Life Satisfaction and Student Engagement in Adolescents

Transcription

1 J Youth Adolescence (2011) 40: DOI /s EMPIRICAL RESEARCH Life Satisfaction and Student Engagement in Adolescents Ashley D. Lewis E. Scott Huebner Patrick S. Malone Robert F. Valois Received: 15 October 2009 / Accepted: 15 February 2010 / Published online: 4 March 2010 Ó Springer Science+Business Media, LLC 2010 Abstract Situated within a positive psychology perspective, this study explored linkages between adolescent students positive subjective well-being and their levels of engagement in schooling. Specifically, using structural equation modeling techniques, we evaluated the nature and directionality of longitudinal relationships between life satisfaction and student engagement variables. It was hypothesized that adolescents life satisfaction and student engagement variables would show bidirectional relationships. To test this hypothesis, 779 students (53% female, 62% Caucasian) in a Southeastern US middle school completed a measure of global life satisfaction and measures of cognitive, emotional, and behavioral engagement at two time points, 5 months apart. A statistically significant bidirectional relationship between life satisfaction and cognitive engagement was found; however, non-significant relationships were found between life satisfaction and emotional and behavioral student engagement. The findings provide important evidence of the role of early adolescents life satisfaction in their engagement in schooling during the important transition grades between elementary and high school. The findings also help extend the positive psychology perspective to the relatively neglected context of education. Keywords Life satisfaction Student engagement Adolescence A. D. Lewis E. S. Huebner (&) P. S. Malone Department of Psychology, University of South Carolina, Columbia, SC 29208, USA huebner@sc.edu R. F. Valois Arnold School of Public Health, University of South Carolina, Columbia, SC, USA Introduction In a provocative book entitled Happiness and Education, Noddings (2003) argues that children s happiness should be a major aim of their schooling. Although her arguments appear reasonable, there is a paucity of relevant empirical research. Research on the happiness of children and adolescents has lagged significantly behind that of adults, particularly with respect to the context of schools (Huebner et al. 2006). The importance of students psychological illbeing relative to their educational performance and behavior has been well-documented (Roeser and Eccles 2000); however, the importance of psychological wellbeing (e.g., happiness) has received little scholarly attention. Although related to academic performance outcomes (e.g., GPA), the facilitation of high levels of student engagement in their schooling has been suggested to be an important outcome in and of itself (Furlong et al. 2003). This study thus sought to add to the existing literature by (1) examining whether one aspect of happiness, life satisfaction, was related to an important school outcome, student engagement, and (2) if so, determining the directionality of the relationship. In doing so, we hoped to shed some light on Noddings contention that happiness is a relevant, but neglected aim of students education. This research is consistent with the positive psychology paradigm, which seeks to incorporate a focus of psychology that goes beyond the remediation of personal deficits to include the development of positive qualities in people (Seligman and Csikszentmihalyi 2000). One area of positive psychology involves the study of individual differences in levels of happiness across individuals. Happiness has been operationally defined as subjective well-being, which specifically refers to an individual s own assessment of his or her own life not the judgments of experts and includes

2 250 J Youth Adolescence (2011) 40: satisfaction (both global and satisfaction with specific domains), pleasant affect, and low negative affect (Diener et al. 2004, p. 189). High pleasant, or positive, affect refers to a high frequency of positive emotions (joy, excitement) while low negative affect refers to a low frequency of negative emotions (anger, anxiety). Global life satisfaction can be defined as a cognitive appraisal of individuals overall quality of life based on their own standards (Pavot and Diener 1993). Measures of life satisfaction allow individuals to report a range of life satisfaction levels below and above a neutral point, providing differentiation among higher levels of life satisfaction. From this perspective, subjective well-being is an umbrella term that can incorporate multiple related, but distinguishable, constructs. Nevertheless, some researchers note that life satisfaction may serve as a proxy for positive emotions, since the constructs demonstrate considerable overlap (Lyubomirsky et al. 2005). Research with adults has demonstrated that life satisfaction is a predictor of positive outcomes in many domains of life. Adults with high life satisfaction show greater success in interpersonal, occupational, and physical functioning (see Lyubomirsky et al. 2005, for a review). Persons with high life satisfaction live longer, fight off illness and feel better, and make more money than those with lower levels of life satisfaction. They also report more positive social relationships, greater job satisfaction and productivity, and lower levels of psychopathology. Although sparse, some studies have investigated linkages between life satisfaction and educational attainment. Frisch et al. (2005) followed a large sample of college students who sought services at a university counseling center over a 4-year period. They found that GPA and life satisfaction, independently and together, predicted which college students would drop out of school 1 3 years in advance. Furthermore, as part of the Chicago Longitudinal Study investigating the outcomes of low income, minority children, Ou (2008) found that students at ages who received their high school diploma reported higher levels of life satisfaction than those who obtained their GED or dropped out of school, after controlling for covariates, such as income. Similarly, Zimmerman et al. s (1995) study of inner-city African-American adolescent males demonstrated that students who completed high school