This project presents a qualitative baseline analysis conducted to understand community perceptions of safety, trust in security actors, and access to essential services in post-conflict areas in Somalia. The findings aim to inform evidence-based programming and security sector interventions.
- Project Overview
- Objectives
- Methodology
- Key insights
- How the Insights Inform Future Programming
- Reflection and Lessons Learned
- Conclusion
This qualitative analysis was conducted as part of a larger baseline assessment to inform the development of a pilot security project in Somalia. The overarching goal was to understand community perceptions of safety, trust in law enforcement, and access to essential health and security services in newly liberated areas.
Between December 2024 and January 2025, I had the opportunity to travel to three post-conflict districts in Somalia, my first time working in such contexts. These were locations emerging from prolonged insecurity, where community structures were rebuilding and formal policing was still limited. This context shaped both the depth of the field experience and my personal growth as an analyst.
The qualitative component of the baseline assessment included 90 Focus Group Discussions (FGDs) – 60 with men and 30 with women – and 23 Key Informant Interviews (KIIs), comprising district commissioners and security actors across three post-conflict districts. These were complemented by household surveys, all designed to capture the perspectives of a wide range of community members, including women, youth, elders, local authorities, and security actors.
I was responsible for leading the analysis of the qualitative data (FGDs and KIIs). My contributions included:
- Cleaning and organizing raw transcripts and survey responses for analysis
- Structuring the dataset into a coding matrix by theme, location, gender, and question
- Applying a predefined thematic coding framework to all responses
- Synthesizing findings thematically across gender and geographic location
- Triangulating FGD and KII responses to identify both dominant trends and outlier perspectives
- Writing structured, evidence-informed narratives for each thematic area
To analyze community perceptions of security, policing, and access to essential services across three post-conflict districts in Somalia. This qualitative analysis aimed to support program design by identifying service gaps, local strengths, and community-defined priorities. While the raw data remains confidential, the process, methods, and analytical approach are shared to demonstrate applied qualitative research and thematic analysis skills.
This qualitative analysis employed thematic analysis using a deductive approach, where themes were predefined based on the project framework and research questions. Data from FGDs and KIIs was manually organized and coded in Microsoft Excel, allowing for structured synthesis without the use of specialized software. Rather than developing themes inductively from scratch, responses were mapped against existing thematic categories. This ensured alignment with the program’s information needs while maintaining flexibility to capture nuanced insights.
The analysis process included:
- Applying predefined thematic categories across all FGD and KII responses
- Manually assigning codes in Excel based on relevance to each theme
- Cross-checking responses by location, gender, and respondent type to identify both common and divergent patterns
- Synthesizing codes into final narratives for each theme using a structured, comparative approach (triangulation)
Due to the sensitivity and confidentiality of the project, specific findings and raw data cannot be disclosed. However, the analysis provides a broader understanding of the structural, social, and institutional dynamics shaping community safety and service access in post-conflict districts.
Across the three assessed locations, notable differences emerged in the availability and accessibility of formal policing infrastructure. Some districts had functioning police stations within reach of residential areas, while others lacked such facilities entirely. These disparities directly influenced how communities experienced and engaged with law enforcement. Where infrastructure was in place, residents reported greater trust, quicker response times, and improved coordination. In contrast, areas without proper facilities faced challenges in accessing justice and supporting security personnel, despite a clear willingness to collaborate.
Throughout all locations, traditional leadership, particularly local elders, remained a cornerstone of community stability. Elders were not only trusted to mediate disputes but also played an essential role in bridging communication between the community and formal institutions. Their involvement was described as necessary to ensure any form of effective engagement.
Overall, the findings underscore the community’s strong internal cohesion, the importance of establishing foundational security infrastructure, and the need to better align formal policing with local governance mechanisms. These insights offer important direction for designing responsive, community-centered programming in similar post-conflict contexts.
While the specific findings remain confidential, the thematic insights extracted from the data offer vital direction for program design and strategic planning. By surfacing community-defined priorities such as the importance of trusted local actors, the need for accessible police infrastructure, and gaps in service delivery, the analysis helps ensure that interventions are locally grounded, evidence-based, and responsive to context.
The triangulated data analyzed across gender, location, and theme also supports targeted programmatic adaptation, identifying which districts require foundational infrastructure versus those needing reinforcement of existing structures. Going forward, the insights will be used to inform community engagement strategies, shape priorities for safety and justice initiatives, and guide resource allocation in post-conflict recovery efforts.
This project marked my first professional experience analyzing qualitative data from post-conflict districts, an opportunity that deepened both my technical and contextual understanding of humanitarian analysis. Working with data collected directly from communities navigating fragile transitions sharpened my ability to listen for nuance, recognize patterns, and synthesize grounded insights in a respectful and ethical manner.
One of the most meaningful aspects of this experience was learning how to balance rigorous thematic analysis with contextual sensitivity. I developed a stronger grasp of how trust, proximity to services, and social cohesion shape people's lived experiences of security and justice. I also learned how to structure qualitative insights, organizing responses by theme, location, and gender while aligning with confidentiality standards and M&E best practices.
Technically, I strengthened my skills in manual coding using Excel, building matrices, and triangulating findings. More importantly, I came away with a clearer vision of what it means to translate voices into actionable recommendations without compromising ethical standards or overstepping what the data can support.
This qualitative analysis was part of a broader effort to understand perceptions of safety, policing, and access to services in newly stabilized areas of Somalia. Through thematic analysis of community voices across three districts, the project surfaced key social dynamics and infrastructure challenges shaping local security outcomes. While the specific insights remain confidential, the process highlighted the importance of community-centered approaches, particularly the central role of local leadership structures and the limitations faced by formal security services in under-resourced settings.
This experience reinforced my capacity to handle sensitive data, apply thematic frameworks, and extract meaningful insights that reflect real-world complexity. It also affirmed my interest in using data storytelling to inform more inclusive, locally grounded programming.