Purposively sampling for qualitative evidence syntheses: Methodological lessons from a synthesis on vaccination communication




Poster session 2 Thursday: Evidence synthesis - methods / improving conduct and reporting


Thursday 14 September 2017 - 12:30 to 14:00


All authors in correct order:

Ames H1, Lewin S2, Glenton C1
1 The Norwegian Institute of Public Health, Norway
2 The Norwegian Institute of Public Health and South African Medical Research Council, Norway
Presenting author and contact person

Presenting author:

Simon Lewin

Contact person:

Abstract text
Background: In a qualitative evidence synthesis (QES), too much data due to a very large number of included studies can undermine thorough analysis. In order to limit the number of included studies included in a QES on vaccination communication, we developed and applied a sampling framework to studies that met our inclusion criteria.

Objectives: To discuss the development and application/strengths and weaknesses of a sampling framework for a QES on vaccination communication.

Methods: We mapped eligible studies by extracting key information from each study, for example: country, study setting, vaccine, data richness, and study objectives. The final sampling frame included the following three steps:
1. Include studies set in low- and middle-income (LMIC) settings
2. Include studies scoring a three or more on a scale of data richness developed for this synthesis
3. Include studies where the study objectives closely match the synthesis objectives

Results: Seventy studies were eligible for inclusion in the review. Thirty-eight studies were sampled for inclusion in the synthesis. Nine studies were sampled in round one from LMIC contexts. These studies contributed to, on average, the least number of findings in the final synthesis. Twenty-four studies were sampled in round two on the basis of data richness. These studies mostly contributed to a larger number of findings. The five studies sampled in round three that, from the studies remaining at that stage, most closely matched the synthesis objectives contributed on average to a large number of findings.

Conclusions: Our approach to purposive sampling allowed us to achieve a wider geographic spread of articles and to increase the number of included studies that had rich data and closely matched the synthesis objective. It is possible that we may have overlooked articles that did not meet our sampling criteria but would have contributed to the synthesis. For example, two studies on migration and access to health services did not meet the sampling criteria but might have contributed to strengthening at least one finding. Ways of cross-checking for under-represented themes are needed.