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1 Assessment and Promotion of Life Satisfaction in Children

As the field of positive psychology advances, researchers have shown that positive mental health is more than the absence of psychopathology. Comprehensive models of mental health require considerations of well-being and ill-being constructs (Antaramian et al. 2010; Greenspoon and Saklofske 2001; Suldo and Shaffer 2008). Life satisfaction (LS) is one key well-being construct in positive psychology. Life satisfaction is defined as a cognitive judgment of one’s perceived quality of life as a whole or with specific domains, such as family relations, work, and health (Diener 1984). Studies of the antecedents, correlates, and consequences of LS of adults have proliferated over the years. However, studies with children and adolescents have been limited. Fortunately, the number of studies of children’s LS has grown significantly in the last decade (see C. L. Proctor et al. 2009a, b for a review).

Life satisfaction is an important factor in positive psychological development; it is not only a by-product of favorable life experiences, but is also a precursor of positive life outcomes. A meta-analysis of cross-sectional, longitudinal, and experimental studies of adults (Lyubomirsky et al. 2005) revealed that high subjective well-being (SWB; including LS) predicts subsequent positive marriages, friendships, work productivity, and mental and physical health. Longitudinal and experimental investigations of children’s LS and positive emotions have been exceedingly scarce. However, short-term longitudinal studies have suggested that low LS in adolescents predicts decreases in emotional support from parents (Saha et al. 2010), decreases in student engagement in schooling (Lewis et al. 2011), and decreases in positive interactions with peers as well as increases in peer relational victimization (Martin et al. 2008). As such, “high” LS is likely a protective factor in the development of healthy child and adolescent development (Suldo and Huebner 2004) as well as a desirable outcome in itself.

1.1 Measurement

Children’s LS measures have been based on three distinct theoretical models: general, global, and domain-specific LS. First, there are instruments based on general models of LS that assume that overall or “general” LS is comprised of bottom-up judgments of specific life domains (e.g., family, peers, and school domains). Thus, a general or total LS score on such instruments reflects a simple (or weighted) sum of scores on items representing responses across a variety of specific domains. Second, there are instruments that attempt to assess satisfaction “as a whole” or overall LS based on a “global” model, which assumes that overall LS is best assessed by an exclusive emphasis on items that are domain-free (e.g., my life is going well) versus domain-specific (e.g., my school life is going well). In contrast to general LS scores, in which the number and nature of the domains are pre-determined by the researcher, global LS scales allow individual children to develop their overall judgments based on their own criteria (Pavot and Diener 1993). Third, multidimensional measures have been developed with the intent of eliciting children’s judgments across various life domains that are considered to be important to most, if not all, individuals of the particular age group. In this fashion, such measures yield profiles of individuals’ reports of LS, providing more differentiated, contextualized LS reports. Hence, a child who has average global LS, along with high school and low family satisfaction could be differentiated from a child who has average global LS, along with low school and high family satisfaction. In all cases, LS judgments generally comprise the full range of experiences from lower levels through a neutral level (neither satisfied nor dissatisfied) and through higher levels of LS. Such scales can provide finely nuanced differentiations (e.g., very high vs. moderately high LS) both above and below the neutral point. The resulting context-specific profiles thus provide more targeted information relevant to the design of more healthy environments for individual students or groups of students. Using the previous example, context-specific profiles allow the tailoring of interventions targeting the specific area of low satisfaction (e.g., low family satisfaction vs. low school satisfaction). Although such measures have existed for adult populations for many years, the development of psychometrically sound measures for children and adolescents has lagged considerably behind.

Based on the above considerations, and like others (e.g., Cummins 1997), we established a research agenda in the 1990s to develop several measures of global and domain-specific LS in our lab. These measures are discussed in detail in Huebner and Hills (in press). These instruments include, but are not limited to the Students’ Life Satisfaction Scale (SLSS; Huebner 1991a) and the Multidimensional Students’ Life Satisfaction Scale (MSLSS; Huebner 1994). The primary goals of the research were to develop LS scales for children ages 8–18 that: (a) tapped their global LS (i.e., SLSS); (b) provided an evidence-based profile of children’s satisfaction with important, specific domains (e.g., school, family, friends) in their lives (i.e., MSLSS); (c) demonstrated acceptable reliability and validity; and (d) could be used effectively with children across a wide range of ability levels (e.g., children with mild developmental disabilities through gifted children). Space considerations do not allow us to elaborate upon the psychometric characteristics of the measures. However, more than two decades of research from our lab as well as others suggests that these self-report instruments demonstrate acceptable reliability and validity for a variety of purposes for children of approximately 8–18 years of age (Huebner and Hills in press; C. Proctor et al. 2009a, b).

These resulting measures have allowed us to study further the determinants and consequences of individual differences in children’s global and domain-specific LS reports. The research has revealed a wide-ranging nomological network of related variables (Huebner 2004). In subsequent research and professional practice, we have used the SLSS and MSLSS in more comprehensive assessments to understand the nature, determinants, and consequences of LS. In professional practice, we have found that we can enhance the usefulness of the SLSS and MSLSS on occasion through conducting more detailed inquiries into the meaning of student responses. The standard measures do not allow examiners to determine the specific content and processes used by individual children to formulate their LS judgments. Open-ended, follow-up interview questions may thus facilitate greater understanding. Adapting procedures recommended by Harter (1985) for self-concept measures, we have used such questions as: “What made you agree/disagree that your life is good?” and “What made you agree/disagree that your family experiences are positive?” In addition to clarifying the meaning of a student’s self-reports, such queries may clarify discrepancies between self-reports and reports from significant others, such as parents or teachers (Huebner et al. 2002).

We have also found that a modified version of Randolph et al. (2009) integrated model of objective and SWB can be useful to guide more complete assessments of students’ LS. Their model includes four levels of indicators of well-being. The highest level refers to a student’s overall quality of life. The second level includes the lower-order component of subjective global LS (along with measures of positive and negative emotions). The third level includes judgments of satisfaction regarding major, specific life domains, such as family, friends, school, self, and living environment (cf. Huebner 1994). The fourth level incorporates key, empirically-validated correlates under each domain. For example, several research-based variables could be assessed that contribute to satisfaction with school experiences, such as teacher behavior, school size, parental involvement, peer relationships, personality characteristics, and academic self-efficacy (see Baker and Maupin 2009 for a review). A similar set of variables could be generated for satisfaction with family, friends, and so forth. This last level includes, but is not limited to objective indicators. Although the hierarchical nature of the model may be debatable, the model reflects the possibilities for comprehensive assessments of the well-being of children for individual assessment purposes or group assessment purposes, such as school-wide or national assessments of children’s well-being. Comprehensive well-being assessments can thus include subjective global and relevant subjective domain-based LS indicators. Furthermore, these measures can then be complemented by considerations of key objective and/or subjective determinants guided by the extant research base. Depending upon the nature and levels of information desired, specific assessment plans can be constructed subsequently to meet specific goals for an individual child or groups of children. For example, the evaluation of the implementation of school-­wide policies or programs to promote students’ academic achievement levels might focus relatively more on school-related conditions and perceptions than community conditions and perceptions, depending upon the evidence base (e.g., strengths of the relationships or ability to alter more distal variables like community-school relationships) and the criterion variables of interest.

The use of this model is consistent with a positive psychology perspective in that it allows for a focus on positive conditions and experiences as well as negative conditions and experiences, accordingly LS indicators allow for differentiated responses above and below the neutral point. Based on the pattern of scores on the various measures, individualized interventions should be developed that target the identified likely determinants of low life LS as well exploit identified student or environmental assets relevant to a given individual or group. There are likely many different determinants of LS for specific individuals and groups of individuals. Thus, it seems imperative to conduct a thorough assessment in order to develop a meaningful understanding of the unique determinants and consequences. Because it is also unlikely that a particular intervention strategy will be effective for all children with low LS, it makes sense to develop a resulting individualized plan for a particular student or group of students who share a common set of antecedent conditions. Such an intervention may not only be helpful for children with existing low levels of LS, but also may also prevent the development of dissatisfaction and promote optimal levels in all students.

