research articles on early childhood development

InBrief: The Science of Early Childhood Development

This brief is part of a series that summarizes essential scientific findings from Center publications.

Content in This Guide

Step 1: why is early childhood important.

• : Brain Hero
• : The Science of ECD (Video)
• You Are Here: The Science of ECD (Text)

Step 2: How Does Early Child Development Happen?

• : 3 Core Concepts in Early Development
• : 8 Things to Remember about Child Development
• : InBrief: The Science of Resilience

Step 3: What Can We Do to Support Child Development?

• : From Best Practices to Breakthrough Impacts
• : 3 Principles to Improve Outcomes

The science of early brain development can inform investments in early childhood. These basic concepts, established over decades of neuroscience and behavioral research, help illustrate why child development—particularly from birth to five years—is a foundation for a prosperous and sustainable society.

Brains are built over time, from the bottom up.

The basic architecture of the brain is constructed through an ongoing process that begins before birth and continues into adulthood. Early experiences affect the quality of that architecture by establishing either a sturdy or a fragile foundation for all of the learning, health and behavior that follow. In the first few years of life, more than 1 million new neural connections are formed every second . After this period of rapid proliferation, connections are reduced through a process called pruning, so that brain circuits become more efficient. Sensory pathways like those for basic vision and hearing are the first to develop, followed by early language skills and higher cognitive functions. Connections proliferate and prune in a prescribed order, with later, more complex brain circuits built upon earlier, simpler circuits.

The interactive influences of genes and experience shape the developing brain.

Scientists now know a major ingredient in this developmental process is the “ serve and return ” relationship between children and their parents and other caregivers in the family or community. Young children naturally reach out for interaction through babbling, facial expressions, and gestures, and adults respond with the same kind of vocalizing and gesturing back at them. In the absence of such responses—or if the responses are unreliable or inappropriate—the brain’s architecture does not form as expected, which can lead to disparities in learning and behavior.

The brain’s capacity for change decreases with age.

The brain is most flexible, or “plastic,” early in life to accommodate a wide range of environments and interactions, but as the maturing brain becomes more specialized to assume more complex functions, it is less capable of reorganizing and adapting to new or unexpected challenges. For example, by the first year, the parts of the brain that differentiate sound are becoming specialized to the language the baby has been exposed to; at the same time, the brain is already starting to lose the ability to recognize different sounds found in other languages. Although the “windows” for language learning and other skills remain open, these brain circuits become increasingly difficult to alter over time. Early plasticity means it’s easier and more effective to influence a baby’s developing brain architecture than to rewire parts of its circuitry in the adult years.

Cognitive, emotional, and social capacities are inextricably intertwined throughout the life course.

The brain is a highly interrelated organ, and its multiple functions operate in a richly coordinated fashion. Emotional well-being and social competence provide a strong foundation for emerging cognitive abilities, and together they are the bricks and mortar that comprise the foundation of human development. The emotional and physical health, social skills, and cognitive-linguistic capacities that emerge in the early years are all important prerequisites for success in school and later in the workplace and community.

Toxic stress damages developing brain architecture, which can lead to lifelong problems in learning, behavior, and physical and mental health.

Scientists now know that chronic, unrelenting stress in early childhood, caused by extreme poverty, repeated abuse, or severe maternal depression, for example, can be toxic to the developing brain. While positive stress (moderate, short-lived physiological responses to uncomfortable experiences) is an important and necessary aspect of healthy development, toxic stress is the strong, unrelieved activation of the body’s stress management system. In the absence of the buffering protection of adult support, toxic stress becomes built into the body by processes that shape the architecture of the developing brain.

Policy Implications

• The basic principles of neuroscience indicate that early preventive intervention will be more efficient and produce more favorable outcomes than remediation later in life.
• A balanced approach to emotional, social, cognitive, and language development will best prepare all children for success in school and later in the workplace and community.
• Supportive relationships and positive learning experiences begin at home but can also be provided through a range of services with proven effectiveness factors. Babies’ brains require stable, caring, interactive relationships with adults — any way or any place they can be provided will benefit healthy brain development.
• Science clearly demonstrates that, in situations where toxic stress is likely, intervening as early as possible is critical to achieving the best outcomes. For children experiencing toxic stress, specialized early interventions are needed to target the cause of the stress and protect the child from its consequences.

Suggested citation: Center on the Developing Child (2007). The Science of Early Childhood Development (InBrief). Retrieved from www.developingchild.harvard.edu .

Related Topics: toxic stress , brain architecture , serve and return

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Early Childhood Development: the Promise, the Problem, and the Path Forward

Subscribe to the center for universal education bulletin, tamar manuelyan atinc and tamar manuelyan atinc nonresident senior fellow - global economy and development , center for universal education emily gustafsson-wright emily gustafsson-wright senior fellow - global economy and development , center for universal education.

November 25, 2013

• 17 min read

Access more content from the Center for Universal Education here , including work on early childhood education .

Early Childhood: The Scale of the Problem

More than 200 million children under the age of five in the developing world are at risk of not reaching their full development potential because they suffer from the negative consequences of poverty, nutritional deficiencies and inadequate learning opportunities (Lancet 2007).  In addition, 165 million children (one in four) are stunted, with 90 percent of those children living in Africa and Asia (UNICEF et al, 2012).  And while some progress has been made globally, child malnutrition remains a serious public health problem with enormous human and economic costs.  Child death is a tragedy.  At 6 million deaths a year, far too many children perish before reaching the age of five, but the near certainty that 200 million children today will fall far below their development potential is no less a tragedy.

There is now an expanding body of literature on the determining influence of early development on the chances of success later in life.  The first 1,000 days from conception to age two are increasingly being recognized as critical to the development of neural pathways that lead to linguistic, cognitive and socio-emotional capacities that are also predictors of labor market outcomes later in life. Poverty, malnutrition, and lack of proper interaction in early childhood can exact large costs on individuals, their communities and society more generally.  The effects are cumulative and the absence of appropriate childcare and education in the three to five age range can exacerbate further the poor outcomes expected for children who suffer from inadequate nurturing during the critical first 1,000 days.

The Good News: ECD Interventions Are Effective

Research shows that there are large gains to be had from investing in early childhood development.  For example, estimates place the gains from the elimination of malnutrition at 1 to 2 percentage points of gross domestic product (GDP) annually (World Bank, 2006).  Analysis of results from OECD’s 2009 Program of International Student Assessment (PISA) reveals that school systems that have a 10 percentage-point advantage in the proportion of students who have attended preprimary school score an average of 12 points higher in the PISA reading assessment (OECD and Statistics Canada, 2011).  Also, a simulation model of the potential long-term economic effects of increasing preschool enrollment to 25 percent or 50 percent in every low-income and middle-income country showed a benefit-to-cost ratio ranging from 6.4 to 17.6, depending on the preschool enrollment rate and the discount rate used (Lancet, 2011).

Indeed, poor and neglected children benefit disproportionately from early childhood development programs, making these interventions among the more compelling policy tools for fighting poverty and reducing inequality.  ECD programs are comprised of a range of interventions that aim for: a healthy pregnancy; proper nutrition with exclusive breast feeding through six months of age and adequate micronutrient content in diet; regular growth monitoring and immunization; frequent and structured interactions with a caring adult; and improving the parenting skills of caregivers.

Related Content

Emily Gustafsson-Wright, Izzy Boggild-Jones, Sophie Gardiner

September 5, 2017

Brookings Institution, Washington DC

Tuesday, 3:00 pm - 5:30 pm EDT

The Reality: ECD Has Not Been a Priority

Yet despite all the evidence on the benefits of ECD, no country in the developing world can boast of comprehensive programs that reach all children, and unfortunately many fall far short.  Programs catering to the very young are typically operated at small scale and usually through external donors or NGOs, but these too remain limited.  For example, a recent study found that the World Bank made only $2.1 billion of investments in ECD in the last 10 years, equivalent to just a little over 3 percent of the overall portfolio of the human development network, which totals some $60 billion (Sayre et al, 2013).

The following are important inputs into the development of healthy and productive children and adults, but unfortunately these issues are often not addressed effectively:

Maternal  Health. Maternal undernutrition affects 10 to 19 percent of women in most developing countries (Lancet, 2011) and 16 percent of births are low birth weight (27 percent in South Asia).  Malnutrition during pregnancy is linked to low birth weight and impaired physical development in children, with possible links also to the development of their social and cognitive skills. Pre-natal care is critical for a healthy pregnancy and birth. Yet data from 49 low-income countries show that only 40 percent of pregnant women have access to four or more antenatal care visits (Taskforce on Innovative International Financing for Health Systems, 2009). Maternal depression also affects the quality of caregiving and compromises early child development.

Child Care and Parenting Practices. The home environment, including parent-child interactions and exposure to stressful experiences, influences the cognitive and socio-emotional development of children.  For instance, only 39 percent of infants aged zero to six months in low and middle-income countries are exclusively breast-fed, despite strong evidence on its benefits (Lancet, 2011).  Also, in half of the 38 countries for which UNICEF collects data, mothers engage in activities that promote learning with less that 40 percent of children under the age of six.  Societal violence and conflict are also detrimental to a child’s development, a fact well known to around 300 million children under the age of four that live in conflict-affected states.

Child Health and Nutrition. Healthy and well-nourished children are more likely to develop to their full physical, cognitive and socio-emotional potential than children who are frequently ill, suffer from vitamin or other deficiencies and are stunted or underweight.  Yet, for instance, an estimated 30 percent of households in the developing world do not consume iodized salt, putting 41 million infants at risk for developing iodine deficiency which is the primary cause of preventable mental retardation and brain damage, and also increases the chance of infant mortality, miscarriage and stillbirth.  An estimated 40 to 50 percent of young children in developing countries are also iron deficient with similarly negative consequences (UNICEF 2008).  Diarrhea, malaria and HIV infection are other dangers with a deficit of treatment in early childhood that lead to various poor outcomes later in life.

Preprimary Schooling. Participation in good quality preprimary programs has been shown to have beneficial effects on the cognitive development of children and their longevity in the school system.  Yet despite gains, enrollment remains woefully inadequate in Sub-Saharan Africa and the Middle East and North Africa.  Moreover, national averages usually hide significant inequalities across socio-economic groups in access and almost certainly in quality. In all regions, except South Asia, there is a strong income gradient for the proportion of 3 and 4 year olds attending preschool.

Impediments to Scaling Up

So what are the impediments to scaling up these known interventions and reaping the benefits of improved learning, higher productivity, lower poverty and lower inequality for societies as a whole?  There are a range of impediments that include knowledge gaps (especially in designing cost-effective and scalable interventions of acceptable quality), fiscal constraints and coordination failures triggered by institutional organization and political economy.

Knowledge Gaps . Despite recent advances in the area, there is still insufficient awareness of the importance of brain development in the early years of life on future well-being and of the benefits of ECD interventions.  Those who work in this area take the science and the evaluation evidence for granted. Yet awareness among crucial actors in developing countries—policymakers, parents and teachers—cannot be taken for granted.

At the same time much of the evaluation evidence from small programs attests to the efficacy of interventions, we do not yet know whether large scale programs are as effective. The early evidence came primarily from small pilots (involving about 10 to 120 children) from developed countries. [1] ;While there is now considerable evidence from developing countries as well, such programs still tend to be boutique operations and therefore questions regarding their scalability and cost-effectiveness.

There are also significant gaps in our knowledge as to what specific intervention design works in which context in terms of both the demand for and the provision of the services. These knowledge gaps include the need for more evidence on:  i) the best delivery mode – center, family or community based, ii) the delivery agents – community health workers, mothers selected by the community, teachers, iii) whether or not the programs should be universal or targeted, national or local, iv) the frequency and duration of interventions, of training for the delivery agents and of supervision, v) the relative value of nutritional versus stimulative interventions and the benefits from the delivery of an integrated package of services versus sector specific services that are coordinated at the point of delivery, vi) the most effective curricula and material to be used, vii) the relative effectiveness of methods for stimulating demand – information via individual contact, group sessions, media, conditional cash transfers etc.  In all these design questions, cost-effectiveness is a concern and leads to the need to explore the possibility of building on an existing infrastructure.  There is also a need for more evidence on the kinds of standards, training and supervision that are conducive to Safeguarding the quality of the intervention at scale.

Fiscal Constraints .  Fiscal concerns at the aggregate level are also an issue and force inter-sectoral trade-offs that are difficult to make.  Is it reasonable to expect countries to put money into ECD when problems persist in terms of both access and poor learning outcomes in primary schools and beyond?  Even though school readiness and teacher quality may be the most important determinants of learning outcomes in primary schools, resource allocation shifts are not easy to make for policymakers.  In addition, as discussed above, we do not yet have good answers to the questions around the cost implications of high quality design at scale.

Institutional Coordination and Political Context.   Successful interventions are multi-sectoral in nature (whether they are integrated from the outset or coordinated at the point of delivery) and neither governments nor donor institutions are structured to address well issues that require cross-sectoral cooperation.  When programs are housed in the education ministry, they tend to focus on preprimary concerns.  When housed in the health ministry, programs ignore early stimulation.  We do not know well what institutional structure works best in different contexts, including how decentralization may affect choices about institutional set ups.

There are also deeper questions about the nature of the social contract in any country that shapes views about the role of government and the distribution of benefits across the different segments of the population.  Some countries consider that the responsibilities of the public sector start when children reach school age and view the issues around the development of children at a younger age to be the purview of families.  And in many countries, policies that benefit children get short shrift because children do not have political voice and their parents are imperfect agents for their children’s needs.  Inadequate political support then means that the legislative framework for early year interventions is lacking and that there is limited public spending on programs that benefit the young.  For example, public spending on social pensions in Brazil is about 1.2 percent of GDP whereas transfers for Bolsa Familia which targets poor children are only 0.4 percent of GDP (Levy and Schady 2013).  In Turkey, only 6.5 percent of central government funds are directed to children ages zero to 6, while the population above 44 receives a per capita transfer of at least 2.5 times as large as children today (World Bank, 2010).  Finally, the long gestation period needed to achieve tangible results compounds the limited appeal of ECD investments given the short planning horizon of many political actors.

The Future: An Agenda for Scaling Up ECD

Addressing the constraints to scaling up ECD requires action across a range of areas, including more research and access to know how, global and country level advocacy, leveraging the private sector, and regular monitoring of progress.

Operational Research and Learning Networks. Within the EDC research agenda, a priority should be the operational research that is needed to go to scale.  This research includes questions around service delivery models, including in particular their cost effectiveness and sustainability.  Beyond individual program design, there are broader institutional and policy questions that need systematic assessment. These questions center on issues including the inter-agency and intergovernmental coordination modalities which are best suited for an integrated delivery of the package of ECD services.  They also cover the institutional set-ups for quality assurance, funding modalities, and the role of the private sector.  Finally, research is also needed to examine the political economy of successful implementation of ECD programs at scale.

Also necessary are learning networks that can play a powerful role in disseminating research findings and in particular good practice across boundaries. Many of the issues regarding the impediments for scaling up are quite context specific and not amenable to generic or off-the-shelf solutions.  A network of peer learning could be a powerful avenue for policymakers to have deeper and face-to-face interactions about successful approaches to scaling up.  South-South exchanges were an enormously valuable tool in the propagation of conditional cash transfer schemes both within Latin America and globally. These types of exchanges could be equally powerful for ECD interventions

Advocacy. There is a need for a more visible global push for the agenda, complemented by advocacy at country or regional levels and a strong role for business leaders.  It should be brought to the attention of policymakers that ECD is not a fringe issue and that it is a matter of economic stability to the entire world. It is also in the interest of business leaders to support the development of young children to ensure a productive work force in the future and a thriving economy.  Currently, there is insufficient recognition of the scale of the issues and the effectiveness of known interventions. And while there are pockets of research excellence, there is a gap in the translation of this work into effective policies on the ground.  The nutrition agenda has recently received a great deal of global attention through the 1000 days campaign and the Scaling up Nutrition Movement led by the United States and others.  Other key ECD interventions and the integration and complementarities between the multi-sectoral interventions have received less attention however.  The packaging of a minimum set of services that all countries should aspire to provide to its children aged zero to six would be an important step towards progress.  The time is ripe as discussions around the post-2015 development framework are in full swing, to position ECD as a critical first step in the development of healthy children, capable of learning and becoming productive adults.

