|USFDC Home | USF Electronic Theses and Dissertations||| RSS|
This item is only available as the following downloads:
School Behavior with a Dual Factor Model of Mental Health by Amanda L. Thalji A thesis submitted in partial fulfillment of the requirements for the degree of Education Specialist Department of Psychological and Social Foundations College of Education University of South Florida Major Professor: Shannon M. Suldo, Ph.D. Kathy Bradley Klug, Ph.D. John Ferron, Ph.D. Date of Approval : April 0 7 20 10 Keywords: psychopathology, subjective well being, academic achievement, longitudinal, positive psychology Copyright 2010 Amanda L. Thalji
Dedication I would like to dedicate this thesis to my father, who taught me that anything can be accomplished with good faith and hard work. It is also dedicated to my mother for always reminding me of the big picture and for offering her proof reading assistance thro ughout this project. My educational pursuits would surely not be what they are today without their endless amounts of encouragem ent and love.
Acknowledgements I would like to thank those individuals who have provided their expert guidance and encouragement during my thesis research. I would especially like to thank my Major Professor, Dr. Shannon Suldo, for her mentoring during my studies at the University of South Florida and particularly for this research project. Her thorough fe edback, accessibility, and enthusiasm for research and teaching made this research project an enjoyable and memorable learning experience. Dr. Suldo is an exceptional teacher, and the skills she has taught me during this project will surely prove useful in my future academic and career endeavors. Next, I would like to express thanks to Dr. Kathy Bradley Klug for her generous assistance and insightful feedback. Dr. Bradley input helped me consider relevant constructs integral to this project, and enco uraged me to explore practical applications. I would also like to extend my gratitude to Dr. John Ferron for his statistical guidance. Dr. Ferron eloquently talked me through many statistical decision points, particularly those regarding outliers. From thi s project and his excellent explanations of statistical analyses I have gained deeper understanding of statistical theory and application as well as gaining confidence in this area. Finally, I would like to thank my family and friends for their love, laugh ter, and supp ort throughout this project.
i Table of Contents List of Tables i v List of Figures v Abstract v i Chapter 1 : Introduction 1 Statement of Problem 1 Definition of Key Terms 5 Subjective Well B eing 5 Psychopathology 6 Academic Achievement and In School Behavior 7 Dual Factor Model 7 Purpose of Current Study 8 Contributions to the Literature 9 Chapter 2 : Review of the Literature 11 Traditional Approaches to Mental Health 11 Modern Alternatives to a Disease Model of Mental Health 12 Positive Indicators of Mental Health 12 Models that Examine Psychopathology and SWB 15 Keyes (2002) 15 Keyes (2006) 17
i Greenspoon and S a klofske (2001) 18 Suldo and Shaffer (2008) 19 Relationships between Youth Mental Health and Academic Functioning 21 Psychopathology and Academic Achievement and In School Behavior 21 Concurrent relationships 21 Predictive relationships 25 SWB and Academic Achievement and In School Behavior 31 Concurrent relationships 32 Predictive relationships 38 Conclusions 40 Chapter 3 : Method 42 Participants 42 Selection of Participants 43 Student participants 43 Teacher participants 4 7 Procedures 4 7 Measures 4 9 Demographics Form 4 9 Students' Life Satisfaction Scale 4 9 Positive and Negative Affect Scale for Children 50 Youth Self Report Form of the Child Behavior Checklist 5 1 Teacher Report Form of the Child Behavior Checklist 5 3
ii Indicators of Academic Achievement and In School Behavior 5 3 Grade Point Average (GPA) 53 Standardized Test Scores 54 Attendance 54 In School Behavior 5 5 Preliminary Analysis: Group Assignments 5 5 Overview of Data Analysis P lan 5 7 The R elation of SWB and Psychopathology to Subsequent Achievement and In School B ehavior 57 Group Membership and Outcomes 5 8 Limitations and Delimitations 5 9 Chapter 4: Results 6 2 Data Screening 6 2 Scale Reliability 6 4 Descriptive Analyses 6 4 Correlational Analyses 6 8 Regression Analyses 7 1 Student Mental Health Group Membership and Academic Outcomes 7 7 Chapter 5: Discussion 90 Relationships between Psychopathology, Academic Achievement, and In School Behavior 90 Internalizing Psychopathology 90 Externalizing Psychopathology 92
iii Relationships between SWB, Academic Achievement, and In School Behavior 94 Relationships between the Dual F actor M odel of Mental Health, Academic Achievement, and In School Behavior 9 7 Implications for School Psychologists 10 2 Contributions to the Literature 10 4 Limitations 10 4 Summary and Future Directions 10 6 References 10 9 Appendices 128 Appendix A: Parent Consent Form 129 Appendix B: Student Assent Form 132 Appendix C: Demographics Form 135 Appendix D: Students' Life Satisfaction Scale 137 Appendix E: Positive and Negative Affect Scale for Children 138
iv List of Tables Table 1. Demographic Characteristics of Participants at Time 1 and Time 2 4 6 Table 2. Means, Standard Deviations, Ranges Skew, and Kurtosis of Raw/Non Transformed Variables 6 5 Table 3. Correlations between Predictor and Outcome Variables 70 Table 4. Student Academic Achievement Predicted by Initial SWB and Previous School Functioning 7 3 Table 5. Student In School Behavior Predicted by Initial SWB and Previous School Functioning 7 4 Table 6. Student Academic Achievement Predicted by Initial Psychopathology and Previous School Functioning 7 6 Table 7. Student In School Behavior Predicted by Initial Psychopathology and Previous School Functioning 7 7 Table 8. ANCOVA Summary Table for Investigating the Relationship of Student Mental Health Group Membership to Subsequent GPA 78 Table 9. ANCOVA Summary Table for Investigating the Relationship of Student Mental Health Group Membership to Subsequent FCAT math 8 1 Table 10. ANCOVA Summary Table for Investigating the Relationship of Student Mental Health Group Membership to Subsequent FCAT reading 8 3
v Table 11. ANCOVA Summary Table for Investigating the Relationship of Student Mental Health Group Membership to Subsequent Absences 8 5 Table 12. ANCOVA Summary Table for Investigating the Relationship of Student Mental Health Group Membership to Subsequent ODRs 87 Table 13. Mean Levels of Academic Achievement and In School Behavior at Time 2 by Group 89 Table 14. Mental Health Groups Yielded from the Dual Factor Model of Mental Health 98
vi List of Figures Figure 1. 79 Figure 2. math scores over time 8 2 Figure 3. Changes in reading scores over time 84 Figure 4. 86 Figure 5. 88
vii School Behavior with a Dual Factor Model of Mental Health Amanda Thalji ABSTRAC T A dual factor model of mental health includes indicators of wellness (i.e., subjective well being) and psychopathology (i.e., internalizing and externalizing behavior problems) in defining psychological wellness. The present empirical investig ation examine d the utility of SWB and psychopathology examined separately and together (as in a dual factor model of mental health ) achievement and in school behavior. Specifically, it determined if SWB, psychopathology, and membership in a specific mental health group yielded by the dual factor model (i.e., complete mental health, vulnerable, symptomatic but content, or troubled) at Time 1 was related to achie vement ( i.e., GPA, FCAT math, FCAT reading, absences, office disciplinary referrals [ODRs] ) the following school year (i.e., Time 2 ). A previously analyzed data set (Time 1) and a different archival data set yielded from student records unique to the current study (Time 2) comprised of data from 300 adolescents were analyzed. Results of regression analyses to explore the predictive initial relationship of mental health to later student achievement indicated that initial SWB predicted student grades one year later, initial internalizing psychopathology predicted absences one year later, and initial externalizing psychopathology predicted gra des, absences, and ODRs one year later. Results o f mixed model AN C OVAs indicated that
viii Specifically, students belonging to the t roubled mental health group declined at a significantly faster rate than youth without psychopathology across time on GPA. In contrast, the slop e of students in the symptomatic but c ontent group w as not significantly different from the slope of peers with low psychopathology. Additionally, at Time 2 the best school attendance and school grades were found by students who had both average/high SWB and low psychopathology one year earlier, suppo rting the long term utility of complete mental h ealth.
1 C hapter 1 Introduction Statement of Problem The current perspective of psychology conceptualizes individuals from a frame of (Seligman & Csikszentmihalyi, 2000). In recent years, behavioral researcher s have advocated for the use of a more comprehensive framework of mental health (Lopez & Guarnaccia, 2000; Maddux, 2005; Seligman, 2005). Specifically, a modern approach stipulates the absence of psychopathology alone does not indicate wellness, and implor es faults (Seligman, 2005). Contemporary evidence suggests there is utility in an approach that focuses on a positive state of mind in youth, rather than just remed iating an Seligman & Csikszentmihalyi, 2000; Suldo & Huebner, 2006). Additionally, this notion of promoting psychological well being is aligned with goals that are integr al of effective school based mental health services (Doll & Cummings, 2008). This paradigm shift to a more comprehensive and preventative psychology is commonly referred to as positive psychology. The current study, which sought in part to provide a longitudinal follow up to research conducted by Suldo and Shaffer (2008), investigate d the relationships between as it pertains to th eir educational functioning the
2 following school year. Mental health is comprised of modern indicators of wellness (specifically, subjective well being [SWB]) as well as traditional indicators of psychopathology (namely, internalizing and externalizing symptoms of mental disorders). 2008), for instance, numerous studies have demonstrated positive concurrent relationships between school grades and SWB (H uebner & Gilman, 2006; Suldo, Shaffer, & Riley, 2008). Additional studies have supported positive linkages from perceptions of school climate, beliefs about learning, and academic self efficacy to a component of SWB, life satisfaction (Gilman & Huebner, 20 06; Kirkcaldy, Furnham, & Siefen, 2004; Reschly, Huebner, Appleton, & Antaramian, 2008; Suldo & Huebner, 2006; Suldo & Shaffer, 2008; Suldo, Shaffer, & Riley, 2008). Life satisfaction has also been found to be bas ed support; specifically, youth who report having high life satisfaction tend to also perceive that adults and peers support their academic endeavors (Nevin, Carr, Shevlin, Dooley, & Breaden, 2005; Suldo & Huebner, 2006; Suldo & Shaffer, 2008), and having these healthy interpersonal relationships with peers and adults promotes achievement motivation (Hall Lande, Eisenberg, Christenson, & Neumark Sztainer 2007; Nelson & DeBacker, 2008). Despite a recent influx in research examining concurrent links between indicators of wellness and developmental outcomes, research examining predictive outcomes of SWB has been largely restricted to adult populations. In fact, a review of the literature yielded no studies that examine SWB in relation to academic outcomes in a dolescents. The current study aimed to address this SWB as they pertain to subsequent educational functioning.
3 Relationships between psychopathology and domains of developmental functioning have long been evaluated in behavioral research. Specifically, studies have demonstrated that the presence of internalizing disorders, such as anxiety and depression, are related to poor academic achievement and reduced academi c engagement in childhood and adolescence ( Fergusson & Woodward, 2002; Lewinsohn, Seeley, & Gotlib, 1997; McCarthy, Downes, & Sherman, 2008; Woodward & Fergusson, 2001). Negative concurrent relationships and predictive relationships regarding externalizing problems such as ADHD and aggression have also been linked to lower school achievement (Eisenberg & Schneider 2007; Frazier, Youngstrom, Clutting, & Watkins, 2007; Loveland, Lounsbury, Welsh, & Buboltz, 2007). Externalizing disorders are also associated w ith lower rates of enrollment in higher education and less successful employment in adulthood (Capaldi, 1992; Caspi, Wright, Moffit t & Silva, 1998; Dubow, Huesman, Boxer, Pulkkinen, & Kokko, 2006; Ingoldsby, Kohl, McMahon, Lengua, & The Conduct Problems Prevention Research Group, 2006; Kokko & Pulkkinen, 2000; Young, Heptinstall, Sonuga Barke, Chadwick, & Taylor, 2005). However, a review of the literature reveals that many studies examining predictive relationships between psychopathology and aca demic achievement are limited by the use of assessments of academic ability that are not necessarily readily available to school personnel (e.g., teacher ratings, normative academic achievement tests) and intelligence tests. The current study explore d stu dent psychopathology in relation to subsequent academic outcomes that are more readily accessible to educators and applicable to long term school achievement: absences, school grades, and performance on a state wide high stakes achievement test.
4 There has been recent evidence to substantiate a transition from traditional to positive psychology, specifically research which supports a distinction between wellness and psychopathology in youth. The dual factor model of mental health (cf. Greenspoon & Saklofske 2001; Suldo & Shaffer, 2008) examines indicators of wellness (i.e., SWB) and psychopathology (i.e., internalizing and externalizing behavior problems). Greenspoon and Saklofske (2001) first examined the utility of the dual factor model in 40 7 elementary a ged students. In this study, they identified subgroups of students who, using traditional assessments of mental health, would typically be overlooked. Specifically, assessing mental health via measures of wellness and psychopathology led to the identificat ion of two unique groups of children: those who reported high SWB and high psychopathology, as well as students who scored low on measures of psychopathology and low on indices of SWB. Two other groups of children were those commonly studied in a tradition al model of psychology: students with high psychopathology and low SWB, and students without psychopathology who reported high SWB. In exploring commonalities among the four groups, it was found that the two groups who reported low SWB (i.e., low SWB and l ow levels of psychopathology; low SWB and high levels of psychopathology) had low self concept related to academic competence as well as poorer interpersonal skills, (2008) replicated and extended the findings by Greenspoon and Saklofske (2001), by utilizing measures of SWB and psychopathology in approximately 350 middle school students to identify four unique mental health groups. Results indicate that students with complet e mental health (i.e., high SWB and low psychopathology) were more academically successful than their vulnerable peers (i.e., low SWB and low
5 psychopathology). Youth categorized as vulnerable did not perform as well as their complete mental health peers on a standardized state test of reading achievement and had more frequent absences from school. Students identified as symptomatic but content (i.e., high SWB and high psychopathology) perceived more positive interpersonal relationships with peers and report ed having more social support from their parents than perceived by peers categorized as troubled (i.e., low SWB and high psychopathology). Additionally, this study proposed that students who are symptomatic but content, reporting high levels of both SWB an d psychopathology, may also have strengths. The current study provide s a longitudinal follow up to research conducted by Suldo and S haffer (2008) that investigate s health status as it pertains to their educational functioning the following school year. Definition of Key Terms Subjective well being. Subjective well being (SWB) is a broad construct that is and negative emotions (Diener, Lucas, & Oishi, 2005; Haybron, 2008). In other words, SWB is comprised of three related, but separate constructs: life satisfaction, positive affect, and negative affect (Diener, 2000). Life satisfaction is the appraisal of the enduring s atisfaction one has with his or her life, based on a set of criteria an individual has constructed from their own beliefs or perceptions (Diener & Diener, 1996; Diener, et al., 2005). Life satisfaction can be assessed globally or within specific domains. M easures assessment, whereas domain specific life satisfaction refers to happiness across both self directed and outer directed domains. Research has supported a high cor relation between
6 global and domain specific life satisfaction (Huebner, Gilman, & Laughlin, 1999). Affective evaluations are conceptualized as pleasant or positive affect, as well as the frequency of negative emotions, referred to as negative affect (Larse n, Diener, & Emmons, 1985). Affect is often considered the hedonic component of subjective well being due to the fact that this emotional component is adjusted based upon situational influences (Larsen & Prizmic, 2008). In the current study, student SWB wa s estimated by adding their standardized scores on measures of life satisfaction and positive affect, and subtracting standardized negative affect scores. Psychopathology. In youth, social, emotional, and behavioral problems are commonly classified by the use of the behavioral dimensions approach. The behavioral dimensions approach employs statistical procedures that yield behavioral clusters (Merrell, 2008). Using this meth od, behavioral researchers have discerned general types of behavioral and emotional problems along two broad band syndromes. Specifically, internalizing problems, also called overcontrolled behaviors (e.g., anxiety, depression, somatic complaints), and ext ernalizing problems, or undercontrolled behaviors (e.g., aggressive behavior, rule breaking behavior, and hyperactivity). Youth diagnosed with internalizing problems or disorders typically deal with difficulties internally, rather than acting them out in t he environment. In contrast, externalizing problems are characterized by behaviors directed outward, typically toward other people or objects in the environment. In the current study, student psychopathology wa s indicated by elevated scores on nationally n ormed inventories of internalizing and externalizing symptoms of mental health problems.
7 Academic achievement and in school behavior. In the current study, student functioning within the context of school has been performance on objective academic indicators, as well as their school behavior. performance on a standardized state test of achievement (i.e., the Florida Comprehensive Assessment Test; F CAT, 2005) in math and reading was examined. In school behavior was explored via student absences and office disciplinary referrals ( ODRs ) Dual factor m odel. A dual factor model of mental health includes indicators of wellness (i.e., subjective well bein g) and psychopathology (i.e., internalizing and externalizing behavior problems) in defining mental health (cf. Greenspoon & Saklofske, 2001; Suldo & Shaffer, 2008). Specifically, this model supports the assumption that psychopathology and SWB are two sepa rate, yet interrelated constructs. Greenspoon and Saklofske (2001) first administered assessments of psychopathology and SWB to identify four mental health categories for children. Two of these classifications were aligned with a traditional model of menta distressed well adjusted levels of psychopathology). Additionally, this model yielded two unique groups that are not observed when a externally maladjusted dissatisfied A recent examination of the dual factor model was conducted by Suldo and Shaffer (2008) who extended the findings of Greenspoon and Saklofske (2001) to yield four distinct mental health
8 of psychopathology and SWB to yield f h psychopathology). The current study has employ ed this conceptualization of the dual factor model of mental health and specific terms used to describe the four mental health groups yielded by Suldo and Shaffer (2008). Purpose of Current Study The curren t study was intend ed to provide a longitudinal examination of the predict ed their academic achievement and in school behavior the following school year. Additionally, this s tudy aim ed to further explore the implications of utilizing the dual factor model (Suldo & Shaffer, 2008) by examining the extent to which student academic derived from l evels of SWB and psychopathology. To date, no studies have looked at the dual factor model in relation to later school functioning, a vital component of adolescent functi adolescent psychosocial functioning with respect to schooling, which includes academic achievement and school attendance as important indicators. School discipline records ( i.e., ODRs) were also examined to determine how membership in a particular mental health group relate s to later in school behavior.
9 The specific research questions answered in this study include : 1. school behavior at Time 2 (controlling for achievement and in school behavior at Time 1) on the following indicators of achievement and in school behavior: GPA, FCAT math, FCAT reading, abs ences, and ODRs ? 2. and in school behavior at Time 2 (controlling for achievement and in school behavior at Time 1) on the following indicators of achievement and in school behavior: GPA FCAT math, FCAT reading, absences, and ODRs ? 3. Is membership in a specific mental health group (i.e., complete mental health, vulnerable, symptomatic but content, or troubled) at Time 1 related to subsequent achievement and in school behavior (i.e., GPA, FCAT math, FCAT reading, absences, ODRs ) at Time 2 (controlling for achievement and in school behavior at Time 1)? Contributions to the Literature There have been a number of studies that have examined academic correlates and predicto adolescent s mental health, but none have examined how measurements of SWB predict academic achievement and in school behavior longitudinally. The current study thus contributes to the literature by providing the first longitudinal examination of SWB and the dual factor model of mental health in relation to later academic achievement and in school behavior. With respect to the dual factor model, the identification of a particular subgroup of youth whose academic performance diminishe d over time may demonst rate the need to provide services or additional supports
10 to this group of individuals Similarly, evidence of diminished performance of students who exhibit characteristics of those in the vulnerable youth category, provides empirical support that psycholo gists and school personnel should attend to mental health beyond psychopathology or illness, as these youth may be at risk despite the absence of psychopathology.