1.2 Research on Life Satisfaction of Children and Adolescents

Numerous predictors of individual differences in children’s LS have been identified. However, some variables are more difficult, impossible, or impractical to alter, even in the most comprehensive LS promotion programs. Such variables range from demographic factors (e.g., ethnicity, socioeconomic status) to temperament/personality characteristics (e.g., extraversion, neuroticism). Thus, rather than offering an exhaustive review of the predictors of LS in children, the following review will focus on more readily alterable environmental variables (e.g., family, school setting) and self variables (e.g., cognitive, behavioral factors) within the context of the aforementioned model of Randolph et al. (2009). It should be noted, however, that some of the more non-malleable factors, such as personality characteristics, demographic variables, and cultural differences, might serve as moderators of the relationships between children’s LS and “alterable” factors. Some examples will be discussed briefly in a later section.

1.3 Contextual Variables

1.3.1 Family-Related Variables

Support from caregivers, usually parents, is the strongest interpersonal resource during children and adolescents’ development. Compared to adolescents who lived in foster care group homes, adolescents who lived at home with parents reported higher global LS (Sastre and Ferriere 2000). Perceived parental support (both intrinsic and extrinsic) is positively related to adolescents’ LS (Oberle et al. 2010). However, intrinsic support (i.e., parental expressions of love, appreciations, and care) was more strongly related to adolescent LS than other forms of support (i.e., extrinsic support, closeness, and informational support) (Stevenson et al. 1999; Young et al. 1995). Research on parenting styles has revealed that all three dimensions of the authoritative parenting style (social support-involvement, strictness-­supervision, and psychological autonomy granting) were all positively related to LS among adolescents, with perceived parental social support showing the strongest correlation (Suldo and Huebner 2004).

Attachment studies have demonstrated that adolescents who are securely attached to their parents display higher LS (Ma and Huebner 2008). Research has also shown that perceived poor parental relationships, but not family status, is associated with reduced LS among adolescents (Grossman and Rowat 1995), with parent–child conflict strongly linked to lower levels of adolescent LS (Phinney and Ong 2002). Further, in both parent and peer relationships, trust and communication have been identified as the strongest components of the attachment-LS linkage in adolescents (Nickerson and Nagle 2004). Suldo et al. (2008) also revealed that students’ perception of their parents’ involvement in their schooling was a significant predictor of students’ global LS.

1.3.2 Peer-Related Variables

Once children reach adolescence, the quality of peer relationships becomes another important contributor to social development and adjustment as well as LS. In general, adolescents with more friends and higher quality peer relationships report higher global LS (Huebner and Alderman 1993). In contrast, students who experience negative interactions with peers report lower levels of LS (Martin and Huebner 2007). Specifically, students who are relationally victimized by peers (e.g., teased by peers or purposefully excluded from peer groups) and in dating relationship (e.g., physical or emotional abuse by a dating partner) experience significantly fewer positive emotions in school and lower global LS (e.g., Martin et al. 2008; Callahan et al. 2003). Besides overt physical or relational victimization, receiving prosocial acts from peers was positively correlated with global LS, with adolescent receiving fewer prosocial acts reporting lower global LS (Martin and Huebner 2007) and higher levels of loneliness (Huebner and Alderman 1993). Thus positive peer relations, as well as family relations, can contribute to children’s LS.

1.3.3 School-Related Variables

The contribution of school experiences to adolescents’ LS has received modest, but increasing attention. Research has shown that how adolescents view their school climate and school experience is significantly related to their SWB, including LS (e.g., Flanagan and Stout 2010; Suldo et al. 2008). For both middle school and high school students, those students with higher LS tend to have more favorable attitudes toward their teachers and toward school in general (Gilman and Huebner 2006). One frequently studied school level variable is school safety. If students don’t feel safe at school (e.g., they fear being threatened or injured by someone with a weapon, having property stolen or damaged), their overall LS is lower (Valois et al. 2001).

Teacher-related variables have been studied primarily with respect school satisfaction (i.e., students’ satisfaction with their school experiences overall). School satisfaction is also a contributor to youth’s evaluation of their overall life; research shows that school satisfaction is significantly associated with global LS among American students (Huebner and Gilman 2002), and even more so among South Korean students (Park and Huebner 2005). Studies demonstrate that higher levels of school satisfaction are associated with a high degree of clarity in classroom rules, predictable teacher behavior and classroom routines (Baker et al. 2003), ample praise for appropriate behaviors (Baker 1999), goals that promote future academic aspirations (Malin and Linnakylae 2001), and curricular activities that promote choice and relevance (Karatzias et al. 2002). The evidence also suggests that adolescents’ perceived quality of student-teacher relationships is the strongest predictor of school satisfaction, even stronger than peer and parent relationships (DeSantis-King et al. 2006).

