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Early Childhood Education Research Alliance

Research Alliance Quicktabs

Research alliances connect practitioners, researchers, and policymakers around regional education challenges. Alliances are tasked with addressing these challenges through regional research, technical assistance, and dissemination projects. Regional Educational Laboratory Midwest supports eight alliances.

Carrie Scholz

Interim Alliance Lead
Carrie Scholz, Senior Researcher at AIR
View Bio

Please connect with us for more information about the alliance or to get involved.

Alliance GoalsAlliance Goals

The Early Childhood Education Research Alliance’s (ECERA) goals are to use research to define, measure, implement, and evaluate the elements of quality in the context of today’s early childhood education systems. The alliance brings together early childhood stakeholders in the region to create a shared research agenda that will ultimately improve the quality of education for young children, birth through age 8. The alliance conducts research and technical assistance projects related to the Quality Rating and Improvement System (QRIS) process, professional development and quality improvement supports, and integrated data systems linking data on young children.

Guiding Research QuestionsGuiding Research Questions

  1. What are the key issues related to assessing, communicating, and improving quality in early childhood education settings in the Midwest?
  2. How do Quality Rating and Improvement Systems (QRISs) in Midwest states rate programs and support quality improvement activities?
  3. In states with early childhood integrated data systems, how is interagency collaboration facilitated and what lessons have been learned?


Carrie Scholz

Interim Alliance Lead
Carrie Scholz, Senior Researcher at AIR
View Bio

Please connect with us for more information about the alliance or to get involved.

Alliance MembersAlliance Members

Bobbie Burnham, Minnesota Department of Education

Wendy Grove, Ohio Department of Education

Jill Haglund, Wisconsin Department of Public Instruction

Erin Kissling, Indiana Department of Education

Richard Lower, Michigan Department of Education

Penny Milburn, Iowa Department of Education

Nathan Williamson, Indiana Department of Education

Kimberly Villoti, Iowa Department of Education


Alliance MembersAlliance Researchers

Ann-Marie Faria, Ph.D., Principal Researcher

Laura Hawkinson, Ph.D., Researcher

Eboni Howard, Ph.D., Managing Researcher

Emily Loney, Researcher


Technical Assistance ProjectsTechnical Assistance Projects Develop systems, surveys, and other tools to help stakeholders apply data to their work

Documenting the “I” in QRIS (Quality Rating and Improvement Systems): Developing a Survey Instrument and Providing Technical Assistance to Support States’ Quality Improvement Efforts in Early Childhood Education Quality (2014)

Goal: To support Iowa in collecting high-quality data on quality improvement activities undertaken by programs participating in Iowa’s QRS (quality rating system).


  • REL Midwest and the Iowa QRS Oversight Committee codeveloped a survey instrument that Iowa and other Midwestern states can use to collect data on the improvement activities and the strategies used by programs participating in a QRIS.
  • REL Midwest trained state administrators in Iowa and other states in survey methods to improve state capacity to conduct high-quality survey research and provided technical assistance to support Iowa in administering the survey.
  • Iowa administered the survey in fall 2014 and will work with REL Midwest to use the data to inform decisions about allocating resources for quality improvement activities across the state.

Quality Rating Improvement Systems in the Midwest: Features, Successes, and Challenges (2013–15)

Goal: To examine the design and the implementation of QRISs in the seven REL Midwest states to help inform states’ decisions as they expand and refine their QRISs.


  • Collect and analyze information about different state approaches to QRIS design and implementation.
  • Describe the unique and common features of QRIS in Midwestern states, including information about defining quality (the “Q”), calculating ratings (the “R”), describing quality improvement activities (the “I”), and detailing features of the system (the “S”).
  • Identify successful strategies in QRIS implementation as well as challenges and state approaches to address these challenges.

Developing Integrated Statewide Early Childhood Data Systems (2014–15)

Goal: To understand how states facilitate interagency collaboration to arrive at an agreed-on and integrated early childhood data system.


  • Conduct a selective scan of states with existing or well-conceptualized early childhood data systems.
  • Gather information from states that have existing or well-conceptualized early childhood data.
  • Gather information on lessons learned from states that have integrated early childhood data systems.
  • Produce a technical report that highlights states’ approaches, successes, and challenges to designing and implementing statewide early childhood data systems.


Research ProjectsResearch Projects Collect and analyze data and summarize information for dissemination

Examining Alternative Calculation Approaches in Michigan’s Quality Rating and Improvement System (2013–14)

Goal: To help stakeholders in Michigan and nationally understand how changes to the calculation system in a QRIS may affect the number of programs rated at each quality level by testing the sensitivity of ratings to alternative rating calculation approaches.


  • Documented how different components of the ratings were related to each other and the overall QRIS rating in Michigan’s original QRIS calculation approach.
  • Described and compared QRIS ratings under Michigan’s original and revised rating calculation approaches. 

Examining Quality Improvement Activities and Associations With Early Childhood Program Ratings in Iowa’s Quality Rating System (2015–16)

Goal: To understand how early childhood programs participating in Iowa’s Quality Rating System (QRS) approach professional development and quality improvement. To identify barriers to professional development and quality improvement. To identify promising approaches to improving QRS ratings over time.


  • Use two sources of data about child development homes and centers participating in Iowa QRS: administrative data on program characteristics and QRS ratings and survey data on professional development and quality improvement activities.
  • Describe programs’ participation in professional development and other quality improvement activities.
  • Examine the relationships between different types of quality improvement activities and increases in Iowa QRS ratings over two time points.