Usability and application of a data integration technique (following the thread) for multi- and mixed methods...

The scope of methodological development and innovation in multi- and mixed methods design is endless and, at times, challenging. The latter is especially true with regards to the integration of data generated through different methods. About a decade ago, Professor Jo Moran-Ellis and her colleagues at the University of Sussex suggested a framework for analytical integration known as "following a thread." Despite an increased focus within health services research on different perspectives and approaches to successful data integration, the framework's usability and application have not yet been well described.

This systematic review aims to integrate and synthesise published accounts of the framework and its applications.

Seven electronic databases were utilised. Included were peer-reviewed scientific papers published in English from 2006 - 2018. The authors independently screened eligible publications by title and abstract.

Thirteen studies were included in our systematic review. One notable finding is that in almost half of the cases (n = 6), the framework had been applied as an analytical integration framework in single studies using multiple qualitative methods. Overall, the descriptions and accounts of the framework were sparse and lacked transparency. Accounts of the analytical integration framework could be said to fall within three overarching areas: (1) applications of the framework, (2) justifications for analytical integration, and (3) benefits and shortfalls of the framework.

Data integration is often one of the major method steps in multi- and mixed methods designs. To further the future development of methodologically sound frameworks for analytical integration, it is essential that they are sufficiently described so as to ensure validation of the framework's usability and replicability. "Following a thread" appears to be an promising analytical integration framework, particularly in that it can be applied with the same datatypes as well as between different types of data.