Enhancing the Quality and Utility of Longitudinal Data for Education Research

纵向调查面临许多挑战，包括高不答复率和数据收集费用增加，这威胁到所收集数据的质量和效用。计划中的研究将侧重于克服这些挑战的两个战略：如果调查数据与其他数据来源相关联，则为非同意的潜在偏差制定调整方法，并在纵向研究中测量和解释非响应偏差。所有研究都将广泛利用国家教育小组调查（NEPS）的数据，旨在制定如何应对NEPS面临的这些挑战的指导方针。许多调查，包括国家环境政策调查，都将其数据与大型行政数据库连接起来，以尽量减少数据收集费用，提高数据的效用。关于链接的主要问题是，链接同意，这需要在链接发生之前获得，是选择性地引入偏见的链接数据分析。我们计划的研究通过开发评估偏倚和评估替代偏倚纠正策略的方法来解决这个问题。对于偏倚评估，我们提出了一种蒙特卡罗方法，使用的倾向联系同意。对于偏差调整，我们提出了两种方法。第一种方法是基于垂直分区数据的思想，这使得可以分析两个不同文件中的变量，而无需实际链接它们;因此，克服了链接同意的要求。第二种方法的基础是将不同意的调查单位与行政数据中统计上类似的单位相匹配。我们提出了一个创新的匹配过程软化的条件独立性假设，需要在大多数统计匹配应用程序。为了解决这个问题的无应答，我们开发了评估和调整单位无应答的方法，并提出了项目无应答的插补策略，特别是考虑到NEPS的多层次纵向设计。使用从先行者调查（ALWA调查）到NEPS成人队列的相关行政数据，我们将研究小组减员的负面影响。即使ALWA的答复者拒绝参加NEPS调查，也可以连续几年获得来自关联行政数据的信息。我们可以利用这些信息来识别小组流失的潜在因素，并评估在NEPS调查中存在小组流失偏见的程度。此外，我们利用相关的行政数据，以提高无应答偏差调整程序，如加权。 最后，我们通过开发新的填补策略来解决多水平纵向设计中的项目无应答问题，该策略用于解释多个聚类来源（如重复测量和嵌套在学校内的学生）的面板数据。我们还将比较这些方法与以前提出的战略填补缺失值的纵向背景。

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.