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Life satisfaction among middle-years children of various language backgrounds Emerson, Scott Daniel 2017

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 LIFE SATISFACTION AMONG MIDDLE -YEARS CHILDREN OF  VARIOUS LANGUAGE BACKGROUNDS   by  Scott Daniel Emerson   A THESIS SUBMITTED IN PARTIAL FULFILLMENT OF  THE REQUIREMENTS FOR THE DEGREE OF  MASTER OF SCIENCE  in  THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES  (Population and Public Health)   THE UNIVERSITY OF BRITISH COLUMBIA  (Vancouver)   December 2017    © Scott Daniel Emerson, 2017   ii Abstract BACKGROUND:  Understanding the measurement of life satisfaction and its association with social supports among children from diverse language backgrounds is a key step of measuring healthy child development in British Columbia (BC). The Satisfaction with Life Scale adapted for Children is a self-report measure of child life satisfaction and has exhibited sound psychometric properties for a representative population of children in BC. If found to show measurement equivalence (ME) across subgroups, SWLS-C means and correlates can be meaningfully compared across such subgroups. Peer and adult support positively relate to SWLS-C scores for BC children overall but such relationships are unknown for specific language background groups. Using language background as a proxy for cultural background, this thesis examined: 1) the cross-cultural ME of the SWLS-C; 2) cross-cultural differences in SWLS-C means; 3) the cross-cultural ME of peer support and adult support scales; 4) cross-cultural associations of peer and adult support with SWLS-C scores.   METHOD: Participants were 20,119 BC children (Mage 9.2; 50.2% male) who completed the SWLS-C, peer support, and adult support scales as part of a child health survey (the Middle-Years Development Instrument). ME of the SWLS-C, peer support, and adult support scales was examined across eight cultural (i.e., language) background groups. Means and inter-relationships of the SWLS-C, peer support, and adult support scales were estimated across cultural background groups.  RESULTS: Findings supported ME between the English group and: all other cultural background groups for the SWLS-C, three other cultural background groups for the peer support   iii scale, and six other cultural background groups for the adult support scale. Relative to the English group, SWLS-C means differed for several cultural background groups - variation consistent with mean differences for the peer support and adult support scales. Within each cultural background group, peer and adult support scale scores positively related to SWLS-C scores.   DISCUSSION: This thesis provided evidence for meaningful comparison of life satisfaction, peer support, and adult support means across diverse cultural background groups, highlighting differences in life satisfaction between cultural background groups of children, but underscored the importance of fostering adult and peer support to promote healthy child development.                iv Lay Summary Despite the cultural diversity of British Columbia and the public health significance of mental health, little is known about the measurement of life satisfaction (a key indicator of quality of life and positive mental health) among children of diverse cultural backgrounds. Additionally, little is known about factors that may help promote life satisfaction for diverse cultural background groups of children in the province. Using children’s language background as a proxy for their cultural background, this investigation found a common measure of child life satisfaction to be appropriate for measuring life satisfaction among 4th grade children from various cultural backgrounds. Levels of life satisfaction differed across the cultural background groups, but positive support from peers and adults had promotive associations with life satisfaction for all cultural background groups of children.                          v Preface  A version of chapter 1 has been published as a review article in Quality of Life Research: Emerson SD, Gadermann AM, Guhn M. Measurement Invariance of the Satisfaction with Life Scale: reviewing three decades of research. Quality of Life Research. 2017 Sep 1; 26(9):2251-64. I conceived the article, was primarily responsible for the writing and analyses, while the two co-authors (Drs. Martin Guhn and Anne M. Gadermann) assisted in the editing and structure.  Chapters 2 and 3 were based on secondary data analyses of the 2010 – 2016 administrations of the Middle-years Development Instrument (MDI), grade 4 version. Access to and analyses of data by a Data Access Request related to the MDI, which was approved in September 2015, as well as approval by the University of British Columbia Behavioural Research Ethnics Board (Project title “Understanding the World of Middle Childhood: The Middle Years Development Instrument (MDI) Survey”; H09-00416-021). A preliminary version of chapter 2 and 3 was presented at the 2017 Canadian Public Health Association conference in Halifax, NS. An article summarizing chapters 2 and 3 will be submitted for publication.           vi Table of Contents Abstract ........................................................................................................................................... ii Lay Summary  ................................................................................................................................. ii Preface..............................................................................................................................................v Table of Contents  .......................................................................................................................... vi List of Tables  ................................................................................................................................ xi List of Tables  ............................................................................................................................... xii List of Figures  .............................................................................................................................. xii List of Abbreviations ................................................................................................................... xiii Acknowledgements ...................................................................................................................... xiv Chapter 1: Introduction and literature review ...........................................................................1 1.1 Positive mental health and child development .......................................................................1 1.1.1 Components of positive mental health and subjective well-being  .................................2 1.2 Life satisfaction ......................................................................................................................3 1.2.1 Researching children’s life satisfaction ...........................................................................4 1.2.2 Conceptual framework – positive youth development ....................................................5 1.2.3 Children’s life satisfaction and physical health ...............................................................7 1.2.4 Children’s life satisfaction and mental health .................................................................9 1.2.5 Child life satisfaction and culture ....................................................................................9 1.2.6 Challenges of interpreting life satisfaction in cross-cultural contexts...........................12 1.2.7 Measuring life satisfaction in middle childhood ...........................................................15 1.3 The Satisfaction With Life Scale adapted for Children (SWLS-C) .....................................16 1.3.1 Validity evidence for the SWLS-C ................................................................................17   vii 1.3.2 Confirmatory factor analysis as validity evidence  .......................................................19 1.4 Measurement equivalence as validity evidence ...................................................................19 1.4.1 Testing measurement equivalence .................................................................................21 1.4.2 Measurement equivalence of the SWLS-C ...................................................................24 1.4.3 Measurement equivalence of the (adult) Satisfaction With Life Scale (SWLS)  ..........24 1.4.4 Measurement equivalence of the adult SWLS across culture  ......................................25 1.5 Correlates of life satisfaction – measurement equivalence of the peer support and adult support scales  ............................................................................................................................27 1.6 Cultural diversity in British Columbia, Canada  ..................................................................27 1.7 Concluding remarks and hypotheses ....................................................................................29 Chapter 2. Methods and analytic approach ..............................................................................31 2.1 Data sources .........................................................................................................................31 2.2 Measures ...............................................................................................................................34 2.2.1 Cultural background  .....................................................................................................34 2.2.2 Life satisfaction  ............................................................................................................35 2.2.3 Peer support  ..................................................................................................................36 2.2.4 Adult support  ................................................................................................................37 2.3 Analytic sample ....................................................................................................................39 2.4 Testing single-group model fit  ............................................................................................42 2.5 Measurement equivalence  ...................................................................................................44 2.5.1 Model fit  .......................................................................................................................45 2.6 Comparison of SWLS-C, peer support, and adult support mean scores ..............................47 2.7 Associations between children’s peer and support and their life satisfaction  ........................47   viii Chapter 4. Discussion  .................................................................................................................61 4.1 Cross-cultural measurement equivalence of the SWLS-C ...................................................61 4.2 Measurement equivalence of the peer support and adult support scales ..............................63 4.3 Comparison of SWLS-C means ...........................................................................................66 4.3.1 SWLS-C among Chinese and Korean background children  ........................................66 4.3.2 SWLS-C among Filipino and Spanish background children  .......................................69 4.3.3 SWLS-C among French background children  .............................................................71 4.3.4 SWLS-C among Punjabi background children  ............................................................71 4.3.5 Socio-cultural contextual influences on child mental health .........................................72 4.4 Relations among SWLS-C, peer support, and adult support scale scores ............................75 4.5 Limitations and challenges ...................................................................................................77 4.5.1 Language background as a proxy for cultural background ...........................................77 4.5.2 Lack of information on immigration status ...................................................................78 4.5.3 Lack of information on acculturation ............................................................................79 4.5.4 English language on the MDI ........................................................................................81 4.5.5 Reliance on self-reported data .......................................................................................82 4.6 Implications ..........................................................................................................................84 4.7 Future inquiry .......................................................................................................................86 References .....................................................................................................................................90 Appendices ..................................................................................................................................106 Appendix A – appendices from Chapter 2 ..............................................................................106 A.1 Measurement equivalence of monolingual and bilingual language backgrounds ................106   ix A.2 MG-CFA results for monolingual and bilingual (with English) comparisons of language background groupings ..................................................................................................................108 A.3 Mean comparisons of mono- and bi-lingual versions of language background groups  ......108 A.4 Single-group confirmatory factor analyses for the SWLS-C one-factor model ...................109 Appendix B – appendices from Chapter 3 ...............................................................................110 B.1 Geographic distribution of language backgrounds in British Columbia ...............................110 B.2 Characteristics of overall MDI sample and analytic sample of respondents ........................111 B.3 Proportion of child respondents who reported a language background other than English across participating BC school districts in the analytic sample (n=28) .......................................113 B.4 Factor loadings for each SWLS-C item across cultural background groups ........................114 B.5 Thresholds for SWLS-C item 1 across cultural background groups .....................................114 B.6 Thresholds for SWLS-C item 2 across cultural background groups .....................................115 B.7 Thresholds for SWLS-C item 3 across cultural background groups .....................................115 B.8 Thresholds for SWLS-C item 4 across cultural background groups .....................................116 B.9 Thresholds for SWLS-C item 5 across cultural background groups .....................................116 B.10 Residual variance for each SWLS-C item across cultural background groups...................117 B.11 Pairwise comparison of model fit for multi-group confirmatory factor analysis of the SWLS-C .......................................................................................................................................118 B.12 Level of ME supported for the pairwise cultural background group comparisons of the SWLS-C .......................................................................................................................................122 B.13 Pairwise comparison of model fit for multi-group confirmatory factor analysis of the peer support scale.................................................................................................................................123   x B.14 Pairwise comparison of model fit for multi-group confirmatory factor analysis of the adult support scale.................................................................................................................................124 B.15 Level of ME supported for the pairwise cultural background group comparisons of the adult support scale.................................................................................................................................126                       xi List of Tables Table 1.1 The Satisfaction with Life Scale Adapted for Children (Table adapted from Gadermann, Schonert-Reichl, & Zumbo, 2010) ............................................................................17 Table 2.1 BC school districts included in dataset of grade 4 respondents to the Middle-years Development Instrument (MDI), by school year ...........................................................................32 Table 2.2 Peer support scale (Schonert-Reichl et al., 2013)  .........................................................37 Table 2.3 Adult support scale (Schonert-Reichl et al., 2013)  .......................................................38 Table 3.1 Ordinal alphas for SWLS-C, peer support, and adult support scales  ...........................49 Table 3.2 Model fit for single-group confirmatory factor analysis of SWLS-C ...........................50 Table 3.3 Model fit for multi-group confirmatory factor analyses of the SWLS-C ......................50 Table 3.4 Model fit for single-group confirmatory factor analysis of the peer support scale .......52 Table 3.5 Model fit for multi-group confirmatory factor analyses of the peer support scale ........53 Table 3.6 Model fit for single-group confirmatory factor analysis of the adult support scale ......54 Table 3.7 Model fit multi-group confirmatory factor analysis of the adult support scale .............55 Table 3.8 Descending unadjusted mean SWLS-C scores for all cultural background groups ......56 Table 3.9 Unadjusted life satisfaction, peer support, and adult support scale means ....................58 Table 3.10 Multi-level linear regression results for SWLS-C scores for each group ....................60         xii List of Figures Figure 1.1 Graphical representation of a hypothesized one-factor model with 5 items ................19 Figure 1.2 Graphical depiction of multi-group confirmatory factor analysis ................................22 Figure 1.3 Linguistic and ethnic diversity* across British Columbia in 2011...............................29 Figure 2.1 Map of BC school districts included in analytic sample of grade 4 respondents to the Middle-years Development Instrument (MDI), 2010 – 2016 ........................................................33 Figure 2.2 Sample refinement process for defining the eligible sample ........................................40 Figure 3.1 Graph of adjusted SWLS-C means for each cultural background group .....................57 Figure 3.2 Graph of unadjusted SWLS-C and adult support means for each cultural background group ..............................................................................................................................................59 Figure 4.1 Spatial distribution of visible minorities in the Vancouver area in 2016 .....................74               xiii List of Abbreviations CFA  Confirmatory Factor Analysis CFI  Comparative Fit Index LS  Life satisfaction ME  Measurement Equivalence MG-CFA Multi-group Confirmatory Factor Analysis PMH  Positive mental health RMSEA Root Mean Squared Error of Approximation SWLS  Satisfaction with Life Scale SWLS-C Satisfaction with Life Scale adapted for Children TLI  Tucker-Lewis Index                xiv Acknowledgements  I am grateful to many people for their support regarding this thesis. Foremost, I am grateful for Dr. Martin Guhn for his relaxed, calm, and agreeable supervisory style. Without his guidance and encouragement throughout I would surely not have ‘crossed the finish line’. I also thank my committee members, Dr. Louise Mâsse and Dr. Ara Norenzayan, as well as my examiner Dr. Bruno Zumbo, for their useful feedback and thought provoking questions that helped improve my thesis. I also acknowledge Dr. Tavinder Ark who provided invaluable insight regarding all things psychometric, assisted with the technical and computational components, and has always been very encouraging to me. I also gained useful insight from discussions with Dr. Anne Gadermann regarding children’s life satisfaction, its measurement, as well as her assistance with the measurement invariance review that formed part of the thesis introduction. I am also grateful for the support from several of my fellow graduate students – including Rami, Luke Andrew, and Pinky – who helped make my experience so enjoyable.   I also thank the thousands of children from diverse backgrounds who shared their voices through the MDI and I hope this thesis can ultimately help to support children’s health. My MSc was financially supported by a Joseph-Armand Bombardier Canada Graduate Scholarship from the Social Sciences and Humanities Research Council and by UBC’s Faculty of Medicine.  I appreciate the kind support from my parents, brothers, and especially my late grandmother who sadly passed away only months before I could share this accomplishment with her. I lastly thank Nicole who has always been there throughout my MSc and thesis providing support and concern for my own life satisfaction.  1 Chapter 1. Introduction and literature review 1.1 Positive mental health and child development Mental health is an integral component of child development [1]. Children with poorer mental health – such as those with mental disorders, low self-esteem, or high levels of negative emotions – are more likely to experience negative social and health outcomes in childhood, young adulthood, and beyond [2]. Conversely, children with higher levels of positive mental health are more likely to present better overall health, be more physically active, have better socio-emotional competencies, and are less likely to suffer from mental or substance abuse disorders [3]. Indeed, many initiatives, programs, and policies aimed at promoting positive child development feature mental health as an outcome [4,3,5]. Hence, it is imperative to better understand the nature of positive mental health among children and to identify factors and contexts that may aid its promotion among all children. This thesis therefore focuses on understanding child positive mental health in terms of its measurement and associations with social support factors in a cross-cultural context (using children’s language background as a proxy for cultural background – see Chapter 2 for details). To preface this focus, however, it is important at this stage to unpack the concept of positive mental health.  Positive mental health (PMH) is not simply the absence of mental illness; rather, it entails a collection of psychological and mental attributes and strengths beneficial for positive functioning (e.g., self-esteem, self-regulation, and an ability to develop meaningful interpersonal relationships) [6]. PMH can be regarded as a broad, overarching concept that comprises positive affect, positive cognitive self-appraisals, as well as competencies to manage and contribute to life meaningfully [7]. Despite its documented importance to healthy child development and positive outcomes in later life, inquiry into child mental health has historically focused on risk   2 factors and mental ill-being in lieu of PMH or its promotion [1]. In recent years, however, a shift has occurred in the mental health literature – away from a focus on risk, deviance, and psychopathology, and toward emphasis on positive mental attributes and their promotion (i.e., prevention through health promotion). Mental illness, justifiably, remains a major focus of mental health research – but PMH has increasingly been regarded as a core component of the broader mental health landscape. Such a recognition was exemplified by the World Health Organization (WHO) description of mental health [8]: Mental health is more than the mere lack of mental disorders. The positive dimension of mental health is stressed in the WHO definition of health as contained in its constitution: “Health is a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity.” Concepts of mental health include subjective well-being, perceived self-efficacy, autonomy, competence, intergenerational dependence and recognition of the ability to realize one’s intellectual and emotional potential.  Resulting from this shift toward positive attributes, policy-makers, researchers, educators, and health professionals have shown a growing interest in better understanding and promoting PMH at a population level [9].  