Archival Data Sets Should not be a Secondary (or Even Last) Choice in Micro-Organizational Research
呼吁微观组织科学领域应更多使用大型档案数据集，而非仅依赖独立研究者收集的原始数据，并指出档案数据在样本代表性、多层次情境和方法论上的优势。
Despite ample access to large, archival datasets, the micro-organizational sciences field seem to consistently cast these datasets aside in favor of primary datasets collected by independent researchers. In the current GoMusing, we argue that these archival datasets should not be a secondary (or even last) choice for the micro-organizational sciences. In fact, large archival datasets can enable researchers to (a) investigate phenomena of interest across generalizable samples, (b) incorporate multiple levels of context into research, and (c) take advantage of several additional methodological benefits. In the hopes of spurring a paradigm shift in the micro-organizational sciences, we begin our article by discussing problems with the standard approach to data collection (i.e., independent researchers collecting their own datasets). We then discuss how archival datasets can remedy many of these issues and advance the range of research questions the field is able to answerer. We conclude by providing a step-by-step process for incorporating these archival datasets into our literature and provide insights into addressing common challenges. We hope this GoMusing will serve as a call to action for researchers and editorial teams alike to move our research forward though a greater usage of large archival datasets.