Collecting, analyzing and communicating data are not neutral activities. The process of creating evidence is dependent on the world view and cultural values of the people involved - from those funding the data project to those doing the analysis, and those being studied. Even an apparently simple mathematical process such as taking an average is highly influenced by your point of view. Understanding quantitative data analysis from an equity perspective will allow for deeper and nuanced work in the areas of grantmaking, impact evaluation, needs assessment and public policy-making.

This session will be devoted to showing step by step processes you can take to embed equity into quantitative work in your portfolio. Participants will learn to use checklists to help surface bias, sexism, racism, and other types of latent discrimination in the way we work with data. We’ll use the seven-step Framework for Equity in quantitative data that includes funding, design, analysis, and communication. Working through an example involving a real-world quantitative evaluation, we’ll demonstrate how different world views and gender perspectives can be used to make different choices in statistical methods and how these lead directly to different results. We’ll share simple tools and checklists to embed equity into these quantitative results. The stories and examples are accessible to funders, leaders, and practitioners with intermediate data analysis experience and also adventurous beginners. Checklists will be provided so you can take away the learning and apply it directly to your own portfolios and projects.