A Dominance Variant Under the Multi-Unidimensional Pairwise-Preference Framework
A Dominance Variant Under the Multi-Unidimensional Pairwise-Preference Framework
复制标题
多维配对偏好框架下的显性变体
DOI：
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发表时间：
2016
影响因子：
1.2
通讯作者：
V. Ponsoda
中科院分区：
文献类型：
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作者：
D. Morillo;I. Leenen;F. J. Abad;P. Hontangas;J. de la Torre;V. Ponsoda
强迫选择问卷被提出作为一种方式来控制与传统问卷形式(例如，Likert-type量表)相关的一些回答偏差。虽然经典的评分方法存在自相矛盾的问题，但项目反应理论(IRT)方法被认为能够准确地解释这些工具的潜在特质结构。在这篇文章中，作者提出了多维配对偏好两参数Logistic(MUPP-2PL)模型，该模型是Stark，Chernyshenko和Drasgow的MUP框架的一个变体，假设符合优势模型。他们还介绍了一种估计模型参数的马尔可夫链蒙特卡罗(MCMC)方法。作者给出了模拟研究的结果，结果表明在所有研究条件下都有适当的回收效果。将新提出的模型与Brown和Maydeu的瑟斯顿IRT模型进行比较，我们得出的结论是，这两个模型在理论上非常相似，并且MUPP-2PL的贝叶斯估计过程可能会提供稍微更好的潜在空间相关性恢复和对潜在性状估计误差的更可靠的评估。模型在实际数据集上的应用表明，这两种估计过程是收敛的。然而，也有证据表明，MCMC在项目参数和潜在特质相关方面可能具有优势。
Forced-choice questionnaires have been proposed as a way to control some response biases associated with traditional questionnaire formats (e.g., Likert-type scales). Whereas classical scoring methods have issues of ipsativity, item response theory (IRT) methods have been claimed to accurately account for the latent trait structure of these instruments. In this article, the authors propose the multi-unidimensional pairwise preference two-parameter logistic (MUPP-2PL) model, a variant within Stark, Chernyshenko, and Drasgow’s MUPP framework for items that are assumed to fit a dominance model. They also introduce a Markov Chain Monte Carlo (MCMC) procedure for estimating the model’s parameters. The authors present the results of a simulation study, which shows appropriate goodness of recovery in all studied conditions. A comparison of the newly proposed model with a Brown and Maydeu’s Thurstonian IRT model led us to the conclusion that both models are theoretically very similar and that the Bayesian estimation procedure of the MUPP-2PL may provide a slightly better recovery of the latent space correlations and a more reliable assessment of the latent trait estimation errors. An application of the model to a real data set shows convergence between the two estimation procedures. However, there is also evidence that the MCMC may be advantageous regarding the item parameters and the latent trait correlations.