论文标题
横向模型的偏置校正估计器与不专心的受访者
A Bias-Corrected Estimator for the Crosswise Model with Inattentive Respondents
论文作者
论文摘要
Crosswise模型是一种越来越受欢迎的调查技术，可从受访者就敏感问题引起坦率的答案。然而，最近的研究指出，在不专心的受访者存在下，敏感属性患病率的常规估计量偏向0.5。为了解决这个问题，我们使用一个具有敏感项目的敏感项目，提出了一个简单的基于设计的偏置校正。我们证明，我们可以轻松地估计并纠正不专心的受访者而不衡量个人水平的专注力而产生的偏见。我们还提供了几种有用的估算器扩展，包括对常规估计器的灵敏度分析，一种加权策略，用于多元回归的框架，其中潜在的敏感性状用作结果或预测因子，以及用于功率分析和参数选择的工具。我们的方法可以通过我们的开源软件CWISE轻松实现。
The crosswise model is an increasingly popular survey technique to elicit candid answers from respondents on sensitive questions. Recent studies, however, point out that in the presence of inattentive respondents, the conventional estimator of the prevalence of a sensitive attribute is biased toward 0.5. To remedy this problem, we propose a simple design-based bias correction using an anchor question that has a sensitive item with known prevalence. We demonstrate that we can easily estimate and correct for the bias arising from inattentive respondents without measuring individual-level attentiveness. We also offer several useful extensions of our estimator, including a sensitivity analysis for the conventional estimator, a strategy for weighting, a framework for multivariate regressions in which a latent sensitive trait is used as an outcome or a predictor, and tools for power analysis and parameter selection. Our method can be easily implemented through our open-source software, cWise.
