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.
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