Leveraging the Private Sector.   The non-state sector already plays a dominant role in providing early childhood care, education and healthcare services in many countries.  This represents both a challenge and an opportunity.  The challenge is that the public sector typically lacks the capacity to ensure quality in the provision of services and research evidence shows that poor quality child care and education services are not just ineffective; they can be detrimental (Lancet 2011).  The challenge is all the greater given that going to scale will require large numbers of providers and we know that regulation works better and is less costly in markets with fewer actors.  On the opportunity side of the ledger, there is scope for expanding the engagement of the organized private sector.  The private sector can contribute by providing universal access for its own workforce, through for-profit investments, and in the context of corporate social responsibility activities.  Public-private partnerships can span the range of activities, including providing educational material for home-based parenting programs; developing and delivering parent education content through media or through the distribution chains of some consumer goods or even financial products; training preprimary teachers; and providing microfinance for home or center-based childcare centers. Innovative financing mechanisms, such as those in the social impact investing arena, may provide necessary financing, important demonstration effects and quality assurance for struggling public systems.  Such innovations are expanding in the United States, paving the way for middle and low-income countries to follow.

ECD Metrics.  A key ingredient for scaling up is the ability to monitor progress. This is important both for galvanizing political support for the desired interventions and to provide a feedback loop for policymakers and practitioners. There are several metrics that are in use by researchers in specific projects but are not yet internationally accepted measures of early child development that can be used to report on outcomes globally.  While we can report on the share of children that are under-weight or stunted, we cannot yet provide the fuller answer to this question which would require a gauge of their cognitive and socio-emotional development.  There are some noteworthy recent initiatives which will help fill this gap.  The UNICEF-administered Multiple Indicator Cluster Survey (MICS) 4 includes an ECD module and a similar initiative from the Inter-American Development Bank collects ECD outcome data in a handful of Latin American countries.  The World Health Organization has launched work that will lead to a proposal on indicators of development for zero to 3 year old children while UNESCO is taking the lead on developing readiness to learn indicators (for children around age 6) as a follow up to the recommendations of the Learning Metrics Task Force (LMTF) which is co-convened by UNESCO and the Center for Universal Education at Brookings.

The LMTF aims to make recommendations for learning goals at the global level and has been a useful mechanism for coordination across agencies and other stakeholders.  A related gap in measurement has to do with the quality of ECD services (e.g., quality of daycare). Overcoming this measurement gap is critical for establishing standards and for monitoring compliance and can be used to inform parental decisions about where to send their kids.

ECD programs have a powerful equalizing potential for societies and ensuring equitable investment in such programs is likely to be far more cost-effective than compensating for the difference in outcomes later in life.  Expanding access to quality ECD services so that they include children from poor and disadvantaged families is an investment in the future of not only those children but also their communities and societies.  Getting there will require concerted action to organize delivery systems that are financially sustainable, monitor the quality of programming and outcomes and reach the needy.

Lancet (2007). Child development in developing countries series. The Lancet, 369, 8-9, 60-70, 145, 57, 229-42.  http://www.thelancet.com/series /child-development-in-developing-countries.

Lancet (2011). Child development in developing countries series 2. The Lancet, 378, 1325-28, 1339- 53.  http://www.thelancet.com/series/child-development-in-developing-countries-2.

Levy, S. and Schady, N. (2013). Latin America’s Social Policy Challenge: Education, social Insurance, Redistribution. Journal of Economic Perspectives 27(2) , 193-218.

OECD and Statistics Canada (2011). Literacy for Life: Further Results from the Adult Literacy and Life Skills Survey. Paris/Ottawa: Organisation for Economic Co-operation and Development/Canada Minister of Industry.

Sayre, R.K., Devercelli, A.E., Neuman, M.J. (2013). World Bank Investments in Early Childhood: Findings from Portfolio Review of World Bank Early Childhood Development Projects from FY01-FY11. Draft, March 2013, Mimeo.

Taskforce on Innovative International Financing for Health Systems (2009). More money for health, and more health for the money: final report. Geneva: International Health Partnership. http://www.internationalhealthpartnership.net//CMS_files/documents/taskforce_report_EN.pdf

United Nations Children’s Fund (2005). Multiple Indicator Cluster Survey 3. UNICEF. http://www.childinfo.org/mics3_surveys.html.

United Nations Children’s Fund (2008). Sustainable Elimination of Iodine Deficiency: Progress since the1990 World Summit on Children. New York: UNICEF.

United Nations Children’s Fund, World Health Organization and The World Bank (2012). UNICEF- WHO-World Bank Joint Child Malnutrition Estimates. New York: UNICEF; Geneva: WHO; Washington D.C.: The World Bank.

World Bank (2006). Repositioning Nutrition as Central to Development: A Strategy for Large-Scale Action. Directions in Development series. Washington D.C.: The World Bank.

World Bank (2010). Turkey: Expanding Opportunities for the Next Generation-  A Report on Life Chances. Report No 48627-TR. Washington D.C.: The World Bank.

World Bank (2013). World Development Indicators 2013. Washington D.C.: The World Bank.

[1] The Perry preschool and Abecedarian programs in the United States have been rigorously studied and show tremendous benefits for children in terms of cognitive ability, academic performance and tenure within the school system and suggest benefits later on in life that include higher incomes, higher incidence of home ownership, lower propensity to be on welfare and lower rates of incarceration and arrest.

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The Development of Self-Regulation across Early Childhood

Janelle j montroy, ryan p bowles, lori e skibbe, megan m mcclelland, frederick j morrison.

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Correspondence should be sent to Janelle J. Montroy, Children’s Learning Institute, Department of Developmental Pediatrics, University of Texas Health Science Center at Houston, 7000 Fannin Street Suite 2373H, Houston, TX 77030, USA. Tel: +1 713 500 3831. [email protected]

Issue date 2016 Nov.

The development of early childhood self-regulation is often considered an early life marker for later life successes. Yet little longitudinal research has evaluated whether there are different trajectories of self-regulation development across children. This study investigates the development of behavioral self-regulation between the ages of three and seven, with a direct focus on possible heterogeneity in the developmental trajectories, and a set of potential indicators that distinguish unique behavioral self-regulation trajectories. Across three diverse samples, 1,386 children were assessed on behavioral self-regulation from preschool through first grade. Results indicated that majority of children develop self-regulation rapidly during early childhood, and that children follow three distinct developmental patterns of growth. These three trajectories were distinguishable based on timing of rapid gains, as well as child gender, early language skills, and maternal education levels. Findings highlight early developmental differences in how self-regulation unfolds with implications for offering individualized support across children.

Keywords: Behavioral self-regulation, developmental trajectories, early childhood, longitudinal

The Development of Self-Regulation Across Early Childhood The development of effective self-regulation is recognized as fundamental to an individual’s functioning, with development during early childhood often considered an early marker for later life successes ( Blair, 2002 ; Bronson, 2000 ; Calkins, 2007 ; Diamond, 2002 ; Gross & Thompson, 2007 ; Kopp, 1982 ; McClelland & Cameron, 2012 ; Mischel et al, 2011 ; Moffitt et al., 2011 ; Vohs & Baumeister, 2011 ; Zelazo et al., 2003 ). Research indicates that between ages three and seven a qualitative shift in self-regulation may take place when children typically progress from reactive or co-regulated behavior to more advanced, cognitive behavioral forms of self -regulation (e.g., Diamond, 2002 ; Kopp 1982 ) that likely require the integration of many skills such as executive functions and language skills ( Calkins, 2007 ; Cole, Armstrong, & Pemberton, 2010 ). Likewise, past research suggests wide variation in the level of self-regulation skills children manifest during early childhood that consistently predicts a multitude of short- and long-term outcomes such as school readiness, academic achievement throughout primary school, adult educational attainment, feelings of higher self-worth, a better ability to cope with stress, as well as less substance use, and less law breaking, even among individuals at risk of maladjustment ( McClelland, Acock, Piccinin, Rhea, & Stallings, 2013 ; Mischel et al., 2011 ; Moffitt et al., 2011 ).

However, despite mounting evidence that early childhood is an important time period for the development of self-regulation, little is known about how children’s trajectories of development might vary across individuals over time ( Bergman, Magnusson, & Khouri, 2002 ; Muthén & Muthén, 2000 ; Nagin, 1999 ). To address this gap, we examined the inter-individual variation in children’s growth trajectories between preschool and early elementary school based on evidence that self-regulation requires the coordination and processing of multiple skills across several domains ( Calkins, 2007 ; Cole et al., 2010 ). More specifically, we posit that there will be differences related to when the integration of these skills begin to manifest as well as differences in the patterns of how they are manifest as regulated behavior ( Blair, 2010 ; Blair & Raver, 2012 ; 2015 ; Calkins, 2007 ; Clark et al., 2013 ). In the current study, we examined the development of behavioral self-regulation via the Head-Toes-Knees-Shoulders task ( Cameron et al., 2008 ) between the ages of three and seven with longitudinal data involving up to eight measurement occasions for individual children across three samples. We evaluated possible heterogeneity in the developmental trajectories of children’s behavioral self-regulation using growth mixture modeling (GMM; Grimm, McArdle, & Hamagami, 2007 ), and potential indicators of trajectory differences.

Defining Self-regulation

Self-regulation is a complex, multi-component construct ( Blair & Raver, 2012 ; McClelland, Cameron Ponitz, Messersmith, & Tominey, 2010 ; Schunk & Zimmerman, 1997 ; Vohs & Baumeister, 2011 ) operating across several levels of function (e.g., motor, physiological, social-emotional, cognitive, behavioral and motivational), that in its broadest sense represents the ability to volitionally plan and, as necessary, modulate one’s behavior(s) to an adaptive end ( Barkley, 2011 ; Gross & Thompson, 2007 ). One approach to the complexity of self-regulation has been to view the multiple functions of self-regulation as hierarchically organized and, eventually, reciprocally integrated ( Blair & Raver, 2012 ; Calkins, 2007 ). Ultimately self-regulation depends on the coordination of many processes across levels of function, with children’s ability to draw on, integrate, and manage these multiple processes increasing across developmental time ( McClelland & Cameron, 2012 ; McClelland et al., 2014 ).

The current study focuses on self-regulation in relation to its role in successful classroom functioning ( McClelland & Cameron, 2012 ; McClelland, Morrison, & Holmes, 2000 ; Nesbitt et al., 2015). Effective self-regulation in the classroom requires that the child seamlessly coordinate multiple aspects of top down control (i.e., executive function) such as attention, working memory, and inhibitory control along with motor or verbal functions to produce overt behaviors, such as remembering multi-step directions amidst distractions ( Cameron Ponitz et al., 2008 ; McClelland et al., 2007 ). This form of self-regulation is therefore typically termed behavioral self-regulation (c.f., emotional self-regulation; Gross & Thompson, 2007 ). To evaluate individual differences in development of self-regulation across multiple years, we used a well validated direct assessment of behavioral self-regulation, the Head-Toes-Knees-Shoulders task (HTKS, Cameron et al., 2008 ) that captures variations in behavioral self-regulation throughout the entire range of early childhood, making it possible to accurately assess developmental change on a common scale across time ( Cameron Ponitz et al., 2008 ; Connor et al., 2010 ; McClelland et al., 2007 ; Skibbe et al., 2012 ). The HTKS is a short, game-like task where children are asked to ‘do the opposite’ in regards to a set of paired rules. For example, if the child is asked to touch their head, instead they must touch their toes. This task taps three executive function skills ( McClelland et al., 2014 ) in order to make a gross motor response: 1. attention (ability to focus on instructions and current stimuli), 2. working memory (ability to process the current trial while holding a rule or set of rules in mind), and 3. inhibition (ability to ignore a well learned response in order to respond in a counter-intuitive way).

Executive functions help an individual understand, monitor, and control their own reaction to the environment, as well as problem solve regarding desired future behaviors and/or outcomes. Put another way, the coordination of these skills often forms the basis of a child’s ability to respond adaptively within the classroom. Notably a distinction has been made in recent years between executive functions at the service of abstract or decontextualized environments, and executive functions at the service of adapting to environments that require the regulation of affect and motivation (e.g., Hongwanishkul et al., 2005). Sometimes referred to as ‘cool’ executive functions and ‘hot’ executive functions within cognitive traditions (e.g., Zelazo & Carlson, 2008 ), these skills can be considered as necessary (although not entirely sufficient; Ursache & Blair, 2011) for behavioral and emotional aspects of self-regulation, respectively (Zhou & Chen, 2008). Both hot and cool aspects are important for development; hot aspects are usually more associated with socio-emotional health and outcomes, while cool aspects are more associated with cognitive and academic outcomes ( Kim et al, 2013 ). The HTKS task generally draws on cool aspects of executive function, although in reality no task is entirely free of an emotional context, with distinctions generally being a matter of degree (Manes et al, 2002).

The Development of Behavioral Self-regulation

The development of self-regulation begins in infancy, with many of the skills that are important for behavioral self-regulation developing first as separate domains, then becoming organized and integrated over time ( Barkley; 2011 ; Corrigan, 1981 ; Diamond et al., 1997 ; Kopp, 1989 ; Stifter & Braungart, 1995 ). Previous work indicates that not only do separate facets of self-regulation appear to develop at different times and rates (such as emotional self-regulation generally preceding the development of behavioral self-regulation; Howse et al., 2003 ) but also the underlying skills may also develop at different times. For example, the ability to delay a response (an outcome most strongly associated with developing inhibitory control) appears to develop earlier than other executive skills ( Lengua, et al., 2015 ). However, despite differences across individual facets and skills associated with self-regulation, previous research consistently indicates that children younger than three have difficulty simultaneously coordinating and utilizing multiple executive function skills to create a behavioral response that also requires a motor or verbal action ( Carlson, Moses, & Breton, 2002 ; Diamond, 2002 ; Zelazo et al., 2003 ). However, after age three and during early childhood, the individual skills that support behavioral self-regulation (e.g., see Cole et al., 2010 ; Diamond et al., 1997 or Rothbart et al., 2006 ), as well as behavioral self-regulation itself as an integration of those skills, rapidly develop(s), signifying a qualitative shift in children’s regulatory abilities ( Best & Miller, 2010 ; Garon et al., 2008 ; Kopp, 1982 ; Zelazo et al., 2008 ).

Specifically, cross sectional work with a multitude of tasks indicates a rapid increase or “leap” in performance on tasks that require the integration of several executive function skills into behavior, such as the HTKS task, the Dimensional Change Card Sort, The Day/Night, Bear/Dragon, Fish Flanker, and Luria’s tapping task ( Diamond, 2002 ; Gerstadt et al., 1994 ; Rothbart et al., 2006 ; Rueda et al., 2004 ; Zelazo et al., 2003 ). For example, there are large group differences in accuracy on a fish flanker task (see Rueda et al., 2004 for a description of the task) between four year olds and six year olds, but by about age seven, children’s accuracy gains level off as performance becomes similar to adults (although reaction time continues to improve; Rothbart et al., 2006 ; Rueda et al., 2004 ). In addition, recent work explicitly evaluating behavioral self-regulation longitudinally ( Cameron Ponitz et al., 2008 ), as well as several studies of underlying executive function skills ( Chang, Shaw, Dishion, Gardner & Wilson, 2014 ; Clark et al., 2013 ; Diamond et al., 1997 ; Wiebe, Sheffield & Espy, 2012) indicate non-linear growth with rapid gains followed by a decelerating rate of gain in performance.

In summary, theory and research both provide evidence of rapid gains in the ability to regulate behavior that are likely linked to the integration of multiple processes, but particularly processes considered under the umbrella of executive function, such as attention, working memory and inhibition ( Cameron Ponitz et al., 2008 ; Chang et al., 2014 ; Diamond, 2002 ; Rothbart, et al., 2006 ). Based on these findings, we expect that the development of behavioral self-regulation in early childhood is likely best represented by a nonlinear function ( Diamond, 2002 ). Specifically, we expect that between the ages of three and seven years, gains in self-regulation will increase rapidly as multiple processes become more coordinated, followed by later decelerated growth (e.g., Cameron Ponitz et al., 2008 ; Chang et al., 2014 ; Wiebe et al., 2012).

