Bayesian Analysis of a Quantile Multilevel Item Response Theory Model.
Bayesian Analysis of a Quantile Multilevel Item Response Theory Model.
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DOI：
10.3389/fpsyg.2020.607731
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发表时间：
2020
影响因子：
3.8
通讯作者：
Zhang X
中科院分区：
文献类型：
--
作者：
Zhu H;Gao W;Zhang X
多级项目反应理论（MLIRT）模型广泛应用于教育和心理学研究。此类建模有两个或多个层次，包括作为测量部分的项目响应理论模型和作为结构部分的线性回归模型，目的是研究解释变量和潜在变量之间的关系。然而，线性回归结构模型关注的是解释变量和潜变量之间的关系，这只是从平均趋势的角度来看。当我们需要探索响应分布中不同位置的变量之间的关系时，分位数回归更合适。为此，在MLIRT框架下引入了分位数回归型结构模型，称为分位数MLIRT（Q-MLIRT）模型。使用吉布斯采样算法估计所提出模型的参数，并通过仿真研究与原始（即线性回归型）MLIRT 模型进行比较。结果表明，Q-MLIRT模型的参数在不同分位数下都能得到很好的恢复。最后，对 PISA 2018 的数据子集进行分析，以说明所提出模型的应用。
Multilevel item response theory (MLIRT) models are used widely in educational and psychological research. This type of modeling has two or more levels, including an item response theory model as the measurement part and a linear-regression model as the structural part, the aim being to investigate the relation between explanatory variables and latent variables. However, the linear-regression structural model focuses on the relation between explanatory variables and latent variables, which is only from the perspective of the average tendency. When we need to explore the relationship between variables at various locations along the response distribution, quantile regression is more appropriate. To this end, a quantile-regression-type structural model named as the quantile MLIRT (Q-MLIRT) model is introduced under the MLIRT framework. The parameters of the proposed model are estimated using the Gibbs sampling algorithm, and comparison with the original (i.e., linear-regression-type) MLIRT model is conducted via a simulation study. The results show that the parameters of the Q-MLIRT model could be recovered well under different quantiles. Finally, a subset of data from PISA 2018 is analyzed to illustrate the application of the proposed model.
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DOI：
10.1016/j.ijedudev.2015.07.005
发表时间：
2015-09-01
影响因子：
2.5
作者：
Giambona, Francesca;Porcu, Mariano
通讯作者：
Porcu, Mariano
影响因子：
3
作者：
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通讯作者：
AITKIN, M
DOI：
10.2307/1165149
发表时间：
1992-09-01
期刊：
JOURNAL OF EDUCATIONAL STATISTICS
影响因子：
--
作者：
ALBERT, JH
通讯作者：
ALBERT, JH
影响因子：
1.2
作者：
Finch, Holmes
通讯作者：
Finch, Holmes
影响因子：
3
作者：
Fox, JP;Glas, CAW
通讯作者：
Glas, CAW