reported higher levels of life satisfaction than those who left school before graduation. Nevertheless, given the lack of research attention, particularly longitudinal work, it is difficult to draw conclusions about the directionality of the relationships between life satisfaction and the ultimate form of school disengagement, that is, drop out. Though less research has been conducted with children and youth, individual differences in global life satisfaction have been shown to be correlated with a number of factors in students lives, such as demographic variables (e.g., higher socioeconomic status), quality of parent and peer relationships, and mental and physical health (see Huebner 2004; Proctor et al. 2008). Longitudinal studies have suggested that lower levels of life satisfaction are antecedents of peer victimization (Martin et al. 2008) and parent withdrawal of support (Saha et al. in press). Adolescents life satisfaction has also been found to be a mediator between stressful life events and internalizing behaviors as well as a moderator between stressful life events and externalizing behaviors (McKnight et al. 2002; Suldo and Huebner 2004a). Thus, similar to studies of adults, life satisfaction appears to play an important role in adolescents overall adaptation. Much remains to be learned about the antecedents, correlates, and consequences of individual differences in youths positive life satisfaction and emotions in relation to school-related experiences. There have been only a few studies of associations between life satisfaction and positive emotions and school-related variables, such as academic achievement and behavior with children and adolescents (see Suldo et al. 2006, for a review). This lack of research regarding schooling and life satisfaction and positive emotions is important, because positive emotions have only recently begun to be viewed as important in their own right and not simply the opposite of negative emotions. For example, Fredrickson s (2001) Broaden and Build Theory of Positive Emotions specifies different functional roles for positive and negative emotions. Negative emotions, such as sadness or anxiety, are thought to narrow individual s cognitions, and result in specific action tendencies, such as fight or flight responses. Such emotions hinder learning and positive coping behavior in the classroom (Roeser and Eccles 2000). On the other hand, positive emotions are thought to broaden a person s viewpoints, increasing attention to learning and flexibility of responding. Research has yielded inconsistent results with respect to students academic achievement. Some variable-centered research has shown that students with high life satisfaction tend to have higher GPAs compared with students with lower life satisfaction (Gilman and Huebner 2006). Nevertheless, other studies have found a non-significant relationship between life satisfaction and academic performance (Bradley and Corwyn 2004; Huebner 1991). Using person-centered research methods, Suldo and Shaffer (2008) found that middle school students with high life satisfaction and low levels of psychopathology had significantly higher GPAs and standardized test scores than peers with low levels of psychopathology and low life satisfaction. More robust relationships between life satisfaction and behavior problems have been reported, with children reporting lower life satisfaction engaging in more problem

3 J Youth Adolescence (2011) 40: behaviors, including internalizing and externalizing problems (Gilman and Huebner 2006; Huebner and Alderman 1993; Suldo and Huebner 2006). Life satisfaction has also been related consistently to psychological variables, such as academic self-concept (Dew and Huebner 1994; Leung et al. 2004). For both middle school and high school age groups, students with higher life satisfaction also tend to have more favorable attitudes toward their teachers and toward school in general (Gilman and Huebner 2006; Gilman et al. 2000). To recapitulate, findings from adult and child studies suggest that there are meaningful associations between life satisfaction and some school-related variables. However, the majority of the research with adolescents has been correlational in nature, making it difficult to determine the directionality of the relationships. Furthermore, the precise mechanisms that account for the relationships remain to be elucidated. Student Engagement One area of inquiry that might help explain linkages between life satisfaction and educational outcomes, such as GPA and school dropout, involves the meta-construct of student engagement. Because of its presumed relationship to school dropout (Finn 1989), student engagement has become a topic of great interest to educators. Student engagement refers to a student s degree of active involvement in school through his or her thoughts, feelings, and actions. That is, student engagement is generally conceptualized as a multidimensional meta-construct, comprised of behavioral, cognitive, and emotional domains (Appleton et al. 2008; Fredericks et al. 2004). These domains represent the most widely agreed upon dimensions of engagement among current researchers (Christenson et al. 2008). Behavioral engagement can be conceptualized as the more objective measure of student engagement. Measures of school absences, academic performance, homework and assignment completion, participation in school activities, and earning credits toward graduation have been used as indicators of behavioral engagement (Finn 1989; Fredericks et al. 2004). Cognitive engagement includes self-regulation, understanding the importance of school, students investment in learning, and desire for challenge (Fredricks et al. 2005). Emotional engagement refers to the affective reactions of students toward school or their teachers. Some scholars have also conceptualized it as liking or disliking school, teachers, or school work (Epstein and McPartland 1976). Of the three components of student engagement described by Fredericks et al. (2004), behavioral and emotional aspects of engagement have received the most research attention, while little research has focused on cognitive engagement. Fredericks et al. (2004) underscore the importance of including all three domains in comprehensive evaluations of student engagement differences. Little is known about how