Self-regulation, family resources, and early child … · Web viewExecutive Summary 4. Results of Study 1 (Q1 and Q2): changes over time in school engagement and differences between

Occasional Paper No. 54

Self-regulation, family resources, and early child care quality as predictors of children’s school engagement in primary school

Jacqueline Homel and Ben Edwards

Occasional Paper No. 54


Jacqueline Homel and Ben Edwards

Australian Institute of Family Studies

© Commonwealth of Australia 2018

ISSN 2205-1422

ISBN 918-1-925318-69-2

This document Self-regulation, family resources, and early child care quality as predictors of children’s school engagement in primary school is licensed under the Creative Commons Attribution 4.0 International Licence

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Acknowledgements

This report uses data from Growing Up in Australia: The Longitudinal Study of Australian Children (LSAC). LSAC is conducted in a partnership between the Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS), with advice provided by a consortium of leading researchers. Findings and views expressed in this publication are those of the individual authors and may not reflect the views of the AIFS, DSS or ABS.

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Executive Summary

Contents

Executive Summary viiFindings and implications viiSummary x

1. Introduction 11.1 Research questions 21.2 Structure of the report 3

2. Background and literature review 42.1 What is school engagement like in early primary school? 42.2 Factors influencing the development of school engagement in early primary school 62.3 The role of ECEC quality in the development of school engagement 92.4 A potential moderating role for disadvantage 102.5 Summary 10

3. Method 113.1 Data 113.2 Dependent variables 123.3 Explanatory variables 143.4 Covariates 19

4. Results of Study 1 (Q1 and Q2): changes over time in school engagement and differences between groups of children. 224.1 Data analysis 224.2 Question 1: How do behavioural and affective school engagement change

between school Years 1–2 and Years 3–4? 234.3 Question 2: Differences in school engagement across sex, Year level, age,

disadvantage, and self-regulation 254.4 Summary 29

5. Results of Study 1 (Q3 and Q4): developmental pathways to school engagement—family resources, disadvantage and self-regulation 305.1 Data analysis 325.2 Results for question 3: approaches to learning 335.3 Results for question 3: absenteeism 375.4 Results for question 3: school liking 395.5 Results for question 3: maths liking 415.6 Results for question 4: Is the effect of self-regulation on school engagement

stronger for disadvantaged children? 445.7 Summary 44

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6. Results of Study 1 (Q5): School engagement and achievement 466.1 Data analysis 466.2 Results 466.3 Summary 48

7. Results of Study 2: ECEC quality and school engagement 497.1 Data analysis 497.2 Results for Question 1: ECEC quality and school engagement 507.3 Results for questions 2 and 3: Are effects of ECEC quality on school engagement

moderated by disadvantage or children’s self-regulation? 55

8. Discussion 568.1 Research Questions—Study 1 568.2 Research Questions—Study 2 618.3 What do the results tell us about the nature and development of engagement in

early primary school? 628.4 What are the key developmental pathways that promote or inhibit school engagement?

638.5 What could be targeted for intervention, and when? 638.6 Limitations 648.7 Conclusions 65

Appendixes 66Appendix A: Measurement of school engagement 66Appendix B: Development of self-regulation measures at waves 2 and 3 77Appendix C: Development of home-learning environment measures at wave 2 81Appendix D: Missing data 84Appendix E: Correlations and estimates for covariates from structural equation models

predicting school engagement in Chapter 6 87

References 90

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Executive Summary

List of Tables

Table 1. Ages and school Year levels of B cohort children in the study subsamples in waves 1 to 5 12

Table 2. Dependent variables: measures of school engagement 13Table 3. Explanatory variables 15Table 4. Distribution of disadvantage index at wave 1 18Table 5. Covariates 21Table 6. Differences in wave 4 school engagement by Year level, delayed enrolment and age 25Table 7. Differences in wave 5 school engagement by Year level, delayed enrolment, and age 26Table 8. Differences in wave 4 school engagement by sex, disadvantage, task attentiveness

and irritability/anger 27Table 9. Differences in wave 5 school engagement by sex, disadvantage, task attentiveness

and irritability/anger 28Table 10. Standardised estimates for indirect paths to approaches to learning 37Table 11. Standardised estimates for indirect paths to school liking at wave 5 41Table 12. Standardised estimates for indirect paths to maths liking 44Table 13. Standardised estimates for associations between wave 4 school engagement and

NAPLAN numeracy scores in Year 3 47Table 14. Standardised estimates for associations between wave 4 school engagement and

NAPLAN reading scores in Year 3 48Table 15. Estimates from regression models predicting wave 4 behavioural engagement from

wave 3 ECEC quality 51Table 16. Estimates from regression models predicting wave 4 affective engagement from

wave 3 ECEC quality 52Table 17. Estimates from regression models predicting wave 5 behavioural engagement from

wave 3 ECEC quality 53Table 18. Estimates from regression models predicting wave 5 affective engagement from

wave 3 ECEC quality 54

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Appendix Tables

Table A1. Measures of school engagement 67Table A2. Summary of approaches to learning models at wave 4 and wave 5 69Table A3. Cross-validation: comparison of model fit for approaches to learning in the

B and K cohorts 69Table A4. Summary of measurement invariance of approaches to learning over sex,

Year level and time 70Table A5. Summary of approaches to learning models at wave 4 and wave 5 71Table A6. Summary of initial affective engagement models at wave 4 and wave 5 72Table A7. Cross-validation—comparison of model fit for affective engagement in the

B and K cohorts 73Table A8. Summary of measurement invariance of affective school engagement over sex,

Year level and time 74Table A9. Summary of final model of school liking across wave 4 and wave 5 75Table B1. Initial pool of self-regulation items 78Table B2. Summary of CFAs for self-regulation at wave 2 79Table B3. Summary of CFAs for self-regulation at wave 3 80Table C1. Initial pool of home-learning environment items 81Table C2. Factor loadings from exploratory factor analysis of home-learning environment items

at waves 2 and 3 82Table C3. Summary of final CFA for home-learning environment at waves 2 and 3 83Table D1. Missing data in Study 1 85Table D2. Missing data in Study 2 86Table E1. Within-time correlations from models presented in Chapter 6 88Table E2. Estimates for regression of wave 1 to 3 variables on gender 89Table E3. Estimates for regression of school engagement outcomes on gender, Year level and

school ICSEA 89

List of Figures

Figure 1. School liking at wave 4 (a) and wave 5 (b) 24Figure 2. Maths liking at wave 4 and wave 5 24Figure 3. Conceptual model for testing 31Figure 4. Estimates from the final model examining approaches to learning. 35Figure 5. Estimates from the final model examining absenteeism. 38Figure 6. Estimates from the final model examining school liking. 40Figure 7. Estimates from the final model examining maths liking. 43

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Executive Summary

Executive Summary

School engagement—commonly characterised by things such as active participation in class, feeling happy and connected to the school environment, having a good attendance record and behaving appropriately—is known to be a crucial contributor to an adolescent’s success at school (Ladd & Dinella, 2009). However, much less is known about school engagement among younger children.

In this report, we focused on four types of school engagement, two behavioural (1 and 2) and two affective (3 and 4):

1. Positive approaches to learning—motivation, persistence and attention to schoolwork and classroom participation

2. Absenteeism—being absent two or more days in the past 4 weeks

3. Liking of school—happy when at school, happy to go to school and finding school fun

4. Liking of mathematics—likes maths and number work at school.

This report aims to examine pathways to early school engagement in a large, nationally representative sample of Australian children followed from infancy to 8–9 years of age. Specifically, we test whether family disadvantage and the home environment in infancy and early childhood are linked to children’s self-regulation. In this study, self-regulation at 4–5 years encompasses:

> task attentiveness—the capacity to focus on a task and persistence

> anger or irritation management—the capacity to manage anger and frustration.

We then test whether self-regulation subsequently affects school engagement in the early primary school years and whether school engagement then influences children’s NAPLAN scores.

The report also considers whether the quality of early childhood education and care (ECEC) experienced by children prior to school is related to their later school engagement.

Findings and implications

Did school engagement change over time? Did it differ across subgroups of children?Overall, changes in school engagement over time varied, depending on the type of school engagement. From 6–7 years to 8–9 years:

> children’s positive approaches to learning tended to increase

> their liking of school and of mathematics decreased

> absenteeism rates were steady.

The increase in approaches to learning may reflect children’s natural cognitive and emotional maturation as it assesses motivation, persistence and attention in class.

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The causes of waning enjoyment of school in primary school-aged children are not well understood but may reflect the tendency of some children to enter school with very high—possibly unrealistically high—levels of academic self-confidence (Stipek & Ryan, 1997), which then drop in response to their social and academic experiences. Finding realistic ways of maintaining students’ high levels of enjoyment and enthusiasm, which can spur their future participation and success, is a worthwhile policy objective.

In terms of differences by child gender, age and family disadvantage, there are a number of notable findings:

> Girls were higher on approaches to learning and more often liked school than boys. Finding ways of increasing boys’ early engagement in school may thus be important.

> At 6–7 years, there were no gender differences on liking for maths; by 8–9 years, girls were becoming less positive about maths. These findings suggest that the earliest school years may be a key intervention period to prevent negative attitudes towards maths developing in girls.

> Older children, and those in more advanced Years, tended to like school less. This is likely to reflect the impact of a longer time in school and a change in the nature of school tasks; it reinforces the need for policy focus on sustaining initial high levels of engagement.

> Disadvantaged children, and those with poorer self-regulation, consistently had less positive approaches to learning in early and mid-primary school and were lower on school liking in mid-primary school. Children from disadvantaged families were also more likely to be absent from school. These findings point to the multiple types of school difficulties these children may experience.

Taken together, the findings point to a need for particular policy focus on:

> building boys’ engagement with school in the early primary years

> halting girls’ growing disaffection with mathematics as they move through the primary school years

> finding ways to lift the school engagement of children from disadvantaged households.

Did children’s task attentiveness and capacity to manage anger and frustration explain the link between family disadvantage and the home environment on school engagement?Self-regulation1, school readiness at primary school entry2 and the home-learning environment3 were important direct influences on all school engagement outcomes.

These three factors were in turn influenced by prior aspects of the family environment, in particular, family disadvantage. Hostile parenting and maternal depressive symptoms were also important early in these pathways, but their indirect influences were less powerful than that of family disadvantage. These family factors are likely to be interconnected: for example, it is well established that the risk of hostile parenting and maternal depression is higher in disadvantaged families (e.g., Conger, Conger & Martin, 2010).

Importantly, as well as influencing children’s self-regulation, family disadvantage was also directly linked to approaches to learning, absenteeism and school liking.

1 for approaches to learning and, to a lesser extent, school liking and maths liking2 for approaches to learning, maths liking, absenteeism3 for approaches to learning, school liking, maths liking

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Executive Summary

These findings carry several implications for policy and practice.

> Encouraging parents to engage in activities that prepare children for school, such as reading to young children, can have long-term payoffs into primary school. This study extended prior research linking parental reading to children with the development of children’s pre-literacy skills and school readiness (e.g. Raikes et al., 2006), showing that home reading can also promote social readiness and prepare children to engage competently in classroom activities.

> School engagement was powerfully influenced by family disadvantage, which can encompass a range of risks, including poorer parenting practices, parental mental health problems, lack of resources and lower educational stimulation for children. Increasing supports and programs for disadvantaged families could benefit parents and children in both the short and longer term.

> Children’s task attentiveness and capacity to manage anger and frustration were crucial influences and are relatively malleable. Hence, interventions that directly aim to build these skills could have beneficial effects and can be delivered quite successfully in ECEC settings. In addition, it is important to continue support of children with poorer task attentiveness and emotional regulation and cognitive skills through the early years of primary school.

Was school engagement in Year 1 related to school achievement in Year 3?Children’s approaches to learning were strongly related to their Year 3 NAPLAN literacy and numeracy results after we controlled for a range of other variables also related to achievement. Other aspects of school engagement investigated were not related to NAPLAN results after all four school engagement measures were taken into account. These results suggest that motivational aspects of school engagement (e.g., persistence) were important for school achievement, but emotional ones were not (e.g., liking).

Was higher-quality ECEC in the year prior to entering school related to higher levels of subsequent school engagement?There was some limited support for higher-quality ECEC in the year before commencing primary school supporting higher levels of school engagement. Specifically:

> Conflict in relationships between ECEC teachers and children was associated with poorer approaches to learning and lower school liking at both 6–7 and 8–9 years. However, other dimensions of ECEC quality (e.g., teacher qualifications, activities with children) were not related to school engagement outcomes.

> When children had higher levels of hyperactivity, they had a relationship with their ECEC teacher characterised by higher levels of conflict.

These findings highlight the importance of:

> children developing good relationships with their ECEC teachers, which can lay the foundation for high-quality teacher–child relationships in primary school

> developing strategies and providing support for hyperactive children to help them adapt to the preschool environment

> supporting teachers to provide environments that foster regulatory control in difficult children.

It is important to note an important caveat for these findings, which is that this research uses data from 2008 which predates two very significant changes in child care policy: the introduction of

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‘universal access’ to ECEC (in 2008) and the introduction of the National Quality Framework (NQF) for ECEC (in January 2012).

Nonetheless, ECEC is also a setting where opportunities to engage in early literacy tasks can be provided for children at high risk of not receiving these opportunities in the home. Thus, various ECEC practices, such as behavioural management and a focus on literacy, are potentially able to promote school engagement, especially among disadvantaged children.

Summary

The report investigated pathways between family disadvantage and resources in infancy and early childhood and children’s school engagement in the early primary school years, with the mediating role of children’s self-regulation skills at 4-5 years also examined. Lower levels of engagement in early primary school could be traced back to family disadvantage, maternal depressive symptoms, higher-hostility parenting and, especially, a less stimulating home-learning environment in the years prior to the start of school. In part, these factors led to poorer school engagement because they contributed to children’s poor self-regulation, which, importantly, can be improved through training children in primary school (e.g., Diamond, Barnett, Thomas, & Munro, 2007) or through training ECEC teachers and carers (e.g., Raver et al., 2011; Webster-Stratton, Reid, & Stoolmiller, 2008).

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4. Results of Study 1 (Q1 and Q2): changes over time in school engagement and differences between groups of children.

1. Introduction

There is research consensus that school engagement during adolescence is a robust predictor of school success. Students who participate actively in the classroom, feel happy and connected to the school setting and have good records of attendance and conduct have higher academic achievement (Ladd & Dinella, 2009) and are much more likely to complete high school (Janosz, Archambault, Morizot, & Pagani, 2008; Wylie & Hogden, 2012). These relationships persist even after extensive controls for ability and socioeconomic status (SES). These positive outcomes have long-term impacts well beyond formal schooling, opening doors to future training and employment opportunities. For instance, higher levels of engagement in Australian high school students have been shown to increase the likelihood of postsecondary education and higher-status occupations 10–20 years later (Abbott-Chapman et al., 2014).

Clearly, it would be desirable to prevent students from disengaging from school. However, little is known about how early disengagement starts or the risk factors for it in early primary school. There is no research consensus on the nature of early school engagement, its development, or its importance for school success. The first aim of this report is to examine pathways to early school engagement in a large, nationally representative sample of Australian children followed from infancy to ages 8–9. We test a developmental model that links family disadvantage and resources in infancy to children’s self-regulation skills at ages 4–5, with these skills subsequently affecting school engagement in the early years of primary school. The second aim is to consider whether the quality of early childhood education and care (ECEC) is related to school engagement.

School engagement has mostly been studied in adolescents. However, by high school, patterns of disengagement are well entrenched for many students (Janosz et al., 2008). Engagement is a cyclical process that begins as soon as children start school. In this cycle, engagement is likely to lead to more engagement, and disengagement is likely to lead to more disengagement. Children who are able to participate attentively in classroom activities and follow instructions evoke positive responses from teachers, such as greater consistency and contingency in teaching and more autonomy support. This in turn increases students’ engagement. On the other hand, children who have difficulty with classroom engagement tend to receive less positive and more coercive involvement from teachers, exacerbating these students’ withdrawal from learning activities (Finn & Zimmer, 2012; Skinner & Belmont, 1993). Therefore, educational researchers emphasise the importance of the early years of primary school as a window of opportunity during which this vicious cycle of disengagement might be prevented (e.g., Finn & Cox, 1992).

Despite widespread acknowledgement of the importance of the early years of school, surprisingly little is known about the nature and development of school engagement in young children. Existing research has suggested that young children’s ability to regulate their attention and emotions may be key prerequisites of early engagement in school. Children who can sustain attention and inhibit emotional outbursts are better able to engage in classroom activities and form more supportive relationships with peers and teachers. This promotes school engagement over time (Blair & Diamond, 2008; Eisenberg, Valiente, & Eggum, 2010; Pagani, Fitzpatrick, & Parent, 2012).

Importantly, children’s self-regulation skills develop in the family well before the start of formal schooling. Children who receive warm and consistent parenting are likely to develop the ability to regulate their attention and emotions (Mathis & Bierman, 2015). Stimulating home-learning environments are also linked to the development of self-regulation (McClelland, Cameron, Wanless, & Murray, 2007; Yu & Daraganova, 2015). Unfortunately, deficiencies in family health and material

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resources can disrupt these developmental processes. Mothers who are experiencing symptoms of depression tend to engage in less responsive and more hostile parenting, which may lead to poor control of negative emotions in children. Many studies also show that financial strain and stressful circumstances associated with disadvantage can compromise responsive parenting and limit the amount of home-learning activities that parents can provide to young children (Conger et al., 2010).

Thus, school engagement may be shaped by a developmental ecology involving children’s individual self-regulation skills which are the product of family resources, including sensitive parenting, stimulating home-learning environments, maternal mental health, and socioeconomic resources.4 While these sorts of developmental pathways are supported by theorists and by work on other aspects of children’s development (Alexander, Entwisle, & Horsey, 1997; Ramey & Ramey, 2004), research has not tested them with regard to early school engagement.

Alongside the family, ECEC is a key context of many children’s lives in the years before the start of school. Given the large literature documenting the benefits of high-quality ECEC for cognitive outcomes and school readiness (Gialamas, Mittinty, Sawyer, Zubrick, & Lynch, 2014), it is likely that better quality ECEC is associated with better school engagement. However, these links have not been examined in any large-scale Australian studies.

1.1 Research questions

In this report, we carry out two sets of analyses. In Study 1, we focus on the development of school engagement and the role of self-regulation. In Study 2, we focus on the role of ECEC quality.

In Study 1, we propose that children’s self-regulation skills at ages 4–5 (before the start of formal schooling) will be the factor driving initial levels and change in school engagement in the early years of school. The dimensions of self-regulation we study are task attentiveness and irritability/anger. We expect that these self-regulation skills arise from interconnections between family resources and disadvantage. In other words, we expect that self-regulation at ages 4–5 will mediate effects of family resources and disadvantage on early school engagement.

In Study 1, we ask the following research questions:

1. How do school engagement outcomes change between Year 1 and Year 3?

2. How does school engagement differ across subgroups of children, including girls and boys, children of different ages and school Year levels, disadvantaged and more advantaged children, and children who are more or less competent with self-regulation?

3. Does children’s task attentiveness and irritability/anger at ages 4–5 mediate the relationship between family resources (parenting, home-learning environment and maternal depressive symptoms) and disadvantage, and school engagement over Years 1 to 3?

4. Is the effect of self-regulation stronger for disadvantaged children?

5. How much does engagement in Year 1 matter for achievement in Year 3?

In Study 2, we expect that high-quality ECEC will be important for school engagement. We ask the following research questions:

1. Is higher-quality ECEC at ages 4–5 related to higher levels of school engagement over Years 1 to 3?

2. Is higher-quality ECEC especially beneficial for children with poorer self-regulation?

4 Sensitive parenting involves responding quickly and appropriately to their child's signals, interacting positively with their child, and providing a secure base for the child to explore their environment.

3


3. Is higher-quality ECEC especially beneficial for disadvantaged children?

Because girls generally score higher on school engagement outcomes at all ages than boys, we examine whether these relationships differ by gender. We also control for school readiness and socioeconomic position of the school in all analyses.

1.2 Structure of the report

Chapter 2 provides a background and literature review.

Chapter 3 is the Method and describes the Longitudinal Study of Australian Children (LSAC) data and the measures used in the analysis.

Chapter 4 presents results for Questions 1 and 2 in Study 1. These results describe changes over time in school engagement and differences between groups of children.

Chapter 5 presents results for Questions 3 and 4 in Study 1. These results highlight specific developmental pathways that either promote or limit early school engagement.

Chapter 6 presents results for Question 5 in Study 1. These results describe how well school engagement in Years 1–2 predicts children’s scores on NAPLAN assessments in Year 3.

Chapter 7 presents all results for Study 2. These results describe the role of ECEC quality in predicting children’s school engagement.

Chapter 8 provides a general discussion.



2. Background and literature review

In this chapter, we review research on the nature of engagement in the early years of primary school. We examine what is known about early-in-life influences on school engagement, focusing on the development of children’s self-regulation and how this is affected by disadvantage, and speculate about the role of ECEC quality in school engagement.

2.1 What is school engagement like in early primary school?

There is no single definition of school engagement. It is widely considered to be a multidimensional construct, but the definitions of these constructs and the measures used to assess them vary considerably from study to study (Reschly & Christenson, 2012). However, researchers agree that engagement in adolescence has behavioural, affective and cognitive dimensions and that these are interrelated. The behavioural dimension reflects positive participation in classroom and school activities and good conduct and attendance. The affective dimension reflects the degree to which students like school and feel bonded to the school environment. The cognitive dimension typically includes students’ learning strategies and achievement goals (Fredericks, Blumenfeld, & Paris, 2004).

Engagement in the early years of primary school is not as well defined. Partly, this is due to the limited research on school engagement in young children. However, existing studies also suggest that some of the ways in which engagement is defined and measured in older children and adolescents are not valid for young children. School engagement comprises complex behaviours and attitudes that are shaped over time, responding to children’s developing cognitive and social capacities as well as to their experiences in school. From a developmental perspective, it is expected that the same dimension of engagement will be expressed by different behaviours at younger and older ages, and also that some behaviours that are important indicators of engagement at one age may no longer be relevant at a later age (Cicchetti & Rogosch, 2002). For instance, participation in extracurricular activities is often used as an indicator of behavioural engagement in adolescents. But young children do not have the same amount of freedom in these behaviours as adolescents, with participation likely to reflect parental motivation. More generally, Skinner and Pitzer (2012) argued that young children do not have the means to form a complex academic identity. Yet, it is clear that the process of engagement begins in the early school years.

The available research suggests that there are two basic components to early engagement: (1) participation in classroom activities (behavioural engagement); and (2) liking the school setting (affective engagement) (Eisenberg et al., 2010).

