Realist Approach to Qualitative Data Analysis

Area of Science:

• Evaluation Research
• Qualitative Inquiry
• Critical Realism

Background:

• Realist approach popular in evaluation research for understanding program causality.
• Focuses on how programs work, contextual factors, and outcomes.
• Enhances theoretical explanations of causal interactions in qualitative studies.

Purpose of the Study:

• To present a realist-informed data analysis approach for qualitative data.
• To explore causal interactions within qualitative datasets.
• To demonstrate a method for robust program evaluation.

Main Methods:

• Employed a four-step realist approach rooted in critical realism.
• Utilized retroductive theorizing for qualitative data analysis.
• Steps included: category identification, context-mechanism-outcome configuration, demi-regularities identification, and generative mechanism refinement.

Main Results:

• The four-step analysis reveals underlying causal mechanisms and interactions.
• Generates comprehensive causal explanations of program functioning.
• Identified patterns, pattern fluctuations, and confirmed mechanisms.

Conclusions:

• Realist data analysis, through retroductive theorizing, underpins causal understanding.
• The four-step method facilitates identification and confirmation of generative mechanisms.
• Provides valuable insights for complex decision-making affecting communities.