Education, Faculty of
Educational and Counselling Psychology, and Special Education (ECPS), Department of
DSpace
UBCV
Parent, Natasha
Doctor of Philosophy - PhD
University of British Columbia
This dissertation provides a contemporary examination of adolescents’ development and well-being in the current socio-technological context through the lens of Self-Determination Theory. Specifically, this work employs a person-centered approach (i.e., Latent Profile Transition Analysis) to investigate the developmental and motivational factors (i.e., basic psychological need satisfaction) underpinning the relationship between adolescents’ smartphone engagement and well-being across two time points. Data was collected from two high schools in British Columbia, Canada, using an online questionnaire between April 27th and May 3rd, 2021 (Time 1), and between February 15th and March 1st, 2022 (Time 2). The final longitudinal sample for this study included 875 adolescents (mage = 14.71; 43.7% girls). Findings from the Latent Profile Analyses across both time points revealed four distinct profiles of adolescents’ online and overall basic psychological need satisfaction and frustration: (1) “Unsatisfied and Frustrated”; (2) “Satisfied and Frustrated” (3) “Online Satisfied and Frustrated”; (4) “Overall Satisfied and Unfrustrated”. Results from Multinomial Logistic Regression analyses indicated that membership into these latent profiles was differently predicted by intra- and inter-personal factors (i.e., gender, ethnicity, fear of missing out, habitual smartphone use, and parent/peer attachment). Girls were more likely to be in profiles with less overall need satisfaction, and adolescents’ who identified as Southeast Asian were less likely to be in the “Satisfied and Frustrated” profile. Moreover, adolescents higher in fear of missing out and habitual smartphone use were more likely to be in profiles with more online need satisfaction and overall frustration, while those higher in parent/peer attachment security were more likely to be in profiles with less need frustration. Additionally, higher habitual smartphone use at Time 1 predicted transitions from the “Unsatisfied and Frustrated” profile into profiles with more need satisfaction at Time 2. Finally, membership in the “Overall Satisfied and Unfrustrated” profile at Time 2 was associated with significantly better mental health (i.e., lower levels of stress, anxiety, and depression) when compared to the other profiles. Findings from this work highlight the nuances with which adolescents’ online and overall basic need satisfaction is experienced in the current socio-technological context and have important implications for interventions and educational programming.
ADOLESCENCE 2.0: AN EXAMINATION OF BASIC NEED SATISFACTION AND WELL-BEING IN A SOCIO-TECHNOLOGICAL WORLD USING LATENT PROFILE TRANSITION ANALYSIS by Natasha Parent B.A., McGill University, 2014 M.A., The University of British Columbia, 2019 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 2023 © Natasha Parent, 2023 ii The following individuals certify that they have read, and recommend to the Faculty of Graduate and Postdoctoral Studies for acceptance, the dissertation entitled: Adolescence 2.0: An examination of basic need satisfaction and well-being in a socio-technological world using latent profile transition analysis submitted by Natasha Parent in partial fulfilment of the requirements for the degree of Doctor of Philosophy in Human Development, Learning, and Culture Examining Committee: Jennifer Shapka, Professor, Educational and Counselling Psychology and Special Education, UBC Supervisor Amery Wu, Associate Professor, Educational and Counselling Psychology and Special Education, UBC Supervisory Committee Member Nancy Perry, Professor, Educational and Counselling Psychology and Special Education, UBC Supervisory Committee Member Suna Eryigit-Madzwamuse, Associate Professor, Educational and Counselling Psychology and Special Education, UBC University Examiner Amori Mikami, Professor, Psychology, UBC University Examiner iii Abstract This dissertation provides a contemporary examination of adolescents’ development and well-being in the current socio-technological context through the lens of Self-Determination Theory. Specifically, this work employs a person-centered approach (i.e., Latent Profile Transition Analysis) to investigate the developmental and motivational factors (i.e., basic psychological need satisfaction) underpinning the relationship between adolescents’ smartphone engagement and well-being across two time points. Data was collected from two high schools in British Columbia, Canada, using an online questionnaire between April 27th and May 3rd, 2021 (Time 1), and between February 15th and March 1st, 2022 (Time 2). The final longitudinal sample for this study included 875 adolescents (mage = 14.71; 43.7% girls). Findings from the Latent Profile Analyses across both time points revealed four distinct profiles of adolescents’ online and overall basic psychological need satisfaction and frustration: (1) “Unsatisfied and Frustrated”; (2) “Satisfied and Frustrated” (3) “Online Satisfied and Frustrated”; (4) “Overall Satisfied and Unfrustrated”. Results from Multinomial Logistic Regression analyses indicated that membership into these latent profiles was differently predicted by intra- and inter-personal factors (i.e., gender, ethnicity, fear of missing out, habitual smartphone use, and parent/peer attachment). Girls were more likely to be in profiles with less overall need satisfaction, and adolescents’ who identified as Southeast Asian were less likely to be in the “Satisfied and Frustrated” profile. Moreover, adolescents higher in fear of missing out and habitual smartphone use were more likely to be in profiles with more online need satisfaction and overall frustration, while those higher in parent/peer attachment security were more likely to be in profiles with less need frustration. Additionally, higher habitual smartphone use at Time 1 predicted transitions from the “Unsatisfied and Frustrated” profile into profiles with more need satisfaction at Time 2. iv Finally, membership in the “Overall Satisfied and Unfrustrated” profile at Time 2 was associated with significantly better mental health (i.e., lower levels of stress, anxiety, and depression) when compared to the other profiles. Findings from this work highlight the nuances with which adolescents’ online and overall basic need satisfaction is experienced in the current socio-technological context and have important implications for interventions and educational programming. v Lay summary This study identified four profiles of adolescents with different levels of online and overall need satisfaction and frustration across two time points, one year apart. Results showed that adolescents’ profile membership was distinguished by their level of fear of missing out (FoMO), smartphone use, and their relationships with parents and peers. Findings from this work illustrate the complex ways in which smartphone use contributes to adolescents’ well-being – suggesting that it may have both positive and negative effects. This study also contributes to the emerging understanding of FoMO as a unique fear of the disruption to close relationships. Moreover, findings from this work showed that adolescents’ need frustration was associated with worse mental health and that, over time, adolescents who were doing well, or doing poorly, tended to stay that way. Overall, this study has important implications for our understanding of the role of smartphone use in adolescent development and well-being. vi Preface This dissertation is an original intellectual property of the author, Natasha Parent. Portions of Chapter 2 have been modified from previous work published in Human Development, for which the author is the sole author (Parent, 2023). The data used in this study was collected as part of a larger body of research led by Dr. Jennifer Shapka, UBC Behavioural Research Ethics Certificate #H19-03035. vii Table of Contents Abstract ____________________________________________________________________ iii Lay summary ________________________________________________________________ v Preface _____________________________________________________________________ vi Table of Contents ___________________________________________________________ vii List of Tables ________________________________________________________________ xi List of Figures ______________________________________________________________ xii Acknowledgements __________________________________________________________ xiii Chapter 1: Introduction _______________________________________________________ 1 Chapter 2: Literature Review __________________________________________________ 4 2.1 Self-Determination Theory & Human Development ___________________________ 5 2.1.1. Self-Determination Theory in Adolescence ________________________________ 8 2.1.2. Adolescent Development in a Contemporary Socio-Technological World ________ 9 2. 