Behavioural engagementMost studies that have examined engagement in the early Year levels have included some teacher-rated assessment of classroom participation. Although measures differ across studies, core elements include children’s:

> attentiveness

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> cooperativeness

> capacity to work independently

> capacity to follow rules and teacher instructions

> persistence in completing tasks and school work.

(DiPerna, Lei, & Reid, 2007; Finn & Cox, 1992; Gruman, Harachi, Abbott, Catalano, & Fleming, 2008; Ladd, Buhs, & Seid, 2000; Ladd & Price, 1987; Pagani et al., 2012; Sasser, Beekman, & Bierman, 2015; Skinner & Belmont, 1993; Sturge-Apple, Davies, & Cummings, 2010; Valiente, Lemery-Chalfant, Swanson, & Reiser, 2008; Yang & Lamb, 2014.)

Collectively, these capacities are often referred to as children’s approaches to learning (DiPerna et al., 2007). Children rated higher on approaches to learning in kindergarten and Year 1 have better test scores both concurrently and up to Years 3 and 4, after controls for prior achievement and SES (Alexander, Entwistle, & Dauber, 1993; DiPerna et al., 2007; Finn & Cox, 1992; Ladd & Dinella, 2009), and are less likely to drop out of high school (Alexander et al., 1997). One reason for this may be that difficulties with attention, persistence and cooperativeness in the classroom reduce children’s exposure to instruction in fundamental pre-literacy and numeracy skills, delaying subsequent skill development (Sasser et al., 2015). In particular, ability to focus on the task at hand is a key element in the development of children’s self-regulated learning (DiPerna et al., 2007; Normandeau & Guay, 1998).

Absenteeism is often considered as an indicator of behavioural engagement for adolescents. However, absenteeism is not included in studies of young children’s engagement, because it is not at the student’s discretion in the same way that it may become by adolescence (Finn & Cox, 1992). Nonetheless, being physically present in the classroom assists children to develop the sorts of attentive, persistent learning practices just described (Silva et al., 2011), and it is clearly related to achievement (Daraganova, Mullan, & Edwards, 2014). Therefore, in this study, we consider how well child, family, and ECEC quality prior to school predict absenteeism across Years 1 to 3.

Affective engagementResearch on the affective aspect of engagement in the early years of school is more limited. Gary Ladd and colleagues in the US have carried out the largest program of research on early affective engagement, examining predictors and consequences of students’ affective engagement from kindergarten through to Year 8 (e.g., Ladd et al., 2000; Ladd & Dinella, 2009; Ladd & Price, 1987). This included the development of an instrument assessing children’s self-reported school liking and school avoidance, which we use in the present study.

In general, early affective engagement does not directly predict higher achievement in the way that approaches to learning does. For instance, Alexander et al. (1997) found that school liking in Year 1 did not predict high school drop out, but behavioural engagement did. However, affective engagement does seem to increase positive classroom behaviours. Ladd et al. (2000) found that children who liked school more at the start of their first year participated more cooperatively and independently in classroom activities over the course of the year. It is possible that children who are more comfortable at school are better equipped to invest effort when confronted by novel and challenging tasks. Thus, affective engagement matters because it is part of a cycle that promotes productive classroom behaviours and subsequent academic achievement (Ladd & Dinella, 2009).

Another aspect of affective engagement is children’s feelings about their competence, or academic self-efficacy. This extends to children’s feelings about specific subjects, such as maths and English. The importance of academic efficacy and motivation for achievement has been extensively demonstrated in adolescents and children (Wigfield, 1994). However, less is known about how



feelings about specific subjects are related to earlier self-regulation skills and family resources (Liew, McTigue, Barrois, & Hughes, 2008). In this study, we examine children’s self-reported assessment of their academic competence, and their liking of maths, as aspects of affective engagement.5

How does engagement change in the early years of school?Longitudinal research on school engagement, in which indicators of engagement are examined repeatedly over time, is rare, and longitudinal studies of engagement in early childhood are rarer still. Available evidence suggests that, on average, classroom participation, and approaches to learning, improve across the early years (Marks, 2000; Skinner & Pitzer, 2012). One reason for this is cognitive maturation: children improve in their abilities to focus and direct attention between the ages of 5 and 8 (Eisenberg & Sulik, 2012). Another is that teaching children to behave productively in the classroom is a priority early in school, so simply spending more time in school leads to improvements for most children (Ladd & Dinella, 2009).

There is some evidence that affective engagement may begin to decline in early Year levels (Ladd et al., 2000), and children’s liking of maths has certainly been shown to be initially quite positive and then to decline over the first few years of school (Taylor, 2014). However, early development of affective engagement is not well understood.

A few studies report substantial stability in both behavioural and affective engagement in early primary school, with correlations between 0.50 and 0.70 over adjacent years (Ladd et al., 2000; Skinner & Belmont, 1993). While high correlations suggest that many children retain their ranking relative to other children in the short term, it is also the case that some children increase or decrease in their level of engagement, while others remain stable (Ladd & Dinella, 2009; Sasser et al., 2015). Little is known about what underlies these diverse patterns. Why might some children decline more than others in school liking in the early years of school? Why might others fail to improve in behavioural engagement? We aim to answer some of these questions in this study.

2.2 Factors influencing the development of school engagement in early primary school

Theorists suggest that school engagement should be viewed as a process that arises from multiple levels of children’s developmental ecology (Alexander et al., 1997). This means that children’s individual abilities will be important, but so too will be key aspects of the child’s social world: quality of parenting, the resources that families can provide, experiences in ECEC contexts, relationships with peers and teachers and, ultimately, broader social and educational policies. In practice, research with young children has mostly focused on children’s abilities and family resources. There is some evidence that more securely attached children were associated with better classroom participation in Year 1 (Yang & Lamb, 2014). Securely attached children have been found to have parents who are more sensitive. Additionally, one of the most robust predictors of disengagement is material and educational family disadvantage. In all the studies reviewed here, diverse indicators of SES consistently predicted behavioural and affective engagement outcomes. We suggest that children’s self-regulation will be one key reason why parenting and disadvantage are related to school engagement.

5 Children’s feelings about maths have been found to have specific predictive power for academic outcomes (Gottfried, 1990; Gottfried, Fleming, & Gottfried, 2001), and concern over maths performance is of general policy relevance. Therefore, we also chose to retain the liking maths item as a separate outcome.

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The development of self-regulationSelf-regulation refers to a number of aspects of children’s abilities to flexibly control and organise their behaviour, attention and emotions. In this study, we focus on two important aspects of self-regulation: task attentiveness and irritability/anger. Task attentiveness refers to children’s ability to maintain focus on a particular task without being distracted by other stimuli in the environment, to persist at a task, and to shift attention to other stimuli when appropriate (Eisenberg et al., 2010). For example, task attentiveness would help a child to continue solving a puzzle despite being in a noisy preschool classroom, and also to interrupt the puzzle to listen to a teacher’s instructions. Irritability/anger describes the extent to which children get upset quickly, have difficulty calming down on their own, react intensely to frustration and cannot easily be distracted from their anger (Eisbenberg et al., 2010; Sawyer et al., 2014).

Differences in children’s abilities to regulate their attention, behaviour and emotions have a basis in temperament. Two major components of temperament are self-regulation and reactivity, or responsiveness to change in the external environment (Rothbart & Bates, 2006). Differences in temperament mean that some children are better than others at controlling their responsiveness to environmental stimuli. While children’s temperament characteristics are genetically determined to a certain extent, there is now a consensus that self-regulation abilities in childhood develop as a result of interactions between temperament, environment and maturation (Blair & Raver, 2012).

Like other skills, children’s regulatory skills improve with the opportunity to practise relevant behaviours. During infancy and early childhood, these opportunities are found in the children’s social and physical environments: in parenting interactions between the child and his or her parents, and in stimulating learning environments in the home (McClelland et al, 2007; Sektnan, McClelland, Acock, & Morrison, 2010). Firstly, a large body of research shows that authoritative parenting, characterised by consistency and low hostility, contributes to the development of strong self-regulation (e.g., Dennis, 2006). For instance, children’s negative emotions can be initially externally regulated by parental behaviour, facilitating the development of the child’s capacity to self-regulate emotional reactivity (Bernier, Carslon, & Whipple, 2010). Children’s styles of self-regulation also affect parents. For example, the behaviour of children who have difficulty controlling anger and frustration tends to elicit reactions from parents that may, in turn, exacerbate children’s emotional reactivity. These parent–child transactions tend to maintain the developmental course of poor self-regulation over time (Blair & Diamond, 2008).

Secondly, more stimulating home-learning environments, involving reading, games and other activities that promote problem-solving, predict stronger behavioural self-regulation (McClelland et al., 2007). This may be because these sorts of activities offer opportunities for children to practise important self-regulatory skills, like focusing attention and persisting at difficult tasks. More attentive children may be more likely to receive such stimulation.

Both sensitive parenting and the provision of a stimulating home-learning environment are severely undermined by disadvantage. The process by which disadvantage creates stressors and hassles that affect parental mental health and quality of parenting is well documented (e.g., Conger et al., 2010). Material disadvantage and low maternal education also limit the extent to which parents can devote resources to providing stimulating home-learning environments (Melhuish, 2010). As a result of these processes, children who experience early-in-life disadvantage do significantly worse than more advantaged children on task attentiveness and irritability/anger by school entry (Blair & Raver, 2012).

Maternal depression has also been shown to compromise parenting capacity and to be associated with poorer self-regulation (Morris, Silk, Steinberg, Myers, & Robinson, 2007). Negative effects of maternal depression may be exacerbated by material and social disadvantage (Canadian Paediatric Society, 2004).



Self-regulation and school engagementMany years of research have shown that self-regulation skills are important for a wide range of developmental outcomes (Blair & Razza, 2007). Adaptive self-regulation skills in infants and preschoolers also predict school readiness (McClelland et al., 2007) and later academic achievement (Morrison, Ponitz, & McClelland, 2010; Howse et al., 2003;Sektnan et al., 2010). For example, using LSAC data, Sawyer and colleagues (2014) found that children who improved more in task attentiveness between ages 2 and 6 had higher teacher-rated literacy and maths achievement. However, the role of self-regulation in the development of early school engagement has received limited attention.

Theoretically, task attentiveness and irritability/anger would influence school engagement via different mechanisms. Firstly, task attentiveness helps children to behave in a number of ways that are critical for early school adjustment. Classrooms can be busy and distracting places. Being better able to pay attention helps children to notice important cues, like teacher gestures, and to manage not to be distracted by noise or other children. Persistence enables children to stay with a task for longer, even in the face of learning new and challenging material. These abilities make it easier for children to engage in academic tasks, increasing the likelihood that they will acquire the foundational numeracy and literacy skills that are the basis of future success (Pagani et al., 2012).

This suggests that task attentiveness should be strongly related to better behavioural engagement. This is supported by several studies that have documented the relationship between task attentiveness and productive/cooperative classroom behaviour. For instance, Ladd and Burgess (2001) found that inattentiveness in kindergarten (the first year of school) was correlated with worse teacher-rated classroom participation at subsequent assessments up to the end of Year 1. Similarly, Sasser et al. (2015) found that low-income children who performed worse on assessments of task attentiveness in kindergarten had difficulties cooperating and attending in the classroom across Years 1 to 5.

Task attentiveness has also been related to children’s affective engagement (Ladd & Burgess, 2001; Valiente et al., 2008). Silva et al. (2011) suggested that children whose attentiveness means that they are better able to participate in classroom activities will receive positive reinforcement from teachers, leading to positive connections to the school environment. Conversely, teachers can become annoyed and frustrated with children who cannot comply with classroom practices, and this makes school less fun for these students (Blair & Diamond, 2008).

The second set of mechanisms connecting self-regulation with school engagement involves irritability/anger. Children who are better at regulating irritability/anger display fewer ‘acting out’ behaviours in the classroom. One of the most important consequences of these abilities is the formation of positive relationships with teachers and peers (Blair & Diamond, 2008; Eisenberg et al., 2010). Children who cannot control their emotions attract more discipline and frustrated responses from teachers and tend to experience peer conflict and peer rejection. These experiences increase negative feelings about school, which may undermine future effort and motivation (Silva et al., 2011). Researchers have not specifically investigated the role of irritability/anger in early school engagement. However, a number of studies show that preschool aggressiveness, impulsivity and oppositional–defiant behaviours, which are characterised by irritability/anger, tend to predict poorer classroom participation (Yang & Lamb, 2014) and school liking (Ladd & Burgess, 2001).

Overall, self-regulation around the time of school entry may be a key starting point for the positive or negative developmental pathway that leads to early engagement or disengagement from school. Early difficulties with self-regulation evoke negative responses from teachers and peers, which set in motion processes that decrease school liking, interrupt opportunities to learn and decrease children’s motivation. This disengagement evokes further negative responses from teachers,

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leading to more disengagement (Blair & Diamond, 2008). One result of this cascading process is that the effects of early problems with self-regulation at school entry may become magnified over time. Demands on children’s self-regulation skills increase substantially in the early years of school, meaning that difficulties arising from poor self-regulation can emerge at later Year levels when deficits were not apparent in the first year or two (Sasser et al., 2015).

Self-regulation as a mediator of disadvantage and family resources in the development of school engagementOne of the ways disadvantage may produce such marked differences between children even early in school may be through its effects on the development of self-regulation.

There is evidence that self-regulation mediates between disadvantage and children’s school readiness and early academic achievement (Raver et al., 2011; Sektnan et al., 2010). However, the role of self-regulation as a potential mediator of early risk in the development of school engagement has not been examined, especially from infancy across the transition to school. In this study, we hypothesise that task attentiveness and irritability/anger will mediate effects of disadvantage, parenting, maternal depressive symptoms and home-learning environment on behavioural and affective engagement in early primary school.

2.3 The role of ECEC quality in the development of school engagement

The benefits of high-quality ECEC for children’s cognitive development, school readiness and academic outcomes have been frequently demonstrated in recent years (Burger, 2010). There are a number of aspects that can be considered in assessing quality of ECEC, including the quality of carer–child relationships, the sorts of activities that take place with children, and the qualifications of the ECEC carer (Sylva, 2010).

In Australia, researchers have shown that higher-quality relationships between the carer and the child when children were aged 2–3 are related to higher vocabulary, literacy and maths scores, lower internalising and externalising behaviour, and better self-regulation at ages 4–5 (Gialamas, Mittinty et al., 2014; Gialamas, Sawyer et al., 2014). However, the quality of activities with children had no effect on these future outcomes. Warren and Haisken-DeNew (2013) used LSAC data to show that children whose preschool teachers had Diploma or Degree level qualifications with a speciality in ECEC showed the largest gains in NAPLAN numeracy, reading and spelling scores, compared to children whose teachers had other levels of qualifications.

The relationship between ECEC quality and early school engagement in Australia has not been previously examined. In this study, we consider whether the quality of ECEC when children were aged 4–5 (including carer–child relationship quality, carer qualifications and different ECEC activities) is related to behavioural and affective engagement in the early years of primary school.

Could high-quality ECEC be especially advantageous for children who have poor self-regulation? High-quality ECEC seems to promote the development of good self-regulation (Taggart, Sylva, Melhuish, Sammons, & Siraj, 2015; Raver et al., 2011), perhaps because it provides the sorts of stimulating tasks that give children opportunities to practise attentional focus, attention shifting and inhibitory control. Children with low levels of self-regulation might be especially benefitted by high-



quality ECEC. For instance, one US study showed that very young children with poor emotion regulation who experienced a warm, sensitive relationship with an ECEC carer were subsequently rated higher in social competence than similar children who did not experience this sort of relationship (Morrison et al., 2010). Therefore, we consider whether experiencing high-quality ECEC might be particularly beneficial for children who have poor regulatory skills before they start school.

2.4 A potential moderating role for disadvantage

Research in child development has highlighted a number of factors that are associated with positive outcomes for children who grow up in adverse circumstances. Warm, consistent parenting and child regulation of attention and emotions seem to protect children against negative developmental outcomes when they are exposed to disadvantage in families and communities (Rhule, McMahon, Spieker, & Munson, 2006; Vitaro, Larose, Brendgen, & Tremblay, 2005). Conversely, children low in self-regulation may be more vulnerable to the detrimental effects of multiple socioeconomic risks (Lengua, 2002).

A fairly large number of the studies reviewed here are based on small, low-income samples, such as Head Start children (e.g., Blair & Razza, 2007; Sasser et al., 2015; Silva et al., 2011). Among these children, variation in self-regulation does predict school engagement, but, given that most children in the samples were experiencing some disadvantage, it is difficult to assess the extent of the protective effect. The LSAC dataset is large and nationally representative, so it is possible to test whether self-regulation is protective for children in families experiencing disadvantage. We examine whether good self-regulation is more strongly associated with school engagement among disadvantaged children.

2.5 Summary

This discussion has highlighted a number of unknowns with regard to school engagement in young children. This report fills some gaps in knowledge with regard to four main issues: (1) the development of engagement in the early years of school; (2) the role of children’s self-regulation in promoting early school engagement; (3) the role of self-regulation as a mediator of family resources and disadvantage; and (4) the role of ECEC quality in promoting early school engagement.

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3. Method

3.1 Data

This report uses data from Growing Up in Australia: the Longitudinal Study of Australian Children (LSAC). The study is conducted in partnership between the Australian Government Department of Social Services (DSS), the Australian Institute of Family Studies (AIFS) and the Australian Bureau of Statistics (ABS). This is Australia’s first nationally representative longitudinal study of children, the overall aim of which is to understand the role of the social, economic and cultural environment on the social, emotional and cognitive development of children in Australia.

The sample consists of two cohorts of children and their families: one cohort of 5,107 children aged 0–1 (the ‘B’ cohort) and another of 4,983 children aged 4–5 (the ‘K’ cohort). Beginning in 2004, data have been collected every two years on children’s physical, emotional and cognitive wellbeing, as well as family, school and community circumstances. Information is collected from multiple sources, including resident and non-resident parents, teachers and carers, and by direct child assessment and self-report. By 2013, five waves of data were available. Detailed descriptions of the study design and procedures can be found in Soloff, Lawrence, and Johnstone (2005) and Gray and Smart (2009).

Samples used for analysisThis report uses data from waves 1 to 5 of the B cohort. Most children entered school in the year before the wave 4 interview, giving two waves of information about school engagement. In Study 1, we used a subsample of 4,351 children who participated in either wave 4 or wave 5, meaning that they had at least some information about school engagement. For these children, information from waves 1, 2 and 3 was used to capture details about our key predictors of interest—self-regulation, parenting, home-learning environment, maternal depressive symptoms and disadvantage. In Study 2, we used a subsample of 3,248 children who were in either preschool or long day care in wave 3, and who also participated in either wave 4 or wave 5. Information about ECEC was drawn from wave 3.6

Table 1 summarises information about children’s ages, school Year levels and ECEC participation across waves. It also shows the number of children participating at each wave for each Study sample. Because LSAC is a birth cohort study, it can be seen that children born in the same year do not all end up in the same Year level once they start school. This is the result of differences in enrolment age cut-offs across states and parental decisions about the timing of children’s enrolment (see Daraganova et al., 2014 for a discussion). Given the differences in Year levels, we analyse the data by wave rather than by Year level. For simplicity, we also refer to the five waves of the study as ‘wave 1, wave 2, etc.’ rather than by the ages children were at these waves.

The reporter for many measures was the child’s primary caregiver. In 98.5 per cent of cases, this was the child’s biological mother, so we refer to the primary caregiver as the child’s mother in the rest of the report. It should also be noted that, when we refer to ‘family’, this means the household of the primary caregiver only.

6 Many children were enrolled in ECEC before wave 3, but we focus on wave 3 enrolment in this study.



Table 1. Ages and school Year levels of B cohort children in the study subsamples in waves 1 to 5

Measure Wave 1 (2004)

Wave 2 (2006)

Wave 3 (2008)

Wave 4 (2010)

Wave 5 (2012)

Age 0–1 year 2–3 years 4–5 years 6–7 years 8–9 years

School Year level - - Kinder/prep (17.4%) Kinder/prep (4.2%)

Year 1 (78.3%)

Year 2 (17.5%)

Year 2 (4.9%)

Year 3 (78.3%)

Year 4 (16.8%)

N, Study 1 4,351 4,189 4,190 4,224 4,065

ECEC participation Long day care (10.8%)

Other/none (89.3%)

Preschool (4.1%)

Long day care (43.0%)

Other/none (52.9%)

Preschool (83.3%)

Long day care (16.8%)

- -

N, Study 2 3,248 3,163 3,248 3,183 3,056

3.2 Dependent variables

School engagementIn this report, we examined four school engagement outcomes, which were assessed at both wave 4 (when children were in school Years 1–2) and wave 5 (when children were in school Years 3–4). There were two behavioural engagement outcomes: approaches to learning and absenteeism. There were two affective engagement outcomes: school liking and maths liking.

Information about these measures is summarised in Table 2. The Table shows whether the measures were observed or latent variables in the analysis and the values and range (where applicable). It also shows the reporter for each measure.

These outcomes were selected because they:

1. reflected constructs that have been used to assess school engagement in young children in the literature

2. were distinct from each other

3. did not assess general externalising or internalising behaviour

4. were present at both waves 4 and 5

5. demonstrated reliability and measurement invariance7 over sex, Year level, age and time.

The development of the four school engagement measures is described in Appendix A: .

Approaches to learning is a subscale from the Social Rating Scale (SRS), developed for use with young children in the US Early Childhood Longitudinal Study. It taps motivation, persistence and attention with regard to schoolwork and classroom participation. The scale has strong psychometric properties, and validity is well established (DiPerna et al., 2007).

7 Measurement invariance is the extent to which the same construct is measured over time or over different groups of participants. Measurement invariance is required to make valid comparisons in school engagement outcomes between genders and Year levels, and over time.

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Absenteeism reflects a child being absent two or more days in the past four weeks. This level of absenteeism was selected because it was associated with increased familial and individual risk factors in previous research (Daraganova et al., 2014).

School liking includes three items from the School Liking and Avoidance Questionnaire (SLAQ; Ladd & Price, 1987), which has been used quite frequently to assess affective engagement in young children.

Maths liking reflects academic self-efficacy (Liew et al., 2008).

NAPLANThe National Assessment Program – Literacy and Numeracy (NAPLAN) is designed to assess all Australian students in Years 3, 5, 7 and 9 on reading, writing, spelling, grammar and numeracy. Technical details of the matching of LSAC children to NAPLAN are described in Daraganova, Edwards, and Sipthorp (2013).

NAPLAN scores on each test are standardised so that children’s and schools’ performance can be compared over time. In the test years we use here, scores were standardised to have a mean of 500 and a standard deviation of 100.