Heterogeneity in behavioral self-regulation development

Several prominent theories suggest the possibility of multiple self-regulation growth trajectories across early childhood (see Blair, 2010 ; Blair & Raver, 2012 ; 2015 ; Calkins, 2007 ; Lerner & Overton, 2008 ). Specifically, theories drawing on psychobiological or dynamic systems models ( Blair & Raver, 2015 ; Lerner & Overton, 2008 ) indicate a back and forth developmental relationship between children’s biological traits and their experiences. These theories contend that how children learn to regulate their behavior can vary widely given that biological predispositions such as temperament and early environmental experiences greatly vary. However, few studies have fully tested whether there are underlying trajectory differences in self-regulation such that children develop behavioral self-regulation in differing ways (i.e., process differences) and/or at different rates ( Posner & Rothbart, 2000 ; Rothbart, Ellis, Rueda & Posner, 2003 ). The majority of work in this area has noted mean differences in the amount of self-regulation children are able to exert at a given age during early childhood; only recently have studies begun to focus on growth in self-regulation and predictors thereof. Of these studies, few have accounted for systematic inter-individual differences across time (but see Vallotton & Ayoub, 2011 ; Wanless et al., 2016 ; Willoughby et al., 2016 ).

Instead, the majority of studies evaluating self-regulation growth have focused on utilizing child and environmental aspects to predict aggregate variation around a general slope and/or rate mean (e.g., Blandon et al., 2008 ; Cameron Ponitz et al., 2008 ; Clark et al., 2013 ), without further consideration for whether this variation may indicate qualitatively distinct developmental change. This makes it is difficult to conclude whether there actually are subgroups of children with systematic differences in how self-regulation processes unfold ( Rogosa, 1988 ). Likewise, findings at the aggregate level do not necessarily describe the relationship among variables for a single individual or subgroup of individuals ( von Eye & Bergman, 2003 ). This makes it equally difficult to accurately map out predictive relations between children’s individual traits and environments and their self-regulation development.

Only one study to date has evaluated multiple trajectories across children in behavioral self-regulation ( Wanless et al., 2016 ). This studied focused specifically on a Taiwanese sample of children and indicated two distinct behavioral self-regulation trajectories: an “increasing” developers trajectory with children rapidly gaining in self-regulation and then leveling off across early childhood, and a “steady-then-increasing” trajectory with children demonstrating few regulatory gains between ages 3 – 5 years and rapid gains after 5 years of age. However, this study only includes a relatively small sample, and focuses on a homogenous population in Taiwan.

The current study builds upon and extends this previous work by directly examining the possibility of qualitatively different behavioral self-regulation growth trajectories between the ages of three and seven in a large heterogeneous population. We focus specifically on behavioral self-regulation as theoretical considerations indicate that the regulation of behavior is expected to include multiple trajectories during early childhood given that the multiple executive function inputs that support it are sensitive to not only genetic inputs but experiential inputs and that these inputs are rapidly developing and differentiating during this time period ( Blair & Raver, 2012 ; Lonigan & Allan, 2014). As part of investigating potential trajectories, we also evaluate one rough environmental proxy and two child level predictors in order to validate potential trajectory differences, and better understand patterns of how and, possibly when, these factors matter for self-regulation development across children.

Child factors

There are several early characteristics that previous studies have identified as having an association with the development of behavioral self-regulation ( Blair et al., 2011 ; Calkins, Dedmon, Gill, Lomax, & Johnson, 2002 ; Cole et al., 2010 ; Matthews et al., 2009 ). The current study focuses on children’s gender and language skills as these attributes are fairly consistently linked to individual differences in self-regulation ( Bohlmann et al., 2015 ; Matthews et al., 2009 ; Ready et al., 2005 ), and potentially trajectory differences ( Vallotton & Ayoub, 2011 ).

Previous findings generally indicate that boys have lower levels of self-regulation than girls ( Kochanska et al., 2001 ; Matthews et al., 2009 , 2014 ; McClelland et al., 2007 ), with gender differences often increasing across time ( Matthews et al., 2014 ). It is not well understood why such gender differences occur (though see Entwistle, Alexander, & Olson, 2007 ), although recent work suggests gender differences may in part relate to cultural beliefs and expectations ( von Suchodoletz et al., 2013 ; Wanless et al., 2016 ). However, there is evidence that, from an early age, gender is associated with what type of self-regulation developmental trajectory a child is likely to follow ( Vallotton & Ayoub, 2011 ). For example, during toddlerhood, boys’ self-regulation generally dips around age two then rises, while girls’ self-regulation rises steadily, resulting in gender differences at ages two and three favoring girls. Additional research focused on kindergarteners suggests that a subset of boys persist in demonstrating very low levels of behavioral self-regulation ( Matthews et al., 2009 ), potentially signifying these boys not only continue to developmentally lag behind girls, but that they may also not be acquiring self-regulation in the same way that peers are. Given these past findings, we expected that boys may be more likely to follow a potentially lagged trajectory.

Language is another child attribute that affects developing self-regulation, and may be an important factor for understanding potential self-regulation trajectory differences across children. Theoretically, language is thought to give children “mental tools” to help them organize and modify their thoughts and behaviors ( Vygotsky, 1934/1986 ). During early childhood, expressive language in particular may be important as it enhances the ability of the child to both name their own current state and manipulate that state in relation to a specific context ( Cole et al., 2010 ). It also seemingly enhances children’s ability to hold task requirements in mind (Karbach, Eber, & Kray, 2008). Research evaluating how expressive language helps toddlers to self-regulate suggests that trajectories of self-regulation vary between children based on the child’s observed expressive vocabulary skills ( Vallotton & Ayoub, 2011 ). Likewise, early expressive language skills are also associated with higher levels of early self-regulation, with greater language gains across preschool and the transition to kindergarten associated with greater self-regulation gains ( Bohlmann, Maier, & Palacios, 2015 ). This suggests that children with higher levels of expressive language develop self-regulation faster compared to children with lower levels of language. We also expected expressive language to be related to self-regulation growth on the HTKS because children use both expressive and receptive language when completing the task (and can answer verbally if needed/verbalize actions). Based on these previous findings, the pattern of associations between expressive language at the start of schooling and potential self-regulation trajectories should follow a similar pattern such that lower levels of expressive language are associated with a distinct, potentially lagged trajectory compared to higher levels of expressive language.

Mother education

In addition to child attributes and competencies, past research consistently demonstrates that children’s environments affect developing behavioral self-regulation ( Blair, 2010 ; Grolnick & Farkas, 2002 ; Landry et al., 2006 ). One particularly salient aspect of children’s environments that may affect developing self-regulation is their mothers’ education levels (e.g., see Miech, Essex, & Goldsmith, 2001 ). Mother education often serves as a rough yet important proxy of family socioeconomic status and resources ( Bradley & Corwyn, 2002 ; Hoff, Laursen & Tardif, 2002 ). Low maternal education levels have been linked to lower socioeconomic resources and higher stress levels that, over time, can affect children’s developing neuroendocrine processes (e.g., such as cortisol levels). These processes are theorized to directly shape developing self-regulatory response patterns (see Blair & Raver, 2015 ). Maternal education levels are also associated with distinct parenting profiles that include mothers’ warmth, responsiveness, use of rich language inputs, and ability to maintain their children’s attention ( Guttentag, Pedrosa-Josic, Landry, Smith & Swank, 2006 ), all factors that predict individual differences in children’s self-regulation levels (see Grolnick & Farkas, 2002 ). Thus, mother education levels are also expected to serve as indicator of valid differences in children’s developing self-regulation patterns.

Current Study

Past theory and research indicate that behavioral self-regulation rapidly develops during early childhood with possible heterogeneity of early self-regulation trajectories (e.g., Blair & Raver, 2015 ; Vallotton & Ayoub, 2011 ; Wanless et al., 2016 ). To better understand behavioral self-regulation development and heterogeneity across children, we used the HTKS measure to assess and evaluate development via latent growth curve modeling. We then directly focused on potential trajectory differences in early childhood utilizing growth mixture modeling. We hypothesized that most children would demonstrate rapid gains in their behavioral self-regulation trajectory (e.g., Cameron Ponitz et al., 2008 ; Matthews et al., 2009 ), compared to peers, with these gains occurring early in schooling ( Blair & Raver, 2015 ). However, we hypothesized a subset of children would demonstrate a lagged behavioral self-regulation trajectory across early childhood as they are not ready to integrate the multiple processes required by advanced behavioral self-regulation when they first reach school ( Wanless et al., 2016 ; Willoughby et al., 2016 ). As part of trajectory validation, we utilized multiple diverse samples that included the same measure of behavioral self-regulation within similar age ranges, and with similar data collection procedures in order to evaluate whether trajectory findings replicate across a diverse population of children in different areas of the United States. We then further validated trajectories in relation to predicted associations between three characteristics: gender, language ability, and maternal education levels. We expected that these factors would distinguish what trajectory a child was likely to follow, with patterns of association matching previous findings, offering evidence indicating that trajectories capture meaningful individual difference as well as increasing our understanding how these characteristics relate to individual differences in development over time.

Participants

Participants consisted of 1,386 children across three samples that had at least two assessments of self-regulation between the ages of three and seven. Children were administered the same direct assessment of behavioral self-regulation in all three studies (the Head-Toes-Knees-Shoulders Task; Cameron Ponitz et al., 2008 ). The samples are described below.

Michigan longitudinal sample

The first sample was collected in predominantly middle- to upper-SES suburban area with a range of ethnic diversity in southeast Michigan. Participants included 351 (51% female) children followed from preschool through second grade as part of the “Pathways to Literacy” longitudinal study evaluating children’s socio-emotional and cognitive development (e.g., ***; blinded for review; 32 of the full sample of 383 were not included due to having fewer than 2 assessments of self-regulation). Students attended 314 classrooms located within 16 schools in a single suburban school district. All schools within this district that included at least one preschool classroom were represented and preschool classrooms included Head Start classrooms ( n = 49) as well as those that charged tuition. On average, children were 48.16 months ( SD = 7.35) old at the start of the study: just over four years of age. The bulk of parents who provided information about their child’s ethnicity ( n =257) reported that their child was White/Caucasian (80%). The remainder of children were described as African-American (4%), Asian/Indian ( 5% ), Hispanic (1%), and Multi-racial (3%). Several parents (8%) noted that another ethnicity would describe their child best. Most ( n = 278) families noted that their child’s native language was English, although some families ( n = 73) did not respond to this question 1 . Median household income was high (i.e., $115,000; Range = $11,000 to $650,000) as were parent education levels, with over 75% of mothers (n = 233) reporting that they had earned at least a bachelor’s degree.

Families were recruited via flyers sent home in children’s backpacks at the beginning of the school year(s). Children’s self-regulation was evaluated in the fall and spring of each year that the child was in the study until they finished first grade (up to 8 times) as part of a battery of measures administered by trained research assistants. Language assessments were administered during the fall of children’s first preschool year. Parents also filled out demographic information including child gender and information related to education level in the fall of their child’s first preschool year.

MLSELD preschool sample

The second sample consisted of 642 (51% female) preschool aged children from middle-SES communities with data waves collected over four years in Michigan as part of the Michigan Longitudinal Study of Early Literacy Development (MLSELD preschool sample; ***; blinded for review). Children were drawn from 78 classrooms across six schools: two in central Michigan and four in western Michigan. Schools in central Michigan were accredited by the National Association for the Education of Young Children. One was associated with a university and the other was a joint public/university preschool that also had a population of Head Start eligible children (less than 5% of the current sample). The four schools in western Michigan were part of the area’s public schools. In western Michigan, families were recruited for participation at a parent information night, while at the central Michigan schools families were recruited via flyers sent home in children’s backpacks. Children were on average approximately four years of age at the start of the study ( M = 47.74, SD = 7.02). Most parents who provided information about their child’s ethnicity ( n = 479) reported that their child was White/Caucasian (81%). Children who were African American (2%), Hispanic (3%), Asian (7%), multi-racial (4%), and those from ‘other’ (3%) ethnicities also participated in the present work. Among families reporting primary language spoken at home, almost all reported English ( n = 443) although some families did not respond to this question ( n = 158). Over half of mothers ( n = 374) reported that they had earned at least a bachelor’s degree. Household income levels were not collected as part of this study.

Children’s self-regulation was collected in the fall and spring of each year by a trained research assistant in a quiet setting; self-regulation was also collected two additional times in winter (about a month and a half apart) in two years of the study, and one additional time in the winter in one year of study (i.e., in the first two years of study self-regulation was assessed four times, year three it was assessed three times, and it was assessed twice in year four). Children’s language skills were tested in the fall of their first year of preschool and parents filled out demographic information including child gender and information related to education level at this time as well. Some children ( n = 160) participated in the study over the course of two years and a small subset of children ( n = 13) were included in the study for three years. Thus these children had their self-regulation evaluated more frequently. Across the larger MLSELD study ( n = 888), 246 children either had only one self-regulation assessment ( n = 133) or no assessments ( n = 113; by design, only half of the sample had self-regulation assessed in year 4).

Oregon sample

The third sample was recruited from a mixed-SES rural site in Oregon and consisted of 393 (50% female) children followed from preschool through kindergarten as part of a measurement study focused on improving measures of school readiness and self-regulation (***; blinded for review; 38 of the full sample of 431 were not included due to having fewer than 2 assessments of self-regulation related to study attrition). Children were drawn from 37 classrooms in 17 schools, with 54% ( n = 209) of children in Head Start programs. Children were on average over four and a half years old at the start of the study ( M = 56.14, SD = 3.65). Most parents who provided information about their child’s ethnicity ( n = 354) reported that their child was White/Caucasian (63%), or Hispanic (19%). Children who were African American (1%), Asian/Pacific Islander (4%), multi-racial (13%), and those from ‘other’(1%) ethnicities also participated in the present work. Families reported that English was the primary language spoken at home for most children ( n = 297); however this sample also included a subsample of children whose primary language was Spanish ( n = 60) who were tested in Spanish (all Spanish speakers were enrolled in Head Start). On average, mothers reported having attended some college, but only 43% reported that they had earned a bachelor’s degree or higher. Of respondents, 58% indicated that their families qualified for public assistance such as WIC or food stamps in the past four years.

Families were recruited through letters sent home with an enrollment packet sent during the summer before the beginning of the preschool year. Self-regulation was assessed each year in the fall and spring by trained research assistants (up to four time points). Language skills were assessed in the fall of children’s preschool year, and parents filled out demographic surveys at this time.

Self-regulation

Children’s self-regulation was measured directly using the Head-Toes-Knees-Shoulders task ( Cameron Ponitz et al., 2008 ; Connor et al., 2010 ; Matthews, et al., 2009 ). During the task, children are provided with paired behavioral rules (e.g., touch your head/touch your toes) and asked to do the opposite of what they were instructed to do. For example, when a child is asked to touch her toes, she should complete the opposite action (touch her head). The first ten items include one paired rule (e.g., head/toe). If children respond correctly to four or more items, they are given ten additional items with two paired rules (e.g., head/toes, knees/shoulders). Children earned two points for each correct response, one point for each self-correction (i.e., an initial movement to the incorrect response, but ultimately ending with the correct response), and zero points for each incorrect response. Scores ranged from 0–40, with higher scores indicating higher self-regulation. In the first year of the Michigan longitudinal sample data collection, when all children were in preschool, only the first half of the HTKS was administered, as the second half had not yet been developed. We therefore used a Rasch measurement approach to extrapolate an expected score on the entire 40 item task (details are provided in Bindman, Hindman, Bowles, & Morrison, 2013 ).

