Enhancing the Quality and Utility of Longitudinal Data for Education Research

纵向调查面临许多挑战，包括较高的无回复率和不断增加的数据收集成本，这威胁到所收集数据的质量和效用。计划中的研究将集中在克服这些挑战的两个战略上：如果调查数据与其他数据来源相关联，则开发针对不同意的潜在偏差的调整方法，以及在纵向研究中测量和考虑无响应偏差。所有研究都将广泛使用国家教育小组调查(NINS)的数据，目的是制定指导方针，如何应对NINS面临的这些挑战。许多调查，包括非政府组织，都将其数据与大型行政数据库联系起来，以便最大限度地减少数据收集成本，提高数据效用。关于关联的主要关切是，在关联发生之前需要获得关联同意，这是选择性地在关联数据分析中引入偏见。我们计划的研究通过开发评估偏差的方法和评估替代的偏差校正策略来解决这个问题。对于偏差评估，我们提出了一种使用关联-同意倾向的蒙特卡罗方法。对于偏差调整，我们提出了两种方法。第一种方法基于垂直分区数据的思想，它使分析来自两个不同文件的变量成为可能，而无需实际链接它们；因此，克服了链接同意的要求。第二种方法基于将未经同意的调查单位与行政数据中统计上相似的单位进行匹配的想法。我们提出了一种创新的匹配过程，软化了大多数统计匹配应用中所需的条件独立性假设。为了解决无反应的问题，我们发展了单元无反应的评估和调整方法，并提出了项目无反应的归因策略，具体说明了NNP的多水平纵向设计。使用从先期调查(ALWA调查)到NEP成人队列的相关管理数据，我们将研究面板磨损的负面影响。即使ALWA的答复者拒绝参加非政府组织的调查，也可以连续几年获得来自相关行政数据的信息。我们可以利用这些信息来确定面板磨损的潜在因素，并评估在NEPS调查中面板磨损偏差存在的程度。此外，我们利用关联的管理数据来增强无响应偏差调整程序，例如加权。最后，我们通过开发新的面板数据归因策略来解决多水平纵向设计中的项目无反应问题，这些策略可以解释多个聚类来源(例如重复测量和学生嵌套在学校中)。我们还将把这些方法与以前提出的在纵向上下文中输入缺失值的策略进行比较。

Longitudinal surveys face many challenges, including high non-response rates and increasing data collection costs, which threaten the quality and utility of the collected data. The planned research will focus on two strategies for overcoming these challenges: developing adjustment methods for potential biases from non-consent if survey data are linked with other data sources and measuring and accounting for nonresponse bias in longitudinal studies. All research will make extensive use of data from the National Educational Panel Survey (NEPS) with the aim of developing guidelines how to address these challenges for the NEPS. Many surveys, including the NEPS, link their data to large-scale administrative databases in order to minimize data collection costs and enhance data utility. The major concern regarding the linkage is that linkage-consent, which needs to be obtained before the linkage can occur, is selective introducing bias in linked-data analyses. Our planned research addresses this issue by developing methods for assessing the bias and evaluating alternative bias correction strategies. For bias assessment we propose a Monte Carlo approach using the propensity of linkage-consent. For bias adjustment, we propose two methods. The first method is based on the idea of vertically partitioned data, which makes it possible to analyze variables from two different files without actually linking them; thus, overcoming the linkage consent requirement. The second method builds on the idea of matching non-consenting survey units to statistically similar units in the administrative data. We propose an innovative matching procedure softening the conditional independence assumption that is required in most statistical matching applications. To address the issue of nonresponse, we develop methods for assessing and adjusting for unit nonresponse and propose imputation strategies for item nonresponse that specifically account for the multilevel longitudinal design of the NEPS. Using linked administrative data from a forerunner survey (the ALWA survey) to the NEPS adult cohort, we will study the negative impacts of panel attrition. Information from the linked-administrative data is available in consecutive years even if the ALWA respondent refused to participate in the NEPS survey. We can use this information to identify potential factors of panel attrition and evaluate the extent to which panel attrition bias exists in the NEPS survey. Furthermore, we utilize the linked administrative data to enhance nonresponse bias adjustment procedures such as weighting. Finally, we address the issue of item nonresponse in multilevel longitudinal designs by developing new imputation strategies for panel data that account for multiple sources of clustering (such as repeated measurements and students nested within schools). We will also compare these methods with previously proposed strategies for imputing missing values in longitudinal contexts.