Education, Faculty of
Educational and Counselling Psychology, and Special Education (ECPS), Department of
DSpace
UBCV
Kitil, Marsha Jennifer
Doctor of Philosophy - PhD
University of British Columbia
Executive functions (EFs) are required for goal-oriented behavior, and comprise three core areas: inhibition, working memory, and cognitive flexibility. EFs form the basis for higher-order cognitive skills (e.g., planning, reasoning, and problem solving). Previous research has shown that EFs as measured in young children reliably predict academic, occupational, and life success. Less is known about the relations of EFs measured in early adolescence to indicators of academic success and well-being in late adolescence. In a framework put forth by Diamond (2012), participation in programs that foster EFs and contain components that promote EFs (e.g., social support) will lead to improvements in EFs and academic achievement. Previous research has found support for this; however, this study was the first to examine EFs in relation to both program participation as well as EF promotive factors within a single sample of adolescents across time. The study aims were (1) to investigate longitudinal relations of EFs in early and late adolescence to academic achievement (i.e., GPA) and well-being (i.e., satisfaction with life and depressive symptoms) in late adolescence; (2) to explore relative contributions of factors that support or hinder EFs in early adolescence to EFs, academic achievement, and well-being in late adolescence; (3) to investigate the relative contribution of students’ participation in school-based activities to EFs, academic achievement, and well-being in late adolescence. As such, the study hypothesized that EFs and the factors that affect them (EF-supportive, EF-promotive, and activity participation) would be positively associated with EFs, academic achievement, and satisfaction with life, and negatively associated with depressive symptoms. Using multiple methods and sources, data were collected at two time points from 65 students; first when they were in the fourth and fifth grades, and again when they were followed up eight years later in the eleventh and twelfth grades. Results indicated that EFs, the factors that support them, and the factors that hinder them negatively predict GPA eight years later. No other statistically significant findings emerged. Findings are discussed in relation to extant theoretical and empirical research in this area and directions for future research are put forth.
PATHWAYS TO SCHOOL AND LIFE SUCCESS: RELATIONS OF EXECUTIVE FUNCTIONS TO ACADEMIC ACHIEVEMENT AND WELL-BEING IN ADOLESCENCE by Marsha Jennifer Kitil B.A., University of California, Los Angeles, 2003 M.P.H., University of California, Los Angeles, 2009 A DISSERTATION SUBMITTED IN PARTIAL FULFILLMENT OF THE REQUIREMENTS FOR THE DEGREE OF DOCTOR OF PHILOSOPHY in THE FACULTY OF GRADUATE AND POSTDOCTORAL STUDIES (Human Development, Learning, and Culture) THE UNIVERSITY OF BRITISH COLUMBIA (Vancouver) April 2020 © Marsha Jennifer Kitil, 2020 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Pathways to School and Life Success: Relations of Executive Functions to Academic Achievement and Well-Being in Adolescence submitted by Marsha Jennifer Kitil in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Human Development, Learning, and Culture Examining Committee: Dr. Kimberly Schonert-Reichl, Professor, Faculty of Education, UBC Supervisor Dr. Adele Diamond, Professor & Tier 1 Canada Research Chair, Department of Psychiatry, UBC Supervisory Committee Member Dr. Martin Guhn, Assistant Professor, School of Population & Public Health, UBC Supervisory Committee Member Dr. Bruno Zumbo, Professor & Distinguished Scholar, Faculty of Education, UBC University Examiner Dr. Andrew Baron, Associate Professor, Department of Psychology, UBC University Examiner iii Abstract Executive functions (EFs) are required for goal-oriented behavior, and comprise three core areas: inhibition, working memory, and cognitive flexibility. EFs form the basis for higher-order cognitive skills (e.g., planning, reasoning, and problem solving). Previous research has shown that EFs as measured in young children reliably predict academic, occupational, and life success. Less is known about the relations of EFs measured in early adolescence to indicators of academic success and well-being in late adolescence. In a framework put forth by Diamond (2012), participation in programs that foster EFs and contain components that promote EFs (e.g., social support) will lead to improvements in EFs and academic achievement. Previous research has found support for this; however, this study was the first to examine EFs in relation to both program participation as well as EF promotive factors within a single sample of adolescents across time. The study aims were (1) to investigate longitudinal relations of EFs in early and late adolescence to academic achievement (i.e., GPA) and well-being (i.e., satisfaction with life and depressive symptoms) in late adolescence; (2) to explore relative contributions of factors that support or hinder EFs in early adolescence to EFs, academic achievement, and well-being in late adolescence; (3) to investigate the relative contribution of students’ participation in school-based activities to EFs, academic achievement, and well-being in late adolescence. As such, the study hypothesized that EFs and the factors that affect them (EF-supportive, EF-promotive, and activity participation) would be positively associated with EFs, academic achievement, and satisfaction with life, and negatively associated with depressive symptoms. Using multiple methods and sources, data were collected at two time points from 65 students; first when they were in the fourth and fifth grades, and again when they were followed up eight years later in the eleventh and twelfth grades. Results indicated that EFs, the factors that support them, and the iv factors that hinder them negatively predict GPA eight years later. No other statistically significant findings emerged. Findings are discussed in relation to extant theoretical and empirical research in this area and directions for future research are put forth. v Lay Summary Executive functions are comprised of three dimensions: (1) inhibition (being able to focus one’s attention, resist temptation, and delay gratification), (2) working memory (being able to play with ideas mentally), and (3) cognitive flexibility (being able to think creatively, take the perspective of others, and adapt flexibly to change). Adolescence is a period of development that is marked by significant cognitive, behavioral, social, and emotional changes. This research project focused on understanding how adolescents’ executive functions are related to their academic achievement and well-being across time. The research project found that early adolescents with better executive functions would have better grades eight years later. Results are discussed in light of the relationship that executive functions have with academic achievement and well-being in adolescence. vi Preface This dissertation is submitted in partial fulfillment of the requirements for University of British Columbia’s Doctor of Philosophy in Education. It contains works completed from September 2011 to April 2020. My supervisor for the project has been Dr. Kimberly Schonert-Reichl, who is the Director of the Human Early Learning Partnership and Principal Investigator of the Schonert-Reichl Social and Emotional Learning Laboratory. The identification and design of the research project was made in consultation with my supervisor. The writing and data analyses for this dissertation were carried out solely by me. Some of the text is based on research conducted by others, and I have done my best to provide references to these sources. Some of the data used in the analyses for this project were collected as part of a larger program evaluation study, which was approved by the Behavioural Research Ethics Board of the University of British Columbia (approval certificate numbers: 06-0131 and H14-00644-8002). vii Table of Contents Abstract .......................................................................................................................................... iii Lay Summary ...................................................................................................................................v Preface ............................................................................................................................................ vi Table of Contents .......................................................................................................................... vii List of Tables ................................................................................................................................. xi List of Figures .............................................................................................................................. xiii Acknowledgements ...................................................................................................................... xiv Dedication .................................................................................................................................... xvi Introduction ......................................................................................................................................1 Overall Aim of Research .............................................................................................................4 Literature Review .............................................................................................................................7 Overview .....................................................................................................................................7 Executive Functions (EFs) ..........................................................................................................8 Adolescence ................................................................................................................................9 Development of EFs ..................................................................................................................10 EFs Across Adolescence ...........................................................................................................11 Academic Achievement ............................................................................................................13 EFs and academic achievement ............................................................................................14 EFs, academic achievement, and school self-concept .....................................................17 Well-Being ................................................................................................................................18 Satisfaction with life .............................................................................................................18 EFs and satisfaction with life ...........................................................................................20 Depressive symptoms ...........................................................................................................22 EFs and depressive symptoms .........................................................................................23 Factors that Affect EFs ..............................................................................................................26 Factors that support EFs .......................................................................................................27 Positive affect and EFs ....................................................................................................27 Connectedness and EFs ...................................................................................................32 Mindful attention and EFs ...............................................................................................36 Physical activity and EFs .................................................................................................41 Summary of factors that support EFs ..............................................................................47 Factors that hinder EFs .........................................................................................................47 Stress and EFs ..................................................................................................................47 Negative affect and EFs ...................................................................................................50 Depressive symptoms and EFs ........................................................................................52 viii Summary of factors that hinder EFs ................................................................................55 School-Based Activities ............................................................................................................55 School-based activities and EFs ...........................................................................................57 Sports and EFs .................................................................................................................57 Music and EFs .................................................................................................................59 Theatre and EFs ...............................................................................................................60 School-based activities and academic achievement .............................................................61 School-based activities and well-being ................................................................................64 Research Aims ..........................................................................................................................67 Method ...........................................................................................................................................69 Participants ................................................................................................................................69 T1 Sample .............................................................................................................................69 T2 Sample .............................................................................................................................70 Recruitment ..........................................................................................................................70 Procedures .................................................................................................................................71 T1 data collection .................................................................................................................71 T2 data collection .................................................................................................................72 School yearbooks .............................................................................................................72 Academic transcripts .......................................................................................................73 Measures ...................................................................................................................................73 Demographic information ....................................................................................................73 Predictors – EFs ....................................................................................................................73 Hearts & Flowers task .....................................................................................................74 Flanker / Reverse Flanker task ........................................................................................75 Scoring of EF tasks ..........................................................................................................76 Predictors – factors that support EFs ....................................................................................77 Positive affect ..................................................................................................................77 Mindful attention .............................................................................................................77 Connectedness .................................................................................................................78 Predictors – factors that hinder EFs ......................................................................................79 Stressful Life Events ........................................................................................................79 Negative affect .................................................................................................................80 Depressive symptoms ......................................................................................................80 Predictors – activities that promote EFs ...............................................................................80 School yearbooks .............................................................................................................81 Academic transcripts .......................................................................................................84 Connectedness derived from activity participation .........................................................84 Physical fitness derived from activity participation ........................................................85 Outcomes – grade point average ..........................................................................................86 Outcomes – well-being .........................................................................................................86 Satisfaction with life ........................................................................................................86 ix Depressive symptoms ......................................................................................................87 Outcomes – EFs ....................................................................................................................87 Covariates .............................................................................................................................87 School self-concept ..........................................................................................................88 Results ............................................................................................................................................89 Overview of Research Aims .....................................................................................................89 Data Analytic Plan ....................................................................................................................89 Preliminary Analyses ................................................................................................................91 Descriptive statistics .............................................................................................................91 Evaluation of statistical assumptions ...............................................................................91 Attrition ................................................................................................................................93 Study Aim 1 ..............................................................................................................................94 Study Aim 2 ..............................................................................................................................97 Study Aim 3 ............................................................................................................................103 Additional Hypotheses ............................................................................................................105 Discussion ....................................................................................................................................107 EFs and Academic Achievement ............................................................................................108 EFs and Well-Being ................................................................................................................112 EFs and satisfaction with life .............................................................................................112 EFs and depressive symptoms ............................................................................................114 Factors that Affect EFs ............................................................................................................116 Factors that support EFs .....................................................................................................116 Factors that hinder EFs .......................................................................................................119 School-Based Activities ..........................................................................................................120 Strengths and Limitations .......................................................................................................121 Future Directions .....................................................................................................................126 Educational Implications .........................................................................................................129 Conclusion ...............................................................................................................................131 References ....................................................................................................................................166 Appendices ...................................................................................................................................248 Appendix A: T1 Demographic Questionnaire ........................................................................248 Appendix B: Positive and Negative Affect Scale for Children ...............................................249 Appendix C: Mindful Attention Awareness Scale adapted for children .................................251 Appendix D: Teacher’s Ratings of Social Behavior ...............................................................253 Appendix E: Peer behavioral assessments of peer acceptance ...............................................254 x Appendix F: Stressful Life Events Scale ................................................................................255 Appendix G: Seattle Personality Questionnaire for Children - Depressive Symptoms ..........257 Appendix H: School Yearbook Data Collection Sheet ...........................................................258 Appendix I: Satisfaction with Life Scale for Children ............................................................259 Appendix J: Marsh’s Self-Description Questionnaire ............................................................260 Appendix K: Analyses including the influential case .............................................................261 xi List of Tables Table 1 Type of construct by data source and time point .......................................................... 132 Table 2 Counts of Student Names in School Yearbook Data by Category and School Type ... 133 Table 3 Connectedness and Energy Expenditure Scores (MET) for School-Based Activities derived from Yearbooks and Co-Curricular Course Completion .......................................... 134 Table 4 Means, standard deviations, kurtosis, and skewness of variables ................................ 136 Table 5 Intercorrelations of variables ........................................................................................ 137 Table 6 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 and T2 EFs 138 Table 7 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 School Self-Concept and T1 and T2 EFs ................................................................................................... 139 Table 8 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T1 and T2 EFs, Accounting for T1 SWLS ...................................................................................................... 140 Table 9 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T1 and T2 EFs, Accounting for T1 Depressive Symptoms ................................................... 141 Table 10 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Hearts & Flowers) from T1 Factors that Support EFs ................................................................................................... 142 Table 11 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Flanker / Reverse Flanker) from T1 Factors that Support EFs ........................................................................... 143 Table 12 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 Factors that Support EFs ............................................................................................................................ 144 Table 13 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T1 Factors that Support EFs ............................................................................................................................ 145 Table 14 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T1 Factors that Support EFs ................................................................................................... 146 Table 15 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Hearts & Flowers) from T1 Factors that Hinder EFs .................................................................................................... 147 Table 16 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Flanker / Reverse Flanker) from T1 Factors that Hinder EFs ............................................................................. 148 Table 17 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 Factors that Hinder EFs ............................................................................................................................. 149 Table 18 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T1 Factors that Hinder EFs ............................................................................................................................. 150 Table 19 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T1 Factors that Hinder EFs .................................................................................................... 151 Table 20 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Hearts & Flowers) from T2 School-Based Activities .................................................................................................... 152 xii Table 21 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Flanker / Reverse Flanker) from T2 School-Based Activities ............................................................................ 153 Table 22 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T2 Participation in School-Based Activities ..................................................................................................... 154 Table 23 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T2 Participation in School-Based Activities ..................................................................................................... 155 Table 24 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T2 Participation in School-Based Activities .......................................................................... 156 xiii List of Figures Figure 1. EFs and related constructs ...................................................................................... 157 Figure 2. Model illustrating hypothesized direct and indirect pathways to improved EFs and academic success ........................................................................................................................ 158 Figure 3. Overall model of the present study ......................................................................... 159 Figure 4. EFs: Hearts & Flowers Task, Congruent and Incongruent trials are shown .......... 160 Figure 5. EFs: Flanker / Reverse Flanker Task, Congruent, Incongruent, and no Distractor Trials are shown .......................................................................................................................... 161 Figure 6. Histograms of accuracy scores for T1 Hearts & Flowers EF Task, including and excluding the influential case ...................................................................................................... 162 Figure 7. Histograms of accuracy scores for T1 Flanker / Reverse Flanker EF Task, including and excluding the influential case ............................................................................................... 163 Figure 8. Histograms of accuracy scores for T2 Hearts & Flowers EF Task, including and excluding the influential case ...................................................................................................... 164 Figure 9. Histograms of accuracy scores for T2 Flanker / Reverse Flanker EF Task, including and excluding the influential case. .............................................................................................. 165 xiv Acknowledgments I express my deepest heartfelt gratitude to my research supervisor and mentor, Dr. Kimberly Schonert-Reichl for her unwavering support, encouragement, and guidance. I offer my appreciation to my committee member, Dr. Adele Diamond, for allowing me to explore the relations proposed in her framework of executive functions as my dissertation topic, and to both Dr. Diamond and Dr. Martin Guhn, for offering insight and guidance while I traveled along on this journey. I extend my appreciation as well to my university examiners, Dr. Bruno Zumbo and Dr. Andrew Baron, and my external examiner, Dr. Claire Crooks, for taking the time to read through my dissertation and provide helpful feedback. I am thankful to the University of British Columbia and Faculty of Education for giving me the opportunity to study, in the pursuit of learning more about myself and discovering how deep self-exploration can serve as an inspiration to guide young people of all ages to support them in leading joyful and meaningful lives. I also acknowledge the financial support received from the university in the form of fellowships and graduate student research grants that have enabled me to pursue my studies, including the University of British Columbia 4 Year Fellowship and the University of British Columbia Faculty of Education Research Grant. I would like to acknowledge the monetary support from the Goldie Hawn Foundation and University of British Columbia Hampton Research Fund that also made this research possible, as well as thank the school district administrators for their enthusiastic support throughout the research process. I would also like to thank all participating students for their willingness to disclose personal information and provide researchers with insight into their social and emotional lives. I give thanks to all the members of the University of British Columbia’s Social and Emotional Learning lab, and numerous friends and family members who cheered me on from the xv sidelines, engaged in deep conversation with me about the research, provided me with helpful feedback, and in some cases, assisted me with some aspects of the research process. And last but not least, I offer my deepest gratitude to my mom who has supported me throughout all these years of schooling. Without her as a life compass, I wouldn’t have been able to grow into the person I am today. Thank you. xvi Dedication I dedicate this work to all my teachers who guided, encouraged, and inspired me to embark on this journey, to learn about myself, and to learn about the nature of the human mind. As I approach the end of this chapter in life and embark upon the next, I am reminded that, no matter if the skies are blue or covered with clouds, the sun that is the constant source of light will always continue to shine brilliantly, smiling upon and lifting all hearts. It must be remembered that the source of this light lies within and is always with us, and it is to this light that I ultimately dedicate this work. 1 Introduction For centuries, philosophers have explored the role that education could have in shaping the lives of young people. Is education merely an avenue through which the traditional subjects of reading, writing, and arithmetic are taught? These competencies are necessary but directing a focus to these conventional subjects may not ultimately fulfill the purpose of education—the full reason of why we aim to educate. Noddings (2005) proposes that policymakers reconsider the goal of education, to restructure the traditional compartmentalized approach by integrating both the goals of society and the goals of the individual being educated so that the needs of both are addressed. Society has always had a firm stake in the education of its people through its investment in basic education for all, at least in the industrialized world. Graduates from the system are expected to contribute to society, not just through the application of knowledge and skills, but also through participation in civic life and engagement in ethical practice. In relation to human development, society also seeks to fulfill aspirations that its youth grow into well-rounded adults (Steinberg & Morris, 2001). From the perspective of the young people themselves, the goal of pursuing an education is perhaps not just to learn the skills necessary to achieve success in life, but also to engage in processes that enable them to cultivate their own individual happiness (Noddings, 2003). As John Dewey (1972) once said, “Cease conceiving of education as mere preparation for later life, and make it the full meaning of the present life” (p. 50). This has led to a shift in recent years in the field of education to ensure that not only cognitive aspects are addressed but social and emotional aspects are considered as well to nurture and meet the needs of the “whole child” (Noddings, 2005). In the past decade, policymakers have begun to recognize the importance of skills that move beyond the application of knowledge in specific contexts. To prepare young people for a 2 rapidly changing and increasingly collaborative future, many stakeholders including educators, parents, and the society-at-large have begun to recognize that youth need to be equipped with 21st Century Skills, which are needed to thrive in a “post-industrial service economy.” These include a broad range of flexible competencies necessary for adapting to increasing levels of social interaction and rapidly changing tasks, such as thinking quickly on one’s feet and adapting knowledge to new contexts (Partnership for 21st Century Skills, 2009; Pellegrino & Hilton, 2013). Also needed are “non-cognitive” traits, such as behaviors, competencies, attitudes, and strategies that are not measured by cognitive tests (Farrington et al., 2012), such as creativity, communication, collaboration, and social and cross-cultural skills (Partnership for 21st Century Skills, 2009). For decades, researchers have delved into identifying the essential ingredients that lead to a successful life. The extant literature suggests a combination of “cognitive” and “non-cognitive” skills are required for supporting success in adulthood – the same ones that promote academic success (Best, Miller, & Naglieri, 2011; Farrington et al., 2012; Moffitt et al., 2011). Among the factors that promote these skills is inhibitory control and cognitive flexibility, both of which have been associated with well-being, aspects of which include physical and psychological health (for a review, see Diamond, 2013; Moffitt et al., 2011); most of the research examining these relations, however, have been correlational in nature. Previous research has found that these factors—the ability to delay gratification, adapt to new circumstances, and think quickly on one’s feet—all fall under one umbrella term, executive functions (Diamond, 2013). Executive functions (EFs) are needed for goal-oriented behavior and encompass three core areas: inhibitory control, working memory, and cognitive flexibility (Miyake et al., 2000). These EFs form the basis for higher order skills such as planning, reasoning, and problem 3 solving. EFs, particularly inhibitory control, also overlap with self-regulation, effortful control, and executive attention (for a model illustrating the interrelationships, see Figure 1). EFs are important because they have been found to be a reliable predictor of school, work, and life success (for a review, see Diamond, 2013), with longitudinal studies discovering the lasting impacts of EFs (e.g., Moffitt et al., 2011). Measurements made in young children show that EFs can have remarkable continuity over time and are reliable predictors of important developmental outcomes that are as varied as stress management skills (Carlson et al., 2018; Mischel, Shoda, & Rodriguez, 1989), substance use and abuse (Moffitt et al., 2011), and altruistic behavior (Aguilar-Pardo, Martínez-Arias, & Colmenares, 2013). To illustrate, a population-based study that followed a birth cohort into adulthood found inhibitory control to be a significant predictor of several indicators of adult success, including financial management, substance use, and criminality, even after controlling for IQ and social class (Moffitt et al., 2011). Considering the long-term repercussions of EFs on so many facets of life and development, research in the past decade has begun to focus on ways to improve them through training (for review, see Diamond & Ling, 2016). Randomized-controlled trials have demonstrated that EFs can be trained, leading to improvements in academic outcomes (Blair & Raver, 2014; Raver et al., 2011). Additionally, researchers have begun to investigate specific factors that either support or hinder EFs. Factors that have been found to promote EFs include having a positive mood (Ashby, Isen, & Turken, 1999) and having a mindful disposition (Creswell, Way, Eisenberger, & Lieberman, 2007); those that negatively impact EFs the most are the presence of stress (Liston, McEwen, & Casey, 2009), lacking a sense of social connectedness (Cacioppo & Hawkley, 2009), being sleep deprived (Barnes, Gozal, & Molfese, 2012), and being sedentary (Verburgh, Konigs, Scherder, & Oosterlaan, 2014). 4 Overall Aim of Research As a result, the overall aim of research in the present study was to examine the ways that EFs affect and are affected by a variety of psychological, behavioral, and social factors, including those that support and those that hinder EFs, in a sample of early adolescents (Grades 4 and 5) with measurements across time into late adolescence (Grades 11 and 12). The present study intended to contribute to the general paucity of longitudinal research of EFs in three primary ways: (1) by investigating the longitudinal relations of EFs in early adolescence and late adolescence to academic achievement and well-being in late adolescence, with an examination into the role that school self-concept has in the relation; (2) by exploring the relative contributions of factors that support or hinder EFs in early adolescence to EFs, academic achievement, and well-being in late adolescence; (3) by investigating the relative contribution of students’ participation in middle and secondary school-based activities to EFs, academic achievement, and well-being in late adolescence. Moreover, an examination also was made into whether EFs played a role in mediating the predictors and outcomes. In a framework put forth by Diamond (2012), the current research on EFs, their predictors, and the pathways by which outcomes would be affected were synthesized. Diamond hypothesized that activities/programs that are designed to promote EFs directly (requiring and challenging EFs) or indirectly (by promoting most or all of the factors related to improving EFs) would lead to enhanced EF performance for its participants. This, in turn, should reduce the severity of certain psychological disorders (e.g., attention deficit/hyperactivity disorder or ADHD) and improve academic outcomes (see Figure 2). Specifically, programs or activities that improve one’s mood, encourages physical activity, promotes a sense of connectedness with 5 others, instills confidence and pride, and requires, trains, and challenges EFs, are hypothesized to lead to better EFs; this, in turn, should lead to academic success. The present study was inspired by the framework outlined above. Although individual pathways among EFs, its predictors, and its outcomes have been examined by prior research, few of the relations have been studied using a longitudinal design, particularly in the context of one individual study. The present study sought to focus on a subset of factors put forth by Diamond—specifically, positive affect, physical activity, and social connectedness. Other factors also examined as predictors in the present study that were not specified in Diamond’s 2012 framework included stress, mindful attention, negative affect, and depressive symptoms. These were included because Diamond posited that these factors have empirical evidence that indicates they can influence EFs. In the present study, factors were first categorized using Diamond’s framework based on whether the factors promoted or hindered EFs. These factors then were examined in relation to academic outcomes over time. Indicators of life success (i.e., psychological well-being) were also examined as outcomes due to evidence from prior research that suggests the factors that affect EFs also have an impact on well-being (e.g., Emerson, Mâsse, Ark, Schonert-Reichl, & Guhn, 2018). The present study is one of the first to examine the longitudinal relations of EFs and the factors that affect them to well-being. Furthermore, to explore the primary hypothesis that was highlighted in the framework by Diamond (2012), the present study sought to examine school-based activity participation, particularly activities that have been found to promote EFs. These activities were also examined in relation to EFs, academic outcomes, and well-being. Given that prior research suggests these predicted relations exist, the present study also sought to investigate whether EFs mediated any of the hypothesized relations. Lastly, as a factor related to self-efficacy, which is a component in the framework by 6 Diamond (2012), the present study also sought to examine the role that school self-concept played in the relation between EFs and academic outcomes. 