The HTKS has good construct and predictive validity within many culturally diverse samples, and across languages ( Cameron Ponitz, McClelland, Matthews, & Morrison, 2009 ; McClelland et al., 2007 ; von Suchodoletz et al., 2013 ; Wanless, et al., 2011 ). Scores on this measure are significantly correlated with reported self-regulation in the classroom, parental reports of attention ( Cameron Ponitz et al., 2009 ; McClelland et al., 2007 ) and other measures of self-regulation and executive function tasks. The HTKS also loads well onto a self-regulation factor with other similar measures ( Allan & Lonigan, 2014 ). In addition, past evidence indicates that growth in HTKS performance does not appear to be a function of practice effects ( Cameron Ponitz et al., 2008 ).

In terms of predictive validity, the HTKS consistently predicts academic achievement across diverse sample populations ( McClelland et al., 2007 ; Montroy et al., 2014 ; von Suchodoletz et al., 2013 ; Wanless et al., 2011 ). Notably, evidence suggests HTKS scores and growth are generally stronger predictors of growth in academic achievement than other self-regulation and executive function measures, particularly measures that mostly capture one skill versus an integration of skills ( Lipsey et al., 2014 ; McClelland et al., 2014 ).

The HTKS has strong reliability ( Cameron Ponitz et al., 2008 ; Matthews et al., 2009 ; Montroy, et al., 2014 ; Wanless, et al., 2011 ). Past studies consistently report high levels of inter-rater reliability (kappa > .90; Cameron Ponitz et al., 2008 ), and internal consistency estimates above .80 ( Montroy et al., 2014 ; Wanless et al., 2011 ). Within the current study internal consistency was also good, with Cronbach’s alpha values ranging from .85–.94 across samples.

Language skills were assessed across all three samples. In the Michigan longitudinal sample and the Oregon sample, the Picture Vocabulary subtest of the Woodcock Johnson III was used as an indicator of language ( Woodcock & Mather, 2001 ), while the Test of Preschool Early Literacy picture vocabulary subtest (TOPEL; Lonigan, Wagner, Torgeson, & Rashotte, 2007 ) was used in the MLSELD preschool sample. Both the TOPEL and WJ vocabulary tests have been well validated and extensively used in the literature as indicators of expressive vocabulary ( Bohlmann et al., 2015 ; Pence, Bojczyk & Williams, 2007; Wilson & Lonigan, 2009 ; Vallotton & Ayoub, 2011 ).

The Woodcock Johnson picture vocabulary subtest is an untimed picture naming task where children are shown a series of pictures and are asked to verbally identify the image. Children speaking Spanish in the Oregon sample were administered the Picture Vocabulary subtest of the Spanish version of the Woodcock-Johnson, the Bateria III Woodcock-Munoz. Picture Vocabulary has strong evidence of reliability (e.g., split half reliability between 0.76–0.81 for English speaking children and 0.88–0.89 for Spanish speakers) and validity. We used W-scores, a Rasch-type measure of ability, for all analyses. This type of score ensures measurement on an equal-interval scale and takes into account the level of item difficulty in relation to a children’s age.

The TOPEL picture vocabulary subtest consists of 35 items including various untimed picture naming tasks where children name pictures (1 point) and then describe aspects or functions associated with the picture presented to them (e.g., What are they for? 1 point) 2 . Thus raw scores range from 0–70. Test-retest reliability for this subtest is .81 and test developers indicated that scores were strongly related to the Early One-Word Picture Vocabulary Test ( r = .71, Brownell, 2000 ). The TOPEL was administered only in years 2 and 3 of the study. The remaining n =77 in year 1 and n = 113 in year 4 were not administered by design.

Mother education and gender

Across samples, demographics questionnaires were provided to parents including information regarding the child’s gender and parent education levels. For the Michigan longitudinal and the Oregon sample, mothers were asked to report education in terms of the number of years of schooling they had completed, while the MLSELD preschool sample was asked to report education levels via an 11-point survey question where education level categorically increased with 1 = less than a high school level education, 7 = a bachelor’s degree, and 11 = an advanced graduate degree (e.g., Ph.D or M.D). For comparability across samples, data from the Michigan longitudinal and Oregon sample were converted to the 11-point scale used by the MLSELD sample.

Analytic Approach

Analyses were done in two parts to (1) describe the general growth trajectory of self-regulation and (2) evaluate heterogeneity in self-regulation trajectories across children. First, we used latent growth curve models ( Bowles & Montroy, 2013 ; McArdle, 1986 ; Meredith & Tisak, 1990 ; Singer & Willett, 2003 ) to examine the general trajectory of development of self-regulation. These models provide information about the average values of children’s self-regulation (level of self-regulation) at a specified time, how rapidly their skills increase or decrease (i.e., slope), and whether this change is constant or might accelerate or decelerate (i.e., linear versus nonlinear growth). The general equation for the latent growth curve models we used was:

where Self-reg [ t ] n is the HTKS score for child n at age t; A[t] or the basis coefficient(s), are a function defining the shape of the growth trajectory, determining both the precise interpretation of the Level and the Slope , and the nature of change; Level n represents child n’s predicted level of self-regulation at the point where A[t] is 0; and Slope n generally reflects child n’s predicted rate of growth on the HTKS per unit of the basis coefficients. We considered five models for the trajectory: linear, quadratic, exponential, logistic, and the latent basis model. Due to variation in what age children received assessments and the time between assessments, scores were grouped by child age into three month windows in each dataset 3 . To evaluate what model optimally described the general growth trajectory of behavioral self-regulation, we utilized the Akaike Information Criterion (AIC) and the Adjusted Bayesian Information Criterion (aBIC) fit indices.

Next, we utilized growth mixture modeling (GMM; Muthén, 2001 ) to evaluate if there were multiple growth trajectories of early childhood behavioral self-regulation. In GMM, the trajectory classes are formed based on the growth factor means and variances (e.g., Level and Slope means and variances) with each class defining a different growth trajectory ( Muthén, 2001 ). GMM also captures individual variation around these growth curves by estimating the growth factor variances within each class ( Muthén & Muthén, 2000 ). Within the GMM models, we chose to restrict trajectory shape to the shape indicated by the latent growth curve models. This is common practice in the GMM literature when there is not a strong theory regarding shape of trajectory differences across the population. However, slope and rate parameters (but not functional form) were ultimately allowed to vary across trajectories, thus providing information regarding different developmental progressions and patterns. In all models, errors were specified to be uncorrelated. We determined best model fit based on AIC and aBIC indices ( Tofighi & Enders, 2007 ), entropy, and bootstrapped likelihood ratio tests (BLRT) which compares the fit of the estimated model with k classes to the same model with one less class (k-1), with p-values less than .05 indicating that the estimated k class model fits better than the k-1 model ( Grimm, Ram & Estabrook, 2010 ). Note, BLRTs can only test differences in relation to what number of classes fits best, they provides little information when comparing within class solutions with differing parameters (e.g., whether a solution with constrained random effects versus variable random effects fit best). In addition we also considered whether results were interpretable and meaningful, and we took into account estimation parameters as well as estimation history as these are all relevant indicators of model comparison and selection ( Grimm et al., 2010 ). To evaluate the predictors of trajectory classes, we assigned each child to the class with the highest probability, and used logistic regression based analyses to predict class membership 4 . All analyses were completed with Mplus version 7.2 ( Muthén & Muthén, 1998–2010 ), utilizing full information maximum likelihood to account for missing data. In all analyses, year of study was included as a saturated covariate given its relationship with missing data in all samples, and the MLR estimator was used as this estimator provides the most accurate parameter estimates when missing data are present ( Enders, 2010 ; Graham, 2003 ).

Descriptive Statistics of Behavioral Self-regulation

On average, children demonstrated gains in behavioral self-regulation as measured by the HTKS between the ages of three and seven; see Table 1 for a comparison of average gains across samples. Individual observed trajectories for a random subset of 25 children’s scores per sample are presented in Figure 1 . In all samples, there were substantial individual differences, and periods of acceleration and deceleration in growth both within and across children. Correlations are provided in the supplementary materials .

Table 1. Descriptive Statistics for Self-regulation by Sample and Age.

Note. HTKS refers to the Head-Toes-Knees-Shoulders task. Mos. refers to months. Dashes represent ages that data were not collected by sample.

Random subset of 25 children per sample’s (75 total) smoothed behavioral self-regulation trajectories

Fit statistics for the five latent growth curve models are reported in Table 2 by sample. In all samples, both AIC and aBIC suggested that the changes and between person differences in early childhood behavioral self-regulation development were best described by an exponential curve; see Figure 2 . Across samples patterns varied such that: in the MLSELD preschool sample, children’s growth accelerated across preschool. However, in the Oregon and Michigan longitudinal samples that followed children across early elementary grades, children demonstrated faster gains early in preschool with gains slowing in early elementary school 5 .

Table 2. Summary of Latent Growth Model Fit Statistics.

Note. AIC refers to the Akaike information criterion, aBIC refers to the adjusted Bayesian information criterion. Bolded values indicate best fit.

Latent growth curve model of the developmental trajectory of behavioral self-regulation by sample.

Heterogeneity in Behavioral Self-regulation Development

Growth mixture modeling allows for the estimation of different trajectories with the possibility of every estimated parameter differing across groups (e.g., means, variances, covariances, and basis coefficients; Grimm et al., 2007 ; McArdle & Bell, 2000 ; McArdle & Nesselroade, 2003 ). Currently, there is no generally accepted strategy for how GMMs should be evaluated in terms of which constraints to relax first ( Grimm et al., 2007 ). However, similar to past studies, we evaluated models based on the principles of factorial invariance studies (e.g., Grimm et al., 2007 ), followed by an examination of models with different constraints related to within trajectory variation patterns (e.g., Kreuter & Muthén, 2008 ). Specifically, we evaluated fit across separate datasets starting with a two growth trajectory model where only level and slope means were allowed to vary across trajectory groups. In all models, the exponential shape indicated by the latent growth curve analyses was specified for all classes. Rate of acceleration/deceleration within the exponential trajectory(ies) was initially constrained to be the equal across curves (i.e., only timing differences were allowed with identical developmental form and rate of change). This would offer strong evidence that variation in behavioral self-regulation trajectory growth across children is similar, but with differences in developmental timing. All within curve variations (random effects) were also initially constrained ( Kreuter & Muthén, 2008 ). We then allowed rates of acceleration/deceleration to vary in order to evaluate possible differences in how children develop self-regulation across early childhood. Specifically across different class/trajectory solutions, one could potentially see changes in the sign for rate of change indicating whether rate was accelerating or decelerating across the specific study time period, as well as differences in rate parameter magnitude (i.e., it was possible for a non-significant rate parameter to be found, which would be similar to if a linear trajectory was specified). Additional growth trajectories were then added to determine what number of trajectories best fit the data. Once number and trajectory rate of change differences were determined, we progressively relaxed within trajectory level and growth variances and covariances to investigate how closely individual children followed group trajectories.

Results indicated that the three trajectory solution fit best in all samples based on the evaluation of global fit statistics in association with bootstrapped LRTs, iteration history, convergences, estimated parameters, and entropy values; see Table 3 for a summary of fit statistics across the different models and samples. In all samples, the three trajectory solution was also interpretable such that children generally demonstrated timing differences in early childhood self-regulatory gains but with some variation in rate across trajectories. As seen in Figure 3 , children demonstrated either early gains, intermediate gains, or later gains relative to sample peers. In general, children’s individual self-regulation trajectories also conformed closely to the three trajectories. Specifically, the models where within trajectory level, slope or rate variations were constrained to zero fit best for all three samples.

Table 3. Summary of Growth Mixture Model Fit Statistics.

Note. Dashes (-) denote that the model did not converge, an indicator of poor fit. Means only model: only level and growth means were allowed to vary between trajectories; means + shape: basis coefficients were allowed to vary (i.e., shape of curve) between trajectories. The “3 trajectory” models also included extra models to test whether relaxing within trajectory constraints resulted in better model fit. AIC refers to the Akaike information criterion, aBIC refers to the adjusted Bayesian information criterion. Bolded values indicate best fit. Ent. refers to entropy values, and BLRT indicates the p-value from the bootstrapped likelihood ratio tests.

Included non-positive definite warnings that affected model trustworthiness, class specification and related entropy values.

Bootstrap LRT p value may not be trustworthy

Predicted developmental trajectories for behavioral self-regulation by sample. A. MLSELD preschool sample, B. Oregon sample, C. Michigan longitudinal sample, D. all samples picture together; MI = Michigan.

As expected, the percent of children predicted to follow a given trajectory varied across samples, yet results still demonstrated clear consistency. Descriptively, in the Michigan longitudinal sample, 20% of children were classified as early developers who demonstrated higher initial levels of self-regulation and earlier gains, 45% as intermediate developers who had low initial self-regulation, followed by rapid gains, and 35% as later developers, who started with lower levels and gained more slowly compared to other groups. A similar pattern occurred within the MLSELD preschool sample with 29% of children classified as early developers, 45% of children as intermediate developers, and 26% as later developers. Likewise in the Oregon sample: 50% of children were early developers, 32% were intermediate developers, and 18% were later developers.

Broad patterns that were replicated across samples indicated that more than half of the children assessed within a given sample (including both early and intermediate developers) demonstrated rapid growth across preschool. On average, these children accurately responded (correct or self-correct) 75% of the time or more by age five (58–60 months). Yet, about 20% of children (later developers) consistently demonstrated relatively few early gains, responding at less than 35% accuracy on the behavioral self-regulation task throughout preschool. This pattern persisted into kindergarten in both samples that spanned into early elementary school with gains for later developers lagging same sample peers; in the Oregon sample gains do not pick up until 70–72 months of age (i.e., nearly 6 years of age). In short, children classified as later developers were, on average, six months to a year behind their intermediate developing peers and at least a year and a half behind early developers.

Descriptively there were also several other similarities across samples, see Table 4 for means across samples. In all three samples boys made up the majority of the later developers group whereas the majority of early developers were girls. Likewise, across all three samples, early developers had the highest mean levels of language and, on average, their mother’s obtained the highest education levels. Children in the later developers group demonstrated the lowest levels of language and had mothers with comparatively lower levels of education compared to the other two groups.

Table 4. Summary of Behavioral Self-regulation Trajectory Descriptives.

Note. For gender, girls are coded 1, boys are coded 0. MLSELD preschool language scores are from TOPEL vocabulary subtest; Michigan longitudinal and Oregon language scores are from the Woodcock Johnson Picture Vocabulary Subtest (w-scores).

Predictors of self-regulation growth trajectories

In this section we investigated whether child attributes and mother education levels were indicative of which children are more likely to follow which trajectory. Given that a subset of children in each sample demonstrated a ‘later developer’ behavioral self-regulation trajectory, we first tested whether these children differed from children who globally demonstrated rapid gains in preschool, classified as ‘preschool developers’ in behavioral self-regulation (i.e., ‘intermediate’ and ‘early developers’ considered together). We followed this analysis up with a second analysis comparing intermediate to early developers. In all analyses, gender, language, and maternal education levels were included as predictors, controlling for children’s age at the first measurement time point, race/ethnicity, and whether English was a indicated as a child’s primary language.

We used the Benjamini-Hochberg procedure to account for multiple predictors ( Benjamini & Hochberg, 1995 ). This procedure controls for the false discovery rate (i.e., Type I errors) associated with conducting multiple comparisons by ranking post-hoc the individual p-value associated with each predictor from smallest p-value to largest. Each individual p-value is then compared to its Benjamini-Hochberg critical value. The predictor with the largest p-value that is less than the Benjamini-Hochberg critical value, and all predictors ranked before it are considered significant. In the current study, we used a false discovery rate of .05, thus all predictors with a Benjamini-Hochberg critical value below .05, regardless of raw p-values, were considered significant. See Table 5 for all results including raw p-values and calculated Benjamini-Hochberg critical values.

Table 5. Predictors of Behavioral Self-regulation Trajectories.

Note. B-H crit. Value refers the Benjamini-Hochberg critical value for a given predictor. Mother ed. refers to mother reported education level. Bolded values indicate significant findings.

Child gender was linked to what trajectory a child was likely to follow in both the Michigan longitudinal sample and the Oregon sample. Girls included in the Michigan longitudinal sample were 1.79 times more likely to be classified as preschool developers (following either an intermediate or early developers’ trajectory) versus later developers. Further, in the Oregon sample, girls were 2.34 times more likely to be classified as early developers versus intermediate developers. No other comparisons were significant.

