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Indices of non-ignorable selection bias for proportions estimated from non-probability samples

机译：从非概率样本估计的比例的不可忽略的选择偏差指标

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Rising costs of survey data collection and declining response rates have caused researchers to turn to non-probability samples to make descriptive statements about populations. However, unlike probability samples, non-probability samples may produce severely biased descriptive estimates due to selection bias. The paper develops and evaluates a simple model-based index of the potential selection bias in estimates of population proportions due to non-ignorable selection mechanisms. The index depends on an inestimable parameter ranging from 0 to 1 that captures the amount of deviation from selection at random and is thus well suited to a sensitivity analysis. We describe modified maximum likelihood and Bayesian estimation approaches and provide new and easy-to-use R functions for their implementation. We use simulation studies to evaluate the ability of the proposed index to reflect selection bias in non-probability samples and show how the index outperforms a previously proposed index that relies on an underlying normality assumption. We demonstrate the use of the index in practice with real data from the National Survey of Family Growth.
机译：调查数据收集成本的上升和响应率的下降已导致研究人员转向非概率样本来对人口进行描述性陈述。但是，与概率样本不同，非概率样本可能由于选择偏差而产生严重偏差的描述性估计。本文开发和评估了基于简单模型的潜在选择偏差的指数，该指数是由于不可忽略的选择机制导致的人口比例估计。索引取决于介于0到1之间的不可估计参数，该参数捕获随机选择的偏差量，因此非常适合敏感性分析。我们描述了修改后的最大似然和贝叶斯估计方法，并提供了新的且易于使用的R函数用于其实现。我们使用模拟研究来评估拟议指数反映非概率样本中选择偏见的能力，并显示该指数如何胜过依赖基础正态性假设的先前提出的指数。我们通过全国家庭增长调查的真实数据证明了该指数在实践中的使用。

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