student engagement changes over time or the developmental contexts that may influence it (Fredericks et al. 2004). Appleton et al. (2008) as well as Fredericks et al. (2004) have reviewed several facilitators of student engagement. Facilitators include school discipline practices, parental and peer attitudes toward school, and peer, classroom and school contexts (Christenson and Anderson 2002; Furlong et al. 2003). Aspects of a child s home, school, or peer relationships can impact students engagement in school and achievement in positive or negative ways (Anderson et al. 2004; Furrer and Skinner 2003; MacDonald and Marsh 2004). For example, peer victimization and rejection are related to disengagement from school, while positive support for learning from the home environment is related to increased student engagement (Christenson and Anderson 2002). Even less is known about how psychological individual difference factors may influence student engagement. In addition to the aforementioned social contextual factors, Reschly et al. (2008) found that individual differences, such as positive and negative affect impact the level of cognitive or psychological engagement students report in school. Students frequent experiences of positive emotions were correlated with a greater perceived cognitive engagement as measured by the relevance of schoolwork and future aspirations, and greater psychological engagement, defined as support for learning from family and peers and positive student teacher relationships. Reschly et al. also found that students who reported less engagement in school experienced more frequent negative emotions while in the school setting. This study was cross-sectional, however, so the directionality of the relationship between emotions and student engagement remains unknown. Purpose of Current Study The aforementioned research has shown the importance of adolescent life satisfaction with regard to various social, family, physical and mental health variables, as well as educational variables. For example, empirical research has shown that life satisfaction predicts future higher levels of student engagement in school among college students (Frisch et al. 2005). This finding fits with Fredrickson s (2001) Broaden and Build Theory of Positive Emotions. Life satisfaction, as a proxy for frequent positive emotions, may also broaden a person s viewpoints, increase flexibility of responding, and build available resources (see Lyubomirsky et al. 2005). Based on this theory, it was expected that high life satisfaction in adolescents would

4 252 J Youth Adolescence (2011) 40: lead to broadened thinking and behavior, such as increased cognitive, emotional, and behavioral student engagement with school. Thus, it was hypothesized that adolescents life satisfaction at Time 1 would predict increases in student engagement at Time 2. However, Shocet et al. (2006) have also shown that lower levels of school connectedness, similar to emotional engagement, predicts decreased negative affect (i.e., anxiety and depression), suggesting the possibility that student engagement may predict increases in adolescents life satisfaction. Considering both sets of findings, a model of bi-directional relationships between global life satisfaction and student engagement variables was evaluated. This model was tested using a sample of students during the middle school years, during the transition time between the elementary school years and the high schools years, the latter years representing the time when students become legally able to drop out of school. Figure 1 shows the overall hypothesized model. Prior to evaluating the model, demographic effects were considered. Relationships among engagement variables and demographic variables have been investigated, with some studies suggesting significant associations (Fredericks et al. 2004). For example, Reschly et al. (2008) found that levels of cognitive engagement showed a pattern of decline from grades 7 to 9. In the same study, however, gender was not significantly associated with cognitive or emotional engagement. When significant differences are observed in emotional engagement as a function of age or T1 LS T1 BE T1 CE T1 EE T2 LS T2 BE T2 CE T2 EE Fig. 1 Hypothesized relationships between life satisfaction and student engagement. Note. Solid lines indicate hypothesized relationships. Dotted lines indicate variables are allowed to covary. T1 Time 1, T2 Time 2, LS life satisfaction, BE behavioral engagement, CE cognitive engagement, EE emotional engagement. Controlled covariates are not diagrammed in the figure gender, the effect sizes are typically small (see Huebner et al. 2009). Furthermore, student achievement and completion outcomes have been consistently related to both socioeconomic status (SES) and ethnicity, although some studies suggest that when SES is controlled, ethnic groups differences in school completion disappear (Rumsberger 1995). Given the possibility of demographic effects (age/ grade, gender, SES, ethnicity), analyses were conducted to determine the associations of the demographic variables with the criterion variables. Method Participants In the Fall of 2008 and in the Spring of 2009, students from a large middle school in the Southeastern United States completed questionnaires administered by school staff investigating their students engagement, life satisfaction, and related variables. During the Fall 2008 administration, 864 students from 7th (50.7%) and 8th (49.3%) grades completed surveys while 779 students completed surveys in Spring 2009, resulting in a return rate of 90.2%. At Time 1, the mean participants age was (SD =.67) and at Time 2 the average age was (SD =.66). Of the participants, 46.4% were male and 53.6% were female at Time 1 and 46.9% male and 53.1% female at Time 2. Based on Time 1, the ethnic/racial distribution was 61.9% Caucasian, 31.4% African American, and 6.7% Asian American, Hispanic or other and at Time 2, the distribution was 62.0% Caucasian, 31.1% African American, and 6.9% Asian American, Hispanic or other. Free and reduced lunch was used as a measure of socio-economic status. At Time 1, 22.6% of students received free or reduced lunch, while at Time % of the students did. Concerning family status, 63.2% of students lived with their biological mother and father, 35.5% of students lived with other combinations of adults, and 1.3% of students did not report their family status at Time 1. At Time 2, 64.8% of students lived with their biological mother and father, 34% of students lived with other combinations of adults, and 1.2% of students did not report their family status. Chi-square tests on demographic variables were conducted to assess bias related to attrition between students who completed surveys at Time 1 only and students who completed surveys at Time 1 and Time 2. There was no association between administration time and race (v 2 (1) =.022, p [.01) or time and gender (v 2 (1) =.429, p [.01), suggesting comparability across groups for these demographic variables. There was a significant difference in the demographics of Time 1 and Time 2 groups for family status (v 2 (1) = 8.52, p \.01.) and SES