We examined numeracy and reading scores from the tests administered when children were in Year 3. When assessed at study wave 4 in 2010, children were in kindergarten, Year 1 and Year 2 (see Table 1); therefore, for most of these children, the Year 3 NAPLAN test was taken in 2011 or 2012. The sample for these analyses was all children who participated in wave 4 of the study for whom Year 3 NAPLAN linked data were available. We excluded 74 children who took the Year 3 NAPLAN in 2010, the same year as wave 4 of the LSAC study. The final sample was 3,523.

Table 2. Dependent variables: measures of school engagement

Measure Assessed at wave

Variable type

Values/range Reporter Items

School liking 4, 5 Continuous latent variable

Child 1. Are you happy when you are at school?

2. When you get up in the morning, do you feel happy about going to school?

3. Is school fun?

Maths liking 4, 5 Observed categorical variable

1 = no; 2 = sometimes; 3 = yes

Child Do you like maths and number work at school?

Approaches to learning

4, 5 Continuous latent variable

Teacher 1. Pays attention well

2. Shows eagerness to learn new things

3. Works independently

4. Easily adapts to changes in routine

5. Persists in completing tasks

6. Keeps belongings organised

Absenteeism 4, 5 Observed binary variable

0 = less than two days absent in past 4 weeks;

1 = 2 or more days absent in the past four weeks

Mother During the previous four weeks of school, how many days has study child been absent?



3.3 Explanatory variables

Measures of children’s self-regulationTwo aspects of children’s self-regulatory skills were assessed at waves 2 and 3: task attentiveness and irritability/anger reactivity. In the analyses, irritability/anger and task attentiveness were latent variables. The items for these constructs were different at wave 2 and wave 3, but it is assumed that they reflect the same underlying capacities at each time point. The development of these measures is described in Appendix B: . Table 3 summarises information about these variables.

At wave 2, task attentiveness was measured with five items from the Short Temperament Scale8 (STS; Fullard, McDevitt, & Carey, 1984). Examples of items include: ‘This child plays continuously for more than 10 minutes at a time with a favourite toy’ and ‘This child stays with a routine task (dressing, picking up toys) for 5 minutes or more.’ At wave 3, task attentiveness was assessed with four items from the STS and one item from the Strengths and Difficulties Questionnaire (SDQ; Goodman, 2001). Examples of items include: ‘When this child starts a project such as a puzzle or model, he/she works on it without stopping until it is completed, even if it takes a long time’ and ‘This child likes to complete one task or activity before going onto the next.’

Higher values of irritability/anger reflect higher levels of irritability/anger in social interactions, especially in response to frustration. At wave 2, irritability/anger was measured with four items from the STS. Examples of items include: ‘This child responds to frustration intensely (screams, yells)’ and ‘This child has moody “off” days when he/she is irritable all day.’ At wave 3, irritability/anger was assessed with three items from the STS and one item from the SDQ. Examples of items include: ‘When shopping together, if I do not buy what this child wants (e.g., sweets, clothing), he/she cries and yells’ and ‘When this child is angry about something, it is difficult to sidetrack him/her.’

8 The STS was created as a parent-report measure of temperament. In the child development literature, parent-report measures of temperament are often used to assess self-regulation. For example, the Children’s Behavior Questionnaire (CBQ; Rothbart, Ahadi, Hersey, & Fisher, 2001), developed to assess temperament, was used as a measure of effortful control in two of the studies discussed earlier (Silva et al., 2011; Yang & Lamb, 2014). The STS has been used by several Australian researchers with the LSAC data to assess self-regulation (Sawyer et al., 2014; Gialamas, Sawyer et al., 2014; Williams, Berthelsen, Walker, & Nicholson, 2015).

Self-regulation is a defining feature of temperament, and temperament in infancy predicts later self-regulation skills. Therefore, the content of parent-report measures of temperament in young children tends to overlap with the content of parent-report measures of self-regulation. Indeed, some authors suggest that relationships between early measures of temperament and later measures of social development (including self-regulation) are actually manifestations of the same construct, but assessed at different ages (Sanson. Hemphill, & Smart, 2004). This is a conceptual and methodological problem which is being debated in the child development field but is not our focus in this report. We follow the example of other researchers and consider that the items in the STS clearly resemble the task attentiveness and irritability/anger reactivity aspects of self-regulation. This is especially useful, given that LSAC does not include direct assessments of self-regulation.

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Table 3. Explanatory variables

Measure Assessed at wave Variable type Values/range Reporter

Self-regulation

Task attentiveness 2, 3 Continuous latent variable Mother

Irritability/anger reactivity

2, 3 Continuous latent variable Mother

Family resources

Parenting Mother

Hostility 1, 2, 3 Continuous observed variable

Range: 1–10

Consistency 3 Continuous observed variable

Range: 1–10

Home-learning environment

Mother

Reading activities 2 Continuous latent variable

In-home activities 2 Continuous latent variable

Maternal depressive symptoms

1 Continuous observed variable

Range: 0 to 4 Mother

Disadvantage

Total score 1 Continuous observed variable

Range: 0–3 Mother

Disadvantage indicator 1 Dichotomous observed variable

0 = no disadvantage indicators; 1 = at least 1 disadvantage indicator

Mother

ECEC quality (Study 2 only)

Activities 3 Frequency spent in four types of activities with children

(0) never, (1) occasionally, (2) often, (3) very often

ECEC carer

Teacher–student relationship

3 ECEC carer

Warmth Continuous observed variable

Range: 1–5

Conflict Continuous observed variable

Range: 1–5

Teacher qualifications 3 Observed categorical variable

0= no ECEC qualifications, 1=Diploma or Associate Diploma in ECEC, 2= Bachelor and above in ECEC, 3 = Bachelor and above in another field

ECEC carer



Measures of family resources: parenting, home-learning environment, and maternal depressive symptomsMeasures of family resources were hypothesised to contribute to children’s self-regulation, and thus indirectly influence school engagement.

Parenting

Measures of hostile parenting (US Department of Education, 2001; Statistics Canada, 2000) were included at waves 1, 2 and 3, and consistent parenting (Statistics Canada, 2000) was measured at wave 3.9 All measures were reported by the child’s mother.

Hostile parenting assesses parents’ feelings of anger and frustration towards the child. There were five items at waves 1 and 2, and four items at wave 3. For example: ‘I have been angry with this child’ and ‘When this child cries, she gets on my nerves.’ Responses were on a 10-point scale from ‘not at all’ to ‘all the time’. At each wave, a weighted composite of the items was created, based on confirmatory factor analyses in Zubrick, Lucas, Westrup, and Nicholson (2014).

Consistent parenting assesses the consistency of parental discipline. The measures consisted of five items, for example: ’If you tell this child she will get punished if she doesn’t stop doing something, but she keeps doing it, how often will you punish her?’ and ’How often does this child get away with things that you feel should have been punished?’ Responses were (1) never/almost never, (2) less than half the time, (3) about half the time, (4) more than half the time, (5) all the time. A weighted composite of the items was created following Zubrick et al. (2014).

Home-learning environment

Parents responded to a number of questions asking about the activities that they did with their child both at home (e.g., doing craft, reading books) and outside the home (e.g., going to sports events, visiting libraries). From these items, we selected eight that clearly presented learning opportunities and were consistent with items used in past research on the home-learning environment (e.g., Melhuish, Phan et al., 2008). We used exploratory factor analysis to determine whether these items clustered meaningfully into one or more latent factors. Details of these analyses are given in Appendix C: . Results supported two latent factors. The first factor included three items relating to reading, including:

> the frequency in the last week that the study child was read to from a book (responses (0) none (1) 1–2 days (2) 3–5 days (3) every day 6–7 days)

> the number of children’s books in the home (1 = 30 and more, 0 = less than 30)10

> whether the study child had visited a library in the past month (1 = yes, 0 = no)

The second factor included four items relating to stimulating activities at home, including the frequency in the last week that the study child:

> was told a story

> had drawn pictures or done arts and crafts with the parent or other adult

9 Parenting warmth was initially included at waves 1, 2 and 3. However, preliminary analyses showed that it was not related to key variables in the model, including school readiness or school engagement outcomes. For ease of interpretation and parsimony, it was decided to focus on hostility and consistency.

10 Having more than 30 books in the home has been found to be an important indicator of child literacy practice at home (Mullan & Daraganova, 2012).

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> had played music, sung songs, danced or done other musical activities with the parent or other adult

> had played with toys or games indoors with the parent or other adult.

Responses to all items (0) none (1) 1–2 days (2) 3–5 days (3) every day 6–7 days.

These two dimensions of the home-learning environment were treated as latent variables in the analysis. We originally included these two dimensions of home-learning environment at both waves 2 and 3. However, results of analyses were very similar using only the wave 2 dimensions, so, for parsimony, we included the wave 2 dimensions only.

Maternal depressive symptoms

Maternal depressive symptoms at wave 1 were assessed with the Kessler 6 (K6), a 6-item scale measuring psychological distress (e.g., In the past 4 weeks about how often did you feel so sad that nothing could cheer you up?). Responses were (1) All of the time; (2) Most of the time; (3) Some of the time; (4) A little of the time; (5) None of the time. Items were reverse-scored and averaged to form a scale ranging from 1 to 5 where higher scores indicated more distress.

DisadvantageWe created an index of disadvantage based on methods developed by Warren and Edwards (2017). This encompassed material, employment and educational resources at wave 111:

> Material disadvantage was indicated if the household had a parental income less than 50 per cent of the median, or if the study child’s mother reported three or more indicators of financial hardship. Indicators of financial hardship were, because of a shortage of money, the family not being able to pay utilities bills on time, not being able to pay mortgage or rent on time, being unable to heat or cool the home, having pawned or sold something, having sought assistance from a community organisation, and having had financial limits on the type of food the family could buy. At wave 1, 19.8 per cent of families were experiencing material disadvantage.

> Employment disadvantage12 was indicated if neither parent had a job, or if the child’s mother did not have a job in a lone-parent household. At wave 1, 9.1 per cent of families were experiencing employment disadvantage.

> Education disadvantage was indicated if both parents had less than a high school education, or if the child’s mother had less than a high school education in a lone-parent household. At wave 1, 6.5 per cent of families were experiencing education disadvantage.

The three indicators were summed to create a disadvantage index ranging from 0 to 3. Table 4 shows the distribution of this index. For analyses where disadvantage was a moderator, we

11 We also estimated models where disadvantage was defined at wave 3, but the results were almost identical. The wave 1 index was preferred due to having less missing data.

12 At wave 1, 309 (92.0 per cent) of the 336 mothers in lone-parent households were classified as disadvantaged. However, as the study children were infants, 71 per cent of lone-parent mothers were not working. To assess how many of these might have been classified as disadvantaged because of the inclusion of the employment disadvantage indicator, we created a disadvantage index based only on the material and education indicators. Based on this index, 294 (87.5 per cent) mothers in lone-parent households were disadvantaged—a reduction of 15 families (4.5 per cent of lone-parent families). This is compared to a reduction of 0.85 per cent for dual-parent families (18.16 per cent disadvantaged using the first index, and 17.31 per cent disadvantaged using the second index). Analyses were carried out using both indices, and results did not differ. Therefore, we report findings from the index that includes employment disadvantage



dichotomised the index so that families with at least one indicator were classified as experiencing disadvantage.

Table 4. Distribution of disadvantage index at wave 1

Number of indicators N %

None 3,313 76.1

One 658 15.1

Two 264 6.1

Three 116 2.7

Total 4,351 100.0

Measures of ECEC quality (Study 2 only)Because this research uses the LSAC B cohort data, the ECEC data is from 2008, which predates the introduction of the National Quality Framework (NQF) for ECEC in January 2012 and ‘universal access’ to ECEC (UA) from 2008. Thus, the LSAC data items used to measure ECEC quality do not map onto the NQF and National Quality Standards currently in use in Australia. The policy changes which introduced the NQF and UA have improved ECEC quality through the introduction of National Quality Standards, including mandatory qualifications for educators, smaller ratio sizes and the Early Years Learning Framework, all of which may support the development of better self-regulation and school readiness in children.

ECEC quality was assessed at wave 3. Information about the care the child received in the year prior to commencing school was obtained from a questionnaire sent to the study child’s non-parental caregiver. Three aspects of ECEC quality were assessed, based on those developed by Gialamas et al. (Gialamas, Mittinty et al., 2014; Gialamas, Sawyer et al., 2014); and Warren and Haisken-DeNew (2013). These aspects of quality were the frequency of time spent on four different types of activities with the child, the quality of the carer–child relationship, and carer qualifications.

ECEC activities

Carers were asked how often in a typical day in the program time was spent on the following activities:

> teacher-directed whole group activities (e.g., language or numeracy activities, story-time, news-time)

> teacher-supported small group activities (e.g., literacy or numeracy activities, science, cooking, art activities)

> teacher-supported individua

> l activities (e.g., reading, doing puzzles, writing or completing worksheets)

> child-initiated activities (e.g., free-choice of activities, free play in outdoor activities, pretend play).

Responses were (0) never, (1) occasionally, (2) often, (3) very often.

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Relationship with carer

The relationship between the study child and the carer was assessed with the warmth and conflict subscales of the Student–Teacher Relationship Scale (STRS, Pianta, 2001). The warmth items describe how much the carer perceived the relationship with the study child to be affectionate. Warmth items were: (1) I share an affectionate, warm relationship with this child; (2) If upset, this child will seek comfort from me; (3) This child is uncomfortable with physical affection from me (reversed); (4) This child values his/her relationship with me; and (5) When I praise this child, he/she beams with pride. Response options ranged from (1) definitely does not apply to (5) definitely applies. Internal consistency as indicated by Cronbach’s alpha for the warmth items was 0.73.

The conflict items assess perceived negativity in the relationship. Conflict items were: (1) This child and I always seem to be struggling with each other; (2) This child easily becomes angry with me; (3) This child remains angry or is resistant after being disciplined; (4) Dealing with this child drains my energy; (5) When this child is in a bad mood, I know we’re in for a long and difficult day; (6) This child’s feelings towards me can be unpredictable or can change suddenly; and (7) This child is manipulative with me. Response options were the same as for the warmth items. Cronbach’s alpha was 0.88.

Carer qualifications

Carers were asked about the level and field of their highest educational qualification. A variable combining this information was created where 0 = no ECEC qualification (24.4 per cent), 1= Diploma or Associate Diploma in ECEC (14.1 per cent), 2 = Bachelor and above in ECEC (44.4 per cent), and 3= Bachelor and above in another field (17.1 per cent). Other fields of study were largely primary/secondary teaching and child care.

3.4 Covariates

We controlled for some variables that research suggests should be related to school engagement and which might confound the relationship between self-regulation and school engagement. Details of these variables are given in Table 5.

School readinessSchool readiness was assessed at wave 3 with the Who Am I (WAI: Australian Council for Educational Research, 1999). The WAI includes five copying tasks (circle, cross, square, triangle, diamond), four writing tasks (numbers, letters, words, sentence) and a drawing task (of self). Previous research shows that scores on the WAI are moderately correlated with measures of child development and academic competency (de Lemos, 2002; Warren & Haisken-DeNew, 2013). We included school readiness as a covariate so that any effects of self-regulation will be independent of school readiness.

Child gender, age, Year level, and delayed school entryWe control for gender because research shows that girls consistently have higher levels of school engagement than boys (e.g., Li & Lerner, 2011; Marks, 2000; Wylie & Hogden, 2012).



It is important to consider potential differences due to both age and Year level. For example, previous studies show that measures of children’s classroom participation, like approaches to learning, tend to improve over time.

Child age was measured in months at wave 4 and wave 5.

Child school starting delay indicates whether a child entered school on time (in the year they were first eligible to enrol) or delayed entry until the next year. In this sample, 15.2 per cent of children were delayed entrants.

School ICSEASince 2009, the Australian Curriculum, Assessment and Reporting Authority (ACARA) has calculated a measure of social, community and educational factors for Australian schools, called the Index of Community Socio-educational advantage (ICSEA). It is a standardised measure where higher scores represent greater levels of advantage. Each school’s score is the average score of all students attending a school in that year. Scores are based on remoteness, proportion of Indigenous students, and parents’ education and occupation (sourced from ABS 2006 Census data and school records). It is important to control for this socio-educational advantage because schools with more or less-advantaged student populations may differ in the sorts of supports they are able to provide students, which may influence early school engagement.

ECEC participation (Study 2 only)For analyses examining the role of ECEC quality in Study 2, we controlled for the number of hours per week that children spent in ECEC in wave 3 and for the ECEC program type.

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Table 5. Covariates

Measure Assessed at wave

Variable type/notes Values/range Reporter

School readiness Child

Who Am I? 3 Continuous observed variable

Child gender na Observed binary variable 1 = male, 0 = female na

Child age 4 Continuous observed variable 73 to 93 months na

5 98 to 118 months

Child Year level 4 Observed categorical variable 0=Kindergarten/prep, 1= Year 1, 2= Year 2

Mother

5 Observed categorical variable 0= Year 2, 1 = Year 3, 2= Year 4

Child school starting delay

Derived over waves 1–4

Observed binary variable 0= entered school early or on time, 1= delayed entry to school

na

School ICSEA For schools attended in 2010 and 2012

Continuous observed variable na

Hours per week in ECEC (Study 2 only)

3 Continuous observed variable Range: 2 to 50 Mother

ECEC program type (Study 2 only)

3 Observed binary variable 0 = preschool; 1 = long daycare

Mother




This chapter presents results of analyses that address the first two research questions in Study 1:

1. How do school engagement outcomes change between Year 1 and Year 3?

Because some research suggests that children’s attention skills tend to improve as they move through school (Skinner & Pitzer, 2012), while affective engagement declines (Ladd et al., 2000), we expected overall approaches to learning scores to increase and school liking scores to decrease. Based on research with the K cohort at the same ages, we expected liking of maths to decline (Taylor, 2014), but rates of absenteeism to stay the same (Daraganova et al., 2014). However, we also expected substantial stability in children’s engagement over the two time points. This would be consistent with research showing that children tend to maintain their level of school engagement relative to other children in the sample over time (Ladd et al., 2000; Skinner & Belmont, 1993).

2. How does school engagement differ across subgroups of children, including girls and boys, children of different ages and school Year levels, disadvantaged and more advantaged children, and children who are more or less competent with self-regulation?

Based on the literature in Chapter 1, we expected all school engagement outcomes to be better for girls, non-disadvantaged children and children with better self-regulation.

4.1 Data analysis

We examined changes in school engagement in different ways, depending on the nature of the school engagement outcome variable. Absenteeism and liking maths were categorical outcomes, so we assessed whether the distribution of responses at wave 5 was significantly different to wave 4, using a 2 goodness-of-fit test for the wave 4 frequencies. For the latent variables (approaches to learning and school liking), we estimated models where the mean of the latent variable at wave 4 was treated as a baseline, and the wave 5 mean was compared to it.13 If the z-test for the wave 5 mean was statistically significant, this indicated that it was significantly different to the wave 4 baseline.

13 Models for examining time- and group- differences in approaches to learning and school liking were confirmatory factor analyses (CFAs). The indicators for the approaches to learning latent variables and the school liking latent variables were ordered categorical (ordinal) variables. Therefore, the models were estimated using weighted least-squares estimation with a mean- and variance-adjusted chi-square (WLSMV; Finney & DiStefano, 2013). This method is especially well-suited to large samples.

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As well as considering overall changes in the level of the outcomes, we were interested in how stable children were in their levels of engagement over time. For absenteeism and liking maths, we considered the percentage of children who remained in the same category between wave 4 and wave 5, compared to those who changed category. For approaches to learning and school liking, we examined the correlation between the latent factors at the two time points.

Differences in the four school engagement outcomes according to Year level, delayed entry, age, sex, self-regulation and disadvantage were examined separately at wave 4 and wave 5.

To examine differences by age, children were divided into three groups at each wave. At wave 4, the groups were children aged 6 to less than 6.5, 6.5 to less than 7 , and 7 years and older. At wave 5, the groups were children aged 8 to less than 8.5, 8.5 to less than 9, and 9 years and older.

Poor self-regulation was based on the wave 3 assessments of task attentiveness and irritability/anger. We estimated the factor scores for each child based on the confirmatory factor analysis described in Appendix B: . Low task attentiveness was defined as children in the bottom quartile (bottom 25 per cent) of the distribution of factor scores. High irritability/anger reactivity was defined as children in the top quartile (top 25 per cent) of the distribution of factor scores.

Subgroup differences for absenteeism and liking maths were examined using 2 tests of association to assess whether the responses frequencies were the same across groups. The 2 statistic was corrected for the survey design and converted into an F-statistic (Rao & Scott, 1984). Subgroup differences in approaches to learning and school liking were examined in the same way as differences over time. We estimated models where the mean of the latent variable in one group was treated as the baseline and was compared to the estimated mean in the other group or groups. As before, the z-test for the estimated mean/s indicated whether the difference between pairs of groups was significant.

4.2 Question 1: How do behavioural and affective school engagement change between school Years 1–2 and Years 3–4?

School liking decreased on average from wave 4 to wave 5 (z = –18.81, p < .001). At the same time, the correlation between the wave 4 and wave 5 factors was 0.51, showing a strong tendency for children to retain their rank on school liking (relative to other children) over time.

The differences in school engagement outcomes over time are illustrated in Figures Figure 1 and Figure 2.

Figure 1 shows the distribution of children’s responses to the three observed school liking items at Years 1–2 (wave 4) and Years 3–4 (wave 5). The observed item percentages are shown to ease interpretation, as factor means and factor scores when indicators are categorical do not have a simple relationship to the scale of the observed items.



Figure 1. School liking at wave 4 (a) and wave 5 (b)

Liking of maths at wave 4 and wave 5 is shown in Figure 2. As expected, the percentage of children who responded ‘yes’ when asked if they liked maths dropped from wave 4 to wave 5, and the percentage who responded ‘sometimes’ rose. These changes were significant (2(2) = 232.30, p < .001). Fifty-four per cent of children retained their maths-liking status over time, 27 per cent moved to liking maths less, and 19 per cent moved to liking maths more.

Figure 2. Maths liking at wave 4 and wave 5

As expected, the average level of approaches to learning was significantly higher at wave 5 than wave 4 (z = 3.75, p < .001), and the correlation between the factors over time was 0.65, showing that children tended to retain their rank over the two waves.

Finally, the percentage of children who were absent for two or more days in the past four weeks at wave 4 was 28.4 per cent and at wave 5 was 29.1 per cent. Consistent with research with the K cohort (Daraganova et al., 2014), this percentage did not change from wave 4 to wave 5 (2(1) = 2.01, p = 0.16). Overall, 65 per cent of children retained their status over time, but stability was higher for children who were not absent at wave 4: 75 per cent of these children remained not absent at wave 5. Forty per cent of children who were absent at wave 4 were also absent at wave 5.

