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A Simulation Study on Methods of Correcting for the Effects of Extreme Response Style

机译：极端反应风格影响校正方法的仿真研究

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摘要

The impact of response styles such as extreme response style (ERS) on trait estimation has long been a matter of concern to researchers and practitioners. This simulation study investigated three methods that have been proposed for the correction of trait estimates for ERS effects: (a) mixed Rasch models, (b) multidimensional item response models, and (c) regression residuals. The methods were compared with respect to their ability of recovering the true latent trait levels. Data were generated according to a unidimensional model with only one trait, a mixed Rasch model with two populations of ERS and non-ERS, and a two-dimensional model incorporating a trait and an ERS dimension. The data were analyzed using the same models as well as linear regression where the trait estimate is regressed on an ERS score and the resulting residual is considered the corrected trait estimate. Over all conditions, the two-dimensional model achieved the best trait recovery, though the difference to the unidimensional model was rather small. Mixed Rasch models were in general inferior to the other correction methods. When the trait and ERS showed no to weak correlations, ERS appeared to have a minor impact on trait estimation.
机译：长期以来，诸如极端反应风格（ERS）之类的反应风格对特质估计的影响一直是研究人员和实践者关注的问题。该模拟研究调查了为校正ERS效应的特征估计而提出的三种方法：（a）混合Rasch模型，（b）多维项目响应模型，以及（c）回归残差。比较了这些方法恢复其真实潜在特征水平的能力。数据是根据仅具有一个特征的一维模型，具有两个ERS和非ERS种群的混合Rasch模型以及包含特征和ERS维的二维模型生成的。使用相同的模型以及线性回归分析数据，在线性回归中，使用ERS分数对特征估计进行回归，并将所得残差视为校正后的特征估计。在所有条件下，二维模型都实现了最佳的性状恢复，尽管与一维模型的差异很小。混合Rasch模型通常不如其他校正方法。当性状和ERS之间没有弱相关性时，ERS似乎对性状估计影响很小。

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