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.

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

🚨New series coming up: How to close the Gender Data Gap in Mobility Research🚨 Mobility data influences how we plan our cities, fund innovations, and decide whose daily life counts in transport planning and design. But too often, that data leaves out large parts of the population 😱. I wrote an article and it is not really accessible and maybe even too scientific for non-scientificy people 🤓. That’s why I want to start this series now and summarise the content over the following posts. Each post will focus on one part of the article and follow the process of empirical research from defining questions to interpreting results. They will shed light on where gender biases can sneak in, and how we can do better. It’s not just about the sample composition (but a huge part of course). It’s about how we build knowledge, whose mobility gets seen, and who stays invisible when we try to find out new things. 📚 The article is, unfortunately, really tricky to access via official channels (don’t ask 🙃 I will probably write about the absurd issues with this publishing experience in another post.), but the full preprint is freely available here: 🔗 https://lnkd.in/eDJCJAVj Here is the official link to purchase the article (and others) for 3.42 €  from the archives (!!!🤦♀️!!!)  https://lnkd.in/eqUPFPpV   But here is the gist of it: In transport planning, desing and policy, data is everything. But what happens when the data we use doesn’t reflect the reality of half the population? The Gender Data Gap describes the problem that women* are often underrepresented in mobility data or not represented correctly. It's not just about how many women are included in surveys, but whether we ask the right questions to understand their daily mobility needs. For example, care-related trips, safety concerns, and complex travel chains are part of many women’s everyday mobility but they’re often left out of standard data collection. This leads to biased conclusions and transport solutions that don't work for everyone. This article offers practical recommendations on how to improve mobility data so it better reflects different user realities. It explains why gender matters in everyday mobility, outlines five common biases in data collection, and shows step by step how to design better surveys and analyses. Using examples from the German Mobility Panel (MiD 2017), it illustrates how gender-sensitive data can lead to more accurate, inclusive, and fair transport planning. Whether you work in transport, urban planning, data, or care about equity in mobility: I hope you'll find the insights useful and apply them to make your data more inclusive and valid.🚶♀️🧾🚲   #GenderDataGap #InclusiveMobility #MobilityJustice #TransportEquity #DataMatters #MobilityResearch