Fitting multilevel models in complex survey data with design weights: Recommendations
Fitting multilevel models in complex survey data with design weights: Recommendations
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DOI：
10.1186/1471-2288-9-49
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发表时间：
2009-07-14
影响因子：
4
通讯作者：
Carle, Adam C.
中科院分区：
文献类型：
--
作者：
Carle, Adam C.
背景：多水平模型(MLM)为复杂的调查数据分析师提供了一种独特的方法来理解公共卫生的个人和背景决定因素。然而，对于复杂调查数据中的MLM与设计权重的拟合，几乎没有总结的指导意见。模拟工作表明，分析师应该使用两种方法来衡量设计权重，并使用未加权和按比例加权的数据来拟合传销。方法：使用2005-2006年全国特殊健康护理需求儿童调查(NS-CSHCN：N=40,723)的数据，收集了各州聚集的儿童的数据，我检查了结果类型(分类与连续)、模型类型(1级、2级或组合)和软件(Mplus、MLwiN和GLLAMM)的缩放方法的性能。结果：缩放加权估计和标准误差与未加权分析略有不同，彼此比未加权分析更一致。然而，观察到的差异很小，并没有导致不同的推论结论。同样，结果显示软件程序之间的差异很小，增加了对结果和推论结论的信心，而不依赖于软件选择。结论：如果在传销中包括设计权重，分析师应该衡量权重，并使用软件在估计中适当地包括调整后的权重。
Background: Multilevel models (MLM) offer complex survey data analysts a unique approach to understanding individual and contextual determinants of public health. However, little summarized guidance exists with regard to fitting MLM in complex survey data with design weights. Simulation work suggests that analysts should scale design weights using two methods and fit the MLM using unweighted and scaled-weighted data. This article examines the performance of scaled-weighted and unweighted analyses across a variety of MLM and software programs.Methods: Using data from the 2005-2006 National Survey of Children with Special Health Care Needs (NS-CSHCN: n = 40,723) that collected data from children clustered within states, I examine the performance of scaling methods across outcome type ( categorical vs. continuous), model type (level-1, level-2, or combined), and software (Mplus, MLwiN, and GLLAMM).Results: Scaled weighted estimates and standard errors differed slightly from unweighted analyses, agreeing more with each other than with unweighted analyses. However, observed differences were minimal and did not lead to different inferential conclusions. Likewise, results demonstrated minimal differences across software programs, increasing confidence in results and inferential conclusions independent of software choice.Conclusion: If including design weights in MLM, analysts should scale the weights and use software that properly includes the scaled weights in the estimation.