5 J Youth Adolescence (2011) 40: (v 2 (1) = 14.25, p \.01). The Time 2 sample had more participants living with their mother and father and had fewer students from a lower SES than expected. Differences in life satisfaction, cognitive engagement, behavioral engagement, and emotional engagement were also assessed between participants at Time 1 only and participants at Time 1 and Time 2. There were no differences in cognitive engagement (F(1, 863) = 4.41, p [.01) and emotional engagement (F (1, 863) = 3.03, p [.01) for Time 1 and Time 2 samples. Students who only participated at Time 1 were older (F (1, 863) = 17.77, p \.01, g 2 =.02), had lower first 9-weeks GPAs (F (1, 863) = 30.72, p \.01, g 2 =.03), life satisfaction (F (1, 863) = 9.51, p \.01, g 2 =.01), and behavioral engagement scores (F (1, 863) = 9.77, p \.01, g 2 =.01). However, the effect of attrition on these levels is considered small (Cohen 1988). Measures Life Satisfaction Students life satisfaction was measured using the Students Life Satisfaction Scale (SLSS: Huebner 1991). The SLSS is a 7-item self-report measure that assesses students global life satisfaction, which involves evaluating life as a whole rather than domains (e.g. school, family). Students responded to items using a Likert scale format indicating how much they agreed or disagreed (1 = Strongly Disagree to 6 = Strongly Agree), with higher scores on the scales expressing higher levels of global life satisfaction. The SLSS has been used in children as young as age 8 (Huebner 1991) and with adolescents (Suldo and Shaffer 2008). It demonstrates adequate 2-week test retest reliability (r =.74) and internal consistency (a =.82). In this study, the coefficient alpha was.86 for the total sample at Time 2. Emotional Engagement Students level of emotional engagement was calculated with the School Satisfaction subscale of the Multidimensional Students Life Satisfaction Scale (MSLSS: Huebner 1994). The School Satisfaction subscale assesses students overall satisfaction with their school experiences. It is part of the MSLSS, a 40-item measure asking about satisfaction with various domains of life (e.g. school, family, friends, self). Adequate internal consistency has been reported with the School Satisfaction subscale (a =.84; Gilman et al. 2000) and adequate test retest reliability was demonstrated with a 4-week test retest coefficient of.70 (Huebner et al. 1998). Concurrent validity was demonstrated with a positive relationship (r =.68) with the Quality of School Life Scale (Epstein and McPartland 1976) in a sample of preadolescent students (Huebner 1994). The current study used a modified version of the School Satisfaction subscale that removed negatively worded items, resulting in a 5 item scale. Previous research has shown that negatively worded items may decrease reliability in self-report measures of children (Marsh 1986). Pilot testing with a sample of 179 middle school students revealed adequate internal consistency reliability for the shortened scale (a =.91). For the current study at Time 2, the coefficient alpha was.89. Cognitive Engagement Students reported their level of cognitive engagement by completing the Future Aspirations and Goals subscale of the Student Engagement Instrument (SEI: Appleton et al. 2006). The SEI is a 35 item self-report measure that assesses various facets of student engagement. The Future Aspirations and Goals subscale consists of 5-items asking about schools importance for students future and their desire to continue their education after high school (i.e. School is important for achieving my future goals ). Items are anchored on a four point ordinal scale (1 = Strongly Disagree to 4 = Strongly Agree), with higher scores indicating higher levels of cognitive engagement. Appleton et al. (2006) found adequate internal consistency (.78) for the Future Aspirations and Goals subscale. Convergent validity is evident in a positive relationship between Future Aspirations and Goals and grade point average and standardized test performance and a negative relationship with school suspensions (Appleton et al. 2006). The coefficient alpha was.86 for the total sample at Time 2. Behavioral Engagement Behavioral engagement was measured with the behavioral subscale of the School Engagement Scale was used (SES- B; Fredricks et al. 2005). This scale consists of items asking students about following the rules at school and participating in class. Previously, the SES-B has been used with children in third through fifth grade (Fredricks et al. 2005). Using this sample of younger children, adequate internal consistency was found for behavioral engagement (a =.72 to a =.77). Fredricks et al. also found scores on the behavioral engagement subscale positively correlated with teacher reports of work and task orientation. In describing the scale, Fredricks et al. noted that modification of these measures may be necessary for older children. (p. 317). The current study only used four of the five original questions, because pilot testing revealed students were confused about the reverse-scored item When I am in class I just act as if I am working. The removal of this