Studies to date suggest that children’s LS and intelligence test scores are not significantly related (Huebner and Alderman 1993), however, several studies have found small, but significant, correlations between LS (and school satisfaction), school engagement (Ladd et al. 2000; Lewis et al. 2011), and school grades (Antaramian et al. 2010; Gilman et al. 2006). Interestingly, self-perceptions of academic abilities have yielded much stronger relationships with LS and school satisfaction (Huebner and Alderman 1993; Suldo et al. 2008), suggesting stronger linkages between children’s positive well-being and their perceived (vs. actual) academic performance.

1.3.4 Living Environment Variables

The quality of children’s living environments (e.g., neighborhood, community) significantly relates to their LS, especially for those from economically disadvantaged families. A longitudinal study of rural adolescents in the U.S. found that family socioeconomic status, community size, and perceived discrepancy between desired residence and actual residence were predictors of LS among economically disadvantaged youth (Wilson et al. 1997). One study with Australian youth and their families revealed that children from neighborhoods with low safety and health levels (e.g., high crime/violence levels, on industrial or commercial streets, in poorly maintained houses, and/or in rented accommodations) reported lower overall LS compared to those who lived in healthy residential areas (Homel and Burns 1989). Moreover, high levels of family mobility seem to be negatively related to adolescents’ LS (Brown and Orthner 1990). However, one contrary result has been found in a cross-cultural study in the U.S. in which the number of moves was positively associated with adolescents’ self-reported quality of life (Bradley and Corwyn 2004), which suggests that although family residential instability is usually viewed as stressful for adolescents (Ackerman et al. 1999), not all relocations are perceived as threats to adolescents’ LS.

The social aspect of the living environment (e.g., culture) also matters. For instance, one study found that U.S. adolescents who lived in neighborhoods with a relatively homogenous ethnic composition reported higher LS than adolescents who resided in more ethnically diverse communities (Sam 1998). Further, the availability of opportunities for bonding with non-familial adults in the neighborhood was found to be a major contributor to children’s LS (Paxton et al. 2006).

1.4 Self-Related Variables

Children’s LS has also been related to multiple, alterable, cognitive “self” constructs. These constructs include global self-esteem and locus of control. A positive relationship between LS and self-esteem has been found in many studies of adolescents (e.g., Casas et al. 2007; Gilman and Huebner 2006). In some studies, high self-esteem was one of the strongest predictors of LS in adolescents (e.g., Neto 2001; Zhang and Leung 2002), which is similar to findings from some adult studies (e.g., Chen et al. 2006). Huebner (1991b) found that children who possess an internal locus of control and high self-esteem tend to be more satisfied with their lives than children who perceive the events of their lives to be controlled by external forces and who have lower self-worth. Children’s self-efficacy beliefs also appear to be strongly related to their LS. For example, a longitudinal study found that academic and social self-efficacy beliefs significantly contributed to predict LS over the course of 5 years (Vecchio et al. 2007). Another longitudinal study conducted in Germany before and after the fall of the Berlin wall showed that students who had higher pre-unification self-efficacy beliefs had higher LS and future optimism after German unification, whereas those with lower pre-unification levels of self-efficacy had lower levels of LS and less future optimism after German unification (Pinquart et al. 2004).

Children’s LS has also been related to individual differences in optimism and hope. For example, a strong positive relationship between LS and optimism has been revealed in studies in the U.S. (Extremera et al. 2007), Singapore (Wong and Lim 2009), and Hong Kong (Ho et al. 2010). Significant positive relationships between children’s levels of hopeful thinking and their global LS have been revealed in cross-sectional and longitudinal studies, with one study suggesting that hope can serve as a buffer against the effects of stressful life events on adolescents (Valle et al. 2004).

