Three alerts. Three entirely different messages from the system. And three different responses required from you.

This is what edit checks and validation rules look like in practice. They are the EDC system's quality safeguards -- programmed logic that evaluates your entries against defined criteria and tells you when something appears wrong. But "appears wrong" covers a spectrum, from "definitely wrong and you cannot proceed" to "possibly wrong and you should investigate." The coordinator who treats all edit checks identically -- either ignoring them reflexively or panicking at every alert -- is not using the system effectively.

Understanding what the system is telling you, and responding appropriately to each type of message, is one of the most consequential skills you will develop as a coordinator. It is also, in my experience, one of the most underappreciated. Edit checks sit in an odd pedagogical space: they are too routine to feel important and too technical to feel interesting. But they are the front line of automated data quality assurance, and your response to them directly determines whether that quality assurance succeeds or fails.

Three alerts. Three entirely different messages from the system. And three different responses required from you.

This is what edit checks and validation rules look like in practice. They are the EDC system's quality safeguards -- programmed logic that evaluates your entries against defined criteria and tells you when something appears wrong. But "appears wrong" covers a spectrum, from "definitely wrong and you cannot proceed" to "possibly wrong and you should investigate." The coordinator who treats all edit checks identically -- either ignoring them reflexively or panicking at every alert -- is not using the system effectively.

Understanding what the system is telling you, and responding appropriately to each type of message, is one of the most consequential skills you will develop as a coordinator. It is also, in my experience, one of the most underappreciated. Edit checks sit in an odd pedagogical space: they are too routine to feel important and too technical to feel interesting. But they are the front line of automated data quality assurance, and your response to them directly determines whether that quality assurance succeeds or fails.