11 C hapter 2 Review of the Literature This chapter reviews the progression of mental health research and practice from a field focused solely on psychopathology to one that also examines positive attributes of wellness. First, a summary of traditional approaches in mental health is provided, f ollowed by an overview of a modern alternative approach to mental health, termed positive psychology. Research which utilizes both of these approaches to form a comprehensive model of mental health is thereafter explored. Relationships between mental illne ss and wellness, academic achievement and in school behavior are delineated. Finally, research exploring the predictive qualities of mental illness and wellness to academic achievement and in school behavior in youth is summarized. Traditional Approaches to Mental Health Traditionally, mental health assessment has focused on diagnosis based on the presence or absence of psychopathology. Psychopathology is often conceptualized as referring to both internalizing disorders (e.g., anxiety, depres sion) and externalizing disorders (e.g., conduct disorder, oppositional defiant disorder; American Psychiatric Association, 2000). The Diagnostic and Statistical Manual of Mental Disorders, Fourth Edition, Text Revision (DSM IV TR; American Psychiatric Ass ociation, 2000) is a common tool used by practitioners which prioritizes an illness oriented psychology as a mechanism of diagnosing or labeling patients with the benefit to facilitate communication between professionals and to aide in informing intervent ions (Maddux, 2005). However,
12 others (e.g., Maddux, 2005, Seligman & Csikszentmihalyi, 2000) have viewed the categories of the disorders featured in the DSM IV TR as a method of maintaining social order from what is perceived as normal or abnormal by thos e in power. Lopez and illustrates the argument that it is necessary to examine me ntal health from different perspectives, perhaps not discounting indices of psychopathology, but including such resiliency in the assessment of mental health (Maddu x, 2005). Modern Alternatives to a Disease Model of Mental Health As previously mentioned, contemporary psychology gives priority to a conception of people that, to an extreme, is based on pathology, faults, and dysfunctions (Seligman & Csikszentmihalyi, 2000). In recent decades there have been calls for a paradigm shift to a psychology based on empowerment and prevention, referred to as positive knesses (Seligman, 2005), empowering people to develop a more positive state of mind by utilizing their strengths and to encourage them to live a life that is fulfilling (Seligman & Csikszentmihalyi, 2000). Positive Indicators of Mental Health In the field of positive psychology, there are several constructs purported to being and satisfaction, those related to the present, including experiences of flow and joy, as well as those
13 associated with the future, such as hope and optimism (Seligman, 2005). In the current study, the evaluation of subjective well being, commonly referred to as happiness, has been emphasized. According to Diener, Lucas, and Oishi (2005), subjective well being (SWB) is a related, but separable constructs: life satisfaction, positive affect, and negative affect (Diener, 2000). Life satisfaction is the personal cognitive evaluation of the enduring satisfaction one has with his or her life, based on his or her own unique set of criteria (Diener & Diener, 1996; Diener, et al., 2005). These cognitive appraisals can be assessed globally and within ely, domain specific life satisfaction has been measured as subjective happiness across both self directed and outer directed domains (i.e., school, friends, and family). Studies have supported a high relationship between global and domain specific life sa tisfaction (Huebner, Gilman, & Laughlin, 1999). Moods and emotions comprise the affective evaluations, and represent the 1999). Affective evaluations are conceptualized as pleasant or positive affect, the frequency of positive emotions such as joy and excitement; as well as negative affect, the frequency of negative emotions such as guilt and gloom. Life satisfaction and affect are conceptualized as different constructs, as life satisfaction judgments are more stable than affect, which are considered temporary emotional experiences (Kim Prieto, Diener, Tamir, Scollon, & Diener, 2005; Pavot & Diener, 1993). Research has demonstrated that
14 most people, including youth, are at l east mildly happy (Biswas Diener, Vitterso, & Diener, 2005; Diener & Diener, 1996; Huebner, Suldo, & Gilman, 2006). Although most youth report levels of satisfaction above a neutral point, few report the highest levels possible of life satisfaction (Huebne r, Suldo, & Gilman, 2006). High SWB is viewed as advantageous because SWB has been found to co occur with good relationships with self and with others and is further associated with positive indicators of school functioning (Suldo & Huebner, 2006; Suldo & Shaffer, 2008). A longitudinal study by Huebner, Funk, and Gilman (2000) demonstrated that global life satisfaction is stable over time. In their study, 99 high school students were administered the Student Life Satisfaction Scale (SLSS; Huebner, 1991) and the Behavior Assessment Scale for Children (BASC; Reynolds & Kamphaus, 1992). The SLSS is a measure of global life satisfaction and the BASC is a norm referenced instrument frequently used to assess behavior and emotional problems, such as depression and anxiety, as well as adaptive behaviors related to healthy development, such as interpersonal relationships and locus of control. Results from this study revealed moderate positive correlations between the BASC adaptive scales and life satisfaction ( r = .22 to .48) as well as moderate negative correlations between life satisfaction and the scales evaluating problem behavior ( r = .12 to .56). Moreover, a one year test retest coefficient suggests moderate stability ( r = .53) of the SLSS. Overall, these findi ngs suggest that SLSS scores are stable and yield meaningful relationships with traditional psychopathology focused mental health factors (e.g., anxiety and depression) and measures frequently used in current mental health assessment (i.e., BASC). Recent
15 s tudies that examined the benefits of evaluating the SWB of adults and youth in addition to the utilization of traditional indicators of mental health are highlighted next. Models that Examine Psychopathology and SWB Recently, there has been a call for a more comprehensive framework for understanding mental health (Seligman, 2005). Specifically, one that conceptualizes an disorder, but via a model that also takes into a ccount positive factors (Keyes, 2007). The mental and social well Research with youth has yielded data t hat demonstrate psychological functioning is not a continuum; rather, combinations of positive and negative indicators provide a more factors related to wellness and psyc hopathology (Greenspoon & Saklofske, 2001; Keyes, 2002; Suldo & Shaffer, 2008). For instance, a dual factor model of mental health (cf. Greenspoon & Saklofske, 2001; Suldo & Shaffer, 2008) examines positive indicators (i.e., SWB) as well as negative indica tors of psychopathology in youth (i.e., internalizing and externalizing behavior problems) and provides a comprehensive assessment of mental health in youth. Keyes (2002). Keyes (2002) has suggested a categorical system of mental health with adults that e In the study, a sample of 3,032 adults ages 24 to 74 years completed a survey of mental illness (i.e., Composite International Diagnostic Interview Short Form; Kessler, Andrews, Mroczek, Ustun, & Wittchen,
16 1998). Participants were also asked to evaluate their emotional mental health as "poor," "fair," "good," "ve ry good," or "excellent." Additionally, participants completed a structured scale of six symptoms of positive affect, six scales of psychological well being (e.g., positive relations with other s and personal growth), and five scales of social well being (e .g., social acceptance and social actualization). In order to be categorized as languishing in life, participants must have had a low level (i.e., lower tertile) on one out of two measures of emotional well being, and low levels on six out of 11 scales of positive functioning. To be considered flourishing in life, participants must have reported a high level (i.e., upper tertile) on one out of two measures of emotional well being and high levels on 6 out of 11 scales of positive functioning. In the study, 1 7.2% of participants were flourishing, or described as having complete mental health, as they reported in the upper tertiles on two scales measuring emotional well being and on six out of 11 scales measuring psychological and social well being. Conversely, 12.1% of the sample size were classified as languishing because they had low levels of well being and positive functioning, or scored in the lower tertile on one of the emotional well being scales and on six of the psychological and social well being scal es. Whereas those classified as moderately mentally healthy (56.6%) reported levels of well being that were in the middle tertile, on at least seven of 13 symptom scales, in other words functioning somewhere between those categorized as flourishing and th ose categorized as languishing. Lastly there were participants who reported one or more types of psychopathology (14.1%), specifically with major depressive episode. Further data demonstrates that participants who are categorized as languishing are twice a s likely as those in the moderately mentally healthy participants to be at risk for a major depressive
17 episode and almost six times as likely as their counterparts in the flourishing category. This study illustrates the importance and applicability of eval uating indicators of wellness as they provide information relative to the prevention of illness. It has not been until recently that similar studies of youth have been conducted as described below. Keyes (2006). Keyes later explored the same categorical system of mental health applied to adolescents (2006). In this study, 1,234 adolescents ages 12 to 18 completed items assessing SWB. Specifically, Keyes included 12 SWB items that had been adapted from a measure used with adults (Keyes & Magyar Moe, 2003), which assessed the emotional, psychological, and social well being of adults. Additionally, three items from the Child Development Supplement II (CDS II) of the Panel Study of Income Dynamics was applied to assess emotional well being. Specifically, youth reported how frequently in the past month they felt: (a) happy, (b) interested in life, and (c) satisfied. Items from the CDS II were also used to assess psychological well being in regards to four dimensions: environmental mastery, personal growth, posi tive relations with others, and lastly, autonomy. Social well being was assessed via items of the CDS II across five dimensions of social well being: social contribution, social integration, social actualization, social acceptance, and social coherence. Ke yes also assessed participants assessed via self report of the number of times they had been truant, been arrested, smoked cigarettes, smoked marijuana, used alcohol, and/or used inhalants to get high. he global self concept scale (Marsh, 1990), a measure consisting of a 6 item scale tapping ho w frequently they feel good
18 about their abilities and themselves. Additionally, five items were used to measure determination (e.g., I try to do my best on all my work.). Participants ndividuals (i.e., mother/ stepmother, father/ stepfather, sibling, friends, teacher, and adults outside of school) to assess the amount of individuals to whom a youth feels close. Lastly, participants were asked to complete four items that assessed childre Based on these assessments, students were identified as flourishing, languishing, or having moderate mental health. In the age range of 12 to 14, the status of flourishing was the most common category, wher eas for those ages 15 to 18 moderate mental health was the most common diagnosis. Further, Keyes explored outcomes associated with SWB and found an inverse relationship among symptoms of depression and SWB. Additionally, conduct problems, including arrest s, truancy, and drug/alcohol use decreased and measures of psychosocial functioning (i.e., global self concept, self determination, closeness to others, and school integration) increased as SWB increased. Greenspoon and Saklofske (2001). Empirical suppor t for a proposed model of mental health in which psychopathology and SWB are two separate yet interrelated constructs in youth was first provided by Greenspoon and Saklofske (2001). In their study, 407 Canadian students in grades 3 through 6 completed ques tionnaires assessing SWB, psychopathology, personality, and other related constructs (e.g., locus of control, interpersonal relations). Greenspoon and Saklofske (2001) identified two unique sub groups of individuals: children who reported high SWB but also scored high on indices of psychopathology, and children who scored low on indices of psychopathology and SWB. The results indicate that students belonging in both groups with low levels of SWB (i.e.,
19 low SWB and low levels of psychopathology, low SWB and high levels of psychopathology) had low self concept related to academic competence as well as poorer interpersonal skills. These findings are unique, as a traditional approach to mental health (i.e., only examining psychopathology) may have disregarded th ose who reported low SWB but were not yet symptomatic. Suldo and Shaffer (2008). The extent to which a dual factor model of mental health applies to another cohort of students, specifically middle school students in grades 6 to 8, was investigated by Sul do and Shaffer (2008). Suldo and Shaffer administered measures assessing SWB (i.e., life satisfaction, positive affect and negative affect), and internalizing psychopathology as well as measures of physical health, social functioning, and attitudes toward schooling to 350 early adolescents ; teachers of these youth provided data regarding symptoms of externalizing psychopathology exhibited Their study supported previous findings, underscoring the utility of assessing positive indicators of self perceived we llness along with more traditional measures of psychopathology. replicating findings that four distinct mental health groups exist. Suldo and Shaffer (2008) determined tha ., low SWB and high psychopathology). were more academically successful than their vulnerable peers. Vulnerable youth performed worse on a standardized measure of readin g achievement and had higher rates
20 of absenteeism than students with complete mental health. Additionally, these vulnerable students reported diminished academic self concept and lower motivation to self regulate their behavior in the classroom. Lastly, vu lnerable students viewed education as less important for long term goals than students in the complete mental health group. Benefits of complete mental health were also identified regarding interpersonal functioning. Students identified as symptomatic but content perceived more positive interpersonal relationships with peers and more social support from their parents than perceived by troubled youth, who perceived the lowest levels of social support from parents. The use of a dual factor model to assess chi functioning identifies two unique groups of students who otherwise may be overlooked with methods which solely assess psychopathology. Specifically, vulnerable students who are non symptomatic of psychopathology, but report r elatively low SWB, may be at risk for later school failure. Conversely, students who have symptoms of psychopathology but report relatively high SWB (i.e., symptomatic but content) may possess strengths that allow them to flourish socially. The aforementio ned research by Greenspoon and Saklosfe (2001) as well as the study by Suldo and Shaffer (2008) has provided evidence for the validity of classifying psychological functioning according to a dual factor model of mental health in youth, as well as illustrat ed how membership in one achievement and in school behavior. However, no studies have looked at long term outcomes associated with a dual factor model through longitudinal re search. One such important outcome is school functioning, a vital component of adolescent functioning
21 (Berk, 2006). School functioning, related to and predicted by psychopathology as well as indices of wellness, will be discussed in the upcoming sections. Relationships between Youth Mental Health and Academic Functioning Psychopathology and Academic Achievement and In School Behavior Historically, the definition of mental health has focused solely on presence or absence of psychopathology. This approach h as demonstrated that childhood psychopathology has lifelong consequences and costs for youth as well as society, and many adult disorders have origins in childhood (Mash & Dozois, 2003). It is estimated that as many as one in five children in the United St ates has some type of mental health difficulty (Brown, Riley, & Wissow, 2007; Roberts, Roberts, & Xing, 2007). A study by the World Health Organization suggests that by the year 2020, childhood neuropsychiatric disorders will increase by over 50% worldwide to become one of the five most common causes of morbidity, mortality, and disability among youth (U.S. Public Health Service, 2000). In the next section, research will be explored that examines concurrent relationships between psychopathology and academi c achievement as well as in school behavior in youth is summarized additionally ways in which disorders predict later academic outcomes over the developmental lifespan of youth are reviewed Concurrent relationships. Internalizing disorders reflect over c ontrolled to maintain maladaptive control of his or her emotional and cognitive state (Merrell, 2008). Depression and anxiety are two of the most common childhood inter nalizing disorders in youth (Albano et al., 2003; Costello et al., 2005; Huberty, 2008; Rushton, Forcier, & Schectman, 2002). Results of prevalence studies estimate that 4.75 % of youth
22 ages 5 to 17 suffer from major depression and 8% of youth have a diagn osable anxiety disorder; variability in exact rates is attributed to the criterion used (Costello, Egger, & Angold, 2005). Both of these disorders are associated with poor academic functioning in youth. Numerous studies have demonstrated a negative relati onship between symptoms of internalizing disorders and grades (Lewinsohn, Seeley, & Gotlib, 1997; McCarthy, Downes, & Sherman, 2008). Puig Antich et al. (1993) interviewed 62 adolescents with major depressive disorder and their mothers. Puig Antich and col leagues found that these students experienced more behavior problems at school, had lower academic achievement, as well as less positive relationships with teachers when compared to peers without psychiatric diagnoses. Other studies have examined specific academic outcomes associated with psychopathology. For example, Lewinsohn, Seeley, and Gotlib (1997) examined psychosocial variables and outcomes associated with three groups of high school students: adolescents with depression (n = 48), adolescents with nonaffective disorder (n = 92), and adolescents who had never been mentally ill ( n = 1,079). Adolescents with depression met criteria according to the third edition of the Diagnostic and Statistical Manual of Mental Disorders (America n Psychiatric Association, 1980) for major depression or dysthymia. The nonaffective disorder group consisted of participants who met criteria, in about equal proportions, for anxiety disorders, disruptive behavior disorders, or substance use disorders. In this study, only those youth in the nonaffective disorder group had academic problems (e.g., significantly lower GPA, report of dissatisfaction with grades, parental dissatisfaction with grades, reported being late for school in the past 6 weeks, reported not completing homework, and repeating a grade). A possible reason for these discrepant results is the unique design of this study.
23 Specifically, the grouping of students with anxiety disorders, disruptive behavior disorders, and substance use disorders u nder the same category may be considered unusual, as anxiety is typically categorized as an internalizing disorder, whereas disruptive behaviors and substance use are often considered to be externalizing disorders. Externalizing disorders refer to a dim ension of problems which includes a broad array of aggressive, acting out, disruptive, antisocial, oppositional, defiant, and hyperactive behaviors (Merrell, 2008). Common childhood externalizing disorders include attention deficit/ hyperactivity disorder (ADHD), oppositional defiant disorder (ODD) and conduct disorder (CD). These three common childhood problems will be discussed as they relate to academic functioning. The median p revalence estimate of ADHD is 3% (Costello, Egger, & Angold, 2005). Among hig h school age adolescents, disruptive behavior disorders are approximately twice as likely for male students than for female students (Lewinsohn, Hops, Roberts, Seeley, & Andrews, 1993). According to Barkley (2006), adolescents with ADHD have significantly poorer academic outcomes than adolescents without ADHD. Specifically, adolescents with ADHD are more likely to fail a grade, have increased school suspensions and expulsions, and have lower levels of academic achievement on standardized tests of math, scie nce, and reading. Additionally, both female and male students with ADHD have worse perceptions about their own academic abilities compared to students without ADHD (Eisenberg & Schneider, 2007). Parents and teachers of these youth also have poor expectatio ns for their academic performance, with more prominent negative perceptions for females (Eisenberg & Schneider, 2007). These academic outcomes associated with ADHD have recently been summarized via a meta analysis conducted by Frazier, Youngstrom, Clutting and
24 Watkins (2007) of literature published since 1990 regarding the breadth of academic achievement problems experienced by youth with ADHD. This analysis yielded effect sizes that were significantly different between youth with and without ADHD. Specifi cally, effect sizes ranged from r = .05 to .44 for studies examining adolescents and ranged from r = 0.14 to .76 for studies examining children. T he largest effect occurred in the academic achievement subject area of reading ( d = .73), followed by math ( d = .67), and then by spelling, ( d = .55). Frazier, Youngstrom, Clutting, and Watkins (2007) provide further discourse on the multitude of achievement problems experienced by youth with ADHD. Forms of externalizing psychopathology other than ADHD have also been found to have negative effects on student achievement (Hinshaw, 1992; Hinshaw & Lee, 2003). One perspective provides evidence which suggests that youth with behavioral problems have slig htly lower levels of intellectual functioning, particularly regarding verbal abilities, than asymptomatic youth (Alvarez & Ollendick, 2003). This slight discrepancy may adversely affect their academic performance in school. Regardless of intellectual abili ty, evidence suggests that antisocial or aggressive behavior in adolescents undermines student achievement dramatically, as demonstrated in a study by Loveland, Lounsbury, Welsh, and Buboltz (2007). Loveland et al. recruited a sample of approximately 990 h igh student students from the United States and found aggression Similar studies in which aggression is measured via observation or adult report (as opposed to self rep ort) are needed. Additional research by Graham, Bellmore, and Mize (2006) offers a different perspective of aggressive or anti social behavior in youth.
25 Graham et al. examined peer victimization and aggression in a diverse sample (46% Latino; 26% African A merican; 11% Asian; 9% White; 8% biracial or multiracial) of 1985 sixth grade students from 11 middle schools. In this study, peer nomination was used to determine which students had reputations of being aggressive, whereas others had reputations of being victims of aggression. Additionally, teacher report was used to assess student school engagement. Results indicate that youth categorized as aggressors by their peers were more likely to have low GPAs and have lower levels of teacher rated engagement. Pre dictive relationships. There have been several studies exploring the developmental effects of the onset of internalizing and externalizing in youth. For instance, Masten et al. (2005) examined predictive relationships related to externalizing disorders in childhood on developmental outcomes. Masten and colleagues followed over 200 children, who at initial assessment were 8 to 12 years old, for 20 years. These youth were assessed at 7, 10, and 20 years. Academic competence was assessed via four indicators: t he total score on the Peabody Individual Achievement Test (Dunn & Markwardt, 1970), grade point average at initial data collection, a teacher rating from the Devereux Elementary School Behavior Rating Scale (Spivack & Swift, 1967), and a composite variable derived from three explicit questions included in a structured parent interview. Overall, findings from this study suggested that externalizing problems in childhood were related to lowered academic competence in adolescence, which was related to internal izing problems in young adulthood. Other studies have focused their efforts more explicitly on specific disorders. For example, Cole, Martin, Powers, and Truglio (1996) conducted a longitudinal study with
26 490 third grade students and 455 sixth grade stud ents. Data were obtained regarding depression, as well as social and academic competence, via self reports completed by students, nominations by peers, as well as teacher and parent reports. Data were collected in the beginning of the school year and at th words, the belief that academic competence deteriorates because of depression was not academic competence was stable over the 6 month period. These findings, however, should be interpreted with caution, as measures of actual academic performance, such as performance on academic standardized tests or course grades may deteriorate over this time. Further, competence in academic domains may deteriorate beyond the 6 month period. A number of studies have provided evidence of negative relationships between symptoms of depression and anxiety, and later academic achievement ( Fergusson & Woodward, 2002; Woodward & Fergusson, 2001 ) For example, Fergusson and Woodward (2002) conducted a longitudinal exploration of 1,265 children ages 14 to 16 in New Zealand over a 21 year period as part of the Christchurch Health and Development ptoms of major depression were evaluated using the self report and parent versions of the Diagnostic Interview Schedule for Children (DISC; Costello, Edelbrock, Kalas, Kessler, & Klaric, 1982) as well as criteria from the Diagnostic and Statistical Manual of Mental Disorders Revised (DSM III R; American Psychiatric Association, 1987) at the onset of the study (ages 14 to 16). Academic achievement was assessed by recording the age at which participants withdrew from school (i.e., dropout rate), their partici pation in tertiary education (i.e., enrollment in a trade or skill based
27 training program), and their enrollment in a university level education program or similar program by the time the participant was 21 years of age. Social, familial, and individual f actors and comorbid disorders were also taken into account. Results indicated that 13% of participants developed depression between ages 14 and 16. These individuals were at increased risk for educational underachievement (i.e., high rates of school dropou t, reduced likelihood of enrolling in a university or tertiary level education) compared to their counterparts. Specifically, of those youth diagnosed with depression at ages 14 and 16, approximately 26% reported leaving school prematurely and only 22% of these youth enrolled in a university. Conversely, only 17% of participants who did not meet the diagnostic requirements for depression left school prematurely and 32% of these non depressed youth enrolled in tertiary education. This study provides further evidence of the need to assess for mental health problems in youth and the need to intervene appropriately, in part to prevent premature school dropout. Another longitudinal study by Woodward and Fergusson (2001) utilizing the same sample from Christchurc h Health and Development Study in the aforementioned study (Fergusson & Woodward, 2002) of over 1200 children in New Zealand. These youth were assessed for the following anxiety disorders: generalized anxiety, specific phobia, separation anxiety, panic dis order, and social phobia, as assessed by self report and parent versions of the DISC (Costello, Edelbrock, Kalas, Kessler, & Klaric, 1982) and diagnoses were based on the DSM III R criteria for anxiety disorders at ages 14 to 16 years. For those adolescent s who did not have anxiety disorders from ages 14 to16, 34% of them entered college by age 21, whereas only 26% of adolescents who were diagnosed with one anxiety disorder attended college. Further, only 19% of participants diagnosed
28 with 2 anxiety disorde rs and 13% of students diagnosed with 3 or more anxiety disorders attended college. Additional research has focused on longitudinal educational outcomes in relation to externalizing disorders. As children diagnosed with ADHD navigate adolescence, they ar e more likely to be retained, have lower performance in classes as evaluated by report cards, and perform more poorly on standardized measures of academic achievement compared to peers without ADHD (Loe & Feldman, 2007). For instance, Young, Heptinstall, S onuga Barke, Chadwick, and Taylor (2005) examined outcomes of females ( n = 70) in England who demonstrated hyperactive behaviors. Rating scales were completed by parents and teachers when the students were at 7 years of age. At the time, these students wer e categorized within one of four groups according to their results on the rating scales: hyperactivity; conduct problems; comorbidity of hyperactivity and conduct problems; non symptomatic control. Measures were completed again when the students were 14 to 16 years old, in addition to a clinical interview with the child. Hyperactivity at age 7 was a risk factor for later school behavior, as it predicted the likelihood of suspensions, whereas conduct problems did not. The detrimental effects of externalizing problems on achievement extend to other global populations as well. Lopes (2007) investigated behavioral, emotional, and academic problems in a sample of 116 students from one public seventh grade school located in an urban area of Portugal. Data collection occurred in the beginning of the Teacher Form (Queirs, 2006). The scale was translated and adapted into Portuguese by Queirs to as sess two factors: externalized (17 items) and internalized (10 items)
29 of six times via school achievement tests. The achievement tests were developed by three classroo areas of their curriculum: the Portuguese language, the English language, and mathematics. Cut off scores based upon those typical of Portuguese high schools were used to eval uate participant performance, such that students who performed below 35% between 36 and finally, a performance above 7 Results include that students rated as having externalizing behavior problems were much more likely to also perform in the However, the majority of stud ents in the study were referred for internalizing problems, rather than externalizing problems. This unbalanced sample size may have attributed to lack of diversity among youth in the externalizing problems category and thus overrepresentation of externali compared to those students with internalizing problems in this group (19%). Other studies have demonstrated that children who experience aggression and conduct problems often experience difficulties later on, such as having low educational attainment, unemployment, lower occupational status, or an unstable career path (Caspi, Wright, Moffit t & Silva, 1998; Dubow, Huesman, Boxer, Pulkkinen, & Kokko, 2006; Ingoldsby, Kohl, McMahon, Lengua, & The Conduc t Problems Prevention Research Group, 2006; Kokko & Pulkkinen, 2000). In a longitudinal investigation by Dubow and colleagues (2006), aggressive behavior, among other variables, was evaluated as related
30 to later adolescent and adult functioning in a sample of youth from the United States and Finland. The United States sample consisted of over 850 third grade students (436 males and 420 females) in a semirural county. Initial data collection occurred in 1960, and additional follow up assessments were conduct ed in 1970 ( n = 427), 1981 ( n = 409), and between the years 1999 and 2002 ( n = 523). At the age of 8, cognitive and academic achievement. A peer nomination procedure cr eated by Eron, Walder, and Lefkowitz (1971) was used to assess aggression. In the North American sample, cognitive and academic achievement were negatively related to aggression ( r = .34). Aggression at age 8 predicted more aggression at age 19, which was inversely associated with education at age 30 and occupation at age 48. The same trend was found in the Finnish sample. aggressive behavior will later experience low edu cational outcomes in early adulthood, and eventually lower occupational attainment in adulthood. Effects of externalizing disorders as well as comorbid externalizing and internalizing have been studied by Capaldi (1992), specifically in an examination of e xamined conduct problems and depressive symptoms in an at risk community sample of 203 early adolescent boys. At sixth grade the participants were divided into the following four groups: (a) conduct problems and depressed mood, (b) conduct problems only, ( c) depressed mood only, and (d) no problem control. The four groups were compared in eighth grade. Those participants with comorbid conduct problems and depressive symptoms were more likely to have been arrested, as well as have poor academic achievement.
