An approach to assessing data richness in primary qualitative studies included in a qualitative evidence synthesis: a worked example

Tags: Poster
Ames H1, Lewin S1, Glenton C1
1The Norwegian Institute of Public Health

Background: in a qualitative evidence synthesis, too much data due to a large number of studies can undermine our ability to perform a thorough analysis. Purposive sampling of primary studies for inclusion in the synthesis is one way of achieving a manageable amount of data. One approach to study sampling is to map the richness of the data in the primary studies that meet the synthesis’ inclusion criteria, and then sample studies with richer data. Rich data can provide more in-depth insights into the phenomenon of interest, allowing the researcher to better interpret the meaning and context of findings presented in the primary studies.

Objectives: to develop, test and reflect on an approach to assess the data richness of primary studies included in a qualitative evidence synthesis.

Methods: to our knowledge, there is no existing tool to map data richness in qualitative studies. We therefore created a simple 1 to 5 scale for assessing data richness. Initially, we looked at the whole study when assessing data richness. However, we realised that much of this data covered topics that were outside of the scope of the synthesis. We therefore adapted the data richness scale. The result is a scale where the richness of data in an included study is not ranked by the total amount of data but by the amount of data that is relevant to the synthesis objectives (see table).

Results: the 5-point data richness scale has been piloted in two Cochrane qualitative evidence syntheses and has been found to be useful. It has helped us identify and include studies with rich data in our syntheses, which we believe allow us to better interpret the data and present more detailed and nuanced findings and explanations. Other review authors who have used our approach have also found it to be an understandable and helpful tool.

Conclusions: our approach for assessing data richness needs to be developed further and tested within other qualitative evidence syntheses to see if it needs further refining. This testing is needed to explore its understandability and application across research teams and syntheses in order to standardize the approach and develop guidance for its use.

Patient or healthcare consumer involvement: this abstract discusses a methodology that facilitates the inclusion of rich data in qualitative evidence synthesis. Qualitative studies with richer data can provide a more detailed understanding of patients’ or health consumers’ thoughts, opinions and experiences. This allows synthesis authors, and users of synthesis findings, to better interpret the meaning and context of findings presented in the primary studies.