1.1.1 Components of positive mental health and subjective well-being Subjective wellbeing is an important, and widely-studied aspect of PMH. In a seminal review, Diener and colleagues identified four components of subjective wellbeing: Pleasant affect, Unpleasant affect, Domain Satisfaction, and Life Satisfaction [10]. The first two components relate to affective reactions to life events (e.g., experiences of positive or negative moods) whereas Domain Satisfaction and Life Satisfaction pertain to cognitive evaluations of life and life circumstances. Domain Satisfaction relates to satisfaction with certain facets of life (e.g., work or family) whereas Life Satisfaction pertains to a global (overall) evaluation of satisfaction with life. The subsequent section discusses the latter component, as it is the mental health outcome of focus for the present thesis.   3 1.2 Life satisfaction Research supports the idea of life satisfaction as a unique construct [11], although some see life satisfaction (LS) as a subordinate component of happiness [12] whereas others regard LS as an important social indicator of quality of life [13]. For the purpose of this thesis, LS is defined as a “cognitive evaluation of one’s life” (p. 550) [14] or as one’s evaluation of the extent to which they are satisfied with life overall [15].  LS is widely considered a key component in one’s quality of life, and is one of the most commonly measured aspects of PMH [16]. Accordingly, much overlap exists between conceptualizations of subjective quality of life and LS, as shown in Pavot and Diener’s definition of LS as a “judgmental process, in which individuals assess the quality of their lives on the basis of their own unique criteria” (p.164) [17].  LS is comparable to self-reported quality of life, as evidenced in the WHO’s inclusion of items about LS in their quality of life measures [18]. Despite the ostensible breadth of quality of life as a concept, Kazdin argued the importance of focusing on quality of life as a measured outcome in health: “we are interested in improving the quality of life. There are few constructs this broad, global, and yet as clinically important as quality of life” (p. 296) [19]. Underscoring the recognition of LS as a key aspect of public health, the annual nationally-representative Canadian Community Health Survey asks respondents to self-report their LS [20]. Judgements of how satisfied a person is with his/her life are likely to be influenced, at least partially, by time- and situation-related factors, such as one’s mood when making such assessments. At the same time, evidence suggests people typically form assessments about their LS by drawing on the same sources of information each time they rate their satisfaction with life [21]. Such informational sources can include life domains, such as (romantic, familial, and   4 social) relationships, work (e.g., job satisfaction, achievements) and educational attainment (e.g., high school graduation; graduate degree). When respondents used different sources to inform LS assessments, their scores on LS measures also differed. Hence, differences in LS seem to be related more closely to the informational source employed when rating her/his LS than to transient situational factors  [21]. One’s affective state or mood at the time of forming a LS judgement has some influence, yet such influences seem to be minor [22]. Over the course of two-decades, approximately 33% - 38% of variance in LS among participants was accounted for by a stable, trait component [23], suggesting a considerable amount of LS is stable over time. Pavot and Diener underline the importance of personality in influencing LS [15]. They highlight work indicating traits such as neuroticism and extraversion are correlates of LS [24]  There is, however, evidence for the malleability of LS due to major life events and experiences. Whereas some theory and research suggests one’s LS tends to ‘adapt’ after various life experiences (i.e., regress to a set-point similar to where it was before such events) [25,26], this is not always the case. Major life events have been shown to have a lasting impact on LS levels. The death of a loved one [27], being a caregiver for a person with a grave illness [28], suffering a long-term disability [29], and marital change (e.g., divorce) [30] may lead to significant and sustained decreases in one’s LS. Unemployment can also have an enduring influence on one’s LS [31]. In summary, there is evidence that LS can be explained not solely by stable, trait-like factors (e.g., disposition), but also by context and experiences (e.g., social relationships, changes in employment status).  1.2.1 Researching children’s life satisfaction The bulk of LS Research has traditionally tended to focus on adults rather than children    5 and youth [32]. Mirroring such discrepancy, until recent years most instruments for assessing positive mental health generally (and LS in particular) were designed for, and validated with, adults.  A shift in both research direction and policy, has however, occurred such that there is a growing interest among various fields in measuring children’s PMH. In 2008 a journal dedicated to research into the measurement of children’s quality of life - Child Indicators Research - was initiated. Within the context of gathering information about children’s mental health, there has been a recent movement away from treating the child as the ‘object’ of research, and toward involving – and giving a voice to – children [33]. This movement is exemplified by the recent rise in self-report measures for children’s PMH in general, and LS especially, such as the Students’ Life Satisfaction Scale (Huebner, 1991), the Multidimensional Students’ Life Satisfaction Scale [34], the School Children’s Happiness Inventory [35], the Satisfaction with Life Scale adapted for Children [36], and the Children’s Happiness Scale [37].   1.2.2 Conceptual framework – positive youth development The present thesis is informed by the positive youth development (PYD) conceptual framework [38]. PYD assumes that both individual-level and contextual factors can influence positive development of children and youth. It is a strengths-based model that emphasizes positive attributes and their promotion in children and youth [1]. In this sense, the mere focus of LS to index child mental health and development (rather than presence of mental health problems) is aligned with the PYD approach. Lazarus theorized that higher levels of PMH enable one to frame and interpret potentially problematic or stressful events in more positive ways, which in turn allows an individual to more effectively manage stressful experiences that could deteriorate mental health/functioning [39].    6 In the strengths-based view of PYD, PMH can be regarded as a resource that enables one to be optimistic, hopeful, and exhibit an openness to new experiences – attributes that can support healthy child development. Within this framework, PMH is a crucial component of child development, and is a useful indicator of various outcomes related to positive health, educational, and social outcomes [40,41]. Thus, the present thesis integrates the PYD conceptual framework in its focus on child PMH not only as a key indicator of healthy child development but also as an invaluable, promotive mental resource that should be fostered.  PYD, as well as other ecological theories of child development [42], highlight the embedded and contextualized nature of human development. Within Bronfenbrenner’s model, proximal contexts – including parents/family, peers, and school teachers – have reciprocal relationships with children [43]. Beyond proximal contexts, there are mid-range contexts (e.g., neighbourhoods) and distal contexts (e.g., culture), which interact with each other and influence child development. In this sense, the present thesis considers two contextual influences: Proximal (peer and adult support) and distal (children’s cultural background). As children progress through various developmental stages, the extra-familial environment gains increasing importance [44] and becomes part of children’s social world in conjunction with parental/familial influences. Consideration of such social contextual factors is hence invaluable to better understand children’s healthy development. Hence, there have been calls for further examination and identification of factors – individual and contextual – that relate to life satisfaction in a developmental context [45]. The present thesis hence incorporates an ecological emphasis on child development [42] through examining associations of multiple developmental contexts, namely perceived support from adults and peers, with children’s LS. LS may serve as a useful indicator of children’s current positive functioning and health    7 states (i.e., physical and mental well-being), but children’s LS also predicts LS in adulthood [46]. To this end, the subsequent section documents various documented associations between life satisfaction and specific aspects of health. 1.2.3 Children’s life satisfaction and physical health  An array of positive developmental associations has been observed between child LS and various physical health outcomes. In the below citations, the term child or children refers to individuals aged below 18, since the extant LS literature has been conducted with a range of ages, but especially those in adolescence (approximately ages 13-17). Child LS has been shown to relate to numerous positive physical health and health-related behaviours and competencies. LS has been found to correlate with nutritional diets and physical exercise in late adolescence [47-49]. Several studies observed higher LS among youth who participate in physical exercise [50-52].  In addition to positive associations between higher LS and positive physical health, lower LS scores among youth were associated with worse general health and a higher number of physical symptoms such as headaches and migraines [53]. Poorer self-rated health, physical health, and mobility were also associated with lower youth LS [54,55]. Zullig and colleagues also found that LS scores were incrementally associated with increases in the number of self-reported ‘poor health’ days [54]. Among children, higher levels of LS positively related to self-rated physical health status, a key marker of morbidity and a predictor of mortality [56,57].  The question of the directionality of health-LS associations – whether LS helps promote health or whether individuals who are healthier tend to also be more satisfied with life – is a major discussion point. To this end, Diener and colleagues reviewed how LS and related constructs may influence health [58]. The authors argued that LS and PMH constructs can   8 influence physical health under at least some conditions. The specific mechanisms through which PMH and LS relate to physical health, as well as the threshold levels at which meaningful influences on health can occur, remain unknown. Nevertheless, Diener et al. highlight multiple longitudinal studies that observed levels of LS at baseline were predictive of morbidity, health behaviours (e.g., physician visits), and mortality many years later, even after adjustment for important confounders, such as genetic, socio-economic, and psychosocial factors [59-61]. Also, several longitudinal experimental studies in which individuals were randomized with respect to programs aimed at enhancing PMH (e.g., mindfulness, self-affirmation exercises) found participants receiving the intervention experienced health benefits relative to the comparison groups (e.g., fewer symptoms, fewer physician visits, and longevity) [62-64]  Regarding the specific mediators through which LS may influence health, Ong   suggested health behaviours (e.g., proactive/preventative health activities such as screening), stress, and physiological systems as candidate mediators [65]. In support of such hypothesized pathways, several studies have found PMH to be associated with immune system functioning (e.g., inflammation and thus stress) [66,67], which in turn impacts health [68]. Hence, immune system functionality may act as a mediator through which PMH may relate to physical health. Also, high levels PMH have been shown to support individuals’ ability to respond effectively to stressors (e.g., negative life events) – that would otherwise have detrimental effects on cardiovascular health [69]. Similarly, a study by Park found that child and youth LS buffered the association between negative life stressors and mental health problems [1]. Thus, LS and other PMH attributes may act as a moderator of the association between stressors and problematic mental health.  Despite the aforementioned evidence, and the plausibility of the hypotheses that LS and    9 PMH enhance physical health, it may also be that a causal effect occurs in the other direction, or that the association is bi-directional. For instance, poor health has been shown to predict decreases in level of PMH several years’ later [70] as well as shortly after onset of a negative physical health event [71]. In sum, it appears that LS may operate as both an outcome and predictor of positive health outcomes – but, as Diener et al. concluded, more research is required to understand the intricacies linking PMH/LS with physical health [58].  1.2.4 Children’s life satisfaction and mental health  Regarding mental health, found LS levels negatively related to both anxiety and  depression levels among youth [52].  Poor overall mental health has also been associated with  lower youth LS [54]. Child and youth LS has been found to relate to several positive  psychological attributes: Hope [72], optimism [73,74], self-efficacy [75,76], motivation [77], and sense of meaning in life [78].   Zullig et al. found that lower LS was associated with adolescent use of several substances (alcohol, tobacco, cannabis) [54]. Kuntsche and Gmel found binge alcohol consumption among adolescents related to lower LS [79]. Similarly, Newcomb et al. found alcohol use in childhood directly predicted lower LS in young adulthood [80]. Piko and colleagues found LS negatively associated with smoking among adolescents [81]. Another study observed negative relationships between youth LS and substance use [52]. Thus, LS among children is well-documented as a correlate of many mental outcomes and behaviours.  1.2.5 Child life satisfaction and culture Some work has cross-culturally examined LS among children, youth, or young adults. Relative to adolescents in the United States (US), lower LS has been observed among adolescents in China [82], in Japan [83,84], and in South Korea [85]. Similar findings have been   10 observed among undergraduates in the US whereby those of East Asian descent reported lower LS than those of European descent [86]. Similarly, one study found that Chinese university students studying in Germany had lower LS scores than German students [87]. In contrast to these disparities, similar levels of LS were observed cross-culturally: between children living in Canada relative to those living in Zambia [88] however the psychometric properties of the measures employed for the Zambian youth were poor. Taken together, findings generally hint at a pattern of lower LS among children of East Asian backgrounds and those from Western societies.  For children of diverse cultural backgrounds, migration and transition to a new nation  (and culture) can have a major impact on youth LS. Leung et al. found that variation in LS among East Asian (Chinese, Filipino, Vietnamese) immigrant youth to Australia was explained by differing emphasis on education, the ability of their family to support the  adolescent, and the circumstances of their immigration (namely, whether they were refugees or voluntary migrants) [89]. Among youth who immigrated to Western nations from various countries, those reporting higher parental support tended to report higher LS [90-93]. LS among immigrant youth negatively relates to perceptions of discrimination, marginalization, and transition problems [94-96,90,93]. Immigrant youth who integrate (i.e., retain cultural identity of both their original culture while adopting cultural values of their host culture) tend to experience higher levels of LS than immigrant youth who fail to integrate with the host culture (i.e., retain solely the culture and values of their heritage cultural background) [97]  Although few studies have examined correlates of PMH for specific cultural groups, across multiple cultural/national settings, people with higher levels of extraversion tend to report higher levels of PMH [98] and spirituality has been shown to positively relate to LS among   11 children in both Canada and Zambia [88]. Similarly, perceived admiration from parents and peer acceptance emerged as significant correlates of LS among youth from 11 nations including the US, Turkey, Israel, China, and India [99]. Also, LS negatively related with smoking among adolescents in four different nations: Hungary, Poland, Turkey, and the USA [81]. In contrast, Chinese adolescents have been found to score higher on School- and Friends-related domains of LS relative to the US youth [100], whereas Korean youth have been found to be more satisfied with the School domain, but less satisfied with Self domain, relative to US students [101]. Relationship harmony was found to be a stronger correlate of adult LS among individuals in Hong Kong relative to US residents, hinting at possible variations in the nature of LS in terms of the relative importance of social relationships and maintaining societal norms to LS [102]. For residents of less economically developed nations, financial satisfaction was a key predictor of LS whereas satisfaction with one’s home life was emerged as the key predictor of LS among residents of wealthier nations [103]. Such findings hint at the cultural context underlying the nature of LS among youth of diverse backgrounds. In addition to observational studies, some work has examined how LS and PMH dimensions differ cross-culturally in controlled designs. Boehm and colleagues compared two student groups in the US – those of Asian background and those of Western European background [104]. Both groups received an optimism, gratitude (e.g., write down things for which you are grateful that week), or a control intervention; LS was the outcome measured to assess the impact of the program. In both the gratitude and optimism conditions, participants of Western European background presented higher improvements in LS relative to those of Asian background. Among students of Asian background, LS improvement was higher for those in the gratitude condition relative to those in the optimism condition [104]. The study authors   12 suggested that the gratitude intervention may have been more apt for the participants of a more collectivistic cultural background as gratitude is an inherently altruistic concept. Layous et al. suggested, however, that gratitude interventions may be less impactful on the positive mental health of South Korean individuals as gratitude primes may evoke thoughts about being an undue burden on loved ones [105,106] and South Korean persons may be more prone to feelings of guilt and indebtedness [107]. Support such suggestions, Layous et al. observed higher levels of LS following the gratitude intervention among US students of Western European background, but a lower level of LS following the intervention among South Korean participants [105]. Hence, one asset and common positive mental health intervention – gratitude – may evoke negative emotions among certain cultural groups.  Establishing the universality of psychological phenomena such as LS is, however, challenging and requires evidence of cross-cultural existence, usage, and accessibility [108]. One example of a (likely) psychological universal is the ‘mere exposure effect’ whereby people tend to experience positive emotions regarding familiar rather than unfamiliar objects (e.g., a preference for culturally-familiar faces). Such a pattern has been shown across  multiple diverse cultures as well as across species [109,110]. 1.2.6 Challenges of interpreting life satisfaction in cross-cultural contexts Despite the documented variations and similarities within and across various cultural groups of children in terms of LS, meaningful interpretation of such research is plagued by major measurement and theoretical challenges. Some have taken a ‘strong’ stance on the issue, arguing that latent construct of life satisfaction and other related, latent constructs, cannot be examined/compared cross-culturally, as definitions and interpretations of certain concepts differ markedly from one culture to another [111].    13 It is possible that the construct of LS truly differs fundamentally across nations and cultural groups. The nature and determinants of LS do appear to be influenced by the cultural context in which a person resides [111]. Broadly-speaking, some claim that LS – defined in the Western, individualistic sense – may be less relevant to collectivistic cultures, in which group-based or social judgements are valued more than individualist perspectives (e.g., Canada or the US) [112,113]. Moreover, the majority of human behavioural research has been conducted on WEIRD participants – those from Western, Educated, Industrialized, Rich, and Democratic societies [114]. As such, item wording in scales, response categories, and the very construct of examination (e.g., LS), may not translate to other economic, political, or cultural settings. Indeed, cross-cultural research has demonstrated markedly different findings on phenomena as basic as visual perception and spatial cognition [115,116]. Also, social desirability bias in self-report measures (e.g., LS measures) may differ by culture – and may lead to differentially distorted response patterns, due to a person’s desire to select responses that better fit with cultural norms, such as selecting responses in keeping with humility/modesty (for East Asian respondents) or self-promotion (for US respondents) [117].  Specific to cross-cultural comparisons of self-reported evaluations on a Likert scale  (i.e., subjective ratings of agreement to items), Heine and colleagues discuss the reference- group effect [118]. In this phenomena, respondents from different cultures compare themselves to similar peers and associated norms (both of which are typically from their own culture). Peers and norms often vary by culture, and hence, subjective self-appraisals that invoke comparisons with norms/peers may thus not be comparable [118]. Fundamentally, individuals from different cultural backgrounds tend to have different reference groups. The reference-group bias when using subjective Likert measures tended to be more pronounced for international than within-  14 nation comparisons across cultural groups – likely because groups in the latter instance share one broad society/community [118]. The authors also suggested that cross-cultural comparisons of LS means may be less undermined as they invoke more introspection and consideration of internal standards rather than comparisons with peer/norm standards. Additional methodological challenges in cross-cultural research and comparisons relate to how data are collected. Sampling methods, mode of administration, and the influence of the environment where data are collected can all vary across cultural contexts. Translated versions of surveys can be problematic, as the same words/statements may have different meanings in different linguistic and cultural contexts [119,120]. Familiarity with the stimuli involved in data collection (e.g., use of a computer, experience with self-report surveys) may also influence cross-cultural comparisons, where the experience of responding to items via an electronic/computerized survey may be a novel experience for respondents in some societies and thus may affect responses. Sampling of respondents can also be problematic in cross-cultural work. Selecting highly homogenous samples within the various cultures of interest may increase comparability but at the cost of under-representing the diversity and heterogeneity within each cultural group.  Having a different way of thinking about oneself – as collectivistic cultures, like India  and China do compared with individualistic cultures like the US and Canada [121] – may impact how one conceptualizes and pursues life satisfaction. For example, individuals from a collectivistic culture tend to consider PMH and LS to be derived from social harmony [122,123], and may therefore benefit more from other-focused activities. In contrast, people from more individualistic cultures may benefit more from activities focused on individual, self-promotive experiences and outcomes [104], as they tend to be driven by personal goals and pursuits rather   15 than group cohesion and harmony [124]. Moreover, whereas in most individualistic societies emotions are often seen as either negative or positive, several collectivistic cultures use a dialectic view of emotions, meaning that positive and negative emotions may be experienced simultaneously [83]. For instance, ratings of experienced positive and negative affect over the course of several weeks were found to be less strongly correlated among Asian than Western nations [21]. Experiences evoked both positive and negative emotions among individuals in Asian societies whereas in many Western societies, positive and negative affect were considerately negatively related – suggesting one typically occurred in the absence of another. Relatedly, relative to those in the US, East Asians have been found to agree more with statements denoting counterfactual, dialectical cognition (e.g., “I sometimes believe two things that contradict each other”) [125].  