31 In summary, psychopathology, manifested as internalizing and externalizing disorders, is predictive of negative outcomes in youth. Specifically, the presence of mental disorders such as anxiety, depression, and ADHD are related to poor academic achievemen t and engagement in school during youth. As adults, these children and adolescents are more likely to be faced with bleak opportunities for higher education and successful employment. In the next section, similar relationships and outcomes of youth functio ning are explored in the context of positive indicators of mental health (i.e., SWB). SWB and Academic Achievement and In School Behavior According to Erikson (1968), the opportunities adolescents are provided by their families, schools, and communities for nurturing their academic aspirations are essential in promoting adolescents' developmental success. Middle schools in particular have be en regarded as one of the most important institutions to assist American youth who, often due to difficult social conditions, are at higher risk for academic failure and low motivation to learn, poor conduct and affiliations with negative peers (Carnegie C ouncil, 1989; Hamburg & Takanishi, 1996 ). Therefore, institutions responsible for socializing full range of functioning, such as those that evaluate student happine ss, as research has demonstrated that happiness provides benefits at the individual, family, and community level, and across different domains of functioning (e.g., social, emotional, academic; Lyubomirsky, King, & Diener, 2005). In the next section, studi es that have examined school behavior are discussed.
32 Concurrent relationships. Life satisfaction has been linked to academic achievement as well as socially desirable behaviors. Youth who repo rt that they are very satisfied with their lives show positive functioning in school related domains, such as high perceptions of quality of school experiences, more perceived social support from peers and teachers, greater academic achievement, and greate r academic self efficacy (Gilman & Huebner, 2006; Suldo & Huebner, 2006). Additionally, life satisfaction may serve as a protective factor from engaging in risky behavior, such as suicide ideation and substance abuse (Valois, Zullig, Huebner, Drane, 2004; Zullig, Valois, Huebner, Oeltmann, & Drane, 2001). Therefore, experiences of high life satisfaction may be advantageous for youth, who are expected to master tasks involving productivity related to their educational pursuits, which are intended to guide yo uth towards securing productive and meaningful employment as adults (Berk, 2006). notion that the experiences of schooling and wellness are intertwined, as school grades, persona 04). A study by Reschly, Huebner, Appleton, and Antaramian (2008) explored a specific component of SWB, affect, and its relation as an antecedent to student engagement among 293 students in grades 7 through 10. Students completed the Positive and Negative Affect Schedule Children (PANAS C; Watson, Clark, & Tellegen, 1988), a 27 item scale comprised of two subscales: Positive Affect (PA) and Negative Affect (NA; Laurent et al., 1999). The PA subscale consisted of 12 items on a Likert scale to elicit
33 informat ion on the frequency of experiencing positive emotions (e.g., proud, energetic) in the school setting. The NA subscale consisted of 15 items also on a Likert scale, measuring the frequency of certain negative emotions (e.g., sad, lonely) in the prior few w eeks within the school setting. Participants also completed the Student Engagement Instrument (SEI; Appleton, Christenson, Kim, & Reschly, 2006), a scale that measures two constructs of engagement at school associated with learning: cognitive engagement an d psychological engagement. Results indicated that students experience more positive emotions than negative emotions during school. Further, there were significant, positive correlations between PA and subscales on the SEI, ranging from 0.37 to 0.47, where as suggests that students who experience more positive emotions, a component of SWB, are more likely to be engaged, both cognitively and psychologically, on school rel ated tasks. school performance. Huebner, Gilman, and Laughlin (1999) examined the relationship ncluded 183 American elementary school students in grades 3 to 5, and 290 American middle school students. Participants completed the SLSS and a measure of self concept related to school, the Self Description Questionnaire II (SDQ II; Marsh, 1990) for the middle school age students or the Self Description Questionnaire I (SDQ I; Marsh, 1988) for the competence was positively correlated with global life satisfaction ( r = .3 6 .37). Suldo and Huebner (2006) also explored the relation between life satisfaction and perceived academic ability via a sample of 698 students from 3 middle and 2 high schools.
34 Perceived academic ability was assessed by academic subscale of the Self Ef ficacy Questionnaire for Children (SEQ C; Muris, 2001). In the SEQ C, academic self efficacy master academic material, and to fulfill academic expectations. Findings in dicated a moderate correlation ( r = .45) between global life satisfaction (measured via the SLSS) and academic self efficacy. These findings have been replicated with non American youth. Specifically, a longitudinal study of Chinese students found that sel f perceptions of performance in core academic subjects (i.e., Chinese, English, and mathematics) were strongly associated with current life satisfaction and predicted global life satisfaction 7 to 9 months later (Leung, McBride Chang, & Lai, 2004). Despi te the evidence that higher perceptions of academic competence predict life (i.e., grades) indicated that these two constructs were unrelated. For example, in a stud y by Huebner (1991) with 79 Caucasian middle school students, average report card grades for the subjects of math, reading, spelling, science and social studies were unrelated to student scores on the SLSS. Similar results were found comparing life satisfa ction of youth who were in a gifted ( n = 61) program to match students in the general education program ( n = 61; Ash & Huebner, 1998). However, more recent research using larger sample sizes have shown otherwise. Chenge and Furnham (2002) explored the rela tion between happiness and academic performance with 49 male and 41 female adolescents, ages 16 18, in the United Kingdom. A small, but significant correlation of .29 between a measure of positive affect (measured via the Affectometer; Headey & Wearing, 19 83) and school grades and of .25 on the Oxford Happiness Inventory (Argyle, Martin, &
35 Crossland, 1989) and school grades was found. In a sample of 341 middle school students, Suldo and Shaffer (2008) found that those who scored in the satisfactory range on measures of SWB (above the 30 th percentile) had better grades in academic subject areas and scores on standardized achievement tests of reading compared to groups who scored at or below the 30 th percentile on measures of SWB. Further, the group that had complete mental health (high SWB and low psychopathology) performed significantly better on a standardized math achievement test compared to students categorized in the vulnerable group (character ized as having low SWB and low scores of psychopathology). Additionally, Kirkcaldy, Furnham, and Siefen (2004) examined archival data from 30 countries to evaluate factors that correlate to happiness internationally. Happiness was evaluated via the World D atabase of Happiness by Veenhoven (2001) and self reported student achievement data was gathered via the Programme for International Students Assessment (2001). Those countries whose youth reported the highest levels of happiness also reported the highest levels of academic achievement in the subjects of science, math, and reading literacy. Specifically, the relationship between happiness and reading literacy was the strongest ( r = .63), followed by math literacy ( r = .59), and finally science literacy ( r = .57). In sum, having high SWB is advantageous in youth. In particular, youth with high life satisfaction not only perceive that they are academically competent (Huebner, Gilman, & Laughlin, 1999), but these students with high SWB also typically perform be tter in academic subject areas (Kirkcaldy, Furnham, & Siefen, 2004; Suldo & Shaffer, 2008). challenges associated with academic tasks. Daily stresses of school (e.g., tests grades,
36 homework, academic and achievement expectations) are cited among the greatest stressors of high school students (Crystal et al., 1994; de Anda et al., 2000; Lohman & Jarvis, 2000). Having high l ife satisfaction may be advantageous for students as it has demonstrated a mediational role in the relationship between stressful environmental experiences and youth behavior problems (McKnight, Huebner, & Suldo, 2002). Students with high life satisfaction may also perceive that their academic aspirations are supported by adults and peers. Adolescents in Ireland and America reporting the highest SWB perceived significantly higher levels of social support from significant adults (i.e., parents, teachers; Nevin, Carr, Shevlin, Dooley, & Breaden, 2005; Suldo & Huebner, 2006; Suldo & Shaffer, 2008) as well as more positive attitudes towards their teachers (Gilman & Huebner, 2006). This is gainful, because having healthy interpersonal relationships with peers and adults promotes achievement motivation (Hall Land e, Eisenberg, Christenson, & Neumark Sztainer 2007; Nelson & DeBacker, 2008). One study in particular underscored the strong links between life satisfaction and perceived social support with a sample of 698 students in middle and high schools (Suldo & Hueb er, 2006). Life satisfaction was assessed via the SLSS and social support was measured using the Child and Adolescent Social Support Scale (CASSS; Malecki & Demaray, 2002). Suldo and Huebner (2006) found that adolescents reporting extremely high life satis faction (i.e., in the top 10% of life satisfaction scores relative to peers) reported the highest levels of social support from parents, a close friend, classmates, and particularly, teachers, compared to peers reporting average and low levels of life sati sfaction.
37 Research also supports concurrent relationships between SWB in youth and their in school behavior. A common problem in schools is bullying and victimization. In fact, the National center for Educational Statistics reported that during the 2005 2006 school year, 24% of public schools indicated bullying as a daily or weekly problem (Dinkes, Kemp, & Baum, 2009). The problem is particularly prevalent in middle schools (Dinkes, Kemp, & Baum, 2009). Student happiness is correlated with their behavior on school grounds, including acts of violence, bullying, and victimization. For example, Martin and Huebner (2007) investigated relationships between peer victimization and SWB (measured via the PANAS and MSLSS) among 571 middle school students. Peer vict Self Report (CSEQ SR; Crick & Grotpeter, 1996). The CSEQ SR assesses the frequency of three types of interactions: overt victimization, relational aggression, and being the recipient of supportive acts by peers. Results indicate that life satisfaction and positive affect were positively related to prosocial acts ( r = .49 and r = .41, respectively) and inversely related to reports of overt victimization and relational aggression ( r = 30 and r = .12, respectively) among participants. Additional studies have demonstrated concurrent linkages between lower levels of life satisfaction and problem behaviors that affect schooling. In a study with approximately 2,000 Caucasian and African Am erican middle school students, those who reported diminished life satisfaction reported carrying a gun or other weapon or being in a physical fight more frequently than peers with moderate to high reports of life satisfaction (Valois, Paxton, Aullig, & Hue bner, 2006). In a sample of 5,414 adolescents attending public high schools in South Carolina, MacDonald, Piquero, Valois, and Zullig
38 (2005) found similar results. Students who reported higher levels of life satisfaction were significantly less likely to r eport having carried a weapon in general ( t = 6.17), or on school property ( t = 5.34) in the past 30 days on a self report measure. Further, life satisfaction was also negatively associated with carrying guns ( t = 2.39) and engaging in physical fights i n the prior 12 months ( t = 8.07). There is very limited research relating life satisfaction and school attendance. Suldo and Shaffer (2008) found that students with high levels of SWB, as well as low psychopathology (i.e., students categorized as having c omplete mental health) had lower numbers of school absences compared to students categorized as having low SWB and low psychopathology (i.e., students categorized as vulnerable), underscoring the importance of SWB to school attendance. The studies previous ly mentioned provide support for the need to foster SWB in youth. SWB is related to higher school achievement and school attendance, as well as serves as a protective factor from engaging in risky behavior. The following section highlight s predictive relat ionships between life satisfaction and academic achievement and in school behavior in order to further solidify the benefits of a complete mental health model, which considers factors beyond psychopathology, and which may act as a ture school functioning. Predictive relationships. Research examining predictive links between SWB and developmental outcomes has been largely restricted to adult populations. This lack of data is unfortunate, because as Park (2004) has suggested, there is a need to target protective factors, such as life satisfaction, locus of control, and/or hope, in order to promote long term experiences of wellness among youth across their development. An example of longitudinal resear ch as indicators of positive functioning related to
39 achievement in youth involves a study of character strengths. Character refers to the individuals to do the right thing (P ark & Peterson, 2008). One study of 190 fifth grade and 131 eighth grade students from one middle school revealed that students who had the character strengths of perseverance, fairness, honesty, hope, gratitude, and perspective had higher end of the year grade point averages, after controlling for IQ scores, compared to students without these character strengths (Park & Peterson, 2006). However, these correlations were small, and results should be interpreted with caution pending replication. A study by F unk, Huebner, and Gilman (2000), evaluated life satisfaction, a component of SWB, in r elation to indicators of school functioning The study consisted of 99 high school students who participated in the longitudinal study at two time points separated by a y ear Results from this study revealed concurrent relationship, but not predictive relationships between life satisfaction and schooling. One study conducted with adults may be pertinent to future academic outcomes predicted by S WB in youth. Lewinsohn, Redner, and Seley (1991) examined a sample of over 2,000 adult participants, and found that adults who report low life satisfaction are at risk for future depression. This link between low life satisfaction and later depression is i mportant, as youth with diagnoses of depression diminished in academic performance in early adulthood ( Fergusson & Woodward, 2002 ). Empirical research examining indicators of wellness (i.e., life satisfaction and SWB) as a predictor of youth academic achie vement (e.g., GPA and standardized test scores) and in school behavior (e.g., disciplinary referrals and attendance) is needed to examine the extent to
40 which various levels of SWB in youth predict improvements and declines in academic achievement and in sc hool behavior. No studies have yet examined the comparative predictive validity of SWB and psychopathology in relation to academic outcomes in adolescents. Conclusions School based mental health services should provide children with the resources to thrive within the school building (Baker, 2008). Thriving results when children possess psychological characteristics that lend to durability, competencies that ensure adapta bility in adversity, and have access to supportive socializing institutions, such as schools, which allow them to be resilient over potential harmful risk environment (Luthar, Cicchetti, & Becker, 2000). Therefore, those in a position to work with students should focus on factors that allow youth to thrive. Despite a historical foundation of psychological research driven by a focus of psychopathology, new research suggests a need to shift from a psychology driven solely by p sychopathology to one that Csikszentmihalyi, 2000). The dual factor model of mental health (cf. Greenspoon & Saklofske, 2001; Suldo & Shaffer, 2008) examines positive indicators (i.e. SWB) as well as indicators of psychopathology (i.e., internalizing and externalizing behavior problems) and thus yields a picture of psychological functioning in youth that is more complete However, indicators of student outcomes predicted by this model have yet to be examined longitudinally Additionally, despite the fact that education is a highly valued aspect of child development in the United States (Berk, 2006), it has not been until recent decades that outcomes associated with SWB and aspects of c hild development, such as
41 school functioning, have been evaluated. The current study thus aim ed to address these two gaps in research. First, the study evaluate s how membership in one of four mental health groups (i.e., complete mental health, vulnerable, symptomatic but content or troubled) derived from integrated scores of wellness and psychopathology yielded from the dual factor model (Suldo & Shaffer, 2008) pr edict s academic achievement and in school behavior over a one year period. Additionally, this study contribute s to the literature by providing more data on the predictive value of SWB on academic achievement and in school behavior during adolescence. The e xtent to which internalizing and externalizing symptoms of psychopathology predicts subsequent achievement was also verified.
42 C hapter 3 Method The present study assess ed the utility of a dual factor model of mental health (cf. Greenspoon & S aklofske, 2001; Suldo & Schaffer, 2008) subsequent academic achievement and in school behavior. This chapter provides an overview of the participants in this current study and the process used to select participants for the study. N ext, procedures for data collection are delineated, including a review of the measures used in data collection. Last, variables examined in this study and analysis procedures are discussed. Participants The dataset analyzed in the current study is part of a larger research project investigating SWB and psychopathology in relation to academic achievement, attitudes towards school, physical health, and social relationships in middle school students (Suldo & Shaffer, 2008). However, data from a second wave of data collection that has not been examined in any prior investigation was also analyzed in the current study. In the spring of 2006 (Time 1 of the current study, data analyzed in Suldo & Shaffer, 2008) p articipants consisted of 349 students (341 of whom were retained for data analyses) enrolled in sixth through eighth at a large public middle school in the Southeastern United States, as well as the teachers from that school who were familiar with the stud ent participants. During the 2007 2008 school year, additional data on students enrolled in the
43 school records for the 2006 2007 school year (Time 2). Due to pr omotion of grade eight students to grade nine, as well as the transient nature of the population (i.e., students used to attain student records from multiple schools at Time 2. Ultimately, usable school records for 300 of the original student participants from the Time 1 data wave were ascertained as these students remained within the school district during the 2006 2007 school year and had mostly complete school reco rds at Time 2 Selection of Participants Student participants Students who initiated participation at Time 1 were required to be enrolled at the middle school and obtain written parental consent to participate in the longitudinal project. Once these two requirements were met, students were asked to sign a student assent form prior to administration of measures and collection of school record data. At Time 1, participant enrollment was limited to 350 students due to financial constraints. Data on one stud ent was incomplete, and therefore not included in the analysis. Additionally, eight students identified as multivariate outliers during determination of student mental health groups were not included. Demographic characteristics of the 341 student particip ants at Time 1 are included in Table 1. In the current study, student participants from Time 1 who (a) remained within the school district and (b) had data regarding course grades, standardized test scores, attendance records, and/or office disciplinary re ferrals (ODRs) were included in the Time 2 wave of data collection. Considering these inclusion criteria, 300 participants remained
44 in the study at Time 2, or approximately 88% of those in the original data set. Demographic characteristics of Time 2 studen t participants are provided in Table 1. A series of chi square and t tests between the student characteristics of the longitudinal sample and the participants lost to attrition was conducted to test for potential effects of sample attrition. First, chi square tests compared demographic characteristics of Wave 1 subjects ( N = 341) to those subjects remaining at Wave 2 ( N = 300) With an alpha level of .05, none of the following effects of demographics were 2 (1, N = 641) = 0.79, p 2 (1, N = 641) = 0.06, p 2 (1, N = 641) = .12, p 2 (1, N = 641) = 0.04, p = 2 (1, N = 641) = 0.06, p 2 (5, N = 641) = .35, p = .95). T hese results indicate that students who withdrew from the longitudinal sample were no more likely to be of a particular grade, gender, socioeconomic status, parental marital status, race, or mental health group, than those students who remained in t he stud y at Time 1 and Time 2. Next, data were analyzed using independent samples t test s These analyses indicated that participants who remained in the study across time ( N = 300) and students lost to attrition ( N = 41) did not differ on any mental health or academic function variable at Time 1, including: global life satisfaction ( t = .35, p = .73), positive affect ( t = .05, p =.96), negative affect ( t = .73, p = .47), internalizing psychopathology ( t = .65, p = .52), externalizing psychopathology ( t = .2 7, p = .79), GPA ( t = 1.44, p = .16), FCAT math ( t = .14, p = .89), FCAT reading ( t = .87, p = .38), absences ( t = 1.24, p = .21), and referrals ( t = .32, p = .75). These analyses indicate that students who withdrew from the longitudinal sample were no m ore
45 likely to have different levels of mental health functioning, academic achievement, or in school behavior than students who participated in both time points of the study.
