, or other strategies such as the degree of closeness between trait level and the median of item category parameters, etc. Up to now, seldom research had systematically concerned about the inherent relationship between the trait level and item category parameters under polytomously scored item types, and its effect on item information. The primary purpose of this research is to systematically investigate the relations of item information to item category parameters and subject trait levels. In this study, we simulated 121 trait values that distributed uniformly between the ranges of -3 to 3. Also, we simulated 504 sets of item parameters, with 4 sets of discrimination parameters which separately matched the 126 sets of difficulty parameters. Each item is graded in terms of 5 categories with differential degrees of difficulty. Based on the results of item information of simulated data, we fred that the trait value that correspondence to the maximum item information matches the difficulty parameter group with high-frequency item categories. We call this principal as ＂item category parameter priority rule＂. Such principle is very different from the previous item selection strategies under computerized adaptive testing situations. The results of this research will be very useful for the construction of computerized adaptive testing with polytomously scored items.