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4.3 Question 2: Differences in school engagement across sex, Year level, age, disadvantage, and self-regulation

Differences in wave 4 school engagement outcomes according to Year level, age and delayed entry are summarised in Table 6. Differences for wave 5 school engagement are summarised in Table 7. For easier interpretation, Tables 6 and 7 show the means of the observed items for approaches to learning and school liking. However, comparisons between groups are based on the latent variables. Only the direction and significance level of the z-tests for differences in school liking and approaches to learning are shown.

Results showed that:

> At both waves, children in earlier Year levels liked school more. At wave 4, children in Year 2 liked maths less than children in younger Year levels, but at wave 5, differences in maths liking between levels were not significant.

> At both waves, younger children liked school more.

> There were no significant age or Year level differences in approaches to learning or absenteeism.

> There were no significant differences on any of the outcomes between children who delayed starting school and those who did not.

Table 6. Differences in wave 4 school engagement by Year level, delayed enrolment and age

School liking Maths likingApproaches

to learning Absenteeism

Mean of observed items

(range = 1–3)No (%)

Sometimes (%)

Yes (%)

Mean of observed

items (range = 1–4) Absent > 2 days (%)

Year level

Kindergarten 2.53 15.89 15.13 68.98 3.23 34.20

Year 1 2.53 15.51 15.21 69.28 3.22 28.80

Year 2 2.47 13.88 20.22 65.90 3.16 28.30

Year 2 < Year1* F(3.9, 1056.8) = 2.43* ns F(2, 541.61) = 1.02

Delayed enrolment

Not delayed 2.53 15.06 16.19 68.76 3.20 28.61

Delayed 2.50 16.16 15.79 68.05 3.24 30.66

ns F(1.98, 536.69) = 0.24 ns F(1, 271) = 0.90

Age

6 –6.5 2.53 17.83 13.91 68.26 3.17 29.9

6.5–7 2.55 14.57 16.07 69.36 3.23 28.5

7+ 2.45 14.98 17.81 67.21 3.19 29.4

7+ < 6–6.5*** F(3.95, 1071.12) = 1.77 ns F(1.98, 535.64)1 = 0.26






(range = 1–3)No (%)

Sometimes (%)

Yes (%)

Mean of observed


7+ < 6.5–7**

Table 7. Differences in wave 5 school engagement by Year level, delayed enrolment, and age




(range = 1–3)No (%)

Sometimes (%)

Yes (%)

Mean of observed


Year level

Year 2 2.54 12.65 20.42 66.92 3.19 34.5

Year 3 2.44 14.11 26.90 58.99 3.25 28.7

Year 4 2.43 15.02 28.65 56.33 3.26 27.4

Year 4 < Year 2* F(3.93, 1065.13) = 1.58 ns F(1.99. 538.25) = 1.30

Delayed entry

Not delayed 2.45 14.45 26.88 58.67 3.24 28.48

Delayed 2.42 13.06 26.99 59.95 3.26 29.89

ns F(1.99, 540.43) = 0.39 ns F(1, 271) = 0.37

Age

8–8.5 2.51 15.1 21.8 63.1 3.22 29.2

8.5–9 2.46 14.4 26.9 58.6 3.28 28.5

9+ 2.41 13.7 28.3 58.1 3.21 28.4

9+ < 8–8.5***9+ < 8.5–9**

8.5–9 < 8–8.5*

F(3.9, 1057.29) = 1.40 ns F(2, 541.79) = 0.04

Notes: * p<0.05; **p<0.01; ***p<0.001; ns = no significant differences

Table 8 summarises differences in wave 4 school engagement according to sex, self-regulation, and disadvantage. Table 9 shows results for wave 5. Results showed:

> At both waves, girls scored higher than boys on approaches to learning and liked school more. At wave 4, there were no gender differences in liking for maths, but by wave 5, girls liked maths less.

> At wave 4, children who experienced early disadvantage liked maths less. At both waves, these children scored lower on approaches to learning and were more likely to be absent. By wave 5, they also liked school less.

> At both waves, children who had low levels of task attentiveness at 4–5 years old scored lower on approaches to learning and liked maths less. By wave 5, they also liked school less and were more likely to be absent.

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> At both waves, children who had difficulties with irritability/anger at 4–5 years old scored lower on approaches to learning. By wave 5, they also liked school less.

Table 8. Differences in wave 4 school engagement by sex, disadvantage, task attentiveness and irritability/anger

School liking Maths likingApproaches to

learning Absenteeism


(range = 1–3)No (%)

Sometimes (%)

Yes (%)

Mean of observed

items (range = 1–4)

Absent > 2 days (%)

Sex

Girls 2.60 14.18 16.52 69.30 3.40 29.12

Boys 2.44 16.23 15.75 68.02 3.04 28.73

Girls > Boys*** F(1.97, 534.75) = 1.35 Girls > Boys*** F(1, 271) = 0.06

Disadvantage

Not disadvantaged 2.53 13.74 17.73 68.53 3.28 26.23

Disadvantaged 2.50 18.70 12.38 68.92 3.04 35.14

ns F(1.99, 539.88) = 11.59*** Not disad > disad***

F(1, 271) = 22.82***

Wave 3 task attentiveness

Average/high 2.53 14.49 16.74 68.77 3.32 28.19

Low 2.50 19.53 15.15 65.32 3.04 26.59

ns F(1.98, 537.60) = 5.92** Low < High*** F(1, 271) = 0.57

Wave 3 irritability/anger

Average/low 2.52 15.45 16.78 67.77 3.30 26.95

High 2.51 16.81 15.05 68.14 3.08 30.08

ns F(1.98, 537.78) = 0.90 High < Low*** F(1, 271) = 2.79



Table 9. Differences in wave 5 school engagement by sex, disadvantage, task attentiveness and irritability/anger

School liking Maths likingApproaches to

learning Absenteeism

Mean of observed


No (%)

Sometimes (%)

Yes (%)

Mean of observed


Absent > 2 days (%)

Sex

Girls 2.53 16.56 31.18 52.26 3.47 27.65

Boys 2.37 12.00 22.79 65.21 3.03 29.70

Girls > Boys*** F(1.99, 538.95) = 30.07***

Girls > Boys*** F(1, 271) = 1.63

Disadvantage

Not disadvantaged

2.45 13.70 27.69 58.61 3.33 24.82

Disadvantaged 2.44 15.59 24.90 59.52 3.03 38.45

Disad < Not disad* F(1.98, 537.12) = 1.73 Not disad > disad*** F(1, 271) = 52.40***

Wave 3 task attentiveness

Average/high 2.46 13.71 26.80 59.49 3.35 26.42

Low 2.39 17.40 28.13 54.48 3.04 30.62

Low < High** F(2, 541.32) = 3.61* Low < High*** F(1, 271) = 4.67*

Wave 3 irritability/anger

Average/low 2.46 14.32 27.69 57.99 3.34 26.56

High 2.39 15.63 25.60 58.77 3.06 30.20

High < Low*** F(1.99, 538.14) = 0.74 High < Low*** F(1, 271) = 3.63, p = 0.06

Notes: * p<0.05; **p<0.01; ***p<0.001; ns = no significant differences

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4.4 Summary

In answer to research question 1, we found that, between wave 4 and wave 5, on average:

> school liking and maths liking declined

> approaches to learning improved

> absenteeism remained steady.

These changes are quite consistent with the limited research on early school engagement. It is also interesting that average levels of school liking and maths liking were quite high at wave 4. Therefore, some of the decline in liking over the two waves could be due to normative declines in children’s initial enthusiasm for school.

In answer to research question 2, analyses showed that:

> girls did better at all aspects of school engagement except maths liking

> older children and children in more advanced years liked school less

> poor self-regulation and disadvantage were related to lower scores on approaches to learning in waves 4 and 5

> poor self-regulation and disadvantage were related to lower school liking in wave 5 only.

By Years 3–4, it is possible that difficulties associated with self-regulation and disadvantage could have led to less school liking. This may be the start of the disengagement process. In the next chapter, we test how disadvantage may hamper school engagement through its effect on family processes that promote children’s self-regulation.



5. Results of Study 1 (Q3 and Q4): developmental pathways to school engagement—family resources, disadvantage and self-regulation

In this chapter, we use structural equation modelling to examine the third and fourth research questions for Study 1.

3. Does children’s task attentiveness and irritability/anger at age 4–5 mediate the relationship between family resources (parenting, home-learning environment and maternal depressive symptoms) and disadvantage, and school engagement over Years 1 to 3?

Based on the literature reviewed in Chapter 2, we expected that:

> task attentiveness, irritability/anger and school readiness would directly influence each of the four school engagement outcomes at wave 4, feeding into wave 5 engagement

> self-regulation and school readiness would also have direct influences on wave 5 engagement. This was based on research suggesting that effects of self-regulation may be magnified over time.

> disadvantage, home-learning environment and parenting would not have a direct influence on school engagement, but would operate indirectly through influences on self-regulation and school readiness, as depicted in Figure 3.

4. Is the effect of self-regulation stronger for disadvantaged children?

We expected effects of self-regulation to be stronger for disadvantaged children.

Figure 3 shows the conceptual model that will be tested. We will examine four models, one for each of the school engagement outcomes. Based on the literature reviewed in Chapter 1, we expect that:

> task attentiveness, emotion regulation and school readiness will directly influence each of the four school engagement outcomes at wave 4, which will then feed into wave 5 engagement

> self-regulation and school readiness will also have direct influences on wave 5 engagement. This is based on research suggesting that the influence of self-regulation may be magnified over time.

> disadvantage, home-learning environment and parenting will not have a direct influence on school engagement, but will operate indirectly through influences on task attentiveness and school readiness, as depicted in Figure 3.

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Figure 3. Conceptual model for testing



5.1 Data analysis

Figure 3 shows the general conceptual model that was to be tested. We examined four models, one for each of the school engagement outcomes. In each model, the specification of waves 1 to 3 was the same.14

For each outcome, we tested models in three steps15:

1. the hypothesised model, as show in Figure 3

2. a model in which non-hypothesised direct paths from parenting, disadvantage and home-learning environment to the school engagement outcomes were added

3. a final model which retained the hypothesised paths from Step 1 and any non-hypothesised but significant paths from Step 2.

Covariates included gender, wave 4 Year level, school readiness at wave 3, and wave 4 school ICSEA. Both wave 4 and wave 5 school engagement outcomes were regressed on wave 4 Year level and ICSEA; wave 5 Year levels and wave 5 ICSEA were not included because they are too highly correlated with these variables at wave 4. Estimates for ICSEA, gender and Year level are not reported in this chapter but can be found in Appendix E: . Similarly, within-time covariances were estimated and are in Appendix D: .

In the discussion of the results from these structural equation models, we refer to direct paths between an explanatory variable and an outcome as ‘direct effects’. This does not imply causation, but is rather short hand for this type of influence. Pathways to an outcome that include the influence of an explanatory variable operating via a third variable, such as home-learning environment to school readiness and school readiness to school engagement, are referred to as ‘indirect effects’. Again, this nomenclature does not imply causation.

Indirect paths (i.e., mediated paths) were tested in the final model using the Delta method (MacKinnon, Warsi, & Dwyer, 1995). In a complicated model like this, there are dozens of indirect paths that could be considered. Because the focus of the report is on self-regulation, we limit the discussion of indirect influences to those operating via wave 3 self-regulation. With regard to this, we consider:

> how much wave 3 self-regulation mediated effects of parenting, disadvantage and home-learning environment on wave 4 engagement

> how much self-regulation mediated effects of parenting, disadvantage and home-learning environment on wave 5 engagement.

To test whether the effects of self-regulation were stronger for disadvantaged children, we divided the sample into children who were living in households with at least one indicator of disadvantage at wave 1, and those who had none. We then estimated the model in Figure 3 for children in both

14 All models were estimated using weighted least squares estimation with a mean and variance-adjusted chi-square (WLSMV), which is appropriate for ordered categorical and binary outcomes.

15 Goodness of fit for the models was evaluated with the root mean square error of approximation (RMSEA), the comparative fit index (CFI), the Tucker-Lewis fit index (TLI) and the weighted root mean square residual (WRMR). Well-fitting models are generally indicated by a RMSEA of < = 0.06, although < 0.08 indicates adequate fit, CFI and TLI of > = 0.95, and WRMR of close to or under 1 (Finney & DiStefano, 2013). The WRMR is a newer, less evaluated index, so we use it as a supplement to the other indices. Model chi-square can also be considered, where a non-significant value is the gold standard. However, as the chi-square is very sensitive to sample size, we expect it to be significant for all models reported here. Therefore, we follow recommendations for researchers working with large samples (Kline, 2011; Meade, Johnson, & Braddy, 2008) and rely more on the other fit indices in evaluating absolute model fit.

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groups. To test whether pathways differed between groups, we compared the fit of a model where structural paths were freely estimated between groups with the fit of a model where structural paths were constrained to be equal across groups. If the fit of the constrained model were significantly worse, it would indicate that some paths were significantly different. Differences in fit were tested with changes in CFA, RMSEA, and 2, as described in Appendix A: .

Standardised estimates are reported throughout.

Missing dataTaking all the variables into account, only 1,818 (42 per cent) of the sample of 4,351 had complete data, meaning that they had no missing variables at any wave. The model estimation approach used allowed all 4,351 cases to be used in the analysis.16 Full details of missingness are reported in Appendix D: .

5.2 Results for question 3: approaches to learning

Step 1 of the analyses involved estimating the model shown in Figure 3 with approaches to learning as the dependent variable at waves 4 and 5. This hypothesised model was a good fit to the data (RMSEA = 0.020 [90%CI = 0.020–0.021]; CFI/TLI= 0.980/0.978; 2(1010) = 2836.624, p< 0.001; WRMR = 1.776).

Step 2 of the analysis involved adding direct paths from parenting, home-learning environment and disadvantage to both wave 4 and wave 5 approaches to learning. Results showed that there were significant direct influences from disadvantage to both wave 4 and 5 approaches to learning, and from wave 2 home-learning environment to wave 4 approaches to learning.

Step 3 of the analysis estimated a final model, building on Steps 1 and 2. In this model, the significant direct paths from disadvantage and home-learning environment from Step 2 were retained. This final model is shown in Figure 4.17

Direct influence of explanatory variables on approaches to learningAll hypothesised paths in the approaches to learning model were significant. As expected:

> Children who were poorer at task attentiveness and had higher irritability/anger reactivity at wave 3 were rated lower on approaches to learning at wave 4.

> School readiness at wave 3 had the strongest direct influence on wave 4 approaches to learning.

> Self-regulation and school readiness were also directly associated with changes in approaches to learning over waves 4 to 5. For example, after accounting for the substantial stability in approaches to learning over the two waves, children who scored one standard

16 In this study, we assume data are missing at random (MAR), which means that missingness on a variable can be related to other variables in the dataset, but should not be due to the values of the variable with missing data.. With WLSMV estimation, all cases are used in the analysis, and a hybrid approach to missing data is taken, with some steps based on full-information maximum likelihood and some on pairwise deletion. However, simulations suggest that, under the MAR assumption, estimates are consistent and efficient, and certainly preferable to listwise deletion (Asparouhov & Muthén, 2010).

17 Note that in Figure 4 and all subsequent Figures, for clarity, estimates for autoregressive paths and cross-lagged paths between the measures of the same construct over time are greyed out. Only statistically significant paths are shown.



deviation higher in school readiness at wave 3 were one-tenth of a standard deviation higher in wave 5 approaches to learning. Effects for wave 3 task attentiveness and irritability/anger were about half this size.

Non-hypothesised paths from home-learning environment factors and disadvantage were also significant. There was a small but significant effect of wave 1 disadvantage on wave 5 approaches to learning only. A more stimulating home-learning environment with regard to reading was related to higher ratings on approaches to learning at wave 4, and this effect was larger than the effects found for self-regulation. Surprisingly, however, a more stimulating environment with regard to arts, crafts and other in-home activities was associated with lower ratings on approaches to learning at wave 4.

Given that the negative effect of stimulating in-home activities was unexpected, and that the reading and in-home activities factors of home-learning environment were highly correlated (about 0.60), we considered that the effect might be due to suppression. Suppression is a situation that can arise in multiple regression when highly correlated independent variables are used, which results in the magnitude of the relationship between an independent variable and the dependent variable becoming larger when another independent variable is added to the model (Conger, 1974). To check for this, we estimated the model twice, once with only the reading factor of home-learning environment, and once with only the in-home activities factor. The reading factor was still positively related to approaches to learning, in the absence of the in-home activities factor. In the absence of reading, however, the in-home activities factor was not significantly related to approaches to learning. The suppression probably arises because parents who engage in a lot of reading activities with their child are highly likely to also engage in other stimulating activities. When this overlapping variability is partialled out from both the predictors and the dependent variable, the leftover part of in-home activities that is uniquely related to approaches to learning is associated with lower levels of approaches to learning. This may simply be another way of indicating that home environments low in reading are related to poorer school adjustment and performance.

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Figure 4. Estimates from the final model examining approaches to learning.

* p < .05; **p < .01; ***p < .001; Model fit: RMSEA (90%CI) = 0.020 (0.019–0.021) CFI/TLI = 0.981/0.978; 2(1007) = 2811.20, p < .001; WRMR = 1.749

Covariates include gender, wave 4 Year level, and wave 4 school ICSEA. Standardised estimates are shown. All hypothesised paths are estimated, but only significant estimates are shown.



Indirect influences on approaches to learning mediated via wave 3 self-regulationTable 10 shows standardised estimates for indirect influences on approaches to learning operating via wave 3 task attentiveness and irritability/anger. These indirect influences could originate with wave 1 disadvantage, wave 1 hostile parenting or wave 1 maternal depressive symptoms. For each of these wave 1 variables, Table 10 shows:

> the total indirect effect operating via all other wave 2 and wave 3 variables in the model, such as self-regulation, parenting, the home-learning environment and school readiness

> the total indirect effect operating via wave 3 task attentiveness

> the total indirect effect operating via wave 3 irritability/anger.

For example, it can be seen that the total indirect effect of disadvantage for wave 4 approaches to learning was –0.176.18 This quantifies how differences in disadvantage at wave 1 relate to differences in wave 4 approaches to learning through all mediators at once (i.e., self-regulation, home-learning environment, later parenting and school readiness). For instance, a child who was one standard deviation higher than another child on wave 1 disadvantage was estimated to be 0.176 standard deviations lower on wave 4 approaches to learning as a result of the ways in which disadvantage influences all subsequent variables in the model that relate to wave 4 approaches to learning. In comparison to the direct influences on wave 4 approaches to learning shown in Figure 4, this is a fairly large influence.

The indirect influence of disadvantage that operated via wave 3 task attentiveness and irritability/anger are shown in column 2 of Table 10. Compared to the total indirect effect of disadvantage (–0.176), these influences are small. Although not hypothesised, the indirect effect of disadvantage operating via the reading component of the home-learning environment was –0.093, about half of the total indirect effect of disadvantage on wave 4 approaches to learning.

The pattern of indirect influences extending to wave 5 approaches to learning (final three columns of Table 10) was similar, although most of the influences of earlier variables on wave 5 approaches to learning operated via their effects on wave 4 approaches to learning. This is likely to be due to the substantial stability in approaches to learning between wave 4 and wave 5 (see Figure 4).

The key implications from Table 10 are:

> Disadvantage exerted a large total indirect effect on wave 4 and wave 5 approaches to learning. This shows that disadvantage affected approaches to learning via many intervening processes.

> Indirect influences of wave 1 disadvantage for approaches to learning were larger than indirect influences of wave 1 hostile parenting or maternal depressive symptoms.

> However, only a minority of this indirect influence of disadvantage operated via wave 3 task attentiveness and irritability/anger. Most operated by the home-learning environment, an unexpected finding we return to in Chapter 8.

> The indirect influences of hostile parenting and maternal depressive symptoms operated mainly via wave 3 task attentiveness and irritability/anger, although these were small effects.

18 Indirect effects are products of coefficients. They are obtained by multiplying coefficients that are part of the specific indirect pathway. For example, in Figure 9, consider the path: wave 1 hostile parenting -> wave 2 emotion regulation -> wave 3 emotion regulation -> wave 4 approaches to learning -> wave 5 approaches to learning. Tracing the coefficients for each link in the pathway, the standardised specific indirect effect is 0.15 x 0.57 x -0.11 x 0.53 = -0.005. The Delta method is used to compute a standard error and confidence interval for this indirect effect, allowing the researcher to make statistical inferences about the effect.

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Table 10. Standardised estimates for indirect paths to approaches to learning

Estimates for wave 4 approaches to learning

Estimates for wave 5 approaches to learning

Indirect effects originating with…

Total indirect

effect

Total indirect effect via

wave 3 task attentiveness


wave 3 irritability/

anger

Total indirect

effect




wave 3 irritability/

anger

Wave 1 disadvantage

–0.176*** –0.018*** –0.016*** –0.156*** –0.017*** –0.019***

Wave 1 hostile parenting

–0.016* –0.008*** –0.014*** –0.021*** –0.008*** –0.017***

Wave 1 maternal depressive symptoms

–0.032*** –0.011*** –0.006*** –0.034*** –0.011*** –0.019***

Notes: *p < .05; **p < .01; ***p < .001

5.3 Results for question 3: absenteeism

The hypothesised model in Step 1 fit the data reasonably well (RMSEA = 0.026 [90%CI = 0.025–0.028]; CFI/TLI = 0.911/0.893; WRMR = 1.964; 2(587) = 2368.17, p< 0.001). The Step 2 model revealed significant paths from disadvantage to absenteeism. The final model (Step 3) is shown in Figure 5.

Only one of the hypothesised paths was significant: children who were more school ready at wave 3 were less likely to be absent by wave 5, but, interestingly, not at wave 4. However, this effect was not very strong in comparison to disadvantage. To illustrate, the predicted probability of being absent for two or more days in the past four weeks at wave 5 for children with no indicators of disadvantage was 0.16, rising to 0.48 for children with three indicators of disadvantage. In comparison, the predicted probability of being absent for children in the lowest decile of school readiness was 0.35, and in the highest decile was 0.30. This shows that absences varied more strongly with disadvantage than with school readiness.

Because neither task attentiveness nor irritability/anger were significantly associated with absenteeism, there were no indirect effects of wave 3 self-regulation to report.



Figure 5. Estimates from the final model examining absenteeism.

