Many studies have shown that higher-quality early care and education (ECE) predicts positive developmental gains for the children who experience it. However, much ECE in the United States is not of sufficiently high quality to produce these benefits.
The purpose of this document is to encourage high-quality QRIS evaluations by providing timely information on evaluation options to those who may be in positions to authorize, finance, design, and refine QRISs and other quality improvement efforts, including state child care administrators, legislators, and other potential funders. This brief presents basic evaluation concepts, useful tools for determining the appropriate design and timing of an evaluation, and evaluation references and resources for those who wish to learn more.
An evaluation of the Colorado Qualistar Early Learning QRIS, including: an assessment of system components and the relationships between them; a comparison of Qualistar measures to other established quality measures; and an examination of the association between quality improvements as measured by Qualistar components and children's socioemotional and cognitive outcomes.
A case study of five early adopting state QRISs including descriptions of the theory of change, including aspects of quality included in each state system, challenges facing system designers and lessons learned from these states.
A research article that investigates how many items are necessary to score and how many classrooms are necessary to rate to achieve a representative score on the ECERS-R based on classrooms sampled in one state
This brief summarizes research that was conducted to assess the validity of the Qualistar Early Learning Quality Rating and Improvement System (QRIS) as a tool for improving child care quality in Colorado. The QRIS includes five components that are generally agreed to contribute to high quality care: (1) classroom environment, (2) child-staff ratios, (3) staff and director training and education, (4) parent involvement, and (5) accreditation. Data suggest that much work needs to be done before such systems can be confidently designed and implemented at scale.