46 Table 1 Demographic Characteristics of Participants at Time 1 and Time 2 Demographic Variable Time 1 Total Sample ( N = 341) % Time 2 Total Sample ( N = 300) % Gender Male 40.76 37.33 Female 59.24 62.67 Grade 6 32.84 33.67 7 36.07 36.00 8 31.09 30.33 Ethnicity Caucasian 55.43 55.67 African American 14.08 14.33 Asian 5.28 5.67 Hispanic/Latino 12.61 12.33 Native American 1.47 1.33 Multi racial 9.68 9.33 Other 1.47 1.33 Socioeconomic status Low 24.63 25.33 Average/High 75.37 74.67 Family Structure Married 60.41 61.33 Not Currently Married 39.59 38.67 Mental Health Group Complete Mental Health 56.89 56.67 Troubled 17.30 17.00 Vulnerable 12.90 14.33 Symptomatic but Content 12.90 12.00
47 Teacher participants At Time 1, participation was sought from teachers familiar with one or more student participants. Selected teachers provided written consent to participate and received incentives (i.e., $5 gift certificates) following valid completion of a behavior ratin g scale for a given student participant. A total of 44 teacher participants were included at Time 1. Further participation from teachers was not solicited at Time 2 st atus (specifically, externalizing psychopathology) at Time 1. Procedures This section reviews the procedures used to construct the archival dataset ( Time 1; Suldo & Shaffer, 2008), as well as procedures used to collect additional student information at Time 2 examined in the current study. The procedures used in the archival dataset were gleaned through written documentation elaborating upon the procedures used to produce the data set in the study by Suldo and Shaffer (2008) and will be summarized below. In November of 2005, approval to conduct the study was obtained from the University of South Florida Institutional Review Board as well as the school district in which the school was located. Consent was obtained from parents via a written parental conse nt form (see Appendix A) that students were asked to take home, share with their parents, and return to school after acquiring a parent signature. These procedures may have induced unequal gender representation in the sample, such that female students may have been more likely to have brought the consent forms home and/or returned them to school at a higher rate than their male counterparts. In January of 2006, students who had obtained parent permission were asked to check during
48 an elective class period on one of two dates allocated for data collection. In the media center, students gathered in groups of approximately 50 75 students. Before students responded to items within the questionnaire packet, the principal investig ator of the study read the student assent form (see Appendix B) aloud to all students in the media center. It was explained to the students that they had the right to withdraw from the study during any point of the data collection process. Following this p rocedure, students completed the demographic questionnaire, as well as other self report measures reviewed later within this chapter. The questionnaires contained in the packet were counterbalanced in order to control for potential order effects. Approxima tely 55 60 minutes were provided to allow students to complete their questionnaire packets. During the administration procedures, the principal investigator, along with graduate student research assistants, attended to all students to be certain they were responding independently. After a student completed his or her questionnaire packet, one member from the research team visually scanned each measure in the packet, checking for skipped items or errors in responding. When e rrors were detected, students were asked to complete the item(s) or correct the item(s) when appropriate. During the 2007 2008 school year the author of the current thesis worked with the data clerk of the targeted middle school in which the study by Su ldo and Shaffer academic achievement during the 2006 2007 school year. Specifically, this author me 2 in order to gather information regarding grades earned in classes, attendance records, and office disciplinary referrals. Student records were de identified by this author. Participants were
49 assigned an identification number. Grade point averages for each 9 week grading period were computed by hand (i.e., A = 4.0, B = 3.0, C = 2.0, D = 1.0, F = 0), and then entered in a SPSS file. This file was used to compute grade point averages by semester and finally, by year. For example, if a student received thr grading period they would be assigned a 3.50 GPA for that grading period. This academic data for Time 2 was then entered into the original SPSS spreadsheet created at Time 1, along with the Time 2 attendance data, ODRs and FCA T math and FCAT reading scores. Measures Demographics form. The questionnaire administered at Time 1 contains items regarding age, grade level, gender, socioeconomic status (SES), race/ethnicity, and family structure (e.g., my biological parents are marri ed, my biological parents are divorced; see Appendix C). The form also contained two sample questions in Likert scale form (e.g., I go to the beach) which were similar to the format of subsequent measures in the assessment packet handed out to students at Time 1. The research team used these items to train students how to complete Likert style questions. (SLSS; Huebner, 1991). The SLSS consists of seven items measuring global life satisfaction (see Appendix D). The question naire is designed for children in grades 3 to 12, and was completed by student participants at Time 1. Students were asked to indicate the extent to which they endorsed general ange many strongly disagree ) to 6 ( strongly agree ). Scaled scores are obtained by reverse scoring the two items that are negatively
50 worded, summing the responses, and finally dividing the sum by the n umber of items to yield an overall score of life satisfaction, such that higher mean scores represent higher levels of life satisfaction. The SLSS has demonstrated utility with diverse samples of youth, including students with emotional handicaps and stud ents diagnosed with learning disabilities (Huebner & Alderman, 1993) as well as children from diverse ethnic and language backgrounds (Huebner, 1995; Marques, Pais Ribiero, & Lopez, 2007). The SLSS has demonstrated high internal consistency (coefficient al pha = .82) as well as test retest reliability in a sample of 202 youth at 1 and 2 week intervals ( r = .74 and r = .68; Huebner, 1991). The SLSS has demonstrated moderate stability across a period of four weeks ( r = .64; Gilman & Huebner, 1997). In regards to construct validity, moderate convergent validity has been found (Huebner, 1991) between the SLSS and other measures of SWB, including the Happiness and Life Satisfaction subscale of the Piers Harris ( r = .53; Piers, 1984) and one item tapping life sati sfaction from the Andrews and Withey Life Satisfaction Scale ( r =.62; Andrews & Withey, 1976). Huebner (1991) has determined that the SLSS yields a small, non significant correlation with a measure of social desirability ( r = .05). Evidence of convergent v alidity has been found by r = provided via significant, negative correlations with measures of depression and loneliness (Huebner & Alderman, 1993). Positive and Negative Affect Scale for Children (PANAS C; Laurent, Catanzaro, Joiner, Rudolph, Potter, Lambert, Osborne, & Gathright, 1999). The PANAS
51 C was administered at Time 1 and is a 27 item self report scale (see Appendix E). Twelve of the items assess the frequency of positive affect and 15 items assess the frequency of negative affect. This scale measures the degree to which individuals experience positive and negative affect, by rating a l ist of 27 words that describe feelings and emotions, such very slightly or not at all ) to 5 ( extremely ). R espondents indicate the extent to which they have felt each mood or feeling in the past few weeks. The PANAS C was adapted to measure negative and positive affect in children and adolescents from the Positive and Negative Affect Scale, which was designed for adults (PANAS; Watson, Cl ark, & Tellegen, 1988). Earlier research has demonstrated a negative small correlation ( r = .16) between the positive affect and negative affect subscales of the PANAS C (Laurent et al., 1999). Internal consistency is high for the positive affect and negative affect subscales (alpha coefficient s of .90 and .94, respectively; Laurent et al., 1999). The PANAS C has demonstrated construct validity via its comparison to constructs which are different, but related (Seligson, Huebner, & Valois, 2005). Specifically, a study by Laurent et al. (1999) con firmed that the subscales have good convergent validity (positive affect, r = .20) and di scriminant validity (negative affect, r = .62) when compared to the Trait Anxiety Scale of the State Trait Anxiety Inventory for Children ( Spielberger, 1973 ). Similar ly, when compared to the ) the PANAS C also demonstrate s good construct validity ( positive affect, r = .42; negative affect, r = .59 ; Laurent et al., 1999). The Youth Self Report F orm of the Child Behavior Checklist (YSR; Achenbach & Rescorla, 2001). The YSR was administered to student participants at Time
52 behavior in children 11 to 18 years of age, including: anxious/depress ed, withdrawn/depressed, somatic complaints, rule breaking behavior, aggressive behavior, social problems, thought problems, and attention problems. This measure is com prised of 112 items aimed to measure these eight dimensions of psychopathology. Students are asked to consider the degree to which feelings or behaviors are accurate for them currently or in the past six months, responding on a 3 point Likert scale. The scale ranges from 0 ( not true ) to 2 ( very true or often true ). Only data from the followin g three subscales was analyzed in the current study: (a) anxious/depressed, (b) withdrawn depressed, and (c) somatic complaints. These three subscales form the internalizing ir own externalizing problems, an i ndex of externalizing symptoms i s provided by a different measure, specifically, one completed by a teacher. The YSR has proved efficacious at discriminating between youth with symptoms of psychopathology and those who h ave not been referred amongst diverse populations symptoms of internalizing problems has been demonstrated via correlations with checklists of diagnostic categories of the DSM IV ( r = .3 7 to .51; Achenbach & Rescorla, 2001) and c orrelations with subscales of the BASC ( r = .38 to .80; Achenbach & Rescorla, 2001). Additionally, the YSR has demonstrated high test retest reliability at 8 days on the internalizing problems, with coe fficient alphas ranging from .67 to .76. This rating scale is not included as an appendix due to copyright restrictions.
53 Teacher Report Form of the Child Behavior Checklist (TRF; Achenbach & Rescorla, 2001). The TRF was completed at Time 1 of the current study. The TRF is a scale which consists of 113 items that examine the same eight dimensions of psychopathology as the YSR. This measure is completed by teachers and school personnel who are familiar with children and adolescents ages 5 to 18. According to the agreement to an item on a Likert scale ranging from 0 to 2, with 0 ( not true ) to 2 ( very true or often true ). In the current study, only items from the TRF that assess externalizing psychopathology (i.e., rule breaking behavior and aggressive behavior subscales) were analyzed. Similar to the YSR, the TRF is efficacious at discriminating between children and adolescents referred for psychopathology and those who were not referred. Additionally, the TRF has demonstrated test retest reliability at 16 days with coefficient alphas ranging from .93 to .95. Finally, the T R F has been compared to the Conners Rating Scale for Teachers Revised (Conners, 1997) to yield high convergent validity (.81; Achenbach & Rescorla, 2001). This rating scale is not included as an appendix due to copyright restrictions. Indicators of Academic Achievement and In School Behavior Grade point average (GPA). Cumulative grade point averages were obtained from student school records during the 2005 2006 school year (Time 1) and 2006 2007 school year (Time 2). GPA was calculated by summing numerical values assigned to = 1.0, F= 0) and dividing by the total number of courses or credit hours attempted within a given
54 grading period (i.e., 9 w eek period or semester). For example, if a student received three h a 3.50 GPA for that grading period. Standardized test scores. In Florida, all students in grades 3 to 11 are administered the Florida Comprehensive Assessment Test (FCAT; Florida Department of Education, 2005) The FCAT is a norm referenced assessment t hat measures student progress towards statewide benchmarks (i.e., the Sunshine State Standards) in reading, math, writing, and science. Scores are assigned along a five level grading criteria (1 5), with Level 1 being the lowest and Level 5 the highest. St udents must score on or within Levels 3 math and FCAT reading during the 2005 2006 (Time 1) and 2006 2007 (Time 2) school years were analyzed. Attendance Time 1 attendance history is operationalized as th e total number of school days missed during the first three 9 week grading periods and 4/9 of the 4 th 9 week grading period (159 student days; the same time frame during which self report and teacher report data collection was conducted). For Time 2, days missed includes the entire 2006 2007 school year (186 student days). Higher scores indicate worse school attendance or in other words, more absences. To account for the discrepancy in total maximum number of days examined during Time 1 (i.e., 3.44 9 week grading periods) and Time 2 (i.e., four 9 is represented by a ratio of the number of days they missed divided by the number of 9 week grading periods i n the specific time period. Thus, if a student missed 10 days of in the curren t study would be 2.91 (i.e., 10 /3.44 ) for Time 1 and 2.5 (i.e., 10/ 4 ) for Time
55 2. In practical terms, this student woul d have m issed approximately 3 days a nd two to three days per 9 week grading period during Time 1 and Time 2, respectively. In school behavior. In school behavior is operationalized as the number of office disciplinary referrals (ODRs) a student received in a give n school year. Similar to attendance records, disciplinary referrals at Time 1 only refer to those ODRs received during the first three 9 week grading periods and 4/9 of the 4 th 9 week grading period (159 student days). For Time 2, the number of ODRs inclu des the entire 2006 2007 school year (186 student days). A higher frequency of ODRs indicates worse in school behavior. To account for the discrepancy in time frames during Time 1 and Time 2, are expressed as a ratio of the number of ODRs received in a given 9 week grading period. Thus, if a student received five ODRs during Time 1 and five (i.e., 5/3.44) for Time 1 and 1.25 (i.e., 5/4) for Time 2. In practical terms, this student would have received approximately 1.5 and 1.25 ODRs per 9 week grading period during Time 1 and Time 2, respectively. Preliminary Analysis: Group Assignments To answer the research question s of interest to the current study, students were first assigned to mental health groups. Mental health groups assigned in previous work with this database (cf. Suldo & Shaffer, 2008), were retained in order to address questions in the current study. In th e study by Suldo and Shaffer (2008), students were assigned to mental health groups based on their composite scores on measures of psychopathology (i.e., externalizing and internalizing symptoms) and on measures of SWB (i.e., life satisfaction, positive an d negative affect). Published T scores provided by
56 Achenbach and Rescorla (2001) were used to establish cut score of 60 on a measure of internalizing symptoms via the YSR (Achenbach & Rescorla, 2001) and/or a measure of externalizing symptoms via the TRF (Achenbach & Rescorla, 2001). In sum, participants who scored at or above the published T scores on either measure were y. Clinical published norms for SWB have not yet been made available. Therefore, o ff point that corresponded to the 30 th percentile was selected, as it would mathematically allow for participants who had been defined as having high psychopathology to also be categorized as having low SWB. More specifically, if students reported their SW B composite was greater than 30% of their peers in the study at Time 1 (i.e., above the 30 th SWB scores that were less than or equal to 30% of their peers (i.e., below t he 30 th The mental health group to which a given participant was assigned at Time 1 (cf. Suldo & Shaff er, 2008), has been retained in the current study in order to determine the
57 relationship between mental health group at Time 1 and educational functioning at Time 2. Overview of Data Analysis Plan The relation of SWB and psychopathology to subsequent achievement and in school behavior. In order to address the first two research questions, if SWB and ps ychopathology at Time 1 predict school behavior (i.e., GPA, FCAT math, FCAT reading, absences, ODRs) at Time 2, two series of multiple regression analyses were conducted five separate times due to the fact that five dependent variables (different indicators of educational achievement) were e xamined In each regression student SES and parental marital status were e ntered as covariates becau s e they were consistently differentially represented among the four mental health groups during Time 1 (Suldo & Shaffer, 2008) T herefore, t hese covariates were entered in all analyses to control for the potential influences of th ese demographic variables on the outcomes of interest. To determine if SWB scores at Time 1 predict student achievement and in school behavior (i.e., GPA, FCAT math scores, FCAT reading scores, absences, and ODRs) at Time 2, regressions were conducted in which SWB at Time 1 wa s regressed on a specific educational outcome variable. A composite SWB variable was created from the measures of global life satisfaction (SLSS) and positive and negative affect (PANAS C). Educational achievement and in school behavi or at Time 1 was controlled for statistically by entering it prior to Time 1 SWB in all equations. Alpha has been set at .05 to determine statistical significance for a given analysis.
58 Similarly, a series of five separate regression equations were conducted to determine the extent to which psychopathology scores at Time 1 predict each indicator of student achievement and in school behavior (i.e., GPA, FCAT math scores, FCAT reading scores, absences, and ODRs) at Time 2. Psychopathology is represent ed by reported externalizing and student reported internalizing symptoms on the Achenbach System of Empirically Based Assessment (Achenbach & Rescorla, 2001). Student demographic characteristics e ducational achievement and in school behavior at Time 1 variable of interest were again controlled for statistically by entering the variables first in all five regression equations. Group membership and outcomes. To d etermine if membership in one of the four subgroups of mental health (i.e., complete mental health, vulnerable, symptomatic but content, or troubled) at Time 1 is related to changes in achievement and in school behavior between Time 1 and Time 2 and relate d to subsequent achievement and in school behavior (at Time 2) after controlling for between group differences in SES and parental mar i tal status a series of mixed model analyses of co variance (AN C OVA), repeated measure design were conducted due to the f act that five dependent variables (different indicators of educational achievement) were examined Specifically, mental health group membership at Time 1 was used as a between subjects factor and time of assessment of academic achievement was used as the level of repeated measures factor. Potential covariates were controlled for (e.g., SES, parental marital status) when determining if mental health groups differ on change in school functioning outcomes (i.e., GPA, FCAT attendance, ODRs ). Addi tionally, pairwise follow up tests were
59 employed to determine how mental groups differed from one another on achievement and in school behavior at Time 2. Limitations and Delimitations Several factors that may compromise the validity of the study will b e reviewed in this section. First, it should be noted that both waves of the data used were gleaned from archival data sets. This factor is a major delimitation of the study, as the author of the current manuscript was not able to control the content of qu estionnaires used (which determined the categorization of the students in mental health groups) during Time 1, nor the procedures that were involved during data collection at Time 1. Nonetheless, written documents completed by the research team who collect ed the archival data set provide evidence that appropriate steps were taken to prevent threats to the validity of the data during collection. For example, researchers monitored student completion of the questionnaire packets such that participants were abl e to ask questions and receive standardized answers from researchers regarding the measures. To ensure the safety and well being of participants, members of the research team were available if student participants asked about withdrawing from the study and /or if students looked as if they were upset ( e.g., tearful, angry). Situations such as these were not reported. Students were allowed privacy while completing forms, and were seated in such a way as to prevent fellow participants from being able to view t heir responses. Additionally, researchers checked student responses to ensure that measures were completed appropriately. Measures included in the questionnaire packets were counterbalanced, which controlled for order effects. Teacher participants who were asked to complete rating scales
60 given contact information for a member of the research team who would be available to answer teacher questions and concerns. In sum no a dverse events which may have compromised the validity of the study co occurred when administering measures to teachers or students during the 2006 wave of data collection. Additional delimitations must be addressed regarding the second wave of data colle ction. Of note is the attrition of 12% of the original sample. Additionally, during the time between Time 1 and Time psychopathology may have changed, which may have impacted their academic achievement and in school behavior. Although the utility of this study is to determine if mental health status as categorized b y the dual factor model is able to predict academic achievement over a one year period, factors that may have impact ed student mental health during that period (i.e., trauma, provision of mental health services) are unknown and may confound results. Ecolo gical validity, also referred to as ecological transferability, is the ability of the researcher to generalize the results of a study across settings (Tashakkori & Teddlie, 2003). Violations to ecological validity include the tendency of the researcher to draw erroneous conclusions to populations with different settings than the population under study. Although students currently reside in different schools, participants used in this study were selected from one middle school in one school district. Therefo re, population and ecological transferability of the research has been minimized (Tashakkori & Teddlie, 2003). Along these lines, the middle school from which participants were originally recruited from in this study is located within a middle to high SES community; therefore generalizations of results to lower SES areas are made cautiously. Generalizations of
61 results to rural communities are also considered since the middle school is located in an urban district. Finally, the majority of students in the c urrent study are Caucasian, therefore extending the findings from the current study to more diverse populations should be done cautiously. At the onset of the study, a convenience sampling method was employed; therefore students who agreed to participate i n the research study may differ from students who declined to participate. These unique characteristics of the sample population may in turn limit the extent to which conclusions drawn from this study can be applied to other populations as well as the scho ol population from which it was drawn.
62 Chapter 4 Results This chapter provides the results of the analyses conducted to address the research questions of interest in the current study. First, correlations among variables are provided to illustrate the relationships between mental health (i.e., subjective well being, psychopathology, and mental health group membership), academic achievement, and in school behavior among ado lescents. Next, results from regression analyses conducted to achievement and in school behavior (i.e., GPA, FCAT math scores, FCAT reading scores, absences, and ODRs) at Time 2 ar e summarized. Then, results of five mixed model AN C OVAs conducted to determine if membership in one of the four subgroups of mental health (i.e., complete mental health, vulnerable, symptomatic but content, or troubled) at Time 1 is related to subsequent academic achievement and in school behavior (at Time 2) are presented. Data Screening During data entry for the original research study that yielded the Time 1dataset analyzed in the current study, data were checked for errors and accuracy (Suldo & Schaffer, 2008). For the current study, Time 2 GPA was manually computed from school records (specifically, letter grades earned in each class per 9 week grading period were clerk. The GPA values for each 9 wee k grading period were then entered into the
63 original SPSS spreadsheet created at Time 1, along with the Time 2 attendance data, office disciplinary referrals, and FCAT math and FCAT reading scores. Time 2 data and data from Time 1 were screened using Stat istical Analysis Software (SAS) to detect the presence of either univariate and/or multivariate outliers. Univariate outliers were defined as participants scoring more than 4 standard deviations from the group mean on any variable of interest (i.e., SWB, i nternalizing problems, externalizing problems, GPA, FCAT reading, FCAT math, absences, office disciplinary referrals). Out of 300 student participants, ten were identified as being extreme univariate outliers, defined by having a standard score of 4 or gre ater on a Time 2 outcome of achievement or in school behavior. Another 9 participants (out of 290), were identified as extreme univariate outliers, defined by having a standard score of 4 or greater on a Time 1 achievement or in school behavior outcome. Ho wever, participants found to be univariate outliers on Time 1 and Time 2 variables were retained in the dataset due to the nature of this study. Specifically, youth with extreme levels of psychopathology and/or SWB were anticipated to have substantially wo rse or better academic or in school behavior than their peers. As a follow up to these univariate methods Cook D values were used. The Cook D value for each participant is the measurement of the parameter estimate change in analysis with that participa nt compared with the estimate without that participant. A larger value indicates that the participant is more different from the remaining participants. All Cook < 1.0 and therefore retained (Stevens, 2009). Thus, the dataset retained for al l subsequent analyses consisted of 300 participants; no univariate outliers were removed.
64 Scale Reliability Prior to further analyses, all scales utilized within the study (i.e., SLSS, PANAS C, YSR internalizing composite, TRF externalizing composite) we re analyzed to determine the internal consistency of each within the sample of 300 students. As previously mentioned SWB is comprised of three separate, but inter related constructs (i.e., life satisfaction, positive affect, and negative affect). Internal consistency (coefficient alpha) for the 7 item SLSS was .90. Coefficient alpha values for the 12 and 15 item subscales of the PANAS C were .87 and .94 for positive and negative affect, respectively. The internal consistency of the 31 item YSR internaliz ing composite was .89. Lastly, the 32 item TRF externalizing composite yielded a coefficient alpha of .89. Descriptive Analyses Descriptive statistics for all variables of interest in the data set are presented in Table 2. As shown in T able 2 PAs in the current sample declined from Time 1 ( M = 3.48) to Time 2 ( M = 3 .16). This declining trend was observed across all four mental health groups and is commensurate with prior research which explores the effect of transitions (i.e., from middle to hi gh school) among youth (Benner & Graham, 2009; Isakson & Jarvis, 1999 ). Of note, regarding the mean rate of student absences by participants, the numbers obtained (i.e., 1.35 days missed per 9 week grading period at Time 1 and 1.70 absences per grading per iod at Time 2) are similar to the school wide attendance data for that school during a recent school year. Specifically, during the 2007 2008 school years, students at the school that participated in the current study missed an average of 2 days during a 9 week grading period ( Florida Department of Education's Bureau of School Improvement 2010)
65 Table 2 Means, Standard Deviations, Ranges, Skew, and Kurtosis of Raw/Non Transformed Variables Variable N M SD Range Skewness Kurtosis Predictor T1 SWB 300 0.02 2.36 8.3 4.0 .91 .68 T1 Internalizing 300 11.17 8.19 0 46.0 1.10 1.21 T1 Externalizing 300 2.14 4.31 0 26.0 2.78 8.28 T1 GPA 298 3.48 0.61 1.1 4.0 1.32 1.09 T1 FCAT Reading 296 3.34 1.15 1.0 5.0 0.39 0.66 T1 FCAT Math 296 3.63 1.23 1.0 5.0 0.51 0.74 T1 Absences 298 1.35 1.42 0 9.9 1.97 6.13 T1 ODRs 298 0.10 0.28 0 1.7 3.57 13.74 Outcome T2 GPA 299 3.16 0.82 0.6 4.0 0.98 0.30 T2 FCAT Reading 298 3.21 1.17 1.0 5.0 0.22 0.78 T2 FCAT Math 298 3.69 1.16 1.0 5.0 0.51 0.63 T2 Absences 300 1.70 2.36 0 20.8 4.26 26.54 T2 ODRs 300 0.19 0.51 0 4.25 3.86 18.72 Note. Higher scores reflect increased levels of the construct indicated by the variable name. To assess univariate normality, skew and kurtosis of each of the 13 variables were calculated. At Time 1, three variables had a normal distribution (skewness and kurtosis between 1.0 and +1.0) and five variables demonstrated values of skew and kurtosis th at
66 were outside normal limits. These five variables include: GPA (skew = 1.32, kurtosis = 1.09), absences (skew = 1.97, kurtosis = 6.13), ODRs (skew = 3.57, kurtosis = 13.74), internalizing problems (skew= 1.10, kurtosis= 1.28), and externalizing problems (skew = 2.78, kurtosis = 8.28). Obtained values for three of five variables at Time 2 were between 1.0 and +1.0, demonstrating a normal distribution of scores on each of these target variables. Two variables with abnormal distributions included absences (skew = 4.26, kurtosis = 26.54) and ODRs (skew = 3.86, kurtosis = 18.74). The seven Time 1 and Time 2 variables that did not meet criteria for normal distribution were then transformed, using procedures recommended in Tabachnick and Fiddell (2001). Four of these transformed variables evidenced distributions that approximated normal distribution s (i .e., skew and kurtosis values near the range of 1 to +1). These four variables included: internalizing psychopathology (transformed by taking the square root of the raw variable; skew = .28, kurtosis = .28), externalizing psychopathology (transformed by taking the logarithm of the raw variable; skew = 1.25, kurtosis= .34), Time 1 GPA (transformed by taking the square root of the raw variable; skew = 1.02, kurt osis = .14), and Time 1 absences (transformed by taking the square root of the raw variable; skew = 1.08, kurtosis = 1.31). After transformation (i.e., transformed by taking the inverse of the Time 1 ODRs variable, square root of the Time 2 absences vari able, and inverse of the Time 2 ODRs variable respectively) the distributions of the remaining three variables (i.e., Time 1 ODRs, Time 2 absences, and Time 2 ODRs) improved, but were still problematic (i.e., yielded values for skewness between 2 and 3, and kurtosis between 3 and 7).