* p < .05; **p < .01; *** p< .001; Model fit: RMSEA (90%CI) = 0.026 (0.025–0.027) CFI/TLI = 0.913/0.985; 2(581) = 2328.21, p < 0.001; WRMR = 1.934

Covariates include gender, wave 4 Year level and wave 4 school ICSESA. Significant standardised estimates are shown.

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5.4 Results for question 3: school liking

In Step 1, the hypothesised model was an adequate fit to the data (RMSEA = 0.020[90%CI = 0.022–0.024]; CFI/TLI = 0.928/0.917; 2(740) = 2388.37, p < .001). In the Step 2 model, there were significant direct paths from disadvantage and wave 2 home-learning environment to wave 4 liking. In Step 3, these were retained in the final model (Figure 6).

Direct effects of explanatory variables on school likingAs expected, task attentiveness and irritability/anger were related to school liking. However, this was not apparent until wave 5. School readiness was not significantly associated with school liking.

There were three significant paths that were not hypothesised in our conceptual model. Firstly, disadvantage had a direct effect on wave 4 school liking.

Secondly and thirdly, a home environment that provided more reading-related stimulation was associated with lower levels of school liking at wave 4, while more stimulation related to games, stories and arts and crafts was related to higher levels of school liking. We suspected that the opposite signs for the reading and in-home factors of home-learning environment were indicative of suppression, as described above with regard to the approaches to learning. In a model without the in-home factor, reading was still negatively related to school liking. In a model without the reading factor, in-home activities were not significantly related to school liking. Therefore, the reading factor of home-learning environment seems to inflate the influence of other in-home activities. It does seem that children who come from home environments with high levels of reading activity like school less in Year 1. There may be good reasons for this, which we discuss in Chapter 8.



Figure 6. Estimates from the final model examining school liking.

* p < .05; **p < .01; ***p < .001; Model fit: RMSEA (90%CI) = 0.026 (0.025–0.027) CFI/TLI = 0.913/0.895; 2(585) = 2335.50, p < 0.001; WRMR = 1.948.

Covariates include gender, wave 4 Year, Year level, and wave 4 school ICSEA. Significant standardised estimates are shown.

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Indirect effects on school liking mediated via wave 3 self-regulationWave 3 self-regulation did not mediate effects of earlier variables on wave 4 school liking, because self-regulation variables were not significantly related to wave 4 school liking. Therefore, we report indirect influences of wave 3 self-regulation on wave 5 school liking only.

These effects are summarised in Table 11. Note that the total indirect effect of disadvantage on wave 5 school liking is positive. This is because disadvantage was associated with less reading in the home-learning environment, which was associated with more wave 4 school liking (see Figure 5). Nonetheless, the total indirect effects of disadvantage via wave 3 task self-regulation are negative, showing that children who were higher on disadvantage at wave 1 were estimated to be significantly lower on school liking in wave 5, because disadvantage was associated with poorer task attentiveness and more irritability/anger reactivity over waves 2 and 3. However, these indirect influences via wave 3 self-regulation were small, accounting for around 0.01 of a standard deviation in wave 5 school liking.

The key results from the school liking model show:

> Direct links between self-regulation at ages 4–5 and school liking were significant by wave 5 but not strong.

> Disadvantage, hostile parenting and maternal depressive symptoms were indirectly related to lower school liking at wave 5 because they were associated with continuing poor self-regulation over waves 2 and 3.

> Children who were in home environments with more reading at ages 2–3 were significantly lower on school liking at wave 4. This was unexpected.

Overall, the results illustrate the complexities of children’s affective engagement in the early years of primary school, which we discuss further in Chapter 8.

Table 11. Standardised estimates for indirect paths to school liking at wave 5

Indirect effects originating with… Total indirect effect Total indirect effect via wave 3 task

attentiveness

Total indirect effect via wave 3 irritability/anger

Wave 1 disadvantage 0.12*** –0.010** –0.013**

Wave 1 hostile parenting –0.012* –0.012* –0.005*


–0.010 –0.005** –0.014**

Notes: *p < .05; **p < .01; ***p < .001

5.5 Results for question 3: maths liking

In Step 1, the hypothesised model was an adequate fit to the data (RMSEA = 0.026[90%CI = 0.025–0.027]; CFI/TLI = 0.913/0.896; 2(587) = 2325.37, p < .001). In Step 2, there were significant direct paths from wave 2 home-learning environment and wave 3 hostile parenting to wave 4 maths liking. These were retained in the final model, which is shown in Figure 7.



Direct effects of explanatory variables on maths likingThe results were in many way similar to those for school liking, except that irritability/anger was not significantly related to maths liking. At both waves, higher wave 3 task attentiveness was associated with more maths liking. School readiness was related to higher maths liking, but not until wave 5. That is, after accounting for stability in maths liking over time, better task attentiveness and higher levels of school readiness at wave 3 predicted increases in school liking over the two-year interval from Years 1–2 to Years 3–4. This suggests that good attention skills and school readiness may be useful in helping children stay engaged with maths in the early grades.

Similar to school liking, a home environment that provided more reading-related stimulation was associated with lower levels of maths liking at wave 4, while more stimulation related to games, stories and arts and crafts was related to higher levels of maths liking. Once again, this is probably due to a suppression effect with the reading factor of home-learning environment. There was also a small but significant tendency for children who experienced more hostile parenting at wave 3 to like maths less at wave 4.

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Figure 7. Estimates from the final model examining maths liking.

*p < .05; **p < .01; ***p < .001; Model fit: RMSEA (90%CI) = 0.026 (0.025–0.027) CFI/TLI = 0.913/0.985; 2(581) = 2328.21, p < .001; WRMR = 1.934.

Covariates include gender, wave 4 Year level, and wave 4 school ICSEA. Significant standardised estimates are shown.



Indirect effects on maths liking mediated via wave 3 self-regulationIndirect influences of disadvantage, hostile parenting and maternal depressive symptoms on maths liking via wave 3 self-regulation are summarised in Table 12. Note that the total indirect effect for disadvantage was positive, because of the negative effect of disadvantage on the reading dimension of the home-learning environment, which predicted higher maths liking in wave 4.

Disadvantage, hostile parenting and maternal depressive symptoms were indirectly related to less maths liking at waves 4 and 5 because they were associated with continuing poor self-regulation over waves 2 and 3. In many cases, these indirect influences were transferred by stability in maths liking over waves 4 and 5.

Table 12. Standardised estimates for indirect paths to maths liking

Estimates for wave 4 maths liking

Estimates for wave 5 maths liking

Indirect effects originating with… Total indirect

effect

Total indirect effect via wave

3 task attentiveness

Total indirect

effect via wave 3

irritability/ anger

Total indirect effect



Total indirect

effect via wave 3

irritability/anger

Wave 1 disadvantage 0.07* –0.008* - 0.02 –0.013*** -

Wave 1 hostile parenting –0.013* –0.004* - –0.009* –0.007** -


–0.005 –0.002* - –0.008 –0.008*** -

Notes: *p < .05; **p < .01; ***p < .001

5.6 Results for question 4: Is the effect of self-regulation on school engagement stronger for disadvantaged children?

For each school engagement outcome, the fit of models in which paths were constrained to be equal between children with better and poorer self-regulation skills was not significantly worse, suggesting that effects of self-regulation (and other variables) were not significantly different for disadvantaged and more advantaged children. We also used this procedure to test for differences between boys and girls and did not find that paths differed significantly by gender.

5.7 Summary

The results of the structural equation models in this chapter presented a nuanced illustration of the developmental pathways to school engagement in Years 1–2 and 3–4. As expected, task attentiveness and irritability/anger at ages 4–5 were related to school engagement outcomes, and they did mediate influences from earlier hostile parenting, disadvantage and maternal depressive

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symptoms. However, these pathways were not consistent across the four outcomes, and there were also strong direct influences of disadvantage and home-learning environment—especially the reading component.

Key points:

> For approaches to learning, school liking, and maths liking:

– Self-regulation (good task attentiveness and low levels of irritability/anger) got children off to a good start in school and helped them stay engaged over the early grades.

– Poor self-regulation at ages 4–5 was related to early disadvantage, hostile parenting and maternal depressive symptoms.

– Early disadvantage, hostile parenting and maternal depressive symptoms influenced later school engagement, partly through their effects on self-regulation.

> However, these influences were strongest for approaches to learning.

> Absenteeism in the early grades was not related to self-regulation and was related most strongly to early disadvantage.

> The home-learning environment emerged as an important precursor of approaches to learning. The strong association between disadvantage and less reading in the home-learning environment suggests that this is a key transformer of the effect of early disadvantage for later school engagement.



6. Results of Study 1 (Q5): School engagement and achievement

This chapter addresses our 5th research question for Study 1:

5. How much does engagement in wave 4 (Years 1–2) matter for achievement in school Year 3?

Because many studies show that children who are more engaged with school achieve higher grades, we expected higher levels of all four school engagement measures to be related to higher achievement.

To answer this question, we examined wave 4 engagement as a predictor of children’s Year 3 NAPLAN scores.

6.1 Data analysis

To examine how school engagement was related to numeracy and reading NAPLAN scores in Year 3, we estimated models in which the NAPLAN test at Year 3 was regressed on all four school engagement outcomes at wave 4 as well as selected covariates, including wave 3 school readiness, wave 3 self-regulation, wave 2 home-learning environment, wave 1 disadvantage, wave 4 Year level, and 2010 school ICSEA. We also controlled for the child’s age in months at the time of the Year 3 test. One model examined numeracy, and the other literacy. Given the gender differences in achievement, especially for numeracy, we estimated these models separately for boys and girls.

We tested models in 3 steps. In Step 1, for each outcome (numeracy and reading), models included only a school engagement dimension as a predictor of numeracy/reading, as well as wave 4 Year level and age in months when the test was taken. Note that, for convenience, estimates for Step 1 are shown in one column, but each school engagement dimension was in its own model. In Step 2, we estimated a model which included all four school engagement dimensions together. In Step 3, models were estimated which added selected covariates shown to be direct predictors of engagement in Chapter 6 and which were likely to also be related to test scores.

6.2 Results

Results for numeracy are summarised in Table 13 and for reading in Table 14. Clearly, approaches to learning was the only dimension of engagement that had consistent consequences for children’s achievement in the short term. In Steps 2 and 3, both boys and girls who were one standard deviation higher in wave 4 approaches to learning were estimated to be about 0.35 to 0.40 of a standard deviation higher in Year 3 NAPLAN scores. The addition of covariates like disadvantage and school readiness reduced the coefficients for approaches to learning, but they remained strong influences. For instance, for girls, approaches to learning was associated with a quarter of a standard deviation higher numeracy and reading scores, independent of the effects of disadvantage

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and school readiness. Overall, effect sizes for approaches to learning were comparable to those for school readiness.

Absenteeism was negatively associated with numeracy scores, but not when approaches to learning was added to the model. Similarly, maths liking predicted slightly higher numeracy scores for boys, but this was not independent of approaches to learning.

It is worthwhile noting that school ICSEA was associated with higher academic achievement for the numeracy and literacy for both boys and girls even after other characteristics were taken into account. Children’s academic achievement was at least one-quarter of a standard deviation higher, which was comparable to most other influences in our statistical models.

Table 13. Standardised estimates for associations between wave 4 school engagement and NAPLAN numeracy scores in Year 3

Girls Boys

Step 1 Step 2 Step 3 Step 1 Step 2 Step 3

School engagement

Approaches to learning 0.42*** 0.43*** 0.25*** 0.40*** 0.40*** 0.19***

School liking 0.01 –0.04 –0.01 0.03 –0.03 0.03

Maths liking 0.02 –0.04 0.01 0.07* –0.01 0.01

Absenteeism –0.07* –0.04 –0.01 –0.08* –0.04 –0.01

School readiness (w3) - - 0.27*** - - 0.29***

Self-regulation (w3)

Task attentiveness - - 0.07* - - 0.08*

Irritability/anger - - 0.01 - - –0.02

Home-learning env. (w3)

Reading dimension - - 0.17** - - 0.32***

In-home activities dimension

- - –0.03 - - –0.18***

Disadvantage (w1) - - 0.02 - - 0.08

School ICSEA (2010) - - 0.24*** - - 0.30***

W4 Year level (ref = Year 1)

Year 2 0.11 0.08 0.05 –0.10 –0.10 –0.08

Age in months at test 0.13*** 0.13*** 0.11** 0.05 0.05 0.05

Notes: *p < .05; **p < .01; ***p < .001



Table 14. Standardised estimates for associations between wave 4 school engagement and NAPLAN reading scores in Year 3

Girls Boys

Step 1 Step 2 Step 3 Step 1 Step 2 Step 3

School engagement

Approaches to learning 0.40*** 0.42*** 0.25*** 0.35*** 0.38*** 0.17***

School liking –0.03 –0.06 –0.02 –0.01 –0.001 0.06

Maths liking –0.04 –0.08 –0.02 –0.02 –0.11* –0.09*

Absenteeism –0.04 –0.02 0.02 –0.03 0.01 0.04

School readiness (w3) - - 0.20*** - - 0.21***

Self-regulation (w3)

Task attentiveness - - 0.02 - - 0.08*

Irritability/anger - - –0.03 - - –0.03

Home-learning env. (w3)

Reading dimension - - 0.28*** - - 0.40***

In-home activities dimension - - –0.09 - - –0.14**

Disadvantage (w1) - - 0.01 - - 0.08

School ICSEA (2010) - - 0.24*** - - 0.31***

W4 Year level (ref = Year 1)

Year 2 0.16 0.12 0.09 –0.16 –0.15 –0.12

Age in months at test 0.13*** 0.13*** 0.11*** 0.10** 0.10** 0.09**

Notes: *p < .05; **p < .01; ***p < .001

6.3 Summary

> Children who rated higher on approaches to learning in Year 1 had significantly higher NAPLAN numeracy and reading scores in Year 3. These were strong influences that persisted after controlling for a range of other variables also related to achievement.

> Children who were absent at least two days out of the past four weeks in Year 1 had lower numeracy scores in Year 3, but this effect was not significant when approaches to learning was in the model. It is possible that children who are frequently absent do worse academically because they are less skilled at classroom participation.

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7. Results of Study 2: ECEC quality and school engagement

1. Is higher-quality ECEC at ages 4–5 related to higher levels of school engagement over Years 1 to 3?

2. Is higher-quality ECEC especially beneficial for children with poorer self-regulation?

3. Is higher-quality ECEC especially beneficial for disadvantaged children?

7.1 Data analysis

We examined the ECEC variables as predictors of school engagement in a series of regression analyses. Linear regression was used for school liking and approaches to learning, ordered logistic regression for maths liking, and logistic regression for absenteeism.19

For each outcome, we report results of the following models:

> a model containing only the ECEC quality variables

> a model adjusted for covariates.

We first examine wave 4 school engagement outcomes, then wave 5 outcomes adjusted for wave 4.

Finally, we examine whether task attentiveness, irritability/anger or disadvantage interact with the ECEC quality variables in the prediction of school engagement.20 The potential moderating effects of task attentiveness, irritability/anger, and disadvantage are tested separately. Also, to aid interpretation, interactions are only examined for one aspect of ECEC quality at a time. For example, in examining moderating effects of task attentiveness, we first test interactions with carer warmth and conflict in one model, then interactions with the four ECEC activities in a second model, and interactions with carer qualifications in a third model. Interactions with child gender were carried out in the same way.

All analyses were carried out in Stata 14.0.

Missing dataThere were missing data for 1,249 (37 per cent of 3,308). Most of the missing data was due to the carer questionnaire not being returned at wave 3—about 22 per cent of children were missing ECEC data. To deal with missing data, we used multiple imputation with chained equations,

19 Measures of approaches to learning, school liking, self-regulation and home-learning environment were factor scores based on the confirmatory factor analyses described in Appendix A.

20 For moderation analyses, disadvantage was dichotomised so that families with at least one indicator were classified as experiencing disadvantage. Task attentiveness was dichotomised into low (bottom quartile) and not low, and irritability/anger as high (top quartile) and not high.



assuming data were missing at random. Details of missingness and the imputation procedure are in Appendix D: .

Analyses were also weighted to take account of the survey design. For wave 4 dependent variables, we used sampling weights designed for analyses using waves 1 to 4, and for wave 5 dependent variables, we used sampling weights designed for analyses using waves 1 to 5. Values are missing on these weights if a child did not participate at wave 4 or 5, respectively. Thus, although we used multiple imputation for missing values, the sample size for the wave 4 analyses was 3,101 and for wave 5 analyses was 2,922.

7.2 Results for Question 1: ECEC quality and school engagement

Results of regressions predicting wave 4 school engagement outcomes from wave 3 ECEC quality variables are shown in Tables 15 and 16. Results of regressions predicting wave 5 school engagement are shown in Table 17 and Table 18. Results were quite similar for both wave 4 and wave 5 outcomes:

> None of the school engagement outcomes at either wave was significantly associated with any of the ECEC activities.

> Children whose ECEC carer had a Bachelor in ECEC liked school at wave 4 significantly more than children whose carer had a Bachelor in another field, and this was true after adjustment for covariates. However, there were not significant differences in school liking between any other pair of qualification levels.

> Higher conflict with an ECEC teacher appears to matter. After adjustment for covariates, higher conflict in a child’s relationship with their ECEC carer was associated with:

– lower scores on approaches to learning at both waves 4 and 5. Follow-up analyses showed that a one standard deviation increase in conflict was associated with a 0.19 standard deviation decrease in wave 4 approaches to learning and a further 0.05 standard deviation decrease in wave 5 approaches to learning.

– less school liking at waves 4 and 5. At both waves, a standard deviation unit increase in conflict was associated with a 0.06 standard deviation decrease in school liking.

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Table 15. Estimates from regression models predicting wave 4 behavioural engagement from wave 3 ECEC quality

Wave 4 Approaches to learning Wave 4 Absenteeism

Unadjusted Adjusted1 Unadjusted Adjusted1

Est. (95% CI) Est. (95% CI) OR (95% CI) OR (95% CI)

ECEC Activities

Teacher-supported individual activities

0.02 (–0.03–0.08) 0.01 (–0.04–0.06) 0.89 (0.76–1.04) 0.90 (0.76–1.05)

Teacher-supported small group activities

0.00 (–0.06–0.06) 0.01 (–0.04–0.07) 1.00 (0.81–1.22) 0.96 (0.78–1.18)

Teacher-directed whole-group activities

–0.05 (–0.11–0.02) –0.04 (–0.11–0.02) 1.05 (0.88–1.26) 1.06 (0.89–1.26)

Child-initiated activities 0.02 (–0.07–0.10) 0.03 (–0.05–0.10) 0.91 (0.72–1.14) 0.91 (0.71–1.16)

Carer qualifications (ref = BA in ECEC)

No ECEC qualification –0.03 (–0.12–0.06) 0.01 (–0.08–0.09) 0.93 (0.72–1.19) 0.98 (0.75–1.29)

Diploma in ECEC –0.03 (–0.16–0.09) –0.05 (–0.17–0.07) 0.94 (0.69–1.29) 0.97 (0.70–1.34)

Bachelor in other field –0.05 (–0.15–0.05) –0.06 (–0.15–0.03) 0.99 (0.75–1.31) 0.97 (0.74–1.28)

Relationship with child

Warmth 0.11 (0.04–0.18)** 0.06 (–0.01–0.12) 0.99 (0.82–1.20) 1.05 (0.87–1.27)

Conflict –0.29 (–0.34– -0.23)*** –0.22 (–0.27– -0.17)*** 0.98 (0.83–1.15) 1.00 (0.84–1.18)

Notes: *p < .05; **p < .01; ***p < .001. OR = Odds ratio. CI = Confidence interval

1. Covariates in the adjusted models include school readiness, wave 3 self-regulation, wave 2 home-learning environment, disadvantage, Year level, school ICSEA, age when first enrolled in ECEC, hours per week in first ECEC enrolment.



Table 16. Estimates from regression models predicting wave 4 affective engagement from wave 3 ECEC quality

Wave 4 School Liking Wave 4 Maths Liking

Unadjusted Adjusted1 Unadjusted Adjusted1


ECEC Activities


0.01 (–0.03–0.06) 0.01 (–0.04–0.06) 0.00 (–0.02–0.02) 0.98 (0.85–1.14)


–0.01 (–0.06–0.04) –0.01 (–0.06–0.04) –0.01 (–0.03–0.02) 1.06 (0.88–1.28)


–0.02 (–0.07–0.03) –0.02 (–0.08–0.03) 0.01 (–0.02–0.03) 0.96 (0.82–1.12)

Child-initiated activities –0.02 (–0.08–0.05) –0.02 (–0.09–0.05) 0.00 (–0.03–0.03) 1.01 (0.81–1.26)


No ECEC qualification 0.00 (–0.06–0.07) 0.00 (–0.07–0.06) 0.00 (–0.03–0.03) 0.98 (0.75–1.27)

Diploma in ECEC –0.03 (–0.11–0.06) –0.02 (–0.11–0.06) 0.01 (–0.03–0.05) 0.94 (0.69–1.28)

Bachelor in other field –0.10 (–0.19– -0.02)* –0.10 (–0.19– -0.02)* 0.00 (–0.04–0.03) 1.04 (0.78–1.40)


Warmth 0.05 (–0.01–0.12) 0.05 (–0.01–0.11) 0.00 (–0.02–0.03) 0.97 (0.80–1.19)

Conflict –0.07 (–0.13–0.02)** –0.06 (–0.12–0.01)* 0.02 (0.0–0.04)* 0.86 (0.73–1.01)


1. Covariates in the adjusted models include school readiness, wave 3 self-regulation, wave 2 home-learning environment, disadvantage, Year level, school ICSEA, age when first enrolled in ECEC, hours per week in first ECEC enrolment.

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Table 17. Estimates from regression models predicting wave 5 behavioural engagement from wave 3 ECEC quality

Wave 5 Approaches to learning Wave 5 Absenteeism

Unadjusted1 Adjusted2 Unadjusted1 Adjusted2


ECEC Activities


0.00 (–0.04–0.04) 0.00 (–0.04–0.03) 0.92 (0.78–1.08)

0.93 (0.79–1.10)


0.01 (–0.04–0.05) 0.02 (–0.02–0.06) 1.20 (0.98–1.47)

1.18 (0.96–1.44)


–0.02 (–0.06–0.03) –0.02 (–0.06–0.02) 0.93 (0.78–1.12)

0.93 (0.77–1.12)

Child-initiated activities 0.01 (–0.04–0.06) 0.00 (–0.05–0.06) 1.17 (0.90–1.53)

1.13 (0.85–1.51)


No ECEC qualification 0.00 (–0.07–0.06) 0.01 (–0.05–0.08) 0.90 (0.68–1.20)

0.91 (0.68–1.22)

Diploma in ECEC –0.06 (–0.15–0.03) –0.07 (–0.15–0.01) 1.19 (0.87–1.62)

1.22 (0.89–1.68)

Bachelor in other field –0.07 (–0.14–0.0) –0.07 (–0.13–0.0) 1.15 (0.84–1.57)

1.14 (0.83–1.58)


Warmth –0.02 (–0.07–0.03) –0.03 (–0.08–0.02) 0.98 (0.81–1.18)

1.05 (0.86–1.28)

Conflict –0.08 (–0.12– -0.04)*** –0.06 (–0.10– -0.03)*** 1.11 (0.93–1.32)

1.08 (0.90–1.30)


1. The unadjusted model includes the dependent variable at wave 4

2. Covariates in the adjusted models include the dependent variable at wave 4, school readiness, wave 3 self-regulation, wave 2 home-learning environment, disadvantage, Year level, school ICSEA, age when first enrolled in ECEC, hours per week in first ECEC enrolment.



