Towards Mixed Methods Narrative Inquiry and Mixed Methods Narrative Research that are Transparent, Rigorous, Ethical, and Equitable (TREE)

Abstract

This article examines the intersection of mixed methods research with both narrative inquiry and narrative
research—what we refer to as mixed methods narrative inquiry and mixed methods narrative research,
respectively—proposing a novel meta-framework based on Transparency, Rigorousness, Equitableness, and
Ethicality (TREE). Through a comprehensive analysis, we explore the philosophical foundations, including social
constructionism and social constructivism, that inform narrative inquiry and narrative research. A systematic
review of the literature that we conducted previously highlights the underutilization of mixed methods research
approaches in narrative studies—revealing only 36 Scopus-indexed works that represent either mixed methods
narrative inquiry or mixed methods narrative research over a 64-year period (1960-2004), which represents less
than 1% of all narrative inquiry and narrative research studies published to date. The scant focus given to
conducting mixed methods narrative inquiry and mixed methods narrative research studies underscores the
necessity of our call for an increased use of these research approaches that promote greater methodological
integration. By introducing a TREE-oriented meta-framework, underscored by a tree metaphor, we advocate for
research approaches that fully integrate the depth of narrative inquiry and narrative research with the empirical
strength of quantitative analysis. This article contributes to the scholarly conversation by offering a framework
that can help researchers navigate and enrich their understandings of the complexity of human experiences through
mixed methods narrative inquiry and mixed methods narrative research. The proposed approach aims to enhance
the richness, depth, and ethical considerations in narrative-based investigations, presenting a compelling case for
the symbiotic relationship between qualitative depth and quantitative clarity.