Thus, substantive differences in the structure and measurement of psychological constructs such as LS may occur between cultures and hence may obfuscate any observed differences/similarities among different cultural groups. It is hence imperative to assess the extent to which measures of child LS and related PMH constructs are useful within and across diverse cultural groups.  1.2.7 Measuring life satisfaction in middle childhood          When measuring child LS, a particularly important period is middle childhood (ages 6 to 12). It represents a key developmental phase, marked by key biological, cognitive, and social, and emotional changes that together set the stage for adolescence, early adulthood, and  beyond [126]. Moreover, middle childhood has been described as an “opportune time to identify modifiable factors associated with well-being and maladjustment so that appropriate prevention and interventions can be implemented to foster competence and deter the emergence of problems   16 in adolescence.” (p. 347) [127]. Despite its developmental significance, childhood is understudied with regard to children’s perceptions of their own mental health and relevant social context factors [127]. Partly, this developmental research gap may have been hampered by a lack of measures for assessing LS in childhood. 1.3 The Satisfaction With Life Scale adapted for Children (SWLS-C) Measurement of LS has mostly been with adults and the commonest measure of LS – the Satisfaction with Life Scale [128] – had originally been designed for adult respondents. This scale has since been modified for children, and thorough validation work has been performed to ensure the appropriateness of items [36,129]. Modifications were guided by consultations with a diverse sample of children and three content area experts with strong backgrounds in child development [36,129]. The modified scale is the Satisfaction with Life Scale adapted for Children (SWLS-C) and its items response format are listed in Table 1.1.  Several features of the SWLS-C are intentional. It contains no reverse scored items, which aids in its interpretability and simplicity for middle-years children. Wording of items and the response format was chosen to ensure it was easier to interpret for children than the SWLS (designed and primarily used with adults). Whereas the SWLS contains a 7-point response scale, the SWLS-C lists 5 options – a range found to be appropriate for middle-years children [130]. Although the general meaning of each SWLS-C item corresponds to like items on the SWLS, the terminology was simplified. For instance item 1 reads “In most ways my life is close to my ideal” on the SWLS, and “In most ways my life is close to the way I would want it to be” on the SWLS-C. Notably, item 4 has identical wording in both the SWLS and SWLS-C. Additionally, a standardized computerized assessment of the reading comprehension level (the Flesh-Kincaid score) yielded a score (1.9) indicating the SWLS-C would be comprehensible at the grade 2 level    17 reading ability [36]. Table 1.1 The Satisfaction with Life Scale Adapted for Children (Table adapted from Gadermann, Schonert-Reichl, & Zumbo, 2010).    Disagree a Lot Disagree a Little Don’t Agree or Disagree Agree a Little Agree a Lot 1. In most ways my life is close to the way I would want it to be. 1 2 3 4 5 2. The things in my life are excellent. 1 2 3 4 5 3. I am happy with my life. 1 2 3 4 5 4. So far I have gotten the important things I want in life. 1 2 3 4 5 5. If I could live my life over, I would have it the same way. 1 2 3 4 5  1.3.1 Validity evidence for the SWLS-C For interpretations and decisions made as a result of SWLS-C scores to be meaningful, validation evidence is needed. Due to various usages and consequences of survey score inferences, as well as the documented possible cross-cultural differences in response styles, it is essential to see how responses to an item may be influenced by aspects of a respondent. Measurement validity experts, such as Messick, have advocated for thoroughly examining the construct validity related to inferences based on a scale by providing evidence and theoretical justification to support “the adequacy and appropriateness of inferences and actions based on test scores” (p. 13) [131]. The substantive component of construct validity relates to examination of processes entailed in measurement tasks, which can be examined via cognitive interviewing. Moreover, the Standards for Educational and Psychological Testing, developed jointly by the   18 American Educational Research Association, the American Psychological Association, and the National Council on Measurement in Education,  explicitly listed evidence related to response processes as one of five validity sources [132].  Previous research primarily examined validity components of the SWLS-C via two methods – quantitative investigation of construct validity components of the SWLS-C [36], and qualitative examination of children’s cognitive processes involved in responding to the items [129]. Specifically, differential item functioning (DIF) analyses were conducted, and found no DIF (i.e., no differential measurement) on the SWLS-C items between children of an English-as-a-second-language background relative to those without such a background. Secondly, cognitive think-aloud protocols on the SWLS-C conducted with children revealed that children (n=55), in general, employed similar informational sources as adults in their response to each SWLS-C item (e.g., referring to social relationships, material belongings, basic needs, competencies, and favorite activities and hobbies as the main criteria for judging their LS) [129]. Social relationships, especially support from adults at home as well as peers, were most commonly brought up by children when asked to ‘think-aloud’ the things they considered when preparing responses to SWLS-C items. Notably, no major differences in the mentioned criteria or other response processes documented via the think-aloud protocols were observed across the diverse sample of children (two-thirds had language backgrounds other than solely English) [36]. Psychometric validity evidence for the SWLS-C for middle-year children has been strong (with factor loadings ranging from .7 to .9) [127], suggesting both that the latent variable (life satisfaction) is explaining an adequate amount of variance in each item, and that together, the items measure a single dimension (life satisfaction) rather than multiple dimensions. Internal consistency for the SWLS-C has been good, represented by a Cronbach’s α of 0.80 and ordinal α   19 of 0.86 for Grade 4 MDI respondents in 2015 [127]. Despite the seminal validation work conducted, more work is needed to extend the measure’s validity. Specifically, although sound psychometric properties of the SWLS-C have been demonstrated for samples of children in BC, it is important to examine such properties and validity evidence for diverse background groups.   1.3.2 Confirmatory factor analysis as validity evidence Confirmatory factor analysis (CFA) is the standard method for assessing cross-cultural comparability of measures of latent variables. Figure 1.1 displays the simple factor model representing the theoretical structure of LS.  Figure 1.1: Graphical representation of a hypothesized one-factor model with 5 items       Note. Following conventional notation, the circle represents the latent variable, squares represent observed variables. Here, a single latent variable of LS is assumed to determine scores on each of the five observed SWLS-C survey items. The arrows pointing from the latent variable to the items represent the factor loadings. 1.4 Measurement equivalence as validity evidence Measurement equivalence (ME; also known as measurement invariance) is a property of a scale whereby the observable measurements (e.g., responses to items on a self-report scale) are found to statistically measure a construct (e.g., life satisfaction) similarly across different subgroups [133]. ME in the context of factor analytic framework is known as factorial   20 equivalence (or factorial invariance), to be distinguished from other approaches such as those via item response theory. ME is a crucial but often neglected validity aspect [133].  ME can be formally defined [134] as conditional independence of observed scores, X, given the underlying latent variable, V: f ( X |V, G) =  f (X | V ) ME occurs if the conditional distribution of the observed scores of X – given the latent variable V – is independent of group membership G. In other words, the distribution of observed scores X solely depends on scores on the latent variable V and is not dependent on group membership G. In the context of the SWLS-C, ME occurs for a given set of groups if variability in observed SWLS-C scores are due to variability in the underlying level of LS – independent of respondents’ membership in such groups (e.g., culture 1 or culture 2). It is highly desirable for a scale to have measurement equivalence as it provides evidence for construct validity since it suggests a scale is measuring its intended concept, and doing so in the same way, for subgroups of respondents. If the SWLS-C is not measuring the construct in the same way for different subgroups then interpretation of scores is problematic, as such scores would have different, incomparable meaning for subgroups [135]. A non-equivalent scale will provide inaccurate estimates of the attribute it purports to measure across subgroups and - in turn - may lead to inferences based on inaccurate numbers.  When ME is not supported, mean differences in observed group means may be due to elements other than actual, underlying differences in the level of the latent variable; for example, cross-group differences in factor loadings of the measurement model relating the latent variable to each item [136]. Wu, Li, & Zumbo illustrated the impact of unequal factor loadings between grade eight students from the US and Japan in terms of a mathematics test question [137]. In   21 their example, a 1-unit increase in the latent factor score (a child’s underlying math ability) corresponded to a 1-unit increase in the observed mathematics item score for US students, but only to a 0.5 increase in the observed item score for Japanese students. The unequal cross-group factor loading meant that, for US students, an observed item score of 2 corresponded to a latent math ability factor score of 2, but for Japanese students, an observed item score of 2 corresponded to a latent math ability factor score of 4 – which nullified cross-group construct comparability for this item and hence would have misconstrued comparisons between observed scores for the two groups [137]. Additionally, a central aspect of modern views of validation processes concerns the implications of inferences based on scale scores. In the context of the SWLS-C and cultural comparisons, the consequences of inferences for specific cultural background groups is a key aspect of validation evidence. Differences between social groups (e.g., by culture, age, gender) must be rigorously examined and done so with due care so as to avoid misinterpretation and potential stereotyping. Hence, ME is crucial to ensure relative differences between groups are not due to differential measurement.  In summary, support for equivalence across subgroups for a measure is highly desirable, as it indicates that relationships between the observed variables and the latent variable are the same (equivalent) across subgroups. In other words, ME essentially indicates we are measuring a construct in the same way for various subgroups and thus there is (more) confidence that observed differences on the latent variable between subgroups occur due to differences in the construct itself rather than differential measurement.  1.4.1 Testing measurement equivalence  Researchers commonly assess ME via testing a hierarchy of four increasingly restrictive    22 assumptions concerning the relations between the observed variables and the latent variable(s), in the context of a multi-group confirmatory factor analysis (MG-CFA) [138]. Figure 1.2 illustrates an MG-CFA visually whereby two subgroups are compared and hypothesized to both represent the SWLS-C in terms of a single latent variable that loads onto the 5 observed scale items. Increasingly more cross-group restrictions are placed on parameters in the measurement model. The Configural level requires equivalent factor structure (i.e., constraining the number of latent factor(s) and the pattern of fixed and free loadings) across groups; its presence suggests that respondents from various groups employ the same conceptual framework when responding [133,139].  Figure 1.2: Graphical depiction of multi-group confirmatory factor analysis.       The Metric level entails holding equal factor loadings for like items across groups, and its support suggests like items share equivalent meaning in terms of their relationship to the factor, across groups [140]. The Scalar level entails equivalent intercepts (for continuous variables) or equivalent thresholds (for ordinal variables), suggesting that the probability of endorsing a response (category), given a certain latent factor score, is equivalent across groups for like items [141]. The Strict level denotes equality of residual variances, and indicates systematic measurement error related to group membership is equivalent across groups for like items [142].   23 Following the configural level, each level of ME requires evidence supporting equivalence at the prior level (i.e., strict ME entails equivalence of residual variances, item intercepts/thresholds, loadings, and factor structure).  Disagreement exists over the level is necessary to establish measurement equivalence. Out of 67 ME studies, less than half considered up to the strict level [133]. An important question in the debate is which level of equivalence enables comparison of observed mean scale scores – an issue of substantive interest in many applications of ME research [143]. While some have asserted the scalar level permits meaningful cross-group comparison of observed means [144-146], others contend that the scalar level permits comparison of solely latent factor means [140,136,137]. Moreover, Deshon explained that the strict level is necessary for observed mean comparison in virtually all situations (with the exception being when item residuals are uncorrelated after accounting for factor scores) [147]. Additionally, using real educational data Wu et al. illustrated unequal cross-group residual variances can yield systematic differences in observed group means [137].  It should be noted, however, that ME per se is not sufficient evidence of complete equivalency of a construct across cultural groups nor is it – per se – evidence of a psychological universal [108]. It is but one aspect of construct comparability, within the broader area of construct validation. ME is limited to inferences of scores based on the sample, setting, and instruments employed. Even if ME is established, a comparison may still be biased if the groups under comparison differ in terms of potentially confounding variables existing within each group [148]. Balancing groups in terms of potentially confounding variables (i.e., pre-existing variables that relate both to the group and the outcome of interest), and then performing ME analyses on the adjusted sample would provide a more accurate understanding of between-group differences.   24 Additionally, examining divergent and convergent validity evidence of the SWLS-C as well as associations with established health outcomes (e.g., stress, physical health) cross-sectionally and longitudinally would provide stronger validation evidence. Nevertheless, examining cross-cultural (estimated using children’s language background as a proxy for cultural background) ME of the SWLS-C is an important step in the ongoing validation process and will help set the stage for future examination of its validation (including its association with external variables). 1.4.2 Measurement equivalence of the SWLS-C In addition to the validation work discussed in section 1.3.1, which employed item-level analyses (differential item functioning) to assess ME, two other studies have examined ME of the SWLS-C. One study examined cross-cultural measurement equivalence of the SWLS-C [149]. Castelli et al. evaluated ME of the SWLS-C among children (mean age = 11.7) in an Italian-speaking canton of Switzerland. Employing MG-CFA, they observed strict ME across gender, grade (6 and 7), and Italian native language status (yes/no). Guhn et al. used a representative sample of BC middle-years children in who completed the SWLS-C in 2009-16. MG-CFA was employed to examine ME across gender and age (grade 4 and grade 7) [150]. Strict ME was observed between gender at grade 4 and between gender at grade 7. The present thesis will extend upon such work to examine cross-cultural ME of the SWLS-C. 1.4.3 Measurement equivalence of the (adult) Satisfaction with Life Scale (SWLS) To provide context for better understanding ME of LS cross-culturally, and since few studies tested ME of the SWLS-C, the literature was searched to identify the extent to which, and for whom, the (adult) SWLS supported ME. The original SWLS contains five items to which respondents self-report their level of agreement on a 7-point Likert scale; some translated versions have used a different number of response options.   25 A literature search was conducted to retrieve publications that tested ME of the SWLS using the commonest approach: MG-CFA. Alternate methods (e.g., item response theory) have been employed to evaluate ME of the SWLS, however, almost all studies have used MG-CFA. Furthermore, as MG-CFA is the approach that will be employed in this thesis for the SWLS-C, reviewing prior studies that used the same methodological approach provides greater comparability and insight for this thesis.  MEDLINE, PsycINFO, EMBASE, and Web of Science databases were searched, limiting to records from January 1985 to August 2016. Articles were included if they (a) quantitatively examined the measurement equivalence of the SWLS across subgroups using MG-CFA, (b) were published in English, and (c) examined ME of the SWLS per se (i.e., not modeled in conjunction with other constructs).  The search identified 27 eligible articles featured participants from 24 nations: China,  Japan, Taiwan, Malaysia, Russia, Iran, Israel, Angola, Togo, Turkey, Bulgaria, Serbia, Norway, Sweden, Germany, France, the United Kingdom, Spain, Portugal, the US, Mexico, Nicaragua, Brazil, and Argentina. In total, 40 unique ME analyses were conducted: 14 across gender, 11 across cultural groups, nine across age groups, and six across other grouping variables (e.g., timepoints). The subsequent section describes the 11 cultural ME studies.   1.4.4 Measurement equivalence of the adult SWLS across culture  Nine analyses examined cross-nation ME [151,152,87,153-157]. One compared equivalence across ethnicities within one nation [158], and one compared equivalence across immigrants residing in three nations  [159]. Of the 11 analyses, one supported scalar ME, four metric ME, four configural ME, and two demonstrated a lack of ME (i.e., even the configural level failed to yield model fit). Scalar ME was observed between university students in Russia   26 and the US [156]. The metric level was observed among Mexican, Nicaraguan, and Argentine university students [153]; older adults in the USA, England, and Japan [157]; adolescents in Spain and Portugal [155]; and adults living in Malaysia of Chinese and Malay descent [158]. Configural ME was supported among university students in Germany, Russia, and China [87], Iran and Serbia [152], China and the US [154], as well as those in Brazil and the US [151]. The configural model was unsupported among immigrants in Israel, Germany, and Bulgaria [159] and between adults in the US and Russia [156]. Inequivalent item parameters can be tested through a process known as partial equivalence whereby cross-group equivalence constraints are removed parameters (i.e., loadings, thresholds/intercepts, or residual variances) associated with each item one at a time, and model fit is re-tested to assess if fit improves. Non-equivalence was thus identified for loadings and/or intercepts associated with item one [152,87], item two [87,153], item three [152,87,153], item four [151,154,157], and item five [151]. Findings indicated that the SWLS was rarely an equivalent measure of adult LS beyond the configural level across cultural groups. Tucker et al. observed scalar ME between Russian and US university students, but no ME at all between adults from the same nations, hinting that cross-cultural experiences may be more similar for US and Russian university than for adults [156]. No clear pattern of non-equivalence emerged in terms of specific items (lack of ME for each SWLS item was found in at least one partial ME cross-cultural analysis). Patterns suggested that the broad frame of reference which respondents use to inform their responses to SWLS items seems to be similar across various cultural systems since configural ME was generally supported [160]. Many of the cultural ME analyses entailed comparing across different translated versions of the SWLS. Despite efforts to ensure comparability across languages, item meanings may have been modified through the translation [119,120].   27 Therefore, the review of studies testing measurement equivalence of the adult SWLS via MGCFA found little evidence for cross-cultural ME of the SWLS, as only 5 of the 11 ME analyses provided evidence beyond the configural level. Such lack of ME across cultural groups undermines cross-cultural comparison of the adult SWLS score means or correlates. In light of the findings regarding cross-cultural ME of the SWLS among adults, it thus seems critical to routinely examine the cross-cultural measurement equivalence of the SWLS-C across different cultural groups of children.  1.5 Correlates of child life satisfaction – measurement equivalence of the peer support and adult support scales In addition to assessing cross-cultural ME of the SWLS-C, the ME of two social support scales were also examined, with the aim of using the social support scales analytically to better understand variation of child LS within and between cultural groups. The types of social support were chosen based on work identifying key correlates of the SWLS-C, with large representative samples of BC children [161,73].  Specifically, higher levels of perceived support from peers and support from adults (e.g., parents, teachers) were the two major correlates of SWLS-C. Although these studies featured culturally diverse samples, correlates of SWLS-C were not explored for specific cultural subgroups [73,161]. The present investigation thus extends this prior work to examine how support from peers and adults relates to SWLS-C for specific cultural background groups of children. 1.6 Cultural diversity in British Columbia, Canada It has been recognized that “the diversity in the ethnic composition of the existing Canadian population has signaled the need for researchers to have a better understanding of the health and well-being of culturally diverse populations” (p.58) [162]. As a culturally diverse   28 province, BC presents a unique opportunity to examine cultural diversity in the context of children’s LS. Of all provinces and territories in Canada, BC had the highest proportion of ethnic minority individuals in 2011 and 2016 (27% and 30%, respectively) [163,164]. BC’s cultural diversity has occurred primarily through Asian immigration in recent decades – particularly from mainland China, Hong Kong, India, and the Philippines. The three largest cultural backgrounds in BC were Chinese, South Asian, and Filipino [163]. After English and French (Canada’s official languages), the five commonest languages learned in childhood among were Punjabi, Mandarin, Cantonese, Tagalog, and Spanish [165] – representing over 70% of all immigrant languages learned in childhood.  Furthermore, the population of Canada is becoming increasingly ethnically diverse.  