This preprint is under consideration at BMC Pediatrics. Preprints are preliminary reports that have not undergone peer review. They should not be considered conclusive, used to inform clinical practice, or referenced by the media as validated information.
Learn more about In Review
Research article
Proximal and distal predictors of self-regulatory change in children aged 4 to 7 years
Kate E Williams
Queensland University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8983-5503
License:
This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
Background Growth in early self-regulation skills has been linked to positive health, wellbeing, and achievement trajectories across the lifespan. While individual studies have documented specific influences on self-regulation competencies in early childhood, few have modelled a comprehensive range of predictors of self-regulation change across health, development, and environment simultaneously. This study aimed to examine the concurrent associations among a range of proximal and distal influences on change in children’s self-regulation skills over 2 years from age 4-5 years.
Methods Data from the Longitudinal Study of Australian Children (N= 4983) were used in a structural equation model, predicting a multi-source composite measure of self-regulation at each of 4-5 years and 6-7 years. By controlling for earlier self-regulation and covariates, the model examined the relative contributions of a comprehensive range of variables to self-regulation change including health, development, educational, home environment, time-use, and neighbourhood characteristics.
Results The significant predictors of children’s self-regulation growth across 4 to 7 years were fewer behavioural sleep problems, higher gross motor and pre-academic skills, lower levels of maternal and paternal angry parenting, and lower levels of financial hardship. There were also marginal effects for high-quality home learning environments and child-educator relationships.
Conclusion Findings suggest that if we are to successfully foster children’s self-regulation skills, interventionists would do well to operate not only on children’s current capacities but also key aspects of their surrounding context.
Keywords
early childhood, self-regulation, self-control, predictive model
Figure 1
Self-regulation refers to the ability to exert control over our cognition, emotion, and behaviour in ways that are adaptive to functioning. These skills develop across the lifespan, but most rapidly in early childhood alongside cortical maturation processes. In terms of self-regulation development, early improvements appear to be better, with strong early childhood self-regulation skills linked with a wide range of health and achievement outcomes across the lifespan, including positive mental and physical health, and educational attainment (1-3). In contrast, poorer self-regulation in early childhood has been linked with school adjustment difficulties (4), behaviour problems (5), adolescent risk-taking (2), and adult disordered behaviour (6).
Early childhood is a period in which growth in self-regulation is not only particularly desirable, but also demonstrably possible. In fact, growth in self-regulation skills in the early years of life (controlling for early self-regulatory levels and family environment) has been found to reduce risk of childhood behaviour problems (5), adolescent crime, self-harm, and mental health problems (2), as well as enhance academic learning trajectories (7). Given limited understanding of antecedents of early self-regulation change that can shift trajectories and outcomes more broadly, intervention approaches remain incongruous. For instance, approaches to self-regulation intervention include computerized executive function training, specialized preschool curricula, physical activities, arts and music, motor skill development, and so forth (8-10). While it is indeed likely that multiple approaches will be effective, and ideally suited to different contexts, needs and children, the design of interventions would nevertheless be improved through a more comprehensive and holistic understanding of early childhood factors and experiences that support self-regulation development.
The individual and environmental conditions that support optimal development in self-regulation across early childhood remain relatively unclear. Various lines of inquiry have identified longitudinal predictors associated with better point-in-time self-regulation in early childhood including rich home learning environments (11), positive parenting approaches (12), stronger motor (13) and language development (14), and well-adjusted sleep behaviours (15). However, very few studies have examined the extent to which these, and other plausible proximal and distal factors predict change in self-regulation over time. The aim of this study is to investigate the concurrent associations among a range of proximal and distal influences on change in children’s self-regulation skills over 2 years beginning at 4-5 years of age.
Participants
This study used data from the population-representative Kindergarten (K) cohort of the Longitudinal Study of Australian Children (LSAC), with full study design details described elsewhere (16). In brief, for the K cohort, 4,983 children aged 4-5-years old were recruited in 2004 with biennial data collection occurring since then. Data collection involves parent and teacher questionnaires, computer assisted interviews with parents and children, and direct assessments with children. The current study uses data collected for the K cohort across two waves (when children were 4- to 5-years old and 6- to 7-years old). Table 1 describes the characteristics of the sample.
Measures
Self-regulation was assessed at 4-5 and 6-7 years of age using a factor score we have previously established as a reliable indicator of children’s self-regulatory capacity with good predictive validity of broad later-life outcomes into adolescence (2). A total of 20 survey items from parent-, teacher-, and observer-report ratings of self-regulation were standardized and then averaged to create a single composite score (M = 0, SD = 1). Constituent items of this factor index the extent to which children can control and sustain their attention, and control their behaviour and emotions (see Table 2). Internal consistency was high (alpha = 0.84 at 4-5 years, 0.86 at 6-7 years).
Predictors of self-regulation change were selected from the domains of health, development, home environment, education, time use, and neighbourhood measured when children were 4-5-years old. Details of each of these are provided in Table 3. Where parent-report is indicated, this was provided by Parent 1 (defined by LSAC as the parent who knows the study child best, which in 97% of cases was the mother).
Control variables included in the analyses were gender, age of assessment (in months) at baseline, birth weight percentile, whether or not the child had ever been breastfed, Aboriginal and Torres Strait Islander status, Non-English speaking home background, maternal education level (on a 6-point scale from incomplete high school to postgraduate degree), and household income bracket. We used data from the age 4-5 years data collection for these variables, providing the most complete data possible (before attrition in the longitudinal study).
Approach to analysis and missing data
A structural equation model was tested in Mplus version 7.11. Fig 1 depicts the model, showing self-regulation at 6-7 years predicted by the full range of variables described above, while controlling for self-regulation measured two years earlier. This approach to modelling means the estimates for the predictors represent their impact on residualized change in self-regulation from 4 to 7 years of age, because the effect of the earlier measure of self-regulatory problems has already been accounted for. Additionally, effects of stable covariates present from birth on earlier self-regulation were controlled for. Correlations among all predictor variables were included in the model, with the strongest significant correlation as r = .45 for the correlation among teacher-reported gross motor and fine motor skills. Due to the large sample size, we use a conservative p value of < .01 to indicate a significant effect and < .02 for a marginally significant effect.
The amount of missing data varied across waves and variables, ranging from no missing data for socio-demographic characteristics at 4-5 years to 45% missing data for the self-regulation scores at 4-5 and 6-7 years due primarily to item-level missing data from teacher non-report. The data were considered missing at random (MAR) because it was unlikely that the presence of a missing value was related to the response that would have been given (31). We used full information maximum likelihood with a robust estimator to address missing data, allowing us to retain 98% of the sample in the statistical models. We used the sampling weights provided for LSAC (32) to account for sampling error.
The model was a good fit for the data and accounted for 42% of variance in self-regulation at 6-7 years, with all estimates shown in Table 4. Self-regulation skills at 6-7 years, after controlling for self-regulation skills at 4-5 years, were predicted by fewer behavioural sleep problems, higher gross motor and pre-academic skills, lower levels of maternal and paternal angry parenting, and lower levels of financial hardship. There were also marginal effects for the home learning environment and child-educator relationships. Covariates associated with stronger self-regulatory skills at 6-7 years including being a female, having a higher birthweight percentile, identifying as non-Aboriginal and Torres Strait Islander, having a mother with a higher level of education, and a higher household income.
This is the first paper to model a comprehensive and concurrent set of predictors across health, development, and environment in relation to self-regulatory development of young children, across a two-year period beginning from age 4-5 years. Controlling for a range of background factors, significant predictors of self-regulatory growth included: fewer behavioural sleep problems; higher gross motor and pre-academic skills; lower levels of maternal and paternal angry parenting; lower levels of financial hardship; and marginal effects for home learning environment and child-educator relationships. As predictors were modelled simultaneously, significant findings provide a (likely conservative) estimate of the associations between each variable and self-regulation change, over and above the combined associations of all other variables in the model. While previous studies have provided insight into the transactional mechanisms between some factors known to influence self-regulation (e.g. parenting and sleep), this model better reflects the complexity of children’s lives and the combined impact of a range of factors on self-regulatory change. Thus the study makes an important contribution toward prevention and intervention efforts by identifying the most salient and high-potential factors to target for self-regulation interventionists, taking a holistic approach to supporting self-regulatory growth in young children.
Substantial research and theory supports both acute and persistent associations of self-regulation with learning and academic skills (33) with self-regulation typically positioned as a predictor of academic skills. In a related finding, but with self-regulation as the outcome, in our model pre-academic skills were one of the strongest predictors of self-regulation growth. It is clear why self-regulation would predict learning and academic skills: the ability to direct and sustain attention, tackle new challenges, resist maladaptive impulses, and work collaboratively and pro-socially with others – all hallmarks of high self-regulation – serve to support on-task behaviour, effort and persistence during learning. However, there is comparatively less research focused on the possible reciprocal effects with pre-academic skills predicting self-regulation growth. A number of explanations are feasible. First, it is likely that self-regulation and early literacy and numeracy skills, as represented by our pre-academic skill assessment, develop in a bidirectional manner across early childhood (34, 35). For example, time spent in focussed literacy and numeracy learning activities provide the opportunity to extend and enhance self-regulatory capacities, particularly in attentional and cognitive control aspects. It is likely that had we had an earlier and multiple measures of both self-regulation and early concept comprehension, literacy, and numeracy, we would have established birdirectional and reciprocal associations across time. A second and related explanation is that the pre-academic assessment used here may have tapped children’s visual-motor skills given it was a pencil and paper task requiring the writing of letters. While there was no visual-motor data available for children in this dataset, scores on the pre-academic test did correlate (r = .40) with the fine motor variable in our model (single item of teacher report of fine motor competence). Recent research has suggested that visual-motor skills and cognitive self-regulation, as enabled by executive functions, co-develop in a bidirectional manner (35) and it may be that our findings are reflecting a small portion of this transactional process at this period of development. That is, children who scored more highly on the pre-academic score may have done so due to higher visual-motor skills, which may themselves co-develop with and support self-regulatory growth.
