CLEAN PROCESS DATA- THE LILLARD METHOD USING THE PSID

描述（由申请人提供）：清洁过程数据代表了一种特定的理念，用于清理和组织复杂的辅助数据，这些数据在数据准备过程的每个阶段以及最终分析产品中留下完全可重复的足迹。这一过程背后的组织方法对数据准备方法有重大贡献，因为每次都可以精确地再现结果。这一点很重要，因为清理和准备辅助数据是一项昂贵且耗时的工作，当处理步骤丢失或未记录时，这种成本会大大增加。作为存档数据收集的一部分，标准化数据准备文件的存在不仅提高了研究过程的效率，而且还为这些研究增加了作为辅助数据来源的持续价值。在资助期间，我们建议完成三项具体任务，以说明利拉德方法的价值-最初，我们建议将利拉德清洁过程数据收集作为直接存档任务进行完整记录。现有Lillard PSID收集文件的完成将极大地提高研究界利用PSID统一纵向文件的广泛收集的能力，并有助于继续使用这一重要的纵向研究。其次，我们建议注释利拉德方法操作的中心方法和组织问题，以及它们在使用复杂数据的计量经济学和社会研究中的应用。本任务将为相关数据过程的使用提供一套明确的指导方针，以帮助研究人员使用PSID文件进行研究。更一般地说，注释指南将记录利拉德清洁过程方法作为应用于任何复杂数据集的组织和管理的系统方法的价值。最后，我们建议探索将清洁过程数据方法的原则和组织结构应用于共享复杂纵向结构的其他次要数据收集的机制。

DESCRIPTION (provided by applicant): Clean Process Data represents a specific philosophy towards the cleaning and organizing complex secondary data that leaves a fully reproducible footprint of each stage of the data preparation process as well as a final analysis product. The organizational approach underlying this process represents a significant contribution to the methodology of data preparation because the outcomes can be reproduced exactly every time. This is important because the cleaning and preparation of secondary data represents an expensive and time consuming endeavor, and this cost is increased substantially when processing steps are lost or undocumented. The presence of standardized data preparation files as part of an archived data collection not only enhances the efficiency of the research process, it also adds ongoing value to these studies as sources of secondary data. During the course of the funding period we propose to accomplish three specific tasks to illustrate the value of the Lillard approach- Initially, we propose to document fully the Lillard Clean Process Data collection as a direct archival task. The completion of the documentation for the existing Lillard PSID collection will greatly enhance the research community's ability to employ this extensive collection of harmonized longitudinal files from the PSID and help contribute to the ongoing use of this important longitudinal study. Secondly, we propose to annotate the central methodological and organizational issues operationalized by the Lillard approach and their application to econometric and social research using complex data. This task will provide a set of clear guidelines for the use of the associated data processes to aid researchers in the use of the PSID files for research. More generically, the annotated guidelines will document the value of the Lillard clean process methodologies as systematic approach with applications to the organization and management of any set of complex data. Finally, we propose to explore mechanisms under which the principles and organizational structures of Clean Process Data methodology could be applied to other secondary data collections that share a complex longitudinal structure.