Projections suggest that, by 2031, up to one third of Canada’s populace will belong to an ethnic minority group [166]. Employing linguistic diversity as a marker for cultural diversity, linguistic diversity has grown in Canada and British Columbia. For instance, the proportion reporting an immigrant language spoken in the household (i.e., a language other than English, French, or an Aboriginal language) rose in BC from 28% in 2011 to 30% in 2016 [165,167]. Although cultural diversity at the national scale is a key feature of Canada, ethnic minority individuals tend to live in more urbanized regions in Canada. This is the case with BC where its most populous region – Greater Vancouver – was home to over 1 million of BC’s 1.38 million ethnic minority residents in 2016 [163]. Figure 1.3 illustrates the variation in diverse language backgrounds and ethnic minority concentration in BC regions in 2011. Cultural diversity tends to co-occur with linguistic diversity, as areas with higher proportions of specific ethnic minorities also tend to have high proportions of corresponding minority languages. This is illustrated in the graphs of Figure 1.3, which indicate the proportion of individuals with   29 immigrant language backgrounds (i.e., languages other than English or French) per census divisions in BC in 2011. Both graphs indicate highest concentrations in Vancouver.  Figure 1.3 Linguistic and ethnic diversity* across British Columbia in 2011   Ethno-cultural data from 2011 National Household Survey administered by Statistics Canada; values indicate proportion of residents in each BC census division (n=29) that self-reported as “persons, other than Aboriginal who are non-Caucasian in race or non-white in “colour”” [168].  Linguistic data from 2011 Census, indicating proportion of residents in each BC census division (n=29) that in childhood learned an immigrant language (i.e., languages other than English or French).   1.7 Concluding remarks and hypotheses Given children’s LS is a core component of their healthy development, understanding correlates of children’s LS, as well as cross-cultural variability in LS and its correlates, is an important goal for public health promotion. To study children’s lives, it is fundamental to have accurate measures of child LS as it allows researchers to study – and societies to monitor – children’s PMH, and to also evaluate the impact of programs, policies, and contexts (e.g., community interventions, school curricula) geared to promote healthy child development. This requires that measures, such as the SWLS-C, be valid – and equivalent – measures of LS for   30 different subgroups including children from various cultural backgrounds. Employing children’s language background as a proxy for cultural background (see Chapter 2), this thesis thus was motivated by the following four research questions: I.a To what extent is the Satisfaction with Life Scale adapted for Children (SWLS-C) an equivalent measure of life satisfaction for middle years British Columbian children of various cultural backgrounds? I.b How does LS vary across various cultural background groups of children? II.a To what extent are the peer support and adult support scales equivalent measures for children of various cultural backgrounds? II.b How are peer and adult support related to LS for children of various cultural backgrounds? This thesis will provide evidence of the extent to which meaningful comparisons can be made across different cultural background groups of children, in terms of life satisfaction scores yielded by the SWLS-C. Measurement equivalence of the SWLS-C is critical for meaningfully interpreting LS data, especially as such data may be used to evaluate impacts of community- and school-based initiatives/policies on children’s health. Ultimately, examining the SWLS-C’s cross-cultural validity will help guide educators, families, and policy-makers in monitoring and fostering factors that promote child health.         31 Chapter 2. Methods and analytic approach 2.1 Data sources Data for the analyses came from the Middle-years Development Instrument (MDI) [127], which contained data on children’s language background, the SWLS-C, the peer support scale, and the adult support scale. The MDI is a population-level self-report instrument that measures children’s developmental outcomes, social relationships, physical health, and time use in middle childhood [169]. The MDI has been administered extensively in British Columbia and has been piloted elsewhere in Canada (Ontario, Nova Scotia, and the Northwest Territories) as well as in Croatia, Peru, Australia, Switzerland, and the United Kingdom [169]. Data for the present analyses were sourced from the British Columbia administrations of the MDI, where the scale was initially developed by researchers, educators, and community organizations and where the most extensive data collection has occurred. The grade 4 version, used for this study, contains 71 survey items and several socio-demographic items [127]. Analyses for the present investigation will employ grade 4 MDI data from the 2009-10, 2010-11, 2011-12, 2012-13, 2013-14, 2014-15, and 2015-16 school years. In total, the MDI grade 4 data come from children attending schools within 28 of the 60 BC school districts (Table 2.1). Some school districts participated in multiple years while some participated only once, however each child completed the grade 4 MDI once. In addition to the MDI data, census-derived data on socio-economic status (SES) at an aggregated area were linked to individual responses. Data came from the 2011 National Household Survey, a voluntary version of the long-form census [170]. Specifically, the following variables were employed: median household income, and the proportion of residents with postsecondary education. Income and education are widely-used indicators of SES, and relate to health outcomes for children in Canada [171].     32 Table 2.1. BC school districts included in dataset of grade 4 respondents to the Middle-years Development Instrument, by school year School district  2010-11 2011-12 2012-13  2013-14 2014-15 2015-16 Kootenay Lake  247 266  227  Revelstoke 79 58 74 53 73 81 Kootenay-Columbia    211   Central Okanagan      1,362 Chilliwack  721 842    Langley      1,260 Delta   1,050  845  Richmond   1,335    Vancouver*    2,526   New Westminster  230 155   377 Burnaby   1,565  1,375  Maple Ridge/ Pitt Meadows    633 686  Coquitlam 1,921  1,916    West Vancouver    418   Sunshine Coast  180 186 173 193 199 Powell River     92 125 Central Coast    ds ds ds Haida Gwaii   47 36  ds Boundary  98 100 106 ds 76 Okanagan- Similkameen    157 131 152 Prince George     732    33 School district  2010-11 2011-12 2012-13  2013-14 2014-15 2015-16 Nicola-Similkameen     128 123 Alberni   230 240 232 237 Gold Trail   71 79   Mission     343  Fraser-Cascade  108 94  100 113 Fort Nelson   56    Nechako Lakes    178   Total 2,000 1,642 7,987 4,830 5,255 4,146 * An additional 3,042 students participated in the grade 4 MDI in the Vancouver School District, as part of a pilot. ds = data supressed (cell size n<35).    Figure 2.1. Map of BC school districts included in analytic sample of grade 4 respondents to the Middle-years Development Instrument, 2010 - 2016   34 2.2 Measures 2.2.1 Cultural background At the start of the MDI, children are asked to provide some demographic information but are not explicitly asked to report their ethnicity or cultural background. They are, however, asked about their language background. Language is a factor that helps shape and contribute to ethnic identity [172,173]. Phinney et al. found heritage language to significantly predict ethnic minority immigrant’s ethnic identity in the United States [174]. Language background may even provide a more nuanced and specific indication of health outcomes of ethnic groups commonly aggregated into a broader group (e.g., a nation). As an example, one study in Seattle observed chronic hepatitis B virus infection rates differed markedly between individuals of different language backgrounds (Oromo and Tigrinya) from Ethiopia, variance that would have been masked had nation of origin been employed to indicate ethnic background [175]. Although it featured a biological rather than mental health outcome, such a pattern nonetheless suggests that a language-based stratification can be a meaningful categorization to examine cross-cultural variability in health outcomes within populations.  Language background may be a particularly salient index of cultural background among immigrant and cultural minority groups of children as language “provides a link to the culture in which their [the children’s] parents were raised” (p. 149) [174]. Moreover, language background has been used to meaningfully distinguish and represent various cultural backgrounds by numerous researchers [176-178]. Indeed, Guhn et al. observed distinct early child development profiles associated with BC children of Chinese, Filipino, and Punjabi language backgrounds. Moreover, patterns showed consistency with results from studies using other methods to approximate cultural background (e.g., the Chinese-language background children exhibited    35 stronger academic competencies than the other groups) [177]. Language background was derived from two MDI items asking children to report their home language (“Which language(s) do you speak at home? You can check more than one if you need to.”) and first language (“What is the first language you learned at home? You can check more than one if you need to.”). In-depth details on the coding of the language background variables are provided in section 2.3. 2.2.2 Life satisfaction   The 5-item self-report Satisfaction with Life Scale adapted for Children (SWLS-C)  [36] provided data on child LS (see Table 1.1). Children rated their level of agreement to the five items via a 5-point Likert response scale ranging from 1: ‘disagree a lot’ to 5: ‘agree a lot’ with higher scores indicating higher life satisfaction. Studies have provided evidence for the reliability, the factor structure, and various components of the validity of the SWLS-C for large, representative samples of middle-years children in BC [36,129]. Sound psychometric properties, including evidence supporting a one-factor structure of the SWLS-C, have also been demonstrated among samples of South Korean [179] and Swiss children [149]. Based on a diverse sample of middle-years children in BC, the SWLS-C was found to have high inter-relations among items (ranging from .56 to .75) – indicative of sufficient inter-item correlations. A principal component analysis (PCA), a parallel analysis, and a factor analysis estimated via a polychoric matrix, all indicated evidence of an (essentially) unidimensional factor structure of the SWLS-C [36]. The PCA revealed the first eigenvalue to explain 71% of variance, compared to 9.6% of variance in the second. Additionally, non-parametric item response theory was employed to graphically display performance of each SWLS-C item. Item response functions (analogous to item characteristic curves), which   36 represent the probability of response associated with its level on the latent variable, indicated the SWLS-C items performed well. Items discriminated well across children of varying levels of life satisfaction – those with higher total expected SWLS-C scores tended to have higher item scores while slopes were steep, increasing from the lower left to upper right corner. At the scale level, predicted score on each item was summed to that of the overall scale and suggested predicted SWLS-C item scores increased monotonically as a function of the overall scale score [36]. Regarding evidence for the concurrent and discriminant validity of the SWLS-C, higher SWLS-C scores more strongly related to concepts more theoretically related to life satisfaction (large/medium effect size: optimism, self-efficacy, and depression) than with conceptually less related constructs (small effect sizes: empathic concern, perspective taking). Also, direction of associations with SWLS-C scores were expected (e.g., positively with optimism, self-concept, and negatively with depression scores) [36]. 2.2.3 Peer support  Children’s sense of support from friends and peers was assessed via a 4-item scale (Table 2.2) that was adapted from the Relational Provisional Loneliness Questionnaire (RPLQ) [180]. Items from the RPLQ have been used in numerous prior studies of middle-years children, and feature in the MDI.  Sound psychometric evidence for the items have been provided in numerous analyses of children in Canada [180,127,181]. Test-retest reliability for the peer support scale was 0.79 over a two-week period; Cronbach alpha’s ranged from .82 to .87 in samples of Chinese youth in Canada [181]. Children reported their level of agreement to each item (e.g., “I feel part of a group of friends that do things together”) from 1: ‘disagree a lot’ to 5: ‘agree a lot’; higher scores represented higher levels of perceived peer support.   37 Table 2.2 Peer support scale (Schonert-Reichl et al., 2013)  Disagree a Lot Disagree a Little Don’t Agree or Disagree Agree a Little Agree a Lot 1. I feel part of a group of friends that do things together. 1 2 3 4 5 2. I feel that I usually fit in with other kids around me. 1 2 3 4 5 3. When I am with other kids my age, I feel I belong. 1 2 3 4 5 4. There is somebody my age who really understands me. 1 2 3 4 5  Similar peer support items were found to positively correlate with SWLS-C among grade 4 BC children [161]. Validation and reliability evidences are not static features of an instrument, however, but are dependent on samples, theory, setting, and the specific usage (i.e., inferences). Thus, reliability of the peer support scale will be assessed via computation of ordinal alpha (comparable to Cronbach’s alpha, but more apt for categorical items) and the factor structure of the scale will also be assessed via a CFA. 2.2.4 Adult support A 4-item subscale, adapted from the California Health Kids Survey – Middle School Questionnaire [182] was used to measure adult support (Table 2.3). Children rated the extent to which they perceived the presence of a parent or non-parent adult in their home and a teacher or other adult in their school who supports and cares for them. Children rated their level of agreement on a 4-point response scale from 1 (not at all true) to 4 (very much true). Acceptable reliability (alpha coefficients) has been demonstrated among the adult support items in numerous studies, with alphas such as .81 [73], .75 [183], and .82 [127]. Prior BC-based research has   38 employed these items to capture supportive relationships with adults (at home and school), and found them to significantly correlate to child life satisfaction scores [161,73]. Table 2.3 Adult support scale (Schonert-Reichl et al., 2013)  Not at all true A little true  Pretty much true  Very much true  1. At my school there is a teacher or another adult who really cares about me 1 2 3 4 2. In my home, there is a parent or another adult who believes that I will be a success 1 2 3 4 3. In my home, there is a parent or another adult who listens to me when I have something to say 1 2 3 4 4. In my home, there is a parent or another adult who I can talk to about my problems 1 2 3 4  Despite the encouraging validity evidence yielded in prior child health studies, reliability and factor structure of the adult support scale will be assessed via the same procedures as for the peer support scale. It should be noted that the focus of the present thesis is on the SWLS-C. Hence, less emphasis is been given to discussion of the peer and adult support scales. The latter scales are included in analyses primarily as a way of better understanding child life satisfaction cross-culturally rather than as a means of explicitly examining the nature of peer support and adult support cross-culturally per se. Nevertheless, the reliability, factor structure, and ME of the peer and adult scales were examined as a way to assess the extent to which they can be used as correlates of SWLS-C. Peer and adult support were chosen as the ‘auxiliary’ variables as the items selected originated in previously used and conceptually coherent scales, and because both concepts have been shown to positively relate to child LS.   39 2.3 Analytic sample  Various exclusion criteria were imposed upon the sample of MDI grade 4 respondents. Figure 2.2 illustrates the refinement of the eligible sample based on the various exclusion and inclusion criteria. Four key aspects of the refinement process are presented. 1. Self-rated English reading ability. As analyses were focused on specific groups of children with various cultural backgrounds who likely had a range of English reading abilities, and the MDI was administered in English, it was important to ensure included respondents had a reasonable level of English comprehension. On one MDI item, respondents are asked to indicate on a 4-point scale (“Very hard”, “Hard”, “Easy”, “Very easy”) their agreement to the item: “How difficult is it for you to read English?”. Those who indicated ‘Hard’, ‘Very hard’, or failed to provide a response, were omitted to ensure any observed group differences in SWLS-C, peer support, or adult support scores were not due to poor English comprehension skills (e.g., inability to interpret items, comprehension of questions). 2. Cultural background via language background. Language background was ascertained by children’s responses to the home and first language items on the MDI.  If, however, their first or home language was not listed as an option, children could check a box entitled “Other” and write out the name they call their language(s). For instance, children for whom Russian was their language would have been able to check ‘Other’ and write in ‘Russian’ to the free-text option adjacent to the ‘Other’ category. Responses to the ‘Other’ box were analyzed and coded accordingly. Some children selected English as well as selecting ‘Other’ and entering a language. Others selected a non-English language from the list (e.g., Punjabi) as well as selecting ‘Other’ and entering a language. Some children also only selected ‘Other’ and inputted their language(s).    40 Figure 2.2. Sample refinement process for defining the eligible sample   * Following Ownership Control Access Possession (OCAP) principles ( data of children reporting Aboriginal language backgrounds were not analyzed in this study.    41 Of the 3,313 respondents who checked the “other” language box, 2,373 text responses were provided. These text responses were grouped into categories if they met certain criteria: (a) The language provided was intelligible/recognizable; b) A language or a nation/region specifically associated with that language was reported (i.e., a land-based language classification); c) Only one language other than English was reported; d) The language formed or joined a minimum group size (n=30). Appendix A lists further related details. 3. Monolingual versus bilingual groupings. Among children who reported both a first language and a home language, most children reported consistent languages (e.g., first language: Punjabi, home language: Punjabi). In some cases, the home and first languages differed. Children who reported multiple non-English languages as their first language(s) and/or home language(s) (e.g., Punjabi as a home language and Spanish as a first language – or vice versa) were omitted from the analytic sample. Children reporting languages or descriptions that agreed with one another (e.g., home language: Cantonese, first language: Chinese) were included. Corresponding monolingual and bilingual language background groups were combined for four main reasons. First, strict ME of the SWLS-C was supported across monolingual (e.g., Punjabi) and bilingual (e.g., Punjabi and English) language background groups. Second, having established ME of the SWLS-C, means of the monolingual and bilingual versions corresponding to each language background group were compared and found to be almost identical (see appendix A). Third, although it was hypothesized that the monolingual/bilingual distinction may have provided a crude indication of acculturation, the strict measurement equivalence of the SWLS-C and equality of means suggested that English as a home and/or first language alongside a non-English language provided no additional differentiation for the present sample. All respondents retained in the analytic sample reported a reasonable English reading ability, were   42 attending schools where instruction and material was primarily in English, completed responses to a survey that was fully in English, and resided in a province were English is the primary language of communication. Hence, theoretically, it is coherent that all children had at least a reasonable level of English language familiarity, and hence the presence or lack of English as a home or first language alongside a non-English language had no significant impact on the level of life satisfaction among cultural groups represented in the present sample. Fourth, merging monolingual and bilingual language background groups enabled larger sample sizes for all groups, yielding more stable estimates of parameters and means.  4. Missing data. Respondents were included in the analytic sample if they responded to over half of the items on each of the three scales. Specifically, those included had: i) at least 3 items on the 5-item SWLS-C, (ii) had at least 3 items on the 4-item adult support scale, and (iii) had at least 3 items on the 4-item peer support scale. These criteria excluded a negligible proportion (approximately 2%) of children from the sample (Figure 2.2). Rather than list-wise deletion (i.e., omission of any respondent with missing data on at least one item), analyses employed all possible pairs of data to estimate correlation matrices for model estimation. After applying all exclusion criteria, a total of 22,435 children were included in the preliminary sample of participants, and the sample represented 21 cultural background groups that were assessed for further inclusion in the final analytic model (see section 2.4).  2.4 Testing single-group model fit  For each cultural background group, and for the three scales, respectively, a separate single-group confirmatory factor analysis (CFAs) was conducted to assess fit for the three scales’ hypothesized one-factor model. This step is a recommended prerequisite for the testing of measurement equivalence via multiple group CFA [133]. CFAs were conducted in Mplus   43 version 7.4 to estimate the model parameters using a means and variance adjusted weighted-least squares (WLSMV) estimation method. Acceptable model fit was assessed via a root mean square error of approximation (RMSEA) value ≤ 0.08 (with an upper-bound 90% confidence interval ≤ 0.10), and a comparative fit index (CFI) value ≥ 0.95 [133,184]. SRMR is not calculated for categorical CFA, and a similar index for categorical data (WRMR) has been proposed recently. WRMR, however, is a nascent and experimental fit index [185,186] and recent simulation studies suggest its sensitivity to sample size [187], so it was not reported. Cultural background groups for whom a one-factor CFA could not be supported by model fit indices were not included in the final analytic sample on which MG-CFAs were performed [133]. Model fit results for the single-group one-factor model for the SWLS-C are available in Appendix A. In light of poor model fit for groups with sample sizes below 300, as well as research suggesting factor analytic procedures yield stable solutions via WLSMV where samples comprise at least 300 individuals [188,187], it was decided to include only groups that contained at least 300 children and exhibited accepted single-group CFA model fit. Thus, eight language background groups, representing 20,119 respondents, were retained: English, Mandarin, French, Cantonese, Punjabi, Filipino, Spanish, and Korean. In Canada, English and French are official languages. French is widely taught in schools nationwide, and is widely spoken in various parts of Canada, especially Eastern Canadian provinces, such as Quebec and New Brunswick. Nevertheless, French was included as a separate cultural group (rather than combining it with Canada’s other official language: English). Although French is spoken in many more nations and regions beyond Canada, representing great cultural diversity, government figures suggest the vast majority (85% in 2011) of BC residents with a French language background were born in Canada [189]. Even though the English and   44 French language background groups may share a ‘Canadian cultural background’, and have a distinct relationship (both official Canadian languages), cognition of speakers of different languages have been regularly found to differ [190,191]; hence it is worth investigating whether differences in the measurement of constructs occur between the solely English language background group and the French background group. Moreover, French and English were the sole language backgrounds represented in the analytic sample that are not regarded as immigrant languages in census definitions [163]. 