Background

 There is solid biological, psychosocial, and economic arguments to promote children’s development as early as possible (Richter et al., 2017), and the evidence to support the immense importance of children’s experiences in the early childhood years (0–5 years) is clear and convincing (Britto et al., 2017; WHO, 2019). The early childhood years is a foundational period for holistic child development, and fundamental aspects of health and wellbeing–with lifelong effects–are laid during this period (Marmot, 2010). For instance, this period is vital for mental and physical health (Irwin et al., 2007) and have been referred to as a “window of opportunity” for promoting life-long healthy habits such as sound physical activity behaviors (Fernandez-Jimenez et al., 2018; WHO, 2019). Early childhood education and care (ECEC) have a central role in children’s early development (Foundation for Child Development, 2020; Irwin et al., 2007) and the quality of these environments is dependent on how practitioners orchestrate and navigate the specific environment in which children’s learning and development take place. For instance, children’s movement and play activities are largely afforded or constrained by the physical and pedagogical environment they are situated in. Besides the basic necessary institutional resources and structures, much of this work rely on how teachers make choices and balance social, psychological and physical aspects of these learning environments. Due to the societal significance of ECEC there is substantial interest in assessing it and its environmental quality on child outcomes. To assess this type of quality, a set of observational tools called Environment Rating Scales (ERS) are commonly used.

ERS have a long history of international use and are frequently used in research, self-assessment, and high-stake settings that help direct the distribution of funding (Brunsek et al., 2017). Leading ERS view quality in ECEC from the children’s perspective (Clifford et al., 2020) whilst increasing observation objectivity, being recognized for their standardization process and ability to measure important environmental characteristics that influence child outcomes (Howard et al., 2018). Still, despite the importance of quality in these early childhood settings, the concept of quality itself is elusive and established tools (like ERS) used to capture this elusive concept have limitations. What we define important to measure (e.g., physical activity, movement, play or other type of activities) and in what way these activities are measured and assessed is of importance for policy creation and ultimately for how institutional settings for young children are organized.

Guiding interventions, targeting educational goals, and child development, quantitative studies and evaluation tools (like ERS) have developed into a distinct research field. These tools are widely heterogenic, at time confusing, and collectively embrace an extensive array of aspects of the ECEC environment. Against this background, this study aims to review the existing literature to identify the relevant characteristics of the most commonly utilized ERS in ECEC. This may serve as a guide for ECEC researchers, practitioners, and stakeholders to make more informed decisions when aiming to evaluate the quality of ECEC settings and for instance organize settings to improve and strengthen children’s opportunities for movement and physical activity.

Methods

 Literature search: To identify ERS used in ECEC research over the past decades, we conducted a structured literature search (2023-12-19) in ERIC via EBSCO, Scopus, PsychInfo, PubMed, ERC, SwePub, and SPORTDiscus, using the Boolean search terms: “Environment Rating Scale*” AND (“Preschool*” OR “Early childhood*” OR “Kindergarten*” OR “Nursery School*” OR “Kindergarden*” OR “Child Care” OR “Day Care”). The search was limited to studies published between 1990-01-01, and 2023-12-31. A PICOS approach (Liberati et al., 2009) was used to specify the search, followed by a pilot search. To complement this initial search, an identical complementary search was performed on 2025-06-09, to retrieve articles published in 2024–2025. 

Inclusion criteria: Included studies had to: (a) use ERS in ECEC, (b) include children in ECEC, (c) have full text availability, (d) be written in English, and (e) be published in a peer reviewed academic journal between 1990 and 2025. ERS focusing exclusively on one-to-one interactions were excluded. To be classified as an ERS, the scale had to assess at least one aspect of the environment and use observations. 

Literature search result: The initial search yielded 1,226 studies, which was reduced to 757 after duplicate screening. These 757 studies (the reference pool, RP) were screened by title, abstract, and method to identify relevant ERS. The complementary search resulted in 39 studies, reduced to 25 after manual duplicate screening and subsequently screened in the same way as the RP of the initial search. During RP screening, an accumulative count of ERS used in original articles was performed, studies analyzing secondary datasets were not included in the count. The complete ERS list contained two sections: section 1 for ERS and section 2 for other mentioned scales/tools requiring additional review. In total, 175 scales were identified (section 1, n=109; section 2, n=66). ERS used ≥5 times in the RP were included in the results, leaving 24 ERS to review. We searched for the original references where these ERS were first described, identifying 7 full texts from the RP and 17 from citation searches. These were read in depth to extract ERS characteristics. Five references were excluded after full text review, leaving 19 for data extraction. 

Data extraction and analysis: From the 19 studies, the following data was extracted: (a) area of assessment, (b) age of targeted population, (c) theoretical basis, (d) design, and (e) country of creation

Conclusions

 Three main findings were identified, firstly, all but one of the reviewed tools originated from an Anglo-Saxon cultural background (and 74% were created in/for a US context), secondly, most of the tools lacked an explicitly stated rigorous theoretical grounding, and lastly, the comparability of the tools psychometric properties is complex. Further, none of the included tools had a specific focus on movement or physical activity. When aiming to measure ECEC quality using observational tools like ERS, we suggest employing tools that are culturally/contextually sound, that is, adapted or created specifically for the particular context of interest. For instance, tools that facilitate practitioners and researchers to develop children’s learning environments with regards to movement and play. This is in line with previous research advocating cultural/context-specific ERS as well as recommending adaptations of already existing ERS when implementing them in new contexts (e.g., Li et al., 2014; Sylva et al., 2006). Further, researchers aiming to assess theoretical constructs by developing new observational tools should employ an explicit rigorous theoretical framework that defines and articulates the constructs of interest since this is a central step in scale construction (e.g., Spector, 1992), something that also could strengthen the validity basis of these tools. To further strengthen these tools, future studies could also focus on validating the tools against relevant outcomes on child level, e.g., objective measures of physical activity. Additionally, since the use of observational tools are resource demanding, in line with previous research (Hay et al., 2024; Ulferts et al., 2019), we recommend streamlining existing ERS or developing less resource-demanding alternatives–that are able to capture quality more practically (e.g., more time efficiently)–for both researchers and practitioners in these environments. Lastly, future research should adopt a meta-analytic approach to enable performing side-by-side comparisons of the psychometric data of ERS.

References 

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