Pre-school gross motor abilities were also significantly, albeit modestly, associated with children’s self-regulation growth. This is consistent with suggestions of common mechanisms (i.e., executive functions) that are implicated in both self-regulation and motor learning (36-38), such that both show common areas of neural activation, are impaired after damage to neural regions for the other, and are often both impaired in cognitive disorders, such as ADHD and dyslexia. Indeed, tasks that are motor-demanding for young children, such as navigating uneven surfaces and/or obstacles, are more cognitively demanding and lead to more cognitive errors than less cognitively demanding motor tasks (39). As such, one possibility is that this finding is indicative of the concomitance between self-regulatory and motor skills. However, that gross motor skills were associated with change in self-regulation may additionally suggest that the acquisition of motor proficiency creates new learning opportunities (40) such as experiences that serve to foster self-regulation (e.g., increased mobility causing children to encounter rules associated with access, involvement in physically active shared play providing opportunities for impulse control and turn-taking, etc.) As such, gross motor skills may open a gateway to important self-regulation-promoting experiences and activities, whereas low levels of gross motor skills might consume much of the cognitive resource that otherwise could be directed toward these same activities.
Another factor that was modestly but significantly and uniquely related to self-regulation growth was sleep problems. This aligns with a large body of existing research that identifies sleep problems as a key contributor to daytime self-regulatory problems in young children both in the short (41) and long term (18, 42). It is possible that behavioural sleep problems in young children reflect an underlying phenotype associated with regulatory problems (43, 44), and/or that early behavioural sleep problems initiate a developmental cascade that disrupts emotional and attentional development over time (15). Either way, brief sleep interventions are known to be safe and effective in improving both sleep behaviours and daytime self-regulatory functioning in young children in both typically-developing (45-47) and clinical populations (48, 49).
Our finding that angry parenting was associated with less growth in self-regulation for children echoes a range of prior studies that have linked aggressive, controlling parenting with poor self-regulation in children (50-54). However, this study extends that work by including not only mothers’ but also fathers’ parenting, a rare inclusion. We suggest that angry parenting as measured here is indicative of dysregulated parenting, and potentially of overall emotional regulation skills of parents. Mechanisms through which this might limit self-regulatory growth in children include heritability pathways in terms of self-regulation capabilities (55), and socialisation pathways in which children learn about self-regulatory behaviours through modelling their parents’ behaviours. It is also important to note that child-driven effects are possible, as reflected in prior studies that show dysregulation in young children is associated with increased parenting stress and more-negative parenting approaches (56, 57). These bidirectional relationships between parenting and children’s self-regulation, which are likely to establish mutual promotion/exacerbation processes over time, were not modelled in this study and should be the focus of future longitudinal work.
A number of socioeconomic variables were associated with enhanced self-regulatory growth including higher household incomes, higher maternal education levels and living in households with lower levels of financial hardship. The experience of significant financial hardships such as those tapped here is likely associated with stressful home environments, which impact on children’s physiology and neurodevelopment in ways that limit their capacity for self-regulation development (58, 59). Indeed, early self-regulation has been identified as one of the foremost mechanisms through which early stressors and socioeconomic disadvantage can lead to poorer academic and wellbeing outcomes (60). For these reasons, much of the prevention and intervention focus to date has been on children from disadvantaged backgrounds in an effort to address socio-economic gradients in achievement likely mediated through early self-regulatory capacity. Our findings suggest this focus is well-placed.
Marginal effects were also found for the association between educator-child relationships and the home learning environment, with self-regulatory change. The finding regarding importance of educator-child relationship in terms of children’s early self-regulation development reflects other similar findings in both Australia (61) and Europe (62). Positive student-teacher relationships likely matter because they set the context within which teachers can enact strategies particularly important for acquiring self-regulation during the preschool developmental period (63) including co-regulation, modelling and coaching (64). Our findings regarding the home learning environment align with a prior American longitudinal study linking parental involvement in home learning activities with children’s self-regulatory development (65).
Limitations
Although this study included a comprehensive array of predictors of self-regulation growth across a specific period in early childhood, there are a number of limitations related primarily to measurement. Most measures were broad and blunt instruments of their constructs. This reflects the nature of the population dataset, in which a broad spectrum of measures capturing child development and the environment were desired, rather than an in-depth measurement of any particular constructs. In addition, our self-regulation composite was only available at two time points in this dataset, meaning that more sophisticated growth curve modelling, which requires a minimum of three time points, could not be undertaken. It is also important to note that although we included a wide array of predictors, nearly 60% of the variance in our self-regulation composite at 6-7 years was still unexplained by the model. This suggests that even large-scale studies such as these are missing key ingredients related to self-regulatory growth. Our understandings could be enhanced through studies which capture potential variables that are not often measured, including chronic stress (e.g. cortisol), psychophysiological arousal and regulation, sensory processing, and more detailed understandings of the nature of home learning and early education and care activities. Finally, it is important to note that participants in this study were recruited in 2004. While it is anticipated that there has been limited change in most lifestyle factors investigated (e.g., parenting), new cohort studies are required to better understand the influence of more prominent societal change such as increased access and use of digital devices.
While we know that self-regulation is important for a broad range of longitudinal achievement and wellbeing outcomes, and that early childhood is a key window for self-regulatory growth, we have not yet been overly effective in intervention efforts. One reason for this might be that we need more holistic and evidence-informed theories and approaches to self-regulatory development, rather than a focus on single factors that appear predictive in isolation. We need more complex modelling of the interactions between these various factors and their association with self-regulation change (not just prediction at one time point). The findings of this study suggest a starting point for further detailed research that aims to achieve this.
Ethics approval and consent to participate
Ethics approval for participants in the Longitudinal Study of Australia Children was approved by the Human Research Ethics Committee of the Australian Institute of Family Studies.
Consent for publication
Not applicable
Availability of data and materials
The dataset analysed for the current study is available from the Australian Data Archive https://dataverse.ada.edu.au/dataset.xhtml?persistentId=doi:10.26193/JOZW2U
Competing interests
The authors declare that they have no competing interests.
Funding
There was no funding attached to this study.
Authors’ contributions
KW co-conceptualised the study, undertook all final analyses and was a major contributor to the writing of the paper. SH created the key outcome composite variables for self-regulation, co-conceptualised the study and was a major contributor to the writing of the paper. All authors have read and approved the manuscript.
Acknowledgements
Not applicable.
K cohort Kindergarten cohort
LSAC Longitudinal Study of Australian Children
- McClelland MM, Acock AC, Piccinin A, Rhea SA, Stallings MC. Relations between preschool attention span-persistence and age 25 educational outcomes. Early Childhood Research Quarterly 2013; 28: 314-324.
- Howard SJ, Williams KE. Early self-regulation, early self-regulatory change, and their longitudinal relation sto adolescents' academic, health, and mental well-being outcomes. Journal of Development and Behavioral Pediatrics 2018; 39:489-96.
- Moffitt TE, Arseneault L, Belsky J, Dickson N, Hancox R, Harington H, et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Science 2011;108(7):2693-8.
- Williams KE, Nicholson JM, Walker S, Berthelsen D. Early childhood profiles of sleep problems and self-regulation predict later school adjustment. The British Journal Of Educational Psychology 2016;86(2):331-50.
- Sawyer ACP, Miller-Lewis LR, Searle AK, Sawyer MG, Lynch JW. Is greater improvement in early self-regulation associated with fewer behavioral problems later in childhood? Developmental Psychology 2015;51(12):1740-55.
- Slutske WS, Moffitt TE, Poulton R, Caspi A. Undercontrolled temperament at age 3 predicts disordered gambling at age 32: A longitudinal study of a complete birth cohort. Psychological Science 2012;23(5):510-6.
- Sawyer ACP, Chittleborough CR, Mittinty MN, Miller-Lewis LR, Sawyer MG, Sullivan T, et al. Are trajectories of self-regulation abilities from ages 2-3 to 6-7 associated with academic achievement in the early school years? Child: Care, Health & Development. 2014;41:744-754.
- Takacs ZK, Kassai R. The efficacy of different interventions to foster children’s executive function skills: A series of meta-analyses. Psychological Bulletin 2019;145(7):653-97.
- Diamond A. Activities and programs that improve children’s executive functions. Current Directions in Psychological Science 2012;21(5):335-41.
- Jacob R, Parkinson J. The potential for school-based interventions that target executive function to improve academic achievement: a review. Review of Educational Research 2015;85(4):512-52.
- Baker CE, Cameron CE, Rimm-Kaufman SE, Grissmer D. Family and sociodemographic predictors of school readiness among African American boys in kindergarten. Early Education and Development 2012;23(6):833-54.
- Hindman AH, Morrison FJ. Differential contributions of three parenting dimensions to preschool literacy and social skills in a middle-income sample. Merrill-Palmer Quarterly 2012;58(2):191-223.
- Kim H, Byers AI, Cameron CE, Brock LL, Cottone EA, Grissmer DW. Unique contributions of attentional control and visuomotor integration on concurrent teacher-reported classroom functioning in early elementary students. Early Childhood Research Quarterly 2016;36:379-90.
- Hanno E, Surrain S. The direct and indirect relations between self-regulation and language development among monolinguals and dual language learners. Clinical Child and Family Psychology Review 2019;22:75-89.
- Williams KE, Berthelsen D, Walker S, Nicholson JM. A developmental cascade model of behavioral sleep problems and emotional and attentional self-regulation across early childhood. Behavioral Sleep Medicine 2017;15(1):1-21.
- Soloff C, Lawrence D, Johnstone R. The Longitudinal Study of Australian Children LSAC Technical Paper 1: Sample design.: Australian Government Department of Family and Community Services; 2005.
- Varni JW, Seid M, Rode CA. The PedsQL: Measurement model for the pediatric quality of life inventory. Medical Care 1999;37(2):126 - 39.
- Quach J, Nguyen CD, Williams KE, Sciberras E. Bidirectional associations between child sleep problems and internalizing and externalizing difficulties from preschool to early adolescence. JAMA Pediatrics. 2018;172(2):e174363.
- Dunn LM, Dunn LM. Examiner’s manual for the Peabody Picture Vocabulary Test (3rd edn.). Circle Pines, MN: American Guidance Service 1997.
