StATS: Non-destructive data editing (November 2, 2005). Category: Data management
I recently worked on a project looking at patients having two different types of operations, with and without collar sutures. The data set that the researchers sent to me had some inconsistencies, though. In one portion of the spreadsheet, the averages for the with and without groups were calculated based on row order, which was effectively the same as the date since the worksheet was sorted by date. In another portion of the spreadsheet, the two groups were defined based on a column indicating the number of collar sutures (0 meant without, of course, and any non-zero meant with). When I asked about this, the researchers fixed the data set by changing all the zeros in that column after a certain date to a non-zero value. But then when I ran some side effect data, the numbers did not match their original calculations. It turns out that one patient was actually ineligible because he/she had an open rather than laproscopic operation and two of the patients after a certain date who seemed to have a positive number of collar sutures actually had zero. There were several other changes along these lines, and I realized that I couldn't very easily backtrack from one version of the data set to another or to document exactly what changes had been made.
So I decided that from then on, I was going to edit the data in a non-destructive mode. For example, if I need to delete several rows of data, I would create a new column that would be 0 for the rows that I wanted to keep and 1 for the rows I needed to delete. Then I would use a programming statement to remove the rows. That way, I would know exactly which rows were deleted and I could "undelete" them just by changing the code. For a column of data like number of collar sutures, I would add a column next to it with the revised values. I would first copy the original columns and then make changes in the copy. That way, I could read off exactly which values changed and I could go back to the original data if necessary.
It's a bit more work and uses a bit of extra storage space, but in data sets with a large number of possibly conflicting changes, this gives you a bit of security.
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