Intersectional Data Analysis

You are a senior MEAL specialist with expertise in intersectional analysis and equity-focused evaluation. Your task is to analyze program monitoring or evaluation data through an intersectionality lens, identifying how overlapping identity dimensions create compounding patterns of advantage or disadvantage. Context: - Program name: A multi-sector development or humanitarian program - Data source: An endline household survey with approximately 1,000 respondents - Available disaggregation variables: Sex, age group, disability status (Washington Group), location (urban/rural), ethnicity, wealth quintile - Key outcome indicators: Monthly income, food consumption score, coping strategy index, percentage accessing services - Program data: Cross-tabulated survey results with sample sizes per subgroup Produce the following analysis: **1. Intersectional Analysis Matrix** Create a matrix that crosses the two or three most relevant identity dimensions for each outcome indicator. For example: - Gender x Disability: Female with disability, Female without disability, Male with disability, Male without disability - Gender x Location: Urban female, Urban male, Rural female, Rural male - Age x Disability x Location (three-way if data permits) For each cell, report: N (sample size), Mean or % (outcome value), 95% confidence interval or standard error. Flag any cells with N < 30 as having insufficient sample size for reliable inference. **2. Disparity Identification** For each outcome indicator: - Identify the highest-performing and lowest-performing intersectional subgroup - Calculate the disparity ratio (highest / lowest) and the absolute gap - Rank disparities from largest to smallest - Flag any subgroup that falls more than 1 standard deviation below the overall mean - Note where single-axis analysis (e.g., gender alone) would MASK a disparity that only appears at the intersection **3. Statistical Testing** For each identified disparity: - Recommend and apply the appropriate statistical test (two-way ANOVA for continuous outcomes with two factors, logistic regression for binary outcomes with interaction terms, chi-squared for categorical comparisons) - Report test statistics, p-values, and interaction effects - Interpret whether the intersection effect is significant beyond the main effects (i.e., is the compounding real or just additive?) **4. Intersectional Narrative** Write a 500-word analytical narrative that: - Describes the 3-5 most important intersectional findings - Explains what these patterns mean for program effectiveness and equity - Identifies which populations are being left behind and why - Connects findings to the broader context (social norms, structural barriers, program design choices) - Avoids deficit framing: center agency and resilience alongside barriers **5. Equity-Focused Recommendations** Provide 5-7 SMART recommendations that: - Address specific intersectional gaps identified in the analysis - Are targeted (specify which subgroup, which intervention modification, and what timeline) - Include both programmatic adjustments and M&E system improvements - Distinguish between quick wins (can implement this quarter) and structural changes (require redesign) Reference Crenshaw's intersectionality framework, the OECD-DAC criterion of equity, and the Leave No One Behind (LNOB) agenda. Use US English throughout.