As researchers, we believe it is your responsibility to account for as many nuances in your data as possible.  This is important to help assure you are reporting reliable and valid findings.  One such challenge that many face is when you are analyzing data across multiple groups that differ in the number of members.  In Dedoose, we use a normalization procedure, so that when you are looking at charts representing frequencies, the relative size of the bars is meaningful.  For example, say we are studying how involved single parents are in teaching their children to read.  If we have 30 women and 5 men and we are interested in the time spent on this task, the raw values for the groups will heavily favor the women—that is, all things equal, larger groups will likely show greater amounts of time spent teaching reading (or more of whatever we are studying).  Using normalization, we can calculate proportional values and use them to generate metrics or in displaying bars that are adjusted proportionally based on the number of members in each group.  Simply, this allows us to visually compare data from groups of differing sizes in relative terms so the metrics or graphs are meaningful. If you are interested in further information please check out our article here

Happy Dedoosing out there and, as always, feel free to contact us if you have any questions