2.5 Measurement equivalence  Multiple-group confirmatory factor analyses (MG-CFA) were performed using the WLSMV estimation method, which is appropriate for the ordered categorical nature of the data [192]. Latent scores were estimated via an ordinal probit link under the assumption that each item reflects a normally distributed latent construct that underlies and determines responses to the observed items/variables. WLSMV estimates missing data using a pairwise present approach; it computes 'limited information' from all available pairs of variables/items - using all respondents with observations on each pair. Rather than limiting analysis to respondents with complete responses on all variables/scales, WLSMV computes pairwise  polychoric correlations between all available pairs of variables [193].  MG-CFA allows one to simultaneously analyze multi-group data sets, and to concurrently constrain parameters to be equal across groups [194]. Here, nested models were sequentially tested, starting with the least constrained model (i.e., all factor loadings, thresholds, and residual variances are freely estimated/ allowed to vary across groups) and then progressively placing equality constraints on the parameters across groups based on Millsap and Yun-Tein’s recommendations [195]:    45 a) Configural equivalence (the least constrained model) requires that the same factor model specification (i.e., constraining the number of factor(s) and the pattern of fixed and free loadings) holds across groups.  b) Metric equivalence requires that, in addition to the requirements of the configural level, model fit is tested after imposing cross-group equality of factor loadings.   c) Scalar equivalence entails the requirements of equal configuration and loadings as well as equality of item thresholds across groups.  c) Strict equivalence is the final level of measurement equivalence via MG-CFA. As such, it requires model fit following cross-group constraints on configuration, factor loadings, thresholds, and residual variances.  Evidence of metric equivalence permits meaningful cross-group comparison of the construct as a correlate (e.g., using SWLS-C scores as a correlate of another variable) [133]. Evidence of scalar equivalence permits meaningful comparison of latent factor score means across groups while strict equivalence enables meaningful cross-group comparison of observed means [137]. Hence, scalar and strict ME is of interest in this thesis. 2.5.1 Model fit   The following guidelines were used to evaluate the adequacy of model fit: RMSEA <.08 (with an upper 90% confidence interval ≤ 0.10) and a comparative fit index (CFI) ≥ .95 [133,196]. The χ2 difference test was used to compare competing and nested models (p<.05). The chi-square difference test was conducted using the Satorra-Bentler (S-B) χ2 because the data were non-normal and categorical, and the S-B correction allows for a better approximation of chi-square under non-normality [197]. The χ2 difference test is a function of sample size, whereby increasing sample sizes tends to inflate Type I error rate – increasing the likelihood of observing   46 model misfit in ME analyses [137,139]. However, following recommendations [139], and recent empirical evidence on the CFI’s robustness to sample size [187], Δ CFI ≤ 0.01 was deemed to indicate a negligible decrease in model fit across nested models  [137,198]. This approach has been the primary index of ME between nested  levels in prior analyses of categorical data via MG-CFA [199,200]   Thus, it should be noted that χ2 and Δ χ2 are associated with a hypothesis test. Hence these indices provide a ‘test’ of model fit between the observed and hypothesized model. RMSEA and CFI are more descriptive in nature, detailing the extent of model fit. Thus, the latter indices are conceptually more akin to indicators of effect size. Even small levels of variability between aggregations such as schools (e.g., ICCs < 5%) have been shown to bias standard error estimates, which could distort model fit indices [201,202]. In the context of confirmatory factor analytic models, failure to account for clustering can distort model fit and parameter estimates [203]. Furthermore, child life satisfaction as well as peer and adult support has been found to vary significantly between BC schools [161,73]. Thus, to control for the influence of school effects on the measurement of LS, peer support, and adult support, school ID (i.e., the school attended by each child in grade 4) was entered as a level-2 covariate via the “COMPLEX=” command in Mplus. This approach adjusts the standard errors, estimates, and test statistics to yield more accurate Type I error rates in the presence of clustering.  In addition to the ME analyses, descriptive analyses of the socio-demographic aspects of the analytic sample, as well as reliability analyses (ordinal alpha) of the three scales were conducted. Ordinal alpha was computed in R version 3.4.2 using a polychoric correlation matrix, which more accurately estimates alpha for categorical data [204].   47 2.6 Comparison of SWLS-C, peer support, and adult support mean scores  After examining the measurement equivalence of all three measures, group means on each scale were compared by obtaining mean estimates and 95% CIs via an adjusted multi-level ANCOVA that also controlled for several socio-demographic variables (gender, age, and neighbourhood-level SES). Specifically, the MIXED command was used in SPSS version 24. Point estimates and 95% confidence intervals (CIs) were obtained from both analyses, to graphically compare group means. As the peer and adult support measures were on different scales (5-point and 4-point), they were standardized. 2.7 Association between children’s peer and adult support and their life satisfaction  The relations among SWLS-C, peer support, and adult support scores were examined in two ways. First, cultural background group means on the SWLS-C were tabulated against group mean scores for (i) peer support and (ii) adult support, respectively. Second, a sequence of multi-level multiple linear regression models were used to regress children’s SWLS-C scores on peer support and adult support (using children’s ‘school’ as the multi-level grouping). Regression models were conducted in SPSS. Model 1: Peer support scores were entered as the independent variable, adjusting for socio-demographic variables (gender, age, and neighbourhood-level SES). Model 2: Adult support scores were entered as the independent variable, adjusting for socio-demographic variables (gender, age, and neighbourhood-level SES). Model 3: Both peer support and adult support scores were entered as independent variables, adjusting for socio-demographic variables (gender, age, and neighbourhood-level SES).       48 Chapter 3. Results 3.1 Characteristics of analytic sample  After applying all exclusion criteria, 69.6% of all grade 4 respondents to the 2009/10 – 2015/16 MDI were included in the analytic sample (n=20,119). The analytic sample was virtually identical to the total MDI grade 4 sample socio-demographically and in terms of its distribution among school districts (see Appendix B). Of the analytic sample, approximately two thirds completed a paper version of the MDI, most children were aged 9 or 10 (88.6%; Mage 9.2), and 49.1% were female. One in three children (32.4%) reported a language background other than solely English. Although the overall MDI dataset had a higher proportion of children with language backgrounds other than solely English (59.9%), many of these individuals belonged to language background groups that were too small to fit the prerequisite one-factor SWLS-C model, or belonged to mixed language backgrounds, which would be too heterogeneous or small to be included as groups per se. The six retained cultural background groups (other than English or French) represented almost 75% of all immigrant languages spoken in BC households as per the 2016 Census while collectively the eight language backgrounds represented around 90% of all language backgrounds in BC [165].  Mean scores SWLS-C (M = 4.1), peer support (M = 4.2), and adult support (M = 3.4) measures were comparable to the overall MDI dataset and were similar to those reported in other analyses of MDI data for BC [73,161]. Unconditional models indicated small between-school variation in SWLS-C scores (ICCs < 5% in each cultural group); however, multi-level models were nonetheless employed to minimise any possible biased estimates.  3.2 Psychometric properties of measures Table 3.1 presents scale reliabilities for each cultural background group – given the    49 ordered-categorical nature of the scales, ordinal alphas were calculated for each cultural background group; with values of at 0.70 or above considered to indicate acceptable levels of  reliability [204]. As can be seen, alphas were acceptable and ranged from .78 to .86 for the SWLS-C, from .82 to .84 for adult support, and from .77 to .83 for peer support. Table 3.1. Ordinal alphas for SWLS-C, peer support, and adult support scales  Ordinal alpha coefficient Cultural background group (n) SWLS-C Adult support Peer support English (13,591) .86 .84 .83 French (1,258) .85 .84 .81 Cantonese (1,251) .84 .83 .80 Mandarin (1,131) .85 .83 .79 Punjabi (872) .85 .83 .79 Filipino (848) .78 .82 .77 Spanish (635) .86 .84 .82 Korean (533) .85 .84 .81  3.3 Measurement equivalence of the SWLS-C  Single-group model fit. For each language group, model fit indices suggested an acceptable fit to the hypothesized one-factor model for the SWLS-C (Table 3.2).  RMSEA, CFI, and the TLI values all met model fit criteria indicating acceptable fit. The prerequisite for testing multi-group factorial equivalence was thus met.        50 Table 3.2. Model fit for single-group confirmatory factor analysis of SWLS-C Cultural  background (n) x2  (p value) CFI TLI RMSEA                      (90% CI) English (13,591) 97.02 (.00) .998 .996 .036 (.030, .043) French (1,258) 10.20 (.07) .999 .997 .029 (.000, .054) Cantonese (1,251)   7.73 (.17) .999 .999 .020 (.000, .046) Mandarin (1,131) 23.47 (.00) .996 .992 .056 (.034, .080) Punjabi (872) 11.27 (.05) .997 .995 .037 (.004, .066) Filipino (848) 14.92 (.01) .993 .986 .048 (.021, .076) Spanish (635)   8.90 (.11) .998 .997 .036 (.000, .073) Korean (533) 13.89 (.02) .995 .990 .058 (.023, .096)  In the multi-group CFA, with all the eight language groups included, results supported measurement equivalence of the SWLS-C up to the most stringent level (equivalent residual variances). Table 3.3 summarizes the findings, for the multi-group ME analysis at the configural, metric, scalar, and strict equivalence levels.  Table 3.3. Model fit for multi-group confirmatory factor analyses of the SWLS-C  Level of ME  x2 (p value) CFI TLI RMSEA Configural 1226.49   .983 .993 .047 (.045, .050)  (00) Metric 1.95 .983 (Δ = .000) .993 .047 (.045, .050)  (.75) Scalar 18.21 .983 (Δ = .000) .993 .046 (.043, .048)  (.20) Strict 9.43 .983 (Δ = .000) .993 .045 (.043, .048) (.09) Note. x2 (p value) for Metric, Scalar, and Strict models indicate the Satorra-Bentler scaled x2  difference test results   51 As can be seen, for the configural (baseline) level, the RMSEA value was below 0.08, and the TLI and CFI exceeded .95, and in all subsequent models (metric, scalar, strict ME), the TLI remained constant, the RMSEA slightly decreased, and the Δ CFI was .000 for each model, indicating that model fit did not decrease as stricter model constraints were sequentially imposed. In other words, results suggested equivalence of factor loadings, item thresholds, and residual item variances between the English background group and the seven other language background groups. (See Appendix B for details on language-group specific item factor loadings, item thresholds, and residual variances for the cross-cultural ME analyses of the SWLS-C.) Although the results section focuses on ME between English and the other groups, pairwise MG-CFA comparisons of each possible pair of the cultural background groups for which single-group CFA models demonstrated acceptable fit for the SWLS-C, peer support, and adult support measures, were performed and are presented in Appendix B.  3.3.1 Sensitivity analysis of a random sample of the English background group To ensure ME analyses and model fit estimates were not distorted because of the vast inequality of group size between the English group (n=13,591) and the other seven groups (ranging from n = 533 to 1,258), a sensitivity analysis was performed using a random sample of 1,000 participants from the English group. Similar results were yielded when performing ME analyses of the SWLS-C using the random sample of 1,000 participants, and model fit indices would have yielded the same conclusion supporting strict ME (configural: RMSEA=.077, CFI=.953, TLI=.979, metric: Δ CFI = .000, scalar: Δ CFI = .002, strict: Δ CFI = .002). Thus, the sensitivity analysis supported the robustness of the model estimation to a smaller subset of the English group and supported the robustness of CFI to sample size [187]. Hence, the original English group was retained for the remaining analyses.   52 3.3.2 Measurement equivalence of the peer support scale  In the pilot administration of the grade 4 MDI (2009/10 years), the response format on the peer support items differed. Thus, to ensure comparability, in analyses involving the peer support scale this pilot year of data was excluded, hence the smaller sample size relative to the SWLS-C and adult support scale analyses (as these latter scales were unaffected).  For the peer support scale, model fit indices suggested the data were an acceptable fit to the hypothesized one-factor model (Table 3.4), as the RMSEA, CFI, and TLI met model fit criteria for the English, French, and Mandarin groups). Due to the lack of an acceptable one-factor, within-group model fit for the Cantonese, Korean, Filipino, Spanish, and Punjabi background groups, multi-group ME analyses of the peer support scale omitted these groups. The same procedures and order of analyses were conducted for the peer support scale as with the SWLS-C: MG-CFA using English background as the reference group.  Table 3.4. Model fit for single-group confirmatory factor analysis of the peer support scale Cultural  background (n) x2 (p value) CFI TLI RMSEA                      (90% CI) English (12,802) 89.02 (.00) .997 .991 .057 (.048, .068) French (1,173) 0.42 (.81) 1.00 1.00 .000 (.000, .036) Mandarin (934) 5.76 (.06) .998 .995 .045 (.000, .099) Cantonese (746) 6.34 (.04) .997 .991 .054 (.009, .104) a Punjabi (743) 7.81 (.02) .994 .982 .063 (.021, .111) a Filipino (644) 18.24 (.00) .989 .967 .112 (.069, .162) a Spanish (557) 4.71 (.10) .998 .994 .049 (.000, .108) a Korean (481) 13.72 (.00) .989 .966 .110 (.060, .169) a a  Model fit unsupported    53 3.5. Model fit for multi-group confirmatory factor analyses of the peer support scale Note. x2 (p value) for Metric, Scalar, and Strict models indicate the Satorra-Bentler scaled x2  difference test results. n/a: strict ME x2 difference test not reported as prior Δ x2 < .05.  Table 3.5 illustrates the results for each level of measurement equivalence for the peer support scale across the 4 cultural groups. In all models, the RMSEA met established criteria for acceptable model fit (≤ 0.08), and both the TLI and CFI met thresholds recommended ≥ .95. Results supported strict ME of the peer support scale, as Δ CFI also did not exceed the cut-off (.01). Findings thus provided evidence that the measurement of peer support (in terms of the loadings, thresholds, and residual variances) for the English background group was equivalent to other two cultural background groups. 3.3.3 Measurement equivalence of the adult support scale  For the adult support scale, single-group model fit indices suggested the data were an acceptable fit to the hypothesized one-factor model (Table 3.6) for all but one group.   Level of ME x2 (p value) CFI TLI RMSEA                      (90% CI) Configural 179.25 .996 .997 .035 (.030, .040) (.00) Metric 1.01 .996 (Δ = .000) .997 .033 (.029, .038)  (.80) Scalar 196.72 .996 (Δ = .000) .997 .032 (.027, .036)  (.00) Strict  .995 (Δ = -.001) .998 .029 (.025, .033) n/a   54 Table 3.6. Model fit for single-group confirmatory factor analysis of the adult support scale a  Model fit unsupported The Korean group yielded an RMSEA suggesting inadequate fit; although the RMSEA point estimate was within acceptable boundaries, the 90% upper confidence interval surpassed the recommended 0.10 cut-off – suggestive of poor model fit. Hence, all groups except the Korean background group were included in the MG-CFA ME analyses.   Table 3.7 illustrates the results for each level of measurement equivalence for the adult support scale across the 7 cultural groups. Results indicated strict equivalence of the adult support measure between the English background group and the six other cultural background groups.      Cultural background group x2 (df) = p value CFI TLI RMSEA                      (90% CI) English (n=13,591) 49.81 (2) = .00 .997 .991 .042 (.032, .052) French (n=1,258) 3.98 (2) = .14 .998 .995 .028 (.000, .069) Cantonese (n=1,251) 10.51 (2) = .01 .995 .984 .058 (.027, .095) Mandarin (n=1,131) 4.90 (2) = .09 .998 .993 .036 (.000, .077) Punjabi (n=872) 1.02 (2) = .60 1.00 1.00 .000 (.000, .055) Filipino (n=848) 4.25 (2) = .01 .997 .991 .036 (.000, .085) Spanish (n=635) 0.39 (2) = .82 1.00 1.00 .000 (.000, .047) Korean (n=533) 5.21 (2) = .07 .996 .988 .055 (.000, .115) a   55  Table 3.7. Model fit multi-group confirmatory factor analysis of the adult support scale        Note. x2 (p value) for Metric, Scalar, and Strict models indicate the Satorra-Bentler scaled x2  difference test results. n/a: strict ME x2 difference test not reported as prior Δ x2 < .05. In summary, findings from the cross-cultural ME analyses indicated evidence of strict equivalence – permitting meaningful comparison of observed means and correlates – between the English background group and: a) all seven cultural background groups on the SWLS-C, b) all cultural background groups except Korean and Filipino, on the peer support measure, and c) all cultural background groups except Korean on the adult support measure.    3.4 Comparison of SWLS-C means across cultural background groups  Table 3.8 shows the adjusted mean SWLS-C scores of each cultural group. Means ranged from 4.33 (Punjabi) to 3.85 (Mandarin). Lack of strict ME between all possible cultural background group combinations (see Appendix B) denied meaningful comparison for any possible combination, however, relative to the English group any other group can be compared due to ME for these combinations.  Whether or not mean scores were adjusted for socio-demographic variables the pattern of SWLS-C group means was very similar, which suggested between-group differences in SWLS-C Level of ME x2 (df)  = p value CFI TLI RMSEA                     (90% CI)      Configural 704.00 .972 .987 .050 (.046, .053)  (.00) Metric 2.75 .972 (Δ = .000) .987 .049 (.045, .052)  (.43) Scalar 66.26 .971 (Δ = -.002) .988 .048 (.044, .051)  (.00) Strict n/a .970 (Δ = -.001) .987 .049 (.046, .052)    56 scores were not simply a product of major between-group demographic and/or socio-economic differences. Relative to the English group mean,   Table 3.8. Descending unadjusted mean SWLS-C scores for all cultural background groups Cultural background M SD  Punjabi (n=872) 4.34* 0.72  English (n=13,591) 4.17 0.81  Spanish (n=635) 4.14 0.85  Filipino (n=848) 4.13 0.73  French (n=1,258) 4.03* 0.85  Korean (n=533) 3.99* 0.83  Cantonese (n=1,251) 3.95* 0.82  Mandarin (n=1,131) 3.86* 0.87  * Indicates a statistically significant (p<.05) difference relative to the English group. the Punjabi group SWLS-C mean was significantly higher, the Spanish and Filipino group means did not differ, whereas the French, Korean, Cantonese, and Mandarin group means each were significantly lower. The effect sizes (Cohen’s d) of SWLS-C differences relative to the English group were as follows: Punjabi: .13, Spanish: .00, Filipino: -.02, French: -.09, Korean: -.07, Cantonese: -.19, Mandarin: -.19. Adjusted group mean SWLS-C scores (controlling for gender, age, school, and neighbourhood-level SES), and their associated 95% CIs, are displayed in figure 3.1. (Group means for which the CIs overlap less than a quarter of the CI width are statistically significant).       57 Figure 3.1. Graph of adjusted SWLS-C means for each cultural group  3.5 Relations of the SWLS-C with the peer support, and adult support scales across cultural background groups  In this section, two sets of findings are presented. First, relations of LS and peer support, and LS and adult support within each cultural background group, respectively, are presented. Second, the within-group associations between children’s LS and their peer and adult support are presented.  Associations of LS with peer support and adult support. Table 3.9 displays the (unadjusted) group means for LS, peer support, and adult support. The tabulated SWLS-C, peer support, and adult support means by group suggest positive relations between SWLS-C means and adult support and peer support means. Lack of model fit for all but three groups on the peer support scale severely limited cross-group mean comparisons. Relative to the English background group, the Cantonese and Mandarin background had lower SWLS-C means – and lower adult support means (as well as a lower peer support scale mean for the Mandarin group). Relative to the English background group, the Punjabi group had a higher SWLS-C mean but their adult support scale means did not differ.    58 Table 3.9 Unadjusted life satisfaction, peer support, and adult support scale means  Cultural background (n)                  SWLS-C Adult support Peer support    M   SD      M   SD M SD English (13,591)     4.17 .81  3.48 .54 4.20 .84 French (1,258)    4.03 .85  3.46 .56 4.09 .91 Cantonese (1,251)    3.92 .82  3.22 .61 a 3.94 .84 Mandarin (1,131)     3.85 .87  3.31 .57  3.98 .85 Punjabi (872)    4.33 .72  3.51 .52 a 4.30 .77 Filipino (848)     4.12 .73  3.30 .59 a 4.13 .82 Spanish (635)     3.99 .83  3.39 .57 a 4.12 .78 Korean (533)      4.14 .85 a 3.44 .57 a 4.14 .87  a Indicates that single-group CFAs did not suggest an acceptable model fit, hence measurement equivalence analyses were not conducted and means cannot be meaningfully compared across groups.  Relative to the English group, the Filipino, Mandarin, and Cantonese cultural background groups had significantly lower adult support means but no significant differences were observed for the Punjabi, Filipino, or Spanish cultural background groups. Regarding the peer support scale, means of the French and Mandarin group were significantly lower when compared to the English group. Figure 3.2 illustrates how SWLS-C means of cultural background groups related to adult support means of cultural background groups. Visually, the cultural groups with lower SWLS-C means tended to have lower adult support means. For instance, the Cantonese and Mandarin cultural background groups had significantly lower SWLS-C means than the English group as well as significantly lower adult support means. Mean scores of cultural background groups on the SWLS-C and Peer support scales were not illustrated as ME was supported for only three groups.        59 Figure 3.2. Graph of unadjusted SWLS-C and adult support means for each cultural background group   Within-group associations of LS with peer support and adult support.  Within each group, analyses were performed (table 3.10) to examine the extent to which (a) peer support related to SWLS-C, (b) adult support related to SWLS-C, and (c) both peer support and adult support related to SWLS-C scores. Analyses adjusted for nestedness and socio-demographics.                    60 Table 3.10 Multi-level linear regression results for SWLS-C scores for each group   * Analyses adjusted for gender, age, neighbourhood-level SES, and included school as a random intercept.  a Measurement equivalence not supported (baseline model unsupported).     ǂ Within-group regression coefficients (peer vs adult support) statistically differed.   As can be seen, within each cultural background group, adult support (left column) had significant positive associations with SWLS-C scores. For peer support (center left column), the pattern was similar, as peer support emerged as a significant positive correlate of SWLS-C within all eight cultural background groups. When entering both the peer support and adult support variables into the regression models, adult support and peer support were independent and significant correlates of SWLS-C scores within each cultural group. In general, the results of the within-group regression models suggested overall that peer support and adult support scores had similarly promotive associations with SWLS-C scores.    