2. Basic Psychological Need Satisfaction and Parent/Peer Attachment _____________ 11 2.2.1. Secure Parent/Peer Attachment and Relatedness Needs ______________________ 14 2.3. Basic Psychological Need Satisfaction and Fear of Missing Out (FoMO) _________ 15 2.3.1. FoMO and Attachment _______________________________________________ 16 2.3.2. FoMO, Social Comparison, & Need Satisfaction ___________________________ 17 2.4. Basic Psychological Need Satisfaction and Smartphone Engagement ____________ 18 2.5 Online and Overall Need Satisfaction______________________________________ 22 2.6 Adolescent’s basic need satisfaction during COVID-19 _______________________ 23 viii 2.7 The Current Study _____________________________________________________ 24 2.5.1 Toward the Identification of Online and Overall Basic Psychological Need Satisfaction and Frustration Profiles __________________________________________ 24 2.5.2 Longitudinal Similarity and Stability of Profiles ____________________________ 26 2.5.3 Covariates of Need Satisfaction Profiles __________________________________ 27 2.5.4 Need Satisfaction Profiles and Mental Health ______________________________ 28 Chapter 3: Methods _________________________________________________________ 30 3.1 Methodological framework ______________________________________________ 32 3.3 Procedure ___________________________________________________________ 32 3.2 Participants __________________________________________________________ 33 3.4 Measures ____________________________________________________________ 36 3.4.1 Demographics ______________________________________________________ 36 3.4.2 Parent and peer attachment ____________________________________________ 36 3.4.3 Fear of missing out (FoMO) ___________________________________________ 37 3.4.4 Habitual Smartphone Use _____________________________________________ 37 3.4.5 Overall Need Satisfaction and Frustration _________________________________ 37 3.4.7 Online Need Satisfaction ______________________________________________ 38 3.4.6 Mental Health_______________________________________________________ 39 3.5 Analyses ____________________________________________________________ 42 3.5.1 Preliminary Analyses _________________________________________________ 42 3.5.3 Preliminary Measurement Models for Latent Profile Indicators ________________ 45 3.5.3 Latent Profile Transition Analyses (LPTA) ________________________________ 52 3.5.4 Predictors and outcomes of profile membership ____________________________ 54 ix Chapter 4: Results___________________________________________________________ 55 4.1 Latent Profile Analyses _________________________________________________ 55 4.1.1 Longitudinally Invariant Latent Profiles __________________________________ 62 4.2 Latent Transitions _____________________________________________________ 62 4.3 Covariates of Profile Membership ________________________________________ 65 4.4 Predictors of Profile Transitions __________________________________________ 67 4.5 Profile Membership and Mental Health ____________________________________ 72 Chapter 5: Discussion ________________________________________________________ 73 5.1 Adolescents’ Online and Overall Basic Psychological Need and Frustration Profiles 74 5.1.1 Adolescents’ Profile Membership over Time ______________________________ 81 5.2 Predicting Adolescents’ Need Profiles _____________________________________ 83 5.2.1 The Relationship between Gender, Ethnicity, and Adolescents’ Profile Membership 83 5.2.2 The Relationship between Fear of Missing Out and Adolescents’ Profile Membership_______________________________________________________________________ 84 5.2.3 The Relationship between Habitual Smartphone Use and Adolescents’ Profile Membership ____________________________________________________________ 86 5.2.4 The Relationship between Parent and Peer Attachment and Adolescents’ Profile Membership ____________________________________________________________ 89 5.3 Adolescents’ Profile Membership and Mental Health _________________________ 91 Chapter 6: Conclusion _______________________________________________________ 93 6.1 Strengths, Limitations and Directions for Future Research _____________________ 93 6.2 Significance and Conclusion_____________________________________________ 95 References _________________________________________________________________ 97 x Appendices ________________________________________________________________ 119 Appendix A: Email to Parents from School Principal (with consent form attached). ___ 119 Appendix B: Parental Consent Form ________________________________________ 120 Appendix C: Student Informed Assent Form __________________________________ 123 Appendix D: Inventory of Parent and Peer Attachment Scale _____________________ 126 Appendix E: Fear of Missing Out Scale ______________________________________ 128 Appendix F: Habitual Smartphone Use Scale _________________________________ 129 Appendix G: Basic Psychological Need Satisfaction and Frustration Scale __________ 130 Appendix H: Online Need Satisfaction and Frustration Scale _____________________ 132 Appendix I: Depression Anxiety and Stress Scale ______________________________ 133 xi List of Tables Table 1 Characteristics of Participants ......................................................................................... 34 Table 2 Descriptive Statistics of Measures ................................................................................... 40 Table 3 Correlations between Variables of Interest ...................................................................... 43 Table 4 Correlations between Variables of Interest (Continued) ................................................. 44 Table 5 Goodness-of-Fit Statistics of the Preliminary Measurement Models of LPA Indicators 47 Table 6 Standardized Factor Loadings for Online and Offline Need Satisfaction and Frustration....................................................................................................................................................... 48 Table 7 Standardized Factor Correlations at Time 1 .................................................................... 50 Table 8 Standardized Factor Correlations at Time 2 .................................................................... 51 Table 9 Results from the Latent Profile Analysis Models Estimated Separately at Each Time Point .............................................................................................................................................. 56 Table 10 Results from the Latent Profile Transition Analysis ..................................................... 57 Table 11 Detailed Results from the Final Longitudinal Latent Profile Analytic Solution (Distributional Similarity) at Time 1 and 2................................................................................... 61 Table 12 Latent Transition Probabilities for Final Model ............................................................ 64 Table 13 Results of the Multinomial Logistic Regression for Covariates of Profile Membership....................................................................................................................................................... 69 Table 14 Results from the Multinomial Logistic Regression for the Effect of Habitual Smartphone Use on Profile Transitions ........................................................................................ 70 Table 15 Associations between Profile Membership and Mental Health ..................................... 