基于双因子模型的测验总分和维度分的合成方法  

Reporting overall scores and domain scores of bi-factor models

作　　者：刘玥 刘红云 LIU Yue LIU Hongyun(School of Psychology, Beijing Normal University,Beijing 100875,China)

机构地区：[1]北京师范大学心理学部,北京100875

出　　处：《心理学报》2017年第9期1234-1246,共13页Acta Psychologica Sinica

基　　金：国家自然科学基金(31571152);北京市与中央在京高校共建项目(019-105812);未来教育高精尖创新中心;中央高校基本科研业务费专项资金资助

摘　　要：双因子模型可以同时包含一个全局因子和多个局部因子,在描述多维测验结构时有其独特优势,近些年应用越来越广泛。文章基于双因子模型,提出了4种合成总分和维度分的方法,分别是:原始分法,加和法,全局题目加权加和法和局部题目加权加和法,并采用模拟的方法,在样本量、测验长度、维度间相关变化的条件下考察了这些方法与传统多维IRT方法的表现。最后,通过实证研究对结果进行了验证。结果显示:(1)全局加权加和法和局部加权加和法,尤其是局部加权加和法合成的总分和维度分与真值最接近、信度最高。(2)在维度间相关较高,测验长度较长的条件下,局部加权加和法的结果较好,部分条件下甚至优于多维IRT法。(3)仅有局部加权加和法合成的维度分能够反应维度间真实的相关关系。In large-scale assessments, most of the tests have a multidimensional structure. There is an increasing interest in reporting overall scores and domain scores simultaneously. The domain scores complement the overall scores by providing finer grained diagnosis of examinees＇ strengths and weaknesses. However, due to the small number of items within each dimension, the lack of sufficiently high reliability is the primary impediment for generating and reporting domain scores. A number of methods have been developed recently to improve the reliability and optimality of the overall scores and domain scores. For overall scores, simply averaging or weighted averaging the scores from different content areas, using maximum information method to compute the weights of composite scores under the MIRT framework were some commonly-used procedures. There were also some subscoring methods in the CTT and IRT framework, such as Kelly＇s（1927） regressed score method, the MIRT method, and the higher order IRT method. Nowadays, the bi-factor model became more and more popular in education measurement. Reporting overall scores and domain scores based on it became an important topic. The purpose of this study was to investigate several methods to generate overall scores anddomain scores based on the bi-factor model, and to compare them with the MIRT method under different condition. Study 1 was a mixed measure design of simulation conditions（between-factors） and methods（withinfactor）. There were three between-factors：（1） 3 sample sizes（500,1000,2000）;（2） 3 test length（18 items, 36 items, 60 items）; and（3） 5 correlations between dimensions（0.0, 0.3, 0.5, 0.7, 0.9）. The methods for generating overall scores and domain scores were：（1） original scores from bi-factor model（Bifactor-M1）;（2） summed original scores from the bi-factor model（Bifactor-M2）;（3） weighted sum original scores from the bi-factor model based on all the items（Bifactor-M3）;（4） weighted sum original sco

关 键 词：双因子模型 多维IRT 总分 维度分 

分 类 号：B841[哲学宗教—基础心理学]