A qualitative evidence synthesis (QES) can provide additional evidence to improve understanding of the complexity of an intervention or research problem by investigating contextual variations, implementation challenges and stakeholder preferences and experiences.
There are two main designs for synthesizing qualitative evidence with evidence of the effects of interventions:
Sequential reviews: where one or more existing intervention review has been published on a similar topic, it is possible to do a sequential qualitative evidence synthesis and then integrate its findings with those of the intervention review to create a mixed-method review.
Convergent mixed-methods review: where no pre-existing intervention review exists, it is possible to do a full convergent ‘mixed-methods’ review where the trials and qualitative evidence are synthesized separately, and then integrated within a third synthesis.
Reference and read more in the Cochrane Handbook for Systematic Reviews of Interventions, Chapter 21.
Qualitative evidence syntheses
Qualitative data can consist of the materials you have collected yourself, for example questionnaires, interviews or observations. A qualitative data-set can also be created by systematically searching for research evidence from primary qualitative studies and drawing the findings together.
Reporting standards and tools designed for intervention reviews such as the PRISMA Statement may not be appropriate for qualitative evidence syntheses or an integrated mixed-method review. ENTREQ statement for qualitative research can be used as a basis.
Read more about qualitative evidence syntheses in the Cochrane Handbook for Sytematic Reviews of Interventions, chapter 21.
NVivo aids qualitative analysis of textual and audiovisual data sources, including:
Nvivo can be downloaded from UCPH Software Library. Note! Download the software and the license key.
Qualitative analysis is a form of analysis that is based on obtaining material that, as a starting point, must be analyzed without various statistical models. It involves the identification, examination, and interpretation of patterns and themes in textual data and determines how these patterns and themes help answer the research questions.
Through learning activities, KUB Datalab supports students and faculty in regards to: