Policy & Regulation

This guide explores opportunities to advance equity in quantitative analysis, including by recognizing common biases (e.g., research and measurement bias) and discussing specific quantitative methods and tools to help quantitative analyses address equity.

This tip sheet highlights the importance of using equitable communication and includes tips, guiding questions, and additional resources. The information is organized around three key strategies for equitable communication:

This guide explores intersectionality and why it is essential for advancing equity. It explores strategies for planning and conducting research with an intersectional lens, describes quantitative measures and methods to examine differences across groups of people with intersecting identities, and provides key considerations for using qualitative data to better understand intersectionality.

This tool describes how to conduct intensive equity assessments of existing programs, policies, and processes. The tool first describes the scoping work that needs to be done before conducting an equity assessment and then describes and provides guiding questions for each of the six steps in the in-depth equity assessment process. Those six steps are:

This tool describes the six steps for conducting equity assessments and provides tips for completing each step. Although specific steps vary by equity assessment tool and context, core steps include:

The Early Childhood Systems Collective Impact Project (ECS Collective Impact Project) will help to re-envision a truly coordinated approach to program implementation designed to advance equitable early childhood and family well-being outcomes across federal programs that support expectant parents, children ages 0 to 8, and their families.

Methodological Description

Using a national Medicaid database, the report shows significant racial/ethnic disparities in mental health service use among children during the COVID-19 pandemic.

In recent years several researchers and child welfare agencies have begun developing predictive risk models to support child welfare decision-making. Predictive analytics is a sophisticated form of risk modeling that uses historical data to understand relationships between myriad factors to estimate a probability score for the outcome of interest.

This Data Point presents changes in HealthCare.gov enrollment by race and ethnicity from 2015-2022 using a validated imputation method for missing information on race and ethnicity.