Numerous studies have demonstrated significant negative associations between LS and antisocial behaviors, such as violent behaviors (e.g., physical fighting, carrying a weapon), risk behaviors (e.g., smoking, alcohol, drug use), and internalizing behaviors (e.g., depression, anxiety) (see C. L. Proctor et al. 2009a, b for a review). The findings of such studies are consistent with the findings of studies of coping behavior that suggest that children with lower LS demonstrate fewer adaptive coping behaviors in response to stress (e.g., Suldo et al. 2008). In a similar vein, Park and Peterson (2006) have shown that character strengths (e.g., love, gratitude, zest) predict higher LS. Taken together, these lines of research suggest that poor mental health and poor social behavior can be significant risk factors for reduced LS in children and adolescents.

For children and adolescents, research shows significant relationships between LS and frequency of participation in structured extracurricular activities (e.g., Gilman 2001). Further, positive links have been found between youth LS and meaningful instrumental activity (Maton 1990), physical exercise (e.g., Valois et al. 2004), and religious participation (e.g., Kelley and Miller 2007).

In summary, numerous environmental and individual difference variables that are considered malleable have been identified as possible determinants of children’s LS. Given their more malleable nature, interventionists would be advised to consider such variables when planning assessments and interventions. Nevertheless, caution should be should be exercised in excluding consideration of antecedents and correlates of LS that are not readily malleable in assessment and intervention design. Although a paucity of research has addressed moderators (e.g., demographic characteristics, personality traits) of children’s LS reports, a few studies have been conducted suggesting the importance of comprehensive models (e.g., person × environment) of the development of differences in LS as well as the effects of intervention strategies. For example, gender has been found to moderate the relationship between parent attachment and early adolescents’ global LS reports (Ma and Huebner 2008). For another example, cultural differences (e.g., individualistic vs. collectivistic) are suggested moderators of the relationship between school experiences and global LS in adolescents (Park and Huebner 2005). As an example of personality as a moderator of intervention effects, Ng and Diener (2009) demonstrated that the personality trait of neuroticism moderated college students’ ability to benefit from positive thinking interventions (e.g., cognitive reappraisal). That is, students who were low in neuroticism and who were taught empirically validated cognitive intervention strategies were able to reduce negative emotions significantly more than students who were high in neuroticism.

1.5 Applications and Interventions

As demonstrated in the previous section of this chapter, LS and other factors of well-being are important constructs to address in children and adolescents. Given the significance of LS in both low and high risk groups, interventions to address LS and other well-being variables are important at all points of the prevention-­intervention continuum. Although psychological and physical health has historically been approached using a reactive, intervention focus (treating the ill), there has been a gradual and significant increase in attention to prevention. Many prevention frameworks have been developed and discussed in the literature and most reflect an overall notion of a multilevel system that increases in intensity based on risk and need. For example, the levels of a prevention framework typically reflect points of prevention beginning with primary prevention, secondary prevention, and tertiary prevention (also known as treatment). Primary prevention typically reflects programming efforts aimed toward an entire population of individuals (e.g., school-­wide programming), whereas secondary prevention targets a select group of individuals with a specific set of risk factors (e.g., preschool for low-income families). Tertiary prevention targets a smaller group of individuals at imminent risk and/or current presentation of targeted difficulties. Response to Intervention is an initiative that has been widely adopted throughout school systems in the U.S. aimed toward early intervention and prevention. Response to Intervention is most often identified as a multi-tiered system with interventions that increase in intensity across the tiers, which parallels the aforementioned prevention framework (Fuchs and Fuchs 2009).

Possible secondary and tertiary interventions have been discussed extensively elsewhere (e.g., Suldo et al. 2011). For examples of secondary prevention, Huebner et al. (2007) and Huebner and Hills (in press) provide in-depth illustrative case studies of how multidimensional assessments of children’s LS yield profiles of individual and environmental deficits and assets that can be used to inform the development and evaluation of individualized intervention plans to promote improved well-being in youth. However, when low LS accompanies an existing psychiatric condition (e.g., mood disorders), more comprehensive assessments and intervention plans are obviously needed. Sin et al. (2011) address some of the complexities associated with applying strategies that are effective for individuals or groups at the primary and secondary intervention levels relative to applying the same strategies at the tertiary level (e.g., individuals with clinical depression). Thus, the following section of this chapter will focus on primary prevention efforts, encompassing family, peers, school, and community environments. Furthermore, we will focus on primary prevention programs that can be delivered within the school context, which reflects a context in which children spend a considerable portion of their time, and one which provides access to almost all children and adolescents in developed countries. The examples are merely illustrative. The examples are not exhaustive, nor do they represent the single “best” program. The components and effects of the programs may not be limited to a single environmental context; they may have cross-context effects. Individual program developers will have to choose programs to best fit their own circumstances. Again, a one-size-fits-­all approach is not recommended.

