Introduction
Research Questions
- How and to what extent do child welfare agencies and their partners collect and use data to advance equity?
- What emerging practices across the data life cycle support child welfare agencies in their efforts to advance equity?
- What factors support the implementation and use of emerging data practices intended to advance equity?
- What challenges and barriers may impede the implementation and use of emerging data practices to advance equity?
- What opportunities exist at agency and system levels to support child welfare agencies in using data to advance equity?
This case study highlights two data practices the Children’s Services Administration (CSA) in the Michigan Department of Health and Human Services (MDHHS) and select counties use to promote equity in their child welfare system. The first data practice, the Race Data Collection Project (RDCP), aims to improve collection of data on youth and families’ race, ethnicity, culture, and heritage. The second, anonymous removals in Kent County, intentionally omits data in hopes of more equitable decisions during removal meetings. The case study describes the motivation and context for CSA’s and the counties’ equity work, identifies facilitators and barriers to implementing the data practices, and explores opportunities for furthering the data practices.
Purpose
This case study is part of a series of case studies that explore data practices child welfare agencies and their partners use to promote equity for families served by the child welfare system. The case studies were conducted as part of the Child Welfare Study to Enhance Equity with Data (CW-SEED) project and explored: how data are used to understand and address equity in service delivery and child and family outcomes; barriers or problematic practices; and child welfare agencies’ and their partners’ efforts to reduce barriers to equity across the continuum of child welfare services. Information in this case study may be useful for child welfare agency staff and their partners who aim to use data to support their equity efforts.
Key Findings and Highlights
Data practices:
Race Data Collection Project: The RDCP aims to improve collection of race, ethnicity, culture, and heritage data from all families involved with child welfare. First, staff are trained to collect these data. Caseworkers then collect youth and families’ self-reported identity and enter it into Michigan’s Statewide Automated Child Welfare Information System (MiSACWIS). The RDCP includes nine counties and some members of the statewide Centralized Intake Unit.
Anonymous removal meetings in Kent County: Kent County intentionally removes key identifiers during removal meetings and analyzes data to determine whether they lessen racial disproportionality among youth of color in out-of-home care in the county.
Implementation supports: Key implementation supports included using leadership to promote staff buy-in; leveraging existing diversity, equity, and inclusion practices; strengthening staff skills and interest through training and support; and having diverse staff representation to support data collection.
Implementation challenges: Key implementation challenges included MiSACWIS race and ethnicity data field limitations; staff discomfort or unfamiliarity with talking about race, ethnicity, culture, and heritage with families; need for ongoing data collection training to onboard new staff and help current staff address data collection challenges; data collection burden on Centralized Intake staff; technical challenges to documenting American Indian/Alaska Native identity and Tribal membership in MiSACWIS; and difficulty analyzing disproportionality.
Opportunities: CSA is developing recommendations for the new Comprehensive Child Welfare Information System (CCWIS)’s data elements on race and considering how the data system can strengthen anti-racist data collection practices. In addition, interviewees discussed various ways data collection could expand and support other data equity practices, such as collection of data on sexual orientation and gender identity and expression (SOGIE).
Methods
Site identification. The CW-SEED project team gathered recommendations for potential case study sites from several sources, including: the project’s environmental scan of equity-focused data practices, project team members, the Administration for Children and Families’ regional program managers, and the CW-SEED expert group. The project team held preliminary information calls with child welfare agency staff in each site.
Data sources and data collection methods. The project team requested and reviewed documents related to the data practices and tailored semi-structured interview protocols to guide the site visit data collection. The primary data sources for each case study include information from the jurisdiction selection process, jurisdiction-specific documents, notes from interviews, focus group discussions, and observations.
Data analysis and case study findings. The project team developed a codebook and conducted qualitative analysis by coding the data sources using NVivo software. They exported codes and used them to identify key findings.
Citation
Caffrey, G., Coccia, A., Spielfogel, J., Weigensberg, E., & Bess, R. (2024). Case Study for the Child Welfare Study to Enhance Equity with Data (CW-SEED): Michigan Department of Health and Human Services’ Children’s Services Administration, OPRE Report #2024-286. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
Related Documents
Fung, N., Sherman, D., Spielfogel, J., Miller, M., Weigensberg, E., & Bess, R. (2024). Case Study for the Child Welfare Study to Enhance Equity with Data (CW-SEED): Allegheny County Department of Human Services, OPRE Report #. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services
Gemignani, J., Coccia, A., Spielfogel, J., Miller, M., Weigensberg, E., & Bess, R. (2024). Case Study for the Child Welfare Study to Enhance Equity with Data (CW-SEED): Talbot County, Maryland, Department of Social Services, OPRE Report #2024-304. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
Gemignani, J., Spielfogel, J., Coccia, A., Weigensberg, E., & Bess, R. (2024). Case Study for the Child Welfare Study to Enhance Equity with Data (CW-SEED): Cuyahoga County Division of Children and Family Services, OPRE Report #2024-285. Washington, DC: Office of Planning, Research, and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
Lewis, G., Spielfogel, J., Weigensberg, E., & Bess, R. (2024). Case Study for the Child Welfare Study to Enhance Equity with Data (CW-SEED): New York Office of Children and Families Child Welfare Services, OPRE Report #. Washington, DC: Office of Planning, Research and Evaluation, Administration for Children and Families, U.S. Department of Health and Human Services.
Glossary
- Data:
- Information collected about individuals and families that come into contact with the child welfare system. Data include information about age, gender identity, disability, race/ ethnicity, and descriptive information such as how a household is structured or the events that led to a child’s placement in out-of-home care. In this study, we are particularly interested in data or information that can help assess and address equity—or inequities—in the child welfare system at the local level.
- Data practices:
- Activities that involve data, which includes data planning, collection, access, and analysis; use of statistical tools and algorithms; and data reporting and dissemination.
- Data life cycle:
- Five sequential stages that depict how data move through the earliest stages of data planning to eventual reporting and dissemination.
- Disparity:
- The unequal outcomes of one group compared with outcomes for another group (Child Welfare Information Gateway 2021).
- Disproportionality:
- The underrepresentation or overrepresentation of a particular group when compared with its percentage in the general population (Child Welfare Information Gateway 2021).
- Equity:
- The consistent and systematic fair, just, and impartial treatment of all individuals, including individuals who belong to underserved communities that have been denied such treatment, such as Black, Latino, and Indigenous and Native American persons; Asian Americans and Pacific Islanders and other persons of color; members of religious minorities; LGBTQI+ persons; persons with disabilities; persons who live in rural areas; and persons otherwise adversely affected by persistent poverty or inequality. This definition is consistent with President Biden’s Executive Order 13985, Advancing Racial Equity and Support for Underserved Communities Through the Federal Government (White House 2021).