Table 18. Estimates from regression models predicting wave 5 affective engagement from wave 3 ECEC quality

Wave 5 School Liking Wave 5 Maths Liking

Unadjusted1 Adjusted2 Unadjusted1 Adjusted2


ECEC Activities


–0.01 (–0.03–0.01) –0.01 (–0.03–0.01) 1.01 (0.86–1.20)

1.01 (0.85–1.19)


0.01 (–0.01–0.03) 0.01 (–0.01–0.03) 0.93 (0.77–1.12)

0.93 (0.77–1.12)


0.0 (–0.02–0.02) 0.00 (–0.02–0.02) 1.04 (0.88–1.24)

1.04 (0.87–1.23)

Child-initiated activities 0.01 (–0.02–0.04) 0.01 (–0.02–0.04) 1.03 (0.82–1.30)

1.03 (0.80–1.31)


No ECEC qualification 0.00 (–0.02–0.03) 0.01 (–0.02–0.03) 1.05 (0.80–1.38)

1.02 (0.76–1.35)

Diploma in ECEC 0.02 (–0.01–0.05) 0.02 (–0.02–0.05) 1.12 (0.83–1.52)

1.13 (0.83–1.53)

Bachelor in other field –0.02 (–0.05–0.02) –0.02 (–0.05–0.02) 0.95 (0.71–1.26)

0.95 (0.71–1.26)


Warmth 0.00 (–0.02–0.03) 0.00 (–0.02–0.02) 1.05 (0.87–1.26)

1.05 (0.87–1.27)

Conflict –0.04 (–0.06– -0.02)*** –0.03 (–0.05– -0.01)** 0.94 (0.80–1.11)

0.94 (0.79–1.12)


1. The unadjusted model includes the dependent variable at wave 4

2. Covariates in the adjusted models include the dependent variable at wave 4, school readiness, wave 3 self-regulation, wave 2 home-learning environment, disadvantage, Year level, school ICSEA, age when first enrolled in ECEC, hours per week in first ECEC enrolment.

It is possible that effects of relationship conflict on school liking and approaches to learning are due to children’s behaviour problems. Specifically, children with behaviour problems who are difficult to teach may have both more conflict in their relationship with ECEC carers and lower school liking and approaches to learning. In models not reported here, we included teacher-reported conduct problems and hyperactivity subscales from the Strengths and Difficulties Questionnaire (SDQ) with the other covariates.

Neither conduct problems or hyperactivity were significantly related to wave 4 or wave 5 school liking, and the influence of teacher conflict remained a significant predictor of lower school liking. However, hyperactivity was strongly related to lower teacher-rated approaches to learning at both waves 4 and 5, and, in these models, the effects of teacher–student conflict were not statistically significant.

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7.3 Results for questions 2 and 3: Are effects of ECEC quality on school engagement moderated by disadvantage or children’s self-regulation?

Interactions between the three aspects of ECEC quality and task attentiveness, irritability/anger, disadvantage and gender in the prediction of school engagement were not significant. Therefore, the effects of ECEC quality on school engagement did not appear to be moderated by children’s self-regulation capacities or family disadvantage.



8. Discussion

The first aim of this report was to examine pathways to early school engagement in school Years 1 to 3. In Study 1, we found:

> Lower levels of engagement in early primary school could be traced back to family disadvantage, maternal depressive symptoms, higher-hostility parenting and, especially, a less stimulating home-learning environment in the years before the start of school. In part, these factors led to poorer school engagement because they contributed to children’s poor self-regulation.

> Developmental pathways involving disadvantage, family resources and self-regulation from infancy to school entry were different for each school engagement outcome.

> Of the four school engagement outcomes, only approaches to learning in Years 1–2 seemed to be important for children’s academic achievement in Year 3.

The second aim was to consider whether the quality of ECEC was related to school engagement. In Study 2, we found:

> Children who had more conflicted relationships with ECEC teachers tended to like school less and to be rated lower by teachers on approaches to learning in Years 1–2 and 3–4 of primary school.

> Other aspects of ECEC quality were not significantly related to school engagement.

In this discussion, we first summarise in more detail how the results answered each of our research questions. We then focus on implications of the findings for research on early school engagement and for policies that might prevent early disengagement from school.

8.1 Research Questions—Study 1

Q1. How do school engagement outcomes change between Years 1–2 and Years 3–4?

In Chapter 4, we examined how each of the four aspects of school engagement changed between wave 4 (when children were in Years 1–2) and wave 5 (when children were in Years 3–4). On average, scores on approaches to learning increased, school liking and maths liking decreased, and absenteeism remained steady.

These results are quite consistent with the few previous studies that have examined changes in school engagement in young children. Increases in measures of classroom behavioural participation such as approaches to learning probably reflect both children’s improving attention skills (Eisenberg & Sulik, 2012) and teacher efforts to improve these skills (Ladd & Dinella, 2009). Declines in affective engagement have been demonstrated in young children (Ladd et al., 2000), but it is not clear why this occurs. Given that some studies show that children start school with very high—possibly unrealistically high—levels of academic self-confidence (Stipek & Ryan, 1997), we suggest that some declines in school liking after Year 1 may be normal as children respond to social experiences in school and to teacher feedback about their competence. In this study, we

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could not examine the social or academic consequences of greater or lesser declines in affective engagement, but this should be examined in future research.

The percentage of children who were absent for two days or more in the past four weeks (around 28 per cent) did not change between the two waves, which is consistent with research with the older LSAC cohort (Daraganova et al., 2014). It will be necessary to examine absenteeism over a longer period—into the secondary school years—to determine when rates begin to rise, and for which children.

Q2. How does school engagement differ between subgroups of children (boys and girls, Year levels, younger and older children, children with poor self-regulation, and disadvantaged and more advantaged children)?

Analyses in Chapter 4 showed that girls liked school more and were rated higher on approaches to learning than boys at both waves. This is consistent with school engagement research, from childhood through to senior high school. In general, research suggests that 4 to 5-year-old girls tend to have more of the socio-emotional skills that facilitate adaptive transitions to school, such as the ability to pay attention, sit still and follow teacher directions (OECD, 2015). In this study, boys were significantly lower than girls on task attentiveness at ages 4–5, suggesting that deficits in self-regulation may be a contributor to early-emerging and persistent gender differences in school engagement. As shown in research with the K cohort (Taylor, 2014), the exception to the female advantage in school engagement was maths liking. There is a large body of research showing that girls have lower levels of maths self-efficacy and enjoyment than boys, and this contributes to the gender gap in maths and science achievement. Consistent with some other research (Herbert & Stipek, 2005), our results suggest that a gender difference in maths enjoyment may not be present until a few years into primary school.

Older children, and those in more advanced Year levels, tended to like school less at each wave. There were no other significant age or Year level differences. Because there were no differences in liking between children who delayed school entry (who would have been older when in younger grades) and those who entered on time, we speculate that these differences reflect time in school rather than chronological age. As children accrue positive and negative school experiences over time, they form more detailed perceptions of themselves as students.

Consistent with expectations, disadvantaged children and children with poor task attentiveness and irritability/anger at wave 3 were rated significantly lower on approaches to learning at both waves, but were lower on school liking only at wave 5. Children who were disadvantaged were more likely to be absent at both waves.

Q3. Does children’s task attentiveness and irritability/anger at wave 3 (ages 4–5) mediate the relationship between disadvantage and family resources (parenting, home-learning environment and maternal depressive symptoms) and school engagement outcomes over Years 1 to 3?

We hypothesised that the main way in which disadvantage and family resources would affect children’s school engagement would be through effects on the development of self-regulation skills. This expectation was based on research showing that: (a) self-regulation may promote school engagement (Eisenberg et al., 2010; Blair & Diamond, 2008; Silva et al., 2011); (b) children’s abilities to regulate their attention and emotions are shaped by warm parenting and stimulating learning environments at home (Blair & Diamond, 2008; McClelland et al., 2007); and (c) parents



who are experiencing disadvantage or mental health problems are hampered in their capacities to provide responsive parenting and rich learning environments for their children.

In Chapter 5, we estimated four structural equation models designed to test this hypothesis by examining the pathways of influence from early disadvantage and family resources to each school engagement outcome via children’s task attentiveness and irritability/anger in wave 3. In general, we did find that self-regulation mediated influences from earlier parenting, disadvantage and maternal depressive symptoms on school engagement. However, pathways differed across the four engagement outcomes.


Of the four school engagement outcomes, approaches to learning was most strongly related to children’s self-regulation. Children whose mothers rated them as better at task attentiveness and lower on irritability/anger at ages 4–5 were rated by teachers as higher on approaches to learning in school Years 1 to 3. The relationship between task attentiveness and approaches to learning is consistent with past research and developmental theory (Eisenberg et al., 2010; Ladd & Burgess, 2001). There is a direct correspondence between the behaviours that make up approaches to learning (e.g., paying attention, working independently and adapting to changes in routine) and task attentiveness. Our results for irritability/anger add to the literature by showing that difficulties with these negative emotions also seem to undermine approaches to learning. It is possible that higher levels of irritability/anger tend to lead to more conflicts with teachers, reducing the time that children can spend acquiring and practicing appropriate classroom behaviours.

The role of self-regulation as a mediator between family resources and school engagement was seen most clearly in the approaches to learning model. Disadvantage, hostile parenting and maternal depressive symptoms in wave 1 were all related to poorer task attentiveness and more irritability/anger in waves 2 and 3. Subsequently, poorer task attentiveness and more irritability/anger were associated with poorer approaches to learning.

As we discuss shortly, we found that approaches to learning was a strong predictor of children’s NAPLAN scores. Given this, it is appropriate to ask two things:

(1) How much do the effects of self-regulation on approaches to learning matter?

Unfortunately, few studies have examined approaches to learning as a dependent variable, so it is difficult to compare our results with effect sizes in the literature. Overall, the effects of task attentiveness and irritability/anger could be considered small to medium. However, these relationships were independent of other variables that were strongly related to approaches to learning, such as school readiness. This shows that self-regulation skills are not the same as school readiness and are salient predictors of children’s approach to learning, which is subsequently related to achievement.

(2) How much do the indirect effects of other variables (like disadvantage and hostile parenting), that operated via self-regulation to affect approaches to learning, matter?

The importance of indirect effects can be assessed by considering the size of effects for specific variables, compared to total indirect effects. For example, Table 10 shows that the total indirect effect of disadvantage on wave 4 approaches to learning was –0.176. This quantifies how much family disadvantage affected approaches to learning when other variables were included in the model. Of this total indirect effect, about 10 per cent operated via task attentiveness and about 9 per cent via irritability/anger. Estimates for indirect effects of disadvantage on wave 5 approaches to learning via self-regulation were similar in magnitude. Research consistently demonstrates that

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disadvantage has many negative consequences for children’s educational progress because of impacts on intervening variables, such as school readiness and parental involvement. In our study, up to one-fifth of this indirect effect of disadvantage was attributable to difficulties with self-regulation.

In comparison, self-regulation accounted for the majority of the indirect effects of hostile parenting and maternal depressive symptoms. However, these total indirect effects were smaller to begin with.

While difficult to quantify in terms of years of schooling or some other concrete outcome, the direct and indirect effects on approaches to learning operating via self-regulation show that: (a) children’s self-regulation matters for their early approaches to learning; and (b) self-regulation has its roots early in life.

School liking

Decreases in school liking between waves 4 and 5 were associated with lower levels of task attentiveness and more irritability/anger in wave 3. These effects were not large (about 0.06 of a standard deviation in school liking) but were still statistically significant over a four-year period. Based on the literature (Morrison et al., 2010; Sektnan et al., 2010), we expected stronger effects of self-regulation on school liking. However, there are some reasons for the pattern of results.

It is possible that the negative effects of deficiencies in self-regulation manifest more strongly as children move through school (Sasser et al., 2015). The academic and behavioural demands placed on children increase over the early primary school years, so children’s self-regulatory skills may be more taxed in later grades, possibly eliciting more negative feedback from teachers than earlier, and decreasing school liking. Also, if children who have trouble regulating attention and emotions do experience more conflict and discipline from teachers (Silva et al., 2011), it may take some time for these experiences to give rise to negative feelings about school.

Task attentiveness and irritability/anger did mediate some influences of parenting, maternal depressive symptoms and disadvantage on wave 5 school liking. However, because the direct links between self-regulation and wave 5 school liking were small, indirect effects were also small.

Maths liking

Children who were more task attentive at wave 4 liked maths more at both waves 4 and 5, although effects were not large. Irritability/anger was not significantly related to maths liking. This may be because persistence and focus are more important for maths achievement. Task attentiveness did mediate some earlier influences from family resources. Effects were small, but the largest indirect pathway was disadvantage, influencing poorer task attentiveness and, subsequently, lower maths liking.

Absenteeism

Self-regulation was not related to absenteeism. In fact, our model accounted for less than 10 per cent of the overall variance in absenteeism at either time point. The predictors of absenteeism were disadvantage and school readiness, which is consistent with Daraganova and colleagues’ (2014) findings using the K cohort. Other studies have not examined self-regulation as a predictor of early absenteeism. We suggest that, at this early age, reasons for children being absent are more strongly related to family difficulties (as indicated by disadvantage) than to children’s abilities. This may change in later years. For example, by adolescence, when children can more



easily avoid attending school, being absent may become more strongly linked to academic and behavioural problems arising from poor behavioural and emotional regulation.

The importance of the home-learning environment

We expected influences from the home-learning environment (in wave 2) on school engagement to operate indirectly, via children’s self-regulation at wave 3. However, the two dimensions of the home-learning environment exhibited a moderate influence on approaches to learning, school liking and maths liking. As described in Chapter 5, the pattern of results showed that children who in wave 2 (ages 2–3) were in home environments where more reading-related activities took place tended to have better approaches to learning scores, but liked school and maths less. Although the results for school and maths liking were unexpected, the finding for approaches to learning is consistent with a large body of research documenting the benefits of reading to children for the development of pre-literacy skills and school readiness (e.g., Raikes et al., 2006). Our results suggest that reading also promotes social readiness, preparing children to engage competently in classroom activities.

The finding that a richer reading home environment was associated with less school and maths liking was perplexing, because theory suggests that children who are more academically prepared for school should have more positive experiences and thus like school more. However, there is not a lot of research on precursors of affective and motivational components of schooling among young children, and no studies that we are aware of have examined the home-learning environment as a predictor of young children’s feelings about school. We suggest that the relationship between the home-learning environment and disadvantage may be key to understanding this finding. Many studies show that disadvantaged families engage in much less reading with children (Melhuish, Phan et al., 2008). In the present study, the disadvantage index was associated with about half a standard deviation reduction in the reading factor. Therefore, children who received more stimulating reading environments prior to starting school were likely to be higher-SES and more academically capable, compared to children who received less stimulating reading environments.

The explanation for why these children liked school less may be the frame of reference for making assessments about their competence. Although school and maths liking are not direct measures of motivation or academic self-efficacy, they are correlated with these constructs and have been used as indicators of motivation and efficacy in young children in the literature. In a study of kindergarten children, Stipek and Ryan (1997) found that all children had high levels of enthusiasm and self-confidence at the beginning of the school year. However, on average, disadvantaged children were even more positive on these affective variables, even though their cognitive capacities were substantially poorer. Classroom observations by teachers also showed that advantaged children tended to make more negative social comparisons about their work. Howse et al. (2003) found that 5–8-year-old economically disadvantaged children had similar levels of motivation to advantaged children, but they had more difficulty regulating attention and poorer academic outcomes. These findings, and ours, may be examples of the ‘big fish little pond’ effect (Seaton, Marsh, & Craven, 2010). This is a robust finding that children have lower academic self-concepts when they attend schools where the school-average ability is high, compared to when it is low. It is interesting to note that, in our results, children attending more advantaged schools (as indicated by school ICSEA) tended to have lower levels of school liking at wave 4 (see Table E3 in Appendix E: ). These findings should be investigated in detail in future research.

Q4. Is the effect of self-regulation stronger for disadvantaged children?

We did not find that effects of task attentiveness or irritability/anger were stronger for children who were experiencing disadvantage at wave 1. The pathways illustrated in Chapter 5 did not differ

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significantly between disadvantaged and more advantaged children. Possibly, finer-grained measures of disadvantage would be needed to illustrate differential effects, such as using separate measures of material, employment and educational disadvantage. However, given the distribution of these measures, it is unlikely that there would be sufficient sample size for robust tests to be undertaken.

Q5. How much does engagement in Year 1 matter for achievement in Year 3?

In Chapter 6, we examined how the four school engagement outcomes at wave 4 (when children were in Years 1–2) were related to Year 3 NAPLAN numeracy and reading scores. After controlling for a range of other variables that are also related to achievement (school readiness, self-regulation, disadvantage, school ICSEA and home-learning environment), only approaches to learning was significantly associated with test scores. A standard deviation increase in Years 1–2 approaches to learning was associated with a quarter of a standard deviation increase in numeracy and reading test scores for girls, and with about one-fifth of a standard deviation increase in test scores for boys. Warren and Haisken-DeNew (2013) calculated that half a standard deviation in NAPLAN scores could be considered equivalent to about a year of schooling. Using this method, two girls who were a standard deviation apart on approaches to learning in Years 1–2 (i.e., 68 per cent of the entire sample were between their scores on approaches to learning) were estimated to be about half a year of schooling apart in numeracy and reading in Year 3. For boys, the effect was about 30–40 per cent of the impact of a year of schooling. These are large effects and suggest that approaches to learning is a key indicator of early underachievement in primary school children.

The lack of significant effects for school liking (and maths liking) on achievement is, on the whole, consistent with previous research, although academic outcomes of school liking in young children have not often been examined. A few studies of older children have shown that affective engagement is not related to achievement and high school dropout (Alexander et al., 1997; Janosz et al., 2008).

Absenteeism was related to lower numeracy scores for both boys and girls when it was the only variable in the model. However, the effect became non-significant when approaches to learning was added. Daraganova, Mullan and Edwards (2014) found strong effects of absenteeism on Year 5 numeracy, but they did not include approaches to learning, and we did not examine Year 5 test scores. Our results suggest that children who are frequently absent in early primary school also have difficulties with approaches to learning.

8.2 Research Questions—Study 2

Q1. Is higher-quality ECEC at ages 4–5 related to higher levels of school engagement over Years 1 to 3?

The only dimension of ECEC quality that was related to school engagement was conflict in the teacher–child relationship, which predicted lower school liking and poorer approaches to learning at both waves 4 and 5. Supportive teacher relationships are consistently associated with positive school outcomes in the literature, and researchers using LSAC data have shown that lower conflict in ECEC is related to higher achievement and better self-regulation (Gialamas, Mittinty et al., 2014; Gialamas, Sawyer et al., 2014). Once we controlled for children’s hyperactivity, however, the relationship between teacher–student conflict and approaches to learning became non-significant. This is consistent with a large literature showing that hyperactive children are difficult to manage in



ECEC and school settings and have poorer relationships with teachers (Liew, 2012). However, the relationship between conflict and lower school liking remained after controlling for hyperactivity. It is possible that children who have poor relationships with ECEC teachers also tend to have poor relationships with teachers once they start school, which may lead to negative classroom interactions and lower school liking.

Other dimensions of ECEC quality (teacher qualifications and activities with children) were not related to any school engagement outcome. Warren and Haisken-DeNew (2013) found better NAPLAN outcomes for children whose ECEC teachers had Degree or Diploma-level qualifications. We found a very small tendency for lower school liking in wave 4 associated with teachers who had non-ECEC Degrees (compared to ECEC Degrees), but, in general, teacher qualifications do not seem to be important for school engagement. The activities with children that we measured were probably not important, because they do not convey information about teaching practices that might support children’s self-regulation and school readiness, like management of problem behaviour.

Q2. Is higher-quality ECEC especially beneficial for children with poorer self-regulation, or (Q3) disadvantaged children?We did not find that any aspect of ECEC quality was protective for children with poor self-regulation or who were experiencing disadvantage. Once again, we probably did not have enough information about teaching practices that promote self-regulation to observe differential effects for children low in task attentiveness or with high levels of irritability/anger. Moreover, many studies that do show beneficial effects of ECEC for children with poor self-regulation are intervention studies, where outcomes can be compared for an intervention and control group (Liew, 2012). Similarly, research shows that ECEC can have compensatory effects for disadvantaged children, but this is not always observed in observational studies like LSAC (Burger, 2010).

8.3 What do the results tell us about the nature and development of engagement in early primary school?

One of the key motivations for undertaking research on the precursors of school engagement at any age is to find ways to prevent academic failure. Approaches to learning was most clearly linked to the earlier developmental processes that we examined, and it was most strongly related to conflicts with an ECEC teacher in Study 2.

Absenteeism and affective engagement were also influential. Firstly, absenteeism is not a desirable outcome at any age, and our results, along with Daraganova, Mullan and Edwards’ (2014), suggest that children who are absent early in primary school are at continuing risk of being absent over time. In the longer term, this will be related to lower achievement. However, absenteeism is not at the discretion of the Year 1 child, as it may be by Year 8. Therefore, absenteeism in early primary school is a good indicator of a child at risk of future difficulties and possibly future disengagement, but not a good indicator of current disengagement.

Secondly, if school liking does respond to positive and negative feedback from teachers over time, as the literature suggests, it is possible that it will become more closely related to achievement as time goes by (e.g., Li & Lerner, 2011). In future research, it would be informative to examine the relationship between children’s school liking in wave 5 (around school Year 3) and their Year 5 NAPLAN scores. It is also possible that school liking works to reinforce approaches to learning (Ladd et al., 2000), in which case indirect effects of school liking on achievement would be

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expected. Finally, our measure of school liking was limited to only three items and may not adequately capture affective engagement in young children. Measures of academic self-efficacy and motivation have been shown to be useful in young children (e.g., Liew et al., 2008) and may be better indicators of affective engagement than general school liking.