67 To determine the extent to which employing the transformed versions of the variables would affect the study results, analyses of interest (i.e., correlational analyses to evaluate relationships between variables, multiple r egression analyses to determine the mixed model AN C function of mental health group membership) were conducted twice (first employing the raw/original versions of all variables, and second using the transformed versions of the seven problematic variables) Results from correlational analyses revealed that the pattern and magnitude of almost all relationships remained the same except for two relationships. Specifically, when the transformed version of Time 2 absences was used, the absolute value of the correl ation between SWB and Time 2 absences equaled .14 (vs. 11, as reported in Table 3), and the probability of the relationship changed from a p = .06 to p = .02. Similarly, when the transformed version of Time 2 ODRs was utilized, the absolute value of the co rrelation between SWB and Time 2 ODRs equaled .16 (vs. 11, as reported in Table 3), and the probability of the relationship changed from p = .06 to p = .01. Regarding results of repeated regression analyses that employed original/raw and then transformed v ariables, nine out of the ten regression analyses yielded the same results with respect to pattern, magnitude, and reliability of relationships between indices of mental health and subsequent academic functioning. The one exception involved the regression that explored the extent to which externalizing psychopathology at Time 1 math at Time 2; when transformed variables were employed, externalizing as a predictor of Time 2 FCAT math strengthened to just beyond the thr eshold needed to be considered statistically significant ( = .07, p = .04,
68 vs. = .04, p = .19, as reported in Table 6). Finally, primary results of the five mixed model AN C OVAs (i.e. significance levels of group x time interactions) were the same wh ether transformed or non transformed variables were employed in the analyses. Because relationships between mental health indicators and academic outcomes were similar in the vast majority of exploratory analyses, results of analyses conducted with the raw /original/non transformed versions of all variables are reported for all subsequent analyses in the current study. Correlational Analyses To determine the relationships between predictor and outcome variables within the sample of students, Pearson product moment correlation coefficients were calculated between all variables. Correlations among all continuous variables included in analyses are presented in Table 3. An alpha level of .05 was used to determine statistical significance. As expected, SWB was ne gatively correlated with internalizing problems and externalizing problems ( r = .68, p < .05, r = .13, p < .05, respectively). Of particular interest are longitudinal relationships between Time 1 mental health indicators and Time 2 academic achievement a nd in school behavior indicators. SWB was correlated in a positive direction with the following Time 2 academic functioning variables: GPA ( r =.30, p < .05), FCAT reading ( r =.23, p < .05), and FCAT math ( r =.24, p < .05). These results indicate that there is a positive correlation between SWB and academic achievement, whereas no relationships between SWB and indicators of later in school behavior emerged. Internalizing problems were significantly, negatively associated with GPA ( r = .16, p < .05) and FCAT math ( r = .17, p < .05) but not FCAT reading Internalizing problems were also positively associated with absences ( r = .15, p < .05),
69 but did not demonstrate a significant relationship with ODRs. Externalizing problems were also significantly, negatively associated with GPA ( r = .49, p < .05), FCAT reading ( r = .27, p < .05) and FCAT math ( r = .29, p < .05), as well as positively associated with absences ( r = .24, p < .05), and ODRs ( r = .46, p < .05). In sum, initial psychopathology (interna lizing and externalizing) was consistently, inversely associated with two of the three indicators of academic functioning (i.e., GPA and FCAT m ath ) and externalizing problems in the sample were related to being absent from school and receiving ODRs, in add ition to FCAT Reading. Regarding longitudinal relationships between indicators of academic functioning, student GPA at Time 1 was positively correlated with the following Time 2 variables: GPA ( r = .67, p < .05), FCAT reading ( r = .51, p < .05), and FCAT math ( r = .58, p < .05), as well as inversely related to absences ( r = .32, p < .05) and ODRs ( r = .45, p < .05) at Time 2. FCAT math at Time 1 was positively associated with the following Time 2 variables: GPA ( r = .60, p < .05), FCAT Reading ( r = .72, p < .05), and FCAT math ( r = .86, p < .05), and inversely associated with absences ( r = .26, p < .05) and ODRs ( r = .24, p < .05) at Time 2. Absences at Time 1 were negatively associated with the following Time 2 variables: GPA ( r = .31, p < 05), FCAT reading ( r = .20, p < .05), FCAT math ( r = .19, p < .05), as well as positively associated with absences ( r = .54, p < .05) and ODRs ( r = .21, p < .05) at Time 2. Finally, ODRs at Time 1 were negatively associated with the following Time 2 variables: GPA ( r = .41, p < .05), FCAT reading ( r = .35, p < .05), and FCAT math ( r = .35, p < .05), and positively associated with absences ( r = .20, p < .05 ) and ODRs ( r = .50, p < .05) at Time 2. In conclusion, all school functioning variables at Time 1 predicted all indicators of school functioning at Time 2.
70 Table 3 Correlations between Predictor and Outcome Variables ( N = 300) Variables Time 1 Variables Time 1 Variables Below the Diagonal Time 2 Variables Above and On the Diagonal Time 1 Variables 1. SWB 2. Int. 3. Ext. 4. GPA 5. Read 6. Math 7. Abs. 8. ODRs 1. SWB ---.30* .23* .24* .11 .11 2. Internalizing Problems .68* --.16* .09 .16* .15* .04 3. Externalizing Problems .13* .02 -.49* .27* .29* .24* .46* 4. GPA .24* .13 .38* .67* .51* .58* .32* .45* 5. FCAT Reading .27* .14 .30* .53* .79* .73* .20* .28* 6. FCAT Math .25* .16* .28* .61* .76* .86* .26* .24* 7. Absences .09 .08 .13* .33* .16* .20* .54* .21* 8. ODRs .08 .02 .53* .43* .36* .33* .13* .50* Note Values on the diagonal and in bold are correlations between the variables measuring the same construct at Time 1 and Time 2. Values below the diagonal are intercorrelations among Time 1 variables, and valu es above the diagonal represent relationships between the same variables at two different time points. p < .05
71 Regression Analyses To determine the extent to which SWB and psychopathology were predictive of math, FCAT reading) and in school behavior (i.e., absences and ODRs), ten multiple regression analyses were conducted E ach of the five outcomes of interest were predicted using control variables and SWB, and then each of the five outcomes were predicted using control variable and psychopathology An alpha level of .05 was used to determine statistical significance. Beta weights (sta ndardized multiple regression coefficients) and uniqueness indices were subsequently reviewed to assess the relative importance of the predictor variables and covariates in the prediction of the five school functioning variables at Time 2. The uniqueness i ndex for a given predictor is the percentage of variance in the criterion accounted for by the predictor, beyond the variance accounted from by the other predictor variables. Five regression equations were computed for estimating the effects of SWB on sub sequent achievement and in school behavior. Beta weights and uniqueness indices are presented in Tables 4 and 5. Time 1 GPA, SES, parent marital status, and SWB accounted for 47% of the variance in GPA at Time 2, F (4, 292) = 66.38, p <.0001, adjusted R 2 = .47. A notable finding from this analysis is that SWB accounted for 1% of the unique variance in GPA at Time 2. Specifically, students with higher SWB at Time 1 were more likely to increase their GPA over the next year. Time 1 FCAT math, SES, parent marita l status, and SWB accounted for 75% of the variance in FCAT math at Time 2, F (4, 290) = 217.05, p <.0001, adjusted R 2 = .75. SWB was not a significant predictor of Time 2 FCAT math after the influence of the other variables in the model was
72 accounted for. Similarly, Time 1 FCAT reading, SES, parent marital status, and SWB accounted for 64% of the variance in FCAT reading at Time 2, F (4, 289) = 130.83, p <.0001, adjusted R 2 = .64, but SWB was not a significant predictor of Time 2 FCAT reading after the influ ence of the additional variables in the model was accounted for. Time 1 absences, SES, parent marital status, and SWB accounted for 30% of the variance in absences at Time 2, F (4, 293) = 32.34, p <.0001, adjusted R 2 = .30. SWB was not a significant predict or of Time 2 absences after the influence of the other variables in the model was accounted for. Time 1 ODRs, SES, parent marital status, and SWB accounted for 24% of the variance in ODRs at Time 2, F (4, 293) = 24.75, p <.0001, adjusted R 2 = .24. SWB was n ot a significant predictor of Time 2 ODRs after the influence of the other variables in the model was accounted for.
73 Table 4 Student Academic Achievement Predicted by Initial SWB and Previous School Functioning Parameter Estimates Uniqueness Indices Outcomes R 2 B SE B sr t T2 GPA .47 1. T1 SES .05 .09 .03 .00 .60 2. T1 Married .18 .08 .11 .01 2.27* 3. TI GPA .78 .06 .59 .26 12.14*** 4. T1 SWB .04 .02 .12 .01 2.75** T2 FCAT math .75 1. T1 SES .08 .09 .03 .00 .87 2. T1 Married .12 .08 .05 .00 1.51 3. T1 FCAT math .79 .03 .83 .52 24.46*** 4. T1 SWB .01 .02 .02 .00 0.59 T2 FCAT reading .64 1. T1 SES .20 .11 .08 .00 1.86 2. T1 Married .22 .09 .09 .01 2.34* 3. T1 FCAT reading .75 .04 .74 .43 18.60*** 4. T1 SWB .01 .02 .02 .00 .41 Note. sr = squared semipartial correlation p < .05, ** p < .01, *** p < .0001
74 Table 5 Student In School Behavior Predicted by Initial SWB and Previous School Functioning Parameter Estimates Uniqueness Indices Outcomes R 2 B SE B sr t T2 Absences .30 1. T1 SES .32 .26 .07 .00 1.21 2. T1 Married .25 .24 .06 .00 1.04 3. T1 Absences .78 .07 .52 .26 10.49*** 4. T1 SWB .04 .05 .04 .00 .85 T2 ODRs .24 1. T1 SES .01 .07 .00 .00 .08 2. T1 Married .05 .06 .05 .00 .91 3. T1 ODRs .85 .09 .48 .21 8.99*** 4. T1 SWB .01 .01 .06 .00 1.13 Note sr = squared semipartial correlation p < .05, ** p < .01, *** p < .0001 Similarly, five separate regression equations were computed for estimating the effects of psychopathology on subsequent achievement and in school behavior. Beta weights and uniqueness indices are presented in Tables 6 and 7. Time 1 GPA, SES, parent marital status, internalizing problems and externalizing problems accounted for 52% of the variance in GPA at Time 2, F (5, 291) = 64.06, p <.0001, adjusted R 2 = .52. Externalizing problems uniquely accounted for 6% of the variance in GPA at Time 2. Time 1 FCAT ma th, SES, parent marital status, internalizing problems, and externalizing problems accounted for 75% of the variance in FCAT math at Time 2, F (5, 289) =
75 174.65, p <.0001, adjusted R 2 = .75. Time 1 FCAT reading, SES, parent marital status, internalizing pr oblems, and externalizing problems accounted for 64% of the variance in FCAT reading at Time 2, F (5, 288) = 105.13, p <.0001, adjusted R 2 = .64. Notably, neither internalizing nor externalizing problems contributed to the variance in FCAT scores. Time 1 ab sences, SES, marital status, internalizing problems, and externalizing problems accounted for 32% of the variance in absences at Time 2, F (5, 292) = 29.58, p <.0001, adjusted R 2 = .32. Internalizing problems uniquely accounted for 1% and externalizing pro blems accounted for 2% of the variance in absences at Time 2. Time 1 ODRs, SES, parent marital status, internalizing problems, and externalizing problems accounted for 29% of the unique variance in ODRs at Time 2, F (5, 292) = 25.36, p <.0001, adjusted R 2 = .29. Externalizing problems at Time1 uniquely accounted for 5% of the variance in ODRs at Time 2. Overall, these analyses indicate that students with more symptoms of externalizing psychopathology at Time 1 were more likely to experience decreases in GPAs as well as increases in ODRs, the following school year. Additionally, higher initial levels of internalizing and externalizing problems accounted for significant increases in school absences at Time 2, even after initial absences were accounted for stat istically.
76 Table 6 Student Academic Achievement Predicted by Initial Psychopathology and Previous School Functioning Parameter Estimates Uniqueness Indices Outcomes and Predictors R 2 B SE B sr t T2 GPA .52 1. T1 SES .01 .09 .01 .00 .12 2. T1 Married .15 .08 .09 .01 1.95 3. T1 GPA .69 .06 .52 .20 11.05*** 4. T1 Internalizing .01 .00 .06 .00 1.37 5. T1 Externalizing .05 .01 .27 .06 6.02*** T2 FCAT math .75 1. T1 SES .07 .09 .03 .00 .74 2. T1 Married .10 .08 .04 .00 1.26 3. FCAT math .78 .03 .82 .50 24.22*** 4. T1 Internalizing .00 .00 .03 .00 .88 5. T1 Externalizing .01 .01 .04 .00 1.31 T2 FCAT reading .64 1. T1 SES .19 .11 .07 .00 1.72 2. T1 Married .22 .10 .09 .01 2.30* 3. T1 FCAT reading .74 .04 .73 .42 18.52*** 4. T1 Internalizing .01 .01 .03 .00 .93 5. T1 Externalizing .01 .01 .03 .00 .84 Note sr = squared semipartial correlation p < .05, ** p < .01, *** p < .0001
77 Table 7 Student In School Behavior Predicted by Initial Psychopathology and Previous School Functioning Parameter Estimates Uniqueness Indices Outcomes and Predictors R 2 B SE B sr t T2 Absences .32 1. T1 SES .18 .26 .04 .00 .68 2. T1 Married .10 .24 .02 .00 .40 3. T1 Absences .75 .07 .50 .24 10.32*** 4. T1 Internalizing .03 .01 .10 .01 2.13* 5. T1 Externalizing .08 .02 .16 .02 3.12* T2 ODRs .29 1. T1 SES .02 .07 .02 .00 .32 2. T1 Married .03 .06 .03 .00 .46 3. T1 ODRs .63 .10 .35 .09 5.99*** 4. T1 Internalizing .00 .00 .04 .00 .76 5. T1 Externalizing .03 .01 .27 .05 4.67*** Note sr = squared semipartial correlation p < .05, ** p < .01, *** p < .0001 Student Mental Health Group Membership and Academic Outcomes Results were analyzed using five mixed model AN C OVAs with repeated measures on one factor in order to determine the extent to which membership in one of the four subgroups of mental health (i.e., complete mental health, vulnerable, symptomatic but content, or troubled) at Time 1 predicted student achiev ement (i.e.,
78 GPA, FCAT math, FCAT reading) and in school behavior (i.e., absences, ODRs) at Time 2. In regards to student GPAs, the Mental Health Group x Time interaction was significant, F (3,291) = 2.93, p < .05, as was the main effect for group F (3,291) = 8.33, p < .05. This analysis did not reveal a significant effect for time F (1,291) = .19, ns Full results are presented in Table 8. Table 8 AN C OVA Summary Table for Investigating the Relationship of Student Mental Health Group Membership to Subsequent G PA Source df SS MS F Between Subjects 296 212.34 T1 Mental Health Group 3 15.01 5.00 8.33* T1 SES 1 12.16 12.16 20.25* T1 Married 1 10.34 10.34 17.22* Residual between 291 174.83 .60 Within Subjects 297 52.89 Time 1 .03 .03 .19 Mental Health Group x Time Interaction 3 1.54 .51 2.93* T1 SES x Time Interaction 1 .05 .05 .29 T1 Married x Time Interaction 1 .36 .36 2.06 Residual within 291 50.91 .18 Total 593 265.23 Note. N = 300 p < .05
79 Adjusted means from Time 1 and Time 2 were used to determine the influence of mental health group on GPA in Figure 1, as well as in each subsequent figure. As shown, the influence of mental health group on GPA appears most influential within the troubled m ental health group. Specifically, the slopes of the regression lines for students in the troubled mental health (slope = .53) were significantly different from youth in the complete mental health (slope = .25, p <.01) and vulnerable (slope = .23, p <.05 ) groups, such that students belonging to the troubled mental health group declined at a significantly faster rate than students in the complete mental health and vulnerable groups .34) was not significantly different from youth with complete mental health vulnerable youth or troubled youth Figure 1 CMH = complete mental health and SBC= symptomatic but c ontent Adjusted means from Time 1 and Time 2 are plotted. 2 2.5 3 3.5 4 T1 T2 CMH Troubled Vulnerable SBC Figure 1 Chan
80 interpret significant interaction effects, follow up tests were employed to determine how mental groups differed from one anoth er on achievement at Time 2. These tests included comparisons of adjusted group means. As illustrated in Table 13, students with complete mental health had higher mean GPA at Time 2 compared to troubled and symptomatic but content youth. Of note, symptomat ic but content youth were similar in regards to GPAs to youth in the vulnerable group. Therefore, having average to high SWB, even in the face of the psychopathology, may put students at the same level (at one point in time) as their peers who do not have clinical levels of psychopathology (despite having low SWB). Perhaps most notably, mental health groups with similar levels of psychopathology at Time 1 had similar GPAs at Time 2, regardless of level of SWB. Examining student performance on the FCAT math the Mental Health Group x Time interaction was not significant, F (3,289) = 0.23, ns as displayed in Table 9. The effect for time was also not significant. In line with the nature of the design of this study, the main effe ct for group was significant, F (3, 289) = 3.78, p < .05.
81 Table 9 AN C OVA Summary Table for Investigating the Relationship of Student Mental Health Group Membership to Subsequent FCAT math Source df SS MS F Between Subjects 294 679.43 T1 Mental Health Group 3 22.46 7.49 3.78* T1 SES 1 63.88 63.88 32.29* T1 Married 1 21.42 21.42 10.83* Residual between 289 571.67 1.98 Within Subjects 295 57.74 Time 1 .34 .34 1.72 Mental Health Group x Time Interaction 3 .13 .04 0.23 T1 SES x Time Interaction 1 .33 .33 1.69 T1 Married x Time Interaction 1 .01 .01 .06 Residual within 289 56.93 .20 Total 589 737.17 Note N = 300 p < .05 As shown in Figure 2, mental health groups did not significantly differ in the slope of the regression lines that depict change across time in FCAT math scores by group. Specific slopes were as follows: .05 for complete mental health, .06 for troubled, .0 4 for vulnerable, and .14 for symptomatic but content.
