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本文引用的文献

1
Comparing Two Algorithms for Calibrating the Restricted Non-Compensatory Multidimensional IRT Model.比较两种用于校准受限非补偿多维IRT模型的算法。
Appl Psychol Meas. 2015 Mar;39(2):119-134. doi: 10.1177/0146621614545983. Epub 2014 Aug 19.
2
Generalized latent variable models with non-linear effects.具有非线性效应的广义潜在变量模型
Br J Math Stat Psychol. 2008 Nov;61(Pt 2):415-38. doi: 10.1348/000711007X213963. Epub 2007 May 24.
3
Multicomponent latent trait models for complex tasks.用于复杂任务的多成分潜在特质模型。
J Appl Meas. 2006;7(3):335-50.

部分补偿多维项目反应理论模型:两种替代模型形式。

Partially Compensatory Multidimensional Item Response Theory Models: Two Alternate Model Forms.

作者信息

DeMars Christine E

机构信息

James Madison University, Harrisonburg, VA, USA.

出版信息

Educ Psychol Meas. 2016 Apr;76(2):231-257. doi: 10.1177/0013164415589595. Epub 2015 Jun 9.

DOI:10.1177/0013164415589595
PMID:29795864
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC5965584/
Abstract

Partially compensatory models may capture the cognitive skills needed to answer test items more realistically than compensatory models, but estimating the model parameters may be a challenge. Data were simulated to follow two different partially compensatory models, a model with an interaction term and a product model. The model parameters were then estimated for both models and for the compensatory model. Either the model used to simulate the data or the compensatory model generally had the best fit, as indexed by information criteria. Interfactor correlations were estimated well by both the correct model and the compensatory model. The predicted response probabilities were most accurate from the model used to simulate the data. Regarding item parameters, root mean square errors seemed reasonable for the interaction model but were quite large for some items for the product model. Thetas were recovered similarly by all models, regardless of the model used to simulate the data.

摘要

部分补偿模型可能比补偿模型更能真实地捕捉回答测试项目所需的认知技能,但估计模型参数可能是一项挑战。数据被模拟为遵循两种不同的部分补偿模型,一种带有交互项的模型和一种乘积模型。然后对这两种模型以及补偿模型的模型参数进行估计。根据信息准则,用于模拟数据的模型或补偿模型通常拟合效果最佳。正确模型和补偿模型对因子间相关性的估计都很好。预测响应概率从用于模拟数据的模型来看最为准确。关于项目参数,交互模型的均方根误差似乎合理,但乘积模型的一些项目的均方根误差相当大。无论用于模拟数据的是哪种模型,所有模型对θ的恢复情况相似。