Wetzel Eunike, Böhnke Jan R, Rose Norman
University of Konstanz, Konstanz, Germany.
Eberhard Karls University Tübingen, Tübingen, Germany.
Educ Psychol Meas. 2016 Apr;76(2):304-324. doi: 10.1177/0013164415591848. Epub 2015 Jun 29.
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.
诸如极端反应风格（ERS）等反应风格对特质估计的影响长期以来一直是研究人员和从业者关注的问题。本模拟研究调查了三种已被提出用于校正ERS效应特质估计的方法：（a）混合Rasch模型，（b）多维项目反应模型，以及（c）回归残差。对这些方法在恢复真实潜在特质水平的能力方面进行了比较。数据是根据仅包含一个特质的单维模型、具有ERS和非ERS两个人群的混合Rasch模型以及包含一个特质和一个ERS维度的二维模型生成的。使用相同的模型以及线性回归对数据进行分析，其中特质估计值以ERS分数为自变量进行回归，所得残差被视为校正后的特质估计值。在所有条件下，二维模型实现了最佳的特质恢复，尽管与单维模型的差异相当小。混合Rasch模型总体上不如其他校正方法。当特质与ERS显示出无至弱的相关性时，ERS似乎对特质估计影响较小。
