The UCLA Data Equity Center (DEC) has launched a video series to improve the quality of research by offering training to ensure that overlooked populations and their needs are accurately represented in data.

From questionnaire design to language access to weighting and imputation techniques, the seven modules provide easy-to-follow, practical techniques on how to apply a data equity lens throughout every stage of a project.

Whether you're a data producer or custodian, or lead of a project or organization who needs assistance in achieving your data equity goals, the DEC is here to help!

Housed at the UCLA Center for Health Policy Research (CHPR), the DEC was founded in 2022 to focus on equity in all aspects of the design, collection, production, and dissemination of population health data, which governments, businesses, advocacy organizations, and philanthropies use to inform their decisions.

High-quality, representative data is achieved through the principles of data equity, which engage community to inform and improve the project lifecycle.

The series is designed to provide overviews and practical examples for implementation of each concept, and features trainings from UCLA CHPR and DEC staff and several technical assistance partners, including NORC at the University of Chicago, Market Decisions Research, and Cardea.

Introduction: Applying a Data Equity Lens to Data Systems: In this introductory video, UCLA CHPR Director Ninez A. Ponce, PhD, MPP, provides an overview of the principles of data equity, the benefits of representative data, and a framework for applying a data equity lens across the data lifecycle.

Using examples from the UCLA CHPR's California Health Interview Survey (CHIS) and Native Hawaiian and Pacific Islander (NHPI) Data Policy Lab, Ponce shares how disaggregated data improves quality and precision, especially for underserved communities.

“Data equity is not just about fairness in numbers — it's about shifting power,” says Ponce. “It means designing data systems that reflect the complexity of people's identities and giving communities the tools to tell their own stories and shape the policies that affect them.”

Engaging Relevant Communities to Achieve Data Equity: This presentation by UCLA Data Equity Center Director AJ Scheitler, EdD, describes the importance of meaningful community engagement, and offers a roadmap for collaborating with community members and organizations in each step of the data collection process.

Applying a Data Equity Lens to Questionnaire Design: This training by Market Decisions Research provides a comprehensive overview of how to apply a data equity lens to questionnaire and discussion guide design, emphasizing inclusive, community-engaged, and bias-reducing practices to improve the accuracy, accessibility, and fairness of data collection.

Language Considerations to Promote Data Equity: This module by Cardea examines the critical role of language in facilitating equitable access to, analysis of, and dissemination of data, including community-level participation and engagement throughout every phase of the data lifecycle, which is essential to advancing equity in health data systems.

Ensuring Fair Representation of All Communities in Data Analysis: Presented by NORC at the University of Chicago, this training equips participants with a structured framework and actionable strategies to ensure that data analysis accurately reflects the experiences and needs of all communities — emphasizing representative data systems and ethical data practices.

Applying Weights to Achieve Fair Representation: This training by NORC focuses on applying sample weighting techniques to improve the representativeness of survey data, covering design weights, nonresponse adjustments, and calibration methods, such as post-stratification and raking, to ensure accurate generalizations of the sample results to population subgroups.

Applying Imputation to Achieve Fair Representation: Presented by NORC, this module explores the application of imputation techniques to promote fair representation in survey research, focusing on identifying missing data patterns and implementing appropriate single and multiple imputation methods to reduce bias and improve analytical validity.

If your organization would like technical assistance applying the principles in the modules to your specific projects, please email AJ Scheitler.

A shared learning and training hub for building fairness in data across all health-related sectors, the DEC provides technical assistance, expertise, and resources to increase the representation in and access to data for marginalized populations.