Children’s language skills also predicted within sample trajectory differences. For a 1 SD increase in expressive vocabulary, children in the MLSELD preschool sample were 1.40 times more likely to be classified as a preschool developer versus a later developer. Furthermore, for a 1 SD increase in vocabulary children were 1.68 times more likely to be identified as early developers versus intermediate developers. For the Oregon sample, children were 1.23 times more likely to be classified as demonstrating a preschool developer trajectory compared to later developer trajectory, and 1.63 times as likely to be identified as early developers versus intermediate developers per a 1 SD increase in expressive vocabulary. No other comparisons were significant.

Mother education predicted trajectory differences in the MLSELD preschool sample and the Oregon sample. In the MLSELD preschool sample, children whose mothers completed one additional level of education (e.g., moving from associate to bachelors) were 1.30 times more likely to be classified as a preschool developer than a later developer. Likewise, of the children who demonstrated growth across preschool, there were significant differences between intermediate developers and early developers such that for a 1 unit difference in mother education, children were 1.26 times more likely to be classified as early developers. Within the Oregon sample, for every 1 unit increase in reported maternal education, children were 1.23 times more likely to be classified as demonstrating a preschool developer trajectory versus a later developer trajectory. No other comparisons were significant.

The development of effective self-regulation is widely recognized as an early marker for later life successes ( Blair & Raver, 2015 ; Calkins, 2007 ; Diamond, 2002 ; Gross & Thompson, 2007 ; Moffitt et al., 2011 ). The primary goal of the current paper was to evaluate the trajectory of behavioral self-regulation across early childhood in three distinct samples across two states in order to determine if there was consistent heterogeneity in this trajectory/ies across children. In addition, we evaluated whether several predictors that are often associated with self-regulation could be used to indicate the type of trajectory a child was likely to follow, with an ultimate goal of validating trajectory differences as meaningful and consistent with previous findings, while further increasing our understanding of developmental differences in early childhood behavioral self-regulation across children. Overall, our findings indicate that self-regulation rapidly increases across early childhood, with children in all samples following three distinct trajectories that were distinguishable based on several important factors.

The general growth trajectory of behavioral self-regulation across early childhood was best represented by an exponential function. This was the case across all three samples, although there were differences across samples in whether growth was accelerating or decelerating, likely related to sample specific differences in relation to what ages were captured. This result is consistent with previous findings suggesting that behavioral self-regulation (and the executive function skills that support behavioral self-regulation) develop(s) in a nonlinear fashion with early, rapid gains during the preschool (e.g., Cameron-Ponitz et al., 2008 ; Diamond, 2002 ; Wiebe et al., 2012).

The growth mixture modeling results indicated heterogeneity across children in the developmental trajectories. Despite differences in sample locations, background characteristics, and study windows, in all three samples children could be characterized as either early developers, intermediate developers or later developers relative to their within sample peers. Likewise, an identifiable overall pattern emerged such that for the majority of (but not all) children in all samples there was a period of rapid development of behavioral self-regulation in preschool with individual differences in when rapid growth occurred, how rapidly it occurred, and what level of behavioral self-regulation children demonstrated at the beginning of preschool.

Yet, overall, approximately 20% of children appear to make few gains in preschool. This was especially the case in the MLSELD preschool and Oregon sample, with a subset of children in both samples showing little to no growth until 60 months (i.e., 5 years) of age. Children classified as later developers in the Michigan longitudinal sample did show gains before age five, but these gains still lagged behind their sample peers (e.g., it takes children classified as later developers an additional nearly three years, with children ~80 months old or age 6.75, to obtain relatively identical mean levels of self-regulation their early developing peers demonstrated at four years of age).

In general, we conclude that many children are still developing behavioral self-regulation skills as they leave preschool and enter kindergarten, and that they may need behavioral supports in kindergarten with a subset of children just beginning to develop advanced behavioral self-regulation. The current study’s findings mirror teacher observations, with nearly half of teachers indicating that they feel many children enter kindergarten without the self-regulation skills necessary to be ready to learn in formal education setting ( Rimm-Kaufman et al., 2000 ). These finding are also fairly consistent with previous growth trajectory findings within a smaller, Taiwanese sample ( Wanless et al., 2016 ). Interestingly, this previous study reports that both a two and three trajectory solution fit the data, but the three class solution produced an extremely small class (p. 109; Wanless et al., 2016 ). It is plausible that three classes were consistently found in the current study due to inclusion of larger, more heterogeneous samples.

The overarching similar patterns observable across samples and in the literature at large, indicate that once children start to utilize executive function skills to carry out complex directions, it is a relatively short developmental time before they are able to accurately and consistently follow multiple abstract rules such as those utilized in the Head-Toes-Knees-Shoulders self-regulation task. Importantly, what is clear from the current study that was less clear in previous work is that: 1) it generally takes just about 2–3 years to go from little ability to self-regulate in the face of complex instructions to task mastery in this context and 2) All children will master the basic skills needed to participate in self-regulatory tasks, however the age at which they do so varies across children and is related to child characteristics and contextual factors Thus, the important relations between children’s levels of behavioral self-regulation and later development outcomes documented in previous work (e.g., McClelland et al., 2006 ) may be better described as reflecting differences in the timing of the development of behavioral self-regulation.

Indicators associated with self-regulation growth trajectories

Our findings indicate that early childhood behavioral self-regulation trajectories are distinguishable based on associations with other variables. Consistent with past findings (e.g., Matthews et al., 2009 ) the identification of a child as a girl was associated with earlier development trajectories. In all samples, there were more boys in the later developers group, with results reaching statistical significance in the Michigan longitudinal sample. Interestingly in the Oregon sample there was also a statistically significant difference in the number of boys in the intermediate trajectory compared to the early developer’s trajectory. These findings are generally consistent with previous findings (e.g., Matthews et al., 2009 ) and suggest that boys may need additional supports at the beginning or during preschool to ensure they develop the self-regulation skills necessary for entry into kindergarten. Notably, gender differences were observed in the two samples that spanned into the formal years of education (kindergarten and beyond), possibly providing support to previous evidence suggesting that gender differences appear more substantially over time ( Entwistle et al., 2007 ; Matthews et al., 2009 ; 2014 ). More work is necessary to determine why gender differences are linked to different pattern of self-regulation development. Likewise systematic investigations of how these associations change across time are also warranted. One possibility beyond the scope of the current study is that boys can be more sensitive to environmental experiences including chaos ( Cameron Ponitz, Rimm-Kaufman, Brock, & Nathanson, 2009 ; Wachs, 1992 ; Wachs et al., 2004 ), as well as parent and teacher expectations of school success ( Entwistle, et al., 2007 ; Wanless et al., 2011 ). It is possible that overtime, continued chaos or continued high or low expectations from parents and teachers may have cumulative effects on skill development.

Likewise, language was also predictive of what behavioral self-regulation trajectory a child was likely to follow. Within the MSELD preschool and Oregon sample, higher levels of expressive language at the start of preschool was associated with earlier development, similar to other field findings ( Bohlmann et al., 2015 ; Vallotton & Ayoub; 2011 ). There is long standing theoretical support for the role of language as an organizational tool used to aid self-regulation development ( Cole et al., 2010 ; Vygotsky 1934/1986 ), and the current study provides some evidence that early language skills may affect the timing and rate of development of early self-regulation growth across early childhood. Higher levels of language may give children the ability to organize and better understand incoming information such as complex behavioral rules, contributing to the use of more complex self-regulation that relies on attending to and keeping track of information (i.e., working memory), while inhibiting a well learned dominant response pattern. Future studies are necessary to better understand the ongoing relations between language and self-regulation development ( Bohlman et al., 2015 ). Specifically, more studies evaluating the potential mechanisms regarding language’s role (e.g., see Winsler et al., 2009 ) and the specifics of what other aspects of language lead to the rapid develop of self-regulation.

Mothers’ education levels also affected what trajectory a child was likely to follow. Early developers generally lived in homes where mothers reported the highest education levels, with children in Oregon and the MLSELD significantly more likely to be classified as an intermediate or early developer based on maternal education. Mother education serves as a proxy for aspects of the environment, including available household resources. These resources include physical resources such as toys, games, learning materials, and books that can support self-regulation development ( Brooks-Gunn & Duncan, 1997 ), but also abstract resources such as a less stressful home environment (see Blair & Raver, 2012 ). Additionally, more highly educated mothers often hold different beliefs compared to their lower educated counterparts that affect their parenting behavior towards their children (see Bradley & Corwyn, 2002 for review), that could in turn affect developing self-regulation. Maternal education may also help to explain why children within the Oregon sample developed self-regulation slightly later relative to both Michigan samples. There was a higher preponderance of mothers with lower education levels and families were more likely to be living at or below the poverty line, perhaps indicating that children had fewer familial supports to help them to develop their self-regulation. Furthermore, although beyond the purview of the current study, it is also important to note that poverty consistently predicts the complexity of children’s developing language ( Fernald, Marchman, & Weisleder, 2013 ; Hart & Risley, 1995 ), and is often related to gender differences in children’s performance ( Entwistle et al., 2007 ). A greater investigation across multiple indicators of a child’s environment is a necessary next step in fully understanding what attributes affect children’s self-regulation development in context.

Practical Implications

These findings have several implications. First, given that majority of children appear to demonstrate rapid gains in behavioral self-regulation between the ages of three and seven (but particularly during the preschool years), this research supports previous work emphasizing this time period as a potential critical period ( Blair, 2002 ; 2010 ; Diamond, et al., 2007 ; McClelland & Cameron, 2012 ). Utilizing preschool curricula such as Tools of the Mind ( Bodrova & Leong, 2007 ) that center on scaffolding children’s early self-regulation skills or targeted games and activities focusing on promoting self-regulation (e.g., Schmitt, McClelland, Tominey & Acock, 2015 ) may provide children the support they need to develop behavioral self-regulation skills early. On the other hand, programs and curricula that focus on self-regulation and consider/target multiple aspects of the developing child may ultimately prove even more impactful for preparing all children for formal education (see Dickinson, McCabe & Essex, 2006 ; Lonigan et al., 2015 ). Future investigations evaluating school contexts and their associations with different self-regulation developmental trajectories are necessary to provide further support to these assertions.

In addition to offering insights into the developmental trajectories of behavioral self-regulation, this work also offers tentative evidence indicating which trajectory a child is likely to follow may be predictable based on background characteristics. Specifically, it may be possible to screen for children who are at risk of entering kindergarten without the self-regulation skills teachers feel are necessary to succeed ( Rimm-Kaufman et al., 2000 ). For example, our study indicates that boys from families with lower education levels who score lower on early vocabulary tests compared to peers may be at risk of starting kindergarten behind in self-regulation. Although a more thorough investigation of predictive traits is warranted, even these few consistent predictors, in conjunction with delays in preschool self-regulation progress, may be enough to indicate a need for support, particular given research indicating that later behavioral self-regulation development can impact school readiness and future academic skills and success for years to come (see McClelland et al., 2006 ; Wanless et al., 2016 ).

This type of developmental work also could enhance progress monitoring by early childhood professionals. Recent research has advocated that early education environments should include a three-tiered system of service delivery that includes increasing instructional intensity to the level of student needs (e.g., Fox, Dunlap, & Cushing, 2002 ). A better understanding of how self-regulation development progresses, and what constitutes normal versus delayed development (i.e., benchmarks related to student needs) gives teachers the tools they need to customize level of instruction appropriately, including information for when to seek outside support for children who may need intervention. Past research indicates that early interventions targeted at increasing behavioral self-regulation skills can result in significant behavioral self-regulatory gains as well as some academic gains, particularly for individuals who are have lower initial levels of behavioral self-regulation compared to peers ( Schmitt, et al., 2015 ; Tominey & McClelland, 2011 ).

As preschool and kindergarten have evolved in focus across the last few decades (and continue to evolve) from social-emotional skills to more academic skills ( Kagan, Kauerz, & Tarrant, 2007 ; Stipek, 2011 ), it is critical for researchers and policymakers alike to remember that social-emotional development such as self-regulation development can have long lasting impacts on children’s school readiness and success, including academic success. Past research indicates that children who begin kindergarten with lower levels of self-regulation skills also lag in math and literacy skills through sixth grade, with the gaps in achievement widening through second grade between children with higher kindergarten self-regulation and children with comparatively lower levels of self-regulation ( McClelland et al., 2006 ). Although future research linking academic achievement to different self-regulation trajectory classifications is needed, we speculate that children classified in the current study as ‘later developing’ may struggle with academic achievement given that these children did not demonstrate the rapid gains their peers demonstrated in preschool with some not showing those types of gains until nearly the end of kindergarten. It is possible that a lagged trajectory in early childhood may have cascading effects on children’s future development as many of the skills that rely in part on self-regulation skills may also be delayed until self-regulation skills are more adequately acquired. Regardless, the fact that all ‘later developers’ across samples in the current study demonstrated markedly less developmental progress compared to peers should be addressed as early as possible given that self-regulation in its own right is an important developmental milestone ( Bronson, 2000 ).

Limitations and Future Studies

The current study utilized one measure of self-regulation, which, although reliable and valid, does not fully capture the multi-dimensional nature of self-regulation. The inclusion of other measures of self-regulation that each focus on a different aspect of self-regulation would certainly be informative in future studies for better understanding how these multiple aspects come together to support behavioral self-regulation. Additional measurement work is also needed, as it is not clear how these aspects integrate, or develop in relation to each other across time ( Allan & Lonigan, 2014 ; Miller, Giesbrecht, Müller, McInerney, & Kerns, 2012 ; Willoughby et al., 2011 ; Zelazo & Carlson, 2008 ). Studying the development of multiple aspects of self-regulation could provide a greater specificity of information, particularly related to the subset of children who demonstrated later development.

In addition, caution must be taken regarding whether the functional form found within the current study is specific to the measure used, or is indicative of the latent construct of behavioral self-regulation. Past evidence and theory (e.g., Diamond, 2002 ; Vallotton & Ayoub, 2011 ) support the current finding that the development of behavioral self-regulation is non-linear across early childhood, yet more research with multiple measures is needed to adequately confirm that this is the case and that the functional form of development is not an artifact of the particular assessment(s) used. Likewise, additional research focused on functional form differences across the trajectories is necessary. Consistent with typical GMM practices, the current work used a common functional form (exponential), while allowing rate of change to vary across trajectories. Our findings suggested that some children may demonstrate a different functional form indicative of a more steady progression of skill development. There is little past work or theory specifying trajectory form differences or how mechanisms of development relate to those differences. In short, it is still not clear whether children may acquire skills differently across trajectories with some children ‘leaping’ in skills indicating a possible rapid integration of multiple skills into a behavior, while others slowly build the skills that support self-regulation.

In the current study we focused on a core set of predictors to validate trajectory differences; more comprehensive work is necessary to determine underlying mechanisms of regulatory differences. As anticipated, the set of chosen indicators we focused on supported that the individual differences captured by the three trajectories found per sample are meaningful, yet a host of other indicators will likely provide more information regarding trajectory differences, as well as indicating what child skills and environments are important to help children optimize their regulatory development. For example, direct evaluation of the early contexts provided to children by aspects of parental warmth and responsiveness and their predictive association with developmental differences in self-regulation is a promising next step ( Grolnick & Farkas, 2002 ; Landry et al., 2002 ).

Likewise there is a need to evaluate genetic and neurological predictors of trajectory differences, particularly given recent findings suggesting that self-regulation and many of the processes, including neural processes, that underlie it may be genetic in nature (e.g., see Friedman, et al., 2008 ; Friedman, Miyake, Robinson & Hewitt, 2011 ) although still open to environmental influences via probabilistic epigenesis (see Blair & Raver, 2012 ; Deater-Deckard, 2014 ). This must be considered given the remarkable regularity of patterns across three diverse samples in the current study.

