In a recent study, University of Delaware College of Education and Human Development (CEHD) assistant professor Anamarie A. Whitaker and her co-authors, CEHD alumna Gerilyn Slicker of the University of Nevada, Las Vegas, and Jing Tang of Child Trends, analyzed how over 5,000 early childhood education (ECE) centers meet different quality indicators (such as teacher qualifications) and identified groups of centers based on the patterns they found. They then determined how program, community and policy factors influenced their group membership. Using data from the publicly available 2012 National Survey of Early Care and Education (NSECE) and benchmarks from the National Institute of Early Education Research (NIEER), Whitaker and her co-authors found that ECE centers, on average, met approximately six of the nine possible NIEER quality benchmarks in the NSECE. Less than 6% of centers met a total of 0, 1, 2, or 3 benchmarks, but only 2.5% of centers met all 9 benchmarks. Whitaker and her co-authors also identified five subgroups of centers based on the quality indicators that they met. Revealing these latent or hidden groups among ECE centers can help policymakers understand the patterns in how centers meet quality indicators, which can then help leaders develop policies that support centers in allocating their limited resources. In “Center-based early care and education programs and quality indicators: A latent class analysis,” published in Early Childhood Research Quarterly, Slicker, Whitaker and Tang conducted a latent class analysis of 5,076 ECE centers represented in the NSECE survey. This study is the first to use this approach with a national sample of publicly and non-publicly funded center-based ECE programs to determine which types of programs meet widely accepted quality indicators. Centers fell into five groups: those with 1) most quality indicators met; 2) smaller classroom ratios, but fewer teacher education and workforce support indicators met; 3) less screening support, but more teacher education and workforce support indicators met; 4) fewest indicators met; and 5) larger teacher-to-student ratios. In addition to determining five subgroups of ECE centers, Whitaker and her co-authors identify program, community and policy characteristics that predict which centers will fall into each subgroup. For example, they found that the age of the children was a significant predictor. In comparison with centers that met the most quality indicators, centers that served infants and toddlers along with preschool children were 2.63 times more likely to be in a group characterized by smaller teacher-to-student ratios, but fewer teacher education and workforce support indicators met. Similarly, the location of the center also predicted group membership. For example, centers located in high-poverty areas were 1.32 times less likely to be in the group characterized by smaller teacher-to-student ratios, but fewer teacher education and workforce quality indicators met, and 1.44 times less likely to be in a group characterized by less screening support, but more teacher education and workforce support indicators met. As the authors note, the fact that subgroups emerged from this analysis suggests that ECE programs may be making decisions about their investment of resources by prioritizing certain indicators of quality. Given these results, the authors emphasize the importance of considering how federal- and state-level support could assist programs in weighing difficult decisions about which quality indicators to prioritize amidst limited funds and resources. “Our research further shows how ECE centers vary in terms of quality indicators commonly used in research and in policy, and highlights the need for increased support in order for all ECE providers to meet quality benchmarks,” Whitaker said. “Our study underscores the importance of additional research in this area to understand how different ECE quality indicators are related to provider well-being and children’s development.” Article by Jessica Henderson.