Cellular-Only Substitution in the United States as Lifestyle Adoption Implications for Telephone Survey Coverage
Cellular-Only Substitution in the United States as Lifestyle Adoption Implications for Telephone Survey Coverage
复制标题
美国仅使用手机替代生活方式对电话调查覆盖范围的影响
DOI：
10.1093/poq/nfm048
复制
发表时间：
2007
影响因子：
3.4
通讯作者：
Patrick Ehlen
中科院分区：
文献类型：
--
作者：
Jon Ehlen;Patrick Ehlen
从历史上看，从调查样本中排除美国手机人口的覆盖偏差一直很小，因为这个群体的规模相对较小。然而，这一部分的持续增长引起了越来越多的关注，即美国公众的电话调查将越来越容易受到覆盖面偏见的影响。虽然有证据表明，人口加权可以用来消除这种偏见，权重的可用性落后于快速变化的细胞人口。为了解释这个问题的严重程度，我们提出了一个可靠的模型来预测细胞的人口规模和人口统计。该模型假定，一个稳定的行为过程，习惯保持率，可以从以前的无线生活方式在美国的采用估计，也可以描述采用的手机的生活方式。使用激励和习惯化的措施，我们通过预测仅细胞群体大小的变化和年龄人口统计学的变化来测试这一假设。预测的准确性证实了这两种采用行为是相似的。然后，我们开发了到2009年的年龄人口统计预测，并展示了仅采用手机的生活方式如何导致潜在的覆盖偏差，通过这种类型的建模，而不是从历史数据中加权，可以更好地解决这个问题。
Historically, the coverage bias from excluding the United States cell-only population from survey samples has been minimal due to the relatively small size of this group. However, the unrelenting growth of this segment has sparked growing concern that telephone surveys of the general public in the United States will become increasingly subject to coverage bias. While there is evidence that demographic weighting can be used to eliminate this bias, the availability of the weights lag behind the rapidly changing cell-only population. To explain the extent of the problem, we propose a reliable model to forecast cell-only population size and demographics. This model posits that a stable behavioral process, the rate of habit retention, can be estimated from prior wireless lifestyle adoption in the United States and may also describe adoption of the cell-only lifestyle. Using measures of incentive and habituation, we test this assumption by predicting changes in the cell-only population size and changes in age demographics. The accuracy of predictions confirms the two adoption behaviors are similar. We then develop forecasts of age demographics through 2009, and show how cell-only lifestyle adoption leads to potential coverage bias that is better addressed through this type of modeling rather than weighting from historical data.