Kang Inhan, Jeon Minjeong
Department of Psychology, College of Liberal Arts, Yonsei University, Seoul 03722, Republic of Korea.
Social Research Methodology, Department of Education, School of Education and Information Studies, University of California, Los Angeles, CA 90095, USA.
J Intell. 2024 Mar 28;12(4):38. doi: 10.3390/jintelligence12040038.
This article aims to provide an overview of the potential advantages and utilities of the recently proposed Latent Space Item Response Model (LSIRM) in the context of intelligence studies. The LSIRM integrates the traditional Rasch IRT model for psychometric data with the latent space model for network data. The model has person-wise latent abilities and item difficulty parameters, capturing the main person and item effects, akin to the Rasch model. However, it additionally assumes that persons and items can be mapped onto the same metric space called a latent space and distances between persons and items represent further decreases in response accuracy uncaptured by the main model parameters. In this way, the model can account for conditional dependence or interactions between persons and items unexplained by the Rasch model. With two empirical datasets, we illustrate that (1) the latent space can provide information on respondents and items that cannot be captured by the Rasch model, (2) the LSIRM can quantify and visualize potential between-person variations in item difficulty, (3) latent dimensions/clusters of persons and items can be detected or extracted based on their latent positions on the map, and (4) personalized feedback can be generated from person-item distances. We conclude with discussions related to the latent space modeling integrated with other psychometric models and potential future directions.
本文旨在概述最近提出的潜在空间项目反应模型（LSIRM）在智力研究背景下的潜在优势和用途。LSIRM将用于心理测量数据的传统拉施IRT模型与用于网络数据的潜在空间模型相结合。该模型具有个体层面的潜在能力和项目难度参数，捕捉主要的个体和项目效应，类似于拉施模型。然而，它还假设个体和项目可以映射到一个称为潜在空间的相同度量空间上，个体与项目之间的距离表示主要模型参数未捕捉到的反应准确性的进一步降低。通过这种方式，该模型可以解释拉施模型无法解释的个体与项目之间的条件依赖性或相互作用。通过两个实证数据集，我们表明：（1）潜在空间可以提供拉施模型无法捕捉的关于受访者和项目的信息；（2）LSIRM可以量化并可视化项目难度在个体之间的潜在差异；（3）可以根据个体和项目在地图上的潜在位置检测或提取它们的潜在维度/聚类；（4）可以根据个体与项目的距离生成个性化反馈。我们最后讨论了与整合其他心理测量模型的潜在空间建模相关的问题以及潜在的未来方向。