6 254 J Youth Adolescence (2011) 40: item increased the internal consistency of this scale from.61 to.70. For this study, the Time 2 coefficient alpha was.78. Procedures The primary researcher analyzed data collected by a middle school in the Southeastern United States about well-being and student engagement in school. Time 1 passive consent forms were mailed by the school to all parents in the Fall asking permission for their child to participate in two waves of data collection (N = 1,044). From this 12 parents denied consent, 1 teacher failed to participate (N = 25) and 79 students were absent on the day of the survey administration removing these students from the participant pool. Students in special education were also removed from data analyses because scale measures have not been validated on that population (N = 39). Also, four students were removed from the Time 1 sample because they did not have identifying information to track them to Time 2. The final sample included 864 students at Time 1, 83% of the total school population and 779 students at Time 2, 75% of the total school population. At Time 1 and Time 2, teachers administered surveys during homeroom to groups of students. Teachers read scripted directions instructing students that their responses would remain confidential, informing them of the right to withdraw from the study at any time, and asking them to complete all measures in the packet. The measure of global life satisfaction was presented first, the other measures were subsequently presented in counterbalanced order. This procedure was used because asking domain specific questions first may influence a person s perception of global life satisfaction (Diener and Fujita 1995). Before completing study measures, students answered a brief series of demographic questions regarding age and if they live with their biological mother and father. Data on race, gender, SES (free or reduced rate lunch) and special education status were derived from school records. Data Analysis Descriptive statistics were calculated. Spearman and Pearson correlations were performed next to determine the zero-order relationships between the predictor and criterion variables with demographic variables. The amount of missing data for life satisfaction and the three engagement variables was small, ranging from 2.2 to 6.8% across Time 1 and Time 2. Thus, preliminary analyses were conducted with mean substitution procedures for missing values (Buhi et al. 2008; Dodeen 2003). For further confirmatory factor and structural equation modeling analyses by the Mplus 5.21 program (Muthen and Muthen ), missing data were estimated using full information maximum likelihood estimation, which assumes data are missing at random (Arbuckle 1996). To test the hypotheses about the relationships between life satisfaction and student engagement across the school year, a recursive structural equation model was used. Strict measurement invariance was imposed and the error terms for Time 1 and Time 2 life satisfaction, behavioral, cognitive, and emotional engagement were allowed to covary. Effects of all Time 1 measures of life satisfaction and behavioral, emotional, and cognitive engagement were modeled on Time 2 measures of life satisfaction and behavioral, emotional, and cognitive engagement. Effects of covariates were modeled as paths from each covariate to all study factors. The model was thus saturated at the structural level. Controlled covariates in the model included gender (female = 1), free or reduced lunch status (regular lunch = 0; free or reduced rate = 1), race (Caucasian = 0; minority status = 1), family status (intact = 0; non-intact = 1), age at Time 1, and GPA at Time 1. The initial model indicated that two emotional engagement items were more correlated than expected, possibly due to semantically similar wording (items 2 and items 3). In confirmatory factor analyses and final analyses the residuals of these two items of the emotional engagement scale were allowed to covary in order to improve model fit. Structural model fit was analyzed using multiple indicators, including chi-square, the non-normed fit index or Tucker Lewis index (TLI: Tucker and Lewis 1973; Bentler and Bonett 1980), comparative fit index (CFI: Bentler 1990), and the root mean-square error of approximation (RMSEA). Previous researchers have indicated that CFI and TLI measures above.95 indicate good fit, with values above.90 indicating adequate fit, and RMSEA value less than.06 indicating good fit (Hu and Bentler 1999; Bentler and Bonett 1980). Results Descriptive Statistics The means and standard deviations for Time 1 and Time 2 life satisfaction, emotional engagement, cognitive engagement, and behavioral engagement are presented in Table 1. For each variable, higher scores indicate a higher level of that psychological construct. Participants mildly to moderately agreed to being satisfied with their lives (T1 M = 4.46, SD = 1.00; T2 M = 4.58, SD = 1.02), which is similar to levels reported by previous research with middle school students (Suldo and Huebner 2004a, b). The mean levels of behavioral (T1 M = 4.04, SD =.65; T2 M = 3.98, SD =.73), emotional (T1 M = 4.38, SD = 1.20; T2 M = 4.29,

7 J Youth Adolescence (2011) 40: Table 1 Descriptive statistics Variable Time 1 Time 2 M SD M SD Life satisfaction * 1.02 Behavioral engagement *.73 Cognitive engagement Emotional engagement Time 1 N = 864, Time 2 N = 779 * Significant mean differences between Time 1 and Time 2 with p \.01 SD = 1.25), and cognitive engagement (T1 M = 3.73, SD =.41; T2 M = 3.71, SD =.47) decreased across the school year. Overall, students reported that they are often behaviorally engaged with school, which is similar to levels reported 3rd 5th grade students in a study by Fredricks et al. (2005). Participants agree to strongly agree that school is important for their future aspirations and also mildly agree to moderately agree to statements about liking and belonging to school. These levels of cognitive engagement are similar to levels reported by adolescents in previous research (Reschly et al. 2008) but levels of emotional engagement in this sample are somewhat higher than reported in previous studies (Huebner and Gilman 2006; Gilman et al. 2000). In order to assess univariate normality, skewness and kurtosis were examined for each of the predictor and criterion variables. The only variable that exhibited questionable skewness or kurtosis was the cognitive engagement variable of Future Aspirations and Goals (T1 skewness =-2.31, T1 kurtosis = 7.19 and T2 skewness =-2.21 and T2 kurtosis = 6.05). Correlational analyses of this variable were conducted using both inverse transformed data and nontransformed