7 Literature Review Overview The overall purpose of the present study was to examine the ways that EFs are affected by a variety of factors (those that support EFs, those that hinder EFs, and school-based activities purported to promote EFs) in a sample of early adolescents and across time into late adolescence. The three primary study aims included: (1) An investigation into the relations of EFs in early adolescence (T1) and late adolescence (T2) to T2 academic achievement and well-being, with an examination into the role that school self-concept plays in this relation; (2) an examination of the factors that support or hinder EFs at T1 and an exploration of their relative contributions to EFs, academic achievement, and well-being at T2; (3) an investigation of the relative contribution of students’ participation in middle and secondary school-based activities to T2 EFs, academic achievement, and well-being. Moreover, an examination was also made into whether EFs played a role in mediating the predictors and outcomes. The literature review begins with an overview of EFs and their relevance during the years of adolescence. Next, an examination of the existing literature investigating pathways leading to school and life success is reviewed: Specifically, academic achievement, as the long-standing benchmark for success in education; and psychological well-being, as an indicator of success in life. This is followed by an examination of the role of EFs and its predictors, which has emerged as an important underlying factor linking the various pathways to success. To conclude this section, a consideration of the role of school-based activities as a potential strategy for promoting EFs is delineated, followed by a summary of research questions examined in this study. 8 Executive Functions (EFs) Executive functions (EFs) include three, interrelated core components—inhibitory control, working memory, and cognitive flexibility—that together comprise a set of cognitive processes that are required for higher-level goal-oriented behavior (Diamond, 2013; Miyake et al., 2000). Although these three components of EFs are related to one another, there are some clear distinctions (Miyake et al., 2000). Inhibitory control is the ability to inhibit dominant or automatic prepotent responses (Diamond, 2013). Working memory involves being able to retain and engage in mental operations with information being temporarily held in mind (Baddeley & Hitch, 1994). Cognitive flexibility refers to the ability to adapt flexibly to changing demands, tasks, situations, or to view something from different perspectives (Kiesel et al., 2010). Several other constructs from other disciplinary areas overlap with EFs: Self-regulation, which refers to the ability to regulate one’s emotions and requires EFs (Eisenberg, Hofer, & Vaughan, 2007; Eisenberg, Spinrad, & Eggum, 2010); effortful control, which is the natural ability to self-regulate, reflecting a dimension of temperament (Rothbart & Bates, 2006; Zhou, Chen, & Main, 2012); and executive attention, which refers to the top-down processes of attentional control or selective attention (Posner & DiGirolamo, 1998). For a model describing the interrelationships amongst these EF-related constructs, see Figure 1. And lastly, self-control is a construct that has been used interchangeably with EFs—with clear overlap of the three core components of EFs (Duckworth, Taxer, Eskreis-Winkler, Galla, & Gross, 2019). Meta-analyses have revealed weak correlations, however, between EF tasks and self-report measures of self-control (Sharma, Markon, & Clark, 2014). EFs seem to lay the foundation for self-control but do not take into account learned strategies and habits that are employed in the service of long-term goals (Duckworth et al., 2019). 9 EFs have been used to explain numerous developmental outcomes including school readiness (Blair & Razza, 2007), poor physical health (H. V. Miller, Barnes, & Beaver, 2011), and psychopathology (Luciana, 2016), such as clinical disorders like ADHD (Krieger & Amador-Campos, 2018) and anxiety (for a review, see Diamond, 2013; Sjöwall, Roth, Lindqvist, & Thorell, 2012). EFs in childhood also have been found to be predictive of a number of adult life outcomes (Moffitt et al., 2011), including financial health and substance use and abuse. Adolescence As a transitional developmental period (Spear, 2000), adolescence bears witness to the greatest magnitude of change relative to all other periods in the human life span, with the exception of the first year of life (Fuhrmann, Knoll, & Blakemore, 2015). This phase of development encompasses early, middle, and late adolescence—with each period experiencing a host of unique cognitive, behavioral, social, and emotional changes. Early adolescence refers to the transition from childhood to adolescence and is often marked by the onset of puberty that typically varies within a window of four to five years among individuals with similar life circumstances (Parent et al., 2003); the age at which puberty begins is approximately age 10 for girls and slightly later for boys (Peper & Dahl, 2013). With puberty comes the onset of hormones that not only spur changes in the physical body, but also sensitizes adolescents toward social and affective cues (Blakemore & Mills, 2014; Guyer, Silk, & Nelson, 2016; Kilford, Garrett, & Blakemore, 2016; Peper & Dahl, 2013; Somerville et al., 2013). It is during this time that personalities, behaviors, and skillsets are solidified into forms that persist into middle and late adolescence, and further into adulthood (Schonert-Reichl et al., 2013). Greater importance is given to peer relationships (Sherman, Payton, Hernandez, Greenfield, & Dapretto, 2016) and increased social interaction; adolescents also experience decreased empathy and emotion 10 recognition (Tousignant, Sirois, Achim, Massicotte, & Jackson, 2017), as well as increased vulnerability to stress (Romeo, 2013; Tottenham & Galván, 2016), particularly in middle adolescence. The early and middle adolescence periods are also marked by an increased risk for psychopathology, as seen with the number of psychological disorders that emerge during this period, for example, anxiety and mood disorders (Kessler et al., 2005; F. S. Lee et al., 2014; Merikangas, 2010). This coincides with structural changes that occur due to the transition of early adolescents to a new schooling environment (Benner, Boyle, & Bakhtiari, 2017; Goldstein, Boxer, & Rudolph, 2015). Although the period when adolescence begins tends to be fairly in agreement, late adolescence and the transition to adulthood vary depending on the geopolitical and sociocultural context, with legal age often used as the marker for passage into adulthood (A. O. Cohen, Breiner, et al., 2016). Delayed timing of major social role transitions have led developmental researchers to expand the period of late adolescence into the mid-20s, when youth undergo socio-structural changes and have transitioned to relative independence from their parents, or have developed the capacity to live independently (Casey, 2015; Sawyer, Azzopardi, Wickremarathne, & Patton, 2018). Development of EFs Across the developmental period of early adolescence (which begins approximately at age 10), EFs appear to follow a linear trajectory, increasing steadily; this incline appears to level off at around middle adolescence (or approximately age 15) in emotionally neutral contexts (Zelazo & Carlson, 2012), resembling the EFs of adults (Hwang, Ghuman, Manoach, Jones, & Luna, 2016; Luna, Marek, Larsen, Tervo-Clemmens, & Chahal, 2015). In terms of individual EF components, each have been found to develop at different rates throughout childhood and adolescence (Huizinga, Dolan, & van der Molen, 2006). Inhibitory control improves steadily all 11 throughout childhood (Davidson, Amso, Anderson, & Diamond, 2006) and into adulthood (Taylor, Barker, Heavey, & McHale, 2015). Working memory develops in infancy and early childhood (Diamond, Towle, & Boyer, 1994; Perlman, Huppert, & Luna, 2016), with speed and accuracy continuing to improve into adolescence and adulthood (Simmonds, Hallquist, & Luna, 2017). Performance on tasks that combine EFs, such as dots or Hearts/Flowers, which measure all three core EFs also steadily improve in speed and accuracy across development (Davidson et al., 2006). Individual differences in EFs have been found to be remarkably stable over time, retaining its consistency from early childhood (Mischel et al., 1989) into early adolescence (Harms, Zayas, Meltzoff, & Carlson, 2014) and into middle adulthood (Casey et al., 2011). Gender differences have been found among adolescents, with girls exhibiting better inhibitory control than boys (Yücel et al., 2012); on the contrary, no gender differences have been found among adults (Rubia et al., 2013), which may be a function of the EF task employed (Malagoli & Usai, 2018). EFs Across Adolescence An examination of the neural mechanisms revealed by fMRI studies of adolescents indicate that the prefrontal cortex is recruited during EF task performance. Specifically, beginning in early adolescence and continuing into middle and late adolescence, this region of the brain experiences synaptic pruning (Schmithorst & Yuan, 2010), decreases in grey matter volume (Tamnes et al., 2017), and increases in white matter volume (Moura et al., 2017). Although adolescent performance on EF tasks resembles those of adults in ideal laboratory conditions, that is, when the adolescent is alone (Luna et al., 2015), this performance is not reflected in their behaviors as situated in real-life. There is growing evidence to support that “self-control varies by content (i.e., actions, emotions, or desires to be suppressed) and by 12 context (e.g., alone, with a parent, or with peers or strangers) during adolescence” (Casey, Galván, & Somerville, 2016, p. 128). fMRI studies over the past two decades have shed light on possible mechanisms that can explain this. Among the most influential of these models is the dual-systems model (Steinberg, 2010; Steinberg et al., 2008), which explains adolescents’ propensity for sensation seeking and risk taking by positing that self-control is a balance of “hot” and “cool” systems, with the “hot” system being emotional, impulsive, and driven by desires, and the “cool” system being emotionally neutral, reflective, and strategic. The “hot” system is hypothesized to develop in a curvilinear, inverted U-shaped pattern, increasing between early adolescence (age 15), and then declining thereafter (Luciana & Collins, 2012). The “cool” system, on the other hand, follows a linear trajectory improving steadily from early adolescence (age 10) and leveling off at around middle adolescence (age 15) in emotionally neutral contexts (Zelazo & Carlson, 2012). This dual systems heuristic is juxtaposed against a more complex maturational imbalance model proposed by Casey, Getz, and Galvan (2008). In this model, regional neurochemical, structural, and functional brain changes occur that underlie the imbalances in neural circuitry that may explain adolescent behavior, and these processes take place across the full developmental trajectory from childhood to adulthood (Casey, 2015). The temporal sequence that these changes undergo begin in childhood with the fine tuning of circuits responsible for information flow to occur within subcortical regions (e.g., amygdala, nucleus accumbens, ventral striatum), followed by cortical-subcortical circuitry, and ending with the cortico-cortical circuitry (e.g., prefrontal cortex) in adulthood (Casey et al., 2016). Although the human brain is inherently malleable and continuously adapting to environmental influences (Zelazo & Carlson, 2012), evidence from the research literature on 13 cognitive neuroscience suggests further that the adolescent brain is particularly plastic due to the rapid growth and reorganization of the prefrontal cortex during this period (Giedd et al., 1999). The relative plasticity during this period suggests adolescence is an opportune time for prevention and intervention, including for programs aiming to foster EFs (Zelazo & Carlson, 2012). Furthermore, there is growing evidence in the extant literature suggesting that EFs are malleable during adolescence (e.g., Hillman, Pontifex, et al., 2009; Homer et al., 2019; Zinke, Einert, Pfennig, & Kliegel, 2012). This provides further rationale for the focus on EFs and the factors that affect them in the adolescent period for the present study. Academic Achievement The relevance of EFs during adolescence extends to its influence on academic achievement, which has long been the quintessential gauge for school success, opening doors to future opportunities that include post-secondary education and beyond. Numerous tools have been used to measure academic achievement. Standardized tests have been used to assess students’ knowledge; these are based on classroom instruction but scoring well on these also require that students acquire knowledge in contexts that are outside of the traditional educational setting. Report card grades, on the other hand, reflect students’ mastery of subject matter; these are based on classroom instruction and are assessed by the same teachers who engage in the instruction of the material to be mastered. Aside from learning assessment, grades also take into account non-cognitive factors that reflect a students’ ability to participate in other aspects of the schooling process (Bowers, 2011), such as academic effort, academic engagement, improvement in assignment and exam grades, class participation, class attendance, as well as academic habits like homework completion or classroom behavior that are often incorporated into a grade earned for a course (Bowers, 2011; Kelly, 2008; McMillan, 2001; J. Randall & Engelhard, 2010). 14 Furthermore, secondary school grades have been found to be the strongest predictor of secondary school dropout (Bowers, Sprott, & Taff, 2012) and college grades (Geiser & Santelices, 2007; Hoffman & Lowitzki, 2005). Relevant adult outcomes such as work-related earnings (French, Homer, Popovici, & Robins, 2015) also have been predicted by grades. As a result, for the purposes of the present study, academic achievement was operationalized as secondary school grade point average (GPA) for core academic subjects. Considering the important implications that secondary school GPA has on indicators of adult success, stakeholders have invested vast resources into understanding the psychosocial and contextual factors that could affect academic achievement. Aside from factors that are directly related to academic activities such as study skills (Credé & Kuncel, 2008), research has found that GPA is predicted by a number of other factors: Psychological characteristics that have been examined include intelligence (Duckworth, Quinn, & Tsukayama, 2012), chronic stress (Schraml, Perski, Grossi, & Makower, 2012), and satisfaction with life (Heffner & Antaramian, 2016). Behaviors like engaging in physical activity also have been studied (Kantomaa et al., 2013). Social competencies (Malecki & Elliot, 2002; Wentzel, 1993; Wentzel & Caldwell, 1997) that lead to an increased sense of relatedness with peers (Walton & Cohen, 2011) and teachers (Allen et al., 2013) also have been studied in relation to GPA. Other contextual factors examined include parental education (Karbach, Gottschling, Spengler, Hegewald, & Spinath, 2013) and parental involvement (Westerlund, Gustafsson, Theorell, Janlert, & Hammarstrom, 2013). Furthermore, emotional competencies similarly have been found to predict students’ progress in academic studies (Qualter, Whiteley, Morley, & Dudiak, 2009). EFs and academic achievement. Pertinent to the present research under investigation, examinations into the ability of EFs and related constructs to predict GPA have grown in the 15 research literature. EFs are a crucial prerequisite for engaging in the learning process: Selective attention, a key component in working memory, plays an especially prominent role in intelligence (Ohtani et al., 2017) and learning (Steinmayr, Ziegler, & Träuble, 2010). Another core component, inhibitory control, is also required for behaviors that support the learning process, for example, completion of homework assignments and appropriate classroom behavior (Diamond, 2013). Moreover, cognitive flexibility or being able to switch flexibly between mindsets is also needed for learning, competing assignments, or studying for exams, so that a new strategy can be used when it is recognized that a current strategy is ineffective (Kercood, Lineweaver, Frank, & Fromm, 2017). In research related to the areas of intelligence, EFs also have been found to support academic success beyond the contributions that IQ make to academic performance (Buckner, Mezzacappa, & Beardslee, 2009; Duckworth & Seligman, 2005). In terms of the specific areas of research, the extant literature has examined EFs (Best et al., 2011), its subcomponents, and their relations to a number of academic outcomes. Specifically, the subcomponents of working memory (K. Lee & Bull, 2016; St. Clair-Thompson & Gathercole, 2006), inhibitory control (Blair & Razza, 2007; Steele, Karmiloff-Smith, Scerif, & Cornish, 2012), and cognitive flexibility (Kercood et al., 2017) have each been studied in relation to academic outcomes. Related constructs such as self-discipline (Duckworth & Seligman, 2005) and effortful control (Valiente et al., 2013) also have been examined. In terms of measures for academic outcomes, standardized test scores have been studied, for example, using scores of reading (Best et al., 2011; St. Clair-Thompson & Gathercole, 2006) and math ability (for a review, see R. Jacob & Parkinson, 2015; K. Lee & Bull, 2016). Relatedly, deficits in EFs, such as poor attention skills have been found to be related to poor reading achievement (Rabiner, 16 Coie, & Group, 2000) and poor academic performance (Diamantopoulou, Rydell, Thorell, & Bohlin, 2007). Cross-sectional research has examined the relation between EFs and academic outcomes in early childhood (Blair & Razza, 2007; K. Lee & Bull, 2016; Steele et al., 2012), early adolescence (K. Lee & Bull, 2016; St. Clair-Thompson & Gathercole, 2006), middle adolescence (K. Lee & Bull, 2016), as well as the full range of school-aged children and adolescents (Best et al., 2011; Buckner et al., 2009). Longitudinal studies also have examined the relations of EFs and its related constructs to standardized measures of achievement—from kindergarten to Grade 6 (McClelland, Acock, & Morrison, 2006) and from 54 months to age 15 (Ahmed, Tang, Waters, & Davis-Kean, 2019; for a review, see R. Jacob & Parkinson, 2015). Aside from scores on standardized tests, fewer investigations have focused on the long-term predictive ability of EFs or its related constructs in relation to middle school or secondary school GPA (Duckworth & Seligman, 2005). One research study found that eighth graders’ levels of self-control as measured at the beginning of the school year predicted their GPA at the end of the school year (Duckworth & Seligman, 2005). Similarly, Valiente et al. (2013) found that 12-year-olds’ level of effortful control and impulsivity predicted their GPA as reported by both teachers and parents two years later. Only one research study investigated the ability of EFs as measured in Grade 6 to predict GPA four years later (Samuels, Tournaki, Blackman, & Zilinski, 2016); these effects held even after accounting for gender, federal lunch program eligibility (an indicator of low socioeconomic status), and individual education plans (for students with learning disabilities). 17 The current study intended to contribute to the extant literature by examining the relations of early adolescents’ EFs in the fourth and fifth grade to their cumulative GPA for core subject areas in secondary school. EFs, academic achievement, and school self-concept. In addition to the examination of relations that EFs have to academic outcomes, psychological factors such as school self-concept may play a role as well. Specifically, in the framework put forth by Diamond (2012), activities that foster students’ sense of pride, self-confidence, or self-efficacy are hypothesized to promote EFs, and this in turn may influence academic outcomes (see Figure 2). Using this framework as a guide, the present study also explored whether adolescents’ school self-concept may be one factor that would contribute to the enhancement of EFs in the areas of academic outcomes. In defining school self-concept, Marsh, Barnes, Cairns, and Tidman (1984) explain that school self-concept is the perception that students have about themselves in areas with concern to academics; this is formed when students interact with the physical and social environment where academic activities take place, and may be influenced by the academic performance of others (Seaton, Marsh, & Craven, 2010). Previous research also has found that the relation between students’ school self-concept and academic achievement becomes stronger as the years of schooling increase and as students become more familiarized with their relative strengths and weaknesses (Spinath & Spinath, 2005). Moreover, an examination of the extant literature indicates longitudinal relations exist between students’ school self-concept and academic achievement (Newman, 1984; Preckel, Niepel, Schneider, & Brunner, 2013), with a number of studies finding them to mutually enforce one another (e.g., Guay, Marsh, & Boivin, 2003; for a review, see Huang, 2011). 18 In terms of the relation that school self-concept and EFs have to academic achievement, few researchers have explored this relation. In one of the few studies to examine this relation, Roebers, Cimeli, Röthlisberger, and Neuenschwander (2012) found significant positive relations between children’s EFs and their academic achievement but did not find their self-concept in specific subject matter to be related to their academic achievement. There also have been few studies examining the association of school self-concept to EFs. In one of these studies using a clinical sample of children and adolescents, Bailey, Andrzejewski, Greif, Svingos, and Heaton (2018) found that standardized measures of achievement were not associated with school self-concept or parent-rated EFs; EF deficits, however, were associated with lower school self-concept. Further support for a relation of students’ EFs to their school self-concept also comes from prior work by R. Jacob and Parkinson (2015) who suggested that relations of EFs to teacher-reports of academic outcomes may be due to unmeasured factors such as improved self-confidence. As a result, the present study sought to contribute to the extant literature by exploring the possible role that early adolescents’ school self-concept could play in the hypothesized relations that EFs have with academic outcomes. Well-Being Satisfaction with life. Although academic achievement is an important developmental outcome in the lives of adolescents, the perceptions that adolescents have about the quality of their own lives are also important. This is reflected in decades of research that have been conducted on the subjective indicators of well-being across the human lifespan (Diener, Lucas, & Oishi, 2018; Gilman & Huebner, 2003). Although well-being has been studied extensively, no universal definitions currently exist (Coleman, 2009), which has led it to be characterized in a number of ways depending on its context and function. These include the constructs of quality of 19 life (Diener & Suh, 1997), psychological well-being (Linley, Maltby, Wood, Osborne, & Hurling, 2009), and subjective well-being (Diener & Ryan, 2009), as well as the absence of physical illness (Weidner, Sieverding, & Chesney, 2016) and psychological disorder (e.g., depression; Kremer et al., 2014). For the purposes of the present study, well-being was defined as the degree to which an individual evaluates the overall quality of her or his life as a whole in a positive way (Diener & Ryan, 2009; Veenhoven, 1998), and was operationalized as satisfaction with life. Satisfaction with life is defined as the cognitive evaluation of the extent to which individuals are currently satisfied with their life as a whole; this is a judgment that an individual makes based on his or her own criteria, rather than standards that have been objectively determined, and does not include an affective component (Diener, Emmons, Larsen, & Griffin, 1985; Diener & Ryan, 2009). The elective use of satisfaction with life in this study over subjective well-being, which includes an affective component, is due to the reactive nature of affect to recent short-term life experiences. Satisfaction with life, on the other hand, takes a more global perspective that is more likely to look past short-term mood states (Diener & Ryan, 2009). Studies that have examined satisfaction with life over time have noted that perceptions of life satisfaction may be sensitive to social and environmental factors (Guhn, Ark, Emerson, Schonert-Reichl, & Gadermann, 2018) and may change as one encounters new experiences (Emerson, Guhn, & Gadermann, 2017). Previous research has found that changes in satisfaction with life are associated with important developmental outcomes (C. L. Proctor, Linley, & Maltby, 2009); life satisfaction, is therefore, considered by some researchers to be a key component of subjective well-being (Veenhoven, 1988). 20 Previous research has found that a number of constructs are associated with satisfaction with life. Psychological factors that have been examined include EFs (Peeters et al., 2015), character strengths (Gillham et al., 2011; Shoshani & Slone, 2013), optimism (Sulkers et al., 2013), positive and negative affect (Hoyt, Craske, Mineka, & Adam, 2015), mindful attention (Klainin-Yobas et al., 2016), perceived stress (Siddique & D'Arcy, 1984), and emotional health (Layard, Clark, Cornaglia, Powdthavee, & Vernoit, 2014). Psychosocial factors that have been studied in relation to satisfaction with life include feelings of social connectedness (Jose, Ryan, & Pryor, 2012; Olsson, McGee, Nada-Raja, & Williams, 2013), social self-esteem (Valkenburg, Peter, & Schouten, 2006), and prosociality (Aknin et al., 2013; Layous, Nelson, Oberle, Schonert-Reichl, & Lyubomirsky, 2012). Behaviors like engagement in physical activity (Kremer et al., 2014; Spengler & Woll, 2013) and leisure screen-time use (Kremer et al., 2014) also have been studied in relation to satisfaction with life. Among children and adolescents, satisfaction with life has been found to be associated with higher levels of academic achievement (Ng, Huebner, & Hills, 2015), extracurricular activity participation (Gilman & Huebner, 2006), and adaptive psychosocial functioning (Gilman & Huebner, 2006), including positive relationships with peers and significant adults (Gadermann et al., 2016). EFs and satisfaction with life. Although satisfaction with life was not included as an outcome in the framework outlined by Diamond (2012), many of the factors hypothesized to be predictive of EFs and academic achievement are also the same ones that that have been found to be associated with satisfaction with life. Research for the latter, however, has been carried out to a far lesser degree, as the connection between EFs and well-being overall are only beginning to emerge in the research literature. Prior research suggests EFs play a role in well-being through its 21 contributions to self-regulatory processes, such as the development and organization of the emotion management system (Bradshaw, Goldweber, Fishbein, & Greenberg, 2012). Through this system, the top-down processes of EFs interact with the emotional processes that are activated in response to pleasant or unpleasant stimuli (Lantrip, Isquith, Koven, Welsh, & Roth, 2016; Zelazo, Qu, & Kesek, 2010). Individuals who are able to engage in these processes (i.e., through cognitive reappraisal) have better stress management skills and better interpersonal relationships, which may have an impact on their overall well-being and satisfaction with life (Luerssen & Ayduk, 2017). Previous research also suggests that the role that EFs play in its connection to well-being is through the processes of cognitive control over thought and behavior in support of short- and long-term goals (Luerssen & Ayduk, 2017; Zelazo et al., 2010). Examples of such processes supported by EFs include being able to stop rumination, change perspectives, or form new habits (Barkley, 2001). In one of the few studies to investigate the mechanism by which EFs contribute to satisfaction with life, researchers found that young adults who were more skillful at positive reappraisal, that is, retaining and updating positive (and not negative) information in mind also reported higher levels of satisfaction with life when compared to those who were less skillful (Pe, Koval, & Kuppens, 2013). Research carried out with adults in middle to late adulthood, however, found that having a greater sense of control (rather than the tendency to positively reframe) explained the relation between EFs and life satisfaction (Toh, Yang, & Hartanto, 2019). Few investigations, however, have been carried out with child or adolescent populations. Of the two research studies that were found, the EF-related construct of self-control was examined in relation to life satisfaction. In the first of the two studies, Ronen, Hamama, Rosenbaum, and Mishely-Yarlap (2016) examined the relation in a sample of adolescents ages 22 13 to 17, finding that self-control was positively related to life satisfaction, with older adolescents reporting lower levels of life satisfaction than younger ones. Wiese et al. (2018) also found similar findings in their series of cross-sectional studies using a number of adolescent samples drawn from middle and secondary school settings. Examinations into the longitudinal relation of EFs to life satisfaction, however, are few or non-existent. The present study, therefore, intended to replicate the cross-sectional findings, and make a contribution to the extant literature by examining whether a longitudinal relation exists between EFs and satisfaction with life. Depressive symptoms. At the other end of the spectrum, prior research has found that adolescents who report lower levels of life satisfaction also have higher levels of depressive symptoms (Moksnes, Løhre, Lillefjell, Byrne, & Haugan, 2016). Symptoms of depression include having decreased energy or feelings of fatigue, a depressed mood, and a declined interest for activities one used to find pleasurable. Other associated symptoms include the loss of confidence or self-esteem, an inability to concentrate, a lack of appetite, and poor sleep quality (Thapar, Collishaw, Pine, & Thapar, 2012); for adolescents, irritability is often another common symptom (Santor, Gregus, & Welch, 2006). To receive a diagnosis for clinical depression, also known as major depressive disorder, an individual must experience at least five or more symptoms over a period of at least two weeks, and these symptoms must cause significant distress or impairment (S. B. Williams, O'Connor, Eder, & Whitlock, 2009). Clinical depression affects between 0.5% and 2.5% of early adolescents and between 2.5% and 8% of middle and late adolescents (Nardi, Francesconi, Catena-Dell'osso, & Bellantuono, 2013). In studies that have examined depressive symptoms in adolescent populations, researchers found that many adolescents may still have symptoms even though they do not meet the cutoff for clinical 23 diagnosis. For example, in a nationally representative study of U.S. adolescents in Grades 7 through 12, almost 30% reported experiencing depressive symptoms yet did not meet the cutoff for diagnosis (Rushton, Forcier, & Schectman, 2002). In terms of duration, results from a longitudinal study also have found that depressive symptoms reported by adolescents are remarkably stable over time (Holsen, Kraft, & Vittersø, 2000). Moreover, significant gender effects have been found with females having a higher overall prevalence rate and reporting more symptoms that persist longer than males (Ge, Conger, & Elder, 2001; Rushton et al., 2002). Adolescents at risk for developing clinical depression include those who have a family history of depression and those who have been exposed to psychosocial stressors, such as parental divorce (Ge, Natsuaki, & Conger, 2006), school suspension (Rushton et al., 2002), and poor quality relationships with teachers (Jellesma, Zee, & Koomen, 2015). Researchers from the area of clinical positive psychiatry have suggested that, rather than being discrete constructs, well-being and depression lie along a bipolar continuum (Siddaway, Wood, & Taylor, 2017; Wood, Taylor, & Joseph, 2010), based on evidence that the two are inversely related. Specifically, as the number of depressive symptoms increase, the number of other psychological problems (i.e., anxious symptoms, aggressive behavior, and substance use) increase; and as well-being increases, the number of psychological problems also decrease. Based on this rationale, the present study also operationalized well-being as low levels of depressive symptoms. EFs and depressive symptoms. Prior research examining EFs and clinical depression have found the link to be fairly established in adult populations. Specifically, adult patients who have been diagnosed with major depressive disorder were also found to have deficits in EFs, memory, and attention relative to healthy controls (Rock, Roiser, Riedel, & Blackwell, 2014). 24 Moreover, impairments in cognition were found to persist even after patients had gone into remission from depression (Rock et al., 2014). Prior research suggests that the mechanism through which EF deficits are believed to contribute to symptoms of depression is due to an inability to filter out and disengage from negative information (Gotlib & Joormann, 2010), as well as having poor emotion regulation in response to negative information (Joormann & D'Avanzato, 2010). Impairments in EFs are also believed to interfere with an individual’s ability to complete routine tasks in everyday life; this could cause feelings of distress and disruption, and foster the onset, maintenance, and recurrence of depression as a result (Letkiewicz et al., 2014). Yet despite the wealth of support that research has found linking EFs to depression, a number of other studies have found no differences in EF performance between individuals with depression or in remission from depression, as compared to healthy controls (Walters & Hines-Martin, 2018). Researchers suggest these differences may be due to the type of neuropsychological tests being used to measure EFs in each of the studies; that is, for certain complex tasks of EFs, individuals with depression were found to responded more slowly yet more accurately than healthy controls (Dillon et al., 2015; Walters & Hines-Martin, 2018). Among child and adolescent populations, prior research also has examined the association between EF deficits and depressive symptoms. Researchers have found that the processes through which EF deficits are believed to play a role are in line with those of adults: For example, adolescents with EF deficits reported employing a greater number of maladaptive strategies to regulate their emotions, such as engaging in rumination, and fewer adaptive strategies, such as cognitive reappraisal (Wante, Mezulis, Van Beveren, & Braet, 2017). 25 Evidence for the relation of EF deficits to depression are similarly mixed in research carried out with children and adolescents. Among the studies that have found a link between the two, depression was found to be related to deficits in working memory (Baune, Fuhr, Air, & Hering, 2014; Goodall et al., 2018), inhibitory control (Bredemeier, Warren, Berenbaum, Miller, & Heller, 2016), and attention and reasoning (Baune et al., 2014). Gender effects also have been found, with girls being more susceptible to developing depression than boys (Agoston & Rudolph, 2015). Other studies, however, found no differences in EFs between youth with and without depression (see Favre et al., 2008; Kyte, Goodyer, & Sahakian, 2005). Furthermore, not all youth with depression were found to have EF deficits (for a review, see Vilgis, Silk, & Vance, 2015; E. B. Weber et al., 2018). As for the presence of depressive symptoms that are not severe enough to meet the threshold for clinical cutoff, a handful of studies have found an association with EF deficits in children and adolescents (Han et al., 2016; Mullin, Perks, Haraden, Snyder, & Hankin, 2018); for example, depressive symptoms were associated with poor working memory (Matthews, Coghill, & Rhodes, 2008), poor attention and concentration (Lundy, Silva, Kaemingk, Goodwin, & Quan, 2010; McClure, A. Rogeness, & Thompson, 1997; Muris, Pennen, Sigmond, & Mayer, 2008), as well as slower processing speed (Ladouceur et al., 2005; McClure et al., 1997; Opris, Cheie, Trifan, & Visu-Petra, 2019; M. K. Singh et al., 2018). Children of parents who have been diagnosed with depression were more likely to have depressive symptoms and deficits in EFs (M. K. Singh et al., 2018). Although numerous cross-sectional studies exist, very few have examined whether the relation of EFs to symptoms of subclinical depression are also longitudinal in nature. In one of the few prospective studies, Nelson et al. (2018) found that preschoolers’ deficits in executive 26 control predicted their onset of depressive symptoms when they reached the fourth grade, even after controlling for their depressive symptoms at baseline (Nelson et al., 2018). Among adolescents, however, Han et al. (2016) failed to find that EFs were predictive of parent-reported depressive symptoms two years later. Nonetheless, findings from one study suggest the presence of depressive symptoms in adolescents may not be as apparent to others due to its internalizing nature (McClure et al., 1997). Considering the paucity of research conducted examining the longitudinal relations of EF deficits to depressive symptoms in this age group, the present study intended to make a contribution to the extant literature by examining whether this longitudinal relation exists in adolescents. Factors that Affect EFs In consideration of the significant role that EFs play in the development of a host of important outcomes across the lifespan, researchers have been interested in studying the factors that relate to their improvement or decline and have found EFs to be influenced by a number of psychosocial and contextual factors. Individual psychosocial factors that have been found to affect EFs include emotion regulation (Casey et al., 2011; A. O. Cohen, Dellarco, et al., 2016; Lantrip et al., 2016), positive affect (Ashby et al., 1999), dispositional mindful attention (H.