Cultural background (n)   Both variables entered in model Adult support Peer  support Adult support Peer  support β (95% CI) β (95% CI) β (95% CI) β (95% CI) English (13,591) .33 (.31, .34)    .38 (.37, .40) .21 (.19, .22) .31 (.29, .32) French (1,258) .32 (.27, .37)    .36 (.32, .40)  ǂ .20 (.15, .25)  .29 (.25, .34)  Cantonese (1,251) .34 (.29, .39)    .39 (.33, .45) .23 (.18, .29) a .28 (.22, .34) Mandarin (1,131) .37 (.32, .42)    .42 (.36, .47)   .24 (.19, .30)  .31 (.26, .37) Punjabi (872) .34 (.29, .39) a .39 (.34, .45) .23 (.18, .28) a .30 (.24, .35) Filipino (848) .24 (.18, .29) a .32 (.26, .37) ǂ  .14 (.08, .20)  a .26 (.20, .32) Spanish (635) .35 (.29, .42) a .34 (.28, .41)       .27 (.20, .33) a .26 (.19, .32) Korean (533) a .33 (.26, .40) a .47 (.40, .54)  a ǂ .19 (.12, .26)  a.39 (.31, .46)   61 Chapter 4. Discussion 4.1 Cross-cultural measurement equivalence of the SWLS-C This investigation first examined the measurement equivalence of the SWLS-C across cultural background groups of children living in BC. Findings supported strict measurement equivalence, suggesting that children of diverse backgrounds interpreted the SWLS-C similarly and employed a similar frame of reference to the children of English language backgrounds [133]. In some senses, these results were surprising, given the documented cultural variation in cognition, response patterns, and interpretation of concepts  [114]. There are, however, some important considerations that may underlie why cross-cultural equivalence of the SWLS-C was supported. The SWLS-C is a brief scale comprising 5 items and has been found to support a simple factor structure [36,179]. Simple models, such as that representing the SWLS-C via a single factor, tend require the estimation of relatively few parameters, which can increase the likelihood of better model fit [205-207]. Furthermore, a validation study among middle-years children in BC found no scale-level differential item functioning (i.e., the predicted scale score per item was summed to estimate a scale-level predicted score) between children from English versus non-English language backgrounds (combined into a single group) [36]. Another study found strict ME between native and non-native Italian speakers in an Italian-speaking canton of Switzerland [149]. A qualitative study by Gadermann et al. employed cognitive think-aloud protocols with culturally diverse children to understand their responses to SWLS-C items [129]. Some children – mostly those of diverse language backgrounds – found items 1 and 5 slightly challenging. Interestingly, items 4 and 5 are temporal (4: “so far”; 5: “If I could live my life over”) while item   62 1 pertains to comparing one’s actual life to one’s ideal life. It is possible the syntactic (and philosophical) intricacy of these three statements led to slight challenges among children of diverse backgrounds. Also, items 2 (“The things in my life are excellent”) and 3 (“I am happy with my life”) have fewer words than 1, 4, and 5, and appear to be more simple statements in that they explicitly evoke thoughts about their present and proximal circumstances/appraisal. Nevertheless, in the present analyses these items did not exhibit sufficiently large cross-group differences so as to render measurement equivalence unsupported. The support for strict cross-cultural ME of the SWLS-C contrasted with the findings summarized in Chapter 1 regarding cross-cultural ME of the (adult) SWLS. Of course, ME is not a static feature of a measure, but is sample- and context-specific [208], a notion that has been confirmed by cross-cultural research on LS. All but 1 of the 11 cross-cultural SWLS ME analyses featured cross-national comparisons and many employed translated SWLS versions; Swami et al., found metric – but not strict – ME between Malay and Chinese adults in Malaysia [158]. Park et al. also observed only metric ME of a multi-dimensional measure of youth life satisfaction between youth in South Korea and those in the US [85]. It should be noted, though, that the multi-dimensional LS measure had many more items and more complexity (a five-factor model) than the SWLS-C – considerations that may partially explain the differences in level of ME observed. Conversely, Zeng et al. found scalar ME for a subset of items from a multi-domain life satisfaction measure between late adolescents from China and the US [82]. Similarly, Tomyn et al. observed strict ME on a personal wellbeing index between adolescents in Portugal and in Australia [209]. It is therefore possible that the sound cross-cultural ME results were aided by the reality that the children who responded to the SWLS-C were of diverse cultural backgrounds yet were also all in BC. Indeed, a key cognitive bias in some self-report   63 scales between cultural groups (the reference group effect) appears to be more pronounced between nations than between the same cultural groups within a single nation [118].  Thus, ME may have been more difficult to obtain had international, cross-cultural comparisons of the SWLS-C been conducted. Cross-cultural ME of life satisfaction measures is often not tested – and if it is tested, it is frequently not supported. It is thus concerning that global organizations such as UNICEF commonly publically compare LS scores of different nations without establishing ME of instruments [210].  4.2 Measurement equivalence of the peer support and adult support scales   Strict measurement equivalence of the adult support scales was supported for all but one cultural background group, relative to the English group. Strict ME of the peer support scale, however, was supported for only three cultural background groups – as a single-factor model could not be supported for several groups.   Measurement equivalence of the peer support scale was not supported for the Filipino, Korean, Punjabi, Cantonese, and Spanish background groups as a one-factor model was unsupported within each of these groups. It is unclear why this may have been the case. It may be that a different underlying conceptual framework was evoked by the peer support scale when children from these groups interpreted the items. For instance, compared to children in the North America, intimacy/closeness in friendships has been perceived as more important by children in South Korean who often develop emotionally strong and intimate friendships [211]. It may be that the lack of ME between the English background and several other background groups for the peer scale was partially an indication of differences in their conceptualization of peer relations. ME was, however, supported between the English group and an East Asian group (Mandarin) –   64 suggesting the lack of ME for the peer support scale was not purely because of a different cultural framework between the majority, English language background children and those of an East Asian language background.   For all groups except the Korean and Filipino background groups, the lack of ME on the peer support scale was due to a lack of single-group CFA fit regarding the RMSEA value. Specifically, the upper 90% CI surrounding the RMSEA value crossed .10 for these groups whereas the point estimate was within expected guidelines. CI estimates tend to be wider as sample size decreases and so it is likely no coincidence that the three groups with acceptable 90% CI values for the peer support scale also were the three largest groups. The issue was exaggerated by the necessary exclusion of the 2009/10 pilot data in analyses involving the peer support measure (due to differing response format in the pilot year). With larger sample sizes, the 90% upper CI related to the RMSEA may well have been narrower and hence model fit at the single-group level would likely have been supported, enabling the testing of cross-cultural ME of the peer support scale for these groups. Despite the lack of evidence for cross-cultural ME of the peer support scale, it is reasonable to assume that higher scores on the peer support scale may generally represent higher – rather than lower - levels of perceived peer support. The specific magnitude of correlates and mean differences on the peer support scale may not be as accurate for the various cultural groups, however. Hence, emphasis is placed on peer support scores as a correlate of SWLS-C within the various cultural background groups (i.e., was peer support significantly related to SWLS-C in each group) rather than coefficient or mean sizes.  Whereas the SWLS-C has undergone rigorous validation examination via quantitative and qualitative approaches with diverse groups of middle-years BC children, and was generally found to be an equivalent measure across diverse samples [36,129], such rigorous validation has   65 not been conducted with the items comprising the peer support and adult support scales in this thesis. It was noteworthy that model fit for the peer support scale was observed for the Mandarin group, as this agreed with a prior study where a similar set of items were found to be meaningful (i.e., exhibit acceptable reliability, sound test-retest reliability, and correlate with related constructs in expected ways, directions) among Chinese youth in Canada [181]. Future work could explore possible reasons why a one-factor model was not tenable with the peer support scale for the other cultural backgrounds (Cantonese, Korean, Filipino, Punjabi, and Spanish) to identify why the peer support items evoked differing conceptualizations of friendship and peer support.  The adult support scale exhibited stronger cross-cultural evidence supporting construct comparability, as acceptable model fit was found in within-group models for all cultural background groups except Korean. Korean culture is well-documented to highly value strong involvement of adults (especially, parents and relatives) in child development [212]. Although parents/adults at home play a key role in child development in the other cultural backgrounds examined in analyses (e.g., Chinese, Filipino), the adult support scale items may have evoked interpretations/conceptualizations regarding adult support that differed between the Korean and other background groups.  Psychometrically, it is noteworthy that the Korean cultural background group was the sole group to show unsatisfactory model fit on the adult support scale and had the smallest sample size. Although not an especially small sample (n=533), the only group to not demonstrate acceptable model fit for the adult support scale and this group had the smallest sample size (n=533). Model fit was sufficient for the Korean group according to CFI, but not according to the RMSEA value, which exceeded the recommended point estimate and 90% CI. The poor model   66 fit of the Korean group for the adult support scale may have been partly due to its smaller sample size. Regardless, further examination, including qualitative think-aloud protocols, and other modes of examining response processes [213] with children in BC may help elucidate why the adult support scale evoked a different measurement model for such children than the others.      It should be noted that the focus of the present thesis was on the measurement of the SWLS-C across cultural background groups of children. The peer and adult support scales were included to serve as auxiliary variables rather than as the central focus. Specifically, peer support and adult support are documented correlates of child LS in BC. Hence, the lack of ME on the adult support scale, and especially on the peer support scale, across all cultural background groups did not fundamentally undermine overall results of this thesis. The two support scales served as auxiliary variables to illustrate whether established correlates of SWLS-C among overall middle-years samples were also correlates of SWLS-C within several cultural background groups of children.  4.3 Comparison of SWLS-C means  For each cultural background group, SWLS-C scores were generally indicative of good levels of life satisfaction since most means were around 4.0 (out of a maximum of 5.0). Relative to the English group, four groups (French, Korean, Cantonese, Mandarin) had lower LS means, two groups had equivalent means (Filipino, Spanish), and one group had a higher LS mean (Punjabi). Intriguingly, the cultural background groups associated with BC’s three largest immigrant cultural groups (Chinese, South Asian, Filipino) each exhibited different LS means relative to the English group. 4.3.1 SWLS-C among Chinese and Korean background children. The two groups with LS means that were the furthest below the English group were language backgrounds   67 predominantly associated with Chinese culture (Cantonese, and Mandarin). Notwithstanding cultural differences between Cantonese and Mandarin language background (e.g., Cantonese is the official language of Hong Kong and Macau whereas Mandarin, the commonest Chinese dialect, is the official language of China and Taiwan), both share a single written language and key cultural commonalities exist [214]. In a previous study of early child development outcomes in BC, Children of Mandarin and Cantonese language backgrounds were found to exhibit very similar social and emotional health profiles based on teacher-ratings, and hence  were combined into a single Chinese background group [177]. Thus, the two language backgrounds are herein discussed as a ‘Chinese’ group.  The lower LS levels observed for the Chinese background relative to the English background children paralleled the results of several previous Canadian studies of children, adolescents, and adults. Middle-years children who immigrated from Hong Kong or China had higher levels of emotional problems relative to Filipino children [215]. Immigrants from China have been found to have significantly lower LS means than the Canadian-born population [216] while adults residing in Canada of Chinese background have been found to have lower LS than the those of European background [217]. Cross-national comparisons of mean SWLS scores based on a subset of items (for which ME was supported) were conducted among adults from 26 countries, and mean SWLS scores for adults from China were significantly lower than those from Canada [218].  Children of Korean background presented LS mean scores that were also lower than the English background group. Such findings were also observed in a Canadian-based adult immigration study [216] . Research has also found lower LS among adolescents in South Korea than those in the USA [85]. Conversely, single-item life satisfaction scores for children of   68 various cultural groups in Canada (including East Asian i.e. Korean and Chinese) did not differ from the Caucasian group [219]. That children of East Asian backgrounds (i.e., Chinese and Korean language backgrounds) had lower LS means than the majority English background group may be due to various reasons. As members of a group-based cultural background, children of East Asian backgrounds may find it harder to relate to individual-focused items such as those on the SWLS-C. Additionally, individual life satisfaction and the positive mental health of one’s self appears to be viewed as less important among East Asian cultures than among Western cultures [220]. Social harmony, obedience, and maintaining culturally prescribed values are the primary foci in many East Asian societies [123]. In light of the theorized contextual nature of LS, cross-cultural longitudinal investigations could examine the associations of LS for children of East Asian (as well as other cultural) backgrounds with other health and social outcomes in childhood and beyond. It has been argued that the great expectations and familial pressures to achieve high academic outcomes experienced by East Asian (especially Chinese and Korean) youth in Canada may negatively impact their PMH [221]. Relatedly, harsh parenting approaches – known to negatively impact the mental health of Chinese background children in Canada – were found to be more prevalent among Chinese immigrant families in Canada relative to Filipino families [215]. Similarly, supportive parenting – which has been shown to help protect children’s mental health – was more commonly reported among Filipino immigrant families than among Chinese background families [215] It may be that the trajectories and long-term associations with key mental health outcomes differ by cultural group, and it may be that more culturally-relevant factors such as adherence to cultural values, social harmony [102], or perceived respect from family members   69 are more important predictors of long-term outcomes for East Asians. Additionally, relative to the Chinese population, much less is known about the health of Korean descent individuals in British Columbia in particular and in Canada overall. 4.3.2 SWLS-C among Filipino and Spanish background children. No mean LS differences were observed for the Filipino and Spanish groups relative to the English group. In an Australian study of adolescents, no mean differences on LS were observed between those of English language backgrounds and those of Filipino language backgrounds [89]. The authors suggested the Filipino youth may have been experiencing similar levels of LS to the native-born population due to the presence of strong cultural supports from the pre-existing Filipino community in Australia [89]. Australia and Canada share similarities in terms of immigrant and cultural diversity; both nations have large foreign-born populations (> 20%), and have large Filipino immigrant populations [222]. Thus, some parallels may be drawn when attempting to understand the LS of the Filipino populations in the two nations the LS and the similar levels of LS observed between the English background children and the Filipino group may be due to reasons alluded to in the Australian-based study. Indeed, Soriano has highlighted the supportive, accommodating, and affectionate Filipino immigrant communities that may aid the transition and quality of life of immigrants into Western societies like Australia and Canada that have large Filipino communities. Indeed, middle-years Filipino immigrant youth in Canada reported fewer emotional problems [215,223] and higher levels of warm, sensitive parenting relative to those from mainland China [215]. Similarly, middle-years Filipino immigrant youth in Vancouver have been found to report higher levels of excellent self-rated overall health (a construct related to youth LS) than immigrant youth from mainland China [224]. Few Canadian studies have compared the PMH or LS of children of Filipino backgrounds    70 to the majority, English-language background cultural group in Canada. In one related study, however, teacher-rated social and emotional competencies (LS-related constructs) among BC children with a Filipino home language were not significantly different than those of an English-only home language background [177]. Thus, the similar levels of LS observed between the middle-years children of Filipino and English backgrounds has some consistency with prior theory and empirical findings. Similar to the Filipino group, children with a Spanish language background experienced levels of LS no different than the English background group. Based on recent census data, the Spanish background children in BC were most likely the children of Latino descent (i.e., from nations in central and south America) rather than Spain [163]. Indeed, despite the large numbers of Spanish-speakers in BC the 2016 census revealed the majority of immigrants in BC from Spanish-speaking nations came from Latin American nations; Mexico, El Salvador, Costa Rica, Colombia, Chile totaled almost 30,000 whereas around 1,500 were from Spain. Hence, it is likely that children of Spanish-speaking language backgrounds were likely of Latino origin (especially Mexican, the largest Spanish-speaking source nation for BC immigration) [165]. BC is home to sizable Latin American communities and the Latino community is similar in size to the Japanese and West Asian populations in BC [163]. Few studies have examined the health of Latino children in Canada, which prevents connections of the present thesis findings to other Canadian studies. A recent literature review attempted to survey studies on mental health among Latin Americans in Canada, but identified no studies on youth [225]. Among the general population of immigrants, two studies suggested Latino immigrants had lower levels of depression than the Canadian population [226,227]. Such findings lend partial support to the present thesis results that children’s LS did not significantly differ from LS among the English-  71 speaking population. No research on LS among Latino youth in Canada could be retrieved, however, and findings will surely be better understood following studies on LS among Spanish-speaking and Latino children in BC. 4.3.3 SWLS-C among French background children. Despite both languages forming Canada’s two official languages, children of a French language background had a slightly lower mean SWLS-C than those of English language background. In BC, the French language is spoken much less than in Eastern Canada (especially Quebec). Thus, the context of French language in BC differs from Eastern regions such as Quebec. Nevertheless, children in the French language background were still likely born in Canada as recent data indicate relatively low numbers of immigration to BC from France or major French-speaking nations [163]. Comparison of mental health outcomes for children in Canada of English-language and French-language backgrounds is scarce. One study of adults, however, did observe lower levels of self-rated health (a variable correlated with LS) among French-Canadians than English-Canadians [228]. From a contextual standpoint, it has been argued that French-speaking persons in Canada may experience worse health outcomes when living in areas where they form the minority – which would be the case for much of BC – due to factors such as language barriers and cultural disconnection [229]. It should be noted, however, that the mean difference between the French language background group and the English background group was a significant but relatively small (English: 4.17, French: 4.03) and small effect size. Future studies can examine possible individual-level and contextual reasons why French background and English background BC children differed. 4.3.4 SWLS-C among Punjabi background children.  Children of a Punjabi background had a significantly higher mean SWLS-C than the    72 English background group. This finding was partially supported by the findings of Guhn et al. who observed stronger social competencies (Respect and responsibility) and less anxious and fearful displays than English language background children [177]. Such a pattern was consistent with the present thesis, as social interpersonal competencies as well as lower levels of internalizing problems (anxiety/fearfulness) are known to relate to higher levels of LS among children [230]. Although conducted among a household survey of mostly adults in Canada, one study identified a higher proportion of respondents belonging to the Sikh religion – the commonest religion among Punjabi individuals in Canada [231] – reported higher life satisfaction than the white ethnic population [232]. Also, out of any visible minority group in Canada the South Asian respondents reported the highest sense of belonging to Canada, a construct known to have promotive associations with LS among immigrants [233]. Additionally, the integration acculturation strategy (assessed via a measure of acculturation attitudes) was more commonly adopted by South Asian youth than the study’s other cultural groups (Korean and Vietnamese) [234]. An integration acculturation profile has been associated with higher LS among youth [233]. It is possible that the combination of a strong sense of belonging to Canada, living in a region with an established Punjabi community, as well as coming from a cultural system rich in familial support and one that fosters respect toward others, together helped to foster high levels of LS among the Punjabi group. Thus, the findings of higher SWLS-C relative to the English group showed several consistencies with prior Canadian and BC studies. 4.3.5 Socio-cultural contextual influences on child mental health. When understanding the mental health of children, and especially when considering different cultural groups, an emerging body of literature suggests that place matters. The context, built environment, and socio-cultural features of one’s surroundings can impact children’s development [235].   