Realist analysis of qualitative data in health professions education research

Abstract

Background: Qualitative realist analysis is gaining in popularity in health professions education research (HPER) as part of theory-driven program evaluation. Although realist approaches such as syntheses and evaluations typically advocate mixed methods, qualitative data dominate currently. Various forms of qualitative analysis have been articulated in HPER, yet realist analysis has not. Although realist analysis is interpretive, it moves beyond description to explain generative causation employing retroductive theorising. Ultimately, it attempts to build and/or 'test' (confirm, refute or refine) theory about how, why, for whom, when and to what extent programs work using the context-mechanism-outcome configuration (CMOC) heuristic. This paper aims to help readers better critique, conduct and report qualitative realist analysis.

Realist analysis methods: We describe four fundamentals of qualitative realist analysis: (1) simultaneous data collection/analysis; (2) retroductive theorising; (3) configurational analysis (involving iterative phases of identifying CMOCs, synthesising CMOCs into demi-regularities and translating demi-regularities into program theory); and (4) realist analysis quality (relevance, rigour, richness). Next, we provide a critical analysis of realist analyses employed in 15 HPER outputs-three evaluations and 12 syntheses. Finally, drawing on our understandings of realist literature and our experiences of conducting qualitative realist analysis (both evaluations and syntheses), we articulate three common analysis challenges (coding, consolidation and mapping) and strategies to mitigate these challenges (teamwork, reflexivity and consultation, use of data analysis software and graphical representations of program theory).

Conclusions: Based on our critical analysis of the literature and realist analysis experiences, we encourage researchers, peer reviewers and readers to better understand qualitative realist analysis fundamentals. Realist analysts should draw on relevant realist reporting standards and literature on realist analysis to improve the quality and reporting of realist analysis. Through better understanding the common challenges and mitigation strategies for realist analysis, we can collectively improve the quality of realist analysis in HPER.

FIGURE 1

The four fundamentals of realist analysis and their relationships (We developed this figure thinking primarily of realist interviews as part of realist evaluation, but much of this figure can also be applied to realist synthesis. Each of the concentric circles represents the four core fundamentals of realist analysis, with arrows within the concentric circles highlighting the iterative (back and forth) nature between the different elements within those realist analysis fundamentals. The dotted porous lines, as well as the double‐headed arrows, between the different concentric circles illustrate the complex and dynamic inter‐relations between these four fundamentals of realist analysis).

FIGURE 2

Our three key challenges and mitigation strategies for realist analysis.