The relational event model (REM) solves a problem for organizational researchers who have access to sequences of time-stamped interactions. It enables them to estimate statistical models without collapsing the data into cross-sectional panels, which removes timing and sequence information. However, there is little guidance in the extant literature regarding issues that may affect REM’s power, precision, and accuracy: How many events or actors are needed? How large should the risk set be? How should statistics be scaled? To gain insights into these issues, we conduct a series of experiments using simulated sequences of relational events under different conditions and using different sampling and scaling strategies. We also provide an empirical example using email communications in a real-life context. Our results indicate that, in most cases, the power and precision levels of REMs are good, making it a strong explanatory model. However, REM suffers from issues of accuracy that can be severe in certain cases, making it a poor predictive model. We provide a set of practical recommendations to guide researchers’ use of REMs in organizational research.

关系事件模型（REM）为有权访问带有时间戳的交互序列的组织研究人员解决了一个问题。它使他们能够估计统计模型，而无需将数据分解为横截面面板，从而消除了时序和序列信息。但是，关于可能影响REM的能力，准确性和准确性的问题，现有文献中几乎没有指导：需要多少个事件或参与者？风险设定应达到多少？统计应如何缩放？为了深入了解这些问题，我们进行了一系列实验，使用了在不同条件下模拟的关联事件序列，并使用了不同的采样和缩放策略。我们还提供了在实际环境中使用电子邮件通信的经验示例。我们的结果表明，在大多数情况下，REM的功能和精度水平都很好，使其成为一个强有力的解释模型。但是，REM存在准确性问题，在某些情况下可能会很严重，从而使其成为不良的预测模型。我们提供了一组实用建议，以指导研究人员在组织研究中使用REM。