- de Lemos M, Doig B. Who Am I Developmental Assessment Manual. Melbourne: ACER Press 2000.
- Australian Council for Educational Research. Who Am I Supplementary Information; 2007.
- Zubrick SR, Lucas RE, Westrupp EM, Nicholson JM. Growing up in Australia: The Longitudinal Study of Australian Children (LSAC). LSAC Technical Paper No. 12. Parenting measures in the Longitudinal Study of Australian Children: construct validity and measurement quality, Waves 1 to 4: Australian Government Department of Social Services; 2014.
- Statistics Canada. National Longitudinal Survey of Children & Youth Cycle 3 Survey Instruments 1998 - 99. Book 1 - Parent & Child; 1999.
- Furukawa TA, Kessler RC, Slade T, Andrews G. The performance of the K6 and K10 screening scales for psychological distress in the Australian National Survey of Mental Health and Well-Being. Psychological Medicine 2003;33(2):357 - 62.
- Hayes N, Berthelsen DC, Nicholson JM, Walker S. Trajectories of parental involvement in home learning activities across the early years: Associations with socio-demographic characteristics and children’s learning outcomes. Early Child Development and Care 2018;188(10):1405 - 18.
- Spencer N, Strazdins L. Socioeconomic disadvantage and onset of childhood chronic disabling conditions: a cohort study. Archives Of Disease In Childhood 2015;100:317 - 22.
- Lewis AJ, Olsson CA. Early life stress and child temperament style as predictors of childhood anxiety and depressive symptoms: Findings from the Longitudinal Study of Australian Children. Depression Research and Treatment. 2011; Article ID 296026.
- Pianta R. Student–Teacher Relationship Scale. Odessa, FL: Psychological
Assessment Resources; 2001
- Gialamas A, Sawyer ACP, Mittinty MN, Zubrick SR, Sawyer MG, Lynch J. Quality of childcare influences children's attentiveness and emotional regulation at school entry. The Journal of Pediatrics 2014;165:813-9.
- Australian Bureau of Statistics. Socio-Economic Indexes for Areas Canberra, Australia: Australian Bureau of Statistics; 2018 [updated 27 March 2018. Available from: https://www.abs.gov.au/websitedbs/censushome.nsf/home/seifa.
- Enders C. Applied Missing Data Analysis. New York, NY: Guildford Press; 2010.
- Soloff C, Lawrence D, Misson S, Johnstone R. The Longitudinal Study of Australian Children LSAC Technical paper No.3: Wave 1 weighting and non-response; 2006.
- Robson DA, Allen MS, Howard SJ. Self-regulation in childhood as a predictor of future outcomes: A meta-analytic review. Psychological Bulletin; 2020. Epub Jan 6.
- Fuhs MW, Nesbitt KT, Farran DC, Dong N. Longitudinal associations between executive functioning and academic skills across content areas. Developmental Psychology 2014;50(6):1698-709.
- McClelland MM, Cameron CE. Developing together: The role of executive function and motor skills in children's academic lives. Early Childhood Research Quarterl 2019;46:143-51.
- Cook CJ, Howard SJ, Scerif G, Twine R, Kahn K, Norris SA, et al. Associations of physical activity and gross motor skills with executive function in preschool children from low‐income south african settings. Developmental Science 2019;22:e12820.
- Diamond A. Close interrelation of motor development and cognitive development and of the cerebellum and prefrontal cortex. Child Development 2000;71(1):44-56.
- Oberer N, Gashaj V, Roebers CM. Motor skills in kindergarten: Internal structure, cognitive correlates and relationships to background variables. Human Movement Science 2017;52:170-80.
- Berger SE. Locomotor expertise predicts infants’ perseverative errors. Developmental Psychology 2010;46(2):326-36.
- Campos JJ, Kermoian R, Zumbahlen MR. Socioemotional transformations in the family system following infant crawling onset. In: Eisenberg N, Fabes RA, editors. Emotion and its regulation in early development. New directions for child development, No. 55: The Jossey-Bass education series; ISSN: 0195-2269 (Print). San Francisco, CA: Jossey-Bass; 1992. p. 25-40.
- Wilson KE, Lumeng JC, Kaciroti N, Chen SY-P, LeBourgeois MK, Chervin RD, et al. Sleep Hygiene Practices and Bedtime Resistance in Low-Income Preschoolers: Does Temperament Matter? Behavioral Sleep Medicine 2015;13(5):412-23.
- Van den Bergh BRH, Mulder EJH. Fetal sleep organization: a biological precursor of self-regulation in childhood and adolescence? Biological Psychology 2012;89(3):584-90.
- Melegari MG, Sette S, Vittori E, Mallia L, Devoto A, Lucidi F, et al. Relations Between Sleep and Temperament in Preschool Children With ADHD. Journal Of Attention Disorders 2018:1087054718757645-.
- Williams KE, Sciberras E. Sleep and self-regulation from birth to 7 years: A retrospective study of children with and without attention-deficit hyperactivity disorder at 8 to 9 years. Journal of Developmental and Behavioral Pediatrics 2016;37(5):385-94.
- Price AMH, Wake M, Ukoumunne OC, Hiscock H. Five-year follow-up of harms and benefits of behavioral infant sleep intervention: randomized trial. Pediatrics 2012;130(4):643-51.
- Price AMH, Wake M, Ukoumunne OC, Hiscock H. Outcomes at six years of age for children with infant sleep problems: longitudinal community-based study. Sleep Medicine 2012;13(8):991-8.
- Quach J, Hiscock H, Ukoumunne OC, Wake M. A brief sleep intervention improves outcomes in the school entry year: a randomized controlled trial. Pediatrics 2011;128(4):692-701.
- Papadopoulos N, Sciberras E, Hiscock H, Mulraney M, McGillivray J, Rinehart N. The efficacy of a brief behavioral sleep intervention in school-aged children with ADHD and comorbid autism spectrum disorder. Journal of Attention Disorders 2019;23(4):341-50.
- Hiscock H, Sciberras E, Mensah FK, Gerner B, Efron D, Khano S, et al. Impact of a behavioural sleep intervention on symptoms and sleep in children with attention deficit hyperactivity disorder, and parental mental health: randomised controlled trial. BMJ 2015;350(h68).
- Graziano PA, Calkins SD, Keane SP. Sustained attention development during the toddlerhood to preschool period: Associations with toddlers' emotion regulation strategies and maternal behaviour. Infant and Child Development 2011;20(6):389-408.
- Nelson JA, O'Brien M, Calkins SD, Leerkes EM, Marcovitch S, Blankson AN. Maternal expressive style and children's emotional development. Infant and Child Development 2012;21(3):267-86.
- Olson SL, Lopez-Duran N, Lunkenheimer ES, Chang H, Sameroff AJ. Individual differences in the development of early peer aggression: Integrating contributions of self-regulation, theory of mind, and parenting. Development and Psychopathology 2011;23(1):253-66.
- Spinrad TL, Eisenberg N, Silva KM, Eggum ND, Reiser M, Edwards A, et al. Longitudinal relations among maternal behaviors, effortful control and young children's committed compliance. Developmental Psychology 2012;48(2):552-66.
- Williams KE, Berthelsen D. The development of prosocial behaviour in early childhood: Contributions of early parenting and self-regulation. International Journal of Early Childhood 2017;49(1):73-94.
- Scott BG, Lemery-Chalfant K, Clifford S, Tein J-Y, Stoll R, Goldsmith HH. A twin factor mixture modeling approach to childhood temperament: Differential heritability. Child Development 2016;87(6):1940-55.
- Barnes JC, Boutwell BB, Beaver KM, Gibson CL. Analyzing the Origins of Childhood Externalizing Behavioral Problems. Developmental Psychology. 2013.
- Healey DM, Flory JD, Miller CJ, Halperin JM. Maternal positive parenting style is associated with better functioning in hyperactive/inattentive preschool children. Infant and Child Development 2011;20(2):148-61.
- Blair C, Raver C, Granger D, Mills-Koonce R, Hibel LC, Investigators FLP. Allostasis and allostatic load in the context of poverty in early childhood. Development and Psychopathology 2011;23(3):845-57
- Farah M. The neuroscience of socioeconomic status: Correlates, causes, and consequences. Neuron 2017;96(1):56-71.
- Blair C, Raver CC. School readiness and self-regulation: A developmental psychobiological approach. Annual Review of Psychology 2015;66:711-31.
- Gialamas A, Alyssa CP, Sawyer A, Mittinty MN, Zubrick SR, Sawyer MG, et al. Quality of childcare influences children's attentiveness and emotional regulation at school entry. The Journal of Pedatrics 2014;165(4):813 - 9.
- Cadima J, Verschueren K, Leal T, Guedes C. Classroom interactions, dyadic teacher–child relationships, and self–regulation in socially disadvantaged young children. Journal of Abnormal Child Psychology 2016;44(1):7-17.
- Kopp CB. Antecedents of self-regulation: A developmental perspective. Developmental Psychology 1982;18(2):199-214.
- Silkenbeumer JR, Schiller E-M, Kärtner J. Co- and self-regulation of emotions in the preschool setting. Early Childhood Research Quarterly 2018;44:72-81.
