review · Evaluation, Analysis, Problem Solving · 120–480 min · 1-10 people · medium energy

Realist Evaluation

Realist evaluation is a theory-driven approach that seeks to understand how and why interventions work (or don't) in specific contexts by identifying the underlying causal mechanisms at play. It helps to develop context-mechanism-outcome (CMO) statements, providing a nuanced understanding of program effectiveness.

When to use Realist Evaluation

Use realist evaluation when you need to understand the complexities of a program, especially when implemented across multiple settings, and when you want to learn how to adapt interventions to suit specific contexts for scaling up or rolling out.

What it solves

Lack of understanding of why a program works in some contexts but not others; difficulty in adapting programs to new settings; superficial evaluation findings that don't provide actionable insights.

How to run Realist Evaluation, step by step

  1. Develop an initial program theory based on existing knowledge, research, and assumptions about how the intervention is expected to work. (60 min)
  2. Collect data (qualitative and quantitative) to test the different elements of the program theory, focusing on context, mechanisms, and outcomes. (Variable)
  3. Analyze the data to identify patterns and develop CMO configurations that explain the observed outcomes. (120 min)
  4. Refine the program theory based on the evaluation findings, modifying it to reflect the complexities of the intervention and its context. (60 min)
  5. Disseminate the findings and use them to inform decision-making about program adaptation and scaling. (30 min)

Materials needed

  • Program documentation
  • Interview guides
  • Data analysis software (e.g., Excel, NVivo)
  • Whiteboard or virtual collaboration tool

Facilitator tips

  • Ensure that the initial program theory is well-defined and testable.
  • Use a mix of qualitative and quantitative data to provide a comprehensive understanding of the intervention.
  • Involve stakeholders in the evaluation process to ensure that the findings are relevant and useful.

Common pitfalls

  • Failing to adequately define the initial program theory.
  • Collecting data that is not relevant to the program theory.
  • Overly complex CMO configurations that are difficult to interpret.
  • Ignoring the role of context in shaping outcomes.

Variations

  • Focus on specific aspects of the program theory.
  • Use different data collection methods.
  • Involve different stakeholders in the evaluation process.

Running it online or hybrid

Use shared documents and virtual whiteboards to collaborate on CMO statements. Schedule regular video check-ins to discuss findings and refine theories.

What it produces

A refined program theory articulated through context-mechanism-outcome (CMO) statements that explain how the intervention works in different contexts.

Origin

Adapted from BetterEvaluation.org — source

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