A number of universal system-wide efforts have been developed to promote children’s well-being and success in school and life. For the purposes of our discussion, they include interventions that address the following contexts: family, peer, school, community, and self. Extensive research on these programs has been conducted at school-wide levels across the U.S. and elsewhere.

1.5.1 Family-Related Variables

As noted in the previous section, strong family support and attachment processes are critically related to youth LS and positive youth outcomes. Given the strong link between family support for learning and family processes with positive youth outcomes, much effort has been aimed toward building relationships between families and schools. A multitude of programs exist to help facilitate the school-family connection. Families and Schools Together, or FAST, is one such effective universal prevention program that is suitable for a number of diverse populations. FAST is a multisession group for families of elementary school children to increase parenting skills and well-being. It includes a blend of developmentally appropriate intervention techniques to improve family functioning and reduce risk factors, such as school failure, violence, delinquency, substance abuse, and family stress (Substance Abuse and Mental Health Services Administration 2008). The FAST program works toward important goals outlined in the family-school partnership literature (e.g., Christenson and Sheridan 2001) that include a focus on: (a) shared school-­family goals of improving student academic, social, behavioral, and emotional skills; (b) both education and positive socialization outcomes; (c) collaborative interactions between family members and schools; and (d) preventative solutions to promote student learning and overall development. Different FAST curricula have been developed to meet the needs of specific target populations. FAST has been replicated in 38 states in both urban and rural settings and in over 600 diverse school communities (Kratochwill et al. 2004) with evaluation of individual program sites yielding mixed results. However, an aggregated analysis (Crozier et al. 2010) and qualitative review (Terrion 2006) indicate that participation in FAST builds social capital by enhancing bonding among family members, enhancing bonding between the family and school, bridging FAST parents and social agencies, and increasing family empowerment and cohesion.

1.5.2 Peer-Related Variables

Children with adequate social skills are more likely to elicit positive attention from others and less likely to be victimized by peers, which also has positive effects on well-being. Without adequate social skills, children are more likely to have lower levels of well-being, including LS, and struggle with adjustment and mental health problems (Bukowski and Adams 2005). Many social skills interventions have been developed and tested at all levels of prevention and intervention. A number of quantitative reviews of Tier III/Targeted Intervention social skills studies that exclusively involve populations of children and adolescents with behavioral and emotional disorders suggest that this type is only minimally effective at this level (Bellini et al. 2007; Quinn et al. 1999) with many studies documenting iatrogenic effects. However, studies that have evaluated social skills programming at a broader, more universal level have yielded more positive results. January et al. (2011) reviewed 28 peer-reviewed journal articles published between 1981 and 2007 to assess the effectiveness of universal prevention programming (i.e., classroom-wide interventions) for the improvement of social skills. Their findings indicated small but positive effects on improving social skills among students, with early intervention most effective across studies. Results of this meta-analysis indicated that active (vs. passive) models of learning are most effective, with better outcomes when participants are engaged in the intervention and play an active and productive role in the learning process (January et al. 2011).

1.5.3 School-Related Variables

Positive Behavioral Interventions and Supports (PBIS; Sugai and Horner 2006; Gottfredson et al. 1993) is a universal, school-wide prevention strategy that is currently implemented in over 7,500 schools across the U.S. and several other countries around the world (Sprague and Walker2010). The purpose of PBIS is to prevent and reduce disruptive behavior problems through the application of behavioral, social learning, and organizational behavioral principles. PBIS aims to alter school environments by creating improved systems and procedures that promote positive change in student behavior by targeting staff and student behaviors. This universal prevention model aims to systematically and consistently manage student behavior problems by creating a school-wide program that clearly articulates positive behavioral expectations, provides incentives to students meeting expectations, and encourages data-based decision-making.