- Baker CE. Fathers' and mothers' home literacy involvement and children's cognitive and social emotional development: Implications for family literacy programs. Applied Developmental Science 2013;17(4):184-97.
|
Table 1: Sample characteristics |
|
|
Study sample characteristic |
Percentage |
|
Boys |
51% |
|
English as main home language |
86% |
|
Aboriginal or Torres Strait Islander |
3.8% |
|
Mothers with incomplete high school education |
22% |
|
Mothers with university education |
28% |
|
Attending preschool program at 4-5 years |
95% |
|
|
M (SD) |
|
Child age at 4-5 year data collection |
56.9 months (2.65) |
|
Child age at 6-7 year data collection |
81.9 months (2.96) |
|
Household income per week |
$1,661.93AUD ($1,294.05) |
|
Table 2: Items included in the self-regulation measure at 4-5 years and 6-7 years |
||
|
Construct |
Respondent |
Item |
|
Impulsive Aggression |
Parent and teacher |
Often has temper tantrums/hot tempers
|
|
|
Parent and teacher |
Often fights with other children or bullies them |
|
|
Parent and teacher |
Often argumentative with adults |
|
Hyperactivity |
Parent and teacher |
Restless, overactive, cannot stay still for long |
|
|
Parent and teacher |
Constantly fidgeting or squirming |
|
|
Parent and teacher |
If this child is upset, it is hard to comfort him/her |
|
Lack of Persistence & Inattention |
Parent and teacher |
The child likes to complete one task or activity before going on to the next (reversed) |
|
|
Parent and teacher |
Sees takes through to the end, good attention span (reversed) |
|
|
Parent and teacher |
The child stays with an activity (e.g., puzzle, construction, kit, reading) for a long time (reversed) |
|
|
Parent, teacher, and observer |
Easily distracted, concentration wanders
|
|
Impulsivity |
Parent and teacher |
Can stop and think things out before acting (reversed) |
|
|
Parent and teacher |
Shares readily with other children (reversed) |
|
|
Observer |
Degree of negative mood (withdrawn, uncooperative, sulky, seeming upset, angry) to interview |
|
Table 3: Predictors of self-regulation growth in the model
|
||
|
Construct |
Data source |
Measure |
|
Health & Health Behaviours |
||
|
Physical health |
Parent |
Physical Health Summary score from the Pediatric Quality of Life Inventory (PedsQL) (17). Summed and average score of 8 items each rated on 5-point scale, tapping a child’s level of functioning in daily activities that rely on good physical health. E.g. problems with running. α = .72 |
|
Diet quality |
Parent |
Units of high sugar drinks consumed in the last week |
|
Behavioural sleep problems |
Parent |
Five items modelled as a latent variable as per prior studies (18). E.g. child has problems on 4 or more nights a week with waking during the night (yes/no); this child’s sleep is a small/moderate/large problem. |
|
Development |
||
|
Receptive vocabulary |
Assessed |
Peabody Picture Vocabulary Test (19) of receptive vocabulary in which children listen to a spoken word and are asked to point to the matching picture given a set of four pictures. Higher scores represent higher receptive vocabulary skills. |
|
Gross motor development |
Teacher |
On a 4-point scale from ‘much less competent than peers’ to ‘more competent than peers’ |
|
Fine motor development |
Teacher |
On a 4-point scale from ‘much less competent than peers’ to ‘more competent than peers’ |
|
Pre-academic skills |
Assessed |
Who Am I test (20). Children write their names, copy shapes, write words and numbers; scored according to skill level. α = .89 (21) |
|
Home environment |
||
|
Maternal parenting anger |
Mother |
Composite measure (weighted mean score) as per LSAC technical advice (22) using four adapted items from the National Longitudinal Study of Children & Youth (23). Each item rated on 5-point scale from ‘never or almost never’ to ‘almost always’. E.g. how often are you angry when you punish this child? H = .72. |
|
Paternal parenting anger |
Father |
|
|
Maternal parenting consistency |
Mother |
Composite measure as per LSAC technical advice (22) using five items from the National Longitudinal Survey of Children and Youth (23). Each item rates on a 10-point scale from ‘not at all’ to ‘all of the time’. E.g. how often does this child get away with things that you feel should have been punished? H = .80 for father; .82 for mothers. |
|
Paternal parenting consistency |
Father |
|
|
Maternal mental health |
Mother |
Kessler K6 screening scale (24) of six items (summed and averaged) about respondents’ feelings over the past four-week period. Rates on 5-point scale from ‘all of the time’ to ‘none of the time’. E.g. in the past 4 week how often have you felt hopeless? α = .84 for mothers, .82 for fathers. |
|
Paternal mental health |
Father |
|
|
Home learning environment |
Parent |
Single item book reading; plus latent variable with five indicators of other home learning activities including music, art, and play as used in other LSAC studies (25). Each rated on 4-point scale of frequency of adult-child for each activity in the last week from ‘not in the past week’ to ‘6-7 days in the week’. |
|
Financial hardship |
Parent |
7-item count index ranging from 0 to 7, based on summing Yes=1, No=0 responses to 7 items including couldn’t pay bills, gone without meals as used in prior LSAC research (26). |
|
Argumentative parental relationships |
Parent |
Composite of 5 items (summed and averaged) rated on a 5-point scale from ‘never’ to ‘always’. E.g. my partner and I argue; disagree over child-rearing etc. α = .80 |
|
Stressful life events |
Parent |
13-item count index ranging from 0 to 13 based on summing Yes = 1, No=0 responses about exposure to adverse life events over the past year including marital breakdown, death of friend, as per prior LSAC research (27). |
|
Education |
||
|
Teacher-child relationship |
Teacher |
8-item composite drawn from the Student Teacher Relationship Scale (28) following prior LSAC factor modelling (29). Each item rated on 5-point scale from ‘definitely does not apply’ to ‘definitely applies’. E.g. share affectionate relationships, easy to be in tune with feelings α = .81 |
|
Time use |
||
|
Extra-curricular sport |
Parent |
Sum of 3 items indicating participation (yes / no) in extra-curricular swimming, gymnastics, or team sport |
|
Extra-curricular music / dance |
Parent |
Sum of 2 items indicating participation (yes / no) in extra-curricular music and dance |
|
Weekday TV hours |
Parent |
Number of hours watching TV on a typical weekday |
|
Weekday computer hours |
Parent |
Number of hours using a computer on a typical weekday |
|
Physical activity |
Parent |
Parent-rated child enjoyment of physical activity on a 5-point scale from ‘very much dislikes physical activities’ to ‘very much likes physical activity’ |
|
Neighbourhood |
||
|
Liveability |
Parent |
Composite (sum) of 8 items each rated on 4-point scale from ‘strongly disagree’ to ‘strongly agree’. E.g. this is a safe neighbourhood, this neighbourhood has good parks. α = .76 |
|
Socio-economic index for area (SEIFA) |
Australian Bureau of Statistics |
Composite of 31 variables (e.g. income, unemployment, occupation and education) computed by the Australian Bureau of Statistics (30).
|
|
Table 4: Standardized coefficients for the predictors of self-regulation at 6-7 years controlling for prior self-regulation and covariates |
||
|
|
β |
95% CI |
|
Covariate associations with self-regulation at 4-5 years |
||
|
Female |
.50** |
.44 - .57 |
|
Age |
.01 |
.04 - .11 |
|
Birthweight percentile |
.07** |
.03 - .11 |
|
Breastfed |
-.15 |
-.29 - -.01 |
|
Aboriginal Torres Strait Islander |
-.53** |
-.81 - -.26 |
|
Non-English home language |
.01 |
-.10 - .11 |
|
Maternal education level |
.13** |
.09 - .17 |
|
Household income |
.13** |
.08 - .17 |
|
Stability of self-regulation 4-5 years to 6-7 years |
.54** |
.49 - .59 |
|
Predictors of self-regulation at 6-7 years controlling for above |
||
|
Health |
|
|
|
Physical health status |
.02 |
-.03 - .05 |
|
High sugar drink intake |
.02 |
-.01 - .06 |
|
Sleep problems |
-.08** |
-.13 - -.04 |
|
Development |
|
|
|
Vocabulary |
.01 |
-.03 - .06 |
|
Gross motor |
.06** |
.02 - .10 |
|
Fine motor |
-.05 |
-.10 - .00 |
|
Pre-academic skills |
.12** |
.09 - .16 |
|
Home environment |
|
|
|
Maternal angry parenting |
-.10** |
-.15 - .06 |
|
Paternal angry parenting |
-.12** |
-.16 - -.07 |
|
Maternal consistent parenting |
-.01 |
-.04 - .05 |
|
Paternal consistent parenting |
.02 |
-.02 - .07 |
|
Maternal mental health |
-.01 |
-.06 - .04 |
|
Paternal mental health |
.02 |
-.03 - .06 |
|
Shared book reading frequency |
.03 |
-.01 - .07 |
|
Home learning activities |
.06* |
.01 - .10 |
|
Financial hardship |
-.07** |
-.12 - -.02 |
|
Argumentative parental relationships |
-.03 |
-.07 - .02 |
|
Stressful life events |
-.00 |
-.04 - .04 |
|
Education |
|
|
|
Educator-child relationship |
.06* |
.01 - .11 |
|
Time use |
|
|
|
Extra-curricular sport |
-.02 |
-.05 - .02 |
|
Extra-curricular music/dance |
.02 |
-.01 - .05 |
|
Weekday TV hours |
.04 |
.00 - .08 |
|
Weekday computer hours |
.01 |
-.03 - .05 |
|
Physical activity |
-.03 |
-.06 - .00 |
|
Neighbourhood |
|
|
|
Liveability |
-.01 |
-.05 - .02 |
|
Socio-economic index |
.01 |
-.03 - .04 |
|
* p < .02; ** p < .01 |
|
|
|
|
|
|
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 3
Posted 15 May, 2020
No community comments so far
Editor assigned
On 29 Apr, 2020
Editor invited
On 29 Apr, 2020
Submission checks complete
On 09 Mar, 2020
No comments provided
Editorial decision: Minor revision
On 28 Apr, 2020
Reviewer #1 agreed
On 16 Apr, 2020
Review #1 received
Received 16 Apr, 2020
Reviewers invited
Invitations sent on 12 Apr, 2020
Editor assigned
On 11 Apr, 2020
Submission checks complete
On 11 Apr, 2020
Editor invited
On 11 Apr, 2020
No comments provided
Editorial decision: Minor revision
On 03 Apr, 2020
Review #1 received
Received 29 Mar, 2020
Reviewer #2 agreed
On 16 Mar, 2020
Review #2 received
Received 16 Mar, 2020
Reviewer #1 agreed
On 11 Mar, 2020
Reviewers invited
Invitations sent on 11 Mar, 2020
Editor assigned
On 09 Mar, 2020
Submission checks complete
On 09 Mar, 2020
Editor invited
On 09 Mar, 2020
First submitted
On 04 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
Learn more about our company.
This preprint is under consideration at BMC Pediatrics. Preprints are preliminary reports that have not undergone peer review. They should not be considered conclusive, used to inform clinical practice, or referenced by the media as validated information.
