Although multiple organizational-level databases are frequently combined into one data set, there is no overview of the matching methods (MMs) that are utilized because the vast majority of studies does not report how this was done. Furthermore, it is unclear what the differences are between the utilized methods, and it is unclear whether research findings might be influenced by the utilized method. This article describes four commonly used methods for matching databases and potential issues. An empirical comparison of those methods used to combine regularly used organizational-level databases reveals large differences in the number of observations obtained. Furthermore, empirical analyses of these different methods reveal that several of them produce both systematic and random errors. These errors can result in erroneous estimations of regression coefficients in terms of direction and/or size as well as an issue where truly significant relationships might be found to be insignificant. This shows that research findings can be influenced by the MM used, which would argue in favor of the establishment of a preferred method as well as more transparency on the utilized method in future studies. This article provides insight into the matching process and methods, suggests a preferred method, and should aid researchers, reviewers, and editors with both combining multiple databases and describing and assessing them.

尽管经常将多个组织级数据库组合到一个数据集中，但是由于绝大多数研究都没有报告如何完成此操作，因此没有概述所使用的匹配方法（MM）。此外，还不清楚所使用的方法之间的区别是什么，也不清楚所使用的方法是否会影响研究结果。本文介绍了四种用于匹配数据库和潜在问题的常用方法。对那些用于组合常规使用的组织级数据库的方法的经验比较表明，所获得的观察结果数量存在很大差异。此外，对这些不同方法的经验分析表明，其中一些方法会产生系统误差和随机误差。这些错误可能导致方向和/或大小方面的回归系数估计错误，并可能导致发现真正重要的关系不重要的问题。这表明研究结果可能会受到所用MM的影响，这将支持建立一种首选方法，并在将来的研究中提高所用方法的透明度。本文提供了有关匹配过程和方法的见解，提出了一种首选方法，并应帮助研究人员，审阅者和编辑者结合多个数据库并对其进行描述和评估。这将支持建立一种首选的方法，并在以后的研究中提高所用方法的透明度。本文提供了有关匹配过程和方法的见解，提出了一种首选方法，并应帮助研究人员，审阅者和编辑者结合多个数据库并对其进行描述和评估。这将支持建立一种首选方法，并在以后的研究中提高所用方法的透明度。本文提供了有关匹配过程和方法的见解，提出了一种首选方法，并应帮助研究人员，审阅者和编辑者结合多个数据库并对其进行描述和评估。