data. Because no differences were observed, analyses of the non-transformed data were reported (see below). Spearman rho correlations were conducted at Time 1 and Time 2 between dichotomized demographic variables (e.g. gender, race, SES, grade level) and the predictor and criterion variables. Pearson correlations between continuous demographic variables (age and GPA) and the predictor and criterion variables are found in Table 2. It was found that Caucasian students had higher levels of life satisfaction at Time 1 than minority students, but this was not a significant relationship at Time 2. At both Time 1 and Time 2, minority students had higher levels of emotional engagement with school than Caucasian students. At Time 1, females had lower life satisfaction and higher behavioral engagement and emotional engagement compared to males. At Time 2, females also had lower life satisfaction than males and higher levels of all three types of engagement. Concerning SES, those receiving free or reduced lunch had lower Time 1 life satisfaction and Time 1 behavioral engagement and lower Time 2 behavioral and cognitive engagement. Age was significantly correlated with Time 1 behavioral engagement in that older students reported lower levels of behavioral engagement (r =-.10). Students who lived with adults other than their parents reported lower Time 1 and Time 2 life satisfaction, behavioral and cognitive engagement. Time 1 GPA was significantly positively correlated with Time 1 and Time 2 life satisfaction, behavioral engagement, and cognitive engagement. Correlation Analyses Interfactor correlations from CFA among Time 1 and Time 2 life satisfaction and cognitive engagement, emotional engagement, and behavioral engagement are found in Table 3. Significant modest to moderate correlations were found among life satisfaction and behavioral, emotional, Table 2 Correlations among Time 1 demographics and life satisfaction and student engagement Time 1 Time 2 LS BE CE EE LS BE CE EE Race -.14** ** ** Sex -.08*.16**.03.09* -.07*.15**.10**.08* SES -.12** -.11** ** -.12**.03 Age ** Family -.19** -.23** -.07* ** -.16** -.11**.01 GPA.19**.45**.21**.05.18**.41**.20**.05 Time 1 N = 864, Time 2 N = 779. Race is coded 1 = minority race/ethnicity and 0 = Caucasian. Sex is coded 0 = male and 1 = female. SES is coded 0 = regular lunch and 1 = free or reduced rate lunch. Family is coded as 0 = live with biological parents and 1 = other adults. GPA = 1st 9 weeks GPA LS life satisfaction, BE behavioral engagement, CE cognitive engagement, EE emotional engagement *p\.05; ** p \.01

8 256 J Youth Adolescence (2011) 40: Table 3 Factor correlations from confirmatory factor analysis EET1 CET1 BET1 LST1 EET2 CET2 BET2 LST2 EET1 CET1 0.36** BET1 0.41** 0.60** LST1 0.34** 0.38** 0.43** EET2 0.64** 0.31** 0.36** 0.30** CET2 0.28** 0.62** 0.44** 0.33** 0.44** BET2 0.33** 0.53** 0.85** 0.39** 0.49** 0.53** LST2 0.27** 0.38** 0.39** 0.63** 0.44** 0.43** 0.47** T1 Time 1, T2 Time 2, N = 779, LS life satisfaction, BE behavioral engagement, CE cognitive engagement, EE emotional engagement ** p \.01 and cognitive engagement at Time 1 and Time 2. Also, positive correlations among the three types of engagement were also modest to moderate at Time 1 and moderate at Time 2. Cognitive engagement, emotional engagement and life satisfaction were moderately stable from Time 1 to Time 2 (r =.62 to.64, respectively). Participants reports of behavioral engagement were quite stable from Time 1 to Time 2 (r =.85). Structural Equation Modeling Confirmatory factor analysis indicated adequate fit of the measurement model, without covariates, v 2 (804, N = 779) = 2,103.76, p = 0.00, CFI =.93, TLI =.92, RMSEA =.05. All items loaded above.60 to the factors, except for 2 items on the life satisfaction scale (LS 3 and LS7, see Fig. 2). Table 3 provides interfactor correlations from the CFA. The recursive structural equation model indicated adequate fit, v 2 (1,008, N = 779) = 2,422.69, p = 0.00, CFI =.92, TLI =.92, RMSEA =.04 (see Fig. 3 for standardized path coefficients). It should be noted that the full model, including covariates, is very similar in approximate fit indices to the measurement model; this indicates that using the covariates at the level of factors rather than indicators is adequate, as differential relations from the covariates are the only new possible source of misfit. See Table 4 for standardized coefficients among covariates, student engagement and life satisfaction from the structural model. The first hypothesis that changes in Time 1 life satisfaction will predict changes in Time 2 behavioral, cognitive, and emotional engagement was partially supported. When controlling for covariates and Time 1 levels of engagement, Time 1 life satisfaction reports significantly predicted Time 2 cognitive engagement (b =.09, p \.05) and approached significant prediction of Time 2 emotional engagement (b =.08, p =.06). Life satisfaction at Time 1 did not add any prediction to Time 2 behavioral engagement beyond that of the covariates and Time 1 engagement variables (b =.03, p [.10). The second hypothesis that Time 1 behavioral, cognitive, and emotional engagement will predict changes in Time 2 life satisfaction was also partially supported. In predicting Time 2 life satisfaction, when controlling for covariates and Time 1 life satisfaction, only cognitive engagement was found to be a significant predictor of life satisfaction (b =.12, p \.05) when all engagement variables are in the model. Neither behavioral engagement nor emotional engagement were significant predictors (b =.10, p [.10; b =.01, p [.10, respectively). Thus, bi-directionality between life satisfaction and student engagement was supported for cognitive engagement, but not for emotional or behavioral engagement. Discussion The purpose of this study was to determine the directionality of the relationship between life satisfaction and multidimensional student engagement in adolescents. The first hypothesis that Time 1 life satisfaction would predict changes in Time 2 student engagement was partially supported, in that life satisfaction predicted changes in cognitive engagement, and approached significance in predicting changes in Time 2 emotional engagement. Thus, middle school students who were satisfied with their lives at the beginning of the school year reported subsequent higher levels of believing school is important for their future, even after controlling for SES, GPA, family status, race, and gender. This finding is not inconsistent with the Broaden and Build Theory of Positive Emotions (Fredrickson 2001) in that life satisfaction, as a proxy for positive emotions, is linked to broadened thinking and openness to experiences, that is, in believing that school experiences will help meet future aspirations and goals. The relationship between