Learn more about In Review
Research article
Proximal and distal predictors of self-regulatory change in children aged 4 to 7 years
Kate E Williams
Queensland University of Technology
Corresponding Author
ORCiD: https://orcid.org/0000-0001-8983-5503
Badges
Prescreen
This manuscript passed our Prescreen assessment
Complete author information
Appropriate declaration statements
Potential risks to human health
Prescreen
Peer Review Timeline
CURRENT STATUS: Under Review
Version 3
Posted 15 May, 2020
No community comments so far
Editor assigned
On 29 Apr, 2020
Editor invited
On 29 Apr, 2020
Submission checks complete
On 09 Mar, 2020
No comments provided
Editorial decision: Minor revision
On 28 Apr, 2020
Reviewer #1 agreed
On 16 Apr, 2020
Review #1 received
Received 16 Apr, 2020
Reviewers invited
Invitations sent on 12 Apr, 2020
Editor assigned
On 11 Apr, 2020
Submission checks complete
On 11 Apr, 2020
Editor invited
On 11 Apr, 2020
No comments provided
Editorial decision: Minor revision
On 03 Apr, 2020
Review #1 received
Received 29 Mar, 2020
Reviewer #2 agreed
On 16 Mar, 2020
Review #2 received
Received 16 Mar, 2020
Reviewer #1 agreed
On 11 Mar, 2020
Reviewers invited
Invitations sent on 11 Mar, 2020
Editor assigned
On 09 Mar, 2020
Submission checks complete
On 09 Mar, 2020
Editor invited
On 09 Mar, 2020
First submitted
On 04 Mar, 2020
Metrics
Comments: 0
PDF Downloads: ...
HTML Views: ...
License:
This work is licensed under a Creative Commons Attribution 4.0 International License. Read Full License
Background Growth in early self-regulation skills has been linked to positive health, wellbeing, and achievement trajectories across the lifespan. While individual studies have documented specific influences on self-regulation competencies in early childhood, few have modelled a comprehensive range of predictors of self-regulation change across health, development, and environment simultaneously. This study aimed to examine the concurrent associations among a range of proximal and distal influences on change in children’s self-regulation skills over 2 years from age 4-5 years.
Methods Data from the Longitudinal Study of Australian Children (N= 4983) were used in a structural equation model, predicting a multi-source composite measure of self-regulation at each of 4-5 years and 6-7 years. By controlling for earlier self-regulation and covariates, the model examined the relative contributions of a comprehensive range of variables to self-regulation change including health, development, educational, home environment, time-use, and neighbourhood characteristics.
Results The significant predictors of children’s self-regulation growth across 4 to 7 years were fewer behavioural sleep problems, higher gross motor and pre-academic skills, lower levels of maternal and paternal angry parenting, and lower levels of financial hardship. There were also marginal effects for high-quality home learning environments and child-educator relationships.
Conclusion Findings suggest that if we are to successfully foster children’s self-regulation skills, interventionists would do well to operate not only on children’s current capacities but also key aspects of their surrounding context.
Figure 1
Self-regulation refers to the ability to exert control over our cognition, emotion, and behaviour in ways that are adaptive to functioning. These skills develop across the lifespan, but most rapidly in early childhood alongside cortical maturation processes. In terms of self-regulation development, early improvements appear to be better, with strong early childhood self-regulation skills linked with a wide range of health and achievement outcomes across the lifespan, including positive mental and physical health, and educational attainment (1-3). In contrast, poorer self-regulation in early childhood has been linked with school adjustment difficulties (4), behaviour problems (5), adolescent risk-taking (2), and adult disordered behaviour (6).
Early childhood is a period in which growth in self-regulation is not only particularly desirable, but also demonstrably possible. In fact, growth in self-regulation skills in the early years of life (controlling for early self-regulatory levels and family environment) has been found to reduce risk of childhood behaviour problems (5), adolescent crime, self-harm, and mental health problems (2), as well as enhance academic learning trajectories (7). Given limited understanding of antecedents of early self-regulation change that can shift trajectories and outcomes more broadly, intervention approaches remain incongruous. For instance, approaches to self-regulation intervention include computerized executive function training, specialized preschool curricula, physical activities, arts and music, motor skill development, and so forth (8-10). While it is indeed likely that multiple approaches will be effective, and ideally suited to different contexts, needs and children, the design of interventions would nevertheless be improved through a more comprehensive and holistic understanding of early childhood factors and experiences that support self-regulation development.
The individual and environmental conditions that support optimal development in self-regulation across early childhood remain relatively unclear. Various lines of inquiry have identified longitudinal predictors associated with better point-in-time self-regulation in early childhood including rich home learning environments (11), positive parenting approaches (12), stronger motor (13) and language development (14), and well-adjusted sleep behaviours (15). However, very few studies have examined the extent to which these, and other plausible proximal and distal factors predict change in self-regulation over time. The aim of this study is to investigate the concurrent associations among a range of proximal and distal influences on change in children’s self-regulation skills over 2 years beginning at 4-5 years of age.
Participants
This study used data from the population-representative Kindergarten (K) cohort of the Longitudinal Study of Australian Children (LSAC), with full study design details described elsewhere (16). In brief, for the K cohort, 4,983 children aged 4-5-years old were recruited in 2004 with biennial data collection occurring since then. Data collection involves parent and teacher questionnaires, computer assisted interviews with parents and children, and direct assessments with children. The current study uses data collected for the K cohort across two waves (when children were 4- to 5-years old and 6- to 7-years old). Table 1 describes the characteristics of the sample.
Measures
Self-regulation was assessed at 4-5 and 6-7 years of age using a factor score we have previously established as a reliable indicator of children’s self-regulatory capacity with good predictive validity of broad later-life outcomes into adolescence (2). A total of 20 survey items from parent-, teacher-, and observer-report ratings of self-regulation were standardized and then averaged to create a single composite score (M = 0, SD = 1). Constituent items of this factor index the extent to which children can control and sustain their attention, and control their behaviour and emotions (see Table 2). Internal consistency was high (alpha = 0.84 at 4-5 years, 0.86 at 6-7 years).
Predictors of self-regulation change were selected from the domains of health, development, home environment, education, time use, and neighbourhood measured when children were 4-5-years old. Details of each of these are provided in Table 3. Where parent-report is indicated, this was provided by Parent 1 (defined by LSAC as the parent who knows the study child best, which in 97% of cases was the mother).
Control variables included in the analyses were gender, age of assessment (in months) at baseline, birth weight percentile, whether or not the child had ever been breastfed, Aboriginal and Torres Strait Islander status, Non-English speaking home background, maternal education level (on a 6-point scale from incomplete high school to postgraduate degree), and household income bracket. We used data from the age 4-5 years data collection for these variables, providing the most complete data possible (before attrition in the longitudinal study).
Approach to analysis and missing data
A structural equation model was tested in Mplus version 7.11. Fig 1 depicts the model, showing self-regulation at 6-7 years predicted by the full range of variables described above, while controlling for self-regulation measured two years earlier. This approach to modelling means the estimates for the predictors represent their impact on residualized change in self-regulation from 4 to 7 years of age, because the effect of the earlier measure of self-regulatory problems has already been accounted for. Additionally, effects of stable covariates present from birth on earlier self-regulation were controlled for. Correlations among all predictor variables were included in the model, with the strongest significant correlation as r = .45 for the correlation among teacher-reported gross motor and fine motor skills. Due to the large sample size, we use a conservative p value of < .01 to indicate a significant effect and < .02 for a marginally significant effect.
The amount of missing data varied across waves and variables, ranging from no missing data for socio-demographic characteristics at 4-5 years to 45% missing data for the self-regulation scores at 4-5 and 6-7 years due primarily to item-level missing data from teacher non-report. The data were considered missing at random (MAR) because it was unlikely that the presence of a missing value was related to the response that would have been given (31). We used full information maximum likelihood with a robust estimator to address missing data, allowing us to retain 98% of the sample in the statistical models. We used the sampling weights provided for LSAC (32) to account for sampling error.
The model was a good fit for the data and accounted for 42% of variance in self-regulation at 6-7 years, with all estimates shown in Table 4. Self-regulation skills at 6-7 years, after controlling for self-regulation skills at 4-5 years, were predicted by fewer behavioural sleep problems, higher gross motor and pre-academic skills, lower levels of maternal and paternal angry parenting, and lower levels of financial hardship. There were also marginal effects for the home learning environment and child-educator relationships. Covariates associated with stronger self-regulatory skills at 6-7 years including being a female, having a higher birthweight percentile, identifying as non-Aboriginal and Torres Strait Islander, having a mother with a higher level of education, and a higher household income.
This is the first paper to model a comprehensive and concurrent set of predictors across health, development, and environment in relation to self-regulatory development of young children, across a two-year period beginning from age 4-5 years. Controlling for a range of background factors, significant predictors of self-regulatory growth included: fewer behavioural sleep problems; higher gross motor and pre-academic skills; lower levels of maternal and paternal angry parenting; lower levels of financial hardship; and marginal effects for home learning environment and child-educator relationships. As predictors were modelled simultaneously, significant findings provide a (likely conservative) estimate of the associations between each variable and self-regulation change, over and above the combined associations of all other variables in the model. While previous studies have provided insight into the transactional mechanisms between some factors known to influence self-regulation (e.g. parenting and sleep), this model better reflects the complexity of children’s lives and the combined impact of a range of factors on self-regulatory change. Thus the study makes an important contribution toward prevention and intervention efforts by identifying the most salient and high-potential factors to target for self-regulation interventionists, taking a holistic approach to supporting self-regulatory growth in young children.
