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Methodological guidance for the conduct of mixed methods systematic reviews.


Cindy Stern

Lucylynn Lizarondo

Judith Carrier

Christina Godfrey

Kendra Rieger

Susan Salmond

Pamela Kirkpatrick

Heather Loveday


Objective: The objective of this paper is to outline the updated methodological approach for conducting a JBI MMSR with a focus on data synthesis, specifically, methods related to how data are combined and the overall integration of the quantitative and qualitative evidence. Introduction: Mixed methods systematic reviews (MMSRs) provide a more complete basis for complex decision making than that currently offered by single method reviews, thereby maximizing their usefulness to clinical and policy decision makers. Although MMSRs are gaining traction, guidance regarding the methodology of combining quantitative and qualitative data is limited. In 2014, the JBI Mixed Methods Review Methodology Group developed guidance for MMSRs; however, since the introduction of this guidance, there have been significant developments in mixed methods synthesis. As such, the methodology group recognized the need to revise the guidance to align it with the current state of knowledge on evidence synthesis methodology Methods: Between 2015 and 2019, the JBI Mixed Methods Review Methodology Group undertook an extensive review of the literature, held annual face-to-face meetings (which were supplemented by teleconferences and regular email correspondence), sought advice from experts in the field and presented at scientific conferences. This process led to the development of guidance in the form of a chapter included in the JBI Reviewer’s Manual, the official guidance for conducting JBI systematic reviews. In 2019, the guidance was ratified by the JBI International Scientific Committee. Results: The updated JBI methodological guidance for conducting an MMSR recommends that reviewers take a convergent approach to synthesis and integration whereby the specific method utilized is dependent on the nature/type of question(s) that is (are) posed in the systematic review. The JBI guidance is primarily based on Hong et al. and Sandelowski’s typology on MMSRs. If the review question can be addressed by both quantitative and qualitative research designs, the convergent integrated approach should be followed, which involves data transformation and allows reviewers to combine quantitative and qualitative data. If the focus of the review is on different aspects or dimensions of a particular phenomenon of interest, the convergent segregated approach is undertaken, which involves independent synthesis of quantitative and qualitative data leading to the generation of quantitative and qualitative evidence, which are then integrated together. Conclusions: The updated guidance on JBI MMSRs provides foundational work to a rapidly evolving methodology and aligns with other seminal work undertaken in the field of mixed methods synthesis. Limitations to the current guidance are acknowledged, and a series of methodological projects identified by the JBI Mixed Methodology Group to further refine the methodology are proposed. Mixed methods review offers an innovative framework for generating unique insights related to the complexities associated with healthcare quality and safety.


STERN, C., LIZARONDO, L., CARRIER, J., GODFREY, C., REIGER, K., SALMOND, S., APOSTOLO, J., KIRKPATRICK, P. and LOVEDAY, H. 2020. Methodological guidance for the conduct of mixed methods systematic reviews. JBI evidence synthesis [online], 18(10), pages 2108-2118. Available from:

Journal Article Type Article
Acceptance Date Sep 26, 2019
Online Publication Date Jan 31, 2020
Publication Date Oct 31, 2020
Deposit Date Jan 28, 2020
Publicly Available Date Feb 1, 2021
Journal JBI evidence synthesis
Print ISSN 2689-8381
Publisher Lippincott, Williams & Wilkins
Peer Reviewed Peer Reviewed
Volume 18
Issue 10
Pages 2108-2118
Keywords Data transformation; Integration; Mixed methods; Synthesis; Systematic review
Public URL
Additional Information This article has also been published in JBI Evidence Implementation, 2021, 19(2), pages 120-129 (