8.4 What are the key developmental pathways that promote or inhibit school engagement?

Across the four school engagement outcomes, the important direct influences were self-regulation (for approaches to learning and, to a lesser extent, school liking and maths liking), school readiness (for approaches to learning, maths liking, absenteeism) and the home-learning environment (for approaches to learning, school liking, maths liking). These three influences—self-regulation, school readiness and home-learning environment—were the product of related developmental processes. The key driver of these processes was disadvantage. For instance, disadvantage at wave 1 was associated with:

> higher levels of irritability/anger at wave 2, affecting task attentiveness and irritability/anger at wave 3, and subsequently affecting approaches to learning and school liking

> infrequent reading at home in wave 2, lower levels of reading leading to lower school readiness in wave 3 and directly to lower ratings on approaches to learning in wave 4.

Hostile parenting and maternal depressive symptoms were also important early in the pathways leading to poor school engagement via self-regulation, school readiness and home-learning environment. Depressive symptoms and hostile parenting were most strongly related to higher irritability/anger at wave 2, which was subsequently associated with poorer task attentiveness and more irritability/anger at wave 3, as described above. This is in line with research showing that negative parenting practices, including low levels of maternal warmth, support, responsivity and sensitivity (all of which can be exacerbated by maternal depression) do not provide children with enough of the sorts of social exchanges that foster adaptive regulation of negative emotions (Eisenberg et al., 2010).

However, the size of the indirect effects on school engagement via self-regulation (and school readiness and home-learning environment) were larger for disadvantage, compared to hostile parenting and maternal depressive symptoms. It is also well established that the risk of hostile parenting and maternal depression are higher in disadvantaged families (Conger et al., 2010). Finally, disadvantage was directly linked to school readiness, wave 3 self-regulation, approaches to learning, absenteeism and school liking.

8.5 What could be targeted for intervention, and when?

Our results suggest a need to target two things: the development of strong self-regulation skills in children; and increased opportunities for engaging with reading and early literacy tasks. Both these elements could be targeted in high-quality ECEC and through supports for disadvantaged families.

Self-regulation skills are quite malleable. Interventions that directly aim to build children’s self-regulation skills could have very beneficial effects for approaches to learning and can be delivered quite successfully in ECEC settings. Some programs focus on explicit training in regulatory skills, such as learning tasks that are designed to improve working memory, inhibitory control, ability to



shift attention and cognitive flexibility across different situations. This training fosters control over emotion, attention and behaviour in a general sense and has shown encouraging effects on children’s classroom participation (Rueda, Posner, & Rothbart, 2005; Diamond et al., 2007). Training may be especially beneficial for children with attention deficit/hyperactivity disorder (Klingberg et al., 2005). Other programs focus on building positive, supportive learning environments that promote the development of emotion regulation and social competence. This involves training ECEC teachers and carers in strategies that provide children with more regulatory support, like implementing clearer rules and routines, rewarding positive behaviour and redirecting negative behaviour (Raver et al., 2011; Webster-Stratton et al., 2008). We found that conflict in the teacher–child relationship in ECEC was a predictor of poorer approaches to learning up to four years in the future. That this relationship was accounted for by children’s hyperactivity suggests that ECEC teachers need more intensive support to provide environments that foster regulatory control in difficult children.

ECEC is also a setting where opportunities to engage in early literacy tasks can be provided for children who are at high risk of not receiving these opportunities in the home. In one study (Wasik, Bond, & Hindman, 2006), preschool teachers in the US Head Start program were trained in book-reading and oral language strategies, like asking questions of children while reading, explicitly building vocabulary, and encouraging children to talk. The program had positive effects on children’s receptive and expressive language skills. While it was effective, ECEC researchers also suggest that programs designed to promote language skills will be most effective if they involve partnerships between ECEC centres and parents in encouraging reading (Melhuish, Phan et al., 2008).

Research on these sorts of ECEC-based interventions generally focuses on 4–5-year-old children in preschool. Therefore, increasing access to preschool in the year before children start school should continue to be a priority. However, some researchers suggest that there are benefits from starting preschool earlier, especially for children who are disadvantaged or low in self-regulation (Sylva et al., 2012). In addition, it is important to continue to support children with poor self-regulation and cognitive skills through the early years of primary school.

Supports to families in disadvantage can also promote the development of self-regulation skills and exposure to reading. Programs like Sure Start in the UK and Communities for Children in Australia, which integrate services to families in disadvantaged areas, have demonstrated that families living in these areas show less hostile parenting and provide better home-learning environments (Edwards et al., 2011; Melhuish, Belsky et al., 2008). In Australia, the Pathways to Prevention Projects showed that intensive supports to disadvantaged families when children were young improved self-regulation and classroom behaviour over Years 1 to 5 (Homel, Freiberg, Branch, & Le, 2015).

We have recommended various practices in ECEC as potentially able to promote school engagement, especially among disadvantaged children. Many of the practices described, like behavioural management and a focus on literacy, are characteristics of high-quality ECEC. Yet, we note that we did not find significant relationships between school engagement and most of our measures of ECEC quality. However, we did not have information about behavioural management in ECEC settings when children were aged 4–5, so our non-significant findings probably reflect insufficient information about quality of ECEC. Several highly regarded intervention programs (e.g., HighScope Perry Preschool Study; Schweinhart et al., 2005; Chicago Child Parent Centres; Reynolds, Temple, Ou, Arteaga, & White, 2011) have shown long-term benefits of high-quality preschool education for disadvantaged children well into adulthood.

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8.6 Limitations

Strengths of this study include the use of a large, nationally representative sample of children followed from infancy, the use of reports from parents, teachers, and children, and the use of nationally standardised NAPLAN scores for achievement. However, our conclusions must be considered alongside this study’s several limitations. The parent-reported assessments of children’s task attentiveness and irritability/anger were not ideal as measures of children’s self-regulation. Ideally, children’s self-regulation would be assessed using well-established instruments that use a range of tasks to directly measure children’s attention, inhibitory control and emotion regulation (e.g., Liew et al., 2008). Direct assessments may have provided more accurate assessments of children’s abilities and, perhaps, larger effects sizes. However, parent-reported measures of children’s regulation are convenient in large studies like LSAC, are widely used in the literature and are strongly correlated with direct assessments.

The two-year gap between assessments in this study is not ideal to capture the sorts of parent–child–environment transactions that give rise to self-regulation in the first five years of life. The two-year gap also meant that children’s engagement could not be observed in each year of school, limiting conclusions that can be drawn about change. However, despite these long gaps, we were able to observe some of these developmental processes, for instance, parenting and self-regulation were reciprocally related over waves 1 to 3. Future research should extend these findings using shorter time frames with more frequent assessments.

It was beyond the scope of the current report to examine schooling influences such as school discipline policies and teaching practices in the classroom. This is a limitation of the current study and could be a focus for future research. When examining the influence of ECEC programs on school engagement, we controlled for hours per week in the first ECEC program but, of course, hours spent in ECEC change as children age.

8.7 Conclusions

Children’s capacities to behave appropriately in the classroom in the early years of primary school may have long-term consequences for their achievement and school engagement. In high-resource environments, characterised by parents with material and educational resources, children receive appropriate support and stimulation and develop the ability to regulate themselves in ways that prime them to engage enthusiastically with the school setting. This may not occur in low-resource environments, where resources are not adequate to provide children with the supportive social interactions and stimulating activities needed to develop abilities to regulate attention and emotion. However, through high-quality ECEC and targeted support to parents, teachers and schools, some of these difficulties may be overcome.



Appendixes

Appendix A: Measurement of school engagement

The measurement of school engagement remains under-researched, especially in early childhood. In part, this is due to the conceptual fuzziness around what school engagement is (see Chapter 2), meaning that the instruments used to measure engagement are rarely the same from study to study. Another problem is that much research is based on small cross-sectional samples. Even in longitudinal studies with larger samples, the measurement invariance of constructs over time, between children of different Year levels, or between boys and girls, is usually not examined. Measurement invariance is the extent to which the same construct is measured over time or over different groups of participants. When there is measurement invariance, all participants across groups and over time interpret the individual items and the underlying latent construct in the same way. Measurement invariance is required to make valid comparisons in school engagement outcomes between genders and Year levels, and over time. Otherwise, any observed differences could be functions of differences in how people interpret the items, rather than true differences on the construct (Wang, Willett, & Eccles, 2011). LSAC is well suited to address some of these problems because the sample is large enough to examine measurement in different subgroups, information is available at more than one point in time, and there is the possibility of cross-validating measures across the B and K cohorts.

There are several potential items reflecting both behavioural and affective engagement available in LSAC, but the ways in which these might be used to represent engagement have not been previously examined. We used these items to develop measures of behavioural and affective school engagement when the B cohort children were in wave 4 (Years 1–2) and wave 5 (Years 3–4). These measures were used as outcomes in the report. We also test for invariance of the outcomes over time, between boys and girls, in younger and older children and in different Year levels.

MethodologyW used confirmatory factor analysis to examine latent variables representing affective and behavioural school engagement.

Measures of school engagement

Engagement items were reported in wave 4 and wave 5 by teachers, parents and the study children themselves. Table A1 shows all the items selected as potential indicators of school engagement. Items were selected that were considered to reflect aspects of affective or behavioural engagement. We excluded items that assessed externalising or internalising behaviour in general, as well as items that were not present at both waves 4 and 5.21

21 We initially also explored using the PEDS QL parent report of school functioning. However, only one item (how often in the past month has the child had a problem paying attention in class) was related to other behavioural engagement items, and it did not provide additional information beyond teachers’ reports in approaches to learning.

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Table A1. Measures of school engagement

Affective school engagement Behavioural school engagement

School likingResponses: 1 = no; 2 = sometimes; 3 = yes1. Are you happy when you are at school?


3. Is school fun?

Approaches to learningHow often does this child demonstrate the following behaviour in the past month or two?Responses: 1 Never; 2 Sometimes; 3 Often; 4 Very often1. Pays attention well

2. Shows eagerness to learn new things

3. Works independently

4. Easily adapts to changes in routine

5. Persists in completing tasks

6. Keeps belongings organised

School avoidanceResponses: 1 = no; 2 = sometimes; 3 = yes4. Do you wish you didn't have to go to school?

5. Do you feel happier when it is time to go home from school?

6. Do you ask your mum or dad to let you stay home from school?

AcademicResponses: 1 = no; 2 = sometimes; 3 = yes7. Do you like maths and number work at school?

8. Do you think you are good at your school work?

AbsenteeismStudy child has been absent for two or more days in the past four weeks1

Notes:

1. Two or more days was selected based on past research showing that this amount of non-attendance was associated with poorer outcomes (Daraganova et al., 2014).

Model estimationThe engagement items were treated as either ordered categorical (ordinal) or binary outcomes. Ordinal distributions are typical when Likert scales are used for responses. Research suggests that, when there are more than five response options and the distribution of responses is symmetric, the variable may be treated as normally distributed. However, if these conditions are not met, treating the outcome as normal may result in biased estimates. Many of the variables to be used had Likert responses, for example, maths liking (no, sometimes, yes), and most were not symmetric. To account for these sorts of distributions, it is recommended to estimate CFAs and structural equation models using weighted least-squares estimation with a mean and variance-adjusted chi-square (WLSMV; Finney & DiStefano, 2013). This method is especially well suited to large samples. With WLSMV, probit regression coefficients are estimated, and thresholds for items, rather than intercepts, are produced.

Goodness of fit for the models was evaluated with the root mean square error of approximation (RMSEA), the comparative fit index (CFI), the Tucker-Lewis fit index (TLI) and the weighted root mean square residual (WRMR). Well-fitting models are generally indicated by a RMSEA of < = 0.06, although < 0.08 indicates adequate fit, CFI and TLI of > = 0.95, and WRMR of close to or under 1 (Finney & DiStefano, 2013). Model chi-square can also be considered, where a non-significant value is the gold standard. However, as the chi-square is very sensitive to sample size, we expect it to be significant for all models reported here. Therefore, we follow recommendations for researchers working with large samples (Kline, 2011; Meade et al., 2008) and rely more on the other fit indices in evaluating absolute model fit. The WRMR is a newer, less evaluated index, so we use it as a supplement to the other indices.



Cross-validation in the K cohortThe B cohort children in waves 4 and 5 were the same age as the K cohort children in waves 2 and 3. Most school engagement items were included in these assessments in both cohorts, making it possible to confirm models established in the B cohort in another sample. This was done by applying the parameters from models for affective and behavioural engagement established in the B cohort to the K cohort data. Adequate model fit in the K cohort provides support for the stability of the factors and suggests that they are not generated by characteristics specific to the B cohort data. We tested the model using data from the K cohort children when they were aged 6–7 (wave 2, N=4,445) and 8–9 (wave 3, N=4,312).

Testing measurement invariance over sex, Year levels, and timeFollowing cross-validation, we tested for measurement invariance across sex, Year levels, and time. To test for measurement invariance between two groups (e.g., boys and girls), a model in which the measurement parameters are constrained to be equal over groups is compared to a model in which they are free. To test for longitudinal invariance, the fit of a model in which the measurement parameters are constrained to be equal over time is compared to a model in which they free over time. For categorical outcomes, the measurement parameters are factor loadings and item thresholds. It is recommended that the loadings and thresholds be constrained in tandem because the item probability curve is influenced by both parameters. In this study, invariance is indicated by a change in CFI (CFI) between models of less than 0.01 (Cheung & Rosveld, 2002), and a change in RMSEA (RMSEA) of less than 0.01, as well as considering whether the RMSEA of the constrained model falls within the 90 per cent confidence interval of the RMSEA of the unconstrained model (Little et al., 2007). The chi-square difference test (2) is also considered, although, in large datasets like LSAC, it is likely to be over sensitive.

If full measurement invariance (across all items) is not supported, it is possible to consider partial invariance. This involves relaxing equality constraints for some items and comparing the fit of this model to the freely estimated baseline model. Modification indices, as well as the size and pattern of the factor loadings, the item R2s, and the residual item correlations, can be used to make decisions about which constraints to relax. The modification index indicates how much the model chi-square would improve if a constrained parameter were freed. Research suggests that, if only one or two parameters of a factor are noninvariant, comparisons of means across groups or time in subsequent analysis will not be seriously biased (Byrne, Shavelson, & Muthén, 1989).

Behavioural engagementPotential measures of behavioural engagement included six teacher-reported items assessing children’s approaches to learning and one item indicating whether the study child was absent for two or more days in the past four weeks. The six approaches to learning items are a subscale from the Social Rating Scale (SRS), developed for use with young children in the US Early Childhood Longitudinal Study. The Approaches to Learning scale has strong psychometric properties, and validity is well established (DiPerna et al., 2007). Because absenteeism is regarded as an aspect of behavioural engagement, we estimated a model that included the six approach to learning items and absenteeism loading on one latent factor. However, factor loadings for absenteeism were low (< 0.20) at both wave 4 and wave 5. Therefore, we decided to examine absenteeism as a separate outcome and focus on the approaches to learning items in the remainder of this section. Model fit and estimates for approaches to learning at wave 4 and wave 5 for the full sample are summarised in Table A2. As expected, item loadings and R2s were strong at both waves.

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Table A2. Summary of approaches to learning models at wave 4 and wave 5

Wave 4 Wave 5

Item Loading R2 Loading R2

1. Pays attention well 0.93 0.55 0.94 0.88

2. Shows eagerness to learn new things 0.84 0.70 0.86 0.74

3. Works independently 0.93 0.86 0.92 0.85

4. Easily adapts to changes in routine 0.77 0.59 0.81 0.66

5. Persists in completing tasks 0.92 0.84 0.93 0.87

6. Keeps belongings organised 0.74 0.87 0.78 0.60

Model fit

RMSEA (90% CI) 0.043 (0.034-0.053) 0.035 (0.026-0.045)

CFI/TLI 0.999/0.999 0.999/0.999

WRMR 0.998 0.940

2(df) 67.002(9)*** 47.79(9)***

Notes: * p < .05; **p < .01; ***p < .001

Cross-validation in the K cohort and tests of invarianceApplying the parameters from the B cohort wave 4 and wave 5 models to the K cohort wave 2 and wave 3 data produced models with good fit, almost identical to the B cohort results (see Table A3). Measurement invariance was supported across sex, between different Year levels at both waves and over time (Table A4). Table A5 shows factor loadings and R2s for the final model of approaches to learning over waves 4 and 5.

Table A3. Cross-validation: comparison of model fit for approaches to learning in the B and K cohorts

Wave 4 (B cohort) and Wave 2 (K cohort) Wave 5 (B cohort) and Wave 3 (K cohort)

B cohort model Model fit when parameters applied to

K cohort

B cohort model – school liking and

academic

Model fit when parameters applied to

K cohort

RMSEA (90% CI) 0.043 (0.034-0.053) 0.043 (0.033-0.052) 0.035 (0.026-0.045) 0.042 (0.033-0.052)

CFI/TLI 0.999/0.999 0.999/0.999 0.999/0.999 0.999/0.999

WRMR 0.998 0.878 0.940 0.834

2(df) 67.002(9)*** 68.10(9)*** 47.79(9)*** 66.94(9)***

Notes: * p < .05; **p < .01; ***p < .001



Table A4. Summary of measurement invariance of approaches to learning over sex, Year level and time

Invariance RMSEA (90% CI) CFI/TLI WRMR 2 (df) CFI RMSEA 2(df)

By sex

Wave 4 Unconstrained1 0.051 (0.042-0.061) 0.998/0.997 1.024 98.90(18)*** - - -

Fully constrained2 0.042 (0.035-0.050) 0.998/0.998 1.774 142.56(35)*** –0.009 0.000 70.31(17)***


Fully constrained2 0.055 (0.048-0.062) 0.997/0.997 2.173 219.18(35)*** 0.002 –0.002 123.88(17)***

By Year level3


Fully constrained2 0.023 (0.015-0.031) 0.999/1.000 1.692 108.18(67)** –0.028 0.000 51.93(40)


Fully constrained2 0.030 (0.023-0.038) 0.998/0.999 1.595 121.23(59)*** –0.021 0.000 44.19(32)

By time Unconstrained1 0.034 (0.031-0.038) 0.998/0.997 1.275 276.54(47)*** - - -


Notes.

1. An unconstrained model with parameters freely estimated over sex, grade or time.

2. All factor loadings and thresholds constrained to be equal over time, sex or grade.

3. At wave 4, grades are kindergarten, Year 1 and Year 2; at wave 5, grades are Year 2, Year 3 and Year 4.

*p < .05; **p < .01; ***p < .001

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Table A5. Summary of approaches to learning models at wave 4 and wave 5

Wave 4 Wave 5


1. Pays attention well 0.93 0.86 0.94 0.88

2. Shows eagerness to learn new things 0.83 0.68 0.85 0.72

3. Works independently 0.92 0.85 0.93 0.86

4. Easily adapts to changes in routine 0.76 0.58 0.81 0.65

5. Persists in completing tasks 0.92 0.84 0.94 0.88

6. Keeps belongings organised 0.76 0.58 0.77 0.59

Model fit

RMSEA (90% CI) 0.033 (0.030-0.037)

CFI/TLI 0.998/0.997

WRMR 1.519

2(df) 347.81(63)***

Notes: Although factor loadings were constrained to equality across time, standardised loadings, as shown here, will differ slightly.

Affective engagementPotential measures of affective engagement included six items from the SLAQ (Ladd & Price, 1987) and two ‘academic’ items, including a report of liking of maths and of perception of level of achievement (see Table A6).

The SLAQ has been used quite frequently to assess affective engagement in young children. Generally, the ‘liking’ items (items 1–3 in Table A6) and ‘avoidance’ items (items 4–6) are used as separate but related scales. Based on studies of engagement in young children, we considered the additional academic items, item 7 (‘like maths’) and 8 (‘good at school work’), to reflect motivation or academic self-efficacy more closely than general affect (Liew et al., 2008). Therefore, we estimated a CFA with a ‘liking’ factor (items 1–3), an ‘avoidance’ factor (items 4–6) and an ‘academic’ factor (items 7 and 8).

This initial model was a good fit to the data at both waves (Table A6). The three 'liking' items from the SLAQ (items 1–3) had strong loadings, and the factor accounted for 50 per cent or more of the item variances. However, loadings for the avoidance and academic items were less strong and less consistent between waves 4 and 5.



Table A6. Summary of initial affective engagement models at wave 4 and wave 5

Wave 4 Wave 5


School liking

1. Are you happy when you are at school? 0.81 0.59 0.78 0.56


0.75 0.57 0.70 0.49

3. Is school fun? 0.77 0.66 0.75 0.61

School avoidance

4. Do you wish you didn't have to go to school? 0.82 0.23 0.82 0.23

5. Do you feel happier when it is time to go home from school? 0.54 0.68 0.65 0.67

6. Do you ask your mum or dad to let you stay home from school? 0.48 0.29 0.48 0.42

Academic

7. Do you like maths and number work at school? 0.63 0.39 0.51 0.26

8. Do you think you are good at your school work? 0.48 0.23 0.53 0.28

Model fit

RMSEA (90% CI) 0.025 (0.019-0.032) 0.034 (0.027-0.041)

CFI/TLI 0.995/0.993 0.992/0.987

WRMR 0.934 1.167

2(df) 61.67(17)*** 94.30(17)***

Cross-validation in the K cohortThe next step in evaluating the liking, avoidance and academic factors was to cross-validate the model in the K cohort (Table A7). Firstly, we applied parameters from the B cohort wave 4 model to the K cohort wave 2 data. This model was a good fit to the data in the K cohort.

Next, we considered the wave 5 data. The three avoidance items were not asked in wave 3 in the K cohort. Therefore, we estimated a model that included only the liking and academic factors. This two-factor model was a good fit to the data in the B cohort, and applying the parameters from this model to the K cohort produced excellent fit. For comparability, we also estimated a two-factor model of liking and academic factors at wave 4 in the B cohort. This model was an excellent fit, and applying these parameters to the wave 2 K cohort data produced good fit.

These tests show that the structure of the affective engagement constructs can also be uncovered in the K cohort data, increasing confidence that the results arise by chance in the B cohort.