Substantial research and theory supports both acute and persistent associations of self-regulation with learning and academic skills (33) with self-regulation typically positioned as a predictor of academic skills. In a related finding, but with self-regulation as the outcome, in our model pre-academic skills were one of the strongest predictors of self-regulation growth. It is clear why self-regulation would predict learning and academic skills: the ability to direct and sustain attention, tackle new challenges, resist maladaptive impulses, and work collaboratively and pro-socially with others – all hallmarks of high self-regulation – serve to support on-task behaviour, effort and persistence during learning. However, there is comparatively less research focused on the possible reciprocal effects with pre-academic skills predicting self-regulation growth. A number of explanations are feasible. First, it is likely that self-regulation and early literacy and numeracy skills, as represented by our pre-academic skill assessment, develop in a bidirectional manner across early childhood (34, 35). For example, time spent in focussed literacy and numeracy learning activities provide the opportunity to extend and enhance self-regulatory capacities, particularly in attentional and cognitive control aspects. It is likely that had we had an earlier and multiple measures of both self-regulation and early concept comprehension, literacy, and numeracy, we would have established birdirectional and reciprocal associations across time. A second and related explanation is that the pre-academic assessment used here may have tapped children’s visual-motor skills given it was a pencil and paper task requiring the writing of letters. While there was no visual-motor data available for children in this dataset, scores on the pre-academic test did correlate (r = .40) with the fine motor variable in our model (single item of teacher report of fine motor competence). Recent research has suggested that visual-motor skills and cognitive self-regulation, as enabled by executive functions, co-develop in a bidirectional manner (35) and it may be that our findings are reflecting a small portion of this transactional process at this period of development. That is, children who scored more highly on the pre-academic score may have done so due to higher visual-motor skills, which may themselves co-develop with and support self-regulatory growth.
Pre-school gross motor abilities were also significantly, albeit modestly, associated with children’s self-regulation growth. This is consistent with suggestions of common mechanisms (i.e., executive functions) that are implicated in both self-regulation and motor learning (36-38), such that both show common areas of neural activation, are impaired after damage to neural regions for the other, and are often both impaired in cognitive disorders, such as ADHD and dyslexia. Indeed, tasks that are motor-demanding for young children, such as navigating uneven surfaces and/or obstacles, are more cognitively demanding and lead to more cognitive errors than less cognitively demanding motor tasks (39). As such, one possibility is that this finding is indicative of the concomitance between self-regulatory and motor skills. However, that gross motor skills were associated with change in self-regulation may additionally suggest that the acquisition of motor proficiency creates new learning opportunities (40) such as experiences that serve to foster self-regulation (e.g., increased mobility causing children to encounter rules associated with access, involvement in physically active shared play providing opportunities for impulse control and turn-taking, etc.) As such, gross motor skills may open a gateway to important self-regulation-promoting experiences and activities, whereas low levels of gross motor skills might consume much of the cognitive resource that otherwise could be directed toward these same activities.
Another factor that was modestly but significantly and uniquely related to self-regulation growth was sleep problems. This aligns with a large body of existing research that identifies sleep problems as a key contributor to daytime self-regulatory problems in young children both in the short (41) and long term (18, 42). It is possible that behavioural sleep problems in young children reflect an underlying phenotype associated with regulatory problems (43, 44), and/or that early behavioural sleep problems initiate a developmental cascade that disrupts emotional and attentional development over time (15). Either way, brief sleep interventions are known to be safe and effective in improving both sleep behaviours and daytime self-regulatory functioning in young children in both typically-developing (45-47) and clinical populations (48, 49).
Our finding that angry parenting was associated with less growth in self-regulation for children echoes a range of prior studies that have linked aggressive, controlling parenting with poor self-regulation in children (50-54). However, this study extends that work by including not only mothers’ but also fathers’ parenting, a rare inclusion. We suggest that angry parenting as measured here is indicative of dysregulated parenting, and potentially of overall emotional regulation skills of parents. Mechanisms through which this might limit self-regulatory growth in children include heritability pathways in terms of self-regulation capabilities (55), and socialisation pathways in which children learn about self-regulatory behaviours through modelling their parents’ behaviours. It is also important to note that child-driven effects are possible, as reflected in prior studies that show dysregulation in young children is associated with increased parenting stress and more-negative parenting approaches (56, 57). These bidirectional relationships between parenting and children’s self-regulation, which are likely to establish mutual promotion/exacerbation processes over time, were not modelled in this study and should be the focus of future longitudinal work.
A number of socioeconomic variables were associated with enhanced self-regulatory growth including higher household incomes, higher maternal education levels and living in households with lower levels of financial hardship. The experience of significant financial hardships such as those tapped here is likely associated with stressful home environments, which impact on children’s physiology and neurodevelopment in ways that limit their capacity for self-regulation development (58, 59). Indeed, early self-regulation has been identified as one of the foremost mechanisms through which early stressors and socioeconomic disadvantage can lead to poorer academic and wellbeing outcomes (60). For these reasons, much of the prevention and intervention focus to date has been on children from disadvantaged backgrounds in an effort to address socio-economic gradients in achievement likely mediated through early self-regulatory capacity. Our findings suggest this focus is well-placed.
Marginal effects were also found for the association between educator-child relationships and the home learning environment, with self-regulatory change. The finding regarding importance of educator-child relationship in terms of children’s early self-regulation development reflects other similar findings in both Australia (61) and Europe (62). Positive student-teacher relationships likely matter because they set the context within which teachers can enact strategies particularly important for acquiring self-regulation during the preschool developmental period (63) including co-regulation, modelling and coaching (64). Our findings regarding the home learning environment align with a prior American longitudinal study linking parental involvement in home learning activities with children’s self-regulatory development (65).
Limitations
Although this study included a comprehensive array of predictors of self-regulation growth across a specific period in early childhood, there are a number of limitations related primarily to measurement. Most measures were broad and blunt instruments of their constructs. This reflects the nature of the population dataset, in which a broad spectrum of measures capturing child development and the environment were desired, rather than an in-depth measurement of any particular constructs. In addition, our self-regulation composite was only available at two time points in this dataset, meaning that more sophisticated growth curve modelling, which requires a minimum of three time points, could not be undertaken. It is also important to note that although we included a wide array of predictors, nearly 60% of the variance in our self-regulation composite at 6-7 years was still unexplained by the model. This suggests that even large-scale studies such as these are missing key ingredients related to self-regulatory growth. Our understandings could be enhanced through studies which capture potential variables that are not often measured, including chronic stress (e.g. cortisol), psychophysiological arousal and regulation, sensory processing, and more detailed understandings of the nature of home learning and early education and care activities. Finally, it is important to note that participants in this study were recruited in 2004. While it is anticipated that there has been limited change in most lifestyle factors investigated (e.g., parenting), new cohort studies are required to better understand the influence of more prominent societal change such as increased access and use of digital devices.
While we know that self-regulation is important for a broad range of longitudinal achievement and wellbeing outcomes, and that early childhood is a key window for self-regulatory growth, we have not yet been overly effective in intervention efforts. One reason for this might be that we need more holistic and evidence-informed theories and approaches to self-regulatory development, rather than a focus on single factors that appear predictive in isolation. We need more complex modelling of the interactions between these various factors and their association with self-regulation change (not just prediction at one time point). The findings of this study suggest a starting point for further detailed research that aims to achieve this.
Ethics approval and consent to participate
Ethics approval for participants in the Longitudinal Study of Australia Children was approved by the Human Research Ethics Committee of the Australian Institute of Family Studies.
Consent for publication
Not applicable
Availability of data and materials
The dataset analysed for the current study is available from the Australian Data Archive https://dataverse.ada.edu.au/dataset.xhtml?persistentId=doi:10.26193/JOZW2U
Competing interests
The authors declare that they have no competing interests.
Funding
There was no funding attached to this study.
Authors’ contributions
KW co-conceptualised the study, undertook all final analyses and was a major contributor to the writing of the paper. SH created the key outcome composite variables for self-regulation, co-conceptualised the study and was a major contributor to the writing of the paper. All authors have read and approved the manuscript.
Acknowledgements
Not applicable.
K cohort Kindergarten cohort
LSAC Longitudinal Study of Australian Children
- McClelland MM, Acock AC, Piccinin A, Rhea SA, Stallings MC. Relations between preschool attention span-persistence and age 25 educational outcomes. Early Childhood Research Quarterly 2013; 28: 314-324.
- Howard SJ, Williams KE. Early self-regulation, early self-regulatory change, and their longitudinal relation sto adolescents' academic, health, and mental well-being outcomes. Journal of Development and Behavioral Pediatrics 2018; 39:489-96.
- Moffitt TE, Arseneault L, Belsky J, Dickson N, Hancox R, Harington H, et al. A gradient of childhood self-control predicts health, wealth, and public safety. Proceedings of the National Academy of Science 2011;108(7):2693-8.
- Williams KE, Nicholson JM, Walker S, Berthelsen D. Early childhood profiles of sleep problems and self-regulation predict later school adjustment. The British Journal Of Educational Psychology 2016;86(2):331-50.
- Sawyer ACP, Miller-Lewis LR, Searle AK, Sawyer MG, Lynch JW. Is greater improvement in early self-regulation associated with fewer behavioral problems later in childhood? Developmental Psychology 2015;51(12):1740-55.
- Slutske WS, Moffitt TE, Poulton R, Caspi A. Undercontrolled temperament at age 3 predicts disordered gambling at age 32: A longitudinal study of a complete birth cohort. Psychological Science 2012;23(5):510-6.
- Sawyer ACP, Chittleborough CR, Mittinty MN, Miller-Lewis LR, Sawyer MG, Sullivan T, et al. Are trajectories of self-regulation abilities from ages 2-3 to 6-7 associated with academic achievement in the early school years? Child: Care, Health & Development. 2014;41:744-754.
- Takacs ZK, Kassai R. The efficacy of different interventions to foster children’s executive function skills: A series of meta-analyses. Psychological Bulletin 2019;145(7):653-97.
- Diamond A. Activities and programs that improve children’s executive functions. Current Directions in Psychological Science 2012;21(5):335-41.
- Jacob R, Parkinson J. The potential for school-based interventions that target executive function to improve academic achievement: a review. Review of Educational Research 2015;85(4):512-52.
- Baker CE, Cameron CE, Rimm-Kaufman SE, Grissmer D. Family and sociodemographic predictors of school readiness among African American boys in kindergarten. Early Education and Development 2012;23(6):833-54.
- Hindman AH, Morrison FJ. Differential contributions of three parenting dimensions to preschool literacy and social skills in a middle-income sample. Merrill-Palmer Quarterly 2012;58(2):191-223.