9 J Youth Adolescence (2011) 40: Fig. 2 CFA measurement model. Note. All loadings are significant at the p \.01 level LS 1 LS LS 1 LS 2 LS 3 LS 4 LS 5 LS 6 LS T1 Life Satisfaction T2 Life Satisfaction LS 3 LS 4 LS 5 LS 6 LS 7 BE 1 BE 2 BE 3 BE T1 Behavioral Engagement T2 Behavioral Engagement BE 1 BE 2 BE 3 BE 4 CE 1 CE2 CE 3 CE 4 CE T1 Cognitive Engagement T2 Cognitive Engagement CE 1 CE2 CE 3 CE 4 CE 5 EE 1 EE T1 Emotional Engagement T2 Emotional Engagement EE 1 EE2 EE 3 EE 4 EE EE 3 EE 4 EE 5 adolescents life satisfaction and subsequent emotional engagement suggests that students who are satisfied with their lives early in the school year also feel more connected and like school more as the school year progresses. However, Time 1 life satisfaction did not predict changes in Time 2 behavioral engagement, suggesting limitations of the application of the Broaden and Build Theory in this context. The results from the current study are also somewhat consistent with the findings of Frisch et al. (2005) that low levels of life satisfaction in college students were predictive of behavioral disengagement and drop out from school. However, the current study only found support for life satisfaction predicting psychological disengagement variables that perhaps operate as psychological precursors of actual drop out in secondary level students. Future longer-term longitudinal research is needed to demonstrate linkages between student drop out and cognitive and emotional disengagement among secondary level school students (Fredericks et al. 2004). The hypothesis that student engagement would predict life satisfaction was also partially supported. Specifically, Time 1 levels of cognitive engagement, but not emotional or behavioral engagement, predicted changes in Time 2 life satisfaction, after controlling for demographic variables and first 9 weeks GPA. Thus, adolescents who were hopeful about their future and also believed that education would help them later became more satisfied with their lives across the school year. This finding is intriguing in light of previous research showing that low levels of school connectedness, a variable that is similar to emotional engagement, predicted subsequent negative emotions or illbeing (Shocet et al. 2006). Given that cognitive, but not emotional engagement, was related to later well-being (i.e., life satisfaction) in this study, these findings underscore the

10 258 J Youth Adolescence (2011) 40: **.46** T1 LS R 2 =.07.43** T1 BE R 2 =.30.58**.37** T1 CE R 2 =.06.36** T1 EE R 2 =.06.54** T2 LS R 2 = *.01 T2 BE R 2 =.73 need to continue to discriminate carefully between wellbeing and psychopathology variables as well as the various engagement variables in studying their interrelationships. Support was demonstrated for a bidirectional relationship between life satisfaction and student engagement. Students who were more satisfied with their overall lives were able to broaden their thinking to perceive the many ways in which schooling can relate to their future goals..09*.85**.54**.03.08^.54**.32**.35**.24** T2 CE R 2 =.41.36**.49** T2 EE R 2 =.43.36** Fig. 3 Structural model of life satisfaction and student engagement. Note. Solid lines indicate relationships among factors, after controlling for covariates. To improve readability, disturbances are not explicitly diagrammed. Disturbance covariances are presented beside dotted lines. T1 Time 1, T2 Time 2, LS life satisfaction, BE behavioral engagement, CE cognitive engagement, EE emotional engagement. Controlled covariates are not diagrammed in the figure This broadened thinking should in turn lead to the building of academic resources in an upward spiral, as suggested in Fredrickson s Broaden and Build Theory (2001), although the building aspect of the theory remains to be tested further. The bi-directional link between life satisfaction and cognitive engagement is interesting because it has implications beyond an adolescent s experience in a particular school. Cognitive engagement is related to students views of education as a whole, instead of being limited to their particular feelings of bonding with a school or behavior in school. Cognitive engagement may be particularly important because it reflects to some degree the kind of lifelong learning attitudes that many educators argue should be an overarching goal for children s educational experiences (Noddings 2003). Focusing on cognitive engagement, beyond behavioral or emotional school engagement, may not only be the most direct way to achieve the goal of such educators, it may also enhance overall adolescent well-being. The bi-directional relationship between life satisfaction and cognitive engagement also underscores the important role of schooling as a determinant of adolescents life satisfaction. The finding that students who see value in their education and schooling become more satisfied with their lives is not inconsistent with other research suggesting that, although multiple contextual factors, including family and peer relationships, likely exert influences on adolescents life satisfaction, the school context is an important, independent contextual influence (DeSantis-King et al. 2006; Ma and Huebner 2008). The importance of the school context in adolescents global well-being is perhaps related to the fact that the majority of their day is spent at school (Roeser and Eccles 2000). Furthermore, the link between life satisfaction and student engagement suggests a possible causal role for life satisfaction in determining individual differences in student Table 4 Standardized coefficients among Time 1 demographics, student engagement and life satisfaction Time 1 Time 2 LS BE CE EE LS BE CE EE Race ** * Sex -.12**.10**.04.10** -.07* SES * -.09*.01 Age Family -.11** -.11** * GPA.16**.48**.22** T1 Time 1, T2 Time 2, N = 779, Race is coded 1 = minority race/ethnicity and 0 = Caucasian. Sex is coded 0 = male and 1 = female. SES is coded 0 = regular lunch and 1 = free or reduced rate lunch. Family is coded as 0 = live with biological parents and 1 = other adults. GPA = 1st 9 weeks GPA LS life satisfaction, BE behavioral engagement, CE cognitive engagement, EE emotional engagement *p\.05; ** p \.01

