Estimating stochastic survey response errors using the multitrait‐multierror model
Estimating stochastic survey response errors using the multitrait‐multierror model
复制标题
使用多特征多误差模型估计随机调查响应误差
DOI：
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发表时间：
2018
期刊：
影响因子：
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通讯作者：
Daniel L. Oberski
中科院分区：
文献类型：
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作者：
A. Cernat;Daniel L. Oberski
众所周知，调查同时包含不同类型的反应误差，包括默认、社会期望、共同方法方差和随机误差。然而，大多数用于估计和纠正此类错误的方法在实践中只考虑一次一个错误源。因此，对回答误差的估计是低效的，它们的相对重要性是未知的，并且可能无法发现最佳的问题格式。为了纠正这种情况，我们展示了如何使用最近引入的“多特征-多误差”（MTME）方法同时估计多种类型的误差。MTME将实验设计理论与潜变量建模相结合，同时估计不同误差类型的响应误差方差。这使研究人员能够评估哪些错误是最具影响力的，并有助于发现最佳的问题格式。我们将这种方法应用于来自英国的代表性数据，用于测量公众舆论研究中常用的对移民态度的六个调查项目。
Surveys are well known to contain response errors of different types, including acquiescence, social desirability, common method variance and random error simultaneously. Nevertheless, a single error source at a time is all that most methods developed to estimate and correct for such errors consider in practice. Consequently, estimation of response errors is inefficient, their relative importance is unknown and the optimal question format may not be discoverable. To remedy this situation, we demonstrate how multiple types of errors can be estimated concurrently with the recently introduced ‘multitrait‐multierror’ (MTME) approach. MTME combines the theory of design of experiments with latent variable modelling to estimate response error variances of different error types simultaneously. This allows researchers to evaluate which errors are most impactful, and aids in the discovery of optimal question formats. We apply this approach using representative data from the United Kingdom to six survey items measuring attitudes towards immigrants that are commonly used across public opinion studies.
影响因子：
1.8
作者：
Klein, Richard A.;Ratliff, Kate A.;Nosek, Brian A.
通讯作者：
Nosek, Brian A.