Integration of additional micro data sets in the core data

将额外的微观数据集集成到核心数据中

• 批准号: 395374378
• 负责人: Professor Dr. Ralf Münnich
• 金额: --
• 依托单位: Lehrstuhl für Empirische Sozialforschung
• 依托单位国家: 德国
• 项目类别: Research Units
• 财政年份: 2018
• 资助国家: 德国
• 起止时间: 2017-12-31 至 2021-12-31
• 项目状态: 已结题
• 来源: https://gepris.dfg.de/gepris/projekt/395374378?language=en
• 关键词: Integration; additional; micro; data; sets

The basis for small-scale segmented microsimulations is a suitable basis population, which realistically reproduces individual and geographical structures.Partial project 1 (TP 1) concentrates on building a basic dataset based on register and census data. For this purpose, further variables for the primary school simulation are implemented using additional sources of data. TP 2 is concerned with a comprehensive expansion of this database in order to enable research in the fields of health care and migration, which are research topics in partial projects 4 and 5.For this data expansion a toolbox of methods is built and extended, which permits to integrate further topic-related sets of variables beyond the scope the two topics of care and migration. In doing so the microstructures in form of individual combinations of characteristics as well as household relations should be preserved in the best possible way. Therefore, in particular, methods of synthetic data generation are applied.The data generating process has to distinguish between micro and macro structures with special emphasis on the harmonization of both levels. In order to capture these structures, social-scientific surveys such as the micro-census or SOEP as well as further topic-specific datasets are used. In addition, the database should reproduce regional differences, since these are fundamental for carrying out geographically differentiated microsimulations. As in many surveys regionally differentiated data are of low quality due to small sample sizes, special statistical procedures from small area estimation are used.The whole expansion of the basic population is conducted in three essential processes. In the modelling process the structures of the variables are first captured as good as possible and then added to the existing database in the prediction process. The estimated models as well as the generated distributions are checked in the validation process. This validation is done both individually for all added variables as well as in a multivariate analysis for the entire basis population.In order to ensure plausibility of the overall basis population, different methods of statistical and logical editing are used. The entire program of methods will be made available to the research community in the sense of Open and Reproducible Research.

小规模分段微观模拟的基础是一个合适的基本人口，它真实地再现了个人和地理结构。为此目的，利用额外的数据来源实施了小学模拟的其他变量。TP 2涉及全面扩展该数据库，以便能够在保健和移民领域进行研究，这是部分项目4和5的研究主题。在这样做时，应以尽可能好的方式保留以个人特征组合和家庭关系为形式的微观结构。因此，特别采用了综合数据生成方法，数据生成过程必须区分微观和宏观结构，特别强调两个层面的协调。 为了捕捉这些结构，使用了社会科学调查，如微型人口普查或SOEP以及其他特定主题的数据集。此外，数据库应再现区域差异，因为这些差异对于进行按地理区域区分的微观模拟至关重要。由于在许多调查中，由于抽样规模小，按区域区分的数据质量很低，因此使用了小面积估计的特殊统计程序。在建模过程中，首先尽可能好地捕捉变量的结构，然后在预测过程中将其添加到现有数据库中。在验证过程中检查估计的模型以及生成的分布。这种验证既可以对所有添加的变量进行单独验证，也可以对整个基础总体进行多变量分析。为了确保整个基础总体的可验证性，使用了不同的统计和逻辑编辑方法。整个方法计划将在开放和可复制研究的意义上提供给研究界。