ISSN 0439-755X
CN 11-1911/B
主办:中国心理学会
   中国科学院心理研究所
出版:科学出版社

心理学报 ›› 2014, Vol. 46 ›› Issue (8): 1208-1222.doi: 10.3724/SP.J.1041.2014.01218

• 论文 • 上一篇    

多维题组效应Rasch模型

詹沛达;王文中;王立君;李晓敏   

  1. (1浙江师范大学心理系, 金华 321004) (2香港教育学院评估研究中心, 香港)
  • 收稿日期:2013-08-10 出版日期:2014-08-25 发布日期:2014-08-25
  • 通讯作者: 王立君
  • 基金资助:

    浙江师范大学人文社科重点研究项目(SKZD201009)资助。

The Multidimensional Testlet-Effect Rasch Model

ZHAN Peida;Wen-Chung WANG;WANG Lijun;LI Xiaomin   

  1. (1 Department of Psychology, Zhejiang Normal University, Jinhua 321004, China) (2 Assessment Research Centre, The HongKong Institute of Education, HongKong, China)
  • Received:2013-08-10 Online:2014-08-25 Published:2014-08-25
  • Contact: WANG Liju

摘要:

首先, 本文诠释了“题组”的本质即一个存在共同刺激的项目集合。并基于此, 将题组效应划分为项目内单维题组效应和项目内多维题组效应。其次, 本文基于Rasch模型开发了二级评分和多级评分的多维题组效应Rasch模型, 以期较好地处理项目内多维题组效应。最后, 模拟研究结果显示新模型有效合理, 与Rasch题组模型、分部评分模型对比研究后表明:(1)测验存在项目内多维题组效应时, 仅把明显的捆绑式题组效应进行分离而忽略其他潜在的题组效应, 仍会导致参数的偏差估计甚或高估测验信度; (2)新模型更具普适性, 即便当被试作答数据不存在题组效应或只存在项目内单维题组效应, 采用新模型进行测验分析也能得到较好的参数估计结果。

关键词: 题组反应模型, 多维项目反应模型, 项目内多维题组效应, 多维题组效应模型, Rasch模型

Abstract:

Testlet design has been widely adopted in educational and psychological assessment. A testlet is a cluster of items that share a common stimulus (e.g., a reading comprehension passage or a figure), and the possible local dependence among items within a testlet is called testlet-effect. Various models have been developed to take into account such testlet effect. Examples included the Rasch testlet model, two-parameter logistic Bayesian testlet model, and higher-order testlet model. However, these existing models all assume that an item is affected by only one single testlet effect. Therefore, they are essentially unidimensional testlet-effect models. In practice, multiple testlet effects may simultaneously affect item responses in a testlet. For example, in addition to common stimulus, items can be grouped according to their domains, knowledge units, or item format, such that multiple testlet effects are involved. In essence, an item measures multiple latent traits, in addition to the target latent trait(s) that the test was designed to measure. Existing unidimensional testlet-effect models become inapplicable when multiple testlet effects are involved. To account for multiple testlet effect, in this study we develop the so-called (within-item) multidimensional testlet-effect Rasch model. The parameters can be estimated with marginal maximum likelihood estimation methods or Bayesian methods with Markov chain Monte Carlo (MCMC) algorithms. In this study, a popular computer program for Rasch models, ConQuest, was used. A series of simulations were conducted to evaluate parameter recovery of the new model, consequences of model misspecification, and the effectiveness of model-data fit statistics. Results show that the parameters of the new model can be recovered fairly well; and ignoring the multiple testlet effects resulted in a biased estimation of item parameters, and an overestimation of test reliability. Additionally, it did little harm on parameter estimation to fit a more complicated model (i.e., the multidimensional testlet-effect Rasch model) to data with a simple structure. In conclusion, the new model is feasible and flexible.

Key words: testlet response models, multidimensional item response models, multidimensional testlet-effect models, Rasch models