For test development in the setting of multidimensional item response theory, the exploratory and confirmatory approaches lie on two ends of a continuum in terms of the loading and residual structures. Inspired by the recent development of the Bayesian Lasso (least absolute shrinkage and selection operator), this research proposes a partially confirmatory approach to estimate both structures using Bayesian regression and a covariance Lasso within a unified framework. The Bayesian hierarchical formulation is implemented using Markov chain Monte Carlo estimation, and the shrinkage parameters are estimated simultaneously. The proposed approach with different model variants and constraints was found to be flexible in addressing loading selection and local dependence. Both simulated and real-life data were analyzed to evaluate the performance of the proposed model across different situations.

中文翻译：

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基于贝叶斯套索的多维项目反应理论的部分验证方法

对于多维项目响应理论中的测试开发，探索性和验证性方法位于加载和残余结构连续体的两端。受贝叶斯套索（最小绝对收缩和选择算子）最近发展的启发，本研究提出了一种部分验证方法，在统一框架内使用贝叶斯回归和协方差套索来估计两种结构。使用马尔可夫链蒙特卡罗估计实现贝叶斯分层公式，同时估计收缩参数。发现所提出的具有不同模型变体和约束的方法在解决加载选择和局部依赖性方面是灵活的。