11 J Youth Adolescence (2011) 40: engagement, as well as vice versa. This study is one of few studies to have investigated the role of individual differences, such as life satisfaction, in explaining students engagement with school. Past research has investigated how school environments and family support have contributed to understanding and facilitating student engagement, neglecting the influence of individual difference factors (Fredericks et al. 2004). Individual difference factors, such as life satisfaction, can be altered through interventions, unlike personality/temperament variables that are more resistant to interventions (Lyubomirsky et al. 2005; Sin and Lyubomirsky 2009). The differential associations between life satisfaction and the student engagement variables provide support for multidimensional models of student engagement. The unique relationships of emotional, behavioral, and cognitive engagement to life satisfaction support the separability of the student engagement variables. For example, individual differences in levels of life satisfaction were not predictive of subsequent behavioral engagement levels, in contrast to cognitive and emotional engagement levels. The reasons for the differences in longitudinal associations are unclear, but may be related to several possibilities. One possibility may involve the fact that cognitive and emotional engagement variables (like life satisfaction) represent internal, psychological variables whereas behavioral engagement represents a more objective, behavioral variable. Another possibility involves the finding that behavioral engagement was extremely stable (r =.85) from Time 1 to Time 2, which reduced the amount of variance that life satisfaction could predict. Finally, relationships between life satisfaction and engagement variables may be differentially moderated by variables. Life satisfaction has been related significantly, but modestly, to emotional engagement in a number of studies of US students. However, in one study, this relationship varied as a function of the achievement levels of the students; the correlation was much stronger for high achieving adolescents (Huebner and Alderman 1993). Another moderator could be the influence of culture. The relationships between life satisfaction and engagement variables may be different across cultural and ethnic groups. For example, the cross-sectional relationship between life satisfaction and emotional engagement is different in Korean students, as liking school is more related to global life satisfaction in Korean students than in American students (Park and Huebner 2005). Therefore, future studies are needed to incorporate such possibilities. This study has several noteworthy strengths and limitations. First, the sample for this study comprised a large proportion of the school population, suggesting that is was not just a sample of volunteers. This sample thus perhaps yielded more unbiased estimates of levels of student engagement and life satisfaction compared to the many similar studies with a smaller percentage of the school population, that is, a smaller return rate. However, the sample was from one school in the Southeastern United States and influences of its unique school culture may have affected the observed relationship between life satisfaction and student engagement and thus limit the generalizability of the inferences that can be drawn from the findings. Future research should include multiple school sites and other age ranges to test these relationships. Another strength of this study is that it is one of the few studies to investigate all three types of engagement simultaneously, instead of one or two types of student engagement (Fredericks et al. 2004). This study is also one of the first studies to provide information about the longitudinal stability of life satisfaction and student engagement. Furthermore, more information is provided about the nature and directionality of the relationship between life satisfaction and student engagement beyond that of cross-sectional studies, since two waves of data were collected. However, future research should also include three or more waves of data in order to more comprehensively assess life satisfaction engagement connections. Another limitation of the study is the exclusive reliance on self-reports of life satisfaction and student engagement. Also, two items on the life satisfaction measure showed relatively low factor loadings, indicating that those items may be measuring a different factor and perhaps attenuating the validity of the measure. Future studies should include a multi-trait, multi-method assessment of life satisfaction and student engagement to improve confidence in the measurement of the constructs. Objective school records, such as attendance as well as teacher and parent reports of student engagement and life satisfaction, could help in further understanding the relationships between life satisfaction and student engagement. These changes may improve model fit, which though adequate for an initial exploration of the relationship between life satisfaction and student engagement, can possibly be improved upon by considering the above limitations. Finally, though the effect of attrition was small and 90% of the initial sample participated in both waves of data, the study did disproportionately lose students who reported lower levels of life satisfaction, behavioral engagement, and first 9 weeks GPA, and who were older. This may impact the ability to generalize findings to these students. This study has implications for practitioners and school personnel in understanding the relationship between wellbeing and school outcomes in adolescents. The finding of bi-directionality between life satisfaction and cognitive engagement has important implications for school professionals and intervention efforts. Support for a model in which student engagement is theorized to cause individual differences in global life satisfaction would suggest that