Although specific PBIS models vary in terms of their theoretical orientation and specific focal activities, they share a common emphasis on altering the school context in order to influence children’s behavior and academic performance. Most of the whole-school strategies aim to provide staff and student behavior with positively oriented, clearly articulated rules and consequences and well-established processes and procedures for problem solving (Sugai and Horner 2006). Bradshaw et al. (2009) conducted a 3-year randomized controlled effectiveness trial of PBIS in 37 elementary schools and found a significant, positive impact on school climate, which is a strong correlate with youth well-being and academic outcomes. Results from non-randomized (Taylor-Greene et al. 1997; Horner et al. 2005) and randomized (Bradshaw et al. 2011; Horner et al. 2009) studies indicate that implementation of school-wide PBIS is associated with reductions in office discipline referrals (Bradshaw et al. 2009; Taylor-Greene et al. 1997), school suspensions (Horner et al. 2005), and improvements in student academic performance (Horner et al. 2009; Sugai and Horner 2006).

1.5.4 Community-Related Variables

Bullying and other forms of peer victimization (e.g., sexual harassment) have become focal points of intervention in school and community settings. The evolution of the Internet and its online social community has expanded the risk for many youth. Interventions targeting the reduction of bullying behavior at an individual level have been found to be only somewhat effective; however, comprehensive bullying prevention programs implemented and evaluated in many countries have been shown to be quite effective at positively influencing knowledge, attitudes, and self-­perceptions related to bullying. However, results are more variable in evaluating the effects of these programs on actual bullying behavior. A meta-analysis conducted by Merrell et al. (2008) suggests that these programs are only minimally effective in reducing bullying behavior; however, other studies have reported as much as a 50 % reduction (Olweus 1993) in bullying incidents. Olweus and Limber’s (1999) bullying prevention program is one example of an effective, comprehensive program. Community-wide programs found to be effective, promote awareness, education, and adult involvement in order to create a community and school climate that discourages bullying. Further, effective bullying prevention also requires an ongoing commitment from adults in the community and school to reduce or eliminate bullying. Effective programs target all individuals involved: bully (perpetrator of bullying behavior), victim (recipient of bullying behavior), and bystanders (children and adults witnessing the bullying behavior), and commit to ongoing implementation and evaluation of the programming. Addressing the online community and the peer victimization risks presented in that context is also a critical component, especially for adolescents. Comprehensive programming that includes these components has been found to also positively impact prosocial behavior and increase students’ satisfaction with their community and school (Olweus 1993).

1.5.5 Self-Related Variables

Social-Emotional Learning (SEL) programs aim to help youth acquire core competencies to recognize and regulate emotions, set and achieve positive goals, recognize and appreciate the perspectives of others, establish and maintain positive relationships, make responsible decisions, and handle interpersonal situations constructively. The immediate goals of SEL programs are to foster the development of five interrelated sets of cognitive, affective, and behavioral competencies: self-­awareness, self-management, social awareness, relationship skills, and responsible decision making (Collaborative for Academic, Social, and Emotional Learning 2005). SEL programming incorporates systematic instruction in processing, integrating, and selectively applying social and emotional skills in developmentally, contextually, and culturally appropriate ways (Crick and Dodge 1994). These skills are taught, modeled, practiced, and applied to diverse situations so that students use them as part of their daily repertoire of behaviors (Collaborative for Academic, Social, and Emotional Learning 2005). A meta-analysis of school-based, universal SEL programs inside and outside the U.S. (Durlak et al. 2011) indicate that these programs yield significant positive effects on targeted social-emotional competencies and attitudes about self, others, and school, which are significantly related to overall well-­being. This universal programming also enhanced students’ behavioral adjustment in the form of increased prosocial behaviors, reduced conduct and internalizing problems, improved academic performance on achievement tests, and grades. SEL programming has also demonstrated similar results at other levels of prevention (e.g., Tier II; Catalano et al. 2004; Hahn et al. 2007; Wilson and Lipsey 2007).

