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.
Model-matching methods and distributed control of networks consisting of a class of heterogeneous dynamic agents
