“Courteous” (and Efficient and Accurate) Data Entry

Data entry is a great illustration of making modeling and data software courteous as discussed in a previous blog. However, with the data entry use case, this is not just a nicety. Accuracy and efficiency are necessities for entering panel and other measurements where results are typed in by humans. Here I share an example based on a recent consulting project where we streamlined a client’s data entry templates and analysis pipeline for the purpose of modeling the correlation between panel data and lab measurement data.  See this separate, Tech Talk blog containing details on how to set this up in a spreadsheet data entry template.

Do you gather data from multiple sources, including manually entered grading or lab data? Do your suppliers? This blog shares food for thought on auditing and improving those data sources for likelihood of generating accurate data. This is also a way to thoughtfully enable your lab personnel to be efficient and happy. Let us know if we can help you and your team in these areas.

In the context of data entry, how do we get the template to behave like a considerate human? The user interface should encourage data entry to occur comfortably in the same rows/columns template that will subsequently be used for analysis. It should guide the researcher to enter the data in the correct cell location, and it should make it easy to find this location and move from value to value. While not shown here, it may also do some checking of the data to alert the researcher to improper values. A 6 on a 0 to 5 scale? Not in our template! The considerate data entry form also shows the progress and what’s left to do. Below is an illustration of this using spreadsheet Conditional Formatting features to dynamically highlight cell locations and status as graders’ results are recorded.

In the illustration, the dark blue box highlights the current data entry location. Lighter blue below highlights what is left as the panel proceeds. Both use Conditional Formatting to dynamically shade cells based on current and previous cell contents. The dark blue box moves as new values are entered. Additionally, Column C illustrates using Conditional Formatting to highlight changes and avoid the visual clutter of repeated values. This was described in the previous blog and is a great technique for discouraging, use of merged cells, which breaks rows/columns structure.  One last UI touch on display here is freezing Row 1 so that column headers remain in view, this avoids the common urge to break data entry up into multiple template copies which then must be combined to analyze the full data set.

These simple user interface innovations greatly lower the risk of data entry errors, and they allow an experimenter to feel comfortable entering data directly into a large rows/columns table.