CAJ | 학술논문

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。
수선，본문전석료“제조”적본질즉일개존재공동자격적항목집합。병기우차，장제조효응화분위항목내단유제조효응화항목내다유제조효응。기차，본문기우Rasch모형개발료이급평분화다급평분적다유제조효응Rasch모형，이기교호지처리항목내다유제조효응。최후，모의연구결과현시신모형유효합리，여Rasch제조모형、분부평분모형대비연구후표명：（1）측험존재항목내다유제조효응시，부파명현적곤방식제조효응진행분리이홀략기타잠재적제조효응，잉회도치삼수적편차고계심혹고고측험신도；（2）신모형경구보괄성，즉편당피시작답수거불존재제조효응혹지존재항목내단유제조효응，채용신모형진행측험분석야능득도교호적삼수고계결과。
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 testier-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 testier-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.