Generating metainferences in mixed methods research: A worked example in convergent mixed methods designs

Abstract

Metainferences, or the insights derived from integrating quantitative and qualitative inferences at the end of a study, are crucial for achieving added value and synergy in mixed methods research. There is an ongoing need to understand how researchers generate metainferences, especially considering their pivotal role in helping researchers achieve full quantitative and qualitative integration. While some examples of metainferences generation are available in the mixed methods literature, more explicit guidance is required. Approaches to developing metainferences must also be contextual, as inferences of this type are contingent on the nature and purpose of the mixed methods study, the type of mixed methods design, and the quality of the research data. This paper describes a seven-step process for generating metainferences using a convergent mixed methods study as an exemplar. These steps consist of identifying knowledge, experience, and data-driven inferences from the quantitative and qualitative data; developing inference association maps to draw metainferences; and assessing the validity of metainferences using backward working heuristics. This paper contributes to mixed methods research by shedding light on the development of metainferences in convergent designs and by providing practical and tangible tools for making sense of the complexity of the analysis and interpretation tasks involved in the process of generating metainferences.