拉什模型
潜在类模型
潜变量
混合模型
多向拉希模型
潜变量模型
项目反应理论
统计
地方独立性
比例(比率)
班级(哲学)
度量(数据仓库)
心理学
计量经济学
数学
计算机科学
人工智能
心理测量学
数据挖掘
量子力学
物理
作者
Ming-Chi Tseng,Wen Chung Wang
标识
DOI:10.3389/fpsyg.2021.564976
摘要
Mixture item response theory (IRT) models include a mixture of latent subpopulations such that there are qualitative differences between subgroups but within each subpopulation the measure model based on a continuous latent variable holds. Under this modeling framework, students can be characterized by both their location on a continuous latent variable and by their latent class membership according to Students’ responses. It is important to identify anchor items for constructing a common scale between latent classes beforehand under the mixture IRT framework. Then, all model parameters across latent classes can be estimated on the common scale. In the study, we proposed Q-matrix anchored mixture Rasch model (QAMRM), including a Q-matrix and the traditional mixture Rasch model. The Q-matrix in QAMRM can use class invariant items to place all model parameter estimates from different latent classes on a common scale regardless of the ability distribution. A simulation study was conducted, and it was found that the estimated parameters of the QAMRM recovered fairly well. A real dataset from the Certificate of Proficiency in English was analyzed with the QAMRM, LCDM. It was found the QAMRM outperformed the LCDM in terms of model fit indices.
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