Data Analysis

Data analysis transforms raw data into meaningful insights that drive evidence-based decision-making in food security assessments. The process includes both quantitative and qualitative approaches, each contributing unique value to understanding food security dynamics.

Quantitative Data Analysis

Quantitative analysis involves applying statistical methods to numerical data collected through surveys and assessments. This process enables teams to summarize, compare, and interpret key indicators such as the food consumption score, food and livelihood-based coping strategies, and food expenditure share.

Key Steps in Quantitative Analysis:

• Indicator Calculation: Compute standard food security indicators using WFP methodologies

• Descriptive Statistics: Calculate frequencies, means, medians, and proportions for key variables

• Disaggregation: Analyze results by relevant subgroups  to identify disparities.

Qualitative Data Analysis

Qualitative analysis complements quantitative findings by providing context and deeper understanding of the “why” behind the numbers. This involves systematically reviewing open-ended responses, focus group discussions, and key informant interviews.

Key Steps in Qualitative Analysis:

• Data Organization: Transcribe and organize data

• Thematic Coding: Identify recurring themes, patterns, and insights

• Triangulation: Cross-check qualitative findings with quantitative results for a comprehensive picture