Guo Xiaojun, Jiao Yuyue, Huang ZhengZheng, Liu TieChuan
School of Education Science, Gannan Normal University, Ganzhou, China.
School of Humanities, Hubei University of Chinese Medicine, Wuhan, China.
Front Psychol. 2022 Apr 11;13:763959. doi: 10.3389/fpsyg.2022.763959. eCollection 2022.
With the popularity of computer-based testing (CBT), it is easier to collect item response times (RTs) in psychological and educational assessments. RTs can provide an important source of information for respondents and tests. To make full use of RTs, the researchers have invested substantial effort in developing statistical models of RTs. Most of the proposed models posit a unidimensional latent speed to account for RTs in tests. In psychological and educational tests, many tests are multidimensional, either deliberately or inadvertently. There may be general effects in between-item multidimensional tests. However, currently there exists no RT model that considers the general effects to analyze between-item multidimensional test RT data. Also, there is no joint hierarchical model that integrates RT and response accuracy (RA) for evaluating the general effects of between-item multidimensional tests. Therefore, a bi-factor joint hierarchical model using between-item multidimensional test is proposed in this study. The simulation indicated that the Hamiltonian Monte Carlo (HMC) algorithm works well in parameter recovery. Meanwhile, the information criteria showed that the bi-factor hierarchical model (BFHM) is the best fit model. This means that it is necessary to take into consideration the general effects (general latent trait) and the multidimensionality of the RT in between-item multidimensional tests.
随着基于计算机的测试（CBT）的普及，在心理和教育评估中收集项目反应时间（RTs）变得更加容易。RTs可以为受访者和测试提供重要的信息来源。为了充分利用RTs，研究人员投入了大量精力来开发RTs的统计模型。大多数提出的模型假定一个单维潜在速度来解释测试中的RTs。在心理和教育测试中，许多测试无论是有意还是无意都是多维的。项目间多维测试可能存在一般效应。然而，目前不存在考虑一般效应来分析项目间多维测试RT数据的RT模型。此外，也没有用于评估项目间多维测试一般效应的整合RT和反应准确性（RA）的联合层次模型。因此，本研究提出了一种使用项目间多维测试的双因素联合层次模型。模拟表明，哈密顿蒙特卡罗（HMC）算法在参数恢复方面表现良好。同时，信息准则表明双因素层次模型（BFHM）是最佳拟合模型。这意味着在项目间多维测试中，有必要考虑一般效应（一般潜在特质）和RT的多维性。
