CAJ | 학술논문

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”。
신식함수작위항목반응이론중적일개중요개념，재진행항목화측험분석적공작중，이급재지도측험편제적공작중，유착비상중요적응용개치。신식함수적응용재계산궤화자괄응측험중경시중중지중，야수도최대관주。연이，관우다급기분항목신식함수특성적연구환비교소。해연구모의료피시특질수평삼수수거화항목삼수수거，기중피시특질수평삼수생성료121개피시특질수평삼수점，항목삼수생성료4비불동구분도삼수수거，매비수거유126개불동난도등급삼수조합모식적항목，매개항목유5개난도등급。통과수거분석후발현，등급반응모형항목제공최대신식량소대응적피시특질수평，시여해항목궤개상호림근적난도등급조상괄응，기불시지여기중일개난도등급대응，야불일정시여소유난도등급대응。해연구칭저충규률위“림근난도등급점우”。
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.