1.6 Conclusion

Numerous scholars have argued for large-scale monitoring of children’s well-being, including their LS reports (Diener and Seligman 2004; Huebner et al. 2009). Meaningful data regarding children’s well-being should be a prerequisite for understanding and developing healthy environments for children. Although there are numerous contexts in which such large-scale, child well-being assessments could be implemented, the school context provides one manageable example. For example, school professionals in the U.S. currently provide services at all three previously mentioned levels of service delivery, all of which can be informed by LS and other well-being data. Services at Tier I, or primary prevention services, involve universal assessments and instructional/intervention activities for all children in a given context (e.g., grade level instruction in a school). An example of the use of LS data at his level can be found in studies of the Dual-Factor Model of Mental Health (Greenspoon and Saklofske 2001), in which researchers have identified the incremental utility of incorporating positive subjective indicators along with traditional negative ones (e.g., behavior problems, including internalizing and externalizing behavior) to identify meaningful groups of children that would not be identified using negative indicators alone. For example, Antaramian et al. (2010) identified a group of students who reported non-significant levels of behavior problems and low SWB (including low LS reports) that showed significantly lower school engagement levels and school grades compared to students who reported non-significant levels of behavior problems and high SWB. In a similar study, Suldo and Shaffer (2008) found a similar group of students who displayed lower academic, interpersonal, and physical functioning in school compared to students who reported non-­significant levels of problem behaviors and high SWB. Services at Tier II, or secondary prevention services, involve more intensive, sometimes group level services, delivered in the context of regular education programs for students experiencing difficulties in the regular classroom. As noted above, Huebner et al. (2007) and Huebner and Hills (in press) provide case examples of the employment of LS measures to identify student assets and environmental resources, which were subsequently used in the design of an individualized, pre-referral intervention for a student. Services at Tier III, or tertiary intervention services, involve the identification and development of special education programs and program monitoring plans for students with disabilities. An example of the usefulness of SWB data at this level is provided by Brantley et al. (2002). In their study, secondary school students’ reports of SWB were measured by multidimensional LS reports. Not only were differences revealed between students with and without mental disabilities, but complex differences were also revealed across domains within special education student groups as a function of amount of time they spent in separate special education classrooms. Such use of SWB data, in conjunction with objective data, is consistent with the arguments of researchers (e.g., Frisch 2006; Gilman and Huebner 2003) that LS data should be routinely collected to monitor the effects of academic, psychosocial, and medical interventions applied to individuals and groups of children. Their argument rests on the notion that assessments of the impact of interventions should include students’ perceptions of their quality of life as well as targeted behaviors and academic outcomes. In this manner, an intervention that both improves functioning (e.g., reduces symptoms of an anxiety disorder or chronic health condition) and also improves subjective quality of life would be distinguishable and preferable to an intervention that improves functioning but is perceived to reduce SWB or quality of life of a student or students.

We advocate the collection of objective and subjective quality of life data within the context of a multi-trait, multi-method, multi-occasion assessment approach to evaluate the success of policies and procedures to promote children’s overall quality of life. The multi-method aspect of the assessment plan would entail collection of objective well-being data as described above using objective sources (e.g., parent and teacher judgments) and indices (e.g., parenting behavior, peer relationships, teacher behavior) as well as SWB data. The multi-trait aspect would involve multidimensional indices, such as global and domain-based LS reports as well as perhaps other data (e.g., behavior problems) of interest in particular contexts. The multi-­occasion component would involve the collection of systematic, longitudinal data across meaningful time periods to track changes in well-being. The collection of subjective data is critical to assess the goodness of fit between child-focused interventions and children’s related well-being. Although efforts to improve the lives of children are likely to be based on “good” motivations and “good” expected outcomes, the results of such efforts should be carefully monitored to determine their actual (vs. intended) effects on the subjective and objective lives of children. Children’s perceptions of the nature and impact of life conditions and interventions can differ from those of key adults (e.g., parents, teachers), underscoring the importance of taking children’s perspectives into account when considering issues of importance to them (Ben-Arieh et al. 2009). The use of evidence-based, developmentally appropriate objective and subjective measures, assessed over multiple periods of time, would facilitate meaningful assessments of the status of children’s well-being from their own perspective.