Alternative Approaches to the Analysis of Complex Sample Survey Data: Applying State-of-the-Art Methods to NCSES Surveys

许多科学领域使用从个人或机构的大规模概率样本收集的数据进行研究。由于抽样策略的复杂性，这些数据的二次分析可能会变得复杂，这是为了提高数据收集效率而采用的。重要的是，任何二次分析都要使用适当的分析技术，以说明复杂的样本设计。这一概念可以被描述为在更广泛的总体调查误差(TSE)框架内检查分析误差，这是一个非常有前途的调查研究领域。该项目将从识别和记录几个复杂的NCSES调查中的潜在分析错误开始。下一步将是综合分析这些数据的最佳做法。在项目的主要重点中，这些最先进的方法将与现有的NCSES数据分析进行比较和对比，以及其他方法对结果的解释可能产生的影响。这项研究有可能改进NCSES数据的分析用途，但也有助于更合理地使用总体上的复杂抽样调查数据。

Many scientific fields conduct research using data collected from large-scale probability samples of individuals or establishments. Secondary analyses of these data can be complicated by the complex nature of the sampling strategies, which are employed for data collection efficiency. It is important that any secondary analyses employ appropriate analytic techniques that account for the complex sample designs. This concept can be described as examining analytic error within the broader framework of Total Survey Error (TSE), which is a very promising area of survey research. This project will begin by identifying and documenting potential analytic errors in several complex NCSES surveys. The next step will be to synthesize best practices for analyses of these data. In the principal focus of the project, these state-of-the-art methods will be compared and contrasted to existing analyses of NCSES data, and the implications that alternative approaches can have for the interpretation of the results. This research has the potential to improve the analytic uses of NCSES data, but also to make contributions to the more appropriate use of complex sample survey data in general.