71 xii List of Figures Figure 1 Theoretical Model of this Dissertation Study ................................................................... 3 Figure 2 Elbow Plot of the Value of the Information Criteria for Solutions Including Different Number of Latent Profiles (Time 1) ............................................................................................. 58 Figure 3 Elbow Plot of the Value of the Information Criteria for Solutions Including Different Number of Latent Profiles (Time 2) ............................................................................................. 59 Figure 4 Longitudinal 4-Profile Solution ...................................................................................... 60 xiii Acknowledgements I gratefully acknowledge that this work was conducted on traditional, ancestral, and unceded Indigenous territories, including the territories of the Sḵwx̱wú7mesh (Squamish), səl̓ilwətaɁɬ (Tsleil-Waututh), and xʷməθkʷəy̓əm (Musqueam) peoples. As this research involves adolescents’ mental health and well-being, I want to acknowledge that these issues remain disproportionally experienced by Indigenous peoples in Canada as a result of colonization, intergenerational trauma, and ever-present health inequities. While Indigenous youth were not a focus of this study, I hope that findings from this work can contribute to improving the well-being of all adolescents by providing a more nuanced understanding of their development in the modern world. I gratefully thank my supervisor, Dr. Jennifer Shapka, who has offered her unwavering support and confidence throughout my many years of graduate studies. Many thanks also go to my committee advisors, Dr. Amery Wu and Dr. Nancy Perry for providing their expertise, insights, and guidance. I would also like to thank my parents, Joanne Wilson and Wayne Parent, for their never-ending support. None of this would have been possible without them. Special thanks also go to my partner, Steven Malynowsky, for his care, patience, and encouragement. Lastly, I would like to thank my friend and colleague, Dr. Bowen Xiao, for the many hours of conversation and moral support. 1 Chapter 1: Introduction The current socio-technological context, including heavy engagement with smartphones and social media (e.g., Instagram, Snapchat, YouTube, TikTok), features as a critical component of modern daily life. This is especially true for adolescents, 97% of whom report using the internet daily and 35% of whom report using a social media platform ‘almost constantly’ (Vogels et al., 2022). Smartphones undoubtably play an important role in youth’s daily lives (Lenhart, 2015), and function as an important context for adolescent development (Nesi et al., 2021; Spies Shapiro & Margolin, 2014; Subrahmanyam & Smahel, 2011). In fact, smartphone and social media use is so integral to modern life as an adolescent that most struggle to differentiate between their “online” and “offline” worlds (Griffith & Kuss, 2017). In this way, the contemporary digital world can be seen as a mere extension of the social context for adolescent development. This has been echoed in an emerging body of work, which has begun to illustrate how fundamental developmental processes of adolescence play out in online or digital spaces. For example, recent work has focused on the experience of identity development (e.g., Bartsch & Subrahmanyam, 2015) and peer affiliation (e.g., Michikyan & Suárez-Orozco, 2015) in relation to smartphone and social media use. In fact, this work has clearly demonstrated that these key developmental tasks of adolescence continue to drive behaviours in the contemporary socio-technological context. Nonetheless, research at the intersection of adolescence and smartphone and social media use remains largely dominated by a moral panic about the risks of adolescent technology engagement, with a disproportionate number of studies focussing on the development of technological addictions (e.g., problematic smartphone use or addiction), and their subsequent impact on mental health (see Busch & McCarthy, 2021 for a systematic review). While this work has been instrumental to understanding the outcomes and precedents of the disordered and/or 2 problematic use of smartphones in adolescence, most adolescents aren’t actually “addicted” to their smartphones, but rather engage in smartphone use as a part of normative development (Davis et al., 2020; Griffith & Kuss, 2017; Panova & Carbonell, 2018). By consequence, there remains a lack of comprehensive research into adolescent smartphone engagement that considers these behaviours within the larger scope of normative adolescent development. The overarching aim of this doctoral work is to provide a contemporary investigation of adolescent development and well-being, which considers features of the socio-technological context (e.g., smartphone engagement). To this end, this dissertation conceptualizes adolescent development and well-being within the lens of Self-Determination Theory (Deci & Ryan, 2000; Ryan & Deci, 2017), wherein healthy psychological growth, and optimal development in general, is facilitated through the satisfaction, and social and contextual support, of the basic psychological needs for autonomy, competence, and relatedness (Chen et al., 2015; Deci & Ryan, 2000; Riggenbach et al., 2019; Valkenburg et al., 2011). More specifically, this work utilized a person-centered approach (i.e., Latent Profile Analysis; Morin & Litalien, 2019), which analyses groups of individuals rather than variables, to identify profiles of online and overall basic psychological need satisfaction and frustration to gain a more nuanced understanding of the distinct patterns of adolescent development and well-being in the contemporary socio-technological context. Importantly, the use of a person-centered approach allowed for the exploration of how these features co-occur at the individual level. Further, to better understand how socio-technological contextual factors relate to adolescents’ development and well-being, the relationship between adolescents’ basic need satisfaction profiles and relevant contemporary factors (i.e., parent/peer attachment, fear of missing out, and habitual smartphone use) was examined. Additionally, the stability of adolescents’ basic need satisfaction 3 profiles, including their transitions between profiles, were examined using Latent Transition Analysis across two time points, one year apart. Lastly, the relationship between adolescents’ basic need satisfaction profiles and their mental health was investigated to better understand the how need satisfaction and frustration relate to adolescents’ experiences of depression, anxiety, and stress. Figure 1 provides the theoretical model for this work, and the following section reviews the relevant literature underlying this model. Figure 1 Theoretical Model of this Dissertation Study Note. Hexagons represent the time-specific latent-profile solutions (which can be more or less similar over time), linked via the latent transition function (reflecting individual changes in profile membership over time, i.e., within-person stability). Time-specific covariates are presented in the rectangular boxes and predict the likelihood of profile membership at Time 1 via a multinomial logistic regression link function (full black arrows). Habitual Smartphone Use Time 1 is specified to predict specific profile transitions (dotted line). Mean-level comparisons of the mental health variables observed in each profile at Time 2 (dashed arrow) are also specified. Time 1 Need Satisfaction Profiles Time 2Need Satisfaction Profiles Latent Transition School, Gender, Age, Ethnicity Time 1 Parent Attachment Time 1Peer Attachment Time 1Habitual Smartphone Use Time 1Fear of Missing Out Time 1Stress Time 2Anxiety Time 2Depression Time 2 4 Chapter 2: Literature Review Adolescent development is richly informed by environmental supports and contexts (La Guardia & Ryan, 2002). Thus, in the contemporary world, features of the socio-technological context may have important implications for adolescents’ development and well-being. For example, the extension of adolescents’ relationships into online or digital spaces can impact how these relationships either directly nourish (e.g., through increased opportunities for disclosure; Davis et al., 