- Kim H, Byers AI, Cameron CE, Brock LL, Cottone EA, Grissmer DW. Unique contributions of attentional control and visuomotor integration on concurrent teacher-reported classroom functioning in early elementary students. Early Childhood Research Quarterly 2016;36:379-90.
- Hanno E, Surrain S. The direct and indirect relations between self-regulation and language development among monolinguals and dual language learners. Clinical Child and Family Psychology Review 2019;22:75-89.
- Williams KE, Berthelsen D, Walker S, Nicholson JM. A developmental cascade model of behavioral sleep problems and emotional and attentional self-regulation across early childhood. Behavioral Sleep Medicine 2017;15(1):1-21.
- Soloff C, Lawrence D, Johnstone R. The Longitudinal Study of Australian Children LSAC Technical Paper 1: Sample design.: Australian Government Department of Family and Community Services; 2005.
- Varni JW, Seid M, Rode CA. The PedsQL: Measurement model for the pediatric quality of life inventory. Medical Care 1999;37(2):126 - 39.
- Quach J, Nguyen CD, Williams KE, Sciberras E. Bidirectional associations between child sleep problems and internalizing and externalizing difficulties from preschool to early adolescence. JAMA Pediatrics. 2018;172(2):e174363.
- Dunn LM, Dunn LM. Examiner’s manual for the Peabody Picture Vocabulary Test (3rd edn.). Circle Pines, MN: American Guidance Service 1997.
- de Lemos M, Doig B. Who Am I Developmental Assessment Manual. Melbourne: ACER Press 2000.
- Australian Council for Educational Research. Who Am I Supplementary Information; 2007.
- Zubrick SR, Lucas RE, Westrupp EM, Nicholson JM. Growing up in Australia: The Longitudinal Study of Australian Children (LSAC). LSAC Technical Paper No. 12. Parenting measures in the Longitudinal Study of Australian Children: construct validity and measurement quality, Waves 1 to 4: Australian Government Department of Social Services; 2014.
- Statistics Canada. National Longitudinal Survey of Children & Youth Cycle 3 Survey Instruments 1998 - 99. Book 1 - Parent & Child; 1999.
- Furukawa TA, Kessler RC, Slade T, Andrews G. The performance of the K6 and K10 screening scales for psychological distress in the Australian National Survey of Mental Health and Well-Being. Psychological Medicine 2003;33(2):357 - 62.
- Hayes N, Berthelsen DC, Nicholson JM, Walker S. Trajectories of parental involvement in home learning activities across the early years: Associations with socio-demographic characteristics and children’s learning outcomes. Early Child Development and Care 2018;188(10):1405 - 18.
- Spencer N, Strazdins L. Socioeconomic disadvantage and onset of childhood chronic disabling conditions: a cohort study. Archives Of Disease In Childhood 2015;100:317 - 22.
- Lewis AJ, Olsson CA. Early life stress and child temperament style as predictors of childhood anxiety and depressive symptoms: Findings from the Longitudinal Study of Australian Children. Depression Research and Treatment. 2011; Article ID 296026.
- Pianta R. Student–Teacher Relationship Scale. Odessa, FL: Psychological
Assessment Resources; 2001
- Gialamas A, Sawyer ACP, Mittinty MN, Zubrick SR, Sawyer MG, Lynch J. Quality of childcare influences children's attentiveness and emotional regulation at school entry. The Journal of Pediatrics 2014;165:813-9.
- Australian Bureau of Statistics. Socio-Economic Indexes for Areas Canberra, Australia: Australian Bureau of Statistics; 2018 [updated 27 March 2018. Available from: https://www.abs.gov.au/websitedbs/censushome.nsf/home/seifa.
- Enders C. Applied Missing Data Analysis. New York, NY: Guildford Press; 2010.
- Soloff C, Lawrence D, Misson S, Johnstone R. The Longitudinal Study of Australian Children LSAC Technical paper No.3: Wave 1 weighting and non-response; 2006.
- Robson DA, Allen MS, Howard SJ. Self-regulation in childhood as a predictor of future outcomes: A meta-analytic review. Psychological Bulletin; 2020. Epub Jan 6.
- Fuhs MW, Nesbitt KT, Farran DC, Dong N. Longitudinal associations between executive functioning and academic skills across content areas. Developmental Psychology 2014;50(6):1698-709.
- McClelland MM, Cameron CE. Developing together: The role of executive function and motor skills in children's academic lives. Early Childhood Research Quarterl 2019;46:143-51.
- Cook CJ, Howard SJ, Scerif G, Twine R, Kahn K, Norris SA, et al. Associations of physical activity and gross motor skills with executive function in preschool children from low‐income south african settings. Developmental Science 2019;22:e12820.
- Diamond A. Close interrelation of motor development and cognitive development and of the cerebellum and prefrontal cortex. Child Development 2000;71(1):44-56.
- Oberer N, Gashaj V, Roebers CM. Motor skills in kindergarten: Internal structure, cognitive correlates and relationships to background variables. Human Movement Science 2017;52:170-80.
- Berger SE. Locomotor expertise predicts infants’ perseverative errors. Developmental Psychology 2010;46(2):326-36.
- Campos JJ, Kermoian R, Zumbahlen MR. Socioemotional transformations in the family system following infant crawling onset. In: Eisenberg N, Fabes RA, editors. Emotion and its regulation in early development. New directions for child development, No. 55: The Jossey-Bass education series; ISSN: 0195-2269 (Print). San Francisco, CA: Jossey-Bass; 1992. p. 25-40.
- Wilson KE, Lumeng JC, Kaciroti N, Chen SY-P, LeBourgeois MK, Chervin RD, et al. Sleep Hygiene Practices and Bedtime Resistance in Low-Income Preschoolers: Does Temperament Matter? Behavioral Sleep Medicine 2015;13(5):412-23.
- Van den Bergh BRH, Mulder EJH. Fetal sleep organization: a biological precursor of self-regulation in childhood and adolescence? Biological Psychology 2012;89(3):584-90.
- Melegari MG, Sette S, Vittori E, Mallia L, Devoto A, Lucidi F, et al. Relations Between Sleep and Temperament in Preschool Children With ADHD. Journal Of Attention Disorders 2018:1087054718757645-.
- Williams KE, Sciberras E. Sleep and self-regulation from birth to 7 years: A retrospective study of children with and without attention-deficit hyperactivity disorder at 8 to 9 years. Journal of Developmental and Behavioral Pediatrics 2016;37(5):385-94.
- Price AMH, Wake M, Ukoumunne OC, Hiscock H. Five-year follow-up of harms and benefits of behavioral infant sleep intervention: randomized trial. Pediatrics 2012;130(4):643-51.
- Price AMH, Wake M, Ukoumunne OC, Hiscock H. Outcomes at six years of age for children with infant sleep problems: longitudinal community-based study. Sleep Medicine 2012;13(8):991-8.
- Quach J, Hiscock H, Ukoumunne OC, Wake M. A brief sleep intervention improves outcomes in the school entry year: a randomized controlled trial. Pediatrics 2011;128(4):692-701.
- Papadopoulos N, Sciberras E, Hiscock H, Mulraney M, McGillivray J, Rinehart N. The efficacy of a brief behavioral sleep intervention in school-aged children with ADHD and comorbid autism spectrum disorder. Journal of Attention Disorders 2019;23(4):341-50.
- Hiscock H, Sciberras E, Mensah FK, Gerner B, Efron D, Khano S, et al. Impact of a behavioural sleep intervention on symptoms and sleep in children with attention deficit hyperactivity disorder, and parental mental health: randomised controlled trial. BMJ 2015;350(h68).
- Graziano PA, Calkins SD, Keane SP. Sustained attention development during the toddlerhood to preschool period: Associations with toddlers' emotion regulation strategies and maternal behaviour. Infant and Child Development 2011;20(6):389-408.
- Nelson JA, O'Brien M, Calkins SD, Leerkes EM, Marcovitch S, Blankson AN. Maternal expressive style and children's emotional development. Infant and Child Development 2012;21(3):267-86.
- Olson SL, Lopez-Duran N, Lunkenheimer ES, Chang H, Sameroff AJ. Individual differences in the development of early peer aggression: Integrating contributions of self-regulation, theory of mind, and parenting. Development and Psychopathology 2011;23(1):253-66.
- Spinrad TL, Eisenberg N, Silva KM, Eggum ND, Reiser M, Edwards A, et al. Longitudinal relations among maternal behaviors, effortful control and young children's committed compliance. Developmental Psychology 2012;48(2):552-66.
- Williams KE, Berthelsen D. The development of prosocial behaviour in early childhood: Contributions of early parenting and self-regulation. International Journal of Early Childhood 2017;49(1):73-94.
- Scott BG, Lemery-Chalfant K, Clifford S, Tein J-Y, Stoll R, Goldsmith HH. A twin factor mixture modeling approach to childhood temperament: Differential heritability. Child Development 2016;87(6):1940-55.
- Barnes JC, Boutwell BB, Beaver KM, Gibson CL. Analyzing the Origins of Childhood Externalizing Behavioral Problems. Developmental Psychology. 2013.
- Healey DM, Flory JD, Miller CJ, Halperin JM. Maternal positive parenting style is associated with better functioning in hyperactive/inattentive preschool children. Infant and Child Development 2011;20(2):148-61.
- Blair C, Raver C, Granger D, Mills-Koonce R, Hibel LC, Investigators FLP. Allostasis and allostatic load in the context of poverty in early childhood. Development and Psychopathology 2011;23(3):845-57
- Farah M. The neuroscience of socioeconomic status: Correlates, causes, and consequences. Neuron 2017;96(1):56-71.
- Blair C, Raver CC. School readiness and self-regulation: A developmental psychobiological approach. Annual Review of Psychology 2015;66:711-31.
- Gialamas A, Alyssa CP, Sawyer A, Mittinty MN, Zubrick SR, Sawyer MG, et al. Quality of childcare influences children's attentiveness and emotional regulation at school entry. The Journal of Pedatrics 2014;165(4):813 - 9.
- Cadima J, Verschueren K, Leal T, Guedes C. Classroom interactions, dyadic teacher–child relationships, and self–regulation in socially disadvantaged young children. Journal of Abnormal Child Psychology 2016;44(1):7-17.