Conclusions

The present study is an important contribution to our understanding of how self-regulation develops during early childhood. Specifically, findings indicate that the development of behavioral self-regulation is exponential in nature. Likewise, there are differences in this trajectory such that majority of children demonstrate rapid gains across the preschool time period, while a subset of children demonstrated low levels of initial behavioral self-regulation and later self-regulation development. Differences in what trajectory a child was likely to follow were linked to different child attributes and background characteristics. Based on these findings researchers and educators alike should consider carefully how best to support children’s development of behavioral self-regulation during early childhood, particularly for children who may be at-risk of making few gains during preschool as early self-regulation development seemingly places children on a trajectory for later school, economic, and health successes.

Supplementary Material

Acknowledgments.

The research reported here was partially supported by the Institute of Education Sciences, U.S. Department of Education, through Grant R305A100566 to Oregon State University. The opinions expressed are those of the authors and do not represent views of the Institute of the U.S. Department of Education. This work was also supported by the National Institute of Child Health and Human Development and the National Science Foundation under grant numbers R01 HD27176 and 0111754, respectively. Additional funding was provided by the U.S. Department of Education, Institute for Education Sciences, Cognition and Student Learning (R305H04013) and the National Institute of Child Health and Human Development (R01 HD48539).

In the Michigan longitudinal sample, > 7 languages other than English were reported as children’s primary language, the largest language sub-group consisted of Arabic (n=4). For the MLSELD preschool sample, 18 languages other than English were reported, the largest language sub-groups consisted of Spanish, and Korean speakers (n = 8).

We also ran analyses with only points for naming an object correctly included (i.e., excluding the points received for describing functions). The results were similar with both scoring methods, thus we present findings using the total (naming and functions) score as this is commonly how the subtest is scored and used in previous studies.

In years one and two of the MLSELD preschool study, most children had two assessments within three months. To ensure local independence within the three month windows, only one assessment was included, chosen at random.

We also ran the 3-step approach discussed in Asparouhov & Muthen (2014) . However, this approach results in listwise deletion of missing data at the predictor level. Thus, in addition to the initial 3-step approach models we also used multiple imputations of missing predictor level data and re-ran all 3-step approach models. The pattern of results was the same in all cases.

In order to rule out the possibility that the exponential shape was a product of floor and ceiling effects across time points, all analyses were re-ran utilizing Tobit growth curve models (Wang, Zhang, McArdle & Salthouse, 2009), which can account for floor or ceiling effects. In all samples, an exponential model still fit best compared to other models, even when accounting for floor or ceilings.

Contributor Information

Janelle J. Montroy, Email: [email protected].

Ryan P. Bowles, Email: [email protected].

Lori E. Skibbe, Email: [email protected].

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Strengthening Emotional Development and Emotion Regulation in Childhood—As a Key Task in Early Childhood Education

Ramona thümmler, eva-maria engel, janieta bartz.

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Received 2021 Dec 31; Accepted 2022 Mar 21; Collection date 2022 Apr.

Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license ( https://creativecommons.org/licenses/by/4.0/ ).

The following article deals with emotional development and the development of emotion regulation skills in children during early childhood education, focusing primarily on the importance of the early childhood teacher. Emotion regulation is important for success and wellbeing in further life. It is developed in interaction with parents as attachment figures. Teachers can also be important persons for the child in the context of bonding. This leads to the question of how early childhood teachers can support children learning to regulate their emotions. We analyze with the content analysis, four programs for promoting social and emotional skills that are currently used in Germany. The main question is if the programs include elements that increase teachers’ skills in supporting the children in regulating their emotions. The categories to analyze the programs are derived from theories of teacher-child interaction. In addition to programs for promoting emotional and social development, we will discuss aspects of shaping interaction as essential elements in promoting emotion regulation. The conclusion outlines some key implications for educational practice and the importance of developing professional behavior for qualitative teacher-child interactions.

Keywords: emotion regulation, teacher-child-interaction, early childhood development, intervention program

1. Introduction

In recent years, supporting children has become increasingly important. In the context of early childhood education in Germany, training in language and mathematics skills have become well established, especially in the context of inclusive education. Well- developed social and emotional skills in children and young people will lead to success in their schooling and for life beyond the classroom. In our view, insufficient attention is paid to the strengthening of social and emotional skills—especially regulating emotions as an aspect of emotional competence—which is fostered at an early age.

In addition to parents, teachers are the most significant role models for supporting the development of these skills. As there has been little research conducted on this topic so far, we present our paper. We ask what opportunities there are in early childhood education for supporting children in regulating their emotions. We analyze the content of four programs that are currently being used in Germany. One of the key aspects of our analysis is the question: “Do these programs include elements that increase teachers’ skills in supporting the children in regulating their emotions?” We focus on knowledge about emotional development and support of emotion regulation. It is also important for us to reflect upon how a teacher interacts with the children as it is in interacting with other people that a child learns how to regulate his or her emotions, thus developing emotional skills.

To answer this question, we begin by taking a closer, theoretical look at a child’s emotional development in the first years of their life ( Section 2 ). Following this, we present the development and influencing factors of emotion regulation ( Section 3 ) within a focus on family interactions. In Section 4 , we assume, based on Ahnert’s empirical research [ 1 ], that teachers are also significant for the children in terms of bonding. We explore how teachers can support children in regulating their emotions based on how our critical application of the programs. In this context, we also refer to research (e.g., [ 1 , 2 , 3 ]) into teacher-child interaction, as well as our own reflections about improving the skills of teachers regarding emotional regulation. At the end of the article, we provide theory-based implications for the practice of teacher-child interaction. Furthermore, we show how important developing professional behavior is for qualitative teacher-child interactions ( Section 5 ).

2. Emotional Development in the First Years of Life

The development of emotional competencies is a lifelong process that goes hand in hand with physical, cognitive, and social development [ 4 ]. Therefore, emotional development is based on the close relationship we have with our primary caregivers [ 5 ]. Mirror neurons enable infants to imitate the facial expressions of others shortly after birth; they are thus “the neural format for an early, basal form of communication and reciprocal social attunement” [ 6 ] (p. 119).

In the first year of life, children develop basic emotions of joy, fear, anger, sadness, surprise, and interest [ 7 ]. More complex self-referential emotions such as pride, shame, compassion, envy, embarrassment, and guilt are developed toward the end of the second year of life [ 7 ]. “In order to feel these emotions, a child must know socially accepted behavioral standards and be able to implement these in their personal behavior” [ 8 ] (p. 16 f.). The development of self-referential emotions goes hand in hand with children’s increasing language development, which allows them to identify their feelings [ 9 ], see Figure 1 .

Development of emotions and emotion regulation from 0 to 6 years (Diagram originates [ 7 ] (p. 36); This diagram has been reproduced with the authors’ permission).

The embodiment of language makes emotions, on the one hand, conscious and communicable; on the other hand, it enables a differentiated perception of emotions. This is important to distinguish between feelings and physiological states that sometimes appear to be similar [ 9 ]. Emotionally competent people “can talk about feelings and express them in a culturally appropriate form” [ 10 ] (p. 131). Frank summed up that for early childhood education, “firstly, conversations about emotions are possible and secondly, they are very important” [ 11 ] (p. 27). In this context, some authors use the term “emotional intelligence” [ 12 ] or “emotional literacy” which is essentially emotional alphabetization [ 13 ] (p. 407). This conceptualization has a certain appeal since the learnability and developmental possibility of emotional competence are inherent in it. In the second year of life, the phase of emotional perspective taking begins, which is further refined particularly between the third and fifth years of life [ 8 ] (p. 17) “Children are increasingly able to distinguish between their own feelings and those of others, to take the perspective of others, and to recognize and empathize with their feelings” [ 8 ] (p. 17).

In addition to emotion regulation, which we will discuss separately in the next section, emotion understanding is highly important for emotional development. Emotion understanding means being able to recognize how another person feels according to their expressive behavior. Emotion understanding also means being able to assign specific situations to specific feelings, or even knowing that two feelings can occur at the same time. Between the ages of four and five, children can perceive and describe multiple or ambivalent emotions, and understand and interpret them better with increasing age [ 7 ]. Their own emotional reactions are influenced by a growing understanding of emotional concerns as well as expectations of the environment and the ability to relate this knowledge to their own behavior.

3. Emotion Regulation—Development and Influencing Factors

3.1. the development of emotion regulation.

According to Ulrich and Petermann [ 14 ], emotion regulation is defined as “a person’s ability to influence his or her own emotions in terms of quality, intensity, frequency, and their timing and expression, according to his or her own goals“ [ 14 ] (p. 134). A person who has strong emotion regulation does not simply surrender to his or her emotions but is able to influence them.

The ability to regulate emotions develops in parallel with emotional development, because of the close interaction between child and caregiver. In the first weeks after birth, it is mainly the parents who regulate the child’s level of emotional arousal, protect him or her from hyperarousal, and calm him or her down [ 15 ]. In this phase of interpsychic emotion regulation, the infant experiences support from his or her caregivers in dealing with his or her needs and emotions. This process is supported by the ability of social referencing [ 16 ], an ability that the child acquires from the age of about nine months: in an unfamiliar or ambiguous situation, the child can “read” from the facial expression of the caregiver how he or she evaluates the situation and adjust his or her own behavior accordingly. Between the ages of two and five, children are continually improving their ability to use self-contained regulation strategies. This means that a shift from interpsychic to intrapsychic emotion regulation takes place. The child learns to regulate his or her feelings and the associated expressions more and more independently and to adapt them to social demands. According to Petermann and Wiedebusch [ 7 ], functional regulation strategies at preschool age also consist of interactive strategies such as seeking social support and comfort, redirecting attention (for instance, dealing with something else or thinking about something else), reframing the situation or eliminating stimuli that produce emotional reactions, and verbally labeling his or her own emotions in a positive manner (for example, autodidactically or through positive self-talk). However, children may also develop dysfunctional regulation strategies: externalizing behaviors (for example, physical acting out or revenge) and dysfunctional thoughts (for instance, negative self-assessment or helplessness) [ 7 ].

In a study on the regulation of fear in early childhood education, Bettina Janke [ 17 ] showed that preschoolers are able to use regulation strategies and they can even say which strategies are helpful—i.e., functional, or effective—and which are not. The terms functional or effective and dysfunctional or ineffective are commonly used in clinical psychology in relation to regulatory strategies and express that a method has a positive or negative impact on mental and physical health in the medium or long term [ 18 ] (p. 9). The three-, four-, five-, and six-year-olds heard six stories about children who found themselves in a frightening situation. For each story in turn, two effective and two ineffective strategies were predefined. The children were then asked to assess whether these strategies helped the main actor from the story to not feel afraid anymore. Most children from the age of five can “distinguish effective strategies for regulating fear from ineffective ones” [ 17 ] (p. 571). This again points to the major development steps in emotion regulation during early childhood education.

Among other things, emotions and the regulation of emotions have an impact on self-concept, self-esteem, and locus of control. When a child sees that he or she can influence situations in his or her environment, he or she develops the conviction of being effective himself or herself. “The experience of self-efficacy is a milestone in the child’s development” [ 11 ] (p. 22). There is no doubt that age-appropriate emotion regulation is key to successful psychological development. This is also underlined by the fact that “difficulties in emotion regulation play a central role in most symptom patterns in child psychopathology” [ 19 ] (p. 140).

3.2. Family Interactions with a Focus on Emotion Regulation

Parents play a crucial role in how children develop emotion regulation, as well as influencing their child’s temperament and neurobiological characteristics [ 17 ]. When parents are sensitive and responsive to their child‘s emotional needs, the child learns to manage his or her emotions more and more effectively [ 4 ] (S. 5), [ 20 ]. Mechthild Papousek [ 21 ] has described these processes of successful and failed interaction between babies and their caregivers in early childhood in detail. Through her research, she found that children‘s emotion dysregulations are related to negative reciprocity. What happens if parents have a lack of intuitive competence and do not respond appropriately to the signals from their child? The child sends out negative feedback signals and it all ends in a negative cycle. Conversely, we talk about a positive cycle when parents are sensitive to the child’s signals and immediately respond to the relevant need, and thus regulate the child’s tension. As a result, the child sends out positive signals and the parents’ experience of competence is reinforced [ 21 ].

In addition to Papousek‘s model, which focuses more on interactional processes, Morris et al. [ 22 ] tripartite model focuses on family processes with respect to the development of children’s emotion regulation [ 14 ] (Sp. 134 f.), [ 23 ](p. 48 et seqq.). According to the tripartite model, three mechanisms are significant:

Observational learning: Parents are a role model for children through how they express their own emotions and their behavior when dealing with emotions [ 14 ].

Emotion-related parenting practices: Parents’ reactions to their children’s positive and negative emotions are related to the appropriateness of their children’s emotion regulation [ 23 ]; parents who struggle with appropriate emotion regulation themselves report “being more likely to respond to their child’s negative emotions with non-supportive behaviors such as minimization or punitive reactions” [ 14 ] (p. 140).

Emotional climate of the family: Here, the parenting style is very important. “Parenting based on acceptance, support, affection, and understanding appears to have an optimal impact on children’s emotion regulation” [ 23 ] (p. 48).

The way that parents regulate—or fail to regulate—their child’s emotions according to their own emotion regulation skills has a strong effect on the child’s emotion regulation [ 14 ]. The importance of the family context is clearly evident, and this is the starting point for any form of intervention.

4. Promoting Emotion Regulation

In this chapter, we show that teachers are also significant for children in how they bond with others and develop their emotion regulation skills ( Section 4.1 ). We explore how teachers can promote emotional regulation based on how critically we apply the programs ( Section 4.2 ). Here, we also refer to research about, for example, ref [ 1 , 2 , 3 ] teacher-child interaction as well as to our reflections about how to improve teachers’ ability to offer support in emotional regulation. This is highly important because prevention programs are considered effective, but the effects do not persist for very long (e.g., [ 24 ]). Furthermore, we ask if programs can be helpful in this process.

Promoting emotional development is beneficial for the development of a child’s personality. The complexity of development contexts outlined above offers a wide potential for consciously supporting and promoting children’s emotional development. Below, we discuss the importance of the teacher-child interaction for emotion regulation, as well as the opportunities provided by, and limitations of, prevention programs in early childhood education. In this context, the main research question is: “Do the programs include aspects of increasing the teachers’ skills in supporting children to regulate their emotions?”

4.1. Teacher-Child-Interaction and Its Relevance for Emotion Regulation

From the beginning of a child’s life, the parents are his or her most important caregivers. However, in this initial phase, other people also become important caregivers. The emotional and social socialization initially framed by the family is continued and refined during early childhood education. The early childhood teacher plays a special role here, supporting and monitoring the child [ 25 ]. As we all know, children develop a meaningful relationship with their teacher during early childhood education [ 1 ]. Children allow their teacher to guide and stimulate them and they refer to him or her in difficult situations. Teachers in turn give them comfort and help them feel more secure. Consequently, the childhood teacher becomes a significant caregiver for the child [ 26 , 27 ]. There is, however, an important difference between the behavior of parents and teachers. Although parents mostly interact intuitively, childhood teachers interact based on their knowledge and skills; these are professionally designed interactions. They also have the ability and willingness of a professional to design relational, stimulating, and developmentally supportive interactions [ 28 ] (p. 8 f.). Regarding the development of emotion regulation, teachers offer children a developmental space where emotions can be experienced and discussed [ 29 ]. The childhood teacher is there to help the child to fulfill his or her needs and has a mediator role in conflicts with other children. In her studies, Remsperger [ 2 ] (p. 287) was able to show that the interactions between children and their teachers are not only determined by the teacher; children are able to influence the response behavior of their teacher with their own behavior. Children can therefore contribute to a sensitive responsiveness. These findings show the reciprocity of the interaction process. The teacher’s handling of the child’s emotional states should be based on the children’s developmental steps in the context of emotion regulation: although children from the age of five can handle their emotions more and more independently, younger children certainly need support and guidance. If we take a closer look at the interactions between childhood teacher and child, we can see similarities to the above-mentioned parent-child interaction. “From the child’s perspective, the shaping of interactions can only have positive effects if the corresponding signals of the child are perceived and adequately understood or interpreted on the basis of professional knowledge and the response behavior is aligned accordingly” [ 28 ] (p. 9).