73 Neighbourhoods and communities with stronger social capital (community organizations, sense of belonging, community resources, and other features) can support children’s healthy physical and mental development. Within the context of ethnicity, the lack or presence of culturally relevant features and resources can be a key determinant of health – especially mental health. Protective associations between area-level ethnic and/or immigrant density in Canada and ethnic minorities and/or immigrants’ mental health have been documented in numerous studies with adults in Canada. Increasing area-level ethnic density has been associated with lower levels of depression [236-238], less suicide ideation [239], and lower prevalence of psychiatric disorders [240]. In the context of the present thesis, the cultural groups were not evenly distributed throughout BC (Appendix B); cultural diversity was highest in the Vancouver area. Figure 4.1 illustrates the distribution of major visible minority groups in the Vancouver area based on 2016 Census data. Certain school districts in BC had high concentrations of certain cultural groups. Most notably 76% of Cantonese background and almost 60% of Mandarin background children resided in the Vancouver and Burnaby districts. Over half of the Punjabi background children lived in the Delta and Vancouver districts while roughly 60% of Korean children resided in the Vancouver and Coquitlam districts. When considering the neighbourhood level, the proportions of own-group cultural concentration was much higher in several instances. It is possible that living in communities with many other culturally-similar children may be associated with greater access to social support, a higher sense of belonging, maintenance of cultural values, as well as reduced discrimination. Supporting this notion, higher immigrant density in schools has been associated with fewer internalizing behaviours among immigrant youth [241].      74 Figure 4.1. Spatial distribution of visible minorities in the Vancouver area in 2016  Higher co-cultural concentration may not, however, be unanimously associated with mental health advantages for cultural groups in Canadian youth, whereas higher ethnic density of urban neighbourhoods was associated with higher levels of self-rated poor health and depression among adolescents [242]. In turn, no associations were observed between subjective overall health status of immigrant children and school-level immigrant density [224]. Regional differences within ethnically dense communities have also been observed within Canada; one study of over 2,000 youth found those from China or the Philippines residing in Toronto had higher levels of emotional problems than those in Vancouver [243]. Although there is general agreement that co-cultural concentration of areas tends to support the mental health of cultural groups residing in the communities [244], the associations seem to differ by cultural group [245]   75 and benefits seem to be pronounced for native-born (multi-generational) immigrants [246]. Consideration of built and socio-cultural features of children’s environments may therefore help elucidate the variation in observed levels of life satisfaction, peer support, and adult support. Thus, contextual cultural factors such as area-level cultural concentration, as well as the implied social capital and stigma, may influence children’s mental health including life satisfaction and should be considered when examining health outcomes of diverse samples of children.  4.4 Relations among SWLS-C, peer support, and adult support scale scores Social support is widely documented as key to positive mental health [247]. Prior work has also underscored the importance of relationships with peers and adult - especially the emotional, supportive aspect of relationships – as central to child life satisfaction [248,249]. In qualitative think-aloud protocols with middle-years children presented with the SWLS-C, Gadermann et al. found social relationships were a key informational source from which many children drew reflections and inspiration when responding to each item [129]. The present investigation, however, was the first to specifically compare how peer and adult support related to the life satisfaction of children from various cultural backgrounds in Canada. To interpret our findings, we will thus review studies that looked at associations between children’s LS (or related PMH constructs) and social relationships in different cultural contexts. Support from parents and peers are important correlates of LS among children and adolescents in the US and South Korea [85]. In China, warmth and support from mothers was predictive of life satisfaction some 8-months later [250] while other research in Hong Kong [251] and South Korea [252] found supportive child-parent relations to be positively associated with life satisfaction in childhood and adolescence. In a study comparing middle-years children from 11 nations   76 (including the US, India, and China), both peer support and adult support emerged as significant positive correlates of LS [99].  In the present investigation, the pattern of mean SWLS-C scores of each cultural background group was partly supported by the pattern of mean adult support and peer support means of each cultural background group. For instance, the Mandarin cultural background group had an SWLS-C mean, peer support mean, and adult support mean that were all significantly lower than the mean scores of the English background group. Relative to the English background group, the Mandarin, and Cantonese all had significantly lower SWLS-C means as well as significantly lower adult support means. Such a pattern between SWLS-C and peer support means was not consistent throughout, however. Relative to the English group, The Filipino background group had a significantly lower adult support mean but a SWLS-C mean that did not significantly differ. Similarly, the Punjabi group had a significantly higher SWLS-C mean but an adult support mean that was not significantly different from the English group. Such a pattern provided limited evidence that the cultural background groups with lower SWLS-C means relative to the English group necessarily had lower adult and peer support means – with the exception of the Mandarin background group, which did exhibit this pattern. A limitation was the lack of ME supported for the peer support measure, which permitted cross-group mean comparisons among only three of the eight cultural background groups. Between-group comparisons of peer and adult support levels were not a central focus of analyses, however. When considering within-culture associations of peer and adult support with SWLS-C scores, a major finding emerged: Both peer and adult support positively related to LS in across all cultural background groups. Findings regarding peer and adult support were also consistent with theorized contextual and ecological influences on child development [42]. The proximal   77 aspects of contexts (e.g., peer support and adult support) were found to significantly relate to child LS, a key aspect of healthy child development. The distal aspect of Bronfenbrenner’s ecological theory, culture in the present thesis, also was an important consideration as LS, peer support, and adult support differed significantly depending on the cultural background to which children belonged. Examination of the specific processes relating LS, peer support, and adult support, as well as the influence of other contexts (e.g., adults in neighbourhoods) was not possible in the present thesis but warrant examination in the future. This study demonstrated the value of examining the inter-relationships of multiple contexts –peer and adult support as well as culture – rather than one context at a time. Another example of such inter-relational contextual research focused on the SWLS-C was an examination of how LS levels change over time across gender rather than over time overall or within one gender at a time [150].  4.5 Limitations and challenges  4.5.1 Language background as a proxy for cultural background  The MDI lacked direct information on cultural identity such as self-reported cultural and/or ethnic identity; in lieu, language(s) spoken in the home, and first language(s) learned were employed. Children’s language background has been used in various studies of child and youth mental health to approximate cultural differences [253,176,254,177] and is considered a core component of cultural identity [255,256]. Moreover, findings from a comparison of teacher-rated developmental health scores among kindergarten-aged children in BC employed children’s language background as a proxy [177], observing findings for Chinese, Punjabi, and Filipino children generally in line with pertinent theory and research. The Chinese language group had a strong literacy and memory profile, reflecting the high value placed on academic achievement in Chinese immigrant families [257]. The Punjabi group displayed a strong socio-emotional   78 competence profile (high levels of respect and self-regulation) consistent with South Asian cultural values of respect and honour [258].  There are of course limitations to language background as a proxy for cultural background. Primarily, language is an insufficient index of culture. Language transcends cultures and national borders. This was especially the case with the Spanish, French, and English languages used to classify children, as each of these languages are spoken by millions of individuals across many diverse nations trans-continentally due to extensive colonization history associated with these languages. English and French are official languages in Canada and are spoken by an extremely diverse array of cultural groups. Members of the French language background may have been children from France, other French-speaking nations, or Canada. Indeed, all languages used to approximate cultural background in the present thesis may have been subject to a degree of misclassification bias.   4.5.2 Lack of information on immigration status Research commonly finds differences between immigrants and non-immigrants in terms of health, including mental health [259]. A widely-documented pattern, known as the healthy immigrant advantage, finds foreign-born individuals experience a health advantage relative to the native-born population in Canada as well as other major ‘settlement’ nations such as Australia and the US [259]. Such advantage tends to be lost over time and across generational status, as immigrants increasingly adopt, for example, the health behaviours, diets, and practices of the host culture/nation. Such a pattern is evident for mental health, too, where some ethnic minority groups in Canada have been found to experience fewer mental health issues relative to the native-born population at first, but start to look more similar to the Canadian population over time [260]. Immigrants in Canada do, however, tend to under-utilize mental health services   79 compared to the Canadian-born population, due to language barriers, lack of knowledge about such services, and cultural differences concerning mental health and its treatment [261].  4.5.3 Lack of information on acculturation  When focusing on minority cultural populations in Western cultures rather than cross-nation comparisons, a major consideration is the extent to which an individual’s value system and sense of belonging has been influenced by the predominant culture. In the context of the present investigation, acculturation would denote the extent to which the children felt part of the Canadian/Western culture and its associated values, norms, and behaviours. Relatedly, acculturation stress is an important consideration when examining the health of migrants or cultural minority individuals. Acculturation stress can be defined as the mental strain and challenges associated with immigration, moving to a new culture/nation, and the stress of being an immigrant in a new context [262]. Among young adults, acculturative stress has been inversely associated with life satisfaction  [263]. Among middle-years immigrant children from China, Hong Kong, and the Philippines, higher levels of acculturative stress emerged as a significant correlate of higher levels of caregiver-reported emotional problems [243,215]. Migration and acculturation-related challenges can be particularly difficult for children and youth who, unlike adults, almost never had a say in the decision to leave.  Berry outlined four acculturation strategies to describe immigrants’ processes of adjustment and acculturation from a home/heritage culture to a new host (i.e., Canadian) culture: Assimilation, Integration, Marginalization, and Separation [262]. Assimilation denotes an adoption of many of the host culture’s values, in lieu of one’s heritage values. Integration is the strategy of retaining aspects of one’s heritage culture as well as adopting the host culture. Separation is associated with the retention of the heritage culture while refusing to participate or   80 adopt cultural values of the host culture. Marginalization is the strategy of participating neither in the culture of one’s heritage, nor that of one’s host culture. Integration of one’s host culture and heritage culture has been associated with higher life satisfaction among minority youth in Germany, relative to the other acculturative approaches [264]. In a representative study of immigrants to Canada, however, life satisfaction did not differ between those with an integrative or assimilative strategy, but together these approaches were associated with higher LS than the other strategies – highlighting the importance of a strong connection to the host culture to promoting life satisfaction [233]. Among immigrant youth residing in several major ‘settlement’ nations (e.g., the US, Australia, Canada), the integration acculturative strategy was associated with the highest levels of life satisfaction. Acculturation strategies are not fixed or finite entities, but are sensitive to individual and situational forces as well as changes in host societal attitudes toward certain cultural groups. For instance, many Muslim individuals in the US had to re-consider their ethnic identity and often changed their acculturative strategies following the terrorist attacks of September 11 [265]. Hence, acculturation is a dynamic process.  Due to lack of specific acculturation information, our analyses were unable to identify specific acculturation strategies of each cultural group. Previous research with adults, however, provides some relevant insights for our findings. A government report found that South Asians (which include the Punjabi language group) reported the highest sense of belonging to Canada of any visible minority group as well as a higher sense of belonging to their own ethnic group (within the broad category of South Asian) than most other cultural groups [231] . Sense of belonging to Canada as well as one’s heritage culture is a useful proxy for the integration strategy [233]. Furthermore, South Asians have strong communities in BC and were one of the first immigrant groups in the province [231]. The report also pointed out that South Asian group   81 had a higher sense of belonging to Canada than the Chinese group (88% relative to 77%) [231]. Taken together, these findings provide context that may help explain why the Punjabi group experienced higher LS relative to other groups.  Related to acculturation of children in Canada is discrimination. Ethnic and/or cultural discrimination negatively impacts mental health and LS for children and across the life course [233,266,91]. Some of the cultural groups in the present study may have experienced higher levels of discrimination, which in turn contributed to lower levels of LS relative to the English language group. Ethnic prejudice and victimization (i.e., bullying) is well-documented to occur among middle-years children of diverse cultural and ethnic backgrounds [267-269]. There is also evidence that middle-years children in Canada of East Asian backgrounds reported higher levels of ethnic-based victimization than those of European backgrounds [270]. Ethnic-based discrimination was also documented among Vancouver immigrant youth in middle-childhood from Hong Kong and Mandarin [223]. Victimization of culturally diverse children appears to be moderated by school climate factors – as higher cultural diversity in classrooms has been found to relate to less victimization among ethnic minority youth [271]. 4.5.4 English language on the MDI  With regard to the data source for this study, the MDI was offered/completed in  English solely. Considering the focus of the present investigation on children from diverse cultural backgrounds, it is possible that an important subset of children were missed:  (1) those whose understanding of English was not sufficient to understand the MDI items and who did not participate, and (2) those whose data were excluded from the analyses because they indicated that it was ‘hard’ or ‘very hard’ for them to read in English. It is therefore possible that results may have differed if a) the MDI had been administered in languages other than English   82 (i.e., had translated versions been employed). It should be noted that most elementary schools in BC have English as the primary language of instruction. Excluding individuals of poor self-rated English comprehension abilities (9.7% of all MDI grade 4 child respondents) likely had some influence on altering the composition of students retained in the final analytic sample. Indeed, poorer perceived English ability may have indexed greater likelihood of being born abroad or having lived abroad in a non-English speaking society. Hence, poor self-rated English reading ability may have indicated lower levels of acculturation to Canadian society (an assumption that could be tested in future analyses by linking MDI data to cultural and/or immigration data). The English comprehension exclusion criterion, however, was necessary to ensure students could read, interpret, and competently respond to the SWLS-C items, as well as two social support scales.  Inclusion of children with poor English comprehension may, however, have contributed significantly to mismeasurement of items due to misinterpretations and hence inaccurate responses/scores on the SWLS-C and other scales. Of course, self-rated English reading ability was an imperfect assessment of reading ability. Some students may have strong English reading abilities but for whatever reason (e.g., low self-confidence or self-efficacy) indicated their ability as weak. Similarly, some children with poor English reading abilities may have perceived their abilities as strong. Absent an objective, unbiased measurement tool indexing children’s English reading ability, self-reported English reading ability was the best available indicator of such a competency in the present MDI data.   4.5.5 Reliance on self-reported data  All findings of the present investigation relied upon associations among self-reported variables. Common method variance may have influenced findings, as self-reported data was the   83 method for all variables. A recommended practice in cross-cultural research of mental variables such as LS, is to employ multiple modes of measurement [118]. Of course, this is difficult with such introspective constructs as LS – yet one may be able to identify associations with external attributes that are less subjective such as frequency of displays of positive affect, frequency of laughter/smiles, and biomarkers for stress such as cortisol levels. Additionally, other-reported indicators of emotional functioning – including clinical assessments, teacher-/caregiver-reported mental health or emotional functioning may be useful and paint a more comprehensive picture of cross-cultural variation in middle-years children’s positive mental health.  To this end, it was encouraging that the self-reported LS levels reported in the present study showed some consistency with a variety of other Canadian studies measuring emotional and mental health related outcomes across cultural groups of children (e.g., teacher-reported emotional maturity in kindergarten, number of anxious/aggressive behaviours; parent/ caregiver-reported number of emotional problems experienced by children).  Longitudinal studies also point to the utility of self-reported LS scores in middle childhood as associated with emotional/mental health more broadly; teacher-reported emotional competence among kindergarten children positively related to the same children’s self-reported well-being (a composite that included the SWLS-C) in grade 4 [272].  Longitudinal research have found life satisfaction positively related to life expectancy, even after adjustment for various socio-economic and psychological factors [273,61]. Longitudinal, population-level studies employing the SWLS-C in middle childhood to predict various adolescent and adult mental and physical health outcomes for various cultural groups would greatly help elucidate the epidemiological usefulness of inferences concerning self-  84 reported child LS via this measure. Nevertheless, extant findings on LS and related concepts lend support to the external validity and usefulness of self-reported LS. In addition to the aforementioned limitations, the present thesis had several major strengths. Principally, analyses were based on a comprehensive dataset representative of middle-years children in British Columbia, and representative of various cultural backgrounds of children in the province via the 8 most common language backgrounds. Approximately half of BC’s school districts were represented in the dataset, while representation of districts in BC’s most ethnically diverse region – Greater Vancouver – was excellent (10 of its 12 districts were included). Few child health studies have employed population-level datasets, and even fewer cross-cultural child health studies have examined large and representative cultural groups of children. No prior cross-cultural research on middle-years children in Canada comes close to the scale (n > 20,000) and representativeness (of the data employed in this thesis. Another advantage of the present thesis was its ability to examine not only the cross-cultural ME of a common child life satisfaction measure, but to also consider relations between LS and two key social contexts (peer support and adult support) across diverse cultural background groups of children. A further strength of the present analyses was the focus on children of diverse cultural groups living within the same nation. Most prior cross-cultural ME studies of LS have compared cross-nationally. In light of the rapid globalization and diversification of countries such as Canada via immigration  [166], understanding diverse populations within a country is invaluable.  4.6 Implications British Columbia and Canada have increasingly culturally diverse populations. Across Canada, there is need for understanding the health of different cultural and immigrant groups [162], and for the capacity to accurately measure the physical and mental health of these   85 increasingly diverse demographics, in order to monitor and promote health. This necessitates group-specific examination of health outcomes, but also requires an examination of specific correlates of the health outcomes (e.g., exploration of culture-specific protective factors).  Life satisfaction is a key aspect of healthy child development. The findings from this study indicate the SWLS-C is an equivalent measure of child life satisfaction for children of English language backgrounds and those of numerous culturally-diverse backgrounds in BC. Moreover, positive support from peers and adults emerged as key assets that had promotive associations with LS among children of various cultural backgrounds. The present work has, however, identified some cross-cultural differences in a) the levels of LS among children, and b) the relations between social supports (adults and peers) and life satisfaction among children. Moreover, the study provided evidence against the claim that observed cultural group differences in child LS were obfuscated by differential measurement via the SWLS-C or adult support scales between the English and other background groups.   