Zhan Peida, Liao Manqian, Bian Yufang
Collaborative Innovation Center of Assessment toward Basic Education Quality, Beijing Normal University, Beijing, China.
Measurement, Statistics and Evaluation, Department of Human Development and Quantitative Methodology, University of Maryland, College Park, MD, United States.
Front Psychol. 2018 Apr 25;9:607. doi: 10.3389/fpsyg.2018.00607. eCollection 2018.
In joint models for item response times (RTs) and response accuracy (RA), local item dependence is composed of local RA dependence and local RT dependence. The two components are usually caused by the same common stimulus and emerge as pairs. Thus, the violation of local item independence in the joint models is called paired local item dependence. To address the issue of paired local item dependence while applying the joint cognitive diagnosis models (CDMs), this study proposed a joint testlet cognitive diagnosis modeling approach. The proposed approach is an extension of Zhan et al. (2017) and it incorporates two types of random testlet effect parameters (one for RA and the other for RTs) to account for paired local item dependence. The model parameters were estimated using the full Bayesian Markov chain Monte Carlo (MCMC) method. The 2015 PISA computer-based mathematics data were analyzed to demonstrate the application of the proposed model. Further, a brief simulation study was conducted to demonstrate the acceptable parameter recovery and the consequence of ignoring paired local item dependence.
在用于项目反应时间（RTs）和反应准确性（RA）的联合模型中，局部项目依赖性由局部RA依赖性和局部RT依赖性组成。这两个组成部分通常由相同的共同刺激引起，并成对出现。因此，联合模型中局部项目独立性的违反被称为成对局部项目依赖性。为了在应用联合认知诊断模型（CDMs）时解决成对局部项目依赖性问题，本研究提出了一种联合测试单元认知诊断建模方法。所提出的方法是Zhan等人（2017年）方法的扩展，它纳入了两种类型的随机测试单元效应参数（一种用于RA，另一种用于RTs）来解释成对局部项目依赖性。使用全贝叶斯马尔可夫链蒙特卡罗（MCMC）方法估计模型参数。对2015年国际学生评估项目（PISA）基于计算机的数学数据进行了分析，以证明所提出模型的应用。此外，进行了一项简短的模拟研究，以证明可接受的参数恢复以及忽略成对局部项目依赖性的后果。