-S. Shin, Black, Shonkoff, Riggs, & Pentz, 2016), and psychopathology such as ADHD (Zelazo & Muller, 2010). Behavioral factors that also have been found to affect EFs include obtaining adequate amounts of sleep (Leong, Chee, Gooley, Ong, & Lo, 2016) and engagement in physical activity (Soga, Shishido, & Nagatomi, 2015; Verburgh et al., 2014). Contextual factors that also have been found to affect EFs include relational trust (Michaelson & Munakata, 2016), parenting behavior (Sosic-Vasic et al., 2017), peer relationships (Agoston & Rudolph, 2015), social isolation (Baumeister, DeWall, 27 Ciarocco, & Twenge, 2005) and socioeconomic status (Hackman, Gallop, Evans, & Farah, 2015; Ursache, Noble, & Blair, 2015). Furthermore, Diamond (2012) outlined some of these factors in her framework that describes the pathways in which a program or intervention is hypothesized to enhance EFs. Some programs that have been examined in the extant literature in relation to their ability to promote EFs include physical activity programs (e.g., Alesi, Bianco, Luppina, Palma, & Pepi, 2016; van der Niet et al., 2016), martial arts programs (e.g., Lakes et al., 2013), and certain school curricula (e.g., Watts, Gandhi, Ibrahim, Masucci, & Raver, 2018). In the present study, several of the factors outlined by Diamond (2012) were examined in addition to other factors as suggested by prior research to be related to EFs. Specifically, the present study sought to examine several EF-supporting factors including the presence of positive affect, connectedness, and mindful attention, as well as engagement in physical activity. The present study also sought to examine several EF-hindering factors as well including exposure to stress, and the presence of negative affect and depressive symptoms. Lastly, as inspired by Diamond’s 2012 framework, school-based extracurricular programming also was examined as a factor hypothesized to promote EFs. Each of these (EF-supporting and -hindering factors, as well as school-based activities) were examined in relation to EFs, academic achievement, and well-being across time from early to late adolescence, and are delineated, in turn, below. Factors that support EFs. Positive affect and EFs. Among the many factors that prior research has found to support EFs, positive affect is one area that has been studied to a great extent. The constructs of affect, mood, and emotion are, likewise, among the most heavily researched areas in the field of psychology and also have been studied in association with EFs. Core affect, the most general type of the three constructs, is composed of two subtypes—emotion and mood (Ekkekakis, 28 2013). Emotions are characterized by its short-lived nature and occur as a reaction to a stimulus. Moods, in contrast, may not necessarily occur in reaction to a stimulus, or the stimulus could be one’s ‘life’ overall, and its duration could last for weeks (Ekkekakis, 2013). Affect can be examined from the perspective of valence (positive vs. negative), but also in terms of level of activation (high vs. low). Pleasurable affect that is deactivating (e.g., calm, relaxed) and pleasurable moods that are activating (e.g., happy, elated; De Dreu, Baas, & Nijstad, 2008) have been found to be associated with greater motivation and engagement (Higgins, 2006). For the purposes of the present study, positive affect was examined as a factor hypothesized to promote EFs. Prior research examining the relation of positive affect to EFs has found that adults who had positive affect (e.g., feeling delighted, calm, or interested) also had increased creativity and cognitive flexibility (Isen, Daubman, & Nowicki, 1987; Isen, Johnson, Mertz, & Robinson, 1985; Murray, Sujan, Hirt, & Sujan, 1990). Specifically, for tasks that were intrinsically rewarding and enjoyable (Hirt, Devers, & McCrea, 2008), positive affect led to more originality (uncommon solutions that arise from nonhabitual thought) and fluency (number of original, nonredundant ideas that are generated) (for a review, see Baas, De Dreu, & Nijstad, 2008; M. A. Davis, 2009). The mechanism by which positive affect is believed to be related to EFs is through the release of dopamine in the anterior cingulate cortex, which has been found to improve switching ability among potential cognitive options (Ashby et al., 1999). Although most of the extant research has found a positive link between positive affect and creativity, there is some evidence suggesting that, in some cases, negative affect leads to creative responding as well; this relation was found to be true specifically for tasks that were considered serious, with performance-driven outcomes (Baas et al., 2008). Nonetheless, in a meta-analysis conducted by 29 Baas et al. (2008), positive affect was found to produce more creativity than negative affect overall regardless of age. Among child and adolescent populations, however, there are comparatively fewer research studies examining the relation of positive affect to EFs. Most of the research carried out with these populations have been with younger age groups (for a review, see M. Davis & Suveg, 2014). Nonetheless, findings are fairly similar to those of adults, with positive affect being found to be related to more creative responding, among first and second graders (Russ & Schafer, 2006), as well as eighth graders (Greene & Noice, 1988). Similar to adults, the effects of positive affect on creativity appeared to be contextual; that is, for tasks requiring attention to detail, positive affect was found to hinder task performance in a sample of 6- to 7-year-old children and 10- to 11-year-old early adolescents (Schnall, Jaswal, & Rowe, 2008). In addition to creativity, prior research has also examined associations between positive affect and other EF-related constructs, such as self-regulation and self-control. Specifically, daily positive affect was related to parent-rated self-regulation in a sample of preschool-aged children (Ludwig & Rauch, 2018). Relatedly, in a delayed gratification task, third and fourth grade children who were assigned to imagine happy events chose larger and more delayed rewards than children who were asked to imagine sad events (Schwarz & Pollack, 1977); this finding, however, was not replicated in subsequent studies by the same author (for a review, see M. Davis & Suveg, 2014). Lastly, there is some evidence to suggest that positive affect may also serve as a protective factor against externalizing symptoms among children with poor cognitive control (Brieant et al., 2018). Nevertheless, most of the extant research in this domain have been cross-sectional in nature, with a paucity examining the longitudinal nature of positive affect to EFs. As a result, the 30 present study adds to the extant literature by examining the longitudinal relations of positive affect to EFs in a sample of adolescents. Positive affect and academic achievement. In terms of the relation that positive affect has to academic achievement and related psychosocial factors, an examination of the extant literature reveals that the two are related. Prior research has found positive affect to be associated with academic-related psychosocial factors that include increases in perceived academic abilities (Nickerson, Diener, & Schwarz, 2010), higher engagement in academic studies (King, McInerney, Ganotice, & Villarosa, 2015) and school in general (King et al., 2015), increases in academic motivation (Armitage, 2008; Richard & Diefendorff, 2011), increases in academic satisfaction and progress (Garriott, Hudyma, Keene, & Santiago, 2015), and lower levels of school disaffection (King et al., 2015). Among adolescents in middle (M. Weber, Wagner, & Ruch, 2016) and secondary school (Lewis, Huebner, Reschly, & Valois, 2009), positive affect has been found to be associated with adaptive school functioning, for example, the tendency to seek social support, higher quality teacher-student relationships, and peer and family support for learning. In terms of the relation that positive affect has to academic achievement, the extant literature reveals mixed findings. In a study of middle school students, positive affect was found to be related to academic outcomes five months later, as measured with cumulative GPA using grades from English, math, and science classes (e.g., Ng et al., 2015). Other studies examining this link, however, found no relations. For example, Lewis et al. (2009) found positive emotionality to be related to school satisfaction, adjustment, and engagement in their sample of seventh to tenth grade students, but no relations were found with GPA. The authors suggested the lack of significant findings between positive emotions and GPA may be due to the use of 31 self-reported GPA, for which adolescents may have included in the calculation grades for non-core subject areas. Due to the relative paucity of longitudinal research that has examined the relations of positive affect to academic achievement as measured with GPA, the present study adds to the extant literature by examining this relation prospectively from early to late adolescence. Positive affect and well-being. Although there is some empirical support for the relation that positive affect has with EFs, the relations between positive affect and well-being have been studied by researchers to a far greater extent. Evidence continues to grow regarding the relations between positive affect and well-being among children and adolescents (e.g., Huebner & Dew, 1996). For example, Lengua (2003) found that children in Grades 3 to 5 who had positive emotions also had higher levels of well-being. In studies that examined life satisfaction specifically, positive emotions were found to be related to satisfaction with life among 8- to 10-year-old children (Businaro, Pons, & Albanese, 2015). At the other end of the well-being spectrum, positive affect has also been found to be negatively correlated with depressive symptoms in samples of college students (Watson, Lee, & Tellegen, 1988). Among adolescents, neuroimaging studies provided support for these relations due to activation of the striatum, that has been found to be both positively associated with positive affect and negatively associated with depressive symptoms (Forbes et al., 2010). In longitudinal research, low levels of positive affect in early childhood was found to be predictive of depressive symptoms in later childhood (Dougherty, Klein, Durbin, Hayden, & Olino, 2010). Similarly, in longitudinal research conducted with sixth to tenth grade students, low levels of positive emotionality predicted the subsequent development of depressive symptoms (Wetter & Hankin, 2009). Given that the relation between positive affect and the presence and absence of 32 well-being has been well-established, the present study aims to contribute to the extant literature by examining this relation across time from early to late adolescence. Connectedness and EFs. Among the many factors found in the extant literature to affect EFs, connectedness is one that has been studied to a great extent. Connectedness, social support, and belonging are basic human needs that facilitate children and adolescents’ cognitive development (Baumeister & Leary, 1995). Research has found that adults (Baumeister, Twenge, & Nuss, 2002) and children who lack positive social relationships may experience impairment completing tasks that require cognitive ability (K. D. Williams, 2009). Among adults, experimental research has found social isolation to be associated with poor EFs (Baumeister et al., 2002), such as difficulties in problem solving (Baumeister et al., 2002; Campbell et al., 2006), poor selective attention (Baumeister et al., 2005; Cacioppo et al., 2000), and lack of perseverance on difficult tasks (Baumeister et al., 2005). Prior research from the neuroscience literature that has examined the link between stressful events and prefrontal cortex activity suggested that individuals who experience social isolation require additional cognitive resources for stress management (Porcelli et al., 2008). Prior research also has found that the type of relationship children have with the significant adults in their lives has an influence on children’s EFs, as suggested by the predictive association of student-teacher relationships on EFs among children in preschool (Cadima, Verschueren, Leal, & Guedes, 2015) and elementary school (de Wilde, Koot, & van Lier, 2015). This relationship also was found to be reciprocal, with EFs also affecting student-teacher relationships. Such relationships between students and teachers are salient because they have been found to be a particularly important factor in contributing to academic success (Hamre & Pianta, 2006). 33 Although significant adult figures continue to play an important role in adolescence (Clark-Lempers, Lempers, & Ho, 1991), social acceptance by peers exert a great deal of influence over most behaviors (Blakemore & Mills, 2014). It is during this time that peer relationships are prioritized over relations with family members (R. Larson & Richards, 1991). Early adolescents between the ages of 10 and 12 feel that peers provide companionship and support; as they transition to middle and late adolescence, however, adolescents between the ages of 13 and 17 report that, although they do enjoy companionship and support from peers, evaluation by peers also affect how they evaluate themselves (O'Brien & Bierman, 1988). It is important to note that the increased salience of peers coincides with the transition of adolescents from smaller educational contexts (elementary schools), where friendships are often formed based on classroom membership, to larger educational contexts (middle and secondary schools), where peer relations are often formed based on membership and participation in extracurricular activities (Eccles & Roeser, 2011; Mahoney, 2014). As a result of these developmental and contextual changes, research has shown that adolescents who experienced social exclusion reported an increase in anxious symptoms and a decreased overall mood (Sebastian, Viding, Williams, & Blakemore, 2010). Prior research that has examined this relationship prospectively has found a reciprocal link between working memory and relationship problems among peers: Peer problems contribute to worse EFs in childhood (de Wilde et al., 2015) but not during adolescence (Holmes, Kim-Spoon, & Deater-Deckard, 2016). The reciprocal relationship also was found, both in the childhood years and from middle childhood (ages 9 to 10) into adolescence (age 15; Holmes et al., 2016), which suggests that the development of EFs is affected by children’s social interactions (Riggs, Greenberg, Kusche, & Pentz, 2006). 34 Although prior research has examined the relation between connectedness and EFs quite extensively, fewer studies have been conducted between the period of early and late adolescence; as such, the present study adds to the extant literature by examining this relation between early adolescence and late adolescence. Connectedness and academic achievement. In light of the relation that prior research has found between connectedness and EFs, an examination into the association between connectedness and academic achievement is of equal interest. Research investigations into such relations have been of great interest to scholars in the field of education. Findings from intervention research in college students have found that feelings of connectedness have led to achievement-related outcomes such as increased motivation to persist on difficult tasks (Walton, Cohen, Cwir, & Spencer, 2012), as well as improvements on objective academic outcomes, such as GPA (Walton & Cohen, 2011). The relation between connectedness and EFs has also been studied among younger populations. Among early adolescents, acceptance and support from peers (Kiuru et al., 2015; Wentzel, Battle, Russell, & Looney, 2010; Wentzel & Caldwell, 1997) and teachers (Kiuru et al., 2015; Wentzel et al., 2010) have been found to be positively related to prosocial and academic motivation (Wentzel et al., 2010), academic skills (Kiuru et al., 2015), and end-of-year grades (Wentzel & Caldwell, 1997). Among children in Grades 4 and 5, positive peer relationships were found to mediate the relation between behavioral inhibition and math performance at the end of the school year (Oberle & Schonert-Reichl, 2013). Interestingly, prior research has found mixed findings as well, with social support being associated with scores on standardized tests but not GPA in eighth grade students (Levitt, Levitt, & Guacci-Franco, 1994). 35 Nevertheless, most of the extant research conducted has been cross-sectional in nature, with fewer studies examining this relation longitudinally. The present study, therefore, adds to the extant literature by examining the relation of connectedness in early adolescence to academic achievement in late adolescence. Connectedness and well-being. The impact that connectedness has on well-being across the human lifespan is likely one of the most documented in the research literature. In adolescent populations, connectedness has been associated with psychological well-being, defined in the study as the ability to cope with stressful events (Bal, Crombez, Van Oost, & Debourdeaudhuij, 2003). Such a relationship has been found to be mediated by hopefulness and self-esteem (Yarcheski, Mahon, & Yarcheski, 2001). Longitudinal studies also have noted the enduring influence of connectedness on well-being, with a strong directional pathway being found between adolescents’ social connectedness and their well-being in adulthood (Olsson et al., 2013). In terms of the relation that connectedness has with life satisfaction, specifically, a growing number of studies have found a positive relation between the two. Most notable among these studies is a population-based study of 20,119 fourth grade children in which Emerson and colleagues (2018) found that perceived adult and peer support predicted children’s levels of life satisfaction. Smaller studies also reported similar findings: In a sample of older adolescents, life satisfaction has been found to be correlated with social support (Ronen et al., 2016) and positive relationships with parents (Trzcinski & Holst, 2007). In addition to cross-sectional examinations, prior research has also examined the relation longitudinally; specifically, in a study that used a composite measure of well-being that included life satisfaction, Jose and colleagues (2012) 36 found well-being was related to school and family connectedness, but not peer connectedness in a sample of older adolescents who were followed over a period of three years. Moreover, there is also a large body of evidence in the research literature to support the inverse relationship between connectedness and depressive symptoms in children and adolescents (for a review, see Platt, Kadosh, & Lau, 2013). This relation was found in measurements using self-reports of social exclusion (Boulard, Quertemont, Gauthier, & Born, 2012) and peer ratings of acceptance and rejection (Prinstein & Aikins, 2004). Some mixed findings are evident as well, as illustrated by a large-scale representative study that found that family and social support were not related to depressive symptoms in a sample of adolescents (Rushton et al., 2002). The present study, therefore, contributes to the extant literature by examining the relation of connectedness in early adolescence to the presence and absence of well-being in late adolescence. Mindful attention and EFs. Mindful attention is another factor previously examined by prior research to affect EFs, with emerging research in the area of contemplative practice lending some support for this relation. Although numerous definitions have been used to describe mindful attention or mindfulness, Kabat-Zinn (2003) has defined it as paying attention to an experience, non-judgmentally, as it unfolds moment-by-moment. The mechanism through which mindful attention is theorized to have an impact on EFs has been outlined in two processes: (1) the iterative reprocessing model, which proposes that bottom-up influences are regulated by enabling an individual to place experiences in perspective, and (2) top-down processes of reflection that enable individuals to have greater psychological distance from potentially arousing aspects of situations (Zelazo & Lyons, 2012). 37 Although a plethora of studies have begun to emerge in regard to the impact that mindful attention practices have on a wide range of outcomes, research examining the relations between mindful attention as a dispositional trait and EFs are beginning to emerge. In association studies, higher levels of mindful attention have been found to be associated with EFs and related constructs, such as sustained attention (Rosenberg, Noonan, DeGutis, & Esterman, 2013), increased inhibitory control (Lyvers, Makin, Tomas, Thorberg, & Samios, 2014), increased shifting ability (Fiocco & Mallya, 2015), and better emotion regulation (Modinos, Ormel, & Aleman, 2010). Aspects of mindful attention, specifically awareness and non-judgment also have been found to be strongly associated with EFs measured four weeks later (Short, Mazmanian, Oinonen, & Mushquash, 2016). Moreover, in intervention studies conducted in adults, mindful attention was found to be associated with greater attention and cognitive flexibility (A. Moore & Malinowski, 2009). Among child and adolescent populations, mindful attention has been found to be associated with EFs as well. Specifically, in a sample of fourth and fifth grade students, mindful attention was found to be associated with inhibitory control, after controlling for biological markers of stress (Oberle, Schonert-Reichl, Lawlor, & Thomson, 2011). In seventh grade students, mindful attention also was found to be associated with self-reports of EF constructs (H.-S. Shin et al., 2016). Among seventh and eighth graders, mindful attention also was found to be associated with aspects of EFs, specifically, inhibitory control and working memory, but not cognitive flexibility (Riggs, Black, & Ritt-Olson, 2015). In summary, most of the extant research examining the relation between mindful attention and EFs in adolescents has been cross-sectional in nature, with few examining the relation longitudinally beyond a period of one year. The present study intended to contribute to 38 the extant literature by examining the relation of early adolescents’ levels of mindful attention to their EF performance in late adolescence. Mindful attention and academic achievement. In line with research that has been carried out with regard to the relations between EFs and mindful attention, prior investigations also have considered examinations into whether mindful attention is predictive of academic achievement. Investigations examining the link between these two constructs thus far have arisen primarily from intervention studies. Among these, a number of studies conducted with college students have found that mindful attention interventions have led to better performance on a math exam (Weger, Hooper, Meier, & Hopthrow, 2012) and participation in a 8-week Mindfulness-Based Stress Reduction course led to improved scores on standardized exams three months later (Bennett & Dorjee, 2015). Similarly, among child and adolescent populations, mindfulness-based social and emotional learning interventions led to improved quarterly grades in reading and science for third grade students (Bakosh, Snow, Tobias, Houlihan, & Barbosa-Leiker, 2016) and higher end-of-year math grades among fourth and fifth grade students (Schonert-Reichl et al., 2015). Moreover, fifth grade students diagnosed with ADHD who received a Samatha mindfulness meditation training also exhibited improvements on their math exam scores (N. N. Singh et al., 2016). Although there is growing interest in the impact of mindful attention interventions on objective measures of academic success, few, if any have examined the relation of dispositional mindful attention to academic outcomes, particularly across different developmental periods. Therefore, the present study sought to examine the relation of early adolescents’ levels of dispositional mindful attention to their academic achievement in late adolescence. 39 Mindful attention and well-being. Although the field of mindfulness and mindful awareness is fairly new to the field of psychology, a relatively large body of research has already emerged that has examined the link between mindful attention and well-being, particularly in adult and clinical populations (e.g., Bajaj & Pande, 2015; K. W. Brown & Ryan, 2003; Klainin-Yobas et al., 2016; Kong, Wang, & Zhao, 2014; Stolarski, Vowinckel, Jankowski, & Zajenkowski, 2015; Wenzel, von Versen, Hirschmüller, & Kubiak, 2015). A growing number of research studies also have begun to examine the relation in children and adolescents (e.g., Agarwal & Dixit, 2017; K. W. Brown, West, Loverich, & Biegel, 2011). Several theories have been put forth by prior research to explain the mechanism in which dispositional mindful attention is purported to enhance well-being (K. W. Brown & Ryan, 2003). The first mechanism has been through self-determination theory (SDT), where attention and awareness are required for self-regulatory processes that occur when decisions are being made about one’s behaviors so that they are in line with meeting one’s needs (Deci & Ryan, 2008). A second mechanism by which mindful attention may be related to well-being has been through an increase in engagement with and having present moment awareness to experiences as they unfold; this experience has been associated with flow activities that are intrinsically motivated, and which lead to great enjoyment while one is engaged in the activity (Csikszentmihalyi, 1997). Prior research has also found that resilience plays a mediating role, suggesting that individuals with greater levels of mindful attention are better able to cope with challenging thoughts and emotions without becoming overwhelmed (Bajaj & Pande, 2015). In terms of its relation to life satisfaction, specifically, emerging research has found mixed findings in regard to the relation that mindful attention has to life satisfaction in adolescent populations. Some studies have found a cross-sectional relation between adolescents’ 40 levels of mindful attention and their levels of life satisfaction (Bluth & Blanton, 2014; West, 2008), yet other studies have found no relation between the two (Agarwal & Dixit, 2017). Nonetheless, emerging research from intervention studies suggests adolescents’ participation in mindfulness-based programming may lead to increased levels of life satisfaction (Bluth, Gaylord, Campo, Mullarkey, & Hobbs, 2016; Bluth, Roberson, & Gaylord, 2015). In terms of the association that dispositional mindful attention has with depressive symptoms, cross-sectional research has found that a negative correlation exists between the two (for a review, see Tomlinson, Yousaf, Vittersø, & Jones, 2018). Research examining the efficacy of mindfulness-based interventions for alleviating depressive symptoms are only beginning to emerge. Among the few that have examined the effectiveness of such programs, Bluth et al. (2016) found that a mindfulness-based intervention with a self-compassion component led to decreased levels of depressive symptoms in adolescents. Similarly, adolescents who participated in mindful movement (e.g., yoga) had fewer depressive symptoms, compared to those who joined a “business-as-usual” physical education class (Felver, Butzer, Olson, Smith, & Khalsa, 2015). In terms of the longitudinal associations between mindful attention and depressive symptoms, research examining this relation is only beginning to emerge, with much of it carried out in adult populations. In the studies that investigated this relation, higher levels of mindful attention have been found to be associated with lower levels of depressive symptoms measured five weeks later (Sugiura & Sugiura, 2015) and two years later (Petrocchi & Ottaviani, 2016). Very few, if any, studies to date have examined this relation longitudinally in adolescents. Taken together, to make a contribution to the extant literature, the present study intended to examine the relation of early adolescents’ dispositional mindful attention to their levels of life satisfaction and depressive symptoms in late adolescence. 41 Physical activity and EFs. Another factor that has been examined to a great extent by prior research in terms of its ability to affect EFs is engagement in physical activity. During the past five years, studies examining the relation between physical activity and cognitive performance in children and adolescents have mushroomed, with some evidence leaning towards a positive association between the two (Biddle, Ciaccioni, Thomas, & Vergeer, 2019), and others asserting that relations are inconclusive (A. S. Singh et al., 2019). Several theories have been put forth to explain this mechanism: Regular engagement in physical activity can promote changes in the formation and development of neurons and blood vessels, as well as improve the metabolism of the central nervous system (van Praag, 2008). Physical activity engagement can also increase growth factors that promote brain plasticity and function (Cotman, 2008; van Praag, 2008). Research studies have typically categorized physical activity into two main subtypes: acute physical activity and longitudinal physical activity. Acute physical activity is thought to instantaneously increase one’s physiological arousal, by way of neurotransmitters, such as dopamine and epinephrine, both of which are thought to promote cognition (Tomporowski, 2003); findings from intervention studies, however, have been mixed regarding whether engagement in acute physical activity leads to improved cognitive performance. Some studies have found a positive effect in children (Altenburg, Chinapaw, & Singh, 2016) and adolescents (Soga et al., 2015), and others have not (Biddle et al., 2019; de Greeff, Bosker, Oosterlaan, Visscher, & Hartman, 2018; Hogan et al., 2015; A. S. Singh et al., 2019; for a review, see Verburgh et al., 2014). Engagement in long-term physical activity, similarly, has been found to promote cognitive performance, with the added benefit of cardiovascular fitness (Hillman, Buck, Themanson, Pontifex, & Castelli, 2009). Positive relations have been found in intervention 42 studies carried out among children (Dalziell, Booth, Boyle, & Mutrie, 2019; van der Niet et al., 2016) and adolescents (Hogan et al., 2015; Subramanian, Sharma, Arunachalam, Radhakrishnan, & Ramamurthy, 2015), although findings have been mixed in some studies (Chaya, Nagendra, Selvam, Kurpad, & Srinivasan, 2012; Schmidt, Jäger, Egger, Roebers, & Conzelmann, 2015; for a review, see A. S. Singh et al., 2019). Long-term association studies have found a positive association between regular engagement in physical activity and cognition in kindergarten children (Vandenbroucke, Seghers, Verschueren, Wijtzes, & Baeyens, 2016), among 8- to 11-year-old pre-adolescents (Chaddock et al., 2012; R. D. Moore et al., 2013; Raine et al., 2018) and among 12- to 17-year-old adolescents (Ardoy et al., 2014; Hogan et al., 2015; Stroth et al., 2009; Subramanian et al., 2015; Westfall et al., 2018). In studies specifically examining EFs among children and adolescents, physical fitness has been found to be associated with better inhibitory control (Chaddock et al., 2012; A.-G. Chen, Yan, Yin, Pan, & Chang, 2014; Hogan et al., 2015; Jäger, Schmidt, Conzelmann, & Roebers, 2014; Lakes et al., 2013; Raine et al., 2018; Soga et al., 2015; Westfall et al., 2018); findings were mixed, however, for EF subtypes, with few studies finding positive relations for working memory (A.-G. Chen et al., 2014; Hillman, Pontifex, et al., 2009), cognitive flexibility (Westfall et al., 2018), planning (C. L. Davis et al., 2011), and some finding no effects at all (for a review see Álvarez-Bueno et al., 2017; Costigan, Eather, Plotnikoff, Hillman, & Lubans, 2016; A. S. Singh et al., 2019; Sjöwall, Hertz, & Klingberg, 2017; Tarp et al., 2016). A sedentary lifestyle, conversely, is associated with poor EF performance (Best, 2010). Despite a large number of studies examining the cognitive benefits that physical activity engagement may confer, D. Lubans et al. (2016) concluded in their review of physical activity programs for young people that conclusions could not be drawn regarding the mechanism 43 through which participation in physical activity programs may operate in their effects on cognition. Furthermore, in a review of programs and approaches that have been examined in relation to their effectiveness for improving EFs, Diamond and Ling (2016) suggested that physical activity programs should have a cognitive component for participants to be able to experience the benefits of enhanced EFs. Examples of such programs given by the authors include martial arts (Lakes & Hoyt, 2004) and mindful yoga (Manjunath & Telles, 2001). The present study will examine the potential relations of physical activities, contextualized within school-based extracurricular programming, to EFs (see section on School-based Activities). In terms of measurement, many research studies that have examined physical activity expenditure have used one metric to facilitate comparability across the multiple studies: the MET value or resting metabolic rate (Ainsworth et al., 1993, 2011). In one study that has used this value to examine the relation of physical activity expenditure to EFs, mixed findings were found in research conducted using a population-based sample of individuals between the ages of 10 and 86; specifically, physical activity as calculated with MET values was related to scores of attention, but no associations were found between MET values and working memory or cognitive flexibility (Swagerman et al., 2015). Using the MET for ease in the calculation of values and for comparability across physical activities, the present study sought to contribute to the extant literature by examining the relations between physical activity levels and EFs in late adolescence. Physical activity and academic achievement. In consideration of the evidence suggesting the predictive relation that engaging in physical activity has on EFs, prior studies also have examined the relation that engagement in physical activity has to academic achievement. Following from the previous section on EFs and physical activity, previous research has 44 suggested that the mechanism by which engagement in physical activity may have an impact on academic achievement is through the enhancement of cognitive functions (Hillman, Buck, et al., 2009) that are involved in learning and memory (Cotman, 2008; van Praag, 2008); emerging evidence also suggests that EFs (Schmidt et al., 2017) and social and emotional competencies (Zhang et al., 2019) mediate the relationship between physical activity and academic achievement. Nonetheless, findings from the research literature have been mixed regarding whether engagement in regular physical activity causally influences academic achievement. For studies that have looked at outcomes on standardized achievement tests in elementary school children, the general consensus is there are causal effects for math (C. L. Davis et al., 2011; Donnelly et al., 2009; Ericsson, 2008; Gao, Hannan, Xiang, Stodden, & Valdez, 2013; Resaland et al., 2016; Telford et al., 2012); yet findings in other areas of proficiency are inconsistent, such as effects that have been found for writing (Ericsson, 2008; Telford et al., 2012) and reading (Donnelly et al., 2009; Ericsson, 2008) in some studies, and no effects for writing (Donnelly et al., 2009) or reading (C. L. Davis et al., 2011; Resaland et al., 2016; Telford et al., 2012) in other studies. Other studies also have found no effects between physical activity engagement and academic achievement at all (Ahamed et al., 2007; de Greeff et al., 2018; for a review see A. S. Singh et al., 2019; Sjöwall et al., 2017; Tarp et al., 2016). Among adolescents, participation in school-based physical activity interventions have led to improvements in academic grades (Ardoy et al., 2014; Butzer, van Over, Noggle Taylor, & Khalsa, 2015), with one study finding a dose-response effect (Hagins & Rundle, 2016). Cardiovascular fitness among adolescents also have been found to be associated with higher grades (Kwak et al., 2009; Santos de Castro & Cabral de Oliveira, 2016) and higher scores on mandatory standardized exams (Andersen et al., 2016; Welk et al., 2010) in cross-sectional 45 studies. Similarly, longitudinal studies conducted with adolescents have found that cardiovascular fitness was associated with higher grades (Suchert, Hanewinkel, & Isensee, 2016) and higher scores on standardized tests (L. J. Chen, Fox, Ku, & Taun, 2013; for a review, see Marques, Santos, Hillman, & Sardinha, 2018). Among the studies that have examined the relation between engagement in physical activity and academic achievement, one study was found to have utilized the MET value (Ainsworth et al., 1993, 2011), which was calculated based on the intensity level and amount of physical activity engaged in. In this study, it was found that adolescents’ self-reported physical activity was associated with their self-reported academic achievement (Kantomaa et al., 2013). In consideration of prior research evidence suggesting a relation between physical activity levels and academic achievement, the present study sought to contribute to the extant literature by examining the predictive relations that levels of engagement in school-based physical activities have with academic achievement. Physical activity and well-being. Although much research has examined the relation between physical activity engagement and EFs, the vast majority of the extant literature in the area of physical activity has focused on the effects that engagement in such activities have on physical health, with research being carried out in age groups across the human lifespan (Dobbins, Husson, DeCorby, & LaRocca, 2013; Penedo & Dahn, 2005). Interest in the relation that engagement in physical activity has to psychological well-being are also beginning to emerge as well (Hyde, Maher, & Elavsky, 2013). In a review conducted of programs targeting adolescents, however, findings were fairly inconclusive in regard to whether physical activity contributes to improved psychological well-being, due to high heterogeneity among previous 46 studies and a general paucity of longitudinal studies that have examined long-term effects (D. R. Lubans, Plotnikoff, & Lubans, 2012). In terms of life satisfaction, specifically, prior research conducted by Valois, Zullig, Huebner, and Drane (2004) found that adolescents who were sedentary had lower levels of life satisfaction than their peers who were more physically active. Yet, engaging into too much physical activity may also have a negative impact on well-being as found by a study that examined the association between weekly sports activities and other measures of well-being (i.e., using the World Health Organization Well-Being Index) among late adolescents (Merglen, Flatz, Belanger, Michaud, & Suris, 2014). In this study, researchers found an inverted-U shape relation, with those engaging in very low or very high levels of sports practice faring worse in well-being than those who engaged in moderate practice. Yet other studies have found no relation at all between engagement in physical activity and well-being (Costigan et al., 2016). At the other end of the well-being spectrum, prior research has found that regular participation in physical activity served as a protective factor against the onset of depressive symptoms in children and adolescents (for a review, see Biddle et al., 2019; H. E. Brown, Pearson, Braithwaite, Brown, & Biddle, 2013). Sedentary behavior, in contrast, also has been found to increase the risk of developing depressive symptoms among adolescents in low- and middle-income countries (Vancampfort, Stubbs, Firth, Van Damme, & Koyanagi, 2018). Researchers have suggested that possible mechanisms by which physical activity may lead to reduction in depressive symptoms may be through neurobiological, psychosocial or behavioral pathways (D. Lubans et al., 2016); yet further research is needed to fully elucidate the impact that physical activity has on depressive symptoms. The conclusion made by Biddle et al. (2019) in a recent review on this subject, however, is that findings are generally inconclusive for 47 whether physical activity could reduce depressive symptoms in children and adolescents. The present study intended to contribute to the extant literature by examining the relations that participation in school-based physical activities have on levels of depressive symptoms in late adolescence. Summary of factors that support EFs. As can be surmised, there is extensive literature examining the relations that connectedness and physical activity have to EFs and academic achievement, while less is known about mindful attention and positive affect, and their relations to EFs and academic achievement. As for well-being, there is a large body of literature that has examined its relations to positive affect and mindful attention, with some investigations into connectedness, physical activity, and a general paucity examining relations longitudinally. Depressive symptoms, similarly, have been examined a great deal in relation to connectedness, but less research has investigated its relations to positive affect, physical activity, and mindful attention, or have found the relations to be inconclusive. The present study sought to examine each of these factors purported to support EFs in relation to EFs, academic achievement, and well-being longitudinally across the period of adolescence. Factors that hinder EFs. Stress and EFs. Among the many factors that prior research has found to hinder EFs, the presence of stress is one area that has been studied to a great extent. Research with animal models have shed light on processes underlying the stress response; specifically, individual levels of biological sensitivity interact with characteristics of an environment, in a relationship exhibiting an inverted-U, with optimal performance occurring across the top curve of the inverted-U, and high or low levels of arousal resulting in impaired performance (Blair, 2010). Factors that may strain an individual’s stress response system includes the physical environment 48 (Rashid & Zimring, 2008) and psychosocial factors, such as exposure to negative events during childhood (Felitti et al., 1998). Such stress has been shown to have a negative impact on the hippocampus and prefrontal cortex (Gee & Casey, 2015), as more resources are required for coping with the negative event (Porcelli et al., 2008). Moreover, stressful life events that occur during childhood have been associated with decreased EF performance in adults (Heyman & Hauser-Cram, 2015). There is some evidence to indicate, however, that those with worse inhibitory control have better shifting abilities, that is, they are better able to adapt to changes in their environment, which suggests that childhood stress may be adaptive in some circumstances (Mittal, Griskevicius, Simpson, Sung, & Young, 2015). Among adolescents, the presence of stress has been found to lead to poor EFs; for example, higher daily stress levels measured in adolescents using ecological momentary assessment predicted worse performance on behavioral inhibition tasks, compared to adolescents with low daily stress levels (Rahdar & Galvan, 2014). Similar findings exhibiting a dose-effect gradient have been found among children (Grades 3 and 5) and early adolescents (ages 9-14), with higher numbers of negative life events predicting worse self-control, both cross-sectionally (Duckworth, Kim, & Tsukayama, 2013; Humphreys et al., 2019) and longitudinally one year later (Duckworth et al., 2013). As such, the present research sought to contribute to the extant literature by examining the longitudinal relations that stressful life events have on EFs across time from early adolescence to late adolescence. Stress and academic achievement. In consideration of the link that the presence of stress has with EFs, an examination into the association between stress and academic achievement is of equal interest. The effects of stress on cognition have been studied extensively and have been found to have a direct impact on academic outcomes (Shankar & Park, 2016). Among middle 49 school students, higher amounts of stress as experienced during the transition to a new school predicted lower grades (Goldstein et al., 2015). Among adolescents in the same age group, school-related stress (e.g., academic performance, social interactions) subsequently predicted poorer grades in secondary school (Kaplan, Liu, & Kaplan, 2005). In the secondary school setting, adolescents who reported experiencing chronic stress when they first began secondary school and again when they finished secondary school earned significantly worse grades when they graduated, as compared to adolescents who experienced stress at one time point only or none at all (Schraml et al., 2012). Contrary to these findings, not all research has found this relationship to be true. For example, in one study, stressful life events as reported by parents of third to fifth graders did not predict GPA two years later (Dubow, Tisak, Causey, Hryshko, & Reid, 1991). In this vein, the present study sought to further elucidate the relation between stressful life events and academic achievement by examining the relations across time from early adolescence to late adolescence. Stress and well-being. Stress is documented to a great extent in the research literature as having a detrimental impact on well-being. To illustrate, perceived stress from family (Siddique & D'Arcy, 1984), school (Moksnes et al., 2016; Siddique & D'Arcy, 1984), peers (Siddique & D'Arcy, 1984), as well as concerns about school performance (Moksnes et al., 2016) significantly predicted lower levels of psychological well-being in adolescents (Siddique & D'Arcy, 1984). Perceived stress also has been found to be inversely correlated with life satisfaction among adolescents (Abolghasemi & Varaniyab, 2010). In another cross-sectional study, a greater number of stressful life events was related to lower levels of life satisfaction for adolescents in Grades 6 to 12 (McKnight, Huebner, & Suldo, 2002). In one of the few studies that examined this relation over time, Suldo and Huebner (2004) found that a greater number of 50 stressful life events were related to lower levels of life satisfaction one year later for adolescents in Grades 6 to 12. The number of stressful life events also predicted an increase in stressful life events and lower levels of life satisfaction one year later. The link between negative life events and depression has been established for some time for the adolescent period (e.g., Swearingen & Cohen, 1985). The relation between stressful life events and depressive symptoms has similarly been researched extensively among this age group. Over a series of studies, Ge and colleagues (1994, 2001, 2006) found that the number of stressful life events as experienced by adolescents after their parents were divorced was found to mediate the effect of divorce on depressive symptoms (Ge et al., 2006). In two longitudinal studies carried out over four years, higher levels of stressful life events were found to predict higher levels of depressive symptoms among adolescents, with girls being found to be more sensitive to changes in the number of stressful life events over time than boys (Ge et al., 1994). Recently experienced stressful life events also have been found to be positively related to an increase in depressive symptoms as well (Ge et al., 2001). Despite numerous examinations that have been conducted by prior research into the relation that stressful life events have on well-being, few studies, if any, have examined the effects that stressful life events have on life satisfaction and depressive symptoms across time from early adolescence to late adolescence; the present study sought to contribute to the extant literature by investigating this relation. Negative affect and EFs. Another factor that has been found to negatively impact EFs in the extant literature is negative affect. Numerous studies have examined the relations between negative affect and EF-related constructs, but only a handful has found any significant relations among children and adolescent populations. In one research study, adolescents with higher 51 negative affect tended to use more cognitive resources to attend to conflict and monitor for errors in an EF (Flanker / Reverse Flanker) task (Ladouceur, Conway, & Dahl, 2010). Even negative emotions that have been induced among 8-, 10-, and 12-year-olds, such as disappointment and frustration, have been found to lead to great variability in EFs within individuals (Pnevmatikos & Trikkaliotis, 2013). Previous research has also shown that adolescents with poor working memory and inhibition may employ maladaptive strategies (such as avoidance or denial), however, which has been found to lead to daily negative mood (Papadakis, Fuller, Brewer, Silton, & Santiago, 2017). In terms of the relations that negative affect have to adaptive EF-related constructs, prior research has found that negative affect has led to more creativity than positive affect for tasks that require problem solving and perseverance (De Dreu et al., 2008). Nonetheless, most of the research conducted examining this relation has found negative affect to be more predictive of maladaptive functioning in EFs than adaptive functioning. Few studies, if any, have examined this relation over time in adolescents. Thus, the present study sought to contribute to the extant literature by examining the relations that negative affect have on EFs across time from early adolescence to late adolescence. Negative affect and academic achievement. In line with the research conducted on EFs and negative affect, an examination into the association between negative affect and academic achievement has also been of interest to stakeholders in the field of education. Among adolescents, negative associations have been reported by prior research between negative affect and academic-related behaviors and achievement. Negative affect has been found, for the most part, to be negatively associated with positive school functioning (M. Weber et al., 2016), standardized tests of achievement (Scrimin, Mason, & Moscardino, 2014), self-reported GPA (Lewis et al., 2009; Ng et al., 2015), teacher-reports of achievement (M. Weber et al., 2016), and 52 cumulative report card grades (Ng et al., 2015); one study, however, did find negative affect to be unrelated to self-reported GPA (Arsenio & Loria, 2014). Two studies from prior research have examined the relation using an experimental design with induced negative affect (Scrimin et al., 2014; Scrimin, Mason, Moscardino, & Altoè, 2015), while other studies have been correlational in nature (Lewis et al., 2009; M. Weber et al., 2016). Only one study examined the relationship prospectively, five months apart within a school year (Ng et al., 2015). Considering the relative paucity of research that has examined the longitudinal relations of negative affect to EFs, the present study sought to contribute to the extant literature by examining the associations between negative affect and EFs from early adolescence to late adolescence. Negative affect and well-being. Prior research has also established the link between negative affect and well-being in adolescents (e.g., Huebner & Dew, 1996). Most research conducted among this population has examined the relation in concurrent study designs, finding that negative affect and well-being as operationalized as life satisfaction to be inversely related in adolescents (McCullough, Huebner, & Laughlin, 2000; C. Proctor, Linley, & Maltby, 2010). Moreover, longitudinal studies examining the relation between negative affect and depression have found negative affect and negative emotionality to predict subsequent development of depressive symptoms among adolescents (Dolcini-Catania et al., 2020). Although a number of studies have examined the longitudinal relations that negative affect have on the presence and absence of well-being, the present study sought to further investigate if such a relation exists in a sample of early adolescents who were followed eight years later. Depressive symptoms and EFs. The present study examined depressive symptoms as both a predictor and an outcome (for a description, see section above on Well-Being). 53 Depressive symptoms and academic achievement. In consideration of the bidirectional relations that have been found between depressive symptoms and EFs in the extant literature, researchers also have examined the relation that depressive symptoms have with academic achievement and academic-related outcomes. Investigations into this relation have found depressive symptoms to be correlated with academic-related outcomes, such as increased risk for school dropout (Quiroga, Janosz, Bisset, & Morin, 2013), history of school suspension, as well as various measures of academic achievement, as reported by parents (Voltas, Hernández-Martínez, Aparicio, Arija, & Canals, 2014), teachers (Keles, Olseth, Idsøe, & Sørlie, 2018), standardized achievement tests (Rothon et al., 2009), and adolescents themselves (Fröjd et al., 2008; McArdle, Hamagami, Chang, & Hishinuma, 2014). Relations between depressive symptoms and academic achievement have been found in cross-sectional studies (Chow, Tan, & Buhrmester, 2015; Fröjd et al., 2008; Rapport, Denney, Chung, & Hustace, 2001; Zychinski & Polo, 2012), as well as prospective studies across a school year (Shahar et al., 2006; Weidman, Augustine, Murayama, & Elliot, 2015), up to three years later (Keles et al., 2018; McArdle et al., 2014; Rothon et al., 2009), and up to four years later (Rapport et al., 2001). Several studies have found a reciprocal relation between depressive symptoms and academic achievement as well (Keles et al., 2018; Weidman et al., 2015), although one study found the link to only be unidirectional (McArdle et al., 2014). Research examining gender effects also have found mixed results, with some studies finding the relation in only boys (Rothon et al., 2009) and others finding that girls experienced a higher severity of symptoms (Fröjd et al., 2008; Zychinski & Polo, 2012). Of the few research studies that operationalized academic achievement as report card grades or GPA (Shahar et al., 2006; Weidman et al., 2015; Zychinski & Polo, 2012), two of the 54 studies examined the relation in middle school students (Shahar et al., 2006; Zychinski & Polo, 2012) and one examined the relation in middle and secondary students across one school year (Weidman et al., 2015). Only one study employed a non-clinical measure of depressive symptoms (Keles et al., 2018). Few, if any, prior research has explored the relation across time between the elementary and secondary school years, using a non-screening tool or non-clinical measure. As such, the present study sought to examine this relation longitudinally using a non-clinical measure of depressive symptoms in a sample of adolescents. Depressive symptoms and well-being. The extant literature also suggests the existence of a strong negative relationship between depressive symptoms and life satisfaction, with a growing number of studies examining these relations in child and adolescent populations. For example, Gilman and Huebner (2006) found that low levels of life satisfaction were related to greater levels of depressive symptoms in a sample of sixth to twelfth grade students. A similar inverse relationship was found in another study with 13- to 18-year-old adolescents (Moksnes et al., 2016). These studies were cross-sectional, however. Few studies to date, however, have examined the relation over time between the period of early and late adolescence. In terms of the ability of depressive symptoms to be able to predict subsequent depressive symptoms among adolescents, prior research using a nationally representative sample of adolescents in Grades 7 to 12 found that adolescents who initially experienced moderate to severe levels of depressive symptoms were at higher risk for developing later depression (Rushton et al., 2002). Similar findings were exhibited in a research study conducted with a large community-based sample of adolescents, ages 14 to 18, who had subclinical depressive symptoms, and these predicted the development of major depressive disorder one year later (Georgiades, Lewinsohn, Monroe, & Seeley, 2006). As a result, the present study sought to 55 examine the relations of depressive symptoms in early adolescence to well-being, as measured with life satisfaction and depressive symptoms, in late adolescence. Summary of factors that hinder EFs. In summary, extensive literature examining the relations that stress and depressive symptoms have to EFs and academic achievement exist, while less is known about negative affect and their relations to EFs and academic achievement. As for well-being, there is a large body of literature that has examined its relations to stress, with some investigations into negative affect and depressive symptoms, and a general paucity of research examining relations longitudinally. Depressive symptoms also have been examined a great deal in relation to stress and negative affect. The present study sought to examine each of these factors purported to hinder EFs in relation to EFs, academic achievement, and well-being across time from early adolescence to late adolescence. School-based Activities Decades of research have examined the role that school-based activities have had on adolescent development (Eccles & Barber, 1999; Farb & Matjasko, 2012; Feldman & Matjasko, 2005; Holland & Andre, 1987). For the purposes of this study, a focus will be placed on school-based organized or extracurricular activities, as well as co-curricular activities, as defined below. Organized activities generally have been defined as structured activities that are not part of the formal school curriculum that provide adult supervision and have a focus on building some competency (Bohnert, Fredricks, & Randall, 2010; R. W. Larson, 2000; Mahoney, Larson, & Eccles, 2005). Other attributes include voluntary participation, regularly scheduled meetings, peer involvement, and goals and rules in the context where the activity is carried out. Extracurricular activities are placed under the umbrella of structured activities and are defined as “academic or non-academic activities that are conducted under the auspices of the school but 56 occur outside of normal classroom time and are not part of the curriculum…do not involve a grade or academic credit and participation is optional…” (Bartkus, Nemelka, Nemelka, & Gardner, 2012, p. 698); these activities include team sports, academic clubs, and prosocial clubs (Eccles & Barber, 1999). Relatedly, activities that require students to participate “outside of normal classroom time as a condition for meeting a curricular requirement” (Bartkus et al., 2012, p. 699) are considered co-curricular activities; an example of this would be performing arts courses, such as music or theatre (Eccles & Barber, 1999). The scope of the present study will be limited to school-based activities that include both extracurricular activities and co-curricular activities. Participation in school-based activities generally has been associated with positive developmental outcomes, including academic achievement (Farb & Matjasko, 2012; Shulruf, 2010), psychological adjustment (Fredricks & Eccles, 2006), prosocial competencies (Forneris, Camiré, & Williamson, 2015; Linver, Roth, & Brooks-Gunn, 2009), and well-being (Forgeard & Benson, 2019); other outcomes also have extended beyond the years of post-secondary education such as civic and charitable involvement, and higher levels of life satisfaction in the mid-30s (Eisman, Stoddard, Bauermeister, Caldwell, & Zimmerman, 2017; Farb & Matjasko, 2012; for a review, see Mahoney, Harris, & Eccles, 2006). The notable exception to this has been participation in school sports that also is associated with an increase in risk-taking behavior, such as higher rates of alcohol use (Darling, Caldwell, & Smith, 2005; Denault, Poulin, & Pedersen, 2009). Prior research has also found, however, that positive outcomes derived from activity participation begin to diminish if participation rates surpass a certain threshold, a phenomenon known as the overscheduling hypothesis (Fredricks, 2012; Fredricks & Eccles, 2010). 57 Although most studies have been based in the U.S. context, findings on the impact of organized and extracurricular activities in U.S. have been found to be consistent with the Canadian context (Forneris et al., 2015; Viau & Poulin, 2015). School-based activities and EFs. In the framework put forth by Diamond (2012), extracurricular activities that imbed factors that promote EFs, such as positive affect, connectedness, and physical activity, while also teaching skills that require and challenge EFs, were hypothesized to produce the greatest benefits for a number of developmental outcomes. Prior research has found that a number of programs do exist that demonstrate evidence-based outcomes of improvement in EFs, yet most school curricula are often already too overloaded to accommodate additional programming (Diamond, Barnett, Thomas, & Munro, 2007); extracurricular, co-curricular, or community-based activities may be practical and cost-efficient replacements to consider (Diamond, 2014). Such school-based activities that are hypothesized to promote EFs, while also teaching skills that require and challenge EFs may include sports, music, and theatre (Diamond, 2015). The relation that each of these school-based activities have to EFs as examined by the extant literature are delineated below. Sports and EFs. Most of the research related to sports are derived from research examining physical activity engagement in relation to EFs and other cognitive outcomes (see section on Physical Activity & EFs). Far fewer studies have examined the relations that participating in extracurricular sports have to EFs. In an experimental study, EFs were not found to mediate the relationship between a school-based physical activity intervention and academic achievement in 10-year-old children (Aadland et al., 2018). In a longitudinal study carried out with elementary school-aged children, Piché, Fitzpatrick, and Pagani (2015) found that parent- 58 reported structured extracurricular physical activity participation in kindergarten was positively related to self-regulation as measured by teacher reports of classroom engagement in Grade 4; the inverse relationship was also found to be true. Among college students, prior research found that students who participated in either extracurricular or recreational sports performed better on laboratory tasks of inhibition and problem-solving tasks when compared to non-participants (Jacobson & Matthaeus, 2014). Further analyses were conducted by sports type categorized according to their impact on EFs (Singer, 2000), based on the theory that tasks that require greater amounts of skill and knowledge would have the strongest transfer effects to areas outside the domain where the skill and knowledge were originally acquired (Taatgen, 2013). Sports were categorized into self-paced sports (e.g., bowling or running, which give athletes time to make decisions about subsequent actions and perform at their own pace) versus externally-paced sports (e.g., basketball or volleyball, which require athletes to flexibly adapt and make quick decisions as a result of external cues). Jacobson and Matthaeus (2014) found students who engaged in self-paced sports had the highest inhibition scores, and those who engaged in externally-paced sports had the highest scores on a problem-solving task. A similar study carried out with secondary school students that examined the relation of student participation in contact required and contact nonrequired sports to student- and teacher-reported EFs, however, found no relation between sport subtype and EFs (Edelsberg, 2018). Among 10- to 12-year-old children, research that compared the effects of team-based physical activity programs, individual-based physical activity programs, and a low physical exertion, low cognitive demand control condition, found that, although participating in the first two activity programs led to improvements in aerobic fitness, only the participants in the team-based activity program experienced improvements in cognitive flexibility (Schmidt et al., 2015). 59 Music and EFs. Similarly, interest regarding the impact that music education has on EFs has grown due to the potential transfer of EF skills acquired during music lessons and practice to other non-music related areas of cognitive function (Schellenberg, 2004); such interest is due to the consideration that characteristics of music education may promote EFs more easily than other activities (Schellenberg, 2006). An examination of EF requirements during the music making process reveal that EFs are engaged during numerous aspects of music performance, such as “anticipation, planning, memory, [and] synchronization with other musicians” (Guhn, Emerson, & Gouzouasis, 2019, p. 2). Music making also requires engagement in daily practice, having focused attention for long periods of time, reading of musical notes, memorization of lengthy musical passages, an understanding of the variety of musical structures (e.g., chords, scales, intervals, chord progressions), an increasing mastery of specialized skills, and application of rules for emotional expression during performance (Holochwost et al., 2017). In comparison to vocal music, evidence suggests playing a musical instrument fosters EFs more greatly due to fewer demands placed on vocal music participants: fewer practice hours, fewer course prerequisites, and optional requirements for playing a musical instrument or reading musical notation (Guhn et al., 2019). Extant research on the relation of music education to EFs follows a similar pattern with sports, with most of the evidence coming from correlational and experimental studies of non-curricular courses outside of the secondary school level, most of which have been conducted with adults and children. Findings from correlational studies have generally been mixed: Young adult musicians were found to score higher than non-musicians on tasks assessing inhibitory control (Bialystok & DePape, 2009) and working memory (Okada & Slevc, 2018), and children who had undergone at least two years of music lessons scored higher on tasks of working 60 memory in some studies (Roden, Grube, Bongard, & Kreutz, 2014), but not others (Zuk, Benjamin, Kenyon, & Gaab, 2014). Findings were similarly mixed for relations to inhibitory control, with no relation being found between music education and inhibitory control in one study (Zuk et al., 2014), but a dose-response effect being found in another study (Degé, Kubicek, & Schwarzer, 2011). In experimental research, older adults who received piano lessons performed better in verbal working memory (Bugos, Perlstein, McCrae, Brophy, & Bedenbaugh, 2007), and among children, Schellenberg (2004) found that one year of music lessons led to improvements in intelligence among 6-year-olds. Holochwost et al. (2017) also found that students in Grades 1 to 8 who received music education exhibited better inhibitory control and working memory performance as well (e.g., accuracy scores on flanker and go/no-go tasks, amongst others). Dosage effects for accuracy performance on the flanker task were also evident for students who participated for three years as compared to those who participated for only one year of the program. In their sample of 6- to 14-year-old children, Alemán et al. (2017) found mixed results, with improvements in parent-rated measures of self-control, but no differences in performance on the flanker, go/no-go, or Tower of London tasks for children who participated in a music intervention for one semester longer than those assigned to a control group. Theatre and EFs. In comparison to sports and music, theatre has enjoyed far fewer empirical investigations into the nature of its relationship to EFs. In a review by the Organization for Economic Cooperation and Development (OECD; 2013), theatre participation was found to be related to verbal skills, perspective taking, and emotion regulation. As for factors that promote EFs, an empirical study examining the effects of drama lessons in 6-year-old children found that children who received drama lessons experienced greater improvements in social skills and 61 behavior than students who received music lessons or no lessons at all (Schellenberg, 2004). In a correlational study that examined peer acceptance, students who were involved in performance art during Grades 9, 10, and 12 had more favorable impressions of their peers (Fredricks & Eccles, 2005) than those who were not involved. Overall investigations into the arts in general (not categorized by subtype) have been mixed, with some finding a relation to cognitive capacity (Deasy, 2002), but others finding no relation to cognitive outcomes (Foster & Marcus Jenkins, 2017). One correlational study also found that participation in the arts was related to lower rates of teamwork and positive relationships in Grade 11 students (R. W. Larson, Hansen, & Moneta, 2006). School-based activities and academic achievement. Decades of research have found that participation in school-based activities are associated with increased academic achievement (Farb & Matjasko, 2012; Shulruf, 2010). Previous research also supports the assertion that school-based extracurricular activities are more associated with school outcomes, such as academic adjustment, than extracurricular activities based in community settings (Gerber, 1996; Marsh & Kleitman, 2002; Martin et al., 2013). This association provides support for the identification/commitment hypothesis, that puts forth that participation in a context-specific activity fosters an identification and commitment to outcomes associated with that setting (Marsh & Kleitman, 2002). Cross-sectional studies examining this association in middle school students found that participants of extracurricular activities had higher overall achievement test scores (E. A. Chambers & Schreiber, 2004) and GPAs (Schwartz, Cappella, & Seidman, 2015) in some studies, yet this relation was not found in other studies (Dotterer, McHale, & Crouter, 2007). Students who did not participate in extracurricular programming were found to have the lowest GPAs (Metzger, Crean, & Forbes-Jones, 2009). Longitudinal studies that have been conducted 62 have found that students who participated in school-based activities also achieved higher scores on math exams (Lipscomb, 2007) and higher GPAs (Im, Hughes, Cao, & Kwok, 2016). In studies based in the secondary school setting, cross-sectional studies have found that participation in extracurricular activities were positively associated with higher math performance (Dumais, 2008; Fredricks, 2012), higher achievement tests scores (E. A. Chambers & Schreiber, 2004), higher GPA in core subject areas (Darling, 2005; Darling et al., 2005), and higher overall GPA (Eccles & Barber, 1999; Fredricks, 2012). Longitudinally, participation in an increased number of activities in the eleventh grade predicted higher self-reports of first semester grades; those who had the highest activity involvement, however, reported lower grades overall (Fredricks & Eccles, 2010). Similarly, in another study, tenth graders who participated in extracurricular activities had higher math performance scores and overall twelfth grade GPA as well (Fredricks, 2012). Moreover, a three-year longitudinal study conducted by Darling (2005) also found that participating in extracurricular activities predicted adolescents’ self-reported grades in core subject areas. In research carried out by Mahoney and colleagues using school yearbooks, consistent participation in extracurricular activities was found to be predictive of college attendance (Mahoney, Cairns, & Farmer, 2003) and was found to serve as a protective role against early dropout (Mahoney, 2000, 2014; Mahoney & Cairns, 1997). In an examination by activity type, sports participation was found to be associated with higher language arts grades (Hughes & Devine, 2019) and higher GPA (Schwartz et al., 2015) in middle school, as well as higher math test scores (Lipscomb, 2007) and higher GPA (Van Boekel et al., 2016) in secondary school. Participation in school clubs, such as academic, prosocial, and school involvement clubs, has also been associated with higher math test scores (Lipscomb, 2007) and higher GPA (Eccles & Barber, 1999) in secondary school, but was not found to be 63 associated with higher language arts grades in middle school (Hughes & Devine, 2019). The effects of performing arts participation on academic achievement has also been mixed, with arts participation being associated with high GPA in some studies (Eccles & Barber, 1999; Im et al., 2016) but not in others (Hughes & Devine, 2019). In a critical review and meta-analysis, Shulruf (2010) reported that most effect sizes for the relation between participation in extracurricular activities and academic achievement have been small, with the largest effect sizes being found for the relation of student leadership to GPA, followed by academic clubs to math, English and science grades. Extracurricular involvement has been found to be associated with higher math grades in general (Shulruf, 2010). Nonetheless, there was insufficient proof to support the claim for causal relations. Mahoney et al. (2006) has asserted, however, that experimental studies of extracurricular activities generally do not exist since the nature of participation in these activities is characterized by intrinsic motivation of participating students. Participation in co-curricular activities, including the performing arts, also have been examined in their relations to academic achievement. In a report by the OECD (2013) on arts education, music education was found to have the strongest evidence for academic outcomes. To illustrate, findings from a nationally representative sample of tenth grade students in the U.S. found that those participating in secondary school-based music ensembles had higher scores on math achievement tests than non-participants (Miksza, 2010). Similarly, recent findings from a population-based sample of Canadian tenth and twelfth grade students found that participating in music was related to higher grades in math and science for tenth graders, as well as English, for both tenth and twelfth graders; stronger effects were also found for instrumental music as compared to vocal music (Guhn et al., 2019). Findings from correlational studies found music 64 education participation to be related to higher scores on the SAT college entrance exam (Vaughn & Winner, 2000) as well as higher grades, both cross-sectionally (Cabanac, Perlovsky, Bonniot-Cabanac, & Cabanac, 2013; Gouzouasis, Guhn, & Kishor, 2007) and longitudinally (Gouzouasis et al., 2007). Similar findings also have been found in studies with middle school students (dos Santos-Luiz, Mónico, Almeida, & Coimbra, 2016). Most research on drama-based curricula have examined the effects of interventions that have been incorporated into language arts curricula; a meta-analysis conducted of these interventions have revealed weak but mostly positive correlations between program participation and academic outcomes (B. K. Lee, Patall, Cawthon, & Steingut, 2015). In one of the few studies to examine theatre education and academic achievement, students who took theatre classes were found to be more likely to have higher SAT scores as compared to students who took the exam but who did not report participating in theatre (Vaughn & Winner, 2000). School-based activities and well-being. In broad definitions of education that extend beyond the traditional school subjects and extend to the plethora of opportunities for learning (Michalos, 2008), whether through informal education, the arts, or experiences as mundane as social interactions, school-based activities may be one way students find personal meaning and purpose in their lives and may be one reason why they decide to remain in school (Haensly, Lupkowski, & Edlind, 1985). Prior research in school-based activity participation has been positively associated with positive developmental outcomes such as higher levels of well-being (i.e., life satisfaction) and lower levels of depressive symptoms (Forgeard & Benson, 2019; Rose-Krasnor, Busseri, Willoughby, & Chalmers, 2006). Correlational studies also have found that higher levels of extracurricular involvement during secondary school predicted higher levels of school satisfaction (Gilman, 2001); similarly, higher levels of extracurricular participation in 65 secondary school predicted life satisfaction in a retrospective study of college students (Forgeard & Benson, 2019). The long-term contribution of extracurricular activity participation to adult outcomes also have been examined in a sample of urban adolescents at risk for drop out; adolescents who participated in incrementally greater numbers of activities over their secondary school years reported higher life satisfaction in their mid-30s, when compared to adolescents who decreased in their levels of participation over time (Eisman et al., 2017). Nonetheless, relations to well-being by type of activity have been mixed. Research on the relation of participation in sports to well-being outcomes have been examined far more than other school-based activities, and have been found to be related to a number of physical health outcomes including increased physical fitness, reduced body fat, decreased risk for cardiovascular and metabolic disease (US Department of Health and Human Services, 2008), as well as increases in some risk behaviors, including dieting (Michaud, Jeannin, & Suris, 2006) and substance use (Denault et al., 2009; Veliz, Boyd, & McCabe, 2015). Psychosocial outcomes that have been found to be related to sports participation include improved self-esteem (Oosterhoff, Kaplow, Wray-Lake, & Gallagher, 2017; Wiersma & Fifer, 2008), improved social interaction (Linver et al., 2009), and increased levels of life satisfaction (for a review, see Eime, Young, Harvey, Charity, & Payne, 2013; Valois et al., 2004). As for other school-based activities (i.e., clubs, music, leadership, theatre), previous research has found that participation in clubs, music, and leadership, but not theatre arts, was positively correlated with a well-being composite of higher levels of self-esteem and optimism, and lower levels of depressive symptoms, social anxiety, and daily hassles (Rose-Krasnor et al., 2006). Music involvement has also been found to be indirectly related to lower levels of substance use and higher levels of self-esteem (Oosterhoff et al., 2017). For life satisfaction specifically, a study on arts participation found that arts 66 engagement and non-school-based arts participation was associated with life satisfaction; although no significant relation was found for the relation of school-based arts participation to life satisfaction (Martin et al., 2013). Participation in school-based activities, for the most part, has been correlated with lower levels of depressive symptoms when compared with non-participation, suggesting that participation in school-based activities serves as a protective factor against the development of depressive symptomatology. For middle school-based activities, the relation of activity participation was found to be related to lower levels of depressive symptoms in cross-sectional studies (Guest & McRee, 2009); some studies conducted with this age group, however, found no relations between activity participation and depressive symptoms (Denault & Poulin, 2009). In the secondary school setting, participation has been found to be related to lower levels of depressive symptoms in cross-sectional studies (Fredricks & Eccles, 2005; Guest & McRee, 2009) as well as longitudinal studies (Fredricks & Eccles, 2006); some studies, however, found no evidence for any relation between the two (Darling, 2005; Darling et al., 2005; Denault & Poulin, 2009). Cross-sectional research has also found that highly involved participants (over 20 hours per week) had higher levels of depressive symptoms compared to non-participating students; students who reported participating approximately 10 hours per week were found to have the lowest levels of depressive symptoms (E. T. Randall & Bohnert, 2009). A retrospective study of college students also confirms this relation, with those who reported higher levels of extracurricular involvement during secondary school also found to report lower levels of depressive symptoms (Forgeard & Benson, 2019). In terms of specific activity type, findings from research have been mixed as to whether participation is related to depressive symptoms. Generally, studies have found that involvement 67 in sports is related to lower levels of depressive symptoms, both cross-sectionally (Fredricks & Eccles, 2006) and longitudinally (Fauth, Roth, & Brooks-Gunn, 2007; Fredricks & Eccles, 2006). Findings for other school-based activities have been mixed with some finding school involvement (student government, pep club, cheerleading) to be related to lower levels of depressive symptoms (Fredricks & Eccles, 2005), yet other activities (school clubs and performing/fine arts) were not found to be related to depressive symptoms (Denault et al., 2009; Fredricks & Eccles, 2006). Research Aims As described in the literature review, the extant research has found EFs to be related to academic achievement and well-being. Similarly, factors purported to support EFs (i.e., positive affect, connectedness, mindful attention, and physical activity) also have been found to be associated with EFs, academic achievement, and well-being. A large body of research has similarly found factors that hinder EFs (e.g., stress, negative affect, and depressive symptoms) to be associated with EFs, academic achievement, and well-being. As described previously, the examinations the present study sought to conduct for each of these relations was inspired by the framework put forth by Diamond (2012). Furthermore, school-based activities with components that support EFs due to the nature of the activity, but that also require, trains, and challenge EFs (e.g., sports, music, and theatre) may lead to improvements in EFs, academic achievement, and well-being. The overall hypothesized relations examined in the present study are depicted in Figure 3. The specific study aims are as follows: Study aim 1. Given the research linking EFs to academic achievement and well-being, the first aim of the present study was to investigate the hypothesized relations of EFs in early adolescence (T1) and late adolescence (T2) to T2 GPA and well-being (i.e., life satisfaction and 68 depressive symptoms), and explore the potential role that school self-concept has in the longitudinal relation of T1 EFs to T2 GPA. Study aim 2. In consideration of the research linking EFs to academic achievement and well-being, the second aim of the present study was to examine the factors that support (i.e., mindful attention, positive affect, connectedness) or hinder (i.e., stressful life events, negative affect, depressive symptoms) EFs at T1 in relation to their relative hypothesized contributions to EFs, GPA, and well-being (i.e., life satisfaction and depressive symptoms) at T2. Study aim 3. Given the hypothesized relations between school-based activities and EFs, the third aim of the present study was to investigate the relative hypothesized contributions of students’ participation in middle/secondary school-based activities to T2 EFs, GPA, and well-being (i.e., life satisfaction and depressive symptoms). Additional hypotheses. This study also sought to examine whether EFs played a role in mediating the predictors and outcomes. 69 Method Participants T1 sample. Participants for this study were drawn from a larger study examining the long-term effectiveness of a school-based social and emotional learning (SEL) program (Schonert-Reichl et al., 2015). The current study was based on data from two time points, referred to as Time 1 (T1) and Time 2 (T2). The T1 sample, recruited in 2008, comprised 100 children (42% female) from four 4th and 5th grade classrooms located in public elementary schools in a diverse suburb of a large city in British Columbia, Canada. Sixty-five children (48% female) who participated at T1 also participated at T2 in 2015 when they were in Grades 11 and 12. Children’s ages at T1 ranged from 9.00 to 11.16 years (M = 10.19, SD = .55). The average annual income for the neighborhoods in which the elementary schools were located was similar to the median annual income for Canada in 2006 ($53,800 CAD; Statistics Canada, 2006). Regarding family composition of the 65 children at T1, 88% reported living in two-parent homes (including biological parents and families with stepparents), 5% reported living only with the mother, and 8% reported living in dual custody arrangements (half-time with mother and half-time with father). This family composition is comparable to the original sample of 100 students in 2008, in which the following family composition was reported: 86% of students reported living in two-parent homes, 5% reported living only with the mother, and 8% reported living in dual custody arrangements. With regard to first language learned at T1, 75% of children indicated English was the first language they learned at home. Other reported first languages included Chinese (4.6%), Spanish (4.6%), Cantonese (3.1%), Korean (3.1%), Romanian (3.1%), Czech (1.5%), Mandarin (1.5%), Persian (1.5%), and Russian (1.5%). Reported first languages differ slightly from those reported by the original sample of 100 children: English (65%), Korean 70 (16%), Chinese (5%), Romanian (4%), Spanish (3%), Cantonese (2%), and several other languages (1% each). The range of language backgrounds reflects the ethnic and cultural diversity of the Canadian city where this research was conducted. T2 sample. At T2, adolescents’ ages ranged from 16.54 to 18.74 years (M = 17.54, SD = .58). T2 gender was asked differently from T1 with the question, “Which gender best describes you?” There was no change, however, in reported T2 gender (48% female) as compared to T1. Family composition changed slightly at T2: 82% of adolescents reported living in two-parent homes, 12% reported living only with the mother, 2% reported living only with the father, and 2% reported living in dual custody arrangements. One student chose not to respond to this question at T2. The average annual income for the neighborhoods in which the secondary schools were located was similar to the median annual income for Canada in 2015 ($80,940; Statistics Canada, 2015). Recruitment. Ethics approval was obtained from the University of British Columbia Behavioral Research Ethics Board and the school board of the participating school district to conduct research at T1, and later at T2. Of the number of children who were recruited for study participation at T1, 98% of their parent/guardians provided consent and 98% gave assent themselves. Upon receiving ethics approval to conduct the follow-up study at T2, district administrators of the participating school district were contacted, and they were able to provide a list of the secondary schools for 72 of the 100 adolescents who had participated in the original study. The remaining 28 adolescents had relocated to another school district and were unable to be contacted. For the 72 adolescents who remained in the school district, school administrators of the secondary schools they were attending contacted the adolescents and provided them with a 71 description of the follow-up study to determine their interest in participating. Of the 72 adolescents recruited at T2, 65 agreed to participate. Study attrition of 35 adolescents between T1 and T2 was due to: (a) two who had stopped attending classes and could not be reached, (b) five opting not to participate at T2, and (c) 28 who had relocated to another school district. The 35 adolescents who attrited were less likely to be female (nonattrited n = 31; attrited n = 11). They were less likely to report learning English as their first language (n = 16) and more likely to report learning Korean (n = 14) or other (n = 5) as their first language. They were less likely to report living in a two-parent household (n = 22) or in dual custody arrangements (n = 2), and more likely to report living with a single parent (n = 11). Based on a conversation with one of the adolescents at T2, it was suggested that several of the students who had participated in the original study at that adolescent’s elementary school were international students, and those students had returned to Korea to continue their secondary school studies (M. J. Kitil, personal communication, June 30, 2016). Final inclusion in the study required that participants have complete data for the primary predictor of interest (EFs), and primary outcomes of interest (academic outcomes and well-being). As a result, seven adolescents were excluded due to missing data for the EF tasks (n = 4; 6%) and missing academic transcripts (n = 3; 5%). The majority of adolescents with missing data were male (n = 5; 8%), came from two-parent households (n = 6; 9%), and spoke English as their first language (n = 6; 9%). The final sample size for the present study was 58. Procedures T1 data collection. Trained research assistants administered all measures to participating children. Student self-report surveys were administered as a group during one 45-min class period in the children’s classroom. EF tasks were administered in a quiet room located elsewhere 72 in the school during the same week in which the surveys were administered. At the end of the school year, all classrooms participating in the study received a pizza party. T2 data collection. Trained research assistants administered measures in individual sessions in a quiet room at each adolescent’s secondary school during one school period (approx. 70 min). After consent and assent procedures, adolescents completed a self-report questionnaire on a laptop computer at their own pace (approx. 30 min). Afterwards, research assistants administered the EF computer task (approx. 30 min), followed by an interview about school experiences (approx. 10 min). Upon completion of the measures, participating students were compensated with $20 gift cards and were given a choice of Starbucks, Subway, or iTunes. Shortly after the T2 data collection, school district administrators facilitated researchers’ request for school records, specifically school yearbooks and academic transcripts. School yearbooks. To obtain access to school yearbooks, district administrators contacted middle and high school administrators of the schools in which the 65 participating students were enrolled. School administrators from nine middle schools lent 30 yearbooks to the research team, which spanned three school years (2008-2012). Middle school administrators were unable to locate one yearbook at two of the schools. School administrators at all of the middle schools permitted research assistants to borrow the yearbooks and return them at a later date. As for the secondary schools, school administrators from 10 schools lent 42 yearbooks, which spanned four school years (2011-2016). Among the 10 secondary schools, eight followed a regular curriculum. One school was a small, technology-focused school with a population of 65 students; no yearbooks were published at this school. One school was an alternative education school, which published yearbooks, but these did not have information regarding student participation in activities. School administrators at seven of the secondary schools permitted 73 research assistants to borrow the yearbooks and return them at a later date; administrators at two secondary schools, however, requested their yearbooks be reviewed onsite. Academic transcripts. School district administrators supplied computerized lists of courses and academic grades for Grade 9 and unofficial academic transcripts for Grades 10 to 12 for 62 students. District administrators stated that the remaining three students likely transferred to another school district during the 2015-2016 school year. Measures Measures were derived from multiple informants as well as from objective measures and administrative data sources comprising: (a) objective measures of EFs, (b) student self-reports, (c) peer reports, (d) teacher reports, (e) school yearbook data, and (f) academic transcripts. This section is categorized into demographic variables, type of predictor variable, type of outcome variable, and covariates. For a summary of the constructs and their sources, see Table 1. Demographic information. To obtain demographic information, participants completed a self-report questionnaire at T1 and again at T2 inquiring about their gender, grade level, date of birth, family composition, and first language learned at home (Appendix A). Predictors – EFs. Participants completed two objective measures at T1 and T2: (1) Hearts and Flowers, and (2) Flanker/Reverse Flanker tasks (Davidson et al., 2006; Diamond et al., 2007; Zaitchik, Iqbal, & Carey, 2014). These measures have been validated for ages 4 through adulthood and assess all three components of core EF skills: inhibitory control, working memory, and cognitive flexibility (Davidson et al., 2006; Diamond et al., 2007; Rueda et al., 2004). The Presentation program by Neurobehavioral Systems (Berkeley, CA) was used to administer the tasks, which presented stimuli on a computer monitor and recorded responses entered via two keys on the left and right side of the keyboard. Students were positioned 74 approximately 50 cm from the computer screen. Trained research assistants administered the two EF tasks in one-on-one sessions with the students, reading aloud instructions specific to each task condition and providing demonstrations for how to complete the tasks. A block of four practice trials preceded each of the experimental conditions, and rule understanding was determined by providing correct responses for at least three out of the four trials. If the student did not understand the condition-specific rule, instructions and another practice block was given. Each student was supervised during the practice trials and was provided feedback and encouragement by the research assistants. During the experimental trials, research assistants continued to provide supervision, but remained neutral by no longer providing trial-by-trial encouragement. Students were asked to complete each trial as quickly and as accurately as possible and were permitted to take breaks between tasks. At the end of each of the two tasks, regardless of the students’ performance, the computerized EF task launched a short celebratory video. Hearts and Flowers task. Previously known as Dots (Davidson et al., 2006; Diamond, 2013), in this task, the central stimulus is a red heart or flower of equal size and luminance that appears on the left or the right side of the computer screen (see Figure 4). During each trial, stimuli are presented for 750 ms. For Block 1 (congruent trials), students are asked to remember one rule: “Press [the button] on the same side as the heart.” For Block 2 (incongruent trials), students are also asked to remember one rule: “Press [the button] on the side opposite of the flower”; this requires students to inhibit the prepotent response of pressing the button on the same side as the stimulus (Georgopoulos, Lurito, Petrides, Schwartz, & Massey, 1989). For Block 3 (the mixed block of trials), incongruent and congruent trials are randomly intermixed, placing demands on all three core EFs. To successfully complete the trials, students need to 75 remember both rules, mentally translate “same side” or “opposite side” into “press left” or “press right” and apply the rules according to what is presented. Flanker / Reverse Flanker task. EFs were also assessed using a computerized Flanker task, derived from the Attention Network Test (ANT; Fan, McCandliss, Sommer, Raz, & Posner, 2002). In the Flanker/Reverse Flanker task (Diamond, 2013; Munro, Chau, Gazarian, & Diamond, 2006), the first block presents the standard Flanker condition (Eriksen & Eriksen, 1974); the stimuli are blue fish that either appear alone or in a horizontal row of five fish on the computer screen (see Figure 5). Students are told that the hungry fish would appear in the middle (central stimulus). To “feed” the hungry fish, students are instructed to press the button on the keyboard matching the direction the center fish is facing, ignoring any flankers (distractors) appearing on its left and right side. On congruent trials, the blue fish on the outside face in the same direction as the center fish. On incongruent trials, they face in the opposite direction. Regardless of the presence of flanker fish, or the direction in which they are facing, students are to focus on the “fish in the middle.” The second block presents a “reverse” Flanker condition. The stimuli are pink fish that appear alone or in a horizontal row on the computer screen (see Figure 6). Students are instructed to ignore the center fish and attend to the fish on the outside, and “feed” them by pressing the button matching the direction in which they are facing. This requires them to flexibly attend to new rules, flexibly shift the focus of their attention, and inhibit their attention to the “middle fish.” In the third block, standard and reverse Flanker trials are randomly intermixed. Students are instructed to remember both rules, and to use the color of fish (pink or blue) to determine which rule to apply on any given trial. Thus, students have to flexibly apply the rules that 76 correspond with the color of the appearing fish (i.e., “Blue means middle, Pink means outside”). This also requires quite a bit of working memory because first students must use the color of the stimuli to inform them of whether the trial is a standard or reverse flanker trial (and compare the color of the stimuli against the two rules held in mind); then students must focus their attention at the proper location (inhibiting attention to incorrect locations) and determine whether the stimulus (or stimuli) there are pointing left or right. During each trial, stimuli were presented for 1500 ms. Scoring of EF tasks. The percent of correct responses (i.e., the number of trials with a correct response divided by the total number of trials) during mixed blocks for the two EF tasks was used as a combined measure of inhibitory control, working memory, and cognitive flexibility. Responses were determined to be correct if students pressed the button on the keyboard (1) that corresponded to the condition-specific rule, (2) no sooner than 200 ms after the trial stimulus had appeared (<200 ms is considered to be an anticipatory response and too fast to be a response to a stimulus; Davidson et al., 2006), and (3) before the trial stimulus vanished from sight. Practice trials and the first trial to come after the practice trials for each block were excluded from analyses. Performance on mixed blocks were chosen because it has been shown to be the most challenging due to the cognitive demand of switching response sets (Kalkut, Han, Lansing, Holdnack, & Delis, 2009). Accuracy scores were chosen because previous research has found that better inhibitory control was related to slower and more accurate responses on the mixed block, after controlling for the variance accounted for by working memory (Brocki & Tillman, 2014). Examining both accuracy and reaction time scores, Davidson et al. (2006) in a study 77 examining children and youth between the ages of four and twenty-six, found that performance on both of these tasks improved incrementally as children got older. Predictors – factors that support EFs. Positive affect. Positive affect at T1 was measured using the Positive and Negative Affect Scale for Children (PANAS-C; Laurent et al., 1999; Appendix B). Items on the 15-item positive affect subscale used at T1 include “excited,” “happy,” and “proud”. Students were asked, “How much you have felt this way during the past few weeks?” The language of the five-point Likert scale was modified to be more child-friendly (1 = very slightly or not at all, 2 = a little bit, 3 = somewhat, 4 = quite a bit, 5 = a lot). Items were averaged, with higher scores indicative of greater positive affect. Previous research has found support for the validity and reliability of the PANAS-C (Laurent et al., 1999), with .87 as the reported Cronbach’s alpha. In this study, the positive affect subscale of the PANAS-C was found to have good internal consistency, with a Cronbach’s alpha of .85 at T1. Mindful attention. Mindful attention at T1 was assessed using the Mindful Attention Awareness Scale adapted for children (MAAS-C; Lawlor, Schonert-Reichl, Gadermann, & Zumbo, 2014; Appendix C). This scale assesses individual differences in the frequency of mindful states over time. In the original version of the MAAS for adults, K. W. Brown and Ryan (2003) put forth that “statements reflecting mindlessness are likely more accessible to most individuals, given that mindless states are much more common than mindful states” (p. 826). Therefore, items on the MAAS assess states of mindlessness (e.g., “It seems that I am doing things automatically without really being aware of what I am doing,” “I walk into a room, and then wonder why I went there,” “I can’t stop thinking about the past or the future”). The MAAS was later modified by Benn (2004) for use with younger populations to have (1) age-appropriate 78 language and (2) a more child-friendly response format, using a six-point Likert scale (1 = almost never, 2 = not very often at all, 3 = not very often, 4 = somewhat often, 5 = very often, 6 = almost always). Items were reverse-scored and averaged, with higher scores reflecting higher mindful attention and lower mindlessness. Lawlor et al. (2014) reported the MAAS-C to be a valid and reliable scale for measuring mindful attention in children with a Cronbach’s alpha of .84. For this sample, internal consistency was good, with a Cronbach’s alpha of .87 T1. Connectedness. Connectedness at T1 was assessed using two measures: (a) teacher ratings of students’ acceptance by peers and (b) peer nominations of peer acceptance. Thus, for the purposes of the present study, connectedness was operationalized as peer acceptance. Teacher ratings of peer acceptance. Teacher-rated peer acceptance at T1 was assessed using the three-item peer acceptance subscale of the Teacher’s Ratings of Social Behavior scale (TRSB; Eisenberg et al., 2003; Appendix D). Items were: “This child finds it hard to make friends,” “This child is popular with others at his or her age,” and “This child has a lot of friends.” Teachers rated their students following a six-point Likert scale (1 = never, 2 = hardly ever, 3 = sometimes, 4 = often, 5 = always). The first item was reverse-scored, and then a mean score was created for the three items, with higher scores reflecting higher levels of peer acceptance. Eisenberg et al. (2003) found this subscale to be a reliable and valid measure, reporting a Cronbach’s alpha of .91. For the present investigation, this subscale had a Cronbach’s alpha of .92 at T1. Peer behavioral assessments of peer acceptance. Based on the procedures described by Parkhurst and Asher (1992), peer behavioral assessments at T1 (Appendix E) were used to gather independent assessments of peer acceptance, whereby students nominated classmates who met relevant behavioral descriptors. The use of ratings by peers is considered a valid and reliable way 79 of obtaining students’ social behaviors in a school context (Schonert-Reichl, Smith, Zaidman-Zait, & Hertzman, 2012; Wentzel, Barry, & Caldwell, 2004). Students were asked to “circle the names of any of your classmates who you would like to be in school activities with.” Below this question, students were presented with a list of all their classmates who were participating in the research study and were instructed to circle as many or as few names as they wished. Students’ nominations were then standardized within each classroom. A “peer acceptance” nomination score was then calculated for each student by taking the proportion of nominations received for each student relative to the total number of participating students by gender. Data collection at T1 took place halfway through the school year; therefore, the assumption that students knew each other adequately enough to nominate peers for this question was within reason. Predictors – factors that hinder EFs. Stressful life events. At T1, stressful life events was measured using the Stressful Life Events Scale (Pryor-Brown, Cowen, Hightower, & Lotyczewski, 1986; Appendix F), which consists of 22 items that describe life events known to be highly stressful (e.g., “Parents divorced,” “Hospitalized,” “Suspended from school”) or represent circumstances that may be more neutral or transitory (e.g., “Best friend moved,” Parents not home much,” “Father away at work”; Pryor-Brown et al., 1986). For the purposes of this study, one item (i.e., “Death of parent”) was removed because it was deemed potentially too stressful for children. The language of the remaining 21 items was modified to be more child-friendly (e.g., “My parents got divorced,” “I got very sick and had to go in the hospital,” “My father had a job in another city and had to be away from home a lot”). Students were asked to circle a response (dichotomous variable: yes, no) for each of the statements. Items were scored (1 = yes, 0 = no) and then summed, with higher numbers reflecting the occurrence of stressful life events. 80 Negative affect. Negative affect at T1 was measured using the Positive and Negative Affect Scale for Children (PANAS-C; Laurent et al., 1999; Appendix B). The measure at T1 comprises a 15-item negative affect subscale, with items that include “scared,” “jittery,” and “disgusted”. Students were asked, “How much you have felt this way during the past few weeks.” The language of the five-point Likert scale was modified to be more child-friendly (1 = very slightly or not at all, 2 = a little bit, 3 = somewhat, 4 = quite a bit, 5 = a lot). Items were averaged, with higher scores indicative of greater negative affect. Previous research has found support for the validity and reliability of the PANAS-C (Laurent et al., 1999), with a reported Chronbach’s alpha of .92. In the present study, the PANAS-C was found to have good internal consistency, with a Cronbach’s alpha of .83 for the negative affect subscale at T1. Depressive symptoms. Depressive symptoms at T1 were assessed using the Seattle Personality Questionnaire for Children (SPQC; Kusche, Greenberg, & Beilke, 1988; Appendix G), and is composed of four constructs: (a) behavioral problems, (b) anxiety, (c) somatization, and (d) depressive symptoms. For the current study, only the 10-item depressive symptoms subscale was used. Items include: “Do you feel unhappy a lot of the time?” and “Do you feel sorry for yourself?” Items were scored on a four-point Likert scale (1 = not at all, 2 = a little bit, 3 = sometimes, 4 = always). The mean score was taken, with higher scores reflecting greater levels of depressive symptoms. Evidence supporting the reliability of the depressive symptoms subscale of the SPQC has been reported, with a Cronbach’s alpha of .78 (Kusche et al., 1988). The depressive symptoms subscale was found to have adequate internal reliability in the current investigation with a Cronbach’s alpha of .68 at T1. Predictors – activities that promote EFs. Information regarding school-based activities that promote EFs were taken from school yearbook data and academic transcripts. 81 School yearbooks. School yearbook data has been found to be a valid source for obtaining measurements of participation in extracurricular activities (see Mahoney, 2014; Mahoney et al., 2003; Mahoney & Cairns, 1997). Yearbooks are published every year by most middle and secondary schools in Canada and are typically available for purchase by students towards the end of the school year. They offer a snapshot of various aspects of school culture, including: individual photographs of each student categorized by grade; a record and group photograph of members involved in each extracurricular activity, including special positions held by members (e.g., president, team captain), often categorized within the yearbook by type of activity (e.g., athletics, clubs); a snapshot of curricular and co-curricular courses (e.g., English class, music); articles featuring individual students; and photographs and descriptions of other aspects of school culture. School yearbooks also feature a time capsule section that captures notable events occurring in Canada and around the world during that school year, for example, elections, trends in popular culture, and sports. Mahoney and Cairns (1997) found that yearbooks provided information related to all extracurricular activity participation for 94% of their sample, ranging from 88% to 100% depending on the grade level. In later research, Mahoney et al. (2003) found information was consistently available across all years for 96% of their sample. Step 1. Recording school yearbook data. School yearbooks were searched systematically by two trained research assistants, following the procedures described by Mahoney and Cairns (1997), for participating students that the school district had on record as attending a school in their district during specific school years. Only one school yearbook, however, included an index in the back of the yearbook with student names and page numbers where students’ names could be found among the yearbook pages; this index, however, was not a standard feature of all the yearbooks at that school. As a result, students’ names were first searched within the individual 82 photos section by grade level, based on the grade they were expected to be in for that school year. If students’ names were not located in the pages within an expected grade, a section towards the end of each grade level was searched; this section often listed names of students who were not present for the original photography session, sometimes without a photograph. If a student’s name was not located within a particular grade level of a yearbook (e.g., Grade 10), the grade level immediately prior (e.g., Grade 9) and the grade level subsequent to the expected grade level (e.g., Grade 11) were searched. After a student’s name was found in the individual photographs section, all subsequent yearbook sections were then searched, page by page. Every time a student’s name was found, the type of information and page number was recorded in a data collection sheet, for example, Grade 10 photo p. 17, Halloween night p. 35, Red Cross Club p. 50 (see Appendix H). Information on participation in secondary school-based activities varied by yearbook and by school. One school, an alternative education school, only listed students’ names alongside individual photos, but none were listed alongside school-based activities. Most yearbooks had information on student participation in athletics (73%), and few yearbooks had information on clubs, classes (19%) and social events (12%). Participation in middle school-based activities could only be obtained from yearbooks at one school. All other school yearbooks only had names for individual photos. In procedures described by Mahoney and Cairns (1997), one middle school yearbook examined in their study included photographs but failed to include student names. For this yearbook, two researchers who were well acquainted with all study participants were able to provide names, using yearbook photographs as a guide. This particular method could not be employed reliably in the present study for several reasons: (1) there was an eight-year gap between the two data collection points, (2) a different team of researchers were employed at each time point, and (3) school yearbooks 83 were not obtained from the schools until one year after the second data collection time point. On the contrary, the annual collection of data by Mahoney and Cairns (1997) as part of a larger longitudinal investigation enabled some researchers to become fully acquainted with all study participants over the length of their study. To ensure no information from secondary school yearbooks was overlooked, trained research assistants reviewed them all twice. Due to lack of yearbook information regarding student participation in middle school activities, those yearbooks were excluded from the second review and ultimately were excluded from further analyses. Data recorded by the research assistants were reconciled. Researchers were unable to contact administrators at one secondary school to coordinate a second review of yearbooks there, resulting in five secondary school yearbooks being searched only once for students’ names. Step 2. Categorizing school yearbook data. All yearbook data collected were categorized into “codable” versus “uncodable” activities (see Table 2 for counts of student names in school yearbook data by category and school type). Codable activities included athletics, clubs, and classes—activities relevant to the research questions. Uncodable activities included individual photos, social events, family or baby photos, grade-level-specific activities, excursions, and other miscellaneous activities (e.g., cars, featured articles written by students). Uncodable activities were not relevant to the research questions and were subsequently excluded from coding and analyses. Step 3. School-based activity participation as recorded in school yearbooks. Variables were created for school-based activities hypothesized to promote EFs (e.g., music, choir, theatre, leadership, chess, and peer tutoring) in accordance with Diamond (2012). Participation in a given activity was coded categorically (1 = participation, 0 = no participation), with any special 84 positions recorded as well. The mean was calculated for the number of activities within each category that students participated in for each school year. Academic transcripts. To supplement the information available in school yearbooks, co-curricular course completion was derived from academic transcripts, specifically, computerized lists of courses for Grade 9 and unofficial academic transcripts from Grades 10 to 12. Co-curricular course completion as recorded in academic transcripts. Variables were created for potential non-academic courses hypothesized to promote EFs (e.g., music, theatre, leadership, chess, and peer tutoring) in accordance with Diamond (2012). Similar courses were grouped under the same category, for example, the theatre group included courses in drama, musical theatre, film, television, acting, directing, and script writing. In accordance with procedures outlined by Guhn et al. (2019), the music group included courses in concert or jazz band, music conservatory, orchestra, and concert choir, but not music theory or guitar. Courses for music theory and composition were excluded because these courses do not typically focus on music making or practice. Guitar courses were also excluded because they do not have prerequisites that its students have music experience prior to joining. Activity participation as reflected by course completion was coded dichotomously (1 = participation, 0 = no participation), and the mean was calculated for the number of courses completed per category in each school year. Connectedness derived from activity participation. All codable school-based activities in secondary school yearbooks and co-curricular courses in academic transcripts were assigned connectedness scores, reflecting the level of social interaction required to participate in the activity. Connectedness was coded dichotomously (1 = activity requires less social interaction, e.g., math club, track and field, wrestling; 2 = activity requires more social interaction, e.g., 85 lacrosse, theatre). The coding guide was developed via consensus by a panel of experts in the field at the University of British Columbia. Yearbook descriptions about a club’s purpose and its activities also informed decisions regarding assigning a connectedness score. Activities with insufficient information about social interaction were coded as missing (see Table 3). Mean connectedness scores were calculated for each participating student. Physical fitness derived from activity participation. Codable school-based activities in secondary school yearbooks and non-academic courses in academic transcripts were assigned physical fitness scores. The physical fitness component for each activity was calculated using an energy expenditure score or the energy cost of physical activity, which can be used to compare self-reported physical activity across a range of studies (Ainsworth et al., 2011a; Ainsworth et al., 1993). The energy expenditure score is calculated using MET (resting metabolic rate) values, where “1 MET is the energy cost of a person at rest” (Ainsworth et al., 2011a). MET scores are publicly available for download (http://download.lww.com/wolterskluwer_vitalstream_com/PermaLink/MSS/A/MSS_43_8_2011_06_13_AINSWORTH_202093_SDC1.pdf) or accessible via the Compendium of Physical Activities website (Ainsworth et al., 2011b). For a number of activities, the compendium provided several MET scores for a particular activity depending on how that activity was carried out. This occurred for activities related to the playing of musical instruments (e.g., band). In this case, a mean MET score of 2.5 was calculated based on nine different musical instruments ranging from 2 (playing the horn) to 4 (playing the drums). For some of the activities, the yearbooks did not have sufficient information about the activity to enable a MET score to be selected. The MET score for activities with insufficient information about level of physical activity or for which no MET score was available were coded 86 as missing. As seen in Table 3, the scores across all activities ranged from 1.5 (sitting, talking in person, on the phone, computer, or text messaging, light effort) to 10 (hockey, ice, competitive); the MET code as listed in the Compendium is also provided as a reference. Physical fitness scores for each participant were calculated using the mean of all MET scores across all activities participated in for each school year. Outcomes – grade point average. Grade point average (GPA) for Grades 9 to12 was calculated for the four core academic subject areas (language arts, math, science, and social studies) for adolescents who had a minimum of three years’ worth of grades. Data for academic achievement measures were obtained from computerized lists of courses for Grade 9 and the unofficial academic transcript for Grades 10 to 12. Achievement indicators were provided on page 2 of the transcript using the following grading scheme: A (86-100%, excellent performance), B (73-85%, very good performance), C+ (67-72%, excellent performance), C (60-66%, satisfactory performance), and C- (50-59%, minimally acceptable performance). One out of the eight secondary schools assigned letter grades; the remaining eight assigned percentages. All letter grades were converted to percentages, using the mean of that letter grade (e.g., 64% was assigned for a C letter grade). For courses that also included midterm percentages on the school transcript, only end-of-year percentages were used. Outcomes – well-being. Satisfaction with life. Well-being at T2 was assessed using the Satisfaction with Life Scale for Children (SWLS-C; Gadermann, Schonert-Reichl, & Zumbo, 2010; Appendix I). The five items on this self-report measure included, “In most ways my life is close to the way I would want it to be.” Students were asked to provide responses to the question, “How true is each statement for you?” for each of the items according to a 5-point Likert scale (1 = disagree a lot, 2 87 = disagree a little, 3 = don’t agree or disagree, 4 = agree a little, 5 = agree a lot). The mean score was taken, with higher scores reflecting greater levels of well-being. Gadermann and colleagues (2010) found this scale to be a reliable and valid measure for assessing satisfaction with life in fourth to seventh grade, reporting a Cronbach’s alpha of .86. Subsequent work by Gadermann, Guhn, and Zumbo (2011) found evidence to support the substantive component of construct validity (Messick, 1995) for the SWLS-C as well. Using a think-aloud protocol, Gadermann et al. (2011) interviewed 55 children in Grades 4 to 7, and found that the majority of children had no difficulty understanding the meaning of each statement on the SWLS-C, and reported that they believed that it was important for researchers to gather information from children about their satisfaction with life. The SWLS-C was also found to be reliable in the current study with a Cronbach’s alpha of .88 at T2. Depressive symptoms. Lower levels of well-being at T2 was assessed using the 10-item depressive symptoms subscale of the Seattle Personality Questionnaire for Children (SPQC; Kusche et al., 1988; Appendix G). See Predictors – factors that hinder EFs section above for a description of the measure. For the present study, Cronbach’s alpha was .84 at T2. Outcomes – EFs. Participants completed the two EF tasks at T2: (1) Hearts and Flowers, and (2) Flanker/Reverse Flanker tasks (Davidson et al., 2006; Diamond et al., 2007; Zaitchik et al., 2014), which measure the three components of core EF skills: inhibitory control, working memory, and cognitive flexibility (Davidson et al., 2006; Diamond et al., 2007; Rueda et al., 2004). See Predictors – EFs section above for a description of the measure. Covariates. In addition to gender and age, satisfaction with life, depressive symptoms, and school self-concept were included as covariates. See Outcomes – well-being section above for a description of the measures for satisfaction of life and depressive symptoms. 88 School self-concept. Students’ self-rated abilities, enjoyment, and interest in all school subjects at T1 was measured using the school self-concept subscale from Marsh’s Self-Description Questionnaire (SDQ; Marsh et al., 1984; Appendix J). Items from this subscale include: “I am good at school subjects” and “I look forward to all school subjects.” Items are rated on a five-point Likert scale (1 = never, 5 = always). Scores were averaged with higher scores indicative of higher levels of school self-concept. Marsh and colleagues (1984) reported this subscale to have good internal consistency with a Cronbach’s alpha of .84. In the current study, Cronbach’s alpha for the school self-concept subscale was .79 at T1. 89 Results Overview of Research Aims The aims of the present study were threefold: (1) to examine relations of EFs in early adolescence (T1) and late adolescence (T2) to GPA and well-being at T2, (2) to examine the factors that support (i.e., positive affect, peer acceptance, mindful attention, and physical activity) or hinder (i.e., stressful life events, negative affect, and depressive symptoms) EFs at T1 and explore their relative contributions to EFs, GPA, and well-being at T2, and (3) to investigate the relative contribution of participation in middle/secondary school-based activities to EFs, GPA, and well-being at T2. As for the additional hypotheses, the potential mediating role of EFs was investigated in the last section. Data Analytic Plan The first section presents preliminary analyses conducted including descriptive statistics for all variables, as well as tests of assumptions for these variables. Each of the subsequent sections presents findings for each of the three study aims using a series of correlational analyses and hierarchical multiple linear regressions. Gender and age were entered into each regression model as covariates to control for potential confounding effects. In statistically significant multiple regression models, the Pratt Index was calculated to determine the relative importance of predictors (Pratt, 1987), by calculating the overall variance each variable explained within the model (Thomas, Hughes, & Zumbo, 1998). The Pratt Index is calculated by multiplying each predictor variable’s β-weight by the value of the correlation between the predictor and outcome variables, and then dividing that product by R2 (d = (βrxy)/R2; Thomas et al., 1998). Specifically, the Pratt Index provides the amount of variance in 90 the total R2 that is accounted for by each predictor variable, which ranges from 0 to 1 for each predictor. The subsequent decimal value can be converted to a percentage by multiplying the given index by 100, indicating the percentage of variance the predictor accounts for within the R2 for a specific model. When summed, the Pratt Indices for all the predictor variables within a model would equal 100%. According to Thomas et al. (1998), the predictor with the largest Pratt Index would be the most important variable in the model, with successively smaller percentages of other Pratt Indices being relatively less important to the model. Each model also requires its own cut-off value to determine the relative importance of each predictor. This value is calculated with the equation d < 1/(2p), where p equals the total number of predictors in the model. For models where Pratt Indices are calculated, cut-off values are also provided for each of those models. Corrections also were made for multiple comparisons and to control for potential Type I error (Tabachnik & Fidell, 2007) whereby a p value of .01 was adopted for all analyses (Armstrong, 2014). Effect sizes were also examined to consider the practical significance of findings (Kraft, 2018). According to Sullivan and Feinn (2012), p values provide an indication that an effect exists, while effect sizes measure the extent to which there is an effect. In the case of multiple regression analyses, Thompson (2000) stated, “all analytic methods are correlational . . . and yield variance-accounted-for effect sizes analogous to r2 (e.g., R2, ŋ2, ω2)” (p. 263). J. Cohen (1988) proposed that R2 values of .02 are considered small, .13 are considered medium, and .26 are considered large (pp. 413-414), and correlation coefficients (r) with values of .10 are considered small, .30 are considered medium, and .50 are considered large (pp. 79-80) effects. Such benchmarks were used to evaluate effect sizes in the present study. 91 Preliminary Analyses Descriptive statistics. The mean, SD, minimum and maximum, and skewness and kurtosis values of all Time 1 (T1) and Time 2 (T2) variables are presented in Table 4. For the EF variables, there was an improvement in accuracy scores from T1 to T2. For depressive symptoms, the range of scores increased from T1 to T2. For satisfaction with life (SWLS), mean, minimum and maximum scores decreased from T1 to T2. The mean and range of scores for all other variables were as expected. Evaluation of statistical assumptions. Prior to conducting all analyses, data were explored for linearity via visual inspection of scatterplots, multicollinearity, independence of residuals, homoscedasticity, and normality of the distribution of residuals. Cook’s Distance values were also examined to determine if any influential cases existed that biased the models (Field, 2009). Tests for normality included a graphical review of histograms to examine the distribution of data and p-p (probability-probability) plots were examined for the extent to which the data departed from the theoretical normal distribution line (J. Chambers, Cleveland, Kleiner, & Tukey, 1983). An examination of histograms for accuracy scores (see Figures 6 to 9) indicated that scores on the T1 Flanker / Reverse Flanker task and T2 Hearts & Flowers task had less variance and thus, fewer, individual differences compared to T1 Hearts & Flowers and T2 Flanker / Reverse Flanker; it should be noted that an examination of the means and SDs revealed that performance on the two tasks were similar. Skewness and kurtosis. Z scores for skewness and kurtosis were calculated (see Table 4). Kline (2011) indicated that skewness and kurtosis values that have absolute values greater than 3 and 10, respectively, may indicate a problem and values over 20 may be of greater concern. As such, most variables in this study were within the acceptable range of absolute values less than 3 92 and less than10 for skewness and kurtosis, respectively (Kline, 2011). The exceptions were the values for music and theatre participation, which exceeded the absolute value of 10 and 20, and were therefore, significantly positively skewed and significantly leptokurtic, indicating a pile up on the left. This positive skew is common for participation rates in North America (Elpus & Abril, 2011), indicating that most adolescents are not enrolled in any music or theatre course, and few (21%) enroll across multiple years. It was determined that one case exceeded the cut off score of 1 for Cook’s Distance. Further examination of skewness and kurtosis values for this case revealed that values for T2 Flanker / Reverse Flanker significantly exceeded acceptability (skewness = 5.27, kurtosis = 33.74). Examination of histograms also revealed this case to be an outlier, due to a T2 Flanker / Reverse Flanker accuracy score of .52, which was over 3 SDs below the next lowest score (see Figure 9). As such, all main analyses excluded this influential case. The mean, SD, minimum and maximum, and skewness and kurtosis values as outlined in Table 4 exclude this influential case. Please refer to Appendix K for values that include this influential case. The scores for all other statistics are comparable, with and without the influential case. Thus, the sample size used for all analyses was 57. School-based activities. Preliminary analyses of physical fitness scores (using MET values) and connectedness scores (using a coding scheme developed by an expert panel at the University of British Columbia) revealed that scores were not related to any of the outcome measures. Further investigation of the connectedness and physical fitness constructs revealed that information about activity participation in regard to frequency, duration, and intensity of participation by individual students were not reliably available for all students. Moreover, information in regard to how activities were taught or conducted also were unavailable. These 93 information are needed to be able to reliably calculate connectivity scores and MET values. As a result, both these scores were excluded from all analyses for the present study. Furthermore, as mentioned previously in the Methods chapter, middle school-based activities were excluded from analyses due to limitations in data availability. Therefore, all analyses conducted in relation to school-based activities (see Study Aim 3) utilized only activities taken from data sources that were found to be the most reliable for all participants: specifically, athletics participation (based on secondary school yearbook data), and music and theatre course completion (based on secondary school transcript data). Attrition. One-way ANOVAs were conducted for baseline measures comparing adolescents who participated at T2 and adolescents who attrited at T2; analyses revealed there were no significant mean differences between the two groups on all baseline measures, except for the measure of stressful life events, F(2,98) = 5.82, p = .018. Adolescents who attrited were more likely to report a higher number of stressful life events (M = 4.37, SD = 3.02) than those who remained in the study at T2 (M = 2.98, SD = 2.58). One-way ANOVAs also revealed that no significant mean differences were found on baseline measures between adolescents with complete data on primary predictors and outcome measures of interest, and adolescents with missing data at T2. One-way ANOVAs were also conducted for the outcome variables of interest in this study comparing adolescents who had participated in the school-based SEL program (Schonert-Reichl et al., 2015) eight years prior at T1 and adolescents who did not participate, and these analyses revealed no significant mean differences between the two groups. 94 Study Aim 1 The first aim of the present study was to investigate the relations of T1 and T2 EFs to T2 GPA and well-being (i.e., SWLS and depressive symptoms), and the potential role that school self-concept plays in these relations. I began by conducting zero-order correlational analyses among all the variables being investigated in Study Aim 1 (see Table 5). Also included in the table were variables relevant to Study Aims 2 and 3. Results revealed that statistically significant correlations were in the expected direction. Specifically, T2 Flanker / Reverse Flanker was significantly and positively associated with T2 SWLS, yielding a medium effect size. No other significant correlations were observed. Hypothesis 1.1. It was hypothesized that T1 and T2 EFs would significantly and positively predict T2 GPA. A series of hierarchical linear regression analyses were conducted to test this prediction. For the outcome of T2 GPA in Model 1, gender and age were entered in the first step, and T1 and T2 EFs were entered in the second step for Model 2 (see Table 6). Results revealed both models to be significant. Gender and age explained 16% of the variance for the outcome of GPA in Step 1 (Model 1) (R2 = .16, F(2,54) = 5.12, p = .009). In the overall Model 2, the four additional predictors in Step 2 explained an additional 24% of the variance (R2 = .40, F(6,50) = 5.64, p < .001; ΔF = 5.13). In this model, it was found that T1 Hearts & Flowers (β = .37, p = .003) and T2 Flanker / Reverse Flanker (β = .38, p = .003) significantly and positively predicted T2 GPA. Gender was also found to significantly predict T2 GPA, in that females had higher GPAs than males (β = .32, p = .006). Pratt Indices were calculated for the overall model. Using a cut-off value of .08, T1 Hearts & Flowers, T2 Flanker / Reverse Flanker, gender, and age were 95 considered relatively important to the model, with each contributing 30%, 32%, 23% and 12% to the model, respectively. The remaining variables (T1 Flanker / Reverse Flanker, T2 Hearts & Flowers) contributed less than 8% to the R2. The effect sizes for the findings from both models ranged from medium to large. Subhypothesis 1.1.1. Next, it was hypothesized that the strength in the relationship between T1 and T2 EFs, and T2 GPA would diminish when T1 school self-concept was entered into the model, with T1 school self-concept hypothesized to play a mediating role. The hierarchical linear regression analysis was conducted to test this prediction. For the outcome of T2 GPA in Model 1, gender and age were entered in the first step, T1 school self-concept was entered in the second step for Model 3, and T1 and T2 EFs were entered in the third step for Model 3 (see Table 7). Results revealed all three models to be significant. Gender and age explained 16% of the variance for the outcome of GPA in Step 1 (Model 1) (R2 = .16, F(2,54) = 5.12, p = .009). In Model 2, T1 school self-concept added to Step 2 explained an additional 10% of the variance (R2 = .27, F(3,53) = 6.28, p = .001; ΔF = 7.40). In this model, school self-concept also significantly and positively predicted T2 GPA (β = .32, p = .009). In the overall Model 3, T1 and T2 EFs added to Step 3 explained an additional 18% of the variance (R2 = .45, F(7,49) = 5.63, p < .001; ΔF = 4.06). In this model, it was found that T1 Hearts & Flowers significantly and positively predicted T2 GPA (β = .34, p = .005). Gender was also found to significantly predict T2 GPA, in that females had higher GPAs than males (β = .32, p = .006). Pratt Indices were calculated for the overall model. Using a cut-off value of .07, school self-concept, T1 Hearts & Flowers, T2 Flanker / Reverse Flanker, gender, and age were considered relatively important to the model, with each contributing 17%, 25%, 24%, 20%, and 10% to the model, respectively. The remaining variables (T1 Flanker / Reverse Flanker, T2 Hearts & 96 Flowers) contributed less than 7% to the R2. The effect sizes for the findings from all three models ranged from medium to large. To examine whether T1 school self-concept could play a mediating role, all variables in the model needed to be significantly correlated. Based on the correlational analyses conducted however, T1 school self-concept was only correlated with T2 GPA (see Table 5). Hypothesis 1.2. Next, it was hypothesized that T1 and T2 EFs would significantly and positively predict T2 well-being (i.e., SWLS and depressive symptoms), above and beyond the effects of T1 well-being. A series of hierarchical linear regression analyses were conducted to test this prediction. 1.2.1. For the outcome of T2 SWLS in Model 1, gender and age were entered in the first step, T1 SWLS was entered in the second step for Model 2, and T1 and T2 EFs were entered in the third step for Model 3 (see Table 8). Results revealed none of the models to be significant. Gender and age explained less than 1% of the variance for the outcome of T2 SWLS in Step 1 (Model 1) (R2 = .025, F(2,53) = .693, p = .51). In Model 2, T1 SWLS added to Step 2 explained an additional 1% of the variance (R2 = .05, F(3,52) = .914, p = .44; ΔF = .25). In the overall Model 3, T1 and T2 EFs added to Step 3 explained an additional 26% of the variance (R2 = .21, F(7,48) = 1.84, p = .10; ΔF = .057). The effect sizes for the findings from these models would be considered to be in the small to medium range. 1.2.2. For the outcome of T2 depressive symptoms in Model 1, gender and age were entered in the first step, T1 depressive symptoms was entered in the second step for Model 2, and T1 and T2 EFs were entered in the third step for Model 3 (see Table 9). Results revealed none of the models to be significant. Gender and age explained less than 1% of the variance for the outcome of T2 depressive symptoms in Step 1 (Model 1) (R2 = .001, F(2,54) = .020, p =.98). In 97 Model 2, T1 depressive symptoms added to Step 2 explained less than 1% of the variance (R2 = .082, F(3,53) = 1.57, p =.21; ΔF = .035). In the overall Model 3, T1 and T2 EFs added to Step 3 explained an additional 29% of the variance (R2 = .22, F(7,49) = 2.02, p = .071; ΔF = .077). The effect size for the overall model would be considered to be in the medium to large range. Note that analyses for these models were statistically significant when the influential case was included (see Appendix K, Table 30). Study aim 1 summary of findings. The findings revealed that (1) T1 and T2 EFs predicted T2 GPA, (2) the prediction held even after school self-concept was added to the model, (3) T1 and T2 EFs did not predict T2 SWLS above and beyond the effects of T1 SWLS, and (4) T1 and T2 EFs did not predict T2 depressive symptoms above and beyond the effects of T1 depressive symptoms. Study Aim 2 The second aim of the present study was to explore the relative contribution of T1 factors that support or hinder EFs to T2 EFs, GPA, and well-being (i.e., SWLS and depressive symptoms). Zero-order correlational analyses among all the variables being investigated were conducted (see Table 5). Among the T1 factors that either support or hinder EFs, intercorrelations among constructs yielded several statistically significant findings. For example, depressive symptoms were found to be significantly and positively related to negative affect, and significantly and negatively related to mindful attention, both yielding large effect sizes. Negative affect was found to be significantly and negatively associated with mindful attention, resulting in a medium effect size. Peer-reported peer acceptance was found to be significantly and positively correlated with teacher-reported peer acceptance, yielding a large effect size. No 98 statistically significant associations were found between positive affect and any of the other T1 factors hypothesized to support or hinder EFs. Correlational analyses were also conducted examining relations of T1 EFs to the T1 factors hypothesized to support or hinder EFs. As can be seen in Table 5, intercorrelations among variables ranged from -.28 to .31, p > .01. Medium to large correlations were found for relations between many of the T1 variables with T2 GPA. Specifically, GPA had statistically significant positive relations with teacher-reported peer acceptance and statistically significant negative relations with T1 depressive symptoms, both resulting in medium effect sizes. Non-significant correlations between GPA and the remaining T1 variables were, for the most part (except for T1 Flanker / Reverse Flanker, r = -.043) in the expected direction. Correlational analyses were also conducted between the two constructs comprising T2 SWLS, T2 depressive symptoms, and all other variables: T2 depressive symptoms was found to be statistically significant and negatively correlated with T2 SWLS. As for the T2 EF scores, T2 Flanker/Reverse Flanker was found to be statistically significant and positively associated with T2 SWLS. Medium to large effect sizes were observed for both relations. No other significant correlations were observed. Hypothesis 2.1. Next, it was hypothesized that T1 factors that support EFs (i.e., positive affect, mindful attention, and peer- and teacher-reported peer acceptance) would significantly and positively predict T2 EFs, GPA, and well-being (i.e., SWLS and depressive symptoms). A series of hierarchical linear regression analyses were carried out to test this prediction. 2.1.1. For the outcome of T2 EFs (Hearts & Flowers) in Model 1, gender and age were entered in the first step, and T1 positive affect, mindful attention, and peer- and teacher-reported 99 peer acceptance were entered in the second step (see Table 10). Results revealed both models to be non-significant. Specifically, gender and age explained 15% of the variance for the outcome of T2 Hearts & Flowers in Step 1 (Model 1) (R2 = .15, F(2,54) = 4.56, p = .015). In the overall Model 2, the four additional predictors in Step 2 explained an additional 7% of the variance (R2 = .22, F(6,50) = 2.30, p = .049). The effect sizes for the findings from both models ranged from medium to large. 2.1.2. For the outcome of T2 EFs (Flanker/Reverse Flanker) in Model 1, gender and age were entered in the first step, and T1 positive affect, mindful attention, and peer- and teacher-reported peer acceptance were entered in the second step (see Table 11). Results revealed both models to be non-significant. Specifically, gender and age explained only 1% of the variance for the T2 Flanker/Reverse Flanker in Step 1 (Model 1) (R2 = .01, F(2,54) = .25, p = .78). In the overall Model 2, the four additional predictors in Step 2 explained an additional 9% of the variance (R2 = .09, F(6,50) = .80, p = .57). The effect sizes for these findings ranged from small to medium. 2.1.3. For the outcome of T2 GPA in Model 1, gender and age were entered in the first step, and T1 positive affect, mindful attention, and peer- and teacher-reported peer acceptance were entered in the second step for Model 2 (see Table 12). Results revealed both models to be significant. Gender and age explained 16% of the variance for the outcome of GPA in Step 1 (Model 1) (R2 = .16, F(2,54) = 5.12, p = .009). In the overall Model 2, the four additional predictors in Step 2 explained an additional 15% of the variance (R2 = .31, F(6,50) = 3.77, p = .004; ΔF = .05). In other words, higher levels of T1 positive affect, mindful attention, and teacher-reported peer acceptance, and lower levels of peer acceptance predicted higher T2 GPA. Examination of the individual beta weights in this model showed that teacher-reported peer 100 acceptance had the largest beta weight (β = .42, p = .025) in comparison to the other predictors in the model. The effect sizes for findings from all three models ranged from medium to large. 2.1.4. For the outcome of T2 SWLS in Model 1, gender and age were entered in the first step, and T1 positive affect, mindful attention, and peer- and teacher-reported peer acceptance were entered in the second step (see Table 13). Results revealed that both models were non-significant. Specifically, gender and age explained only 3% of the variance for the outcome of T2 SWLS in Step 1 (Model 1) (R2 = .03, F(2,54) = .75, p = .48). In the overall Model 2, the four additional predictors in Step 2 explained an additional 3% of the variance (R2 = .06, F(6,50) = .50, p = .81). The effect sizes for these findings would be considered small. 2.1.5. For the outcome of T2 depressive symptoms in Model 1, gender and age were entered in the first step, and T1 positive affect, mindful attention, and peer- and teacher-reported peer acceptance were entered in the second step (see Table 14). Results revealed that both models were non-significant. Specifically, gender and age explained less than 1% of the variance for the outcome of T2 depressive symptoms in Step 1 (Model 1) (R2 = .001, F(2,54) = .020, p = .98). In the overall Model 2, the four additional predictors in Step 2 explained an additional 14% of the variance (R2 = .14, F(6,50) = 1.31, p = .27). The effect size for this finding would be considered to be in the medium range. Hypothesis 2.2. Next, it was hypothesized that T1 factors that hinder EFs (stressful life events, negative affect, and depressive symptoms) would significantly and negatively predict T2 EFs, GPA, and well-being (i.e., SWLS and depressive symptoms). A series of hierarchical linear regression analyses were conducted to test the prediction. 2.2.1. For the outcome of T2 EFs (Hearts & Flowers) in Model 1, gender and age were entered in the first step, and T1 stressful life events, negative affect, and depressive symptoms 101 were entered in the second step (see Table 15). Results revealed both models to be non-significant. Specifically, gender and age explained 15% of the variance for the outcome of T2 Hearts & Flowers in Step 1 (Model 1) (R2 = .15, F(2,54) = 4.57, p = .015). In the overall Model 2, the three additional predictors in Step 2 explained 1% of the additional variance (R2 = .16, F(5,51) = 1.93, p = .11). The effect sizes for findings from both models would be categorized as medium. 2.2.2. For the outcome of T2 EFs (Flanker/Reverse Flanker) in Model 3, gender and age were entered in the first step, and T1 stressful life events, negative affect, and depressive symptoms were entered in the second step (see Table 16). Results revealed both models were non-significant. Specifically, gender and age explained only 1% of the variance for the outcome of T2 Flanker/Reverse Flanker in Step 1 (Model 1) (R2 = .01, F(2,54) = .25, p = .78). In the overall Model 2, the three additional predictors in Step 2 explained an additional 2% of the variance (R2 = .03, F(5,51) = .33, p = .89). The effect sizes for these findings would be considered to be small. 2.2.3. For the outcome of T2 GPA in Model 1, gender and age were entered in the first step, and T1 stressful life events, negative affect, and depressive symptoms were entered in the second step for Model 2 (see Table 17). Results revealed both models to be significant. Gender and age explained 21% of the variance for the outcome of GPA in Step 1 (Model 1) (R2 = .21, F(2,54) = 6.93, p =.002). In the overall Model 2, the three additional predictors in Step 2 explained an additional 12% of the variance (R2 = .33, F(5,51) = 4.93, p = .001; ΔF = 3.07). In other words, lower numbers of T1 stressful life events, and higher levels of T1 negative affect and depressive symptoms predicted lower T2 GPA. An examination of the individual beta weights showed that T1 depressive symptoms had the largest beta weight (β = -7.25, p = .023) in 102 comparison to the other predictors in the model. The effect sizes from both models are considered to be large. 2.2.4. For the outcome of T2 SWLS in Model 1, gender and age were entered in the first step, and T1 stressful life events, negative affect, and depressive symptoms were entered in the second step (see Table 18). Results revealed that both models were non-significant. Specifically, gender and age explained only 3% of the variance for the outcome of T2 SWLS in Step 1 (Model 1) (R2 = .027, F(2,54) = .75, p = .48). In the overall Model 2, the three additional predictors in Step 2 explained an additional 5% of the variance (R2 = .078, F(5,51) = .859, p = .52). The effect sizes for these findings are small. 2.2.5. For the outcome of T2 depressive symptoms in Model 1, gender and age were entered in the first step, and T1 stressful life events, negative affect, and depressive symptoms were entered in the second step (see Table 19). Results revealed that both models were non-significant. Specifically, gender and age explained less than 1% of the variance for the outcome of T2 depressive symptoms in Step 1 (Model 1) (R2 = .001, F(2,54) = .020, p = .90). In the overall Model 2, the four additional predictors in Step 2 explained an additional 12% of the variance (R2 = .12, F(5,51) = 1.34, p = .26). The effect size for the finding from the overall model is medium. Study aim 2 summary of findings. The findings revealed that (1) T1 factors that support EFs (i.e., positive affect, mindful attention, and peer- and teacher-reported peer acceptance) predicted T2 GPA, with teacher-reported peer acceptance being found to have the largest beta weight, and (2) T1 factors that hinder EFs (i.e., stressful life events, negative affect, and depressive symptoms) predicted T2 GPA, with T1 depressive symptoms being found to have the largest beta weight. The remaining findings were not statistically significant: (1) T1 factors that 103 support EFs (i.e., positive affect, mindful attention, and peer- and teacher-reported peer acceptance) were not found to significantly predict T2 EFs, SWLS, or depressive symptoms, and (2) T1 factors that hinder EFs (i.e., stressful life events, negative affect, and depressive symptoms) were not found to significantly predict T2 EFs, SWLS, or depressive symptoms. Study Aim 3 The third aim of the present study was to investigate the relative contribution of students’ participation in middle/secondary school-based activities (i.e., athletics participation, and music and theatre course completion) to T2 EFs, GPA, and well-being (i.e., SWLS and depressive symptoms). Zero-order correlational analyses were conducted between the T2 school-based activities with T2 EFs, GPA, and well-being. As can be seen in Table 5, intercorrelations among variables ranged from -.12 to .25, p > .01. No statistically significant correlations were found among the variables. Hypothesis 3.1. It was hypothesized that school-based activities would significantly and positively predict T2 EFs, GPA, and well-being (i.e., SWLS and depressive symptoms). To test this prediction, a series of hierarchical linear regression analyses were conducted. 3.1.1. For the outcome of T2 EFs (Hearts & Flowers) in Model 1, gender and age were entered in the first step, and school-based activities were entered in the second step for Model 2 (see Table 20). Results revealed neither model to be significant. Gender and age explained 8% of the variance for the outcome of T2 EFs (Hearts & Flowers) in Step 1 (Model 1) (R2 = .082, F(2,50) = 2.25, p = .12). In the overall Model 2, the three additional predictors of school-based activities in Step 2 explained an additional 1% of the variance (R2 = .092, F(5,47) = .96, p =.45; ΔF = .17). The effect sizes for these findings are considered to be in the small to medium range. 104 3.1.2. For the outcome of T2 EFs (Flanker / Reverse Flanker) in Model 1, gender and age were entered in the first step, and school-based activities were entered in the second step for Model 2 (see Table 21). Results revealed neither model to be significant. Gender and age explained 1% of the variance for the outcome of T2 EFs (Flanker / Reverse Flanker) in Step 1 (Model 1) (R2 = .010, F(2,50) = .25, p = .76). In the overall Model 2, the three additional predictors of school-based activities in Step 2 explained an additional 4% of the variance (R2 = .054, F(5,47) = .54, p =.74; ΔF = .73). The effect sizes for the findings from these models are considered small. 3.1.3. For the outcome of T2 GPA in Model 1, gender and age were entered in the first step, and school-based activities were entered in the second step for Model 2 (see Table 22). Results revealed neither model to be significant. Gender and age explained 19% of the variance for the outcome of GPA in Step 1 (Model 1) (R2 = .23, F(2,50) = 7.45, p = .001). In the overall Model 2, the three additional predictors of school-based activities in Step 2 explained an additional 36% of the variance (R2 = .26, F(5,47) = 3.31, p =.012; ΔF = .66). The effect size for the findings from both models are considered to be large. 3.1.4. For the outcome of T2 SWLS in Model 1, gender and age were entered in the first step, and school-based activities were entered in the second step for Model 2 (see Table 23). Results revealed neither model to be significant. Gender and age explained less than 1% of the variance for the outcome of SWLS in Step 1 (Model 1) (R2 = .023, F(2,50) = .59, p = .59). In the overall Model 2, the three additional predictors of school-based activities in Step 2 explained an additional 6% of the variance (R2 = .080, F(5,47) = .82, p =.54; ΔF = .97). The effect sizes for these findings range from small to medium. 105 3.1.5. For the outcome of T2 depressive symptoms in Model 1, gender and age were entered in the first step, and school-based activities were entered in the second step for Model 2 (see Table 24). Results revealed neither model to be significant. Gender and age explained less than 2% of the variance for the outcome of depressive symptoms in Step 1 (Model 1) (R2 = .018, F(2,50) = .47, p = .63). In the overall Model 2, the three additional predictors of school-based activities in Step 2 explained an additional 3% of the variance (R2 = .044, F(5,47) = .43, p =.83; ΔF = .41). The effect sizes for these findings are small. Hypothesis 3.2. It was hypothesized that participation in school-based activities that promote connectedness and physical fitness at T2 would predict higher T2 EFs, GPA, and well-being (i.e., SWLS and depressive symptoms). As mentioned in the Preliminary Analysis section, this hypothesis could not be tested because not all school yearbooks had sufficient information across all relevant activities to generate complete connectedness and physical fitness scores for all students. Study aim 3 summary of findings. The findings revealed that (1) school-based activity (music, theatre, and athletics) participation was not found to significantly predict T2 EFs, GPA, SWLS, or depressive symptoms, and (2) there was insufficient data to investigate the questions related to whether participation in school-based activities that promote connectedness and physical fitness were related to the outcome variables of interest. Additional Hypotheses An additional hypothesis examining the role of EFs in mediating the predictors and outcomes was also explored. A prerequisite for mediation analyses is the emergence of significant correlations between all three variables of interest. Upon examination of correlational 106 analyses (see Table 5), however, relations were not significant between these three variables; therefore, meditational analyses were not possible. 107 Discussion The current study adds to the theoretical and empirical literature on the nature of EFs by studying EFs from early to late adolescence over the course of eight years in a sample of Canadian youth. Indeed, to date much of the extant research on EFs has been conducted with samples of children. Moreover, much of the research on EFs in adolescence has been conducted via cross-sectional study designs, with relatively little research examining EFs and its correlates over time in adolescence. Study designs that are longitudinal in nature, such as the design employed in the present study, can provide valuable insight into the stability of these abilities. This study had three primary study aims: (1) to examine the relations of EFs in early adolescence and late adolescence to GPA and well-being in late adolescence, (2) to examine the factors that support (i.e., positive affect, peer acceptance, mindful attention, and physical activity) or hinder (i.e., stressful life events, negative affect, and depressive symptoms) EFs in early adolescence and explore their relative contributions to EFs, GPA, and well-being (i.e., satisfaction with life and depressive symptoms) in late adolescence, and (3) to examine the relative contribution of participation in middle/secondary school-based activities to EFs, GPA, and well-being in late adolescence. In the sections that follow, the findings of the current study are summarized and discussed in the context of extant theory and research, as it relates to our current understanding of EFs, academic achievement, and well-being during adolescence. Strengths related to study design are delineated, followed by a consideration of study limitations. Suggestions for future research are then discussed. This section concludes with a discussion related to implications for educational practice in light of findings from the present study. 108 EFs and Academic Achievement The first aim of the present study was to investigate the relations of EFs in early and late adolescence to GPA in late adolescence, and the potential role that school self-concept plays in these relations. In accordance with research evidence that academic achievement is affected by EFs and its related constructs, the present study found that EFs in early adolescence predicted GPA in late adolescence. These findings are in line with those from Samuels et al. (2016) who examined the relation of EFs to grades in core subject areas among sixth to ninth grade adolescents (N = 322). The authors found that lower scores on the teacher-rated Behavior Rating Inventory of Executive Function measure (denoting better EFs) were correlated with higher grades when the adolescents were in their first year of secondary school. Similarly, M. Miller and Hinshaw (2010) found among their sample of adolescent girls, that those who had EF deficits at baseline (ages 6 to 12) measured using the Rey Osterrieth Complex Figure (Osterrieth, 1944) and Conners’ Continuous Performance Test (Conners, 1995) also performed worse on a standardized math achievement test five years later, regardless of whether they had been diagnosed with ADHD or were part of a matched comparison group; this relation was still true as the girls entered young adulthood (M. Miller, Nevado-Montenegro, & Hinshaw, 2012). In the present study, effect sizes for the findings from correlational (r = .34) and multiple regression analyses predicting Grade 9 to 12 GPA from EFs (Hearts & Flowers) in early adolescence (R2 = .40) are in line with those found by Samuels et al. (2016) and M. Miller et al. (2012), with effect sizes of r = -.45 to -.68, and R2 = .54, respectively, which according to J. Cohen (1988) would be considered large effects. The findings from the present study also extend the literature by focusing on a longer period of development, that is, measuring adolescents’ EFs 109 when they were in the fourth and fifth grades, and examining their cumulative grades after they completed four years of secondary school eight years later. A concurrent relation was also found in the present study between EFs in late adolescence and GPA. The findings are in line with previous research that have found a relation between EF performance and academic achievement. Specifically, the findings are in accordance with Best et al. (2011) who examined the relations of EFs to standardized measures of math and reading achievement in a large nationally representative sample of children and adolescents. Effect sizes for the findings from correlational (r = .29) and multiple regression analyses (R2 = .40) predicting Grade 9 to 12 GPA from EFs (Flanker / Reverse Flanker) in late adolescence in the present study, are comparable to the findings by Best et al. (2011) who assessed EFs using the Cognitive Assessment System, finding a range of r = .28 to .50 for 16- and 17-year-old adolescents. Gender effects were also found with female adolescents earning higher grades than males, replicating findings from prior research (Buddin, 2014). B. A. Jacob (2002) suggested that male students receive lower grades due to greater conduct issues and are less engaged in school than female students. In the framework put forth by Diamond (2012), activities that cultivate joy, pride, or self-confidence are hypothesized to promote EFs, which in turn may have an impact on academic outcomes. An examination of an individual’s concept of oneself in the area of academics, known as school self-concept, was hypothesized to be one potential mechanism whereby EFs could be enhanced in areas related to academic achievement. As hypothesized, school self-concept as measured in early adolescence was found to be related to GPA in late adolescence (see Table 5), even after controlling for gender and age (see Table 7). Once EFs were added to the overall model, however, school self-concept was no longer statistically significant, with EFs being a 110 statistically significant predictor of Grade 9 to 12 GPA. These findings suggest that adolescents’ EFs are a more important predictor of GPA than their perception of themselves in academic areas. The findings from the present study are in line with those from prior research. Specifically, Roebers et al. (2012) examined the relation of EFs and specific school subject self-concept to mathematics and language arts performance in their sample of second grade children and found a significant positive relation between EFs and academic outcomes, but not between self-concept and academic outcomes. In comparison to the present study, larger effect sizes were also found by Roebers et al. (2012), which may be due to the cross-sectional nature of the relations examined and the use of a more homogeneous sample. Although the effect sizes in the present study for the relation between school self-concept and GPA were in the medium range, Roebers et al. (2012) found this relation to be non-existent, which may be due to the grade level in which the data were collected. Previous research has suggested that in the early years of schooling, students’ school self-concept is weakly related to their academic performance with the relation becoming stronger as the years of schooling increases (Spinath & Spinath, 2005). Researchers have suggested that children’s overconfidence in their academic abilities may operate as a protective factor for maintaining motivation despite earning poor grades (Bjorklund & Bering, 2002; H. Shin, Bjorklund, & Beck, 2007). Moreover, results from a meta-analysis conducted by Huang (2011) examining the longitudinal relation between school self-concept and academic achievement indicate that prior studies had comparable effect sizes to those reported in the present study; age effects were also found, with the size of correlations decreasing as age at study inception increased. Thus, the large effect sizes seen in the present study are noteworthy given the eight-year gap between measurements, suggesting early adolescents’ concept of their own academic abilities in fourth and fifth grade are predictive of how well they will perform in 111 core subject areas in secondary school. The current study is, to my knowledge, the first to examine the relation of EFs to GPA across the specific period of early adolescence to late adolescence, while taking into account school self-concept. Furthermore, in their review article of EFs and academic achievement, R. Jacob and Parkinson (2015) suggested GPA is an assessment that combines both scholastic achievement and academic behaviors and skillsets. They also suggested the mechanism whereby an EF intervention may lead to improved academic achievement may be due to factors often unmeasured such as improved self-confidence. Previous research has further underscored the importance of GPA as a developmental outcome finding it to be a better predictor of college academic performance than standardized test scores (Geiser & Santelices, 2007). This may be due to the fact that teacher reports of academic achievement incorporate other aspects of the learning process, including academic behavior and discipline necessary for academic-related task completion that are not captured by scores on standardized academic exams. Surprisingly, differential effects were found for each of the EF tasks utilized in the present study, with outcomes dependent on the presence or exclusion of an influential case (see Appendix K for analyses including the influential case). Analyses that included the influential case examining relations between EFs and GPA revealed significant time by task effects that emerged with only the Hearts & Flowers task as measured in early adolescence (see Appendix K, Table 27). Upon exclusion of the influential case (see Table 6), scores on the Flanker / Reverse Flanker in late adolescence also emerged as a statistically significant predictor. Specifically, for early adolescents, it appears that the Hearts & Flowers task may have been more sensitive to individual differences, and Flanker / Reverse Flanker task was potentially too difficult of a task for that age group. This may be due to the requirement that only one rule be remembered per trial 112 in the Hearts & Flowers task, while the Flanker / Reverse Flanker task requires that two rules be remembered per trial. A growing body of research has demonstrated the incremental improvement of EFs as children and adolescents develop over time (Davidson et al., 2006), finding the structure of EFs changes over time in 8- to 10-year-old children (Brydges, Fox, Reid, & Anderson, 2014). In contrast, some research that has examined working memory specifically, has found that its components do not vary in a sample of 5- to 12-year-olds (Michalczyk, Malstädt, Worgt, Könen, & Hasselhorn, 2012). Willoughby, Wirth, Blair, and Family Life Project Investigators (2012) found similar results in their investigation of measurement invariance for a battery of six EF tasks in children 3- to 5-years-old. Such inconsistent findings lead to the need for further research to elucidate the nature of EF development and measurement of EFs over time. EFs and Well-Being EFs and satisfaction with life. The second aim of the present study was to investigate the relations of EFs in early and late adolescence to well-being in late adolescence. No statistically significant relation was found between EFs in early adolescence and life satisfaction in late adolescence, after taking into account baseline life satisfaction; thus, the hypothesis that they would be related was not supported. The sample size for the current study was relatively small, however, and given that significance testing for p values rely on sufficiently large sample sizes, effect sizes should not be overlooked. An effect size in the medium range (J. Cohen, 1988), as revealed by this analysis, suggests practical significance of the findings. Nonetheless, the only significant beta weights emerging from the overall model, with the inclusion of covariates, were of the correlational relations between EFs and life satisfaction in late adolescence. This aligns with findings emerging from the correlational analyses carried out in the 113 current study examining the relation of EFs to life satisfaction in late adolescence; this relation emerged as statistically significant, with an effect size in the medium to large range. This correlational finding aligns with previous cross-sectional research conducted with adolescent populations, in which significant positive relations between the EF-related construct of self-control and life satisfaction have been found (Ronen et al., 2016; Wiese et al., 2018). The effect size from the present study is comparable to findings from the aforementioned studies, which found a range of small to large effect sizes (Ronen et al., 2016; Wiese et al., 2018). In contrast, previous work by Kruger (2011) found no relation between EFs as assessed with the Executive Function Index (Spinella, 2005) and life satisfaction in a cross-sectional study of college students. Inconsistent findings in the literature could be a function of the measures used given that EFs have a number of related constructs (e.g., self-control, self-regulation). Nonetheless, the current study is the first, to my knowledge, to examine longitudinal relations between EFs and life satisfaction in adolescence. Furthermore, the lack of statistically significant findings for the longitudinal relation between EFs and life satisfaction may be due to the nature of the construct of life satisfaction itself. Studies that have examined life satisfaction over time have noted that perceptions of life satisfaction may be sensitive to contextual factors (Guhn et al., 2018) and may change as one ages and encounters new experiences (Emerson et al., 2017). Specifically, with regard to the SWLS-C, items 4 (“So far I have gotten the important things I want in life”) and 5 (“If I could live my life over, I would have it the same way”) are distinct from the others as responses are shaped by one’s life experiences (Pavot & Diener, 2008). This is in line with work by Gadermann et al. (2011) who examined construct validity for the SWLS-C among early adolescents, and found that younger groups (Grades 4 and 5) tended to utilize absolute strategies 114 (e.g., “Because most of the time I like to play,” p. 45), while older groups (Grades 6 and 7) tended to employ relative strategies (e.g., “We should realize how it will be to be like other people with struggles in other parts of the world,” p. 45) when responding to these two items. This is in line with theories of cognitive development that have posited that early adolescents experience increased complexity in their cognitive skills (Civita et al., 2005) and often use comparative statements to describe the self (Bee, 1989). A growing body of research also suggests EFs and its related constructs have an impact on other aspects of well-being. People who are able to recognize unhelpful habits, switch mindsets, or who are able to inhibit behaviors that could potentially irritate others are employing EFs (Barkley, 2001). These individuals are likely to have better stress management skills and better interpersonal relationships, which may have an impact on aspects of well-being (Luerssen & Ayduk, 2017). Given inconsistent findings in the literature regarding the associations that EFs have with well-being, however, further research is necessary to shed light on its true relations, both concurrently and across development. EFs and depressive symptoms. Given the large body of research that has examined the relation between EF deficits and depressive symptoms, the present study sought to contribute to the literature by examining the relation in a sample of youth. The present study, however, found no statistically significant relation between EFs in early adolescence and depressive symptoms in late adolescence, after taking into account baseline depressive symptoms (although the effect size was in the moderate range). Nevertheless, results from the existing literature with children and adolescents have been mixed. In line with the findings from the present study, Han et al. (2016), similarly, failed to find that EFs were predictive of parent-reported depressive symptoms two years later in a sample of 220 adolescents. It is important to note that while the present study 115 recruited its sample from public schools, Han et al. (2016) relied on community recruitment and oversampled for adolescents who reported internalizing symptoms. Findings from other research, however, do align with results from the present study: Nelson et al. (2018) found that preschool children who had lower levels of effortful control also had higher levels of depressive symptoms when they were followed up in the fourth grade. Taken together, the findings from prior research as well as the present study suggest further research is needed to elucidate the nature of the longitudinal relations between EFs and depressive symptoms in children and adolescents. Moreover, correlational analyses in the present study examining the relation between EFs and depressive symptoms, similarly, failed to yield significant results, with effect sizes between .10 and .32, which according to J. Cohen (1988) range from small to medium effect sizes. Inconsistent findings in the literature could be a function of the measures used given that EFs are related to a number of other constructs (e.g., effortful control, self-regulation), or could be a byproduct of a small sample size. Alternatively, non-significant findings in the present study regarding the relation between depressive symptoms and EFs may be due to the fact that a non-clinical, relatively healthy population of adolescents were sampled. It is important to note that the adolescents who attrited from the present study had a higher number of stressful life events when compared to those who remained in the study. This may explain the lack of statistically significant findings for this relation in the present study. Nonetheless, in the framework put forth by De Raedt and Koster (2010), EF deficits (specifically, attentional control) have been found to lead to rumination, which subsequently leads to lasting negative affect and depressive symptomatology; this may lead further to the development of major depressive episodes as time goes on. After individuals experience a depressive episode for the first time, they may become more susceptible to experiencing 116 successive depressive episodes due to increased sensitivity to progressively milder stressors, and weaker cognitive and attentional control (Kertz, Petersen, & Stevens, 2019). Nevertheless, most of the research conducted thus far has focused on the developmental pathways to major depressive disorder; further research, however, is needed to clarify the relations between EFs and subclinical depressive symptomatology in children and adolescents, both concurrently and over time. Factors that Affect EFs Factors that support EFs. One of the primary aims of the current study is to also investigate EFs in relation to factors purported to support EFs based on findings from prior work that EFs affect academic achievement and dimensions of well-being. To examine aspects that support EFs in early adolescence, a series of hierarchical linear regression analyses were conducted to examine whether these aspects would, singularly or in combination, predict EFs, academic achievement, and well-being in late adolescence. Among these analyses, a finding emerged indicating that factors that support EFs in early adolescence (i.e., positive affect, mindful attention, and peer- and teacher-reported peer acceptance) predicted academic achievement, with teacher-reported peer acceptance found to have the largest beta weight compared to the other predictors in the model. This is in line with prior work by Oberle and Schonert-Reichl (2013) who found that teacher-rated peer acceptance was predictive of end-of-year math performance among early adolescents. Interestingly, the effect size for the longitudinal relation in the present study was found to be larger than the results by Oberle and Schonert-Reichl (2013), potentially due to the presence of a greater number of predictors in the model for the present study. 117 Although teacher and peer ratings of peer acceptance were significantly related in the correlational analyses, exhibiting a large effect size, peer acceptance did not emerge as an important predictor of GPA. This may be due to the differential nature of the items that each of the measures assessed. The teacher ratings of peer acceptance comprised three items that tapped into student popularity and difficulties in establishing friendships, the latter of which was reverse scored (Eisenberg et al., 2003). The peer-rated peer acceptance measure, on the other hand, was a one-item measure that only assessed a student’s preference for engaging in school activities with a peer, decisions that may be a function of both a peer’s popularity and likeability. Another potential reason peer acceptance did not emerge as a significant predictor of GPA may be due to the context in which peer interactions take place, which changes over time. Adolescents experience a transition from a smaller elementary school, where the majority of time is spent with the same group of peers in a single classroom, to middle and/or secondary school settings, where peer groups are no longer closely linked to the classroom environment due to specific subject matters being taught by different teachers in different classrooms (Eccles & Roeser, 2011). Instead, dyadic or small-group friendships often derived from peers who share a single classroom reorganize into larger peer groups based on status hierarchy that are contextualized within a larger, more diverse student body (B. B. Brown, Eicher, & Petrie, 1986). Moreover, adolescents are given the opportunity to participate in a greater variety of school activities in the secondary school setting, within which new peer groups are often formed (Mahoney, 2014). It is within these new peer cultures where the formation of personal identity and development of positive peer relationships may take place, particularly for adolescents who may have experienced social rejection in earlier educational contexts (Kinney, 1993). 118 As a result of these changing developmental contexts, peer acceptance may have played a more significant role in its influence on academic achievement had it been assessed annually across the eight years of the study, and had a different method been used once adolescents transitioned to their middle and secondary schools. In those contexts, it may be more appropriate for researchers to ask adolescents to nominate as many peers as they can think of by writing in the names of those peers, instead of circling names from a classroom-based list of peers (Ferguson & Ryan, 2019). As for the investigations into the relations that EF-supportive factors in early adolescence have on late adolescent EFs and well-being, these findings emerged as non-significant. Correlational analyses examining the relations between factors that support EFs in early adolescence and late adolescent EFs and well-being also revealed non-significant findings. These findings do not align with prior research that have found a link between positive peer relationships and EFs (Oberle & Schonert-Reichl, 2013). Contrary to the present findings, prior studies have found that children and adolescents who were socially well-adjusted were less likely to have externalizing or internalizing symptoms (Bornstein, Hahn, & Haynes, 2010) that could be attributable to deficits in EFs (Woltering, Lishak, Hodgson, Granic, & Zelazo, 2016). Furthermore, researchers have suggested that adolescents with better EFs are able to develop and maintain positive student-teacher and peer relationships, which may further support student engagement and academic outcomes (Oberle & Schonert-Reichl, 2013), which is a statistically significant finding that emerged in the present study. Conversely, students who are not accepted by their peers, as found in the present study, may experience difficulties with academic subjects (Holmes et al., 2016; O'Neil, Welsh, Parke, Wang, & Strand, 1997). 119 Factors that hinder EFs. One of the primary aims of the current study is to also investigate EFs, academic achievement, and well-being, in relation to factors purported to hinder EFs. To examine the aspects that hinder EFs, a series of hierarchical linear regression analyses were conducted to determine whether these aspects would, singularly or in combination, predict EFs, academic achievement, and well-being. An examination of the relations between factors purported to hinder EFs and outcome variables (i.e., T2 EFs, academic achievement and well-being) revealed that the factors that hinder EFs in early adolescence (i.e., stressful life events, negative affect, and depressive symptoms) predicted Grade 9 to 12 GPA, with depressive symptoms being found to have the largest beta weight compared to the other predictors in the model. The effect size for this finding is also considered to be large. Findings from the present study align with work by Rapport et al. (2001) who found that children and adolescents in Grades 2 to 9 who had higher levels of depressive symptoms as rated by teachers also performed worse academically three to four years later. Similarly, McArdle et al. (2014) found ninth through twelfth grade adolescents who had higher levels of depressive symptoms also had worse academic performance three years later. This study found the relation to be unidirectional rather than bidirectional, suggesting that adolescents’ inability to cope with school stressors could have a negative impact on academic performance (Lehtinen, Räikkönen, Heinonen, Raitakari, & Keltikangas-Järvinen, 2006). Responses on the Stressful Life Events Scale may lack variability, however, due to the smaller range of responses. In the present study, participants were asked to report on the number of stressful life events in early adolescence but were not asked to complete this measure again in late adolescence. The present study was, therefore, not able to determine if adolescents had experienced additional stressful life events subsequent to the first wave of data collection. The presence of negative events that could have occurred over the eight-year period 120 between early and late adolescence may have had an impact on hypothesized outcomes. Surprisingly, the effect size in the present study (r = .57) was slightly larger than those found by McArdle et al. (2014) (r = .40 to .50). No statistically significant relations were found between the factors that hinder EFs in early adolescence, and EFs and well-being in late adolescence. Correlational analyses examining the relations between factors that hinder EFs in early adolescence and early adolescent EFs and well-being also revealed non-significant findings in both zero-order correlations. School-Based Activities The third study aim was to investigate school-based activities in relation to EFs, academic achievement, and well-being, as adapted from the framework put forth by Diamond (2012), who proposed that programs that require, engage, and challenge EFs, and whose programmatic components inherently have factors that support EFs (e.g., activities that encourage teamwork, and instills confidence and pride) would lead to improved EFs and academic achievement. Dimensions of well-being were also examined as an outcome based on findings from the extant literature. To examine school-based activities, a series of hierarchical linear regression analyses were conducted to examine whether participation in music, theatre, or athletics would, singularly or in combination, predict EFs, academic achievement, and well-being. An examination of these relations revealed no statistically significant relations between school-based activities and outcome variables (i.e., EFs, academic achievement, and well-being in late adolescence); the hypotheses were, therefore, not supported. An examination of the research literature reveals that findings are mixed. For sports participation, in particular, the findings from the present study align with prior work by Edelsberg (2018), who found no 121 relations between participation in sports and student- and teacher-reported EFs. Yet other studies from prior research have found that sports participation and EFs were related (Piché et al., 2015). Similarly, Alemán et al. (2017) found mixed results, depending on the EF task, whereby music participation led to improvements in parent-rated measures of self-control, but no change was found in performance-based measures of EFs (e.g., Flanker or Tower of London tasks). In contrast, other findings from previous work have suggested that participation in music is related to EFs (Degé et al., 2011). Very little prior work has examined the relations of theatre participation to EFs (Foster & Marcus Jenkins, 2017). As for the relations to academic outcomes, the findings from the current study are not supported by prior research that found participation in music (Gouzouasis et al., 2007; Guhn et al., 2019), theatre (Vaughn & Winner, 2000), or sports (Van Boekel et al., 2016) positively predicted academic achievement. Lastly, the findings from the present study do align with findings from previous research that suggested that participation in school-based activities were not related to well-being (Denault & Poulin, 2009), or life satisfaction specifically (Forgeard & Benson, 2019), and depressive symptoms (Fredricks & Eccles, 2006). The hypotheses put forth in the present study intended to explore these relations further. Yet the quality and consistency of data found in the school yearbooks for which this study relied heavily on for data related to activity participation may have precluded any potential relations from emerging. Further research is needed to explore the relations of school-based activity participation to EFs, academic achievement, and well-being. Strengths and Limitations Strengths. The present study makes several important contributions to the extant literature. First, this study is one of the few to examine EFs in the same group of students 122 prospectively across a period of eight years between the transitional period of early adolescence to late adolescence. Thus, the study was not subject to cohort effects that would otherwise arise as an issue in concurrent studies (Carlson et al., 2018). Another strength of this study is the utilization of a multi-method, multi-informant study design that also incorporates self-reports, teacher- and peer-ratings, performance-based objective measures of EFs, and the use of administrative records such as school transcripts and yearbooks; such varied data sources allows for triangulation so that a comprehensive understanding of data is achieved. At the time of writing, this is the first study to examine performance-based measures of EFs in relation to well-being in a sample of adolescents over time. It is also the first study to examine the relation of positive and negative factors specifically hypothesized to affect EF performance in adolescents over time in a single sample of youth. Despite a relatively small sample size in the current study, robust effect sizes were still found and are in line with previous research as previously outlined. Effect sizes from targeted samples (e.g., disadvantaged, clinical) tend to be larger in general because of lower variability in outcomes among participants eligible to participate in a study. The participants of the present study, however, are typically developing, healthy adolescents from more diverse backgrounds. Thus, the robust effect sizes found may suggest a greater potential for generalizability of findings to adolescents of similar backgrounds. Limitations. In the present study, several hypotheses were not supported. It is important to note that, as outlined by de Graaf and Sack (2018), several reasons could contribute to the lack of statistically significant findings in research. Perhaps because the sample size might be considered small, the current study may not have had sufficient power to detect statistical significance. Thus, future research may consider conducting a power analysis to determine 123 sample size. Emerging research, however, is finding that effect size may be more valid than p values given the reliance of p values on both sample size and effect size (Sullivan & Feinn, 2012); often statistically significant results only reflect the use of a very large sample size that may be susceptible to Type II errors, indicating that p values are confounded. A related limitation of this study is that of the initial 100 fourth and fifth grade participants in early adolescence, 65 of the students participated in late adolescence, yielding an attrition rate of 35%. Had these attrited adolescents remained in the study, the pattern of findings that emerged might have been different. Nonetheless, a number of the effect sizes that emerged from the current study would be considered medium to large effects, despite being statistically non-significant. For a few of these investigations, a larger sample size may have led to statistical significance. To illustrate, several correlational analyses yielded an r > .30, yet did not meet the cutoff for statistical significance (e.g., the relation between Flanker / Reverse Flanker and depressive symptoms in late adolescence yielded an r = -.321). Had the sample size been larger (e.g., N = 100), it is likely that a number of these relations would have emerged as statistically significant. Although the design of this study is prospective and longitudinal, the results are, nevertheless, correlational and should not imply causation. Numerous individual and contextual factors may be associated with EFs. Although gender and age were controlled for, a limitation of the present study was the inability to control for other student background characteristics found in prior research to be associated with EFs, such as socioeconomic status (Hackman et al., 2015) or parent education (Ardila, Rosselli, Matute, & Guajardo, 2005; Watts, Duncan, & Quan, 2018). The generalizability of findings to other demographics may be limited as well, as findings may be unique to the context, geographic region, or even school district, where the study took place. 124 Relatedly, school yearbooks are published annually in most Canadian secondary schools, thereby providing a convenient, public record of student participation in school-based activities. Yet, the quality and consistency of data found in yearbooks from this school district was surprisingly quite low. In the context of the United States, school yearbooks have been found to be a reliable source of data, as activity advisors provide yearbook staff with name rosters and photographs on an annual basis (Mahoney & Cairns, 1997); indices are then created to facilitate searches for individual students’ participation in activities. Furthermore, in those studies, for school yearbooks that did not publish names alongside photographs, researchers who were familiar with all study participants were enlisted to help identify students who were photographed as activity participants (Mahoney & Cairns, 1997). The longitudinal nature of that study, which employed a study design in which data were collected annually from students, enabled some of the researchers to become fully acquainted with all study participants. In the present study, however, the gap between the first and second data collection points lasted eight years, with a different team of researchers employed at both timepoints; school yearbooks were also not obtained from schools until one year after the second wave of data collection had occurred. As a result, researchers in the present study were not acquainted enough with study participants to reliably identify students based on photographs taken of members in extracurricular activities. Moreover, given the lower rates of reliability for identification of student membership in extracurricular activities for the present study, school yearbooks may be less valid in the Canadian context for collecting information for research purposes on individual students; for example, names or indices may not be necessary due to the familiarity that students may have with one another. Therefore, reliance on school yearbooks may have led to the lack of significant findings for school-based activity participation in the present study. Although more 125 reliable data was found in relation to school athletics, school yearbooks also do not capture all aspects of an adolescent’s life; for example, some youth have been found to participate extensively in community-based activities, such as sports leagues (Jekauc, Reimers, Wagner, & Woll, 2013) or theatre programming (OECD, 2013), which could potentially have an impact on their EF levels. Nonetheless, future research may consider utilizing other methods for collecting data on school-based activities, as budgetary restrictions for extracurricular programming have encouraged some schools to phase out the production of traditional school yearbooks completely and encouraged others to convert to a digital medium instead (Ellis, 2011, June 7). Furthermore, given the limitations of using a secondary dataset, the factors that support EFs selected for this investigation may not have reflected the factors that truly affect EFs. For example, according to Diamond (2012), activities or programs that foster self-confidence or pride are hypothesized to also promote EFs. Although the present study explored the role that academic self-concept had on the relation between EFs and academic achievement, the use of a secondary dataset precluded the ability to include a measure that would have enabled an exploration into the role that self-confidence or pride would have had in promoting EFs. As a result, future research may want to consider exploring these factors as well. Finally, the instruments selected to measure the constructs selected for this study may not have been sensitive enough to detect longitudinal relations to EFs, academic achievement, or well-being, or to detect individual differences between study participants. It would, therefore, be important for future research to utilize measures that are both developmentally appropriate and sensitive to change across time. 126 Future Directions Taken together, future research in this area may want to consider a study design that involves the use of primary data. One such avenue would be through administration of surveys to adolescents that gather information about both in-school experiences and out-of-school activities. Existing tools such as the Middle Years Development Instrument (Schonert-Reichl et al., 2013), which is currently administered in Grades 4 and 7 in public schools throughout British Columbia, Canada, could be collected in conjunction with performance-based measures of EFs. This would enable researchers to explore the effects that participating in all in- and out-of-school activities could have on EFs, as well as other factors related to EFs, such as the number of adults that adolescents feel they can turn to for support (Oberle, Schonert-Reichl, Guhn, Zumbo, & Hertzman, 2014). School transcript data could be collected as well to support triangulation of data related to in-school activities. In addition to collecting self-report data, interviews could be conducted with adolescents about the nature and quality of activities they participate in to ascertain whether these activities require and promote EFs. Interviews could also include inquiries related to whether participation in such activities instills a sense of pride or self-confidence, which as proposed by Diamond (2012) are psychological factors believed to promote EFs. Activity advisors could also be interviewed to gather information about the characteristics of the activities they oversee; they could also complete surveys on participating students’ levels of engagement and involvement in activities. In light of previous research that suggests that leadership involvement at the secondary school level may have positive impacts on postsecondary educational attainment (Rouse, 2012), future research may also want to consider interviewing students who hold leadership positions to inquire about relevant tasks and mindsets 127 that students may have while holding a leadership role and to explore whether engaging in such a role would be related to EF enhancement. With the advent of technology, experience sampling method or ESM has emerged as a way to obtain information about the activities people engage in (Csikszentmihalyi, 2014). ESM asks people to complete short questionnaires at random times throughout the day over a period of time about where they are, who they’re with, what they’re doing, what they’re thinking, and how they’re feeling. Such day-to-day and moment-by-moment information would enable researchers to obtain rich data related to the activities and psychosocial factors that could potentially affect adolescents’ EFs. To support data triangulation, physical activity data could also be collected using cell phone accelerometers, to conduct a cross check with data gathered via ESM (Kwapisz, Weiss, & Moore, 2011). With this method, data could be gathered across several weeks at specific time frames throughout the school year, as well as on an annual basis over time. Considering the growing number of longitudinal studies that have uncovered the effects of childhood traits on adulthood (Moffitt et al., 2011), the collection of data from early to late adolescence and into adulthood would enable us to gain a more comprehensive understanding regarding the long-term relations among engagement in specific activities, psychosocial factors related to those activities, EF performance, and important developmental outcomes such as those examined in the current study. Moreover, given the growing rates of presentation for mental health services by children and adolescents (Bitsko et al., 2018; Gunnell, Kidger, & Elvidge, 2018), future studies could also include an examination of anxious symptoms in addition to depressive symptoms. Recent studies have found these growing rates may be due to a shift in the use of out-of-school time where adolescents spend more time on their mobile devices and subsequently have increased social 128 media usage. Such usage has been found to be linked to the decrease in adolescent psychological well-being in recent years (Twenge, Martin, & Campbell, 2018). Some studies, however, have found that the increase in prevalence of anxiety symptoms may be spurious and actually is a result of greater awareness of anxiety disorders or use of mental health services by the general public (Gosmann et al., 2016). Nonetheless, given the impact of screen time use on psychological well-being, it would be of interest to also include measures of screen time quality and quantity to determine if they moderate the relation between EFs and well-being. Alternative measures for the constructs chosen could be another direction for future research. Other performance-based EF measures may be better suited for adolescent populations due to their widespread use, which would allow for better comparability between studies (Nyongesa et al., 2019), such as the Tower of London task that measures planning skills (Phillips, Wynn, McPherson, & Gilhooly, 2001) or the Wisconsin Card Sorting Test (Heaton, 1981) that assesses working memory and cognitive flexibility. Inclusion of measures that assess other positive emotions that promote EFs, such as joy, may also be more suitable than the PANAS-C (Laurent et al., 1999), such as the recently developed Dispositional Joy Scale or State Joy Scale (Watkins, Emmons, Greaves, & Bell, 2018). Although such scales would first need to be adapted and validated for child and adolescent populations. Similarly, alternatives exist for assessing mindful attention, such as the Child and Adolescent Mindfulness Measure (Greco, Baer, & Smith, 2011) or the Comprehensive Inventory of Mindfulness Experiences – Adolescents (Johnson, Burke, Brinkman, & Wade, 2017), which could provide insight into the multifaceted dimensions of mindful attention that may potentially be more appropriate for detecting relations with EFs, academic achievement, or well-being. 129 Educational Implications The main findings that emerged from the present study were the cross-sectional and longitudinal link between EFs and academic achievement. In consideration of findings from intervention research that suggests EFs are malleable and can be improved through training (e.g., Zinke et al., 2012), interventions that focus on improving EFs may be one way to improve academic outcomes. A great number of studies exist examining the efficacy of programs promoting EFs (e.g., Diamond & Ling, 2016). Many effects of EF interventions, however, have not been found to persist beyond three to six months (Diamond & Ling, 2016). In some instances, the benefits were not apparent until a year after the intervention took place (e.g., Alemán et al., 2017). The only studies that have found effects beyond five years have been among older adults (e.g., Willis, 2010). Further emphasis, therefore, needs to be placed on longitudinal research in this area as there is still much that is unknown regarding whether programmatic effects that are found remain stable or can last beyond the initial intervention efforts (Diamond & Ling, 2016; Kassai, Futo, Demetrovics, & Takacs, 2019). Despite growing empirical evidence supporting the implementation of evidence-based programming, however, educational research often has had little impact on educational practice due to the role of politics, finances, or other conventions that traditionally have had a greater influence on education than research evidence has had. Slavin (2019) argues for incentives to be provided by governmental institutions so that schools are more motivated to adopt evidence-based programming. Educators, however, traditionally tend to prioritize advice from friends or trusted colleagues, or sales representatives over evidence-based programming (Morrison, Ross, Corcoran, & Reid, 2014). Policy changes, nevertheless, have led to a shift towards increased interest in research in recent years. In 2015, the United States Congress passed the Every Student 130 Succeeds Act (ESSA) into law. The Evidence for ESSA website requires that research studies have a minimum duration of 12 weeks or one semester. Most studies have a duration between 12 weeks and one to three years. Students, however, attend school for 12 years; therefore, for stakeholders to feel confident about implementing evidence-based programming, positive outcomes need to arise from not just one positive, intense program, but from years of proven programs (Slavin, 2019). In recent years, there has also been a movement towards the promotion of well-being in schools due to the increasing levels of stress that the current generation of students have been experiencing (Bitsko et al., 2018). School administrators could promote a culture of student well-being in their schools that may also have a positive impact on student EFs. As a developmental period that is sensitive to the emergence of internalizing symptoms, adolescence is also an opportune time for engaging in prevention and intervention by creating the conditions and cultivating the skills that have been found to reduce mental health issues and promote resilience (Schonert-Reichl et al., 2013). Middle and secondary school students, in particular, have been experiencing high levels of anxious and depressive symptoms. Increasing reports of symptoms in recent years are a concerning trend for society (Bitsko et al., 2018). This high level of stress is deleterious for EFs and psychological well-being, and adults in general are underestimating the level of stress students are experiencing (American Psychological Association, 2009, 2015). Increases in anxiety may be due to adolescents obtaining fewer than the recommended ten hours of sleep each night; the impact of inadequate amounts of sleep on adolescents have led some schools to implement a sleep curriculum to help students meet their sleep needs so they have the cognitive resources to be able to learn (Gruber, Somerville, & Finn, 2019). Stress contagion may also be affecting adolescents, not just from their peers, but from teachers as well. Attending to 131 the well-being of students necessitates equal attention being paid to the well-being of the entire school community, such as teachers and other school staff (Coleman, 2009), as stress that teachers experience may have a trickledown effect to their students (Oberle & Schonert-Reichl, 2016). Conclusion The combined findings of the present longitudinal study suggest that EFs are predictive of important developmental outcomes, such as academic achievement, both cross-sectionally and longitudinally, over a period of eight years from early to late adolescence. Factors that affect EFs, such as peer acceptance and the presence of depressive symptoms in early adolescence were also found to be predictive of academic outcomes across this important developmental period. Bidirectional relations were also found for EFs and life satisfaction in late adolescence. Further research, however, is needed to elucidate the relations between school-based activity participation and EFs, academic outcomes, or well-being in late adolescence. To my knowledge, this is the first study to examine these relations using the same sample of adolescents. To conclude, the present study offers some support for the argument that EFs and the factors that affect them are important predictors of developmental outcomes in adolescents. These factors may be foci for which educators and other stakeholders may want to concentrate their efforts in for improving outcomes for all youth. 132 Table 1 Type of construct by data source and time point Construct Data Source Time Point Predictor Outcome Covariate Student Self-Report Teacher Report Peer Report Computer Task School Yearbooks Academic Transcripts 2008 (T1) 2015 (T2) 2011-2016 (T2) Demographic information     Predictor – EFs Hearts & Flowers Flanker / Reverse Flanker           Predictors – Factors that promote EFs Positive affect    MAAS-Ca    Peer acceptance – Tb    Peer acceptance – Pc    Predictors – Factors that hinder EFs Stressful life events    Negative affect    Depressive symptoms       Predictors – Activities that promote EFs School-based activities    Non-academic courses    Connectedness     Physical fitness     Outcomes* GPA   Satisfaction with life      Covariates** School self- concept    *Outcomes also include constructs: EFs and depressive symptoms. **Covariates also include constructs: Demographic information (gender, age), satisfaction with life, and depressive symptoms. aMAAS-C: Mindful Attention Awareness Scale – Children. bPeer acceptance – T: Teacher. cPeer Acceptance – P: Peer 133 Table 2 Counts of Student Names in School Yearbook Data by Category and School Type Middle School Secondary School Total Codable Sports 23 61 84 Clubs 8 25 33 Classes 12 8 20 Total 43 94 137 Uncodable Individual photos 152 239 391 School social events 24 24 Family photos 5 5 Baby photos 6 4 10 Grade-level activities 12 12 Excursions 5 5 Other 6 6 12 Total 164 295 459 134 Table 3 Connectedness and Energy Expenditure Scores (MET) for School-Based Activities derived from Yearbooks and Co-Curricular Course Completion Connectedness Score MET Score MET Code Description in energy expenditure guide (Ainsworth et al., 1993; 2011a) Sports: Individual Ski & Snowboard 1 8 19170 Skiing, downhill, vigorous effort, racing Swimming 1 9.8 18230 Swimming laps, freestyle, fast, vigorous effort Wrestling 1 6 15730 Wrestling (one match = 5 min.) Cross country 1 9 12140 Running, cross country Tennis 1 7.3 15675 Tennis, general Sports: Team Basketball 2 8 15040 Basketball, game Soccer 2 10 15605 Soccer, competitive Lacrosse 2 8 15460 Lacrosse Field hockey 2 7.8 15350 Hockey, field Rugby 2 8.3 15560 Rugby, union, team, competitive Volleyball 2 6 15711 Volleyball, competitive, in gymnasium Cheer 2 6 15138 Cheerleading, gymnastic moves, competitive Athletic leadership 2 * * Performing arts Theatre, drama, musical 2 3 11870 Working in scene shop, theater actor, backstage employee Band, concert band, jazz band, music conservatory, music 2 2.5 2 4 2 2 2.5 3.5 2.5 2.5 2 10020 10040 10050 10060 10070 10080 10090 10100 10110 Mean of all instruments: Cello Drums Flute (sitting) Horn Piano or organ Trombone Trumpet Violin Woodwind Choir, concert choir 2 * Guitar 2 2 10120 Guitar, classical folk (sitting) Dance 2 4.5 3025 Flamenco, belly, and swing dancing Clubs: Academic Math league 1 1.8 9065 Sitting, in class, general, including notetaking or class discussion Debate Club 1 1.8 9050 Standing, talking in person, on the phone, computer, or text messaging, light effort Peer tutors 1 1.5 9055 Sitting, talking in person, on the phone, computer, or text messaging, light effort Chess 1 1.5 9010 Sitting – card playing, play board games Clubs: Prosocial Red Cross 2 * Green Team * * Indian Umbrella Club * * 135 Connectedness Score MET Score MET Code Description in energy expenditure guide (Ainsworth et al., 1993, 2011a) School Involvement Student Body, Student Council, Leadership, Con X Student Leadership 2 * Other Business club 2 * Yearbook 1 * Note: *No MET score available or not enough information about the activity to assign a score 136 Table 4 Means, standard deviations, kurtosis, and skewness of variables T1 T2 Variable (n = 57)a M SD Min Max Skew Kurtosis M SD Min Max Skew Kurtosis EFs Hearts & Flowers .82 .12 .42 1.00 -.74 .96 .92 .077 .63 1.00 -1.79 3.69 Flanker / Reverse Flanker .91 .077 .65 1.00 -1.93 3.88 .97 .032 .84 1.00 -1.54 3.04 Predictors – Factors that promote EFs Positive affect 3.75 .64 1.93 4.80 -.69 .16 Mindful attention 4.59 .81 3.13 5.87 .020 -1.21 Connectedness – peer report .32 .16 .04 .66 -.038 -1.041 Connectedness – teacher report 3.73 .87 1.67 5.00 -.49 -.239 Predictors – Factors that hinder EFs Stressful life events 3.09 2.69 0.00 10.00 .83 -.006 Negative affect 1.87 .54 1.07 3.33 .25 -.65 Depressive symptoms 2.06 .47 1.18 3.27 .39 -.21 1.95 .53 1.00 3.18 .69 -.14 Predictors – Activities that promote EFs Music participation .10 .30 .00 1.75 3.95 17.34 Theatre participation .15 .32 .00 1.50 2.75 7.58 Athletics participation .28 .44 .00 1.75 1.57 1.70 Outcomesb GPA 75.25 11.08 56.00 97.27 .031 -1.06 SWLS 4.16 .71 1.80 5.00 -1.13 1.19 3.64 .92 1.40 5.00 -.63 -.41 Covariatesc School self-concept 3.74 .58 2.25 4.75 -.43 -.029 Note. aFor all variables except for T1 SWLS (n = 56) and T2 athletics participation (n = 53). bOutcomes also include: T2 EFs and depressive symptoms. cCovariates also include T1 demographic information, SWLS, and depressive symptoms. 137 Table 5 Intercorrelations of variables Constructs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 T1 1. Hearts & Flowers - 2. Flanker / Reverse Flanker .198 - 3. Positive affect .029 -.111 - 4. Mindful attention .160 -.016 -.127 - 5. Peer acceptance – P .125 .243 .080 .252 - 6. Peer acceptance – T .176 .309 .118 .029 .710** - 7. Stressful life events -.019 -.235 .146 -.101 -.002 -.096 - 8. Negative affect -.230 -.199 .002 .398* -.171 -.189 .266 - 9. Depressive symptoms -.282 .060 -.048 -.589** -.207 -.316 .195 .626** - 10. School self-concept .074 -.072 .314 .125 .139 .109 -.179 -.030 -.279 - T2 11. Hearts & Flowers .257 .068 .025 -.004 .315 .300 .048 -.037 .012 .071 - 12. Flanker / Reverse Flanker -.068 -.183 .126 .101 .267 .208 .024 .029 .101 .229 .324 - 13. GPA .337 -.043 .035 .296 .231 .369* -.207 -.231 -.444* .365* .186 .285 - 14. SWLS .048 .149 .036 .137 .127 .140 -.177 -.057 -.194 .229 .071 .415* .230 - 15. Depressive symptoms .116 .144 -.266 -.135 -.117 -.198 -.005 .040 .279 -.250 -.102 -.321 -.172 -.615** 16. Music participation .135 -.081 -.172 .301 .075 -.182 -.104 .023 .054 .158 .089 .100 .253 .098 .128 17. Theatre participation .023 -.175 -.155 .283 -.359* -.292 -.148 -.173 -.129 -.041 .042 -.122 -.072 -.044 -.083 .044 18. Athletics participation .178 .187 .142 -.152 .161 .238 -.119 -.011 -.084 -.070 .115 .170 .014 .216 -.071 -.084 -.222 Note. * p<.01; ** p<.001. aPeer acceptance – Peer. bPeer acceptance – Teacher. 138 Table 6 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 and T2 EFs T2 GPA Predictors Model 1 Model 2 B SE β t value B SE β t value p value PRATT (d) Step 1 Gender 7.00 2.76 .32 2.54 7.07 2.49 .32 2.85 .006 .23 Age 5.59 2.54 .28 2.20 4.17 2.38 .21 1.75 .086 .12 Step 2 T1 Hearts & Flowers 34.52 11.06 .37 3.12 .003 .30 Flanker / Reverse Flanker -34.45 16.65 -.24 -2.07 .044 .018 T2 Hearts & Flowers .38 18.61 .003 .20 .98 .001 Flanker / Reverse Flanker 130.30 41.49 .38 3.14 .003 .32 Note. F(6,50) on overall regression analysis was 5.64, p < .001, ΔF = 5.13, p < .01 Model 1 R2 = .16, Model 2 R2 = .40, ΔR2 = .24. 139 Table 7 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 School Self-Concept and T1 and T2 EFs T2 GPA Predictors Model 1 Model 2 Model 3 B SE β t value B SE β t value p value B SE β t value p value PRATT (d) Step 1 Gender 7.00 2.76 .32 2.54 6.68 2.61 .30 2.56 .013 6.89 2.42 .31 2.84 .006 .20 Age 5.59 2.54 .28 2.20 4.79 2.42 .24 1.98 .053 3.65 2.33 .18 1.56 .12 .098 Step 2 T1 School self-concept 6.10 2.24 .32 2.72 .009 4.07 2.11 .22 1.93 .059 .17 Step 3 T1 Hearts & Flowers 32.01 10.84 .34 2.95 .005 .25 Flanker / Reverse Flanker -29.54 16.41 -.20 -1.80 .078 .01 T2 Hearts & Flowers 2.74 18.16 .019 .15 .88 .009 Flanker / Reverse Flanker 109.44 41.82 .32 2.62 .012 .24 Note. F(7,49) on overall regression analysis was 5.64, p < .001, ΔF = 4.06, p < .01, Model 1 R2 = .16, Model 2 R2 = .26, Δ R2 = .10, Model 3 R2 = .45, Δ R2 = .18. 140 Table 8 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T1 and T2 EFs, Accounting for T1 SWLS T2 SWLS Predictors Model 1 Model 2 Model 3 B SE β t value B SE β t value p value B SE β t value p value Step 1 Gender .30 .25 .16 1.17 .28 .25 .15 1.11 .27 .21 .24 .12 .87 .39 Age .048 .23 .028 .21 .085 .23 .05 .37 .72 .021 .24 .013 .09 .93 Step 2 T1 SWLS .21 .18 .16 1.16 .25 .15 .17 .11 .84 .41 Step 3 T1 Hearts & Flowers .51 1.11 .065 .46 .65 Flanker / Reverse Flanker .56 1.63 .046 .34 .74 T2 Hearts & Flowers -.69 1.86 -.057 -.37 .71 Flanker / Reverse Flanker 11.92 4.14 .41 2.88 .006 Note. F(7,48) on overall regression analysis was 1.85, p = .10, ΔF = 2.47, p = .057, Model 1 R2 = .025, Model 2 R2 = .050, ΔR2 = .025, Model 3 R2 = .21, ΔR2 = .16 141 Table 9 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T1 and T2 EFs, Accounting for T1 Depressive Symptoms Predictors Model 1 Model 2 Model 3 B SE β t value B SE β t value p value B SE β t value p value Step 1 Gender .015 .14 .015 .11 .05 .14 .045 .34 .74 .026 .14 .025 .19 .85 Age -.020 .13 -.021 -.16 .71 .13 .051 .37 .71 .063 .14 .065 .46 .65 Step 2 T1 Depressive Symptoms .33 .15 .29 2.16 .035 .34 .16 .31 2.18 .034 Step 3 T1 Hearts & Flowers .75 .65 .17 1.15 .25 Flanker / Reverse Flanker .98 .93 .14 1.05 .30 T2 Hearts & Flowers -.58 1.05 -.085 -.56 .58 Flanker / Reverse Flanker 4.67 2.34 -.28 -2.00 .052 Note. F(7,49) on overall regression analysis was 2.02, p = .071, ΔF = 2.25, p = .077, Model 1 R2 = .001, Model 2 R2 = .082, ΔR2 = .081, Model 3 R2 = .22, Δ R2 = .14. 142 Table 10 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Hearts & Flowers) from T1 Factors that Support EFs T2 Hearts & Flowers T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender -.021 .019 -.14 -1.07 -.032 .022 -.21 -1.44 .16 Age .048 .018 .34 2.70 .032 .020 .23 1.61 .11 Step 2 Positive affect -.008 .017 -.069 -.50 .62 Mindful attention -.004 .013 -.044 -.32 .75 Peer acceptance - P .095 .089 .20 1.07 .29 Peer acceptance - T .011 .017 -.12 .64 .53 Note. P = peer reports; T = teacher reports. F(6,50) on overall regression analysis was 2.30, p = .049, ΔF = 1.14, p = .35, Model 1 R2 = .15, Model 2 R2 = .22, ΔR2 = .07. 143 Table 11 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Flanker / Reverse Flanker) from T1 Factors that Support EFs T2 Flanker / Reverse Flanker T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .004 .009 .064 .47 .003 .010 .050 .32 .75 Age .005 .008 .078 .57 -.006 .009 -.001 -.007 .99 Step 2 Positive affect .006 .007 .13 .86 .40 Mindful attention .002 .006 .049 .33 .74 Peer acceptance - P .042 .040 .22 1.06 .29 Peer acceptance - T .001 .008 .029 .14 .89 Note. P = peer reports; T = teacher reports. F(6,50) on overall regression analysis was 0.80, p = .57, ΔF = 1.08, p = .38, Model 1 R2 = .01, Model 2 R2 = .09, Δ R2 = .079. 144 Table 12 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 Factors that Support EFs T2 GPA T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender 7.00 2.76 .32 2.54 4.99 3.01 .23 1.65 .10 Age 5.59 2.54 .29 2.20 2.87 2.68 .14 1.07 .29 Step 2 Positive affect 2.13 2.23 .12 .96 .34 Mindful attention 3.54 1.78 .26 1.99 .052 Peer acceptance - P -10 .70 11.94 -.16 -.90 .37 Peer acceptance - T 5.31 2.23 .42 2.31 .025 Note. P = peer reports; T = teacher reports. F(6,50) on overall regression analysis was 3.77, p = .004, ΔF = 2.76, Model 1 R2 = .16, Model 2 R2 = .31 Δ R2 = .15 145 Table 13 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T1 Factors that Support EFs T2 SWLS T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .30 .25 .17 1.22 .26 .29 .14 .87 .39 Age .043 .23 .025 .19 -.045 .26 -.026 -.17 .87 Step 2 Positive affect .12 .22 .083 .55 .59 Mindful attention .13 .17 .12 .73 .47 Peer acceptance - P .026 1.17 .005 .023 .98 Peer acceptance - T .11 .23 .107 .50 .62 Note. P = peer reports; T = teacher reports. F(6,50) on overall regression analysis was 0.75, p = .48, ΔF = 0.39, p = .82, Model 1 R2 = .027, Model 2 R2 = .056, Δ R2 = .029. 146 Table 14 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T1 Factors that Support EFs T2 Depressive Symptoms T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .015 .14 .015 .12 .016 .16 .015 .10 .92 Age -.020 .13 -.021 -.16 .044 .14 .045 .31 .76 Step 2 Positive affect -.22 .12 -.26 -1.81 .077 Mindful attention -.13 .10 -.20 -1.40 .17 Peer acceptance - P .43 .64 .13 .68 .50 Peer acceptance - T -.17 .12 -.28 -1.35 .18 Note. P = peer reports; T = teacher reports. F(6,50) on overall regression analysis was 1.31, p = .27, ΔF = 1.95, p = .12, Model 1 R2 = .001, Model 2 R2 = .14 ΔR2 = .14. 147 Table 15 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Hearts & Flowers) from T1 Factors that Hinder EFs T2 Hearts & Flowers T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender -.021 .019 -.14 -1.07 -.013 .023 -.084 -.56 .58 Age .048 .018 .34 2.70 .055 .020 .39 2.8 .007 Step 2 Stressful life events .003 .004 .102 .68 .50 Negative affect -.004 .025 -.028 -.16 .88 Depressive symptoms .015 .028 .094 .55 .57 Note. F(5,51) on overall regression analysis was 1.93, p = .11, ΔF = 0.30, p = .78, Model 1 R2 = .15, Model 2 R2 = .16, Δ R2 = .015. 148 Table 16 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Flanker / Reverse Flanker) from T1 Factors that Hinder EFs T2 Flanker / Reverse Flanker T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .004 .009 .064 .47 .003 .010 .054 .34 .74 Age .005 .008 .078 .57 .005 .009 .080 .54 .59 Step 2 Stressful life events .001 .002 .061 .38 .71 Negative affect .008 .011 .13 .68 .50 Depressive symptoms -.012 .012 -.17 -.93 .36 Note. F(5,51) on overall regression analysis was 0.33, p = .89 ΔF = 0.39, p = .76, Model 1 R2 = .009, Model 2 R2 = .031, ΔR2 = .022. 149 Table 17 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 Factors that Hinder EFs T2 GPA T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender 7.86 2.32 .41 3.39 7.33 2.52 .39 2.91 .005 Age 4.01 2.14 .23 1.88 2.57 2.16 .15 1.19 .24 Step 2 Stressful life events .080 .48 .023 .17 .87 Negative affect -.11 2.81 -.006 -.040 .96 Depressive symptoms -7.25 3.09 -.36 -2.34 .023 Note. F(5,51) on overall regression analysis was 4.93, p = .001, ΔF = 3.07, p = .033, Model 1 R2 = .20, Model 2 R2 = .33, ΔR2 = .12. 150 Table 18 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T1 Factors that Hinder EFs T2 SWLS T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .30 .25 .17 1.22 .132 .28 .072 .47 .64 Age .043 .23 .025 .19 -.088 .24 -.052 -.36 .72 Step 2 Stressful life events -.050 .054 -.15 -.93 .36 Negative affect .19 .32 .12 .60 .55 Depressive symptoms -.47 .35 -.24 -1.35 .18 Note. F(5,51) on overall regression analysis was 0.86, p = .52, ΔF = 0.93, p = .43, Model 1 R2 = .027, Model 2 R2 = .078, ΔR2 = .051. 151 Table 19 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T1 Factors that Hinder EFs T2 Depressive Symptoms T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .015 .14 .015 .11 .11 .16 .10 .67 .51 Age -.020 .13 -.021 -.16 .044 .14 .046 .32 .75 Step 2 Stressful life events .006 .030 .031 .20 .84 Negative affect -.25 .18 -.25 -1.39 .17 Depressive symptoms .50 .20 .45 2.56 .013 Note. F(5,51) on overall regression analysis was 1.34, p = .26, Δ F = 2.21, p = .098, Model 1 R2 = .001, Model 2 R2 = .112 Δ R2 = .12. 152 Table 20 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Hearts & Flowers) from T2 School-Based Activities T2 Hearts & Flowers Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender -.010 .018 -.079 -.58 -.013 .019 -.099 -.70 .49 Age .035 .017 .28 2.04 .033 .018 .26 1.80 .078 Step 2 T2 Participation in Music .019 .030 .090 .63 .53 Theatre -.003 .028 -.013 -.091 .93 Athletics .008 .022 .054 .36 .72 Note. F(5,47) on overall regression analysis was 0.96, p = .45, ΔF = 0.17, p = .92, Model 1 R2 = .082 Model 2 R2 = .092, ΔR2 = .010. 153 Table 21 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Flanker / Reverse Flanker) from T2 School-Based Activities T2 Flanker / Reverse Flanker Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .003 .009 .048 .34 .001 .009 .021 .14 .89 Age .006 .009 .089 .63 .003 .009 .053 .36 .72 Step 2 T2 Participation in Music .013 .015 .12 .85 .40 Theatre -.008 .014 -.085 -.57 .57 Athletics .011 .011 .15 .98 .33 Note. F(5,47) on overall regression analysis was 0.54, p = .74, ΔF = 0.73, p = .54, Model 1 R2 = .010, Model 2 R2 = .054, ΔR2 = .044. 154 Table 22 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T2 Participation in School-Based Activities T2 GPA Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender 7.55 2.31 .41 3.26 7.03 2.40 .38 2.93 .005 Age 4.63 2.24 .26 2.07 4.85 2.36 .27 2.05 .046 Step 2 T2 Participation in Music 5.00 3.90 .13 1.03 .31 Theatre -3.57 3.66 -.13 -.97 .34 Athletics -.64 2.87 -.030 -.22 .83 Note. F(5,47) on overall regression analysis was 3.31, p = .012, ΔF =0.66, p = .58, Model 1 R2 =.23, Model 2 R2 = .26, Δ R2 = .031. 155 Table 23 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T2 Participation in School-Based Activities T2 SWLS Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .27 .26 .15 1.05 .23 .27 .12 .85 .40 Age .066 .25 .036 .26 -.05 .26 -.028 -.19 .85 Step 2 T2 Participation in Music .33 .43 .108 .75 .46 Theatre .074 .41 .026 .18 .86 Athletics .51 .32 .24 1.58 .12 Note. F(5,47) on overall regression analysis was 0.82, p = .54, ΔF =0.97, p = .38, Model 1 R2 = .023, Model 2 R2 = .080, ΔR2 = .057. 156 Table 24 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T2 Participation in School-Based Activities T2 Depressive Symptoms Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .081 .15 .077 .55 .055 .15 .052 .36 .72 Age -.11 .14 -.11 -.80 -.094 .15 -.093 -.63 .54 Step 2 T2 Participation in Music .20 .25 .12 .81 .43 Theatre -.17 .23 -.12 -.71 .48 Athletics -.073 .18 -.061 -.40 .69 Note. F(5,47) on overall regression analysis was 0.47, p = .63, ΔF = 0.41, p = .74, Model 1 R2 = .018, Model 2 R2 = .044, ΔR2 = .025. 157 Figure 1. Model of EFs and related constructs. Adapted from “Executive Functions,” by Diamond, A., 2013, Annual Review of Psychology, 64, p. 152. Copyright 2013. Reprinted with permission from Annual Reviews, Inc. 158 Figure 2. Model illustrating hypothesized direct and indirect pathways to improved EFs and academic success. Adapted from “Activities and programs that improve children’s executive functions,” by Diamond, A., 2012, Current Directions in Psychological Science, 21, p. 339. Copyright 2012. Reprinted with permission from the Association for Psychological Science. 159 Figure 3. Overall model of the present study. 160 Figure 4. EFs: Hearts & Flowers Task, Congruent and Incongruent trials are shown. Adapted from “Preschool program improves cognitive control,” by Diamond, A., Barnett, W. S., Thomas, J., & Munro, S.,2007, Science, 318, Supplementary Online Material, p. 11. Copyright 2007. Reprinted with permission from AAAS. 161 “WHEN THE FISH ARE BLUE, PRESS WHERE THE MIDDLE FISH IS FACING” Flanker trial no distractor Flanker trial with congruent distractor Flanker trial with incongruent distractor Flanker trial with incongruent distractor Figure 5. 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(Check all the adults you live with). _____ Mother _____ Grandmother _____ ½ Mom, ½ Dad _____ Father _____ Grandfather _____ Stepfather _____ Stepmother _____ Other adults (EXPLAIN, for example, aunt, uncle, mom’s boyfriend, foster parents) ________________________________ 4. What is the first language you learned at home? ______________________ 249 Appendix B: Positive and Negative Affect Scale for Children (PANAS-C; Laurent et al., 1999) The following words are feeling and emotions that children might feel. Read each word carefully and indicate how much you have felt this way DURING THE PAST FEW WEEKS by circling the appropriate answer. Feeling or Emotion Very slightly or not at all A little bit Somewhat Quite a bit A lot 1. Interested 1 2 3 4 5 2. Sad 1 2 3 4 5 3. Frightened 1 2 3 4 5 4. Alert 1 2 3 4 5 5. Excited 1 2 3 4 5 6. Ashamed 1 2 3 4 5 7. Upset 1 2 3 4 5 8. Happy 1 2 3 4 5 9. Strong 1 2 3 4 5 10. Nervous 1 2 3 4 5 11. Guilty 1 2 3 4 5 12. Energetic 1 2 3 4 5 13. Scared 1 2 3 4 5 14. Calm 1 2 3 4 5 250 Feeling or Emotion Very slightly or not at all A little bit Somewhat Quite a bit A lot 15. Miserable 1 2 3 4 5 16. Jittery 1 2 3 4 5 17. Cheerful 1 2 3 4 5 18. Active 1 2 3 4 5 19. Proud 1 2 3 4 5 20. Afraid 1 2 3 4 5 21. Joyful 1 2 3 4 5 22. Lonely 1 2 3 4 5 23. Mad 1 2 3 4 5 24. Fearless 1 2 3 4 5 25. Disgusted 1 2 3 4 5 26. Delighted 1 2 3 4 5 27. Blue 1 2 3 4 5 28. Daring 1 2 3 4 5 29. Gloomy 1 2 3 4 5 30. Lively 1 2 3 4 5 251 Appendix C: Mindful Attention Awareness Scale adapted for children (MAAS-C; Lawlor, Schonert-Reichl, Gadermann, & Zumbo, 2014) Directions: For each sentence, indicate how well it describes you by circling the number that describes HOW TRUE it is for you. Read each sentence carefully. Answer honestly. Thank you!! Experiences Almost Never Not Very Often at All Not Very Often Somewhat Often Very Often Almost Always 1. I could be feeling a certain way and not realize it until later. 1 2 3 4 5 6 2. I break or spill things because of carelessness, not paying attention, or thinking of something else. 1 2 3 4 5 6 3. I find it hard to stay focused on what’s happening in the present moment. 1 2 3 4 5 6 4. Usually, I walk quickly to get where I’m going without paying attention to what I experience along the way. 1 2 3 4 5 6 5. Usually, I do not notice if my body feels tense or uncomfortable until it gets really bad. 1 2 3 4 5 6 6. I forget a person’s name almost as soon as I’ve been told it for the first time. 1 2 3 4 5 6 252 7. It seems that I am doing things automatically without really being aware of what I am doing. 1 2 3 4 5 6 8. I rush through activities without being really attentive to them. 1 2 3 4 5 6 9. I focus so much on a future goal I want to achieve that I don’t pay attention to what I am doing right now to reach it. 1 2 3 4 5 6 10. I do jobs, chores, or schoolwork automatically without being aware of what I’m doing. 1 2 3 4 5 6 11. I find myself listening to someone with one ear, doing something else at the same time. 1 2 3 4 5 6 12. I walk into a room, and then wonder why I went there. 1 2 3 4 5 6 13. I can’t stop thinking about the past or the future. 1 2 3 4 5 6 14. I find myself doing things without paying attention. 1 2 3 4 5 6 15. I snack without being aware that I’m eating. 1 2 3 4 5 6 253 Appendix D: Teacher’s Ratings of Social Behavior (Eisenberg et al., 2003) For each item, please circle the number that corresponds to the single best response: Item Never Hardly Ever Sometimes Often Always 1. This child finds it hard to make friends. 1 2 3 4 5 2. This child is popular with others at his or her age. 1 2 3 4 5 3. This child has a lot of friends. 1 2 3 4 5 254 Appendix E: Peer behavioral assessments of peer acceptance Based on the procedures described by Parkhurst and Asher (1992) Directions: On the following pages is a list of your classmates. We would like to get some information about your feelings about them and their behaviors. Please follow the directions carefully. YOU MAY CIRCLE YOUR OWN NAME if you believe the description applies to you. Students who you would like to be in school activities with. Name Name Name Name Name Name Name Name Name Name Name Name 255 Appendix F: Stressful Life Events Scale (Pryor-Brown, Cowen, Hightower, & Lotyczewski, 1986) Directions: For each of the following statements, please circle either Yes or No. Read each sentence carefully. Answer honestly. Thank you. Question YES NO 1. My best friend moved out of town. Yes No 2. A new baby brother or sister was born in my family. Yes No 3. A new relative moved into my home. Yes No 4. I had to change to a new school. Yes No 5. One of my parents lost his or her job. Yes No 6. My parents got divorced. Yes No 7. My father had a job in another city and had to be away from home a lot. Yes No 8. I was suspended from school. Yes No 9. One of my parents got remarried. Yes No 10.My parents had to work and weren’t home much. Yes No 11. I wasn’t allowed to see a favourite TV program. Yes No 12. I got very sick and had to go in the hospital. Yes No 13. A friend of the family moved into our home. Yes No 14. I got into trouble and was sent to see the principal. Yes No 15. One of my relatives (not mom or dad) died. Yes No 16. I got a bad mark on a test. Yes No 17. I had an operation. Yes No 256 18. My family had to move to a new place. Yes No 19. One of my parents had a serious accident. Yes No 20. My parents separated. Yes No 21. My older brother or sister left home (like went to college or another city). Yes No 257 Appendix G: Seattle Personality Questionnaire for Children - Depressive Symptoms (SPQC; Kusche, Greenberg, & Beilke, 1988) Question Not at all A little bit Sometimes Always Don’t Know 1. Do you feel unhappy a lot of the time? 1 2 3 4 5 2. Do you feel like crying a lot of the time? 1 2 3 4 5 3. Do you feel upset about things? 1 2 3 4 5 4. Do you have trouble paying attention in class? 1 2 3 4 5 5. Do you feel that you do things wrong a lot? 1 2 3 4 5 6. Do you feel that most things are not much fun? 1 2 3 4 5 7. Do you feel sorry for yourself? 1 2 3 4 5 8. Do you have trouble falling or staying asleep? 1 2 3 4 5 9. Do you feel tired a lot of the time? 1 2 3 4 5 10. Do you often feel like not eating even though it is meal time? 1 2 3 4 5 11. Do you want to be by yourself a lot? 1 2 3 4 5 258 Appendix H: School Yearbook Data Collection Sheet Last name, First name Grade in School Year School Name Photo type / Page no. Photo type / Page no. Student name 1 12 2015-2016 Student name 2 12 2015-2016 Student name 3 12 2014-2015 Student name 4 12 2014-2015 Student name 1 11 2014-2015 Student name 2 11 2014-2015 Student name 3 11 2013-2014 Student name 4 11 2013-2014 Student name 1 10 2013-2014 Student name 2 10 2013-2014 Student name 3 10 2012-2014 Student name 4 10 2012-2014 Student name 1 9 2012-2013 Student name 2 9 2012-2013 Student name 3 9 2011-2012 Student name 4 9 2011-2012 259 Appendix I: Satisfaction with Life Scale for Children (SWLS-C; Gadermann, Schonert-Reichl, & Zumbo, 2010) For each of the following statements, please circle the number that describes YOU the best. Read each sentence carefully. Answer honestly. Thank you. Question Disagree a Lot Disagree a Little Don’t Agree or Disagree Agree a Little Agree a Lot 1. In most ways my life is close to the way I would want it to be. 1 2 3 4 5 2. The things in my life are excellent. 1 2 3 4 5 3. I am happy with life. 1 2 3 4 5 4. So far I have gotten the important things I want in life. 1 2 3 4 5 5. If I could live my life over, I would have it the same way. 1 2 3 4 5 260 Appendix J: Marsh’s Self-Description Questionnaire (SDQ; Marsh, Barnes, Cairns, & Tidman, 1984) Directions: The following sentences describe ways children might feel about themselves. For each sentence, indicate how well it describes you by circling the number that describes HOW TRUE it is for you. Read each sentence carefully. Answer honestly. Thank you!! Description Never Hardly Ever Sometimes Often Always 1. I am good at school subjects. 1 2 3 4 5 2. I enjoy doing work in all school subjects. 1 2 3 4 5 3. I do lots of important things. 1 2 3 4 5 4. In general, I like being the way I am. 1 2 3 4 5 5. I get good marks in all school subjects. 1 2 3 4 5 6. Overall, I have a lot to be proud of. 1 2 3 4 5 7. I learn things quickly in all school subjects. 1 2 3 4 5 8. I can do things as well as most other people. 1 2 3 4 5 9. I am interested in all school subjects. 1 2 3 4 5 10. Other people think that I am a good person. 1 2 3 4 5 11. I look forward to all school subjects. 1 2 3 4 5 12. A lot of things about me are good. 1 2 3 4 5 13. Work in all school subjects is easy for me. 1 2 3 4 5 14. I’m as good as most other people. 1 2 3 4 5 15. I like all school subjects. 1 2 3 4 5 16. When I do something, I do it well. 1 2 3 4 5 261 Appendix K: Analyses including the influential case Table 25 Means, standard deviations, kurtosis, and skewness of variables (includes influential case) T1 T2 Variable (n = 58)a M SD Min Max Skew Kurtosis M SD Min Max Skew Kurtosis EFs Hearts & Flowers .82 .12 .42 1.00 -.76 .96 .92 .077 .63 1.00 -1.72 3.44 Flanker / Reverse Flanker .91 .077 .65 1.00 -1.90 3.85 .96 .067 .52 1.00 -5.27 33.74 Predictors – Factors that promote EFs Positive affect 3.75 .64 1.93 4.80 -.67 .17 Mindful attention 4.59 .80 3.13 5.87 -.011 -1.17 Connectedness – peer report .33 .16 .04 .66 -.072 -1.05 Connectedness – teacher report 3.73 .87 1.67 5.00 -.49 -.19 Predictors – Factors that hinder EFs Stressful life events 3.03 2.70 0.00 10.00 .85 .006 Negative affect 1.87 .54 1.07 3.33 .28 -.65 Depressive symptoms 2.05 .47 1.18 3.27 .42 -.18 1.98 .57 1.00 3.73 .90 .546 Predictors – Activities that promote EFs Music participation .10 .30 .00 1.75 3.77 16.11 Theatre participation .15 .32 .00 1.50 2.75 7.65 Athletics participation .28 .44 .00 1.75 1.59 1.79 Outcomesb GPA 75.49 11.12 56.00 97.27 -.002 -1.09 SWLS 4.16 .70 1.80 5.00 -1.13 1.26 3.60 .98 1.00 5.00 -.73 -.15 Covariatesc School self-concept 3.74 .58 2.25 4.75 -.43 .024 Note. an = 58 for all variables except for T1 SWLS (n = 57) and T2 athletics participation (n = 54). bOutcomes also include: T2 EFs and depressive symptoms. cCovariates also include T1 demographic information, SWLS, and depressive symptoms. 262 Table 26 Intercorrelations of variables (includes influential case) Constructs 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 T1 1. Hearts & Flowers - 2. Flanker / Reverse Flanker .214 - 3. Positive affect .021 -.119 - 4. Mindful attention .162 .012 -.128 - 5. Peer acceptance – Pa .138 .257 .073 .253 - 6. Peer acceptance – Tb .173 .303 .118 .029 .704** - 7. Stressful life events -.038 -.253 .153 -.104 -.019 -.093 - 8. Negative affect -.242 -.214 .009 -.398** -.182 -.186 .278 - 9. Depressive symptoms -.289 .047 -.043 -.590** -.214 -.314* .203 .628** - 10. School self-concept .073 -.071 .313 .125 .138 .109 -.178 -.030 -.279 - T2 11. Hearts & Flowers .235 .045 .033 -.008 .295 .298 .067 -.021 .022 .099 - 12. Flanker / Reverse Flanker -.145 -.056 .111 .027 .025 .108 .143 .115 .015 .107 .271 - 13. GPA .351** -.016 .024 .296 .246 .362** -.227 -.245 -.448** .357* .160 -.012 - 14. SWLS -.001 .081 .054 .120 .077 .134 -.110 -.012 -.155 .213 .114 .496** .154 - 15. Depressive symptoms .158 .195 -.266 -.114 -.060 -.185 -.056 -.010 .225 -.227 -.147 -.497** -.088 -.669** 16. Music participation .154 -.050 -.180 .300 .093 -.180 -.127 .002 .041 .156 .063 -.107 .275 .028 .186 17. Theatre participation .028 -.166 -.157 .284 -.351 -.292 -.153 -.176 -.132 -.041 .036 -.094 -.064 -.056 -.059 .051 18. Athletics participation .165 .169 .146 -.154 .148 .238 -.103 -.077 -.077 -.070 .128 .156 -.001 .232 -.101 -.098 -.224 Note. *p < .01; ** p < .001. aPeer acceptance – Peer. bPeer acceptance – Teacher. 263 Table 27 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 and T2 EFs (includes influential case) T2 GPA Predictors Model 1 Model 2 B SE β t value B SE β t value p value PRATT (d) Step 1 Gender 7.29 2.71 .33 2.68 8.09 2.68 .37 3.01 .004 .38 Age 5.78 2.51 .28 2.3 4.36 2.59 .21 1.68 .098 .18 Step 2 T1 Hearts & Flowers 30.03 11.94 .32 2.52 .015 .37 Flanker / Reverse Flanker -22.39 17.65 -.16 -1.27 .21 .003 T2 Hearts & Flowers 14.86 19.64 .10 .76 .45 .065 Flanker / Reverse Flanker 11.96 20.94 .072 .57 .57 .004 Note. F(6,51) on overall regression analysis was 3.62, p = .005, ΔF = 2.30, p = .072, Model 1 R2 = .17, Model 2 R2 = .30, Δ R2 = .13 264 Table 28 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 School Self-Concept and T1 and T2 EFs (includes influential case) T2 GPA Predictors Model 1 Model 2 Model 3 B SE β t value B SE β t value p value B SE β t value p value PRATT (d) Step 1 Gender 7.29 2.72 .33 2.68 6.99 2.57 .32 2.72 .009 7.66 2.56 .35 2.99 .004 .28 Age 5.78 2.52 .28 2.30 5.00 2.40 .25 2.09 .042 3.63 2.48 .18 1.46 .15 .12 Step 2 T1 School self-concept 6.07 2.24 .32 2.72 .009 5.41 2.18 .28 2.48 .017 .27 Step 3 T1 Hearts & Flowers 27.52 11.42 .29 2.41 .02 .27 Flanker / Reverse Flanker -18.07 16.91 -.13 -1.07 .29 .002 T2 Hearts & Flowers 15.36 18.72 .11 .82 .42 .055 Flanker / Reverse Flanker 5.87 20.11 .04 .29 .77 .001 Note. F(7,50) on overall regression analysis was 4.29, p = .001, ΔF = 2.07, p = .099, Model 1 R2 = .17, Model 2 R2 = .27, ΔR2 = .10, Model 3 R2 = .37, ΔR2 = .10 265 Table 29 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 and T2 EFs, Accounting for T1 SWLS (includes influential case) T2 SWLS Predictors Model 1 Model 2 Model 3 B SE β t value B SE β t value p value B SE β t value p value PRATT (d) Step 1 Gender .19 .27 .097 .71 .17 .27 .088 .65 .52 .25 .24 .13 1.01 .32 .042 Age -.020 .25 -.011 -.081 .016 .25 .009 .064 .95 .034 .24 .019 .14 .89 -.001 Step 2 T1 SWLS .20 .19 .14 1.05 .30 .17 .17 .12 .99 .33 .063 Step 3 T1 Hearts & Flowers .31 1.10 .037 .28 .78 -.0002 Flanker / Reverse Flanker 1.01 1.59 .079 .63 .53 .022 T2 Hearts & Flowers -.11 1.80 -.008 -.060 .95 -.003 Flanker / Reverse Flanker 7.55 1.91 .52 3.96 <.001 .88 Note. F(7,49) on overall regression analysis was 2.87, p = .014, ΔF = 4.50, p < .01, Model 1 R2 = .010, Model 2 R2 = .030, ΔR2 = .020, Model 3 R2 = .29, ΔR2 = .26 266 Table 30 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T1 and T2 EFs, Accounting for T1 Depressive Symptoms (includes influential case) T2 Depressive Symptoms Predictors Model 1 Model 2 Model 3 B SE β t value B SE β t value p value B SE β t value p value PRATT (d) Step 1 Gender .081 .15 .071 .53 .11 .15 .099 .74 .46 .020 .14 .017 .15 .89 .003 Age .024 .14 .022 .17 .09 .14 .085 .63 .53 .064 .14 .061 .48 .64 .002 Step 2 T1 Depressive Symptoms .31 .17 .25 1.86 .068 .36 .15 .29 2.32 .025 .18 Step 3 T1 Hearts & Flowers .80 .63 .16 1.26 .21 .074 Flanker / Reverse Flanker .88 .90 .12 .99 .33 .065 T2 Hearts & Flowers -.71 1.00 -.095 -.70 .48 .039 Flanker / Reverse Flanker 3.78 1.06 -.44 -3.58 .001 .63 Note. F(7,50) on overall regression analysis was 3.84, p = .002, ΔF = 5.47, p = .001, Model 1 R2 = .005, Model 2 R2 = .065, ΔR2 = .060, Model 3 R2 = .35, Δ R2 = .29. 267 Table 31 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Hearts & Flowers) from T1 Factors that Support EFs (includes influential case) T2 Hearts & Flowers T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender -.024 .019 -.16 -1.24 -.037 .022 -.24 -1.64 .11 Age .046 .018 .33 2.59 .029 .020 .21 1.48 .15 Step 2 Positive affect -.009 .017 -.07 -.52 .61 Mindful attention -.003 .013 -.03 -.22 .83 Peer acceptance - P .077 .088 .16 -.87 .40 Peer acceptance - T .015 .017 -.16 -.86 .39 Note. P = peer reports; T = teacher reports. F(6,51) on overall regression analysis for T2 Hearts & Flowers was 2.24, p = .054, ΔF = 1.12, p = .36, Model 1 R2 = .14, Model 2 R2 = .21, ΔR2 = .07. 268 Table 32 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Flanker / Reverse Flanker) from T1 Factors that Support EFs (includes influential case) T2 Flanker / Reverse Flanker T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender -.013 .018 -.095 -.71 -.018 .021 -.14 -.87 .39 Age -.007 .017 -.054 -.40 -.014 .019 -.12 -.77 .45 Step 2 Positive affect .005 .016 .049 .33 .75 Mindful attention .009 .013 .11 .71 .48 Peer acceptance - P .019 .016 .25 1.214 .23 Peer acceptance - T -.051 .083 -.13 -.61 .55 Note. P = peer reports; T = teacher reports. F(6,51) on overall regression analysis for T2 Flanker / Reverse Flanker was 0.45, p = .84, ΔF = 0.53, p = .72, Model 1 R2 = .01, Model 2 R2 = .05, Δ R2 = .039. 269 Table 33 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 Factors that Support EFs (includes influential case) T2 GPA T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender 7.29 2.72 .33 2.68 5.56 2.99 .25 1.86 .068 Age 5.78 2.52 .28 2.30 3.24 2.67 .16 1.22 .23 Step 2 Positive affect 2.17 2.24 .12 .97 .34 Mindful attention 3.36 1.78 .24 1.89 .065 Peer acceptance - P -8.25 11.80 -.12 -.70 .038 Peer acceptance - T 4.83 2.27 .38 2.13 .49 Note. P = peer reports; T = teacher reports. F(6,51) on overall regression analysis was 3.83, p = .003, ΔF = .05, Model 1 R2 = .17, Model 2 R2 = .31 Δ R2 = .14 270 Table 34 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T1 Factors that Support EFs (includes influential case) T2 SWLS T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .20 .26 .10 .76 .13 .31 .065 .41 .68 Age -.027 .24 -.015 -.11 .13 .28 -.073 -.47 .64 Step 2 Positive affect .11 .23 .07 .48 .63 Mindful attention .17 .18 .14 .92 .36 Peer acceptance - P -.53 1.22 -.089 -.44 .35 Peer acceptance - T .22 .24 .20 .95 .66 Note. P = peer reports; T = teacher reports. F(6,51) on overall regression analysis for T2 SWLS was 0.44, p = .85, ΔF = 0.51, p = .731, Model 1 R2 = .01, Model 2 R2 = .049, Δ R2 = .038. 271 Table 35 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T1 Factors that Support EFs (includes influential case) T2 Depressive Symptoms T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .081 .15 .071 .53 .093 .17 .082 .54 .59 Age .024 .14 .022 .17 .096 .15 .091 .62 .54 Step 2 Positive affect -.21 .13 -.24 -1.65 .11 Mindful attention -.16 .10 -.22 -1.55 .13 Peer acceptance - P .77 .68 .22 1.14 .080 Peer acceptance - T -.23 .13 .35 -1.79 .26 Note. P = peer reports; T = teacher reports. F(6,51) on overall regression analysis for depressive symptoms was 1.47, p = .21, ΔF = 2.12, p = .092, Model 1 R2 = .005, Model 2 R2 = .15 ΔR2 = .14. 272 Table 36 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Hearts & Flowers) from T1 Factors that Hinder EFs (includes influential case) T2 Hearts & Flowers T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender -.024 .019 -.16 -1.24 -.016 .023 -.103 -.70 .49 Age .046 .018 .33 2.59 .053 .020 .38 2.74 .008 Step 2 Stressful life events .003 .004 .11 .73 .47 Negative affect -.001 .025 -.009 -.052 .96 Depressive symptoms .014 .028 .086 .50 .62 Note. F(5,52) on overall regression analysis was as follows: T2 Hearts & Flowers = 1.92, p = .11, ΔF = 0.35, p = .79, Model 1 R2 = .14, Model 2 R2 = .16, Δ R2 = .017. 273 Table 37 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Flanker / Reverse Flanker) from T1 Factors that Hinder EFs (includes influential case) T2 Flanker / Reverse Flanker T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender -.013 .018 -.095 -.71 -.013 .021 -.097 -.62 .54 Age -.007 .017 -.054 .40 -.003 .018 -.025 -.17 .87 Step 2 Stressful life events .002 .004 .078 .49 .63 Negative affect .022 .023 .18 .96 .34 Depressive symptoms -.018 .026 -.13 -.71 .48 Note. F(5,52) on overall regression analysis was as follows: T2 Flanker / Reverse Flanker = 0.43, p = .82, ΔF = 0.52, p = .67, Model 1 R2 = .011, Model 2 R2 = .040, ΔR2 = .029. 274 Table 38 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T1 Factors that Hinder EFs (includes influential case) T2 GPA T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender 7.98 2.28 .42 3.51 7.42 2.48 .39 3.30 .004 Age 4.09 2.11 .23 1.94 2.61 2.14 .15 1.22 .23 Step 2 Stressful life events .074 .48 .021 .16 .88 Negative affect -.19 2.77 -.011 -.068 .95 Depressive symptoms -7.21 3.06 -.36 -2.36 .022 Note. F(5,52) on overall regression analysis was 5.23, p = .001, ΔF = 3.15, p = .033, Model 1 R2 = .21, Model 2 R2 = .33, ΔR2 = .12 275 Table 39 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T1 Factors that Hinder EFs (includes influential case) T2 SWLS T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .20 .26 .10 .76 .028 .30 .015 .094 .93 Age -.027 .24 -.015 -.11 -.14 .26 -.076 -.53 .60 Step 2 Stressful life events .043 .058 -.12 -.74 .47 Negative affect .28 .34 .16 .84 .41 Depressive symptoms -.51 .37 -.25 -1.37 .18 Note. F(5,52) on overall regression analysis was as follows: T2 SWLS = 0.58, p = .72, ΔF = 0.77, p = .52, Model 1 R2 = .011, Model 2 R2 = .053, ΔR2 = .042. 276 Table 40 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T1 Factors that Hinder EFs (includes influential case) T2 Depressive Symptoms T1 Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .08 .15 .071 .53 .17 .17 .15 .99 .33 Age .024 .14 .022 .17 .074 .15 .07 .50 .62 Step 2 Stressful life events .001 .033 .007 .044 .97 Negative affect -.30 .19 -.28 -1.58 .12 Depressive symptoms .53 .21 .43 2.48 .016 Note. F(5,52) on overall regression analysis was as follows: T2 depressive symptoms = 1.30, p = .28, Δ F = 2.07, p = .12, Model 1 R2 = .005, Model 2 R2 = .11, Δ R2 = .11. 277 Table 41 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Hearts & Flowers) from T2 School-Based Activities (includes influential case) T2 Hearts & Flowers Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender -.014 .018 -.11 -.78 -.016 .019 -.12 -.84 .41 Age .032 .017 -.25 1.88 .030 .018 .23 1.63 .11 Step 2 T2 Participation in Music .013 .030 .063 .44 .66 Theatre -.003 .029 -.013 -.090 .93 Athletics .011 .022 .074 .50 .62 Note. F(5,48) on overall regression analysis was as follows: T2 Hearts & Flowers = 0.87, p = .51, ΔF = 0.15, p = .93, Model 1 R2 = .075, Model 2 R2 = .083, ΔR2 = .008. 278 Table 42 Hierarchical Multiple Regression Analysis Predicting T2 EFs (Flanker / Reverse Flanker) from T2 School-Based Activities (includes influential case) T2 Flanker / Reverse Flanker Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender -.013 .019 -.099 -.708 -.011 .020 -.082 -.57 .57 Age -.006 .019 -.045 -.326 -.011 .020 -.079 -.54 .59 Step 2 T2 Participation in Music -.013 .032 -.059 -.41 .69 Theatre -.008 .030 -.040 -.27 .79 Athletics .025 .024 .16 1.06 .29 Note. F(5,48) on overall regression analysis was as follows: T2 Flanker / Reverse Flanker = 0.46, p = .80, ΔF = 0.57, p = .64, Model 1 R2 = .012, Model 2 R2 = .046, ΔR2 = .034. 279 Table 43 Hierarchical Multiple Regression Analysis Predicting T2 GPA from T2 Participation in School-Based Activities (includes influential case) T2 GPA Predictors Model 1 Model 2 B SE β t value B SE β t value p value PRATT (d) Step 1 Gender 7.17 2.75 .33 2.61 6.17 2.80 .28 2.20 .032 .37 Age 6.10 2.67 .29 2.28 6.54 2.76 .31 2.37 .022 .35 Step 2 T2 Participation in Music 6.98 4.54 .20 1.54 .13 .22 Theatre -5.38 4.30 -.16 -1.25 .22 .07 Athletics -1.82 3.35 -.072 -.54 .59 -.004 Note. F(5,48) on overall regression analysis was 3.30, p = .012, ΔF = 1.36, p = .27, Model 1 R2 =.19, Model 2 R2 = .27 Δ R2 = .063 280 Table 44 Hierarchical Multiple Regression Analysis Predicting T2 SWLS from T2 Participation in School-Based Activities (includes influential case) T2 SWLS Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .17 .28 .088 .627 .15 .28 .077 .54 .59 Age -.005 .27 -.003 -.020 -.13 .28 -.069 -.48 .64 Step 2 T2 Participation in Music .17 .46 .054 .37 .71 Theatre .074 .43 .024 .17 .87 Athletics .59 .34 .26 1.74 .088 Note. F(5,48) on overall regression analysis was as follows: T2 SWLS = 0.71, p = .62, ΔF = 1.05, p = .38, Model 1 R2 = .008, Model 2 R2 = .069, ΔR2 = .061. 281 Table 45 Hierarchical Multiple Regression Analysis Predicting T2 Depressive Symptoms from T2 Participation in School-Based Activities (includes influential case) T2 Depressive Symptoms Predictors Model 1 Model 2 B SE β t value B SE β t value p value Step 1 Gender .15 .16 .13 .92 .10 .17 .089 .62 .54 Age -.068 .15 -.061 -.44 -.042 .16 -.038 -.26 .80 Step 2 T2 Participation in Music .30 .27 .16 1.12 .27 Theatre -.17 .25 -.095 -.66 .51 Athletics -.13 .20 -.096 -.64 .52 Note. F(5,48) on overall regression analysis was as follows: T2 depressive symptoms = 0.62, p = .69, ΔF =0 .69, p = .56, Model 1 R2 = .020, Model 2 R2 = .060, ΔR2 = .040.
Thesis/Dissertation
eng
Human Development, Learning, and Culture
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Pathways to school and life success : relations of executive functions to academic achievement and well-being in adolescence
Text