Feminist data collection methods

Gender-sensitive data collection and estimation are essential for producing statistics that reflect the realities of both women and men. This training module was developed under the Asia-Pacific Network of Statistical Training Institutes to provide statisticians, researchers and civil society with practical guidance on integrating gender perspectives into data processes, from collection to estimation and analysis . This module covers the following key aspects: – Rationale and learning objectives for mainstreaming gender in data systems – Integration of gender considerations in censuses, administrative records, registries and household surveys – Specific guidance for time-use surveys and violence against women surveys, addressing design, sampling and interviewer training – Common gender biases in data processes and strategies to minimise them through careful design and training – Methods for gender data estimation, including identifying research questions, applying international standards and developing tabulation plans – Use of internationally agreed metadata and repositories (UNSD, ILO, WHO, UNESCO, FAO) to align concepts and methods – Recommendations for multi-level sex disaggregation and intersectional analysis across population groups The content emphasises that gender must be integrated at all stages of statistical work—from questionnaire design and sample selection to interviewer training and coding—to avoid bias and ensure relevance. By using international standards, engaging gender specialists and applying careful disaggregation, the module equips practitioners to generate more accurate, inclusive and policy-relevant gender statistics that can inform sustainable development and social equity.

Sometimes in gender programming, it can feel like we’re doing enough because we’re collecting “gender data.” But not all gender data tells the same story. A quick reminder as you design, implement, or review programmes: We often rely heavily on sex-disaggregated data and stop there. It tells us 𝑤ℎ𝑜 is affected, but it doesn’t always explain ℎ𝑜𝑤 or 𝑤ℎ𝑦 inequality is playing out. That’s where other layers of data matter. 👉 Gender-disaggregated data helps us understand different experiences and barriers. 👉 Intersectional data shows how gender overlaps with age, disability, class, location, and more. 👉 Qualitative data brings out lived realities—norms, safety concerns, and power dynamics that numbers miss. When we only rely on one type of data, we risk designing programmes that look inclusive on paper, but miss the real barriers in practice. Worth keeping close when designing or reviewing interventions. #GenderData #GenderAnalysis #GenderResponsiveProgramming #MEL #GenderEquality

When doing surveys I see often - people think inclusion is something we can add toward the end. Like inclusion is the final filter before the data collection tool. What we can miss in that dangerous assumption is - it (inclusion) shows up earlier—quietly—inside the answer options. If your survey has: ● one box for identity, ● one box for gender, ● one box for “race/ethnicity” with three choices, ● and an “Other” option that feels like a catch-all or afterthought, you didn’t just create messy data. You created a message. That message is: we didn’t design this with your experiences in mind. The thing is – surveys (or for that matter any data collection tool) is not neutral. It can create harm. It is a mirror of your values as well as boundaries. A few tiny practices that change everything: ● allow multiple selections ● make “prefer not to say” normal ● add “self-describe” where it matters ● don’t treat open text as a nuisance ● stop confusing “simple” with “respectful” Thinking of engaging in data collection work this year? Review those tools carefully and expand those boxes to reflect more experiences. #nonprofits #nonprofitleadership #community

What exactly is a gender analysis - and how do you actually do one? This guide breaks it down step-by-step. It helps you to... Understand what a gender analysis is → It’s not just about “adding women”—it’s about examining roles, responsibilities, access, control, and decision-making based on gender and other intersecting identities. Gather background information → Review existing policies, statistics, and literature relevant to gender in your sector and context. Collect data through multiple sources → Use interviews, focus group discussions, surveys, and observations—with both women and men, across age and ability. Analyse power and inequality → Look at who has access to resources, who makes decisions, whose voices are heard—and who is invisible. Disaggregate everything → Break down data by sex, age, disability, and other identity markers to spot patterns and disparities. I love that the guide includes checklists, sample questions, and planning templates. ----- 🔔 Join the Monitoring and Evaluation Academy for more tips https://lnkd.in/epqEsMF6 #GenderAnalysis