The framework model of the research group headed by Robert C. Pianta and Bridget K. Hamre provides important information and starting points regarding interaction quality in a professional setting. They proved, based on several studies, that there are essentially three important domains for the teacher-child interaction [ 3 ]: 1. teacher-child interactions for emotional support, 2. activities to organize the classroom, and 3. instructional support that facilitates quality of feedback or concept development. The valid observation instrument CLASS (Classroom Scoring Assessment System) was used in the various studies in daycare centers, kindergartens, and elementary schools. CLASS operationalizes interactions according to the three areas of emotional support, organization of the classroom, and instructional support [ 3 , 28 ] (p. 17). Since this paper focuses on emotion regulation, the area of emotional support and relationship and attachment building will be elaborated below. This “refers to the building of high-quality relationships between adults and children” and is based on work on attachment theory that can be applied to the teacher-child relationship (e.g) [ 1 ]. Through her research, Ahnert was able to show that attachment relationships between teacher and children are possible and necessary. She was able to identify five factors that promote attachment characteristics in everyday life in a kindergarten.

Professionals show affection through loving and emotionally warm communication, which makes the children and the professional alike enjoy the interaction.

A key task of the professional is to convey security.

Teachers help to reduce stress by supporting the regulation of emotions.

Exploration support combined with the availability of the teacher in the event of uncertainty contributes to successful attachment.

When the child reaches his or her limits, the teacher offers assistance and guides the child back to being able to act.

These relation characteristics change depending on age: younger children are more dependent on safety aspects and methods of stress reduction than preschoolers. In addition, Ahnert [ 1 ] pointed out the importance of the social group, as it has been found that it is the professional’s activity in the children’s group that has an impact on the child’s attachment security rather than the individual care of individual children. If the work in the group is characterized by group-oriented, empathic behavior by the teacher, the dynamics in this group can be regulated and the needs of the individual child can be served at the right time, taking the requirements of the group into consideration. Remsperger’s study also showed that structural conditions have an effect. Sensitive responsiveness is not a character trait of the relevant teacher, but it depends primarily on the situations in the kindergarten: in noisy, troubled situations, the teacher is able to show little responsive behavior and offer little stimulation for the child [ 2 ] (p. 280 f.).

Let us come back to Pianta and Hamre’s model. These researchers were able to demonstrate “stronger effects of teacher-child interaction on the learning and development of children who show some vulnerability or developmental risk” [ 3 ] (p. 25). They conclude, “that interaction quality is of even greater importance for children with developmental disabilities” [ 3 ] (p. 26).

The importance of teacher-child interactions for children’s developmental opportunities is undisputed in professional policy and well documented empirically. However, various studies show how rarely linguistically and cognitively stimulating interactions are observed in kindergartens (for the US see [ 3 ]). Against this background, various concepts for the qualitative further development of the teacher-child interaction have been elaborated based on research in recent years. Coaching approaches to train teachers’ observational behaviors through video-based microanalysis of everyday interaction situations show promise [ 3 ]. “Children whose educators had participated in coaching showed higher gains in literacy skills and lower levels of problem behavior” [ 3 ] (p. 28). The data refer to the coaching program MyTeachingPartner, a combination of knowledge transfer and video analysis in 14 three-hour sessions conducted by local colleges. Weltzien et al. [ 28 ] developed a similar tool for Germany: the video-based evaluation tool for designing interactions (GinA-E) was developed and evaluated in various practice research projects. With the aid of GinA-E, the quality of interaction becomes observable and can be reflected based upon the 22 specified criteria on three scales: Shaping Relationships, Stimulating Thought and Action, and Stimulating Speech and Language. What is important here is the behavior of the teacher in everyday interactions, about which a dialogue can be entered into with colleagues during the evaluation. In addition, the importance of consulting with colleagues or even intervision and supervision groups should be pointed out here.

4.2. Promoting a Child’s Emotional Development through Development Training

In the last few years, several international programs stemming from the field of primary prevention for the social and emotional development of children have become established. These programs play an important role in pedagogical practice in Germany. What now follows is an analysis of four programs that are often used in German early childhood education, and which serve as examples: 1. Faustlos [ 30 ], 2. Lubo aus dem All [ 31 ], 3. Papilio [ 32 ], and 4. Prävention und Resilienzförderung in Kitas—PRiK [ 33 ]. Some are adaptations of American programs; for example, the Faustlos program is based on the US program Second Step. These programs have all been evaluated empirically and have proven to promote the development [ 19 , 24 , 34 , 35 , 36 , 37 ]. We have selected these programs because they are frequently recommended for practice in Germany and are, therefore, used very often. Each program has a different focus on preventing behavioral problems, and they support social and emotional development. Some of them have already been analyzed regarding both the aims and scope of the program, and the didactical methods [ 19 ]. The programs Faustlos, Lubo aus dem All, and Papilio have similar objectives: 1. the promotion of a socially acceptable expression of one’s own emotions, 2. an appropriate perception of one’s own emotions and the emotions of others, 3. socially acceptable regulation of negative emotions, and 4. the promotion of prosocial behavior. The aim here is to teach a “canon of values containing cooperative and socially acceptable behavior and an appropriate expression of emotions” [ 19 ]. A positive group climate helps all children to be integrated well into the group. The methods used in these three programs are similar since they all use psychology-based means of reinforcement. In addition to knowledge transfer on emotions and setting an example of model behavior, imitation and praise are specifically used, as well as a token system in the case of Lubo and Papilio. The starting point of the PRiK program [ 33 ] is different to the others as it represents a positive relationship with the child as a basis for promoting resilience. It also focuses on strengthening personal resilience (e.g., self-awareness, self-control, and self-efficacy). The concept refers to skills that the children already have and promotes them in 26 units. The units are, in turn, described in the manual and feature many games, exercises, and proposals for materials. The idea is not to work through the program units by following the instructions closely—it is rather that the children are empowered to contribute and reflect on their experiences, allowing for deeper engagement with the content. The authors propose using the manual as a golden thread that “must always be related to the particular group and situation” [ 33 ] (p. 31).

In our analysis, in addition to the mentioned aspects, the focus is on teacher-child interaction to support children’s emotion regulation in their early education. To achieve this, we work deductively according to Mayring’s qualitative content analysis [ 38 ]. We refer to Ahnert [ 1 ] and Remsperger [ 2 ], who show the importance of the teacher for conveying security, helping the children reduce stress by supporting them in regulating their emotions, and offering assistance for a child that has reached their limits and that requires support ( Section 4.1 ). Our main categories are guidance of teacher-child interaction [ 1 , 2 , 25 ], knowledge transfer concerning emotion regulation development [ 27 ], guidance for supporting emotion regulation [ 1 ], offering security in small groups [ 1 ], and focusing on interactions to provide emotional support to children [ 3 ].

When we examine the programs more closely, we see that Faustlos, Lubo aus dem All, and PRiK are prescribed, and have a pre-structured procedure, additional materials, and at least 20 units, which are carried out over several weeks in the kindergarten. In the manual instruction, there are often written dialogs, which the teacher reproduces with hand puppets or using picture stories. Papilio’s aims are changes in the everyday life in the kindergarten by, for example, initiating a toy-free day. The analysis of the programs focuses on the question of whether the programs can improve a teacher’s skills to support the children’s emotion regulation. The table below ( Table 1 ) illustrates which aspects are considered in each program.

Results of analysis of four prevention programs for social and emotional development—categories.

The Table 1 shows that most of the programs do provide little knowledge about the development and processes of emotion regulation. There is also not enough information on how the educators can transfer their knowledge into everyday practice. Furthermore, we examined the approach the program takes on reflecting children’s emotions. It is noticeable that these are often very cognitive, learning psychology approaches, which are not easy to grasp in the early childhood phase. There is therefore a lack of impetus for interactions between educators and children. This is also evident in the last point of the analysis. The interactions in the program are not bonding oriented. Our main findings are as follows.

Hermann and Holodynski [ 19 ] (p. 154) have also analyzed the programs Lubo aus dem All, Faustlos, and Papilio and criticized the fact that they are more similar to exercises in which the focus is on desired prosocial target behavior rather than on a sociodramatic free play in which children play a fascinating and dramatic part. Finally, they evaluated the attractiveness of the programs as too knowledge heavy and somewhat dry. However, if they are carried out with enthusiasm and commitment to the children, the programs can contribute to the promotion of social-emotional development. Our analysis shows that not enough attention is paid to the interaction between teacher and child as a part of emotional development. In doing so, the programs push too much for the children to develop their emotional skills, disregarding the teacher’s responsibility as a significant figure in the child’s life; only PRiK adopts a bonding-oriented approach.

Several aspects seem to be relevant in the question of the benefits of such programs. The fact that all children in a group can be reached with the prevention program and can participate in this speaks in favor of applying it in the kindergarten. The topic of emotions thus gains a certain significance over a certain period in the kindergarten. The added values of such programs lie primarily in the process that they trigger. There is a discussion of the topic of emotions in childhood. Involvement of the team, the continuous transfer into everyday life, and sustainability are all important factors. If an institution succeeds in transferring elements of the specific program into everyday life, for example, if every team member carries out the training and is thus familiar with it, the teacher can tie their work in with the topics of the program. In addition, preparing for and engaging with a program provides a good opportunity to update one’s knowledge of emotional development, but as shown in our analysis, the programs still need to be expanded in this respect.

Critically, in addition to the points made by Hermann and Holodynski above, we would like to note that some programs convey a certain image of children and pedagogy. Sometimes the impression cannot be denied that children are to be “made fit” to fit into peer groups and educational institutions. Behind this is the seductive thought that manualized programs achieve universal effectiveness. In addition, the material and the effort required are manageable. There is a risk that the individual child with his or her specific needs is out of sight and the teacher relies on the effect of a program. This can contribute to an apparent simplification of an everyday pedagogical life that is generally characterized by uncertainty and marked by antinomies [ 39 ]. Programs may tempt to oversimplify the complex interplay of individual and contextual factors in child development. It is possible that a child who has been “overlooked” in this way does not need manualized intervention over a period of several weeks but, rather, a vigilant teacher who can recognize and offer the necessary freedom for the child and phases of intensive support, in accordance with the aspects of successful teacher-child interaction described above.

The programs and concepts mentioned above bring practical added value if institutions and teachers can be motivated to set out and further develop their own concepts. Not everything offered in manuals or programs is new: some of the activities compiled in the concepts are already being used in various institutions.

5. Conclusions

In this article, the importance of emotion regulation and the interaction between adults and children are described in detail, based on our analysis. Teachers need to have expertise in developmental psychology related to the emotional development of children and to understand how important a highly qualitative teacher-child interaction is. Training and development can help teachers to keep strengthening their knowledge. The teacher’s behavior toward the children is highly important as part of the interaction. During these developmental steps of the child, the teacher’s behavior sensitively addresses the child’s emotions, reflects them, and offers himself or herself as a container for those emotions. Thus, the behavior of the teacher promotes the self-awareness of the child and the perception of emotions in others.

Teachers can provide a framework for children in their day-to-day activities, allowing them to talk about emotions and deal with conflicts as significant figures in a child’s life. Moreover, the potential of the group can be of advantage. In his text about inclusion and emotions, Markus Dederich explained the correlations of emotions—using the term “affective resonance” in this context—and the social group [ 40 ]. These aspects are relatively new, and it is possible that they will be more important in the future.

The reflection of one’s own behavior is becoming more and more important. The chance to take a closer look at complex situations in a slowdown mode offers valuable food for thought. Video analyses [ 28 , 41 ], supervision, or consulting with colleagues, for example, have proven to be possible methods for taking a closer look at one’s own behavior and interactions during the slowdown. It is important here that responsible bodies provide sufficient resources, such as time and funding, for these team-related measures.

Aspects of emotional development and emotion regulation are also important when working with parents. When we work with parents, the aspects of emotional development and emotion regulation should also be dealt with. As part of the kindergarten’s mission to support parenting skills, this topic area can be addressed in the context of development discussions, parents’ evenings, or as a specific parents’ education program [ 42 , 43 , 44 ]. It is important that the teachers support the children in a thoughtful and sensitive way. The aim is to focus necessary offers of support (e.g., counseling services in the context of early child intervention), and develop an awareness for the life situation of the family and think about meaningful perspectives from the viewpoint of the family.

Relation experiences in the first six years of life are fundamental to the development and strengthening of emotional competences. This requires valuable learning stimuli and successful interactions that are consciously designed in early childhood education. We demonstrate that the commonly used programs in Germany look closely at the processes of change concerning the children, and the programs have a certain effect on their emotional and social development. The reality is very complex, and the effects recorded in the studies only are really the tip of the iceberg. Future research in this field is necessary, especially into how teacher-child interaction determines the quality of early childhood development. Further studies should also be conducted into the effects such programs could have on increasing knowledge early childhood teachers have as to how to support children in regulating their emotions, and what they learn about emotional regulation before and after using such a program in their kindergarten. These and other perspectives focusing on professional skills should be examined further, in mixed methods-based research.

Author Contributions

Conceptualization, R.T., E.-M.E. and J.B.; methodology, R.T. E.-M.E., and J.B.; formal analysis, R.T. and E.-M.E.; data curation, R.T. and E.-M.E.; writing—original draft preparation, R.T.; writing—review and editing, R.T., E.-M.E. and J.B.; visualization, R.T.; funding acquisition, R.T. All authors have read and agreed to the published version of the manuscript.

This research received no external funding.

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Data availability statement, conflicts of interest.

The authors declare no conflict of interest.

Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

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Language and Literacy Development: Research-Based, Teacher-Tested Strategies

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“Why does it tick and why does it tock?”

“Why don’t we call it a granddaughter clock?”

“Why are there pointy things stuck to a rose?”

“Why are there hairs up inside of your nose?”

She started with Why? and then What? How? and When? By bedtime she came back to Why? once again. She drifted to sleep as her dazed parents smiled at the curious thoughts of their curious child, who wanted to know what the world was about. They kissed her and whispered, “You’ll figure it out.”

—Andrea Beaty, Ada Twist, Scientist

I have dozens of favorite children’s books, but while working on this cluster about language and literacy development, Ada Twist, Scientist kept coming to mind. Ada is an African American girl who depicts the very essence of what it means to be a scientist. The book is a celebration of children’s curiosity, wonder, and desire to learn.

The more I thought about language and literacy, the more Ada became my model. All children should have books as good as Ada Twist, Scientist read to them. All children should be able to read books like Ada Twist, Scientist by the end of third grade. All children should be encouraged to ask questions about their world and be supported in developing the literacy tools (along with broad knowledge, inquiring minds, and other tools!) to answer those questions. All children should see themselves in books that rejoice in learning.

Early childhood teachers play a key role as children develop literacy. While this cluster does not cover the basics of reading instruction, it offers classroom-tested ways to make common practices like read alouds and discussions even more effective.

The cluster begins with “ Enhancing Toddlers’ Communication Skills: Partnerships with Speech-Language Pathologists ,” by Janet L. Gooch. In a mutually beneficial partnership, interns from a university communication disorders program supported Early Head Start teachers in learning several effective ways to boost toddlers’ language development, such as modeling the use of new vocabulary and expanding on what toddlers say. (One quirk of Ada Twist, Scientist is that Ada doesn’t speak until she is 3; in real life, that would be cause for significant concern. Having a submission about early speech interventions was pure serendipity.) Focusing on preschoolers, Kathleen M. Horst, Lisa H. Stewart, and Susan True offer a framework for enhancing social, emotional, and academic learning. In “ Joyful Learning with Stories: Making the Most of Read Alouds ,” they explain how to establish emotionally supportive routines that are attentive to each child’s strengths and needs while also increasing group discussions. During three to five read alouds of a book, teachers engage children in building knowledge, vocabulary, phonological awareness, and concepts of print.

Next up, readers go inside the lab school at Stepping Stones Museum for Children. In “ Equalizing Opportunities to Learn: A Collaborative Approach to Language and Literacy Development in Preschool ,” Laura B. Raynolds, Margie B. Gillis, Cristina Matos, and Kate Delli Carpini share the engaging, challenging activities they designed with and for preschoolers growing up in an under-resourced community. Devondre finds out how hard Michelangelo had to work to paint the ceiling of the Sistine Chapel, and Sayo serves as a guide in the children’s classroom minimuseum— taking visitors to her artwork!