Results from the present study have important public health implications. Cross-cultural differences in SWLS-C scores can be shared with child development centres, educators, community workers, policy-makers, families, and the children themselves. Such information can initiate discussion about why some cultural groups of children had higher levels of LS than others – moving toward dialogue regarding modifiable factors such as social supports, parenting approaches, cultural sensitivity, and poverty reduction. Moreover, a meta-analysis has shown socio-emotional learning such as through empathic reading and classroom activities as well as mindfulness, can help improve children’s mental health (including life satisfaction) [4]. The impact of curricula and programs that incorporate such socio-emotional learning as a means to enhance the LS can be assessed for various culturally diverse groups of grade 4 children in BC.   86 Additionally, this thesis found evidence that perceived support from peers and adults – modifiable social contexts – had important promotive associations with the LS of children from diverse cultural backgrounds. Such results can be shared with culturally diverse families, educators, and community workers as a means to motivate a focus on promoting positive mental health in the children in their worlds. Fortunately, within BC, strong dissemination outlets exist for sharing of such information through the Human Early Learning Partnership (HELP) – an interdisciplinary child development network based at the University of British Columbia. HELP produces insightful videos, talks, and detailed reports at the neighbourhood and school district level that are shared with a diverse audience. A key implication for future dissemination efforts is that the results of this thesis can be included, with the hope of underscoring the centrality of peer and adult supports not only for the majority (English-only language background group) but for various cultural groups in BC. Results can also be shared through the South Asian Health Institute, a research centre within BC’s Fraser Health Authority. The Institute produces reports on the health of the South Asian population, including measures of positive mental health. The Institute may thus be a useful outlet for dissemination of study findings to the South Asian community, who may have insight into why the children of Punjabi backgrounds (Punjabi is the major South Asian cultural group in BC) experienced the highest level of LS in the study. Such insights may help identify modifiable factors (e.g., familial behaviours, socialization) that could thus be fostered or more widely implemented to help promote LS among children of other cultural backgrounds.   5.0 Future inquiry  The findings from this thesis set the stage for meaningful exploration of why children of diverse cultural backgrounds living in BC differed in their level of life satisfaction relative to the   87 English background group. Indeed, other areas of public health have benefitted from examining variation in health outcomes across different ethnic groups. Although not directly related to SWLS-C, the following example serves as a useful analogy for how cross-cultural examination of a health outcome in a single nation helped to improve public health of children. As Chaturvedi explained, cross-cultural comparisons of the prevalence of sudden infant death syndrome indicated lower levels among South Asian and African immigrant groups compared to those from Western backgrounds in England. Such variation motivated attempts to uncover possible explanations [274]. It was eventually revealed that infant nursing practices among these two cultural groups differed from the general approaches of Western families – namely, these groups tended to nurse the infant supine (face up) instead of prone (face down – the method that had been traditionally emphasized in the West) [275]. This finding partially led to a shift in the West away from advising prone in favour of supine nursing positons for babies, and a notable decrease in cot deaths due to sudden infant death syndrome accompanied this change [274]. In the context of the present thesis, future work could explore why the Punjabi children experienced higher levels of LS relative to the majority, English background group. It may be that there are specific, cultural or otherwise, practices and values shared among children of a Punjabi background that helped promote their PMH. Similarly, it is worth further studying why the East Asian background groups had lower LS than the English background group to assess potentially modifiable factors.  Another important future endeavour would be to assess ME (and compare results attained) via approaches other than that used in the present investigation: MG-CFA. Multiple Indicators Multiple Causes (MIMIC) as well as item response theory (IRT) approaches could be used to estimate the extent to which the SWLS-C is an equivalent measure of child life   88 satisfaction across the various cultural background groups. Additionally, a novel approach by Wu et al. [148] of performing DIF analyses after balancing groups in terms of pre-existing potential confounders (e.g., level of acculturation, socio-demographic factors) via propensity score matching also warrants application to better understand the SWLS-C cross-culturally.   More generally-speaking, future research could further explore individual- and context-level cultural aspects that may influence children’s mental health. Person-level factors such as self-reported ethnic identity/background, immigration status (e.g., foreign-born status of child and parents), immigration type (e.g., economic, family reunification, refugee), and acculturation (e.g., sense of belonging to Canada and host culture) would better inform cross-cultural examinations of child mental health. Contextual factors such as family environment/structure, type of residence, built environment, neighbourhood socio-cultural capital, and the cultural density of the surrounding community or neighbourhood would additionally provide richer understandings of child LS across different cultural backgrounds.  Also, from a developmental perspective, understanding the cross-cultural measurement equivalence of the SWLS-C at multiple phases of childhood/adolescence would expand limited knowledge of how the measurement and conceptualization of life satisfaction may operate for different cultural groups of children across key developmental stages. It may be that cultural differences in conceptual frameworks and meanings ascribed to the SWLS-C content emerge more strongly concomitant with the developmental emergence of  identity and values as children pass through middle childhood [211]. Examination of cross-cultural ME in the present thesis is a key step in providing validation evidence for the appropriateness of inferences based on SWLS-C mean scores. Validation is, however, and ongoing process and pertains to inferences based on scores rather than an inherent property of an instrument [208] and inferences of the SWLS-C   89 scores of various cultural background groups are influenced by individual and ecological factors that warrant future examination.  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A.1.1 Measurement equivalence of monolingual and bilingual language backgrounds a) The language provided was intelligible/recognizable. Spelling errors were permitted to the extent that the language could be reasonably inferred from the word(s) (e.g., the spelled word was phonetically similar to the likely intended word). Similarly, children who wrote the nation or ethnicity associated with the language rather than the language (e.g., Spain, rather than Spanish) were included if the likely target language could be reasonably inferred. b) A language or a nation/region specifically associated with that language was reported (i.e., a land-based language classification). Some children wrote the nation/region rather than the language and/or adjective associated with that nation. For example, children who wrote “Russia” were coded as a member of the “Russian” language background since the purpose of the language background variable in the present analysis was to provide a proxy for cultural background. Similarly, we included all individuals who wrote “Iranian”, “Persian”, and/or “Farsi” as members of one “Persian/Farsi” language background group. It is acknowledged that Iranian languages other than (the most commonly spoken language) Persian/Farsi represent differences culturally and geographically (e.g., languages other than Persian/Farsi are more common in Iran’s Northwest and Southeast regions), however it was deemed that children living in BC who live in households where Persian/Farsi or Iranian languages are spoken likely share a substantial amount of cultural background since all options corresponded to a cultural background associated with Iran. Moreover, it is acknowledged that there is a substantial degree of heterogeneity within each of the language groups (e.g., regional and cultural variation among   107 children of the same language background) however language was the sole indication of cultural background available in the present dataset. Several children indicated ‘sign language’ in the free-text box, however, as sign language is not associated with a corresponding cultural background, such responses were not coded as a separate language background per se. Those reporting ‘sign language’ were included if they selected a language from the 12 listed languages, or a second ‘other’ language, and met the other inclusion criteria outlined in this section. c) Only one language other than English was reported. Several children reported two or more non-English languages.  Due to the small numbers of children reporting any combination of non-English languages (e.g., French and Spanish), as well as the complexity and heterogeneity of such mixed language backgrounds, it was not possible to create and include composite language background groupings. d) The language formed or joined a minimum group size. As larger sample sizes are typically required for analyses such as MG-CFA, a lenient cut-off of 30 ensured that groupings with 30 or more respondents could at least be tested in the model. Although larger samples (e.g., n = 600) are recommended for yielding stable estimates of model parameters in MG-CFA (Sass, Schmitt, & Marsh, 2014), at the preliminary stage such smaller groups were nonetheless considered for inclusion and their measurement properties were assessed at the prerequisite step (testing model fit of single-group CFAs for the SWLS-C).        108 A.2.1 MG-CFA results for monolingual and bilingual (with English) comparisons of language background groupings    Configural Metric Scalar Strict Ref Focal X2 RMSEA 90% CI CFI Δ X2 Δ CFI Δ X2 Δ CFI Δ X2 Δ CFI Can B Can  M 11.54 = .32 .02 (.00, .05) 1.000 10.76 (.03) .003 11.77 (.62) .001 22.52 (.00) .005 Fil B Fil  M 18.80 = .04 .05  (.01, .08) .994 2.83 (.59) .004 10.65 (.71) .001 4.92 (.43) .000 Kor B Kor  M 16.51 = .09 .05 (.00, .09) .997 1.05 (.90) .003 9.41 (.80) .000 5.12 (.40) .000 Man B Man  M 26.04 = .00 .05  (.03, .08) .996 6.71 (.15) .000 21.8 (.08) .001 4.6 (.46) .000 Note. Configural models for the French, Punjabi, and Spanish groups did not converge (likely due to small sample sizes of the monolingual groups). B: Bilingual (i.e., non-English and English language background. M: Monolingual (i.e., solely the non-English language).  A.3.1 Mean comparisons of mono- and bi-lingual versions of language background groups.                 Monolingual                 Bilingual M difference (p value) Language N M (SD) N M (SD)    Cantonese 126 3.93 (.93) 1,125 3.92 (.81) .00 (.96)    Filipino 67 4.05 (.77) 781 4.12 (.73) .07 (.45)    Korean 119 3.91 (.92) 414 4.01 (.80) .10 (.30)    Mandarin 174 3.83 (.89) 957 3.85 (.87) .02 (.79)    Punjabi 108 4.33 (.73) 764 4.33 (.72) .00 (.97)    Spanish 53 4.04 (.87) 582 4.15 (.85) .11 (.36) Note. Due to a very small sample size of 10, French means of the monolingual French group were not compared with those of the bilingual French group.   109 A.4.1 Single-group confirmatory factor analyses for the SWLS-C one-factor model  Cultural background (n) x2 (df) = p value CFI TLI RMSEA                      (90% CI) English (13,591) 97.02 (5) = .00 .998 .996 .036 (.030, .043) French (1,258) 10.20 (5) = .07 .999 .997 .029 (.000, .054) Cantonese (1,251) 7.73 (5) = .17 .999 .999 .020 (.000, .046) Mandarin (1,131) 23.47 (5) = .00 .996 .992 .056 (.034, .080) Punjabi (872) 11.27 (5) = .05 .997 .995 .037 (.004, .066) Filipino (848) 14.92 (5) = .01 .993 .986 .048 (.021, .076) Spanish (635) 8.90 (5) = .11 .998 .997 .036 (.000, .073) Korean (533) 13.89 (5) = .02 .995 .990 .058 (.023, .096) Vietnamese (310) 9.30 (5) = .10 .996 .991 .053 (.000, .105)* Japanese (306) 11.34 (5) = .05 .992 .985 .064 (.009, .115)* Farsi (272) 12.61 (5) = .03 .992 .983 .075 (.023, .128)* Russian (252) 9.01 (5) = .11 .997 .995 .056 (.000, .115)* German (220) 2.75 (5) = .74 1.00 1.00 .000 (.000, .067) Hindi (216) 9.05 (5) = .11 .994 .988 .061 (.000, .124)* Chinese (132) 8.23 (5) = .14 .994 .988 .070 (.000, .152)* Italian (124) 15.25 (5) = .01 .986 .973 .129 (.058, .205)* Arabic (103) 7.77 (5) = .17 .990 .980 .073 (.000, .168)* Serbian (92) 4.08 (5) = .54 1.00 1.00 .000 (.000, .131)* Portuguese (72) 6.58 (5) = .25 .998 .995 .066 (.000, .186)* Dutch (64) 5.79 (5) = .33 .998 .997 .050 (.000, .186)* Greek (63) 18.73 (5) = .00 .965 .930 .209 (.114, .313)* * Poor model fit was indicated by an RMSEA value ≥ 0.08 and/or an upper 90% confidence interval ≥ 0.10. Groups above line were those n > 300 and with acceptable model fit.      110 Appendix B – appendices from Chapter 3  Appendix B contains appendices from Chapter 3.  B.1.1 Geographic distribution of language backgrounds in British Columbia Cultural/language background diversity was present among many of the 28 BC school districts represented by the analytic sample. Variation was, however, most pronounced among those in BC’s most urbanized and populous area - the Greater Vancouver region. More than one third of students in the Richmond, Vancouver, Burnaby, New Westminster,  Coquitlam, and Delta school districts had a language background other than English – districts that together comprised over half of all children in the analytic sample (n=10,655). Indeed, over half of children in the Richmond, Burnaby, and Vancouver school districts had a non-English language background. Mirroring the geographic pattern of ethnic density (concentration of ethnic minorities) in BC, most children in the rural/remote school districts such as Central Coast, Gold Trail, Fort Nelson, and the Nicola-Similkameen had an English language background. Despite representing 25% of all children in the analytic sample, five school districts (Vancouver, Coquitlam, Burnaby, Richmond, and Delta) were home to the majority of children (78.2%) with a non-English language background.  Vancouver and Burnaby were home to 17% of children with an English language background but home to 76% children with a Cantonese background (n=926) and 58% of children with a Mandarin background (n=660).Vancouver and Coquitlam were the home districts of 59% of Korean language background children whereas 56% of children with a Punjabi language background lived in Delta or Vancouver. Among the 91 most populous neighbourhoods in which children lived, the proportion of children with a non-English language background ranged from 7% to 88%.   111 B.2.1 Characteristics of overall MDI sample and analytic sample of respondents  Overall sample  (n=28,904) Analytic sample (n=20,119) Year of collection Count % Count % 2009/10 3,044 10.5 2,039 10.1 2010/11 2,000 6.9 1,454 7.2 2011/12 1,642 5.7 1,317 6.5 2012/13 7,987 27.6 5,215 25.9 2013/14 4,829 16.7 3,373 16.8 2014/15 5,255 18.2 3,639 18.1 2015/16 4,147 14.3 3,082 15.3 MDI version     Electronic 9,584 33.2 6,795 33.8 Paper 19,320 66.8 13,324 66.2 District     Vancouver 5,570 19.3 3,666 18.2 Coquitlam 3,837 13.3 2,636 13.1 Burnaby 2,940 10.2 1,632 8.1 Delta 1,895 6.6 1,351 6.7 Chilliwack 1,563 5.4 1,221 6.1 Central Okanagan 1,363 4.7 1,049 5.2 Richmond 1,335 4.6 871 4.3 Maple Ridge - Pitt Meadows 1,319 4.6 996 5.0 Langley 1,260 4.4 944 4.7 Alberni 939 3.2 702 3.5 Sunshine Coast 931 3.2 688 3.4 New Westminster 762 2.6 499 2.5 Kootenay Lake 740 2.6 586 2.9 Prince George 732 2.5 526 2.6 Boundary 465 1.6 356 1.8 Okanagan - Similkameen 440 1.5 327 1.6 Revelstoke 418 1.4 345 1.7 West Vancouver 418 1.4 281 1.4 Fraser - Cascade 415 1.4 306 1.5 Mission 343 1.2 253 1.3 Nicola - Similkameen 251 0.9 180 0.9 Powell River 217 0.8 144 0.7 Kootenay-Columbia 211 0.7 163 0.8 Nechako Lakes 178 0.6 142 0.7 Gold Trail 150 0.5 99 0.5 Haida Gwaii 111 0.4 84 0.4 Fort Nelson 56 0.2 44 0.2   112  Overall sample  (n=28,904) Analytic sample  (n=20,119) Central Coast 45 0.2 ds 0.1 Gender     Female 14,095 48.8 9,870 49.1 Male 14,807 51.2 10,247 50.9    Age     M (SD) 9.2 (0.54)  9.2 (0.54)  8 3,064 10.6 2,246 11.2 9 17,005 58.8 11,887 59.1 10 8,760 30.3 5,943 29.5 7, 11, or 12 71 0.2 41 0.2 Non-English language background 15,231 59.9 6,528 32.4 SWLS-C M (SD) 4.1 (0.8)  4.1 (0.8)  Peer support M (SD) 4.1 (0.9)   4.2 (0.9)  Adults support M (SD) 3.4 (0.6)  3.4 (0.6)  Neighbourhood-level variables     Completed post-secondary education (%: M, SD) 65.5  (9.1)  65.2  (9.1)  Household income (Cdn $; M, SD) 77,272 (15,292)  77,729  (15,481)  ds = data supressed (cell size n<35)           113 B.3.1 Proportion of children in analytic sample who reported a language background other than English across participating BC school districts in the analytic sample (n=28)                          114 B.4.1 Factor loadings for each SWLS-C item across cultural background groups   B.5.1 Thresholds for SWLS-C item 1 across cultural background groups        115 B.5.2 Thresholds for SWLS-C item 2 across cultural background groups  B.5.3 Thresholds for SWLS-C item 3 across cultural background groups          116 B.5.4 Thresholds for SWLS-C item 4 across cultural background groups  B.5.5 Thresholds for SWLS-C item 5 across cultural background groups           117 B.6.1 Residual variance for each SWLS-C item across cultural background groups                    118 B.7.1 Pairwise comparison of model fit for MG-CFA analysis of the SWLS-C     Configural Metric Scalar Strict Ref Focal X2 RMSEA 90% CI CFI Δ X2 (P value) Δ CFI Δ X2 (P value) Δ CFI Δ X2 (P value) Δ CFI Eng Can 95.48 =.00 .034 (.028, .040) .998 2.18 (.70) .001 14.57 (.41) .000 42.86 (.00) .002 Eng Fil 97.47 = .00 .035 (.029, .041) .998 8.45 (.08) .000 22.04 (.08) .000 16.83 (.00) .000 Eng Spa 101.46 = .00 .036 (.030, .042) .998 5.66 (.23) .001 32.28 (.00) .000 .61 (.99) .000 Eng Fre 98.55 = .00 .035 (.029, .041) .998 2.03 (.73) .001 14.35 (.42) .000 7.98 (.16) .000 Eng Kor 108.92 = .00 .037 (.031, .044) .998 2.04 (.73) .000 28.73 (.01) .000 18.72 (.00) .000 Eng Man 108.90 = .00 .037 (.031, .043) .998 1.84 (.76) .001 18.92 (.17) .000 34.91 (.00) .002 Eng Pun 92.36 = .00 .034 (.028, .040) .998 5.35 (.25) .001 18.86 (.17) .000 12.65 (.03) .000 Can Fili 25.53 = .00 .038 (.020, .057) .997 16.87 (.00) .002 39.48 (.00) .004 64.86 (.00) .000              119   Configural Metric Scalar Strict Ref Focal X2 RMSEA 90% CI CFI Δ X2 (P value) Δ CFI Δ X2 (P value) Δ CFI Δ X2 (P value) Δ CFI Can Spa 17.28 = .07 .028 (.000, .049) .999 3.07 (.55) .000 27.44 (.02) .002 36.01 (.00) .005 Can Fre 14.27 = .16 .018 (.000, .038) .999 3.31 (.51) .001 15.48 (.35) .000 54.62 (.00) .008 Can Kor 19.39 = .04 .032 (.008, .054) .998 6.14 (.19) .000 35.96 (.00) .004 9.78 (.08) .000 Can Man 27.05 = .00 .038 (.021, .055) .998 1.78 (.78) .001 25.19 (.03) .001 10.21 (.07) .001 Can Pun 15.65 = .11 .023 (.000, .044) .999 1.98 (.74) .001 27.89 (.01) .002 75.78 (.00) .016 Fil Spa 29.04 = .00 .051 (.030, .073) .995 17.59 (.00) .005 44.00 (.00) .005 14.28 (.01) .001 Fil Fre 26.43 = .00 .040 (.022, .058) .997 10.19 (.04) .001 42.62 (.00) .005 11.32 (.05) .000 Fil Kor 31.27 = .00 .056 (.340, .078) .993 13.49 (.01) .003 54.45 (.00) .012 41.71 (.00) n/a Fil Man 37.41 = .00 .053 (.035, .071) .995 19.50 (.00) .002 47.64 (.00) .005 66.83 (.00) .012   120    Configural Metric Scalar Strict Ref Focal X2 RMSEA 90% CI CFI Δ X2 (P value) Δ CFI Δ X2 (P value) Δ CFI Δ X2 (P value) Δ CFI Fil Pun 26.86 = .00 .044 (.024, .065) .995 18.51 (.00) .005 23.77 (.05) .008 10.15 (.07) .010 Spa Fre 18.22 = .05 .029 (.000, .051) .999 18.22 (.05) .001 6.36 (.17) .002 29.83 (.01) .000 Spa Kor 23.74 = .01 .049 (.023, .074) .996 8.26 (.08) .000 25.3 (.03) .002 18.48 (.00) .004 Spa Man 31.19 = .01 .049 (.030, .069) .997 5.96 (.20) .000 29.52 (.01) .002 31.00 (.00) .004 Spa Pun 19.26 = .04 .035 (.008, .058) .998 1.18 (.88) .001 24.11 (.04) .002 10.97 (.05) .001 Fre Kor 20.49 = .02 .034 (.012, .055) .998 1.71 (.79) .001 22.91 (.06) .001 29.42 (.00) .006 Fre Man 28.21 = .00 .039 (.022, .056) .998 2.39 (.67) .001 17.30 (.24) .001 59.48 (.00) .007 Fre Pun 16.46 = .09 .025 (.000, .045) .999 5.58 (.23) .000 26.81 (.02) .002 12.29 (.03) .002 Kor Man 34.09 = .00 .054 (.035, .074) .996 3.30 (.51) .001 8.83 (.84) .001 3.03 (.70) .001   121 Kor Pun 21.32 = .02 .040 (.016, .064) .997 7.18 (.13) .001 21.37 (.09) .002 43.74 (.00) .016 Man Pun 28.37 = .02 .043 (.025, .062) .997 3.55 (.47) .001 21.32 (.09) .001 74.28 (.00) .013  Eng = English; Can = Cantonese; Fil = Filipino; Spa = Spanish; Fre = French;  Kor = Korean; Man = Mandarin; Pun = Punjabi.  Bolded values indicate rejection of model fit (Δ CFI > .010).  n/a = fit not assessed if subsequent level not supported                                122 B.7.2 Level of ME supported for the pairwise cultural background group comparisons of the SWLS-C     Eng = English; Can = Cantonese; Fil = Filipino; Spa = Spanish; Fre = French; Kor = Korean; Man = Mandarin; Pun = Punjabi. Str: Strict ME; Sca: Scalar ME; Met: Metric ME. Support for ME indicated by Δ CFI ≤ .01.                    Eng Fre Man Can Pun Fil Spa Fre Str       Man Str Str      Can Str Str Str     Pun Str Str Sca Sca    Fil Str Str Str Str Str   Spa Str Str Str Str Str Str  Kor Str Str Str Str Sca Met Str   123 B.7.3 Pairwise comparison of model fit for MG-CFA of the peer support scale    Configural Metric Scalar Strict Ref Focal X2 RMSEA (90% CI) CFI Δ  X2 Δ  CFI Δ  X2 Δ  CFI Δ  X2 Δ  CFI Eng Fre 99.39 = .00 .057 (.048, .067) .997 1.37 (.72)  .001 82.50 (.00) .002 n/a .002 Eng Man 83.85 = .00 .053 (.044, .063) .998 2.14 (.54)  .000 188.12 (.00) .005 n/a .002 Fren Man 7.32 = .12 .028 (.000, .060) .999 184.55 (.00)  .033 n/a n/a n/a n/a Eng = English; Can = Cantonese; Spa = Spanish; Fre = French; Man = Mandarin;                    Pun = Punjabi. Bolded values indicate rejection of model fit (CFI). n/a = fit not assessed if subsequent level not supported                         124 B.7.4 Pairwise comparison of model fit for MG-CFA of the adult support scale   Configural Metric Scalar Strict Ref Focal X2 RMSEA (90% CI) CFI Δ X2 Δ CFI Δ  X2 Δ CFI Δ X2 Δ CFI  Eng Can 68.95 = .00 .05 (.04, .06) .996 5.75 (.12) .001 61.64 (.00) .002 n/a  .001 Eng Fil 70.82 = .00 .05 (.04, .06) .996 1.01 (.80) .002 13.14 (.07) .001 .88 (.93) .001 Eng Spa 75.12 = .00 .05 (.04, .06) .996 3.46 (.33) .001 60.57 (.00) .003 n/a .001 Eng Fre 76.81 = .00 .05 (.04, .06) .996 1.53 (.68) .001 30.31 (.00) .001 n/a .000 Eng Man 77.96 = .00 .05 (.04, .06) .996 6.96 (.07) .001 49.29 (.00) .003 n/a .000   Pun 69.69 = .00 .05 (.04, .06) .996 5.60 (.13) .001 37.99 (.00) .002 n/a .000 Can Fil 12.14 = .02 .04 (.02, .07) .997 .90 (.83) .002 11.55 (.12) .002 8.90 (.06) .002 Can Spa 10.93 = .03 .04 (.01, .07) .997 5.02 (.17) .000 10.38 (.17) .002 16.69 (.00) .006 Can Fre 12.86 = .01 .04 (.02, .07) .997 4.14 (.25) .000 76.79 (.00) .024 n/a n/a Can Man 14.08 = .01 .05 (.02, .07) .996 10.03 (.02) .002 n/a .009 n/a .001 Can Pun 12.05 = .02 .04 (.02, .07) .997 8.12 (.04) .002 n/a .001 n/a .006 Fil Spa 6.08 = .19 .03 (.00, .07) .999 2.64 (.45) .000 23.45 (.00) .010 n/a .005  Fili Fre 7.99 = .09 .04 (.03, .06) .998 1.65 (.65) .001 37.07 (.00) .013 n/a n/a              125              Configural Metric Scalar Strict Ref Focal X2 RMSEA (90% CI) CFI Δ X2 Δ CFI Δ  X2 Δ CFI Δ X2 Δ CFI Fili Man 9.71 = .05 .04 (.01, .07) .997 6.54 (.09) .002 22.83 (.00) .003 n/a .013 Fili Pun 7.57 = .11 .03 (.00, .07) .998 4.62 (.20) .000 12.30 (.09) .004 23.89 (.00) .010 Spa Fre 6.40 = .17 .03 (.00, .06) .999 .35 (.95) .001 85.77 (.00) .040 n/a  n/a Spa Man 8.43 = .08 .04 (.00, .07) .997 4.18 (.24) .000 28.66 (.00) .012 n/a  n/a Spa Pun 6.07 = .19 .03 (.00, .07) .999 6.70 (.08) .003 3.43 (.84) .002 16.78 (.00) .008 Fre Man 10.21 = .04 .04 (.01, .06) .997 2.45 (.48) .001 57.86 (.00) .021 n/a  n/a Fre Pun 7.94 = .09 .03 (.00, .06) .998 2.90 (.41) .001 63.76 (.00) .024 n/a  n/a Man Pun 9.68 = .05 .04 (.00, .07) .997 1.36 (.72) .003 18.63 (.01) .000 n/a  .002 Eng = English; Can = Cantonese; Fil = Filipino; Spa = Spanish; Fre = French;  Kor = Korean; Man = Mandarin; Pun = Punjabi. Bolded values indicate rejection of model fit (CFI). n/a = fit not assessed if subsequent level not supported           126 B.7.5 Level of ME supported for the pairwise cultural background group comparisons of the Adult support scale               Eng = English; Can = Cantonese; Fil = Filipino; Spa = Spanish; Fre = French;  Man = Mandarin; Pun = Punjabi. Str: Strict ME; Sca: Scalar ME; Met: Metric ME. Support for ME indicated by a Δ CFI ≤ .01.   Eng Fre Man Can Pun Fil Fre Str      Man Str Met     Can Str Met Str    Pun Str Met Str Str   Fil Str Met Sca Str Str  Spa Str Met Met Str Str Str 


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