REDCap: Best practices for designing data collection

Whether you’re brand new to REDCap or have been developing projects for a while, if you’ve never been the one to analyze data from a REDCap project you might not be aware how the way you create the project in the beginning can have a huge effect on the data that you end up with. Collecting your data may seem like the first step in a research or quality improvement project – but planning out your project to get good, clean data that will facilitate analysis comes long before data collection begins.

You may have heard the phrase “quality in, quality out” –meaning that cleaner, more precise input will lead to higher-value, more meaningful analysis. This relates to REDCap projects, too. If you want a good analysis for publication in the end, make sure you set yourself up for success in the beginning.

Your first step is to think Big Picture: What do you or your statistician need the data to look like in order to conduct an analysis? If you’re a researcher, this often means looking through your research protocol with your statistician to make sure everyone understands what variables are needed to conduct the planned analysis. Consider this in the early stages of designing your REDCap project. Make sure to keep the final analysis in mind as you design your fields, and keep in contact with your analytic team as you refine your project.

In this series of articles on specific REDCap data quality topics, we’ll cover: