15.4 Analyzing and reporting evaluation results

Health campaigns rely on data analysis to measure their impact and improve strategies. This process involves preparing data, exploring trends, and selecting appropriate analysis methods. Evaluating effectiveness requires both quantitative and qualitative techniques, with triangulation strengthening conclusions.

Interpreting results goes beyond statistical significance, considering practical impact and context. Effective communication of findings to stakeholders is crucial, involving structured reports, data visualizations, and tailored messaging. Transparency and actionable recommendations are key to driving improvements in future campaigns.

Data Analysis and Interpretation

Process of campaign data analysis

• Data preparation involves cleaning and organizing raw data, checking for missing or inconsistent values, and coding qualitative data for analysis
• Exploratory data analysis examines data distributions and trends, identifies outliers or anomalies, and conducts descriptive statistics (mean, median, standard deviation)
• Selecting appropriate analysis methods determines the type of data (quantitative or qualitative), considers the research questions and hypotheses, and assesses the sample size and data distribution

Techniques for effectiveness evaluation

• Quantitative analysis techniques:
  • t-tests compare means between two groups
  • ANOVA compares means among multiple groups
  • Correlation analysis examines relationships between variables
  • Regression analysis predicts outcomes based on predictors
• Qualitative analysis techniques:
  • Thematic analysis identifies patterns and themes in data
  • Content analysis categorizes and quantifies qualitative data
  • Discourse analysis examines language and communication patterns
• Triangulation of data combines quantitative and qualitative findings to strengthen the validity and reliability of conclusions

Interpretation of evaluation results

• Assessing statistical significance determines if observed differences are likely due to chance or intervention using p-values and confidence intervals
• Considering practical significance evaluates the magnitude and direction of effects and assesses the real-world impact of findings
• Contextualizing results compares findings to previous research or benchmarks and considers the limitations and generalizability of the study
• Drawing conclusions and making recommendations synthesizes key findings and insights and identifies areas for improvement or future research

Reporting and Communication

Communication of findings to stakeholders

• Structuring the report includes:
  1. Executive summary for key takeaways
  2. Introduction and background
  3. Methodology and data collection
  4. Results and findings
  5. Discussion and conclusions
  6. Recommendations and next steps
• Presenting data effectively uses tables, graphs, and visualizations to convey information and ensures data visualizations are accurate, clear, and accessible
• Tailoring the report to the audience considers the needs and interests of different stakeholders (policymakers, funders, community members) and uses appropriate language and level of detail
• Highlighting key insights and actionable recommendations emphasizes the most important findings and conclusions and provides specific, feasible recommendations for improvement
• Ensuring transparency and accountability discloses limitations and potential biases and provides access to raw data and analysis files when appropriate