Conquer the Chaos of Questionnaires: Item harmonisation made easy Researchers in psychology and social sciences frequently face a challenge: comparing data from different questionnaires. For example, they may be combining cohort studies or longitudinal studies or pooling data for cross cohort research. Imagine trying to compare responses to “I often feel anxious” with “Feeling nervous, anxious or afraid” – even though they seem similar, they might not capture the same level of anxiety.
Data Harmonisation in Education: Overview The term ‘harmonisation’ has often been used in different contexts – for example, to describe similar phenomena, such as collaboration, coherence, alignment, integration, partnership, etc. However, we might argue that these concepts might do nothing more than indicate the extent and scale of integration among different entities when it comes to regional cooperation. Now, the underlying degree of interaction between all the players involved can run a lot deeper and tighter when we transition from collaboration, partnership, and cooperation to integration, community, harmonisation, and interdependence.
When researchers take on the task of analysing data from surveys and questionnaires, they often encounter a significant obstacle: finding matching or common items across different sources. This challenge is due to the many different ways questions are asked or formatted. This makes it tough to compare and merge data effectively. According to Forbes, researchers spend up to 80% of their time just getting data ready for analysis, and a big part of that time goes into harmonising data.
Overview Public administrations today are tasked with managing massive volumes of data in multiple formats, often using different management methods, as per the demands of their individual organisations. It’s also become common for them to host multiple copies of that data across different repositories. As a result, the data can often be disseminated across multiple regions, especially in terms of content and presentation, unless it is ‘harmonised’. This is one reason why there is so much re-use at the low level of existing information on citizens and businesses, for example.
More than thirty years ago, John Naisbitt put into words a feeling many of us recognise today in “Megatrends,” saying, “We are drowning in information but starved for knowledge.” This statement is incredibly relevant in today’s world, filled to the brim with data for research, analysis, and making decisions. The job of pulling and refining data from questionnaires is key, as these are so many insights and so much valuable feedback.
