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。这些数据的二次分析可能会因采样策略的复杂性而变得复杂，而采样策略是为了提高数据收集效率而采用的。重要的是，任何二次分析都应采用适当的分析技术，以解释复杂的样本设计。这个概念可以被描述为在更广泛的总调查误差（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。这个项目将开始通过识别和记录在几个复杂的NCSES调查潜在的分析错误。下一步将是综合分析这些数据的最佳做法。在该项目的主要重点，这些国家的最先进的方法将进行比较和对比现有的分析NCSES数据，以及替代方法可能对结果的解释的影响。这项研究有可能提高NCSES数据的分析使用，但也作出贡献，更适当地使用复杂的抽样调查数据一般。

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.