Dr. Deanna Varley and Associate Professor Matthew Sunderland, both based at the Matilda Centre at the University of Sydney, have been using Harmony on a data harmonisation project with large population-based Australian surveys that have measured psychological distress with different scales and sometimes with different versions of the same scale.
Their goal was to identify pre-existing data in Australia that has measured psychological distress and to combine and harmonise multiple large-scale Australian population surveys, each employing various psychological distress measures, into a unified dataset.
The primary challenge was the heterogeneity of the psychological distress measures used across the different surveys. While many surveys employed the Kessler Psychological Distress Scale (K10), others used variations of the K10 or entirely different scales such as the mental health component of the SF-36.
Examples of the kinds of items Dr Varley was matching include
During the past four weeks, have you accomplished less than you would like as a result of any emotional problems, such as feeling depressed or anxious?
and
During the past four weeks, how much of the time have you had any of the following problems as a result of any emotional problems (such as feeling depressed or anxious)? Accomplished less than you would like.
Dr. Varley compiled a list of 282 surveys, each containing various psychological distress measures, from various sources including the Australian Data Archive.
She manually extracted the exact wording of each item from the survey instruments, and then used Harmony’s R library to match the items by pairs and identify which surveys contained matching or partially matching instruments.
Dr Varley also provided feedback on how to improve the tool and example R scripts which we have included in the R examples repository.
Dr Varley wrote to us on the Harmony Discord server,
Thanks so much for developing this great tool! It’s been so useful for our research and has made our work significantly faster and easier.
