适用于多维迫选测验的IRT计分模型

IRT-based scoring methods for multidimensional forced choice tests

迫选(forced-choice,FC)测验由于可以控制传统李克特方法带来的反应偏差,被广泛应用于非认知测验中,而迫选测验的传统计分方式会产生自模式数据,这种数据由于不适合于个体间的比较,一直备受批评。近年来,多种迫选IRT模型的发展使研究者能够从迫选测验中获得接近常模性的数据,再次引起了研究者与实践人员对迫选IRT模型的兴趣。首先,依据所采纳的决策模型和题目反应模型对6种较为主流的迫选IRT模型进行分类和介绍。然后,从模型构建思路、参数估计方法两个角度对各模型进行比较与总结。其次,从参数不变性检验、计算机化自适应测验(computerized adaptive testing, CAT)和效度研究3个应用研究方面进行述评。最后提出未来研究可以在模型拓展、参数不变性检验、迫选CAT测验和效度研究4个方向深入。

Forced-choice(FC) test is widely used in non-cognitive tests because it can control the response bias caused by the traditional Likert method, while traditional scoring of forced-choice test produces ipsative data that has been criticized for being unsuitable for inter-individual comparisons. In recent years,the development of multiple forced-choice IRT models that allow researchers to obtain normative information from forced-choice test has re-ignited the interest of researchers and practitioners in forced-choice IRT models. First, the six prevailing forced-choice IRT models are classified and introduced according to the adopted decision models and item response models. Then, the models are compared and summarized from two perspectives: model construction ideology and parameter estimation methods. Next, it reviews the applied research of the model in three aspects: parameter invariance testing, computerized adaptive testing(CAT) and validity study. Finally, it is suggested that future research can move forward in four directions: model expansion, parameter invariance testing, forced-choice CAT, and validity research.