2020) or hinder (e.g., through exposure to cyberbullying; Nixon, 2014) the attainment of their developmental goals. That said, the basic motivational principles that underly adolescents’ behaviour in these relationships remain the same despite this new context. For example, establishing intimacy and emotional connection with others is an important developmental milestone in adolescence (Shulman et al., 1997), and research suggests that youth use smartphones and social media to connect with friends, support and cultivate emotional ties, and sometimes even foster new relationships (Lenhart, 2015). In fact, youth report that the desire to feel more connected to others is one of the key drivers of their smartphone and social media use (Anderson & Jiang, 2018). Taken together, this research clearly exemplifies how developmental social goals continue to drive behaviours in the contemporary socio-technological context. Moreover, many adolescents find it easier to engage in self-disclosure in online interactions when compared to face-to-face (Valkenburg et al., 2011), providing a mechanism through which online communications can facilitate relationship closeness and lead to more intimate and higher quality friendships (Michikyan & Suárez-Orozco, 2015). In sum, while the contemporary socio-technological world provides a new context for adolescent development, empirically validated theories of development (e.g., Self-Determination Theory) continue to provide useful frameworks for understanding adolescents’ development and 5 well-being – since these same principles of human development and motivation continue to drive behaviours in a contemporary context. Specifically, Self-Determination Theory (SDT), which posits that intrinsic motivation, social development, and well-being, is driven by the satisfaction of basic psychological needs (Deci & Ryan, 2000), continues to provide an especially insightful framework for understanding adolescent development in the contemporary context. Indeed, though SDT posits that tendencies toward growth and integration operate naturally, the social environment, and thus the contemporary socio-technological context, is theorized to support or thwart this innate trajectory, thereby affecting personal well-being and relational functioning (La Guardia, 2008). In this way, applying SDT to understanding adolescent development in a modern context allows for a comprehensive investigation of relational and developmental processes (and well-being) in the current socio-technological context. As such, this chapter begins with an overview of SDT as a framework for conceptualizing adolescent development and well-being. Next, the chapter introduces socio-technological factors associated with adolescent basic psychological need satisfaction and frustration (i.e., parent/peer attachment, fear of missing out, and habitual smartphone use). Then, the last section of this chapter outlines the research questions for this dissertation. 2.1 Self-Determination Theory & Human Development Rooted in the understanding that each individual is goal oriented with an innate tendency towards growth and development (Ryan & Deci, 2017), SDT assumes that human beings naturally develop in the direction of increasing adaptation, integration, and coherence where possible. Specifically, individuals are believed to be self-determined to the extent that they achieve this sense of integration and self-regulation, which itself is dependent on the realization of their basic psychological needs (i.e., for autonomy, competence, and relatedness; Ryan & 6 Deci, 2017). Indeed, the critical role of these psychological needs is outlined in a central mini-theory of the broader SDT framework known as Basic Psychological Needs Theory (Ryan & Deci, 2017; Vansteenkiste et al., 2020). According to Basic Psychological Needs Theory, healthy psychological growth, and optimal development in general, is facilitated through the satisfaction, and social and contextual support, of basic psychological needs (Chen et al., 2015; Deci & Ryan, 2000; Riggenbach et al., 2019; Valkenburg et al., 2011). In this way, satisfaction of basic psychological needs is conceptualized as an intrinsic force driving behaviour across the life span (Deci & Ryan, 2000), and thus provides a useful framework within which to conceptualize adolescent development and well-being. Within this framework, the term ‘psychological need’ is defined as a psychological nutrient that is essential for individuals’ adjustment, integrity, and growth (Ryan et al., 1995). The satisfaction of basic psychological needs is understood to not only be conducive to, but essential to an individual’s well-being, while frustration is theorized to contribute to feelings of passivity and ill-being (Deci & Ryan, 2000; Vansteenkiste et al., 2020). By this criterion, Basic Psychological Needs Theory (Ryan & Deci, 2017; Vansteenkiste et al., 2020) posits that the three basic psychological needs essential to human development and flourishing are the needs for autonomy, competence, and relatedness. Specifically, the need for autonomy describes the need for a sense of agency and control, while the need for competence refers to the need to feel effective and experience mastery when interacting with the environment, and the need for relatedness describes the need to feel connected to others and maintain close and meaningful relationships (Deci & Ryan, 2000). According to Ryan and Deci (2017) these needs represent universal nutriments for psychological growth and well-being, as well as the attainment of various developmental outcomes, including pro-social tendencies (Tian et al., 2018; Wray-Lake 7 et al., 2019), identity consolidation (Luyckx et al., 2009; Skhirtladze et al., 2019) and self-regulation (Roth et al., 2019). Moreover, the positive association between satisfaction of basic psychological needs and various indexes of well-being has been well-supported in the relevant literature (e.g., Gagne, 2003; La Guardia et al., 2000). For example, La Guardia et al. (2000) found that psychological need satisfaction within specific relationships (e.g., romantic, parental, friendships) significantly contributed to well-being. Additionally, cross-sectional (e.g., Felton & Jowett, 2013; Gagne, 2003; La Guardia et al., 2000), and longitudinal (Reinboth & Duda, 2006), studies have demonstrated that psychological need satisfaction within various contexts results in the experience of greater well-being. Just as the satisfaction of basic psychological needs is associated with optimal growth and positive development, social environments that thwart satisfaction of these fundamental needs are associated with greater passivity, alienation, and ill-being (Ryan & Deci, 2017). For example, as described by Vansteenkiste and colleagues (2019) while satisfaction of the need for autonomy is characterized by a sense of integrity as one’s actions, thoughts, and feelings are perceived to be self-endorsed and authentic, frustration of the need for autonomy is associated with the experience of a sense of pressure and conflict. Additionally, Vansteenkiste et al. (2019) described relatedness satisfaction as the experience of warmth, bonding, and care that is experienced upon feeling connected and significant to others, while relatedness frustration comes with a sense of social alienation, exclusion, and loneliness. Lastly, competence satisfaction is described as feelings of effectiveness and mastery when one experiences opportunities for using and extending skills while capably engaging in activities, and competence frustration as the experience of a sense of ineffectiveness or even failure and helplessness (Vansteenkiste et al., 2019). Overall, several studies have linked need frustration with various indicators of ill-being, 8 including stress (Campbell et al., 2017; Weinstein & Ryan, 2011), depressive symptoms (Cordeiro, Paixão, Lens, Lacante, & Luyckx, 2016), and anxiety (Ng et al., 2012), emphasizing the potentially detrimental impacts of need thwarting environments. Taken together, while the satisfaction of basic psychological needs is understood to contribute to human development, psychological growth, and well-being (Ryan & Deci, 2017), frustration of these needs can lead to negative outcomes (Vansteenkiste et al., 2019). However, within SDT, need frustration is not conceptually equivalent to the mere lack of need satisfaction (Bartholomew et al., 2011). Indeed, Vansteenkiste and Ryan (2013) stated that there is an asymmetrical association between need satisfaction and frustration, such that low need satisfaction does not necessarily imply high need frustration, while high need frustration, by definition, comes with low need satisfaction. Accordingly, recent research has shown that the additional assessment of need frustration, above and beyond need satisfaction, better accounts for various adverse mental health consequences such as depression (e.g., Brenning & Soenens, 2017; Verstuyf et al., 2013) and anxiety (e.g., Cordeiro et al., 2016). 