A simulation-based assessment of the bias produced when using averages from small DHS clusters as contextual variables in multilevel models
A simulation-based assessment of the bias produced when using averages from small DHS clusters as contextual variables in multilevel models
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DOI：
10.4054/demres.2006.15.1
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发表时间：
2006-07-18
影响因子：
2.1
通讯作者：
Kravdal, Oystein
中科院分区：
文献类型：
--
作者：
Kravdal, Oystein
如今，人们对社区机构和资源对个人死亡率和生育率的重要性很感兴趣。国土安全部的数据似乎是这种多层次分析的一个有价值的来源。例如，研究人员可以考虑在他们的模型中包括在每个主要抽样单位（PSU）中采访的大约25名妇女的样本（集群）内的平均教育程度。然而，这只是PSU中所有女性理论上更有趣的平均值的代理，原则上，样本平均值的估计效果可能与后一变量的效果明显不同。幸运的是，模拟实验表明，当根据国土安全部在撒哈拉以南非洲的调查估计教育对首次生育时间的影响时，这种偏差实际上相当小--不到14%。如果使用其他数据，或者如果重点转向教育以外的其他自变量，当然，偏差可能会非常不同。在某些情况下，它可能更小;在其他情况下，它可能大得令人无法接受。这取决于集群的规模，以及独立变量如何在社区内和社区之间分布。提供了一些一般性建议。
There is much interest these days in the importance of community institutions and resources for individual mortality and fertility. DHS data may seem to be a valuable source for such multilevel analysis. For example, researchers may consider including in their models the average education within the sample (cluster) of approximately 25 women interviewed in each primary sampling unit (PSU). However, this is only a proxy for the theoretically more interesting average among all women in the PSU, and, in principle, the estimated effect of the sample mean may differ markedly from the effect of the latter variable. Fortunately, simulation experiments show that the bias actually is fairly small - less than 14% - when education effects on first birth timing are estimated from DHS surveys in sub-Saharan Africa. If other data are used, or if the focus is turned to other independent variables than education, the bias may, of course, be very different. In some situations, it may be even smaller; in others, it may be unacceptably large. That depends on the size of the clusters, and on how the independent variables are distributed within and across communities. Some general advice is provided.