Robust Measurement via A Fused Latent and Graphical Item Response Theory Model
Robust Measurement via A Fused Latent and Graphical Item Response Theory Model
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DOI：
10.1007/s11336-018-9610-4
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发表时间：
2018-09-01
期刊：
影响因子：
3
通讯作者：
Ying, Zhiliang
中科院分区：
文献类型：
--
作者：
Chen, Yunxiao;Li, Xiaoou;Ying, Zhiliang
项目反应理论（IRT）在心理测量和教育测量中起着重要的作用。与经典的测试理论不同，IRT模型聚合了项目水平的信息，产生了更准确的测量。大多数IRT模型假设局部独立性，这一假设在实践中不太可能得到满足，特别是当项目数量很大时。在本文的文献和模拟研究的结果表明，错误指定的本地独立性假设可能会导致不准确的测量和差异项目功能。为了提供更强大的测量，我们提出了一个集成的方法，通过添加一个图形组件的多维IRT模型，可以抵消未知的局部依赖的影响。新的模型包含一个验证性的潜在变量组件，它的措施有针对性的潜在性状，和一个图形组件，它捕获的本地依赖。提出了一种有效的近似算法，用于局部依赖的参数估计和结构学习。这种方法可以大大提高测量，没有本地依赖结构的先验信息。该模型既可用于测量一维潜在特质，也可用于测量多维潜在特质。
Item response theory (IRT) plays an important role in psychological and educational measurement. Unlike the classical testing theory, IRT models aggregate the item level information, yielding more accurate measurements. Most IRT models assume local independence, an assumption not likely to be satisfied in practice, especially when the number of items is large. Results in the literature and simulation studies in this paper reveal that misspecifying the local independence assumption may result in inaccurate measurements and differential item functioning. To provide more robust measurements, we propose an integrated approach by adding a graphical component to a multidimensional IRT model that can offset the effect of unknown local dependence. The new model contains a confirmatory latent variable component, which measures the targeted latent traits, and a graphical component, which captures the local dependence. An efficient proximal algorithm is proposed for the parameter estimation and structure learning of the local dependence. This approach can substantially improve the measurement, given no prior information on the local dependence structure. The model can be applied to measure both a unidimensional latent trait and multidimensional latent traits.