Early Care and Education Center Quality: A Latent Class Analysis of Quality Indicators

• In Event: Early Care and Education Quality Indicators, Implementation, and Policy

Abstract

Children in early care and education (ECE) programs where quality of care is high tend to show increases in their academic school readiness, including emerging literacy and language skills, and reductions in problem behavior (e.g., Burchinal et al., 2016; Sabol et al., 2013). Researchers from the National Institute of Early Education Research (NIEER) developed a host of research-based indicators (e.g., classroom ratio, teacher education, professional development opportunities) to assess the quality of state pre-kindergarten programs (Friedman-Krauss et al., 2021). Given evidence that ECE programs meet quality indicators in different types of ways (e.g., Tout et al., 2017), latent class analysis (LCA) that determines whether sets of quality indicators are more commonly met within ECE centers is necessary to understand the heterogeneity of quality benchmarks met simultaneously, as they are likely to differentially impact children’s experiences in ECE centers.

In this study, we consider the relative utility of a range of quality indicators, similar to the most recent NIEER benchmarks (Friedman-Krauss et al., 2021), that may distinguish latent class, or subgroup, center membership. We consider these research questions: 1) How do centers group into latent classes based on the types of NIEER benchmarks met?; 2) What program, community, and policy characteristics predict class membership?

We use nationally representative data from the 2012 National Survey of Early Care and Education (NSECE; n=5,076) to identify classes of ECE centers. To answer research question one, we used a latent class analysis (LCA) to determine how ECE centers align into groups based on the types of NIEER benchmarks met (Table 1). To answer research question two, we examined program, community, and policy characteristics that were predictive of latent class membership (Table 2). Analyses were performed in MPlus 8.2. Missing data were handled using FIML and MI.

Results suggest that centers align into five classes. The majority of centers aligned into two classes: 1) meeting most of the NIEER benchmarks (55% of centers); and 2) having smaller classroom ratios, but meeting fewer teacher education and workforce support benchmarks (26%), with smaller proportions of centers in the remaining three classes: 3) less screening support, but more teacher education and workforce support benchmarks met (7%); 4) fewest benchmarks met (6%); and 5) larger classroom ratios (4%). Our results suggest that ECE centers, which are frequently characterized by limited funding and resources, may make decisions about quality investments by prioritizing certain features of quality. While these priorities could be determined at the program-level, they may also be influenced by specific systems (e.g., Head Start, pre-K) or state requirements.

Our analyses also suggest that program, community, and policy characteristics were predictive of class membership. These results indicate that factors beyond the control of individual ECE centers, including but not limited to ECE policies, workforce availability, and features of the community, are driving quality benchmarks met within centers. Our findings detect potential leverage points for policymakers and practitioners as they aim to improve the quality of children’s ECE experiences that positively shape children’s development.

Authors

• Gerilyn Slicker, University of Nevada Las Vegas
  Presenting Author

• Ana Whitaker, University of Delaware

• Jing Tang, Child Trends -Maryland