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Table A7. Cross-validation—comparison of model fit for affective engagement in the B and K cohorts

Wave 4 (B cohort) and Wave 2 (K cohort)

B cohort model— school liking,

academic, school avoidance


K cohort

B cohort model— school liking and

academic


K cohort

RMSEA (90% CI) 0.025 (0.019-0.032) 0.039 (0.033-0.046) 0.01 (0.00-0.027) 0.043 (0.031-0.056)

CFI/TLI 0.995/0.993 0.989/0.982 1.000/0.999 0.995/0.987

WRMR 0.934 1.374 0.392 0.967

2(df) 61.67(17)*** 132.50(17)*** 5.81(4) 36.26(4)***

Wave 5 (B cohort) and Wave 3 (K cohort)

B cohort model— school liking and

academic


K cohort

RMSEA (90% CI) - - 0.027 (0.014-0.042) 0.013 (0.00-0.029)

CFI/TLI - - 0.997/0.993 1.000/0.999

WRMR - - 0.675 0.424

2(df) - 15.89(4)** 7.03(4)

Notes: * p < .05; **p < .01; ***p < .001

Tests of invarianceTests of invariance for affective engagement were carried out over sex, Year level and time. Details of results are given in Table A8.



Table A8. Summary of measurement invariance of affective school engagement over sex, Year level and time

Invariance RMSEA (90% CI) CFI/TLI WRMR 2 (df) CFI RMSEA 2(df)

By sex


Fully constrained2 0.026 (0.020-0.032) 0.993/0.992 1.398 112.62(47)*** –0.001 –0.001 31.61(13)**



Partial3: Academic items free 0.036 (0.030-0.042) 0.988/0.985 1.637 155.11 (44)*** 0.002 –0.003 42.56(10)***

By Year level4




Fully constrained2 0.028 (0.022-0.034) 0.992/0.991 1.733 158.479(77)*** –0.008 0.001 38.29(26)

By time Unconstrained1 0.019 (0.015-0.022) 0.994/0.991 1.012 201.69(81)*** - - -


Partial3: Academic items free 0.031 (0.028-0.034) 0.981/0.974 1.576 454.20(89)*** 0.012 –0.013 255.87(8)***

Partial3: Academic and avoidance items free 0.021 (0.018-0.024) 0.991/0.988 1.138 249.65(85)*** 0.002 –0.003 54.24(4)***

Notes.

1. An unconstrained model with parameters freely estimated over sex, grade or time.

2. All factor loadings and thresholds constrained to be equal over time, sex or grade.

3. Factor loadings and thresholds for the specified items freely estimated over sex, grade, or time.

4. At wave 4, grades are kindergarten, Year 1 and Year 2; at wave 5, grades are Year 2, Year 3 and Year 4.

* p < .05; **p < .01; ***p < .001

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The three affective engagement factors were largely invariant over sex at wave 4. However, at wave 5, change in fit indices were all within acceptable limits only when factor loadings and thresholds for the two academic items were not constrained. Therefore, there is some non-invariance between boys and girls in the academic aspect of affective engagement at wave 5 only.

Invariance between different Year levels at both waves was supported. However, full invariance over time was not supported. It was necessary to free factor loadings and thresholds for the academic and avoidance items to obtain differences in indices that were within acceptable limits. Therefore, only the school liking factor was invariant between wave 4 and wave 5. This suggests that the way in which the academic items and school avoidance items were related to these underlying constructs changed as children progressed through school.

Table A9 shows factor loadings and R2s for the final model of school liking over waves 4 and 5.

Table A9. Summary of final model of school liking across wave 4 and wave 5

Wave 4 Wave 5


1. Are you happy when you are at school? 0.84 0.70 0.79 0.63


0.69 0.47 0.67 0.45

3. Is school fun? 0.81 0.65 0.77 0.59

Model fit

RMSEA (90% CI) 0.038 (0.030-0.047)

CFI/TLI 0.993/0.988

WRMR 1.284

2(df) 65.18(9)***

Notes: Although factor loadings were constrained to equality across time, standardised loadings, as shown here, will differ slightly.

SummaryThe approaches to learning scale showed good fit across sex, Year level and time and was cross-validated in the K cohort. However, absenteeism did not fit as part of this construct. Based on these results, behavioural engagement will be represented by two separate outcomes: approaches to learning and absenteeism.

The model of the three affective engagement factors initially showed good fit at both waves 4 and 5. The models were confirmed in the K cohort and were invariant across Year level at each wave. However, only school liking proved to be invariant across both sex and time. This is probably because the avoidance and liking factors were not particularly strong. However, the factor loadings and R2s for the avoidance and academic items accounted for much less overall variance in their indicators, compared with the items for school liking. Taken together, these results suggest that it is only appropriate to examine school liking over time. However, children’s feelings about maths have been found to have specific predictive power for academic outcomes (Gottfried, 1990; Gottfried, Fleming, & Gottfried, 2001), and concern over maths performance is of general policy relevance. Therefore, we also chose to retain the liking maths item as a separate outcome.



To summarise, the four school engagement factors that were examined in the report are:

> Approaches to learning—a latent variable, items 1–6 under ‘behavioural engagement’ in Table A6

> Absenteeism—an observed binary variable indicating whether the child had been absent for two or more days in the past four weeks

> School liking—a latent variable, items 1–3 under ‘affective engagement’ in Table A1

> Liking of maths – an observed ordered categorical variable, item 7 under ‘affective engagement’ in Table A1.

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Appendix B: Development of self-regulation measures at waves 2 and 3

Sawyer et al. (2014) identified items measuring task attentiveness and emotion regulation at waves 2 and 3. These items were largely drawn from the Short Temperament Scale (STS; Sanson, Smart, Prior, Oberklaid, & Pedlow, 1994) but, at wave 2, three items were from the Brief Infant Toddler Social-Emotional Assessment (BITSEA; Brigg-Gowan, Carter, Irwin, Wachtel, & Cicchetti, 2004) and, at wave 3, two were from the Strengths and Difficulties Questionnaire (Goodman, 2001). Sawyer et al. used exploratory factor analysis to determine which items loaded on a task attentiveness factor and which loaded on an emotion regulation factor. In the present study, we refined the pool of items using confirmatory factor analysis (CFA). We used CFA because we wanted to use latent factors of task attentiveness and emotion regulation in the analysis, and it is important to have a well-fitting measurement model to interpret parameters in a structural equation model (Mueller & Hancock, 2008). Based on these analyses, we slightly modified the set of items used to assess self-regulation. This Appendix includes details of these CFAs.

Table B1 shows the items selected by Sawyer et al. reflecting task attentiveness and emotion regulation at waves 2 and 3. CFAs were carried out separately for the wave 2 and wave 3 items. All items were treated as normally distributed, and models were estimated with robust maximum likelihood estimation, taking account of the survey structure.



Table B1. Initial pool of self-regulation items

Wave 2 Wave 3

Item Variable name

Item Variable name

Task attentiveness

STS persistence subscale1 STS persistence subscale

1. This child plays continuously for more than 10 minutes at a time with a favourite toy

btplay 1. When this child starts a project such as a puzzle or model, he/she works on it without stopping until it is completed, even if it takes a long time

ctnostop

2. This child goes back to the same activity after a brief interruption (snack, trip to toilet)

btsame 2. This child likes to complete one task or activity before going onto the next

ctfinish

3. This child stays with a routine task (dressing, picking up toys) for 5 minutes or more

btstay 3. This child stays with an activity (e.g. puzzle, construction, kit, reading) for a long time

ctltime

4. This child stops to examine objects thoroughly (5 minutes or more)

btstop 4. When a toy or game is difficult, this child quickly turns to another activity

ctturn

5. This child practices a new skill (throwing, building, drawing) for 10 or more minutes

btprac

BITSEA2 SDQ3

6. Can pay attention for a long time (not including TV)

btattn 5. Sees tasks through to the end, good attention span

ctaspan

Emotion regulation

STS reactivity subscale STS reactivity subscale

7. This child responds to frustration intensely (screams, yells)

brscrftn 6. If this child wants a toy or sweet while shopping, he/she will easily accept something else instead

craccept

8. This child has moody ’off’ days when he/she is irritable all day

brmoody 7. When shopping together, if I do not buy what this child wants (e.g., sweets, clothing), he/she cries and yells

crscrbuy

9. This child shows much bodily movement (stomps, writhes, swings arms) when upset or crying

brmoves 8. When this child is angry about something, it is difficult to sidetrack him/her.

crsidetk

10. This child reacts strongly (cries, screams) when unable to complete a play activity

brscrply 9. If this child is upset, it is hard to comfort him/her

crcomft

BITSEA SDQ

11. Cries or tantrums until he/she is exhausted

brtanty 10. Often has temper tantrums or hot tempers

crtanty

12. Often gets very upset brupset

Notes:

1. STS instructions: For each statement, please tick the answer that best describes the study child’s behaviour at the present time: (1) almost never to (6) almost always.

2. BITSEA instructions: Tick one box to describe the study child in the last month: (1) not true/rarely, (2) somewhat true/sometimes, (3) very true/often.

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3. SDQ instructions: Please tick one box for each of the following statements to best describe the study child’s behaviour over the past six months: (1) not true, (2) somewhat true, (3) certainly true.

Results for wave 2Table B2 summarises factor loadings, item R2s and model fit for CFAs of self-regulation at wave 2. The initial CFA included all 12 wave 2 self-regulation items (items 1–6 assessing task attentiveness and items 7–12 assessing emotion regulation). Model fit was adequate, but the three BITSEA items had loadings that were substantially lower than the items from the STS. Inspection of the modification indices also showed that item 6 from the BITSEA did not load clearly on the task attentiveness factor. Although the model was improved with the removal of this item, the best fitting model with consistently high loadings was one in which all three of the BITSEA items were removed. Additionally, the residuals for items 4 (This child stops to examine objects thoroughly (5 minutes or more)) and 5 (This child practices a new skill (throwing, building, drawing) for 10 or more minutes) were correlated.

Table B2. Summary of CFAs for self-regulation at wave 2

Initial model Final model


Task attentiveness

1. btplay 0.64 0.41 0.64 0.41

2. btsame 0.58 0.34 0.61 0.37

3. btstay 0.62 0.38 0.63 0.40

4. btstop 0.54 0.29 0.49 0.24

5. btprac 0.62 0.38 0.58 0.34

6. btattn 0.43 0.19

Emotion regulation

7. brscrftn 0.76 0.58 0.77 0.59

8. brmoody 0.60 0.36 0.59 0.35

9. brmoves 0.65 0.42 0.65 0.43

10. brscrply 0.65 0.42 0.65 0.43

11. brtanty 0.44 0.19 - -

12. brupset 0.42 0.18 - -

Correlation between factors

–0.11*** –0.06*

Model fit

RMSEA (90% CI) 0.043 (0.040-0.047) 0.039 (0.033-0.045)

CFI/TLI 0.940/0.925 0.975/0.965

2 (df) 459.59(53)*** 148.506(25)***

SRMR 0.042 0.028

Notes: * p < .05; **p < .01; ***p < .001



Results for wave 3Table B3 summarises factor loadings, item R2s and model fit for CFAs of self-regulation at wave 3. The initial CFA included all 10 wave 3 self-regulation items (items 1–5 assessing task attentiveness and items 6–10 assessing emotion regulation). Fit for this model was poor. Modification indices suggested that item 6 (If this child wants a toy or sweet while shopping, he/she will easily accept something else instead) did not load clearly on emotion regulation. Removal of this item improved model fit substantially. It was also necessary to correlate the residuals for item 8 (When this child is angry about something, it is difficult to sidetrack him/her) and item 9 (If this child is upset, it is hard to comfort him/her). This is probably because these items have very similar wording.

Table B3. Summary of CFAs for self-regulation at wave 3

Initial model Final model


Task attentiveness

1. ctnostop 0.79 0.62 0.78 0.62

2. ctfinish 0.73 0.53 0.73 0.53

3. ctltime 0.77 0.59 0.77 0.59

4. ctturn 0.50 0.25 0.50 0.25

5. ctaspan 0.60 0.36 0.60 0.36

Emotion regulation

6. craccept 0.61 0.37 - -

7. crscrbuy 0.52 0.27 0.65 0.28

8. crsidetk 0.73 0.53 0.53 0.43

9. crcomft 0.52 0.27 0.51 0.26

10. crtanty 0.53 0.28 0.61 0.37

Correlation between factors

–0.34*** –0.31***

Model fit

RMSEA (90% CI) 0.064 (0.059–0.069) 0.039 (0.034–0.045)

CFI/TLI 0.930/0.908 0.978/0.968

2 (df) 545.99(34)*** 166.38(25)***

SRMR 0.043 0.028

Note: * p < .05; **p < .01; ***p < .001

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Appendix C: Development of home-learning environment measures at wave 2

We selected eight items from the parent questionnaire at wave 2 that represented a stimulating home-learning environment based on other studies in the literature (e.g., Melhuish, 2010). These items are listed in Table C1.

We wanted to use a latent factor (or factors) to measure home-learning environment but, because we did not have firm prior knowledge of whether the items would cluster into one or more factors, we firstly used exploratory factor analysis to investigate this. All items were treated as categorical indicators. Models were estimated using full-information maximum likelihood (FIML) estimation with weighted least-squares adjusted for mean and variance (WLSMV; see Chapter 3 for details), taking account of the survey structure. Oblique (geomin) rotation was used, allowing the factors to correlate. Factors were retained based on eigenvalue (> 1.0) and interpretability. Items were retained if they had a factor loading of at least 0.40.

Table C1. Initial pool of home-learning environment items

Item Variable name

1. Study child has been read to in the past week1 bihread

2. Number of books for children in the home2 bbook30

3. Study child has visited library in the past month3 bohlib

4. Study child has played with toys and games indoors in the past week1 bihindr

5. Study child has played music, sung songs, danced or done other musical activities in the past week1 bihmusic

6. Study child has drawn pictures or done arts and crafts in the past week1 bihcraft

7. Parent or other adult has told the study child a story in the past week1 bihstory

8. Study child has gone to a concert, play, museum, art gallery or community or school event in the past month3

bohcult

Notes:

1. Instructions: In the past week, on how many days have you, or an adult in your family….[item]. Responses: (0) None; (1) 1 or 2 days; (2) 3-5 days; (3) Every day (6-7 days).

2. 1 = 30 and more, 0 = less than 30.

3. 1 = yes, 0 = no.

We followed up the exploratory factor analysis with confirmatory factor analysis to investigate how well the items functioned as latent variables.



EFA resultsTwo factors were supported (Table C2). Items 4 to 7 loaded on the first factor, while items 1 to 3 and 8 loaded on the second factor. However, item 8 (concerts, plays, museums etc.) did not load strongly on the second factor and was not retained. Thus, the first factor was characterised mostly by activities taking place in the home (except reading), while the second factor was characterised by reading and books.

Table C2. Factor loadings from exploratory factor analysis of home-learning environment items at waves 2 and 3

Wave 2

Item Factor 1 loadings Factor 2 loadings

1. b/cihread 0.16 0.64

2. b/cbook30 –0.01 0.59

3. b/cohlib –0.04 0.46

4. b/cihindr 0.73 0.00

5. b/cihmusic 0.69 –0.05

6. b/cihcraft 0.65 0.08

7. b/cihstory 0.41 0.06

8. b/cohcult 0.004 0.39

Correlation between factors 0.46

Eigenvalues 2.79 1.30

83


CFA resultsConfirmatory factor analyses of the two selected home-learning environment items showed good fit, and loadings are summarised in Table C3.

Table C3. Summary of final CFA for home-learning environment at waves 2 and 3

Wave 2

Item Loading R2

Reading

1. b/cihread 0.86 0.74

2. b/cbook30 0.49 0.24

3. b/cohlib 0.40 0.16

Activities at home

4. b/cihindr 0.73 0.53

5. b/cihmusic 0.64 0.41

6. b/cihcraft 0.70 0.49

7. b/cihstory 0.47 0.22

Correlation between factors 0.54

Model fit

RMSEA (90% CI) 0.020 (0.016–0.023)

CFI/TLI 0.992/0.989

2 (df) 173.608(65)***

WRMR 1.064

Notes: * p < .05; **p < .01; ***p < .001



Appendix D: Missing data

This Appendix gives details of missing data on the variables used in Study 1 and Study 2.

Study 1Taking all the variables into account, only 1,818 (42 per cent) of the sample of 4,351 had complete data, meaning that they had no missing variables at any wave. The largest source of missingness was the parenting and self-regulation variables at wave 2, missing for about 24 per cent of the sample. This was because they were assessed with a self-report questionnaire that was left behind for the child’s parent to complete, and many did not return it. Teacher-reported approaches to learning items were missing for about 22 per cent, again because questionnaires were not returned. Wave 2 parenting and self-regulation were missing for about 15 per cent and wave 1 mother’s depressive symptoms for about 12 per cent. Other variables were missing for less than 10 per cent of the sample.

With maximum-likelihood estimation, missing data are handled by full-information maximum likelihood (FIML), which provides efficient and unbiased estimates comparable to multiple imputation, assuming that data are missing at random (MAR). ‘Missing at random’ means that missingness on a variable can be related to other variables in the dataset but should not be due to the values of the variable with missing data. For example, if a child’s mother did not complete the wave 2 self-regulation items, this should not be attributable to the child’s level of self-regulation. In this study, we assume that data are MAR. With WLSMV estimation, all cases are used in the analysis, and a hybrid approach to missing data is taken, with some steps based on FIML and some on pairwise deletion. However, simulations suggest that, under the MAR assumption, estimates are consistent and efficient and certainly preferable to listwise deletion (Asparouhov & Muthén, 2010).

85


Table D1. Missing data in Study 1

Variable Complete Missing % missing

Wave 5 school engagement

Approaches to learning variables 3,466 885 20.3

School liking variables 3,992 359 8.3

Maths liking 3,992 359 8.3

Absenteeism 4,052 299 6.9

Wave 4 school engagement

Approaches to learning variables 3,417 934 21.5

School liking variables 4,157 194 4.5

Maths liking 4,156 195 4.5

Absenteeism 4,224 127 2.9

Wave 3 variables

Task attentiveness variables 3,694 657 15.1

Emotion regulation variables 3,694 657 15.1

Consistent parenting 3,605 746 17.1

Hostile parenting 3,661 690 15.9

Home-learning environment variables 4,190 161 3.7

Wave 2 variables

Task attentiveness variables 3,328 1,023 23.5

Emotion regulation variables 3,326 1,025 23.6

Hostile parenting 3,274 1,077 24.8

Home-learning environment variables 4,189 162 3.7

Wave 1 variables

Maternal depressive symptoms 3,822 529 12.2

Hostile parenting 4,321 30 0.7

Study 2There were missing data for 1,249 (37 per cent of 3,308). Most of the missing data was due to carer questionnaire not being returned at wave 3—about 22 per cent of children were missing ECEC data. The next biggest source of missing data was missing parent reports of wave 3 self-regulation (13 per cent missing). There was less than 10 per cent missing on all other variables. Refer to Table D2 for details.

It was not feasible to carry out the analyses in Study 2 in a structural equation modelling framework, because the categorical variable for teacher qualifications could not be treated as ordered categorical. A categorical variable that is nominal is more difficult to model in a structural equation context, especially when it is involved in interaction terms. Therefore, it was necessary to impute



missing data. We used multiple imputation with chained equations, assuming data were missing at random. The imputation model included all dependent and independent variables in the models, as well as young maternal age at the study child’s birth (younger than 25), whether the mother was Aboriginal or Torres Strait Islander, and wave 3 parenting hostility. These auxiliary variables were included because they were related to missingness. It is recommended that researchers wishing to examine interactions with imputed data should include the interaction terms in the imputation model in order to avoid serious bias in the estimates (Sterne et al., 2009). A separate imputation model was estimated for each interaction model that we examined (e.g., task attentiveness with carer warmth and conflict; task attentiveness with ECEC activities).

In all cases, 10 datasets were imputed. Coefficients were combined using Rubin’s Rules, which adjust standard errors to account for variation between and across imputed datasets (Sterne et al., 2009).

Table D2. Missing data in Study 2

Variable Complete Missing % missing

ECEC activities wave 2

Teacher-supported individual activities 2,573 735 22.2

Teacher-supported small group activities 2,583 725 21.9

Teacher-directed whole-group activities 2,585 723 21.9

Child-initiated activities 2,589 719 21.7

Carer qualifications wave 2 2,577 731 22.1

Relationship with carer wave 2

Warmth 2,626 682 20.6

Conflict 2,626 682 20.6

School readiness w3 3,144 164 5.0

ICSEA w4 3,156 152 4.6

87


Appendix E: Correlations and estimates for covariates from structural equation models predicting school engagement in Chapter 6

This Appendix presents correlations between variables and constructs measured at the same wave and estimates for covariates (gender, Year level and school ICSEA) not presented in Chapter 6. The correlations and effects of gender for variables at waves 1 to 3 are presented only once, because they were almost identical in each of the four models.



Table E1. Within-time correlations from models presented in Chapter 6

1 2 4 5 6 7 9 10 11 12

Wave 1 Wave 2 Wave 3

1. Maternal depressive symptoms

- 4. Task attentiveness - 9. School readiness

-

2. Disadvantage 0.16*** - 5. Emotion regulation –0.02 - 10. Emotion regulation

–0.07* -

3. Hostile parenting 0.20*** 0.003 6. Home-learning environment—in-home activities

0.29*** –0.04 - 11. Task attentiveness

0.27*** –0.13*** -

7. Home-learning environment—reading

0.19*** –0.22*** 0.62*** - 12. Hostile parenting

–0.03 0.34*** –0.14*** -

8. Hostile parenting –0.16*** 0.39*** –0.13*** -0.09** 13. Consistent parenting

0.01 –0.31*** 0.12*** –0.17***

Notes: * p < .05; **p < .01; ***p < .001

89


Table E2. Estimates for regression of wave 1 to 3 variables on gender

Est.

Wave 1

Hostile parenting 0.02

Wave 2

Task attentiveness 0.01

Emotion regulation 0.04*

Home-learning environment—in-home activities –0.05*

Home-learning environment—reading –0.05*

Wave 3

School readiness –0.27***

Emotion regulation 0.04*

Task attentiveness –0.12***

Consistent parenting 0.004

Notes: * p < .05; **p < .01; ***p < .001

Table E3. Estimates for regression of school engagement outcomes on gender, Year level and school ICSEA


Absenteeism School liking Maths liking

Wave 4 Wave 5 Wave 4 Wave 5 Wave 4 Wave 5 Wave 4 Wave 5

Wave 4 Year level

(ref = Year 1)

Kindergarten 0.11 - 0.19 - 0.11 - 0.02 -

Year 2 –0.17*** - –0.03 - –0.19*** - –0.07 -

2010 school ICSEA –0.004 0.04 –0.02 0.05 –0.08** 0.02 –0.02 –0.03

Gender –0.34*** –0.26*** –0.02 0.01 –0.40*** –0.21*** –0.05 0.39***

Notes: * p < .05; **p < .01; ***p < .001



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Self-regulation, family resources, and early child … · Web viewExecutive Summary 4. Results of Study 1 (Q1 and Q2): changes over time in school engagement and differences between