82 Figure 2. CMH = complete mental health and SBC= symptomatic but content. Adjusted means from Time 1 and Time 2 are plotted. At Time 2, as depicted in Table 13, student in the complete mental health group performed significantly higher on the FCAT math assessment than students in the troubled group. Of note, symptomatic but content youth and vulnerable youth were similar to youth i n the troubled group, as well as students in the complete mental health group in regards to FCAT math scores. Therefore, having average to high SWB, despite clinical levels of psychopathology, may again place students at the same level as their peers who d o not have clinical levels of psychopathology (despite having low SWB). reading scores, the Mental Health Group x Time interaction was also not significant, F (3,288) = 1.26, ns but as anticipated the main effect for group F (3,288 ) = 2.71, p < .05 was significant. The main effect of time was not significant. Full results of the AN C OVA are presented in Table 10. Similar to FCAT math, mental health groups did not significantly differ in the slope of the regression lines, 3 4 5 T1 T2 CMH Troubled Vulnerble SBC Figure 2. math scores over time
83 as depicted in Figure 3. Specific values of slopes are as follows: .19 for complete mental health, .00 for troubled, .01 for vulnerable, and .14 for symptomatic but content. Table 10 AN C OVA Summary Table for Investigating the Relationship of Student Mental Health G roup Membership to Subsequent FCAT reading Source df SS MS F Between Subjects 293 624.92 T1 Mental Health Group 3 15.07 5.02 2.71* T1 SES 1 61.08 61.08 32.93* T1 Married 1 14.47 14.47 7.80* Residual between 288 534.30 1.86 Within Subjects 294 81.42 Time 1 .06 .06 .21 Mental Health Group x Time Interaction 3 1.04 .35 1.26 T1 SES x Time Interaction 1 .00 .00 .00 T1 Married x Time Interaction 1 .66 .66 2.37 Residual within 288 79.66 .28 Total 586 705.84 Note N = 300 p < .05
84 Figure 3. CMH = complete mental health and SBC= symptomatic but content. Adjusted means from Time 1 and Time 2 are plotted. Regarding C OVA are presented in Table 11. The Mental Health Group x Time interaction was significant, F (3, 292) = 4.44, p < .05 as was the main effect for group, F (3, 292) = 4.85, p < .05. This analysis did not re veal a significant effect for time F (1,292) = 1.42, ns 3 4 5 T1 T2 CMH Troubled Vulnerable SBC Figure 3 reading scores over time
85 Table 11 AN C OVA Summary Table for Investigating the Relationship of Student Mental Health Group Membership to Subsequent Absences Source df SS MS F Between Subjects 297 1407.87 T1 Mental Health Group 3 65.16 21.72 4.85* T1 SES 1 .21 0.21 .05 T1 Married 1 33.82 33.82 7.55* Residual between 292 1308.68 4.48 Within Subjects 298 480.79 Time 1 2.22 2.22 1.42 Mental Health Group x Time Interaction 3 20.78 6.93 4.44* T1 SES x Time Interaction 1 1.63 1.63 1.04 T1 Married x Time Interaction 1 .17 .17 .11 Residual within 292 455.99 1.56 Total 595 1888.66 Note N = 300 p < .05 As shown in Figure 4, the influence of mental health group on absences appears most influential with youth with high psychopathology. Specifically, the slopes of the regression lines for students in the troubled mental health (slope = .68) and symptomatic but content group (slope = 1.06) were significantly different ( p < .05) from youth in the complete mental health and vulnerable groups (slope = .06 and slope = .05, respectively). Overall, tests of slope revealed that students with psychopathology at Time 1 accrue more absences over time, regardless of SWB. In other words, the absence of
86 psychopathology (rather than the presence of SWB) was the primary predictor of intact attendance. Pairwise comparisons were again used to determine how these mental health groups differed from one another in regards to absences at Time 2. As depicted in Table 13, at Time 2, the group with the best attendance had both high SWB and no psychopathology (i.e., complete mental h ealth). Troubled students were not significantly different than vulnerable students. Again, students with similar levels of psychopathology at Time 1 had similar attendance scores at Time 2, reg ardless of level of SWB. Figure 4. CMH = complete mental health and SBC= symptomatic but content. Adjusted means from Time 1 and Time 2 are plotted. significant, F (3, 292) = 7.17, p < .05, as summarized in Table 12. The Mental Health Group x Time interaction was not significant, F (3, 292) = 2.60, ns neither was a significant effect for time F (1,292) = .04, ns 0 1 2 3 T1 T2 CMH Troubled Vulnerable SBC Figure 4
87 Table 12 AN C OVA Summary Table for Investigating the Relationship of Student Mental Health Group Membership to Subsequent ODRs Source df SS MS F Between Subjects 297 68.51 T1 Mental Health Group 3 4.54 1.51 7.17* T1 SES 1 1.52 1.52 7.22* T1 Married 1 .78 .78 3.69 Residual between 292 61.67 .21 Within Subjects 298 29.07 Time 1 .00 .00 .04 Mental Health Group x Time Interaction 3 .75 .25 2.60 T1 SES x Time Interaction 1 .02 .02 .16 T1 Married x Time Interaction 1 .04 .04 .44 Residual within 292 28.26 .10 Total 595 97.58 Note. N = 300 p < .05 As shown in Figure 5, although the Mental Health Group x Time interaction effect is not significant, there appears to be a trend regarding number of ODRs received by students in different mental health group. Specifically, when the slope of the regression lines were explored, students in the symptomatic but content group (slope = .26) had a pattern of being different from y outh in the complete mental health (slope = .04, p < .05) group, but not different from vulnerable or troubled youth. Additionally, as
88 symptomatic but content incurred signific antly more ODRs than any other group at Time 2. Perhaps, having high SWB and symptoms of psychopathology is a risk factor for not attending school regularly one year later, however because the Mental Health Group x Time interaction did not achieve statisti cal significance (specifically, p = .0526), further study is needed in order to make more definitive conclusions on this topic. Figure 5. CMH = complete mental health and SBC= symptomatic but content. Adjusted means from Time 1 and Time 2 are plotted. 0 0.25 0.5 T1 T2 CMH Troubled Vulnerable SBC Figure 5
89 Table 13 Mean Levels of Academic Achievement and In School Behavior at Time 2 by Group (N = 300) Mental Health Group Complete Mental Health ( n = 170) Vulnerable ( n = 43) Symptomatic but Content ( n = 36) Troubled ( n = 51) School Functioning M SD M SD M SD M SD GPA 3.40 a (3.33) .64 3.12 a,c (3.19) .71 2.86 b,c (2.93) 1.06 2.63 b (2.80) .92 FCAT math 3.96 a (3.86) 1.08 3.42 a,b (3.52) .85 3.46 a,b 3.55 1.35 3.14 b (3.38) 1.29 FCAT reading 3.44 a (3.33) 1.11 2.93 a (3.07) 1.06 3.06 a (3.17) 1.29 2.78 a (3.05) 1.16 Absences 1.18 a (1.25) 1.25 1.65 a,b (1.58) 1.47 3.13 c (2.66) 4.68 2.48 b,c (2.20) 2.80 ODRs 0.09 a (0.11) .29 0.21 a (0.19) .40 0.51 b (0.49) .92 0.30 a (0.26) .64 Note Significant differences between group means ( p < .05) are indicated by different letters. Means having the same subscript are not significantly different. Adjusted means are presented in parentheses.
90 Chapter 5 Discussion The current study examined longitudinal relationships between mental health as conceptualized by the dual factor model of mental health and school functioning (i.e., GPA, FCAT math, FCAT reading, absences, ODRs). Specifically, research questions evaluated: predict their academic achievement one year later, and (2) the extent to which student membership, as derived from the dual factor model of mental health. The subsequent discussion explores the findings of this study in relation to the research questions posed, as well as in relation to previous findings in the literature. Next, implications of the cur rent study for practice and contributions to the literature are reviewed, followed by limitations of the study. Finally, suggestions for future research are presented. Relationships B etween Psychopathology, Academic Achievement, and In School Behavior Int ernalizing Psychopathology Traditionally, mental health has only been explored in relation to the presence or absence of psychopathology. I n general, internalizing psychopathology refers to problems that manif est in within person disruption typically in the form of anxiety and/or de depressive disorders, rather than problems that are acted out in the environment. In the current study, internalizing psychopathology was measured by a
91 student self report questio nnaire (i.e., YSR; Achenbach & Rescorla, 2001) of symptoms of anxiety, depression, social withdrawal, and somatic complaints experienced by the adolescent Negative concurrent and longitudinal associations between childhood internalizing psychopathology an d developmental outcomes have long been supported by research (Lewinsohn, Seeley, & Gotlib, 1997; McCarthy, Downes, & Sherman, 2008). However, in the current study, internalizing psychopathology was not a unique indicator of academic achievement (i.e., GPA FCAT math or FCAT reading), after initial levels of academic achievement, parent marital status, and socioeconomic status were controlled for statistically. These findings are similar to results yielded from the longitudinal study by Cole, Martin, Powers and Truglio (1996), which found that in a sample of 490 third grade students, and 455 sixth grade students, academic competence did not deteriorate because of depression over a 6 month period. Notably, academic competence was measured by how teachers and may in turn rely on the types of grades students earn at school. Regarding predictions of in school behavior, the current study found that internalizing problems accounted for a significant proportion of the unique variance in subsequent absences, but not in ODRs, after initial school functioning and participant demographic characteristics (i.e., SES, parental marital status) were controlled for statistically. The increased likelihood of stud ents with internalizing problems to have poor school attendance is aligned with studies that found that youth with anxiety and depression were more likely to eventually drop out of school and not pursue higher education ( Fergusson & Woodward, 2002; Woodward & Fergusson, 2001 ). The current study extends this finding to a younger population, and suggests that even in middle
92 school, internalizing symptoms are a risk factor for reduced attendance. Opportunity to engage in academic tasks (i.e., necessitat ing attendance) has been found to be directly related to student achievement (Shapiro, 2004), therefore, identification followed by intervention for youth with internalizing symptoms is critical. Externalizing Psychopathology Externalizing psycho patho logy refers to an array of defiant, aggressive, and hyperactive behaviors. These behaviors may also be referred to as undercontrolled problems, because these behaviors are often directed toward others. In the current study, e xternalizing psychopathology was m easured by a rating scale (i.e., TRF; Achenbach & Rescorla, 2001) completed by teachers familiar with student participants The relationships between indicators of school functioning and externalizing psychopathology evidenced some of the strongest longitu dinal relationships yielded in the current study. Specifically, s earned the following school year, even after controlling for initial GPA, parental marital status, and SES. These r esults are similar to previous findings in which a positive predictive relationship was yielded between externalizing problems (e.g., non compliance, aggression) and indicators of academic underachievement ( Caspi, Wright, Moffit t & Silva, 1998; Dubow, Hue sman, Boxer, Pulkkinen, & Kokko, 2006; Ingoldsby, Kohl, McMahon, Lengua, & The Conduct Problems Prevention Research Group, 2006; Kokko & Pulkkinen, 2000; Masten et al., 2005 ). A similar link between externalizing psychopathology and scores on a standardi zed achievement test was not found in the current study, as externalizing math and FCAT
93 accoun ted for the majority of the unique variance in math and reading scores (42% to 50%) the following school year. This high stability in scores across time made it challenging for any variable to contribute to the (small) amount of change in student performan ce. A nother possible reason why externalizing problems predict GPA, but not FCAT scores, particularly reading, is that as students age, their performance on the FCAT is related more to constructs related to intelligence (i.e., verbal knowledge, non verbal reasoning, working memory) In turn, FCAT performance is less related to phonemic decoding and comprehension which are tasks that may be more critical to the classroom curriculum, and thus are more likely to change with course grades (Schatschneider, Buck, Torgeson, Wagner, Hassler, et al., 2004) It should also be noted that SWB and externalizing psychopathology were significantly correlated to both FCAT assessments at Time 2, and internalizing psychopathology was significantly correlated to FCAT math at Time 2. Thus, the predictors had the most challenge demonstrating associations with change in FCAT scores, rather than performance on the test at a given time One plausible reason for the negative association between externalizing problems and grade s earned in courses (i.e., GPA) is that high levels of externalizing behaviors predict higher rates of out of school suspension (Reinke, Herman, Petras, & Ialongo, 2008) and truant behavior (Hunt & Hopko, 2009; Steinhausen, Mller, & Metzke, 2008), both of which limit academic engaged time. This is also consistent with results yielded from the current study, in which externalizing problems accounted for a significant amount of the unique variance in school absences the following school year, even after init ial absences were accounted for statistically. Specifically, students with higher scores
94 of psychopathology of any nature at the beginning of the study were more likely to accrue more absences over the next year. Additionally, initial symptoms of externali zing problems accounted for 5% of the variance in ODRs the following year, even after initial number of ODRs was accounted for statistically. Therefore, because the presence of externalizing problems in this sample is also associated to poor attendance and misbehavior at school, students with externalizing problems are likely to be more frequently absent from class and may not have access to instruction and course work during suspensions or visits to the office. This diminished academic engaged time, includ ing reduced access to academic instruction and class work, may therefore negatively affect student grades in class, but not their performance on a state standardized test, which may reflect a more basic and general skill set that is stable over time. Becau se these students may have less access to the curriculum and may eventually engage in behaviors that adversely affect their learning and potentially the learning of their peers to an extent that may ultimately result in expulsion, it is important to attend to identification followed by intervention for these mental health problems. Relationships B etween SWB, Academic Achievement, and In School Behavior In recent decades, proponents of the positive psychology movement have advocated for a focus on a positiv e state of mind in youth, rather than only remediating Seligman & Csikszentmihalyi, 2000; Suldo & Huebner, 2006). Studies of wellness commonly include the assessment of subjective well being (SWB). SWB is a broad construct that is comprised as both cognitive judgments of the enduring satisfaction one has with his or her life (i.e., life satisfaction) as well as experiences of positive and
95 negative emotions ( i.e., positive and negative affect; Diener, Lucas, & Oishi, 2005; Haybron, 2008). SWB has been found to be inversely related to internalizing and externalizing psychopathology in youth (Huebner, Frunk, & Gillman, 2000). Additionally, previous studies have found that high SWB is associated with optimal functioning in school related domains, including academic achievement (Gilman & Huebner, 2006; Suldo & Shaffer, 2008). The current study was the first to examine longitudinal relationships between student SWB SWB yielded positive, bivariate correlations with their GPAs and scores earned on the FCAT math and init ial SWB predicted their GPAs the following year, even after initial GPA and relevant student demographic characteristics were controlled for statistically. Specifically, students with greater SWB at the beginning of the study were more likely to experience gains in GPA over the next year. Although small, positive correlations were yielded between initial SWB and performance on the FCAT the following year, SWB did not emerge as a reliable predictor of change in FCAT scores in multiple regression analyses, su ggesting that SWB does not help predict subsequent FCAT performance above and beyond what can be predicted based on knowledge of earlier FCAT performance alone These weak longitudinal relationships between SWB and some indicators of academic achievement a re consistent with results from t he only published longitudinal study of any aspect of SWB in relation to any construct of academic functioning in youth (i.e.,
96 Huebner an d colleagues found that life satisfaction yielded concurrent, but not predictive, associations with high school school The results of the current study with respect to bivariate and multivariate analyses of the re lationship between student SWB and subsequent grades earned in courses serve to underscore the important association between psychological wellness and authentic indicators of academic achievement, consistent with prior cross sectional studies that demonst rated significant links between measures of wellness (i.e., positive affect, SWB, happiness) and academic achievement ( Chenge & Furnham, 2002; Kirkcaldy, Furnham, & Siefen, 2004; Suldo & Shaffer, 2008). Regarding associations between schooling and SWB, Sul courses, beliefs about learning and academic ability, and perceptions of school climate were correlated with their life satisfaction in a positive direction. Life satisfaction is al so linked to higher self perceptions of academic performance (Leung, McBride Chang, & Lai, 2004; Suldo & Huebner, 2006). Similarly, research on the affective component of SWB found that experiences of positive emotion predicted desirable school functioning related tasks (Reschly, Huebner, Appleton, & Antaramian, 2008). Taken together, a gr ades earned in courses, as well as likely related to thoughts and behaviors pertinent to school climate, perceived academic abilities, and engagement in learning. Previous research has found negative concurrent relationships between indicators of wellnes s (i.e., life satisfaction, SWB) and engagement in aggression and risky behaviors (MacDonald, Piquero, Valois, & Zullig, 2005), as well as poor attendance
97 initial levels of SWB to predict their attendance or the ir misbehavior the following year (after controlling for demographic variables and initial in school behavior variables). Thus, preliminary longitudinal data suggests SWB is more strongly tied to later achievement, su ch as GPA and to a lesser degree scores on standardized assessments of skills, than to subsequent in school behavior. Potential reasons for this discrepancy may (Schatschneider, e t al., 2004), whereas course grades are more malleable across time and rely heavily upon students attending class on a consistent basis and completing coursework. Relationships B etween the Dual factor Model of Mental Health, Academic Achievement, and In School Behavior A dual factor model of mental health is comprised of modern indicators of wellness (i.e., SWB) as well as traditional indicators of psychopathology (i.e., internalizing and externalizing symptoms indicative of mental disorders). Using the dual factor model of mental health yields two unique groups of students: those who reported high SWB and high psychopathology (i.e., symptomatic but content), as well as youth who scored low on measures of psychopathology and low on indices of SWB (i.e., v ulnerable). The two other categories yielded from this model are youth who have historically been studied in a traditional model of psychology: youth with high psychopathology and low SWB (i.e., troubled), and youth without psychopathology and high SWB (i. e., complete mental health). These four mental health groups yielded from the dual factor model of mental health are presented in Table 14.
98 The current study was the first examination of longitudinal student outcomes predicted by the dual factor model of mental health. Results from the current study schoo l functioning on two indicators: GPA and absences. Specifically, the troubled students evidenced greater declines in GPA than their peers initially classified as vulnerable or complete mental health. Essentially, students without clinical levels of psychop athology at the beginning of the study fared better with regard to course grades than students whose mental health profile involved the presence of psychopathology coupled with low SWB. On the other hand, students who initially demonstrated average to high SWB as well as clinical levels of high psychopathology (i.e., symptomatic but content students) did not deteriorate more over time, with regards to GPA, than students initially classified as complete mental health or vulnerable (i.e., absence of psychopat hology). Thus, although the initial low levels of SWB demonstrated by the vulnerable students at the beginning of the study did not seem to predispose them to experiencing relatively steep declines in GPA (i.e., as compared to students with complete mental health), it is plausible that the average to high levels of SWB found Table 14 Mental Health Groups Yielded from the Dual Factor Model of Mental Health High SWB Low SWB High Psychopathology Symptomatic but Content Troubled Low Psychopathology Complete Mental Health Vulnerable
99 among the symptomatic but content youth protected these students from experiencing the greatest declines in GPA. It is p ossible that despite the presence of psychopathology, the average to high levels of SWB initially experienced by the symptomatic but content youth may enable them to perform better academically, perhaps by drawing on their relatively intact social relations w ith adults and peers (Greenspoon & Saklofske, 2001; Suldo & Sh affer, 2008). Although no longitudinal research exists examining such hypotheses, results are somewhat consistent with the cross research, which found that both the presence of average to high SWB and the ab sence of psychopathology were associated with optimal academic success. In contrast to Suldo multiple aspects of academic achievement (i.e., FCAT reading, absences and positive attitudes predi ctive of academic success such as academic self confidence, valuing of school, and motivation and self regulation for completion of academic tasks ) between students who were vulnerable and had complete mental health, these groups did not differ one year la ter in regards to change over time GPA or FCAT scores, or even Time 2 mean performance on these variables. A possible reason for the later similarities in GPA and FCAT scores between these two groups that began the study with different levels of SWB pertai ns to a possible lack of stability in regards to mental health group membership across time. For example, it is possible that a student who met criteria for the symptomatic but content mental health group at Time 1 would have met criteria for the troubled mental health group at Time 2 if assessed again using measures of psychopathology and SWB at this later time.
100 symptoms of psychopathology) fluctuate longitudinally is needed to evaluat e this area of inquiry. In regards to the relationship between mental health group status and change in absences across time, results of the current study suggested that youth with psychopathology (i.e., students classified as symptomatic but content or troubled) accrued more abs ences over time regardless of their initial low vs. average to high SWB status. However, at the end of the study (Time 2), the best school attendance and school grades were found by students who had both average/high SWB and low psychopathology one year ea rlier (i.e., complete mental health). This finding speaks to the notion that the absence of psychopathology alone may not be the best predictor of subsequent in school behavior Having low psychopathology as well as high levels of SWB appears to be associated with the best developmental outcomes in youth, and therefore wellness should also be attended to. An unanticipated finding was that youth in the troubled mental health grou p were no different than their peers in the vulnerable mental health group. Additionally, youth in the symptomatic but content mental health group had more absences than students without psychopathology (i.e., youth in complete mental health and vulnerable groups) at Time 2 and across time. A possible reason why youth in the symptomatic but content group were not protected from worse attendance may be linked to student characteristics that are unknown. For example, students in this group may be more likely to have externalizing problems and therefore be more truant from school. group as externally maladjusted. More research examining student characteristics within a specific mental health group is needed to determine the unique features of these
101 students. Taken together, these findings from the longitudinal examination of the dual indicators of welln ess and psychopathology) may be useful, as SWB may serve as a protective factor, and in some cases as a risk factor, for healthy school functioning. Of note, the current study did not yield longitudinal associations between student mental health group mem bership and ODRs across time. This contrasts previous research that demonstrated negative concurrent relationships between group membership and ODRs (Suldo & Shaffer, 2008) The null result is also discrepant from studies that identified concurrent links b etween risky behaviors and life satisfaction (MacDonald, Piquero, Valois, & Zullig, 2005) as well as concurrent and predictive relationships between poor in school behavior and psychopathology ( Hunt & Hopko, 2009 ; Loe & Feldman, 2007 ; Reinke, Herman, Petr as, & Ialongo, 2008; Steinhausen, Mller, & Metzke, 2008) health status across the one year school behavior during the second wave of the study was more tied to their current (and possibly greatly improved or diminished) mental health status. Alternatively, other variables not examined in the current study such as affiliations with deviant peer groups, motivation to excel academically, and diminishe d valuing of school, may serve as the primar y school behavior (as opposed to being primarily influenced ODRs is that youth in the sympt omatic but content group had significantly more ODRs than youth without psychopathology and even students in the troubled group at Time 2. One hypothesis for this finding is that in general, symptomatic but content students were
102 more likely to have clinica l levels of externalizing (vs. internalizing) problems. More information regarding the specific symptom clusters manifested by students identified as having clinical levels of psychopathology is needed to best understand the relationship between mental hea lth group membership and in school behavior. Implications for School Psychologists Early adolescence is a critical stage of change and growth for youth. Therefore, institutions responsible for educating and socializing children, such as schools, should mo strengths as well as maladaptive dysfunctions. The current study and previous studies support that s tudents with low levels of psychopathology and average to high levels of SWB have been shown to demonstrate superior functioning within the areas of achievement, perceived academic abilities, motivation, and social functioning, compared to their peers (Suldo & Schaffer, 2008). The current study provides further support for c oll functioning (i.e., grades earned in class, attendance), both short term and long term. This i strengths and overall wellness ( Doll & Cummings, 2008; Maddux, 2005) Notably, results of the current study also underscore the importance of effective mental healt h prevention programs and intervention efforts geared toward youth with psychopathology in order to promote the best school functioning across time. School psychologists should advocate for promoting complete mental health in youth, including the presenc e of satisfactory SWB, as a form of prevention. Such
103 prevention efforts might begin with school and/or classroom wide screenings to assess of teacher nominations to serv e as a mechanism to identify students with clinical levels of psychopathology. In addition to identification activities, teacher training and classroom activities could include information regarding curriculum that can be used within the classroom to incre ase exercises intended to facilitate gratitude ; Froh, Sefick & Emmons, 2008 ). Because the current study demonstrated that average to high SWB, coupled with minimal symptoms of psychopathology, were associated with improved grades across time, interventions that purposefully target these indicators of mental health are warranted. For instance school psychologists should aim to provide parent and teacher consultation, as well as individual and group counseling interventions aimed at increasing SWB and decreasing symptoms of mental disorders. Such interventions can occur at either the school wide level, or be implemented with small groups of students, or psycho logist evidences low scores on a self report measure of life satisfaction, such as the SLSS (Huebner, 1991), and scores in the average range on a measure of psychopathology, such as the BASC 2 (Reynolds & Kamphaus, 2004), it would be advantageous to admini ster an additional rating scale to inform interventions For example, the Multidimensional Satisfaction Scale (Huebne r, 1994 ) would yield information about five domains of life (i.e., friends, family, schoo l, self, living environment) and a school psychologist could develop
104 interventions that target the specific domain(s) (e.g., social skills training for the domain of friends). Contributions to the Literature There have been a number of studies that have examined academic and behavioral examined how measurements of SWB can predict academic achievement and in school behavior longitudinally; the current study has filled thi s gap. The current study also contributed to the literature by providing the first examination of the dual factor model of mental health in relation to later academic achievement and in school behavior. The current study revealed that students who were sym ptomatic but content were somewhat protected from the worst academic achievement (i.e., that experienced by troubled youth) as symptomatic but content students evidenced change in GPAs similar to that of their asymptomatic peers. Additionally, because stu dents without psychopathology and high SWB had the best school functioning outcomes this study provided support for sum, this study provided empirical support for using the dual factor model of mental health to assess adolescent psychological functioning. Limitations Although the utility of this study is to determine if mental health status as categorized by the dual factor model is able to predict academic ac hievement over a one year period, factors that may impact student mental health during that period (i.e., trauma, provision of mental health services) are unknown and may confound results. Thus, students who began the study in a given mental health group m ay have changed their
105 mental health status at some point for an unknown reason and in an unknown direction. The stability of the dual factor model classifications is unknown. Ecological validity, or ecological transferability, refers to the ability of the researcher to generalize the findings of a study to other settings (Tashakkori & Teddlie, 2003). Violations to population validity pertain to incidences when the researcher draw s invalid conclusions to populations with different characteristics than the p opulation under current study. In the current study, 56% of students are Caucasian, therefore extending the findings from the current study to students of other ethnicities should be done cautiously. Additionally, a lthough students reside in different scho ols, participants used in the current study were selected from one middle school in one school district. Therefore, ecological and population transferability of the current study is minimized (Tashakkori & Teddlie, 2003). Applications of these results to r ural communities are also cautioned, since the middle school is located in an urban district. Furthermore in the current study students were initially recruited from a middle school located within a middle to high SES community; therefore, generalizations of these results to low er SES regions are not recommended. At Wave 1 of the study, a convenie nce sampling method was used ; therefore, students who agreed to participate in the current study may differ from students who declined to participate. In sum, t hese unique characteristics of the sample population may limit the extent to which findings yielded from this study can be valid for other populations, including the school population from which it was drawn. Another limitation of the current study entail s the non normal distributions on four of the Time 1 variables and three of the later outcome variables. Employing variables with large skew and/or kurtosis in the analyses may have reduced the power to
106 detect a significant effect in the event(s) that a si gnificant effect actually existed. Finally, 41 students were lost to attrition throughout the duration of the study (i.e., only included in Wave 1 of data collection). Fortunately, the sample did not appear to be biased as a result of attrition, as a serie s of chi square and t tests between the longitudinal sample (i.e., 300 students who participated at both Waves 1 and 2) and the 41 participants lost to attrition revealed that students who withdrew from the longitudinal sample were no more likely to be of a particular mental health group or demographic group than peers who remained in the study at Time 2 nor were they unique on any indicator of initial mental health functioning (i.e., SWB variable or psychopathology), academic achievement, or in school beh avior. Summary and Future Directions The current study has added to the literature by providing the first examination of longitudinal relationships between SWB and objective indicators of school functioning, as well as the first longitudinal examination o f academic outcomes associated with factor model of mental health. Further, although longitudinal relationships exploring psychopathology and developmental outcomes in youth are prevalent in the literature, many of these studies did not use outcome measures that are readily accessible and relevant to student school functioning. The current study identified important trends in the relationship between mental health and varying levels of academic ac hievement and in school behavior. Specifically, findings support that a lack of psychopathology alone is not enough to ensure the best school functioning in youth across time. Specifically, students who had low psychopathology coupled with average to high SWB had the best outcomes in two
107 domains (GPA and school attendance). Findings also support the importance of focusing on assessment and intervention appropriate for youth with psychopathology. Additional studies examining the association between mental he alth, as conceptualized by the dual factor model, and student outcomes relevant to education will contribute to the field by providing a better understanding of the utility of SWB in youth. In order to gain a better understanding of how to best conceptualize mental health and provide the most impactful services to youth, there are several natural directions for future research. First, more information is needed regarding the categories yielded in the dual factor model of mental health. Specifical ly, it would be beneficial to know how relatively stable group membership in one of the four groups is, on average. In other words, for what extent of time do typical students in the symptomatic but content group meet the mental health cut score requiremen ts to remain in this group (e.g., six months, one year) ? Additionally, information regarding common student characteristics associated with each of these four groups is necessary to provide a richer understanding of the model and to inform practice. For ex ample, knowing that students in the symptomatic but content group typically have higher levels of externalizing problems would point the school psychologist in the appropriate direction for intervention or prevention. Another direction would be to explore which aspects of SWB tend to be associated with better outcomes in youth, both concurrently and longitudinally. For instance, is life satisfaction or positive affect associated more with desirable academic and in school behavior outcomes? Information from this type of exploration would provide relevant information to direct prevention and intervention procedures aimed to increase wellness in youth. This is especially important for early adolescents as Colarossi
108 and Eccles (2003) have suggested that this sta ge of development is critical to prevent the development of negative mental health outcomes in late adolescence and adulthood. Thus, continuing to research the role of SWB in various outcomes in this developmental time period will be especially important a s the literature begins to inform prevention efforts in schools as well as clinical settings.