Moving into first grade, Laura Beth Kelly, Meridith K. Ogden, and Lindsey Moses explain how they helped children learn to lead and participate in meaningful discussions of literature. “ Collaborative Conversations: Speaking and Listening in the Primary Grades ” details the children’s progress (and the teacher’s methods) as they developed discussion-related social and academic skills. Although the first graders still required some teacher facilitation at the end of the school year, they made great strides in preparing for conversations, listening to their peers, extending others’ comments, asking questions, and reflecting on discussions.

Rounding out the cluster are two articles on different aspects of learning to read. In “ Sounding It Out Is Just the First Step: Supporting Young Readers ,” Sharon Ruth Gill briefly explains the complexity of the English language and suggests several ways teachers can support children as they learn to decode fluently. Her tips include giving children time to self-correct, helping them use semantic and syntactic cues, and analyzing children’s miscues to decide what to teach next.

In “ Climbing Fry’s Mountain: A Home–School Partnership for Learning Sight Words ,” Lynda M. Valerie and Kathleen A. Simoneau describe a fun program for families. With game-like activities that require only basic household items, children in kindergarten through second grade practice reading 300 sight words. Children feel successful as they begin reading, and teachers reserve instructional time for phonological awareness, phonics, vocabulary, and other essentials of early reading.

At the end of Ada Twist, Scientist , there is a marvelous illustration of Ada’s whole family reading. “They remade their world—now they’re all in the act / of helping young Ada sort fiction from fact.” It reminds me of the power of reading and of the important language and literacy work that early childhood educators do every day.

—Lisa Hansel

We’d love to hear from you!

Send your thoughts on this issue, as well as topics you’d like to read about in future issues of Young Children , to [email protected] .

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Math Talk in Play Contexts: Relations Between Parent and Child Math Language and Early Math Skills

• Published: 19 October 2024

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• Hsin-Hui Huang   ORCID: orcid.org/0000-0002-8121-5665 1  

Math language plays a crucial role in early math skill development. However, previous studies have measured math language using children’s knowledge or the environmental input of mathematical language, often limiting the scope to specific types of mathematical language. This study examined both numerical (e.g., number-related) and non-numerical (e.g., geometry, measurement, and spatial relations) mathematical expressions used by parents and young children during play and explored how combinations of these math language types related to early math skills. Fifty-eight children aged four to six years and their parents participated in laboratory-based tasks, including a 15-minute play session and a standardized math assessment. The results showed that numerical and non-numerical expressions were used at similar frequencies, and there were strong correlations between parents’ and children’s math language across various types. Mediation analyses revealed that children’s use of math language mediated the relation between the mathematical input they received and their early math skills, but this effect emerged only when advanced numerical and geometry language was considered. These findings emphasize the significance of children’s active use of math language and highlight the need to integrate numerical and non-numerical content in research on early mathematical development.

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Acknowledgements

This study was funded by Taiwan’s National Science and Technology Council (MOST-110-2635-H-227-001). The author thanks the participating families.

This project was supported by a grant from the National Science and Technology Council of Taiwan (grant numbers: MOST-110-2635-H-227-001).

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Huang, HH. Math Talk in Play Contexts: Relations Between Parent and Child Math Language and Early Math Skills. Early Childhood Educ J (2024). https://doi.org/10.1007/s10643-024-01783-w

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Early Childhood Development and Education

Education Access and Quality

About This Literature Summary

This summary of the literature on Early Childhood Development and Education as a social determinant of health is a narrowly defined examination that is not intended to be exhaustive and may not address all dimensions of the issue. Please note: The terminology used in each summary is consistent with the respective references. For additional information on cross-cutting topics, please see the Language and Literacy literature summary. 

Related Objectives (6)

Here's a snapshot of the objectives related to topics covered in this literature summary. Browse all objectives .

• Increase the proportion of 4th-graders with reading skills at or above the proficient level — AH‑05
• Increase the proportion of 4th-graders with math skills at or above the proficient level — AH‑06
• Increase the proportion of children and adolescents who communicate positively with their parents — EMC‑01
• Increase the proportion of children whose family read to them at least 4 days per week — EMC‑02
• Increase the proportion of children who are developmentally ready for school — EMC‑D01
• Increase the proportion of children who participate in high-quality early childhood education programs — EMC‑D03

Related Evidence-Based Resources (4)

Here's a snapshot of the evidence-based resources related to topics covered in this literature summary. Browse all evidence-based resources .

• Social Determinants of Health: Center-Based Early Childhood Education
• Social Determinants of Health: Full Day Kindergarten Programs
• Social Determinants of Health: Out-of-School-Time Academic Programs – General
• Social Determinants of Health: School-Based Health Centers

Literature Summary

Early childhood, particularly the first 5 years of life, impacts long-term social, cognitive, emotional, and physical development. 1 , 2 Healthy development in early childhood helps prepare children for the educational experiences of kindergarten and beyond. 3 Early childhood development and education opportunities are affected by various environmental and social factors, including early life stress, socioeconomic status, relationships with parents and caregivers, and access to early education programs. 4 , 5 , 6 , 7

Early life stress and adverse events can have a lasting impact on the mental and physical health of children. 3 Specifically, early life stress can contribute to developmental delays and poor health outcomes in the future. 3 , 4 , 8 , 9 Stressors such as physical abuse, family instability, unsafe neighborhoods, and poverty can cause children to have inadequate coping skills, difficulty regulating emotions, and reduced social functioning compared to other children their age. 3 , 4 , 9

Additionally, exposure to environmental hazards, such as lead in the home, can negatively affect a child’s health and cause cognitive developmental delays. 3 Research shows that lead exposure disproportionally affects children from racial/ethnic minority and low-income households and can adversely affect their readiness for school. 10 , 11

The socioeconomic status of young children’s families and communities also significantly affects their educational outcomes. 6 Specifically, poverty has been shown to negatively influence the academic achievement of young children. 6 Research shows that, in their later years, children from disadvantaged backgrounds are more likely to repeat grades and drop out of high school. 1 Children from communities with higher socioeconomic status and more resources experience safer and more supportive environments and better early education programs. 1

Early childhood programs are a critical outlet for fostering the mental and physical development of young children. 2 Some indicators of a high-quality early childhood development and education program include highly educated teachers, smaller classes, and lower child-staff ratios. 7 , 12 High-quality early childhood programs can increase earning potential and encourage and support educational attainment. 13

Early childhood development and education programs can also help reduce educational gaps. 7 , 14 , 15 For example, Head Start is a federally funded early childhood program that provides comprehensive services for children from low-income families. 15 Head Start aims to improve health outcomes, increase learning and social skills, and close the gap in readiness to learn for children from low-income families and at-risk children. 15 Enrolling children in full-day kindergarten after the completion of preschool has also been shown to improve academic achievement. 14

Furthermore, extended early childhood programs for children up to 3rd grade, also referred to as booster programs, can provide comprehensive educational, health, and social services to complement standard early childhood and kindergarten programs. 14 , 16 These programs help sustain and bolster early developmental and academic gains. 14 , 16 Characteristics of such programs include: 14

• Low student-teacher ratio
• Focus on basic skills
• Teacher training
• Creation of school-parent liaisons
• School meals
• Provision of transportation to and from school
• Night courses for parents
• Health care services and referrals
• Home visitation
• Supportive social services

Quality education in elementary school is necessary to reinforce early childhood interventions and prevent their positive effects from fading over time. 14 Research also shows that the quality, length, and intensity of early education programs has an impact on well-being, including physical and mental health. 17 For example, children who enroll in low-quality schools with limited health resources, safety concerns, and low teacher support are more likely to have poorer physical and mental health. 18 , 19 , 20 , 21   

The developmental and educational opportunities that children have access to in their early years have a lasting impact on their health as adults. 13 , 22 , 23 The Carolina Abecedarian Project found that the children in the study who participated in a high-quality and comprehensive early childhood education program that included health care and nutritional components were in better health than those who did not. 22 The study found that, at age 21 years, the people who participated in the comprehensive early education program exhibited fewer risky health behaviors; for example, they were less likely to binge drink alcohol, smoke cigarettes, and use illegal drugs. 22 This group also self-reported better health and had a lower number of deaths. 22

Furthermore, by their mid-30s, the children who participated in the comprehensive early childhood development and education program had a lower risk for heart disease and associated risk factors, including obesity, high blood pressure, elevated blood sugar, and high cholesterol. 13 These studies show that quality early childhood development and education programs can play a key role in reducing risky health behaviors and preventing or delaying the onset of chronic disease in adulthood. 13 , 22

Early childhood development and education are key determinants of future health and well-being. 14 , 24 Addressing the disparities in access to early childhood development and education opportunities can greatly bolster young children’s future health outcomes. 9 , 14 , 15 , 22 , 23 , 25 – 31

Additional research is needed to increase the evidence base for what can successfully impact the effects of childhood development and education on health outcomes and disparities. This additional evidence will facilitate public health efforts to address early childhood development and education as social determinants of health.

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Anderson, L. M., Shinn, C., Fullilove, M. T., Scrimshaw, S. C., Fielding, J. E., Normand, J., ... & Task Force on Community Preventive Services. (2003). The effectiveness of early childhood development programs: A systematic review. American Journal of Preventive Medicine, 24 (3), 32–46.

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Arnold, D. H., & Doctoroff, G. L. (2003). The early education of socioeconomically disadvantaged children. Annual Review of Psychology, 54 (1), 517–545.

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Whitehead, L. S., & Buchanan, S. D. (2019). Childhood lead poisoning: A perpetual environmental justice issue? Journal of Public Health Management and Practice, 25 , S115–S120.

Zhang, N., Baker, H. W., Tufts, M., Raymond, R. E., Salihu, H., & Elliott, M. R. (2013). Early childhood lead exposure and academic achievement: Evidence from Detroit public schools, 2008–2010. American Journal of Public Health, 103 (3), e72–e77.

NICHD Early Child Care Research Network. (2002). Child-care structure→ process→ outcome: Direct and indirect effects of child-care quality on young children’s development. Psychological Science, 13 (3), 199–206.

Campbell, F., Conti, G., Heckman, J. J., Moon, S. H., Pinto, R., Pungello, E., & Pan, Y. (2014). Early childhood investments substantially boost adult health. Science, 343 (6178), 1478–1485.

Hahn, R. A., Rammohan, V., Truman, B. I., Milstein, B., Johnson, R. L., Muntañer, C., ... & Community Preventive Services Task Force. (2014). Effects of full-day kindergarten on the long-term health prospects of children in low-income and racial/ethnic-minority populations: A community guide systematic review. American Journal of Preventive Medicine, 46 (3), 312–323.

Aber, J., Brooks-Gunn, J., Burchinal, M., Carta, J., Cook, T., Cunningham, G., ... & Zaslow, M. (2012). Advisory committee on Head Start research and evaluation . US Department of Health and Human Services.

Reynolds, A. J., Magnuson, K. A., & Ou, S. R. (2010). Preschool-to-3rd grade programs and practices: A review of research. Children and Youth Services Review, 32 (8), 1121–1131.

Reynolds, A. J., Mondi, C. F., Ou, S. R., & Hayakawa, M. (2017). Generative mechanisms of early childhood interventions to well-being. Child Development, 88 (2), 378.

Huang, K. Y., Cheng, S., & Theise, R. (2013). School contexts as social determinants of child health: Current practices and implications for future public health practice. Public Health Reports, 128 (6_suppl3), 21–28.

Muennig, P., & Woolf, S. H. (2007). Health and economic benefits of reducing the number of students per classroom in U.S. primary schools. American Journal of Public Health, 97 (11), 2020–2027.

Pianta, R. C., La Paro, K. M., Payne, C., Cox, M. J., & Bradley, R. (2002). The relation of kindergarten classroom environment to teacher, family, and school characteristics and child outcomes. Elementary School Journal, 102 (3), 225–238.

Crosnoe, R. (2005). Double disadvantage or signs of resilience? The elementary school contexts of children from Mexican immigrant families. American Educational Research Journal, 42 (2), 269–303.

Muennig, P., Robertson, D., Johnson, G., Campbell, F., Pungello, E. P., & Neidell, M. (2011). The effect of an early education program on adult health: The Carolina Abecedarian Project randomized controlled trial. American Journal of Public Health, 101 (3), 512–516.

Pianta, R. C., Barnett, W. S., Burchinal, M., & Thornburg, K. R. (2009). The effects of preschool education: What we know, how public policy is or is not aligned with the evidence base, and what we need to know. Psychological Science in the Public Interest, 10 (2), 49–88.

Maggi, S., Irwin, L. J., Siddiqi, A., & Hertzman, C. (2010). The social determinants of early child development: An overview. Journal of Pediatrics and Child Health, 46 (11), 627–635.

Abbott-Shim, M., Lambert, R., & McCarty, F. (2003). A comparison of school readiness outcomes for children randomly assigned to a Head Start program and the program’s wait list. Journal of Education for Students Placed at Risk, 8 (2), 191–214.

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New Research: Free, All-Day Early Care Sets Families on a Better Financial Trajectory

Families who access free, full-day, universal early care earn more—for years to come

Research identifies key elements that make early care effective as labor market policy

Over five decades, U.S. cities, states, and the federal government have increased funding to an array of pre-kindergarten and child care programs. While the impact of many such programs on child development has been extensively researched, their influence on parents' work and earnings has remained understudied. 

A central rationale for early care is that it enables workforce participation, thereby strengthening families’ economic stability. But understanding to what degree it does so, under what circumstances, and with what return on public investment is essential for informed policy design. Researchers widely acknowledge the absence of rigorous evidence, and policymakers have long called for better answers. 

To address this need, Yale’s Tobin Center supported a comprehensive study linking 20 years of randomized school district lottery data with state data on earnings, K-12 outcomes, and more. The study ' Parents' Earnings and the Returns to Universal Pre-Kindergarten ,' by economists John Eric Humphries, Christopher Neilson, Xiaoyang Ye, and Seth D. Zimmerman, was published in the National Bureau of Economic Research (NBER).

The novel research identifies the key elements of early care design that unlock parents’ ability to work and earn to support their families. The study calculates the scale of earnings results for families who were admitted versus those who were not. This, in turn, enables a first-of-kind cost-benefit analysis of the returns to public investment. 

Researchers find the earnings gains for admitted families are high and last long after the completion of pre-K.  They identify the three key elements of care that unlocked these earnings gains: it was free, offered at times that match typical work days, and was not means tested. In terms of return on public investment, as seen in the figure below, researchers state: “Our main finding is that UPK is one of the most cost-effective active labor market policies ever evaluated in the U.S.”

This first-of-kind evidence charts a clear path for early care policy to measurably and significantly enhance family economic well being: enabling parents to work the hours they want, achieve greater economic stability, and reduce financial stress. 

The incremental cost increases of work-enabling care offer high return on public investment. The study demonstrates that care policies designed to maximize economic opportunities align with providing high-quality childcare and promoting child welfare. It is the most instructive study to date on what aspects of care most effectively enhance earnings:

Despite state-to-state differences in early care environments, these findings can inform both pre-K and childcare program designs nationwide, including those serving younger children, ages 0-3. 

About the study

The Yale Tobin Center supported a comprehensive study to examine a large-scale pre-K program for 3- and 4-year-olds in New Haven, Connecticut, spanning from 2003 to 2022. Since New Haven uses random admissions lotteries, researchers were able to compare earnings outcomes for families assigned to pre-K programs with those who were not. Through collaboration with New Haven Public Schools and relevant state agencies, the researchers were able to track parental earnings and children's academic achievement longitudinally. The persistent gap in parental earnings evidence has been commonly attributed to the complexity of data linkage across both levels (local/state) and agencies of government. With Tobin support as well as the active collaboration of the New Haven School District, and Connecticut’s P20 WIN system, researchers combined NHPS lottery records with administrative data of multiple programs across five different state agencies to yield these groundbreaking results.

Consequently, this research provides the most definitive evidence to date on the positive impact of non-means-tested, free, all-day pre-K on family economic outcomes.

To help illustrate what happens when families access free, full-day, universal early care, the research team put together an infographic with key findings.

Media Inquiries? Contact Shannon Bradford: [email protected]