- Kopp CB. Antecedents of self-regulation: A developmental perspective. Developmental Psychology 1982;18(2):199-214.
- Silkenbeumer JR, Schiller E-M, Kärtner J. Co- and self-regulation of emotions in the preschool setting. Early Childhood Research Quarterly 2018;44:72-81.
- Baker CE. Fathers' and mothers' home literacy involvement and children's cognitive and social emotional development: Implications for family literacy programs. Applied Developmental Science 2013;17(4):184-97.
|
Table 1: Sample characteristics |
|
|
Study sample characteristic |
Percentage |
|
Boys |
51% |
|
English as main home language |
86% |
|
Aboriginal or Torres Strait Islander |
3.8% |
|
Mothers with incomplete high school education |
22% |
|
Mothers with university education |
28% |
|
Attending preschool program at 4-5 years |
95% |
|
|
M (SD) |
|
Child age at 4-5 year data collection |
56.9 months (2.65) |
|
Child age at 6-7 year data collection |
81.9 months (2.96) |
|
Household income per week |
$1,661.93AUD ($1,294.05) |
|
Table 2: Items included in the self-regulation measure at 4-5 years and 6-7 years |
||
|
Construct |
Respondent |
Item |
|
Impulsive Aggression |
Parent and teacher |
Often has temper tantrums/hot tempers
|
|
|
Parent and teacher |
Often fights with other children or bullies them |
|
|
Parent and teacher |
Often argumentative with adults |
|
Hyperactivity |
Parent and teacher |
Restless, overactive, cannot stay still for long |
|
|
Parent and teacher |
Constantly fidgeting or squirming |
|
|
Parent and teacher |
If this child is upset, it is hard to comfort him/her |
|
Lack of Persistence & Inattention |
Parent and teacher |
The child likes to complete one task or activity before going on to the next (reversed) |
|
|
Parent and teacher |
Sees takes through to the end, good attention span (reversed) |
|
|
Parent and teacher |
The child stays with an activity (e.g., puzzle, construction, kit, reading) for a long time (reversed) |
|
|
Parent, teacher, and observer |
Easily distracted, concentration wanders
|
|
Impulsivity |
Parent and teacher |
Can stop and think things out before acting (reversed) |
|
|
Parent and teacher |
Shares readily with other children (reversed) |
|
|
Observer |
Degree of negative mood (withdrawn, uncooperative, sulky, seeming upset, angry) to interview |
|
Table 3: Predictors of self-regulation growth in the model
|
||
|
Construct |
Data source |
Measure |
|
Health & Health Behaviours |
||
|
Physical health |
Parent |
Physical Health Summary score from the Pediatric Quality of Life Inventory (PedsQL) (17). Summed and average score of 8 items each rated on 5-point scale, tapping a child’s level of functioning in daily activities that rely on good physical health. E.g. problems with running. α = .72 |
|
Diet quality |
Parent |
Units of high sugar drinks consumed in the last week |
|
Behavioural sleep problems |
Parent |
Five items modelled as a latent variable as per prior studies (18). E.g. child has problems on 4 or more nights a week with waking during the night (yes/no); this child’s sleep is a small/moderate/large problem. |
|
Development |
||
|
Receptive vocabulary |
Assessed |
Peabody Picture Vocabulary Test (19) of receptive vocabulary in which children listen to a spoken word and are asked to point to the matching picture given a set of four pictures. Higher scores represent higher receptive vocabulary skills. |
|
Gross motor development |
Teacher |
On a 4-point scale from ‘much less competent than peers’ to ‘more competent than peers’ |
|
Fine motor development |
Teacher |
On a 4-point scale from ‘much less competent than peers’ to ‘more competent than peers’ |
|
Pre-academic skills |
Assessed |
Who Am I test (20). Children write their names, copy shapes, write words and numbers; scored according to skill level. α = .89 (21) |
|
Home environment |
||
|
Maternal parenting anger |
Mother |
Composite measure (weighted mean score) as per LSAC technical advice (22) using four adapted items from the National Longitudinal Study of Children & Youth (23). Each item rated on 5-point scale from ‘never or almost never’ to ‘almost always’. E.g. how often are you angry when you punish this child? H = .72. |
|
Paternal parenting anger |
Father |
|
|
Maternal parenting consistency |
Mother |
Composite measure as per LSAC technical advice (22) using five items from the National Longitudinal Survey of Children and Youth (23). Each item rates on a 10-point scale from ‘not at all’ to ‘all of the time’. E.g. how often does this child get away with things that you feel should have been punished? H = .80 for father; .82 for mothers. |
|
Paternal parenting consistency |
Father |
|
|
Maternal mental health |
Mother |
Kessler K6 screening scale (24) of six items (summed and averaged) about respondents’ feelings over the past four-week period. Rates on 5-point scale from ‘all of the time’ to ‘none of the time’. E.g. in the past 4 week how often have you felt hopeless? α = .84 for mothers, .82 for fathers. |
|
Paternal mental health |
Father |
|
|
Home learning environment |
Parent |
Single item book reading; plus latent variable with five indicators of other home learning activities including music, art, and play as used in other LSAC studies (25). Each rated on 4-point scale of frequency of adult-child for each activity in the last week from ‘not in the past week’ to ‘6-7 days in the week’. |
|
Financial hardship |
Parent |
7-item count index ranging from 0 to 7, based on summing Yes=1, No=0 responses to 7 items including couldn’t pay bills, gone without meals as used in prior LSAC research (26). |
|
Argumentative parental relationships |
Parent |
Composite of 5 items (summed and averaged) rated on a 5-point scale from ‘never’ to ‘always’. E.g. my partner and I argue; disagree over child-rearing etc. α = .80 |
|
Stressful life events |
Parent |
13-item count index ranging from 0 to 13 based on summing Yes = 1, No=0 responses about exposure to adverse life events over the past year including marital breakdown, death of friend, as per prior LSAC research (27). |
|
Education |
||
|
Teacher-child relationship |
Teacher |
8-item composite drawn from the Student Teacher Relationship Scale (28) following prior LSAC factor modelling (29). Each item rated on 5-point scale from ‘definitely does not apply’ to ‘definitely applies’. E.g. share affectionate relationships, easy to be in tune with feelings α = .81 |
|
Time use |
||
|
Extra-curricular sport |
Parent |
Sum of 3 items indicating participation (yes / no) in extra-curricular swimming, gymnastics, or team sport |
|
Extra-curricular music / dance |
Parent |
Sum of 2 items indicating participation (yes / no) in extra-curricular music and dance |
|
Weekday TV hours |
Parent |
Number of hours watching TV on a typical weekday |
|
Weekday computer hours |
Parent |
Number of hours using a computer on a typical weekday |
|
Physical activity |
Parent |
Parent-rated child enjoyment of physical activity on a 5-point scale from ‘very much dislikes physical activities’ to ‘very much likes physical activity’ |
|
Neighbourhood |
||
|
Liveability |
Parent |
Composite (sum) of 8 items each rated on 4-point scale from ‘strongly disagree’ to ‘strongly agree’. E.g. this is a safe neighbourhood, this neighbourhood has good parks. α = .76 |
|
Socio-economic index for area (SEIFA) |
Australian Bureau of Statistics |
Composite of 31 variables (e.g. income, unemployment, occupation and education) computed by the Australian Bureau of Statistics (30).
|
|
Table 4: Standardized coefficients for the predictors of self-regulation at 6-7 years controlling for prior self-regulation and covariates |
||
|
|
β |
95% CI |
|
Covariate associations with self-regulation at 4-5 years |
||
|
Female |
.50** |
.44 - .57 |
|
Age |
.01 |
.04 - .11 |
|
Birthweight percentile |
.07** |
.03 - .11 |
|
Breastfed |
-.15 |
-.29 - -.01 |
|
Aboriginal Torres Strait Islander |
-.53** |
-.81 - -.26 |
|
Non-English home language |
.01 |
-.10 - .11 |
|
Maternal education level |
.13** |
.09 - .17 |
|
Household income |
.13** |
.08 - .17 |
|
Stability of self-regulation 4-5 years to 6-7 years |
.54** |
.49 - .59 |
|
Predictors of self-regulation at 6-7 years controlling for above |
||
|
Health |
|
|
|
Physical health status |
.02 |
-.03 - .05 |
|
High sugar drink intake |
.02 |
-.01 - .06 |
|
Sleep problems |
-.08** |
-.13 - -.04 |
|
Development |
|
|
|
Vocabulary |
.01 |
-.03 - .06 |
|
Gross motor |
.06** |
.02 - .10 |
|
Fine motor |
-.05 |
-.10 - .00 |
|
Pre-academic skills |
.12** |
.09 - .16 |
|
Home environment |
|
|
|
Maternal angry parenting |
-.10** |
-.15 - .06 |
|
Paternal angry parenting |
-.12** |
-.16 - -.07 |
|
Maternal consistent parenting |
-.01 |
-.04 - .05 |
|
Paternal consistent parenting |
.02 |
-.02 - .07 |
|
Maternal mental health |
-.01 |
-.06 - .04 |
|
Paternal mental health |
.02 |
-.03 - .06 |
|
Shared book reading frequency |
.03 |
-.01 - .07 |
|
Home learning activities |
.06* |
.01 - .10 |
|
Financial hardship |
-.07** |
-.12 - -.02 |
|
Argumentative parental relationships |
-.03 |
-.07 - .02 |
|
Stressful life events |
-.00 |
-.04 - .04 |
|
Education |
|
|
|
Educator-child relationship |
.06* |
.01 - .11 |
|
Time use |
|
|
|
Extra-curricular sport |
-.02 |
-.05 - .02 |
|
Extra-curricular music/dance |
.02 |
-.01 - .05 |
|
Weekday TV hours |
.04 |
.00 - .08 |
|
Weekday computer hours |
.01 |
-.03 - .05 |
|
Physical activity |
-.03 |
-.06 - .00 |
|
Neighbourhood |
|
|
|
Liveability |
-.01 |
-.05 - .02 |
|
Socio-economic index |
.01 |
-.03 - .04 |
|
* p < .02; ** p < .01 |
|
|
|
|
|
|