109 References Achenbach, T.M., Dumenci, L., & Rescorla, L.A. (2001). Ratings of relations between DSM IV diagnostic categories and items of the CBCL/6 18. TRF, and YSR. Burlington, VT: University of Vermont, Research Center for Children, Youth, & Families. Achenbach, T. M., & Edelbrock, C. S. (1991). Child Behavior Checkli st and Youth Self Report Burlington, VT: University of Vermont, Research Center for Children, Youth, and Families. Achenbach, T.M. & Rescorla, L.A. (2001). Manual for the ASEBA school age forms and profiles. Burlington, VT: University of Vermont, Research Center for Children, Youth, and Families. Albano, A. M., Chorpita, B. F., & Barlow, D. H. (2003). Childhood mood disorders. In Mash, E.J. & Barkley, R. A. (Eds.), Child psychopathology (pp. 279 329). New York: The Guilford Press. Alvarez, H. K., & Ollend ick, T. H. (2003). Individual and psychosocial risk factors. In C. A. Essau (Ed.), Conduct and oppositional defiant disorders: Epidemiology, risk factors, and treatment (pp. 97 116). Mahwah, NJ: Lawrence Erlbaum Associates. American Psychiatric Association (1987). Diagnostic and statistical manual of mental disorders, third edition (DSM III). Washington, DC: American Psychiatric Association.
110 American Psychiatric Association. (2000). Diagnostic and statistical manual of mental disorders, fou rth edition, text revision (DSM IV TR). Washington, DC: American Psychiatric Association. Andrews F. M., & Withey, S. B. (1976). Social indicators of well perceptions of life quality. New York: Plenum Press. Appleton, J. J., Christenson, S. L., Kim, D., & Reschly, A. (2006). Measuring cognitive and psychological engagement: Validation of the Student Engagement Instrument. Journal of School Psychology, 44 427 445. Argyle, M., Martin, M., & Crossland, J. (1989). Happiness as a function of personality and social encounters. In J. Forgas & J. Innes (Eds.), Recent advances in social psychology: An international perspective Amsterdam: North Holland. Asarnow, J. R., Carlson, G. A., & Guthrie, D. (1987). Coping strategies, self perceptions, hopelessness, and perceived family environments in depressed and suicidal children. Journal of Consulting and Clinical Psychology, 55 361 366. Ash, C. & Huebner, E. S. (1998). Life satisfaction reports of gift ed middle school children. School Psychology Quarterly, 13 (4), 310 321. Baker, J. A. (2008). Assessing school risk and protective factors. In B. Doll and J. A. Cummings (Eds.), Transforming school mental health services: Population based approaches to prom oting competency and wellness of children (pp. 43 65). Thousand Oaks, CA: Corwin Press. Barkley, R. A. (2006). Attention deficit hyperactivity disorder: A handbook for diagnosis and treatment (3 rd ed.). New York: Guilford Press.
111 Barkley, R. A. (2003). Atte ntion deficit/hyperactivity disorder. In E. J. Mash & R. A. Barkley (Eds.). Child psychopathology (2 nd ed.). New York: Guilford Press. Benner, A. D., & Graham, S. (2009). Transition to high school as a developmental process among multiethnic urban youth. Child D evelopment, 90 (2), 356 375. Berk L. E. (2006). Child development (7 th ed.). Boston: Pearson Education. Biswas Diener, R., Vitterso, J., & Diener E. (2005). Most people are pretty happy, but there is cultural variation: The Inughuit, the Amish, and the Massai. Journal of Happiness Studies, 6 205 226. Brown, J. D., Riley, A. W., & Wissow, L. S. (2007). Identification of youth psychosocial probl ems during pediatric primary care visits. Administration & Policy in Mental Health, 34 269 281. Capaldi, D. M. (1992). Co occurrence of conduct problems and depressive symptoms in early adolescent boys: II. A 2 year follow up at grade 8. Development and P sychopathology, 4 125 144. Carnegie Council on Adolescent Development. (1989). Turning points: Preparing American youth for the 21st century New York: Carnegie Corp. Caspi, A.,Wright, B. R. E., Moffit t T. E., & Silva, P. A. (1998). Early failure in the labor market: Childhood and adolescent predictors of unemployment in the transition to adulthood. American Sociological Review, 63 424 451. Cheng, H., & Furnham, A. (2002). Personality, peer relations, and self confidence as predictors of happiness and loneliness. Journal of Adolescence, 25 327 339. Colarossi, L. G., & Eccles, J. S., (2003). Differential effects of support providers on adolescents' mental health. Social Work Research 27 (1), 19 30.
112 Cole, D. A., Martin, J. M., Powers, B., & Truglio, R. (1996). Modeling causal relations between academic and social competence and depression: A multitrait multimethod longitudinal study of children. Journal of Abnormal Psychology, 105, 258 270. Conners, C. K. (1997). Revised North Tonawanda, NY: Multi Health Systems. Costello, E. J., & Angold, A. (1995). Epidemiology. In J. S. March (Ed.), Anxiety disorders in children and adolescents (pp. 109 124). New York: Guilford. Costello, A., Edelbrock, C., Kalas, R., Kessler, M., Klaric, S. A. (1982). Diagnostic Interview Schedule for Children (DISC) Bethesda, MD: National Institute of Mental Health. Costello, E. J., Egger, H., & Angold, A. (2005). 10 year research update review : The epidemiology of child and adolescent psychiatric disorders: I. Methods and public health burden. Journal of the American Academy of Child and Adolescent Psychiatry, 45 972 986. Victims of relational and overt aggression. Development and Psychopathology 8 367 380. de Anda, D., Baroni, S., Boskin, L., Buchwald, L., Morgan, J., Ow, J., et al. (2000). Stress, stressors and coping among high school students. Children and Youth Serv ices Review, 22 441 463. Diener, E. (2000). Subjective well being: The science of happiness and a proposal for a national index. American Psychologist, 55 34 43.
1 13 Diener, E., & Diener, C. (1996). Most people are happy. Psychological Science, 7 (3), 181 185. Diener, E., Lucas, R. E., & Oishi, S. (2005). Subjective well being: Thee science of happiness and life satisfaction. In C. R. Snyder and S. J. Lopez (Eds.), Handbook of positive psychology (pp. 3 12). New York: Oxford University Press. Diener, E., Suh, E. M., Lucas, R. E., & Smith, H. L. (1999). Subjective well being: Three decades of progress. Psychological Bulletin 125 276 302. Dinkes, R., Kemp, J., & Baum, K. (2009). Indicators of School Crime and Safety: 2008 (NCES 2009 022/NCJ 226 343). National Center for Education Statistics, Institute of Education Sciences, U.S. Department of Education, and Bureau of Justice Statistics, Office of Justice Programs, U.S. Department of Justice. Washington, DC. Doll, B., & Cummings, J. A. (2008). Why population based services are essential for school mental health, and how to make them happen in your school. In B. Doll & J. A. Cummings (Eds.), Transforming school mental health services: Population based approaches to promoting the competency and welln ess of children (pp. 1 20) Thousand Oaks, CA: Corwin Press. Dubow, E. F., Huesman, L. R., Boxer, P., Pulkkinen, L., & Kokko, K. (2006). Middle childhood and adolescent contextual and personal predictors of adult educational and occupational outcomes: A meditational model in two countries. Developmental Psychology, 42 937 949. Eisenberg, D. & Schneider, H. (2007). Perce ptions of academic skills of children diagnosed with ADHD. Journal of Attention Disorders, 10 390 397.
114 Erikson, E. H. (1968). Identity, youth and crisis New York: Norton. Eron, L. D., Walder, L. O., & Lefkowitz, M. M. (1971). Learning of aggression in ch ildren. Boston: Little, Brown. Fergusson, D. M., & Woodward, L. J. (2002). Mental health, educational, and social role outcomes of adolescents with depression. Archives of General Psychiatry, 59 225 331. Florida Department of Education. (2005). Assessmen t and school performance: FCAT Florida Comprehensive Assessment Test. Retrieved from http://www.fldoe.org/faq/default.asp?Dept=179&ID=972#Q972 F lorida Department of Education's Bureau of School Improvement (2010). Florida differentiated accountability pro gram 2009 2010 school improvement plan Retrieved from http://www.flbsi.org/0910_SIP/Public/print.aspx?uid=292471 Frazier, T. W., Youngstrom, E. A., Clutting, J. J., & Watkins M. W. (2007). ADHD and achievement: Meta analysis of the child, adolescent, and adult literatures and a concomitant study with college students. Journal of Learning Disabilities, 40 49 65. Froh, J. J., Sefick, W. J., & Emmons, R. A. (2008). Counting blessings in early adolescents: An experimental study of gratitude and subjective well being. Journal of School Psychology, 46 213 233. being: Convergence across rates, times, and response forma ts. School Psychology International,18 229 243.
115 Gilman, R., & Huebner, E. S. (2006). Characteristics of adolescents who report very high life satisfaction. Journal of Youth and Adolescence, 35 311 319. Greenspoon, P. J., & Saklofske, D. H. (2001). Toward an integration of subjective well being and psychopathology. Social Indicators Research, 54 81 108. Hall Lande, J. A., Eisenberg, M. E., Christenson, S. L., & Neumark Sztainer, D. (2007). Social isolation, psychological health, and protective factors in adolescence. Adolescence, 42 265 286. Hamburg, D. A. & Takanishi, R. (1996). Great transitions: Preparing American youth for the 21 st century the role of research. Journal of Research on Adolescence, 6 379 396. Haslam, N., Whelan, J., & Bastian, B. (2009). Big five traits mediate associations between values and subjective well being. Personality and Individual Differences, 46 40 42. Haybron, D. M (2008). Philosophy and science of subjective well being. In M. Eid & R. J. Larsen (Eds.), T he science of subjective well being (pp. 17 43) New York, NY: The Guilford Press. Headey, B. & Wearing, A. (1990). A stock and flow model of subjective well being. In F. Strack, M. Argyle, N. Schwartz (Eds.), Subjective wellbeing Oxford: Pergamon Press. Hinshaw, S. P. (1992). Externalizing behavior problems and academic underachievement in childhood and adolescence: Causal relationships and underlying mechanisms. Psychological Bulletin, 111 127 155.
116 Hinshaw S., & Lee S. (2003).Conduct and oppositional defiant disorders. In E. J. Mash & R. A. Barkley (Eds.), Child psychopathology (2 nd ed., pp. 144 198). New York, NY: Guilford Press. Huberty, T. J. (2008). Best practices in school based interventions for anxiety and depression. In A. Thomas & J. Grimes (Eds.), Best practices in school psychology V (pp. 1473 1486). Bethesda: The National Association of School Psychologists. School Psychology International, 6, 1 03 111. Huebner, E. S. (1994). Preliminary development and validation of a multidimensional life satisfaction scale for children. Psychological Assessment, 6 149 158. psychom etric properties with black and white elementary school students. Social Indicators Research, 34 315 323. Huebner, E. S., & Alderman, G. (1993). Convergent and discriminant validation of a and teacher reported psychological problems and school functioning. Social Indicators Research,30 71 82. Huebner, E. S., Funk, B.A., III, & Gilman, R. (2000). Cross sectional and longitudinal psychosocial correlates of adolescent life satisfaction reports. C anadian Journal of School Psychology, 16 53 64. Huebner, E. S., Gilman, R., & Laughlin, J. E. (1999). A multimethod investigation of the multidimensionality of children's well being reports: Discriminant validity of life satisfaction and self esteem. So cial Indicators Research, 46 1 22.
117 Huebner, E. S., Suldo, S. M., & Gilman, R. (2006). Life satisfaction. In G. Bear & K. Minke (Eds.), III Washington, DC: National Association of School Psychologists. Hunt, M. K., & Hopko, D. R. (2009). Predicting high school truancy among students in the Apalachian south. The Journal of Primary Prevention, 30 (5), 549 567. Ingoldsby, E. M., Kohl, G. O., McMahon, R. J., Lengua, L., & The Conduct Problems Prevention Research Group (2006). Conduct problems, depressive symptomatology and their co occurring presentation in childhood as predictors of adjustment in early adolescence. Journal of Abnormal Child Psychology 34 603 621. Isakson, K., & Jarv is, P. ( 1999 ). The adjustment of adolescents during the transition into high school: A short term longitudinal study. Journal of Youth and Adolescence, 28 (1), 1 26. Kammann, R. and Flett, R. (1983). Affectometer 2: A scale to measure current level of gener al happiness. Australian Journal of Psychology, 35 259 265. Kessler, Ronald C., G. Andrews, D. Mroczek, B. Ustun, & H U. Wittchen. (1998). The World Health Organization Composite International Diagnostic Interview Short Form (CIDI SF). International Journal of Methods in Psychiatric Research 7, 171 85. Keyes, C. L. M. (2002). The mental health continuum: From languishing to flourishing in life. Journal of Health and Social Research, 43 207 222.
118 Keyes, C. L. M. (2007). Promoting and protecting menta l health as flourishing: A complementary strategy for improving national mental health. American Psychologist, 62 95 108. Keyes, C. L. M., & Magyar Moe, J. L. (2003). The measurement and utility of adult subjective well being. In S. J. Lopez & C. R. Snyde r (Eds.), Positive psychological assessment: A handbook of models and measures (pp. 411 425). Washington, DC: American Psychological Association. Kim Prieto, C., Diener, E., Tamir, M., Scollon, C., & Diener, M. (2005). Integrating the diverse definitions o f happiness: A time sequential framework of subjective well being. Journal of Happiness Studies, 6 261 300. Kirkcaldy, B., Furnham, A., & Siefen, G. (2004). The relationship between health, efficacy, educational attainment, and well being among 30 nations European Psychologist, 9 107 119. Kokko, K., & Pulkkinen, L. (2000). Aggression in childhood and long term unemployment in adulthood: A cycle of maladaption and some protective factors. Developmental Psychology, 36 463 472. Kovacs, M. (1985). The Child Psychopharmacology Bulletin, 21, 995 998. Kovacs, M. (1992). North Tonawanda, NY: Multi Health Systems. Larsen, R. J., Diener, E., & Emmons, R. A. (1985). A multitrait multimethod examination of affect structure: Hedonic level and emotional intensity. Personality and Individual Differences, 6 631 636.
119 Larsen, R. J., & Prizmic, Z. (2008). Regulation of emotional well being: Overcoming the hedonic treadmill. In M. Eid & R. J. Larsen (Eds.), The science of subjective well being (pp. 258 289) New York, NY: The Guilford Press. Laurent, J., Cantanzaro, S. J., Joiner, T. E., Jr., Rudolph, K. D., Potter, K. I., Lambert, S., et al. (1999). A measure of positive and negative affect for children: Scale development and preliminary validation. Psychological Assessment, 11 326 338. Leung, C. Y. W., McBride Chang, C., & Lai, B. P. Y. (2004). Relations among maternal parenting style, academic compe tence, and life satisfaction in Chinese early adolescents. Journal of Early Adolescence, 24 113 143. Lewinsohn, P. M., Hops, H., Roberts, R. E., Seeley, J. R., & Andrews, J. A. (1993). Adolescent psychopathology: I. prevalence and incidence of depression and other DSM III R disorders in high school students. Journal of Abnormal Psychology, 102 133 144. Lewinsohn, P., Redner, J., & Seeley, J. (1991). The relationship between life satisfaction and psychological variables: New perspectives. In F. Strack, M. Argyle, & N. Schwartz (Eds.), Subjective well being (pp. 193 212). New York: Plenum Press. Lewinsohn, P. M., Seeley, J. R., & Gotlib, I. H. (1997). Depression related psychosocial variables: Are they specific to depression in adolescents? Journal of Abno rmal Psychology, 106 (3), 365 375. Loe, I. M., & Feldman, H. M. (2007). Academic and educational outcomes of children with ADHD. Journal of Pediatric Psychology, 32 643 654. Lohman, J. B., & Jarvis, P. A. (2000). Adolescent stressors, coping strategies, an d psychological health studied in the family context. Journal of Youth and
120 Adolescence, 29 15 43. Lopes, J. A. (2007). Prevalence and comorbidity of emotional, beh avioral and learning problems: A study of 7 th grade students. Education and Treatment of Children, 30 165 181. Lopez, S. R., & Guarnaccia, P. J. J. (2000). Cultural psychopathology: Uncovering the social world of mental illness. Annual Review of Psychology, 51 571 598. Lounsbury, J. W., Steel, R. P., Loveland, J. M., & Gibson, L. W. (2004). An investigation of personality traits in relation to adolescent school absenteeism. Journal of Youth and Adolescence, 33 457 466. Loveland, J. M., Lounsbury, J. W., Welsh, D., & Buboltz, W. C. (2007). The validity of physical aggression in predicting ad olescent academic performance. British Journal of Educational Psychology, 77 167 176. Luthar, S. S., Cicchetti, D., & Becker, B. (2000). The construct of resilience: A critical evaluation and guidelines for future work. Child Development, 71 543 562. Lyu bomirsky, S., Sheldon, K. M., & Schkade, D. (2005). Pursuing happiness: The architecture of sustainable change. Review of General Psychology, 9 111 131. MacDonald, J. M., Piquero, A. R., Valois, R. F., & Zullig, K. J. (2005). The relationship between lif e satisfaction, risk taking behaviors, and youth violence. Journal of Interpersonal Violence, 20 1495 1518. Handbook of positive psychology (pp. 3 12). New York: Oxford University Press.
121 Malecki, C. K., & Demaray, M. K. (2002). Measuring perceived social support: Development of the Child and Adolescent Social Support Scale (CASSS). Psychology in the Schools 39 1 18. Marques, S. C., Pais Ribiero, J. L., & Lopez, S. J. (2007). Validation of a Portuguese Applied Research in Quality of Life, 2 83 94. Martin, K., Huebner, E. S., & Valois, R. F. (2008). Does life satisfaction predict victimization experiences in adolescence? Psychology in the Schools, 45 705 714. Masten, A. S., Roisman, G. I., Long, J. D., Burt, K. B., Obradovic, J., Riley, J. R., et al. (2005). Developmental cascades: Linking academic achievement and externalizing and internalizing symptoms over 20 years. D evelopmental Psychology, 41 733 746. Marsh, H. W. (1988). Self Description Questionnaire I Manual Sydney Australi a: University of Western Sydney, SELF Research Centre. Marsh, H. W. (1990). Self Description Questionnaire II Manual Sydney Australia : University of Western Sydney, SELF Research Centre. Martin, K. M., & Huebner, E. T. (2007). Peer victimization and prosocial experiences and emotional well being of middle school students. Psychology in the Schools, 44 199 208. Mash, E. J., & Barkley, R A. (2003). Child psychopathology (2 nd ed.). New York, NY: Guilford Press.
122 Mash, E. J., & Dozois, D. J. A. (2003). Child psychopathology: A developmental systems perspective. In E. J. Mash & R. A. Barkley (Eds.), Child psychopathology (2 nd ed., pp. 3 71) New York: Guilford Press. McCarthy, J., Downes, E. J., & Sherman, C. A. (2008). Looking back at adolescent depression: A qualitative study. Journal of Mental Health Counseling, 30, 49 68. McKnight, C. G., Huebner, E. S., & Suldo, S. M. (2002). Relationsh ips among stressful life events, temperament, problem behavior, and global life satisfaction in adolescents. Psychology in the Schools, 39 677 687. Merrell, K. W. (2007). Behavioral, social, and emotional assessment of children and adolescents (3 rd ed.). Mahwah, NJ: Lawrence Erlbaum Associates, Inc. Merrell, K. W. (2008). Helping students overcome depression and anxiety: A practical guide (2 nd ed.) New York: Guilford Press. Muris, P. (2001). A brief questionnaire for measuring self efficacy in youths. Jo urnal of Psychopathology and Behavioral Assessment 23 145 149. Nelson, R. M. & DeBacker, T. K. (2008). Achievement motivation in adolescents: The role of peer climate and best friends. The Journal of Experimental Education, 76 170 189. Nevin, S., Carr, A., Shelvin, M., Dooley, B., & Breaden, C. (2005) Factors related to well being in Irish adolescents. The Irish Journal of Psychology, 26 123 136. Pavot, W., & Diener, E. (1993). Review of the Satisfaction with Life Scale. Psychological Assessment, 5 1 64 172. Park, N. (2004). The role of subjective well being in positive youth development. Annals of the American Academy of Political and Social Science, 591 25 39.
123 Park, N., & Peterson, C. (2006). Moral competence and character strengths among adolescent s: The development and validation of the Values in Action Inventory of Strengths for Youth. Journal of Adolescence, 29 891 909. Park, N., & Peterson, C. (2008). The cultivation of character strengths. In M. Ferrari & G. Potwowrowski (Eds.), Teaching for wisdom (pp. 57 75). NJ: Springer Business Media B. V. Piers, E. V. (1984). Piers Los Angeles, CA: Western Psychological Services. Programme for International Student Assessment (2001). Knowledge and skills for life. First results of the OECD programme for international students assessment Organisation for Economic Cooperation and Development. Puig Antich, J., Kaufman, J., Ryan, N. D., Williamson, D. E., Dahl, R. E., et al. (1993). The psychosocial functioning and fam ily environment of depressed adolescents. Journal of the American Academy of Child and Adolescent Psychiatry 32 244 253. Reinke, W. M., Herman, K. C., Petras, H., & Ialongo, N. S. (2008). Empirically derived subtypes of child academic and behavior proble ms: Co Occurrence and distal outcomes. Journal of Abnormal Child Psychology, 36 759 770. Reschly, A. L., Huebner, E. S., Appleton, & Antaramian, J. J. (2008). Engagement as enga gement at school and with learning. Psychology in the Schools, 45 419 431. Reynolds, C. R., & Kamphaus, R. (1992). Behavior Assessment System for Children Circle Pines, MN: American Guidance Services.
124 Roberts, R. E., Atkisson, C. C., & Rosenblatt, A. (19 98). Prevalence of psychopathology among children and adolescents. American Journal of Psychiatry, 155 715 725. Roberts, R. E., Roberts, C. R., & Xing, Y. (2007). Rates of DSM IV psychiatric disorders among adolescents in a large metropolitan area. Journal of Psychiatric Research, 41, 959 967. Roeser, R. W., Eccles, J. S., & Sameroff, A. J. (2000). School as a context of early emotional development: A summary of research findings. The Elementary School Journal, 100 4 43 471. Rushton, J. L., Forcier, M, & Schectman, R. M. (2002). Epidemiology of depressive symptoms in the National Longitudinal Study of Adolescent Health. Journal of American Child Adolescent Psychiatry, 41 199 205. Schatschneider, C., Buck, J., Torgesen J., Wagner, R., Hassler, et al. (2004). A multivariate study of individual differences in performance on the reading portion of the Florida Comprehensive Assessment Test: A brief report. Florida Center for Reading Research. Schwarz, N. (1999). Self repor ts: How the questions shape the answers. American Psychologist 54 93 105. Seligman, E. P. (2005). Positive psychology, positive prevention, and positive therapy. In C. R. Snyder and S. J. Lopez (Eds.), Handbook of positive psychology (pp. 3 12). New York : Oxford University Press. Seligman, M., & Csikszentmihalyi, M. (2000). Positive psychology: An introduction. American Psychologist, 55 5 14.
125 Seligson, J. L., Huebner, E. S., & Valois, R. F. (2005). An investigation of a brief life satisfaction scale with elementary school children. Social Indicators Research, 73 355 374. Shapiro, E. S. (2004). Academic skills problems: Direct assessment and intervention (3 rd ed.). New York: Th e Guilford Press. Spielberger, C. D. (1973). Preliminary manual for the State Trait Anxiety Inventory for Children ("How I Feel Questionnaire"). Palo Alto, CA: Consulting Psychologists Press. Steinhausen, H. C., Mller, N., & Metzke, C. W. (2008). Frequency, stability and differentiation of self reported school fear and truancy in a community sample. Child and Adolescent Ps ychiatry and Mental Health 2 (17), 1 11. Stevens, J. (2009). Applied Multivariate Statistics for the Soci al Sciences (5 th Ed.) Mahwah, NJ: Erlbaum. Suldo, S. M & Huebner, E. S. (2006). Is extremely high life satisfaction during adolescence advantageous? Social Indicators Research, 78 179 203. Suldo, S. M & Shaffer, E. J. (2008). Looking beyond psychopathology: The dual factor model of mental health in youth. School Psychology Review, 37, 52 68 S uldo, S. M., Shaffer, E. S., & Riley, K. (2008). A social cognitive behavioral model of School Ps ychology Quarterly, 23, 56 69 Szatmari, P. (1992). The epidemiology of attention deficit hyperactivity disorders. Child and Adolescent Psychiatric Clinics of North America, 1 361 372.
126 Tabachnick, B. G., & Fiddell, E. S. (2001). Using Multivariate Statist ics (4 th Ed.). Needlham Heights, MA: Allyn and Bacon. Tashakkori, A., & Teddlie, C. (2003). Major issues and controversies in the use of mixed methods in the social and behavioral sciences. In A. Tashakkori & C. Teddlie (Eds.), Handbook of mixed methods i n social and behavioral research (1 st ed., pp. 3 50). Thousand Oaks, CA: Sage Publications. U. S. Public Health Service (2000). Washington, DC: Department of Health and Human Services. Retrieved from http://www.surgeongeneral.gov/topics/cmh/childreport.html Valois, R. V., Paxton, R. J., Zullig, K.J., & Huebner, E. S. (2006). Life satisfaction and violent behavior among middle school students. Journal of Child and Family Studies, 15 695 707. Valois R. F., Zullig K. J., Huebner E. S., Drane J. W. (2004). Life satisfaction and suicide among high school adolescents. Social Indicators Research, 66 81 105. Watson, D., Clark, L. A., & Tellegen, A. (1988). Developm ent and validation of brief measures of positive and negative affect: The PANAS scales. Journal of Personality and Social Psychology, 54 1063 1070. Woodward, L. J., & Fergusson, D. M. (2001). Life course outcomes of young people with anxiety disorders in adolescence. Journal of the American Academy of Child & Adolescent Psychiatry, 40 1086 1093.
127 World Health Organization. (1993). The European Network of Health Promoting Schools Copenhagen: World Health Organization Regional Office. World Health Organization (2006). Constitution. Geneva: Author. Retrieved from http://www.searo.who.int/LinkFiles/About_SEARO_const.pdf Young, S., Heptinstall, E., Sonuga Barke, E. J S., Chadwick, O., & Taylor, E. (2005). The adolescent outcome of hyperactive girls: Self report of psychosocial status. Journal of Child Psychology and Psychiatry, 46 255 262. Zullig, K. J., Valois, R. F., Huebner, E. S., & Drane, J. W. (2005). Adolescent health related quality of life and perceived satisfaction with life. Quality of Life Research, 14 1573 1584. Zullig, K. J., Valois, R. F., Huebner, E. S., Oeltmann, J. E., & Drane, J. W. (2001). Relationship between perceived life satisfaction Journal of Adolescent Health, 29 279 288.
129 Appendix A Parent Consent Form Dear Parent or Caregiver: This letter provides information about a research study that will be conducted at Liberty Middle School by investigators from the University of South Florida. Our goal in conducting n their school performance, physical health, and social relationships. Who We Are : The research team consists of Shannon Suldo, Ph.D., a professor in the School Psychology Program at the University of South Florida (USF), and several doctoral students in the USF College of Education. We are planning the study in cooperation with the pr incipal of Liberty Middle School (LMS) to make sure that the study provides information that will be useful to the school. : This study is being conducted as Bei being asked to participate because he or she is a student at Liberty. Why Your Child Should Participate : We need to learn more about what leads to happiness and health during the pre teen years! The informatio n that we collect from students during adolescence. In addition, group level results of the study will be shared with the teachers and administrators at LMS in orde r to increase their knowledge of the relationship between specific school experiences and psychological wellness in students. Please note all students who partici pate in the study will be entered into a drawing for one of several gift certificates. What Participation Requires : If your child is given permission to participate in the study, he or she will be asked to complete several paper and pencil questionnaires These physical health. Completion is expected to take your child betwee n 45 and 60 minutes. We will personally administer the questionnaires at LMS, during regular school hours, to large groups of students who have parent permission to participate. Participation will occur during one class period this school year. If your chi ld is at LMS next year, your child will be asked to complete the same surveys again so that we can examine change over time. In total, participation involves a review of school administrators, we will retrieve the following information about your child: grade point average, FCAT scores, attendance, and history of discipline referrals. Finally, one of behavior at school.
130 Appendix A (Continued) Please Note : Your decision to allow your child to participate in this research study must be completely voluntary. You are free to allow your child to participate in this research study or to withdraw him or her at any time. Your decision to participate, not to parti cipate, student status, his or her grades, or your relationship with LMS, USF, or any other party. : There is minimal r isk to your child for participating in this research. We will be present during administration of the questionnaires in order to provide assistance to your child if he or she has any questions or concerns. Additionally, school guidance counselors will be a vailable to students in the unlikely event privacy and research records will be kept confidential to the extent of the law. Authorized research personnel, employees of the Department of Health and Human Services, the USF Institutional Review Board and its staff, and other individuals acting on behalf of USF may be shared wit h school system personnel or anyone other than us and our research assistants. confidentiality of his or her responses. Only we will have access to the locked file cabinet stored at USF that will 2) all information gathered from school records. All records from the study (completed surveys, information from school records) will be destroyed in four year s. Please note that although your if your child indicates that he or she intends to harm him or herself, we will contact district mental health counselors to ensure your : We plan to use the information from psychological wellness (particularly their subjective well being, al so referred to as happiness) and their school performance, physical health, and social relationships. The results of this study may be published. However, the data obtained from your child will be combined with data from other people in the publication. Th name or any other information that would in any way personally identify your child. Questions? If you have any questions about this research study, please contact Dr. Suldo at (813) 974 taking part in a research study, you may contact a member of the Division of Research Compli ance of the USF at (813) 974 9343. Want Your Child to Participate? To permit your child to participate in this study, please complete the attached consent form and have your child turn it in to his or her homeroom teacher. Sincerely, Shannon Suldo, Ph .D. Assistant Professor of School Psychology Department of Psychological and Social Foundations
131 Appendix A (Continued) --------------------------------------------------------------------------------------------------------------------Consent for C hild to Take Part in this Research Study I freely give my permission to let my child take part in this study. I understand that this is research. I have received a copy of this letter and consent form for my records. ________________________________ ________________ Printed name of child Grade level of child ________________ ______________________ __________ Signature of parent Printed name of parent of child taking part in the study Date Statement of Person Obtaining Informed Consent I certify that participants have been provided with an informed consent form that has been nature, demands, risks, and benefits involved in participating in th is study. I further certify that a phone number has been provided in the event of additional questions. ______________________________________________________________________________ Signature of person obtaining consent ____________________________________ Printed name of person obtaining consent ___________________________________ Date _______________________
132 Appendix B Student Assent Form Hello! Today you will be asked to take part in a research study by filling out several surveys. Our goal in conducting the study is to determine the mental health on their school performance, physical health, and social relationships. Who We Are : The research team is led by Shannon Suldo, Ph.D., a professor in the School Psychology Program at the University of South Florida (USF). Several doctoral students in the USF College of Education are on the team. We are working with your principal to make sure this study will be helpful to your school. Why We Are Asking You to Take Part in the Study : This study is part of a project Subjective Well Being of Middle School Students. take part because you are a student at Lib erty Middle School (LMS). Why You Should Take Part in the Study : We need to learn more about what leads to happiness and health during the pre teen years! The information that we collect may help us better understand why we should monitor ess In addition, results from the study will be shared with LMS to show them how happiness is related to school grades and behavior, physical health, and social relationships. You will not be paid for taking part in the study. Filling Out the Surveys : These surveys will ask you about your thoughts, behaviors, and attitudes towards school, family, and life in general. The surveys will also ask about your physical health. It will probably take between 45 and 60 minutes to fill out the surveys. We will a lso ask you to complete these surveys again one year from now. What Else Will Happen if You Are in the Study : If you choose to take part in the study, we will look at some of your school records grades, discipline record, attendance, and FCAT scores. We will gather this information under the guidance of school administrators. Please Note : Your involvement in this study is voluntary (your choice). By signing this form, you are agreeing to take part in this study. Your decision to take part, not to take part, or to stop taking part in the study at any time will not affect your student status or your grades; you will not be punished in any way. If you choose not to take part, it will not affect your relationship with LMS, USF, or anyone else. Privacy of Your Responses : Your school guidance counselors are also on hand in case you become upset. Your privacy and research records will be kept confidential (private, secret) to the extent of the law. People approved to do
133 Appendix B (Continued) research at U SF, people who work for the Department of Health and Human Services, the USF Institutional Review Board, and its staff, and other individuals acting on behalf of USF may look at the records from this research project. However, your individual responses wi ll not be shared with people in the school system or anyone other than us and our research assistants. Your completed surveys will be given a code number to protect the privacy of your responses. Only we will have the ability to open the locked file cabine t stored at USF that will contain: 1) all records linking code numbers to names, and 2) all information gathered from school records. All records from the study (completed surveys, information from school records) will be destroyed in four years. Again, yo ur specific responses will not be shared with school staff. However, if you respond on the surveys that you plan to harm yourself, we will let district counselors know in order to make sure you are safe. : We plan to use the information from this physical health, and social relationships. The results of this study may be published. However, your responses will be combined with other student responses in the publication. The published results will not include your name or any other information that would in any way identify you. Questions? If you have any questions about this research study, please raise your hand now or at any point durin g the study. Also, you may contact us later at (813) 974 2223 (Dr. Suldo). If you have questions about your rights as a person who is taking part in a research study, contact a member of the Division of Research Compliance of the USF at (813) 974 9343. Als o call the Florida Department of Health, Review Council for Human Subjects at 1 850 245 4585 or toll free at 1 866 433 2775. Thank you for taking the time to take part in this study. Sincerely, Shannon Suldo, Ph.D. Assistant Professor of School Psychology Department of Psychological and Social Foundations -----------------------------------------------------------------------------------------------------------Assent to Take Part in this Research Study I give my permission to take part in this study. I understand that this is research. I have received a copy of this letter and assent form. __________________________ _______________________ __________ Signature of child taking Printed name of child Date part in this study
134 Appendix B (Continued) Statement of Person Obtaining Informed Consent I certify that participants have been provided with an informed consent form that Review Board and that explains the nature, demands, risks, and benefits involved in participating in this study. I further certify that a phone number has been provided in the event of additional questions. ______________________ ____________________ __________ Signature of person Printed name of person Date obtaining consent obtaining consent
135 Appendix C Demographics Form ID # ______________ Spring 2006 ________________________________________________________________________ Birthdate _____ _____ _____ (month) (day) (year) PLEASE READ EACH QUESTION AND CIRCLE ONE ANSWER PER QUESTION: 1. I am in grade: 6 7 8 2. My gender is: Male Female 3. Do you receive free or reduced lunch? Yes No 4. My race/ethnic identity is: a. American Indian or Alaska Native e. Native Hawaiian or Other Pacific Islander b. Asian f. White c. Black or African American g. Multi racial (please specify):______________________ d. Hispanic or Latino h. Other (please specify):___ ________________________ 5. My biological parents are: a. Married d. Never married b. Divorced e. Never married but living together c. Separated f. Widowed 6. On average, how much time per week do you spend doing your homework: a. Less than 1 hour e. From 10 hours to less than 15 hours b. From 1 hour to less than 3 hours f. From 15 hours to less than 20 hours c. From 3 hours to less than 5 hours g. From 20 hours to less than 25 hours d. From 5 hours to less than 10 hours h. 25 hours or more _________________ ___________________________________________________________________________________________
136 Appendix C (Continued) Sample Questions: Never Almost Never Sometimes Fairly Often Very Often 1. I go to the beach 1 2 3 4 5 Strongly Disagree Disagree Not Sure Agree Strongly Agree 2. Going to the beach is fun 1 2 3 4 5
137 Appendix D We would like to know what thoughts about life you've had during the past several weeks Think about how you spend each day and night and then think about how your life has been during most of this time. Here are some questions that ask you to indicate your satisfaction with life. In answering each statement, circle a number from ( 1 ) to ( 6 ) where ( 1 ) indicates you strongly dis agree with the statement and ( 6 ) indicates you strongly agree with the statement. Strongly Disagree Mostly Disagree Mildly Disagree Mildly Agree Mostly Agree Strongly Agree 1. My life is going well 1 2 3 4 5 6 2. My life is just right 1 2 3 4 5 6 3. I would like to change many things in my life 1 2 3 4 5 6 4. I wish I had a different kind of life 1 2 3 4 5 6 5. I have a good life 1 2 3 4 5 6 6. I have what I want in life 1 2 3 4 5 6 7. My life is better than most kids' 1 2 3 4 5 6
138 Appendix E Positive and Negative Affect Scale for Children (PANAS C; Laurent et al., 1999) This scale consists of a number of words that describe different feelings and emotions. Read each item and then circle the appropriate answer next to that word. Indicate to what extent you have felt this way during the past few weeks. Feeling or emotion: Very slightly or not at all A little Moderately Quite a bit Extremely 1. Interested 1 2 3 4 5 2. Sad 1 2 3 4 5 3. Frightened 1 2 3 4 5 4. Excited 1 2 3 4 5 5. Ashamed 1 2 3 4 5 6. Upset 1 2 3 4 5 7. Happy 1 2 3 4 5 8. Strong 1 2 3 4 5 9. Nervous 1 2 3 4 5 10. Guilty 1 2 3 4 5 11. Energetic 1 2 3 4 5 12. Scared 1 2 3 4 5 13. Calm 1 2 3 4 5 14. Miserable 1 2 3 4 5 15. Jittery 1 2 3 4 5 16. Cheerful 1 2 3 4 5 17. Active 1 2 3 4 5 18. Proud 1 2 3 4 5 19. Afraid 1 2 3 4 5 20. Joyful 1 2 3 4 5 21. Lonely 1 2 3 4 5 22. Mad 1 2 3 4 5 23. Disgusted 1 2 3 4 5 24. Delighted 1 2 3 4 5 25. Blue 1 2 3 4 5 26. Gloomy 1 2 3 4 5 27. Lively 1 2 3 4 5 Note. This appendix has been modified in font size to comply with margin requirements.
xml version 1.0 encoding UTF-8 standalone no
record xmlns http:www.loc.govMARC21slim xmlns:xsi http:www.w3.org2001XMLSchema-instance xsi:schemaLocation http:www.loc.govstandardsmarcxmlschemaMARC21slim.xsd
leader nam 22 Ka 4500
controlfield tag 007 cr-bnu---uuuuu
008 s2010 flu s 000 0 eng d
datafield ind1 8 ind2 024
subfield code a E14-SFE0003347
Predicting early adolescents' academic achievement and in-school behavior with a dual-factor model of mental health
h [electronic resource] /
by Amanda Thalji.
[Tampa, Fla] :
b University of South Florida,
Title from PDF of title page.
Document formatted into pages; contains X pages.
Thesis (Ed.S.)--University of South Florida, 2010.
Includes bibliographical references.
Text (Electronic thesis) in PDF format.
Mode of access: World Wide Web.
System requirements: World Wide Web browser and PDF reader.
ABSTRACT: A dual-factor model of mental health includes indicators of wellness (i.e., subjective well-being) and psychopathology (i.e., internalizing and externalizing behavior problems) in defining psychological wellness. The present empirical investigation examined the utility of SWB and psychopathology examined separately and together (as in a dual-factor model of mental health) in predicting students' subsequent academic achievement and in-school behavior. Specifically, it determined if SWB, psychopathology, and membership in a specific mental health group yielded by the dual-factor model (i.e., complete mental health, vulnerable, symptomatic but content, or troubled) at Time 1 was related to achievement (i.e., GPA, FCAT-math, FCAT-reading, absences, office disciplinary referrals [ODRs]) the following school year (i.e., Time 2). A previously analyzed data set (Time 1) and a different archival data set yielded from student records unique to the current study (Time 2) comprised of data from 300 adolescents were analyzed. Results of regression analyses to explore the predictive initial relationship of mental health to later student achievement indicated that initial SWB predicted student grades one year later, initial internalizing psychopathology predicted absences one year later, and initial externalizing psychopathology predicted grades, absences, and ODRs one year later. Results of mixed model ANCOVAs indicated that students' grades and attendance across time varied as a function of mental health group. Specifically, students belonging to the troubled mental health group declined at a significantly faster rate than youth without psychopathology across time on GPA. In contrast, the slope of students in the symptomatic but content group was not significantly different from the slope of peers with low psychopathology. Additionally, at Time 2, the best school attendance and school grades were found by students who had both average/high SWB and low psychopathology one year earlier, supporting the long-term utility of complete mental health.
Advisor: Shannon Suldo, Ph.D.
x Psychological & Social Foundations
t USF Electronic Theses and Dissertations.