2.1.1. Self-Determination Theory in Adolescence SDT posits that this core set of psychological needs are universally essential for human functioning, regardless of developmental epoch or cultural setting (Ryan & Deci, 2017). Indeed, decades of research has demonstrated that basic psychological need satisfaction drives behaviour across cultures (Ryan & Deci, 2017). Moreover, Ryan and Deci (2000) clearly state that SDT applies, in principle, to individuals of all age groups, “the basic needs for autonomy, competence, and relatedness must be satisfied across the life span for an individual to experience an ongoing sense of integrity and well-being” (p. 75). However, despite the importance of SDT across the lifespan, implications of this theory may be particularly important in the context of 9 adolescence, as the fulfillment of these basic psychological needs plays an important role in supporting youth in the attainment of key developmental milestones such as peer affiliation and identity formation. In fact, although the foundational skills for self-determination develop in early childhood (Palmer, 2010; Wehmeyer & Shogren, 2017; Wehmeyer & Palmer, 2000), youth don’t actualized these skills until adolescence (i.e., they come to internalize, regulate and self-endorse extrinsically-motivated behaviours; Deci & Ryan, 2000) as they learn, refine, and practice knowledge, skills, beliefs and actions that enable them to respond to contextual and environmental challenges that help satisfy their basic psychological needs (Wehmeyer et al., 2017). 2.1.2. Adolescent Development in a Contemporary Socio-Technological World From the perspective of Basic Psychological Needs Theory (Ryan & Deci, 2017; Vansteenkiste et al., 2020), the need to experience autonomy, competence, and relatedness is considered important independent of demographics and contexts. However, different contexts have distinct characteristics and may therefore differently serve aspects of basic psychological need satisfaction and subsequent well-being. Contemporarily, the socio-technological context is a particularly salient context for adolescent development, which may hold important implications for the satisfaction of adolescents’ basic psychological needs. For example, while the sense control over what to watch, listen to, or who to engage with online can contribute to adolescents’ autonomy need satisfaction, addictive features of their devices can frustrate their autonomy needs. Similarly, while connecting with others online can satisfy relatedness needs, being exposed to constant information about what one might be missing out on can frustrate relatedness needs. Lastly, receiving positive feedback in the form of likes or comments online can contribute to competence need satisfaction, while engaging in social comparisons or feeling 10 one has failed in meeting their developmental goals can frustrate competence needs. Indeed, research has begun to examine basic need satisfaction in the context of technology engagement. Notably, most of the work in this area has focused on the context of video games, indicating that satisfaction of the needs for autonomy, competence, and relatedness through gaming is associated with overall game enjoyment and well-being (see Ryan & Deci, 2017 for a review). However, recent work has also begun to look at basic psychological need satisfaction through social media engagement or other online communication technologies (Halfmann & Rieger, 2019; Lin, 2016; Masur et al., 2014; Reinecke et al., 2014), with one study looking at adolescents (Ang et al., 2015). Overall, most of this work has found that this type of technology engagement can contribute to the satisfaction of basic psychological needs, and that this is associated individuals’ overall well-being (Ang et al., 2015; Lin, 2016; Reinecke et al., 2014). That said, preliminary evidence also indicates that smartphone and social media engagement can contribute to thwarting psychological need satisfaction (Halfmann & Rieger, 2019). Additionally, research has begun to investigate the relationship between need satisfaction and problematic patterns of technology use (e.g., Internet Game Disorder; Allen & Anderson, 2018; Social Network Site Addiction, Li et al., 2020; Problematic Internet Use; Okur & Ozekes, 2020), with findings suggesting that need frustration may contribute to maladaptive patterns of technology use. Given the technological aspects of adolescents’ contemporary social environments, a comprehensive examination of adolescents’ basic psychological needs must consider both online and offline aspects of their social environments. To this end, recent work has identified several aspects of the socio-technological context which may be particularly relevant to adolescents’ development and well-being. For example, this work has consistently found attachment security to be a strong predictor of positive outcomes, such as popularity, social acceptance, prosocial 11 behavior, higher quality friendships and lower stress in peer relationships (Allen, Porter, McFarland, McElhaney, & Marsh, 2007; Dykas, Ziv, & Cassidy, 2008; Seiffge-Krenke, 2006; Shomaker & Furman, 2009). Moreover, recent work has identified Fear of Missing Out (FoMO) and habitual and/or problematic use of smartphones as important predictors of negative outcomes such as social disconnection (Beyens et al., 2016; Lai et al., 2016; Parent et al., 2021) and problematic smartphone use (Alt & Boniel-Nissim, 2018; Coskun & Karayagız Muslu, 2019; Wang et al., 2019). Thus, the next section of this chapter will review how these aspects of the socio-technological context relate to adolescents’ basic psychological need satisfaction as a basis for understanding how they may impact adolescents’ development and well-being. 2. 2. Basic Psychological Need Satisfaction and Parent/Peer Attachment Both SDT (Ryan & Deci, 1985) and attachment theory (Bowlby, 1969) have been extensively employed to better understand relationship processes and psychological functioning (e.g., Davis et al., 2013; Patrick et al., 2007; Vansteenkiste & Ryan, 2013). Both theories outline processes that are assumed to be innate, have developmental relevance, and rely on support from close others across the lifespan for optimal functioning. As such, an emerging body of work has begun to investigate the relationship between attachment relationships and basic psychological need satisfaction (Avery & Ryan, 1988; La Guardia et al., 2000; Ryan & Lynch, 1989). According to Attachment Theory (Bowlby, 1969), the fundamental and intrinsic human need for social connection and relatedness drives individuals to form attachment bonds with close others as a means of protecting them from danger by ensuring that they maintain proximity to caring and supportive others. In this way, attachment bonds are said to result from an enduring motivational system designed by natural selection to serve human survival needs through protecting the attached individual from physical and psychological harm. While primary attachment bonds 12 develop in infancy (i.e., with primary caregivers/parents), these relationships are theorized to provide internal representations of the functioning and significance of close relationships throughout the lifespan (Ainsworth et al., 1978; Bowlby, 1969). Specifically, interactions with available and responsive attachment figures (e.g., primary caregivers/parents) facilitate the optimal functioning of the attachment system and promote a sense of attachment security. However, when attachment figures are not reliably available and supportive, defensive secondary attachment strategies develop, resulting in a sense of attachment insecurity. These attachment predispositions (i.e., attachment security vs. insecurity) guide future expectations about the availability and accessibility of support from close others (Bowlby, 1969), and thus likely play a key role in predicting basic need satisfaction throughout development. Indeed, LaGuardia (2008) suggests that secure attachment relationships are a necessary antecedent to overall basic psychological need satisfaction since secure relationships provide the supportive context foundational to need fulfilment. That is, as need-supportive attachment targets convey that an individual is significant, cared for, and loved wholeheartedly, they provide the supportive backdrop for that individual to optimally face challenges (La Guardia, 2008). With this supportive foundation in place, the individual can adeptly explore, challenge themselves, try new experiences, and take interpersonal risks, all of which contribute to their development and well-being. Indeed, a series of studies (e.g., Felton & Jowett, 2013; La Guardia et al., 2000; Patrick et al., 2007; Wei et al., 2005) have demonstrated that attachment security is associated with basic psychological need satisfaction, and that people are more securely attached to those who meet their needs for autonomy, competence, and relatedness (La Guardia et al., 2000). During adolescence, relationships with parents (which usually consist of the primary attachment bond; Ainsworth, 1989; Bowlby, 1969), may play a particularly important role in 13 supporting youth’s basic psychological need satisfaction. According to attachment theory, when parents/caregivers are sensitive to their children’s emotional and physical needs, children experience a sense of security, which provides a secure base to explore their environment with self-confidence, build a sense of autonomy and self-competence, and feel a sense of closeness with others (Pietromonaco & Barrett, 2000; Sroufe & Waters, 1977) – thus directly contributing to the satisfaction of their basic psychological needs. Indeed, a study by Avery and Ryan (1988) found that children’s attachment security was positively related to their perceptions of their parents’ support of their needs for autonomy and relatedness. Similarly, work by Ryan and Lynch (1989) found that adolescents’ who perceived their parents as high in autonomy support, acceptance, and warmth were more willing to rely on and be close to them. Lastly, a study by La Guardia and colleagues (2000), found that college students’ attachment security was positively associated with the satisfaction of their needs for autonomy, competence, and relatedness in their relationships with parents and romantic partners, and that this, in turn, was associated with their overall well-being. Contrary to some conceptualizations of adolescent development, SDT views adolescence as a time of movement towards autonomy (as volitional action), not a time of separation from adults (Guardia & Ryan, 2002). Indeed, SDT posits that relatedness to parents and significant others (e.g., teachers, coaches and peers) provides adolescents with a secure base from which they can pursue their innate tendency towards growth (Deci & Ryan, 2000). In this way, socially responsible autonomous functioning depends upon continuing attachments and connections to others (Grolnick et al., 2002). However, in adolescence, youth begin to rely more and more on their peers, in addition to parents, as peer networks expand and interactions with peers become more intimate (Brown & Larson, 2009; Lerner & Steinberg, 2014). As a result, peers become 14 primary sources of social support (Bokhorst et al., 2010; Furman & Buhrmester, 1992), and relationships with peers begin to take on critical attachment functions (J. P. Allen et al., 2016) – thus providing an important social context for basic psychological need satisfaction in adolescence (Knee et al., 2013). 2.2.1. Secure Parent/Peer Attachment and Relatedness Needs While secure parent and peer attachment relationships can contribute to the satisfaction of all three basic psychological needs (i.e., autonomy, competence, and relatedness), the link between attachment relationships and relatedness needs is particularly evident. Indeed, within SDT, the need for relatedness is conceptualized as a satisfaction derived from a sense of connectedness with others, characterized by a need for belonging, closeness, and intimacy (Deci & Ryan, 2000; Ryan & Deci, 2017). As described by Deci and Ryan (2000), relatedness is the need, “to love and care and to be loved and cared for” (p. 231). Thus, as with attachment security, individuals experience relatedness when they interact with others who are involved and emotionally available, and who express affection, warmth, caring, and nurturance (Ryan & Deci, 2017). Moreover, in the contemporary socio-technological context being explored here, many of adolescents’ peer attachment relationships are built and/or maintained through smartphone and social media use (Nadkarni & Hofmann, 2012). Indeed, research suggests that youth use smartphones to connect with friends, support and cultivate emotional ties, and sometimes even foster new relationships (Lenhart, 2015). In fact, youth report that the desire to feel more connected to others is one of the key drivers of their smartphone and social media use (Anderson & Jiang, 2018) – thus illustrating just how important these tools are to satisfying their relatedness needs. One means by which smartphone engagement can help youth foster feelings of connection and intimacy is through providing opportunities for self-disclosure, something that many 15 adolescents consider to be easier to do in online interactions when compared to face-to-face (Valkenburg et al., 2011). As self-disclosure facilitates relationship closeness, communications through smartphones and social media can lead to more intimate and higher quality friendships among adolescents (Michikyan & Suárez-Orozco, 2015), and thus support the development of secure peer attachments/relatedness need satisfaction. 2.3. Basic Psychological Need Satisfaction and Fear of Missing Out (FoMO) In addition to the role of secure attachment relationships, recent work has begun exploring an emerging construct; the fear of missing out (FoMO; Przybylski et al., 2013), as an important antecedent of adolescent basic psychological need satisfaction. Described as a pervasive apprehension that others might be having rewarding experiences from which one is absent (Przybylski et al., 2013), FoMO is theorized to originate from deficits in relatedness needs. Though the idea of developing anxieties around missing out on rewarding experiences has been around for decades (Simson, 1982), the widespread adoption of smartphones and social media, allowing youth to constantly check in on their peers, has recently intensified these experiences. Indeed, the experience of FoMO is said to result from attempts at satisfying relatedness needs through social media engagement (Przybylski et al., 2013), which instead result in feelings of social exclusion. For example, new technologies such as ‘Instagram Stories’ provide youth with instant access to videos and pictures of what others are doing. This real-time access to information about the activities and social gatherings that they may be missing out on can heighten youth’s feelings of social exclusion and/or rejection, and further thwart their relatedness need satisfaction. In this way, when adolescents turn to smartphones and social media to satisfy their need for relatedness, and decrease their anxieties around being socially excluded, this may actually lead them to experiencing feelings of FoMO. In turn, these experiences of FoMO can 16 drive further smartphone and social media use as a means of satisfying youth’s thwarted relatedness needs, since checking up on other people can lead to a temporary relief of these feelings of anxiety (Oberst et al., 2017). Ironically, however, the more adolescents check their social media accounts, the more they may find social events that they are missing out on. As such, using smartphones and social media to satisfy relatedness needs, and reduce anxiety around missing out, can actually end up being another source of FoMO, and thus further thwart adolescent’s relatedness needs. 2.3.1. FoMO and Attachment In contrast to the conceptualization of FoMO as a result of thwarted relatedness needs (as outlined above; Przybylski et al., 2013), an emerging body of work has begun to conceptualize FoMO within an attachment theory framework (e.g., Holte & Ferraro, 2020; Parent et al., 2021; Throuvala et al., 2019). This work has found a relationship between attachment insecurity and FoMO, whereby those who are more prone to feelings of apprehension that close others are not available and/or will abandon them will experience higher levels of FoMO (Holte & Ferraro, 2020). Specifically, Holte and Ferraro (2020) found that individuals with this attachment profile inherently worry about being separated from close others and fear missing out on spending enjoyable time with them. Within this framework, it has been theorized that when people are anxious about their relationships (as in the case of attachment insecurity), they fear being socially excluded (i.e., they experience FoMO). While this theorizing clearly links FoMO with attachment insecurity, it is also possible that FoMO reflects a unique dimension of the peer attachment dynamic, similar, yet independent from, attachment insecurity. From this perspective, FoMO can be interpreted as an expression of the peer attachment dynamic, reflecting the fear of disruption to these attachment relationships (Throuvala et al., 2019). In this way, while 17 attachment insecurity refers to the perceived reliability and availability of close others (Bowlby, 1969), FoMO can be conceptualized as a unique fear of the disruption to these relationships, which is characterised by a pervasive apprehension that one is being socially excluded or left out (Przybylski et al., 2013). Thus, when conceptualized within an attachment framework, FoMO continues to have important implications for adolescents’ relatedness need satisfaction. 2.3.2. FoMO, Social Comparison, & Need Satisfaction Another means by which FoMO can contribute to basic psychological need frustration is through adolescents’ social comparisons. From a developmental perspective, social comparison is a particularly common behaviour in adolescence (Krayer et al., 2008), as youth typically engage in comparisons in an effort to affirm and re-affirm that their actions, decisions, and abilities are being accepted by others (Griffin et al., 2017). Be it upward or downward, these comparisons can have a strong impact on adolescents’ competence need satisfaction and overall self-esteem (Krayer et al., 2008). Specifically, while downward social comparison to others with inferior status, attractiveness, or success may foster feelings of competence, upward social comparison can thwart competence need satisfaction (Haferkamp & Krämer, 2010). Smartphones and social media in particular may make it especially easy for adolescents to compare themselves to their peers through providing constant access to information about what others are doing. Indeed, as adolescents become aware of all the things that they may be missing out on, they may experience heightened feelings of FoMO, and engage in upward social comparisons, thus simultaneously frustrating their needs for relatedness and competence. Given that adolescents are developmentally motivated to construct positive self-presentations demonstrating their strengths and abilities (Masur et al., 2014), and that smartphones and social media provide youth with an opportunity to carefully curate the 18 information they share with others, social comparisons in these contexts are likely more dramatic than warranted. Indeed, a recent qualitative study by Throuvala et al. (2019) found that adolescents have a powerful need to present an ideal image of themselves online via social media. Additionally, given the importance of popularity and social acceptance in adolescence (Brown & Larson, 2009; Steinberg, 2014), these positive self-presentations likely include sharing favourable information about youths’ social capital and popularity. In this way, it is likely that adolescents are only sharing social information online that makes them look popular and well-liked among their peers – such that social comparisons in these contexts are done with biased accounts of others’ social lives. In other words, adolescents are likely comparing their own levels of social inclusion with curated and/or embellished versions of those of their peers – placing them at a heightened risk for experiencing FoMO. Indeed, as adolescents compare themselves to biased accounts of their peers’ social lives, they may experience heightened feelings of FoMO, as they perceive themselves to be socially excluded and left out by their peers. This, in turn, can contribute to frustrating their needs for relatedness, as they feel rejected by their peers, and for competence, as they feel inferior to their peers or as though they have failed to meet their developmental social goals. 2.4. Basic Psychological Need Satisfaction and Smartphone Engagement Another construct that is relevant to adolescents’ basic psychological need satisfaction, is technology engagement (e.g., habitual smartphone use). Indeed, previous work has indicated that establishing relationships and connecting with others is a strong motivator for smartphone use among adolescents (Anderson & Jiang, 2018), and that youth often turn to social media as a means of fostering feelings of intimacy (Michikyan & Suárez-Orozco, 2015), all of which can contribute to the satisfaction of their relatedness needs. Moreover, in line with notions of SDT, 19 which posits that thwarted psychological needs may function as particularly strong motivators for behaviour (Ryan & Deci, 2017), some have proposed that smartphone engagement may be driven by a desire to satisfy thwarted psychological needs (e.g., relatedness need frustration). For example, a series of studies by Sheldon and colleagues (2011) has demonstrated that feelings of disconnectedness resulted in attempts to satisfy thwarted relatedness needs through increased use of Facebook. In this way, using Facebook appeared to function as a coping strategy allowing for the compensation of thwarted relatedness need satisfaction in the offline context. This same logic can be applied to smartphones and other social media (e.g., Instagram, Tik Tok, Snapchat) whereby youth may turn to these technologies to satisfy their thwarted needs for relatedness. In this way, it is possible that youth who feel disconnected in their offline lives turn to smartphones and social media to satisfy their need for relatedness. Notably, the idea of using smartphones to overcome thwarted psychological needs may be particularly relevant for adolescents who have difficulty connecting with others in face-to-face contexts. For these youth, smartphones may be used as a means of social compensation and social facilitation to offset a lack of social skills or difficulties with peer face-to-face interactions (Valkenburg et al., 2011). This reflects the theorizing of Compensatory Internet Use Theory (Kardefelt-Winther, 2014), which suggests that individuals engage in technology use as a means of alleviating negative emotions and psychological distress which, in this case, results from psychological need frustration. The freedom of choice and sense of control afforded to adolescents through their use of social media serves as a developmentally rooted motivation for their smartphone and social media engagement. Specifically, the opportunities for self-presentation (Kaplan & Haenlein, 2010), content sharing (Halpern & Gibbs, 2013), and choosing with whom to connect as well as what to read, listen to or watch afforded to youth through social media, can support adolescents’ 20 autonomy need satisfaction (Karahanna et al., 2018). This is amplified by the fact that most adolescents have their own devices (Vogels et al., 2022), which provide them with a quasi-unlimited and mostly unsupervised access to social media. In line with notions of thwarted basic need satisfaction (Sheldon et al., 2011; Vansteenkiste et al., 2019), this motivation may be particularly strong for adolescents who lack an overall sense of autonomy in their daily lives. For these youth, social media engagement may help compensate for their thwarted autonomy needs by providing them with choice, control, and volition in their actions. Put differently, using smartphones and social media may provide youth whose offline lives feel limited and controlled (e.g., by parents, teachers, scheduled activities, etc.) with the opportunity to exercise a sense agency, and thus satisfy their need for autonomy. Despite this autonomy-supportive use of smartphones, smartphone engagement can, conversely, thwart autonomy need satisfaction through the addictive features of these technologies which can drive habit-forming behaviours, and thus contribute to extrinsically motivated use. Indeed, similar to games, where the acquisition of rewards reinforces the gamer into prolonging the gaming experience (Griffiths & Nuyens, 2017), smartphones and social media exhibit a plethora of structural features that engage adolescents further in habitual behaviours (Osatuyi & Turel, 2018). In line with the principles of operant conditioning (Roberts et al., 2014), these habitual behaviours towards smartphones are extrinsically motivated by the anticipation of a reward, and operate outside of awareness, attention, intention, and control (LaRose et al., 2010). Indeed, work is beginning to show that not all technology use is intentional and that the alerts, notifications, and ‘likes’ that are designed to draw one’s attention to their device (e.g., to check social media or respond to a new message) eventually become cues that elicit an automatic, reflexive behaviour response (Soror et al., 2015). In this way, as youth use 21 smartphones repeatedly, their brains develop a cognitive structure of triggers and actions that surround their usage behaviors and associated responses (Osatuyi & Turel, 2018). For instance, as adolescents check their smartphones upon receiving a notification and learn that this is enjoyable, over time, they form a habit of checking every notification that they receive. Consequently, they come to automatically check their smartphones every time that they receive a notification, without paying any regard to the appropriateness of the action, or whether or not it is actually still enjoyable. Moreover, adolescents may be particularly vulnerable to the development of media habits, as their neural architecture makes them particularly susceptible to context specific social stimuli (Crone & Dahl, 2012). That is, the maturation of the limbic system (i.e., the social-affective processing centre in the brain) in adolescence leads to an intensification of reward-based neural pathways from social stimuli specifically (Crone & Dahl, 2012). Thus, for adolescents, the salience of social rewards (Crone & Dahl, 2012; Steinberg, 2014) may heighten the feedback loop between the cue and behaviour response, thereby speeding up habit formation and subsequently increasing the strength of the habit (Shapka, 2019). This, in turn, can thwart their autonomy need satisfaction as what had originated as an intrinsically motivated behaviour to act with choice and volition becomes extrinsically motivated and automatic. Additionally, relatedness need frustration fuelled by FoMO (as discussed above) can also contribute to the development of habitual and/or problematic smartphone use, and thus further contribute to autonomy need frustration. Indeed, research on FoMO has consistently found it to be a key antecedent of problematic smartphone use (i.e., a compulsive use of smartphones which disrupts daily functioning; Ezoe et al., 2009; Lepp et al., 2016). For example, a recent study using machine learning identified FoMO as the strongest predictor of problematic smartphone use among Canadian adolescents (Xiao et al., 2023). In this way, as youth higher in FoMO 22 experience heightened relatedness need frustration, they may engage in increased smartphone use as a means of checking up on others and reducing some of their anxiety about missing out, which, in turn, can make them more likely to develop problematic smartphone use, and subsequently experience autonomy need frustration. Moreover, experiencing relatedness and autonomy need frustration can also contribute to adolescents’ competence need frustration, as they might feel they have failed at their developmental social goals. 2.5 Online and Overall Need Satisfaction The previous sections describe potential implications of the contemporary socio-technological context on adolescents’ basic psychological need satisfaction and frustration. This review suggests that a comprehensive investigation of adolescents’ positive development and well-being must include the examination how their basic psychological need satisfaction and frustration is impacted by socio-technological aspects of the current context. Thus, since adolescents’ contemporary social environments include both “online” and “offline” elements, it is also important to consider how their basic psychological needs may be satisfied or frustrated in online contexts specifically. Indeed, an emerging body of work has begun to investigate online basic psychological need satisfaction (Liu et al., 2016; Shen et al., 2013; L. Wang et al., 2015). For example, in line with the theorizing of Compensatory Internet Use Theory (Kardefelt-Winther, 2014), Wang and colleagues (2015) identified online need satisfaction as an important predictor of psychological well-being among adolescents and young adults, particularly for those with lower overall need satisfaction. Similarly, findings from Liu and colleagues (2016) identified a complete mediation effect of online need satisfaction on the relationship between adolescents’ low overall need satisfaction and pathological internet use, such that those lower in overall need satisfaction seemed to be meeting their basic needs online, resulting in problematic patterns of 23 internet use. While this work has been instrumental to understanding the relative role of online and overall need satisfaction in contributing to well-being, it fails to consider how need frustration may also be contributing to these outcomes – and thus highlights an important avenue for future work. 2.6 Adolescent’s basic need satisfaction during COVID-19 The COVID-19 global pandemic had a drastic effect on adolescents’ daily lives – and likely impacted their online and overall basic psychological need satisfaction. Notably, as governments around the world imposed restrictions on physical social gatherings, many of adolescents’ interactions were forced online. In this context, technologies such as smartphones and social media became essential tools for adolescents to meet their basic psychological needs. Indeed, research during COVID-19 found that smartphones and social media played a pivotal role in fostering adolescents’ feelings of connection and relatedness (Ellis et al., 2020; Parent et al., 2021). Moreover, using these technologies likely helped youth satisfy their needs for autonomy amidst government and parental restrictions (which limited their opportunities for choice or volition) – all of which likely contributed to their online need satisfaction. However, as many interactions were forced online, it is also likely that addictive features of these technologies contributed to frustrating youth’s autonomy needs through increasing habit-forming behaviours. In fact, despite this well-intentioned use of smartphones, some studies found an increase in problematic smartphone use during COVID-19 (e.g., (Li et al., 2021; Serra et al., 2021) which may hold important implications for adolescents’ online and overall need satisfaction. As such, it is likely that the COVID-19 pandemic drastically altered youth’s relationships with technology such as social media and intensified many of the experiences previously outlined in this paper. 24 2.7 The Current Study 2.5.1 Toward the Identification of Online and Overall Basic Psychological Need Satisfaction and Frustration Profiles This doctoral work focuses on how aspects of the contemporary socio-technological context are related to adolescents’ development and well-being. Specifically, this work examines adolescents’ online and overall basic psychological need satisfaction and frustration in relation to their habitual smartphone use, FoMO, parent/peer attachment, and mental health (i.e., depression, anxiety, stress). Seven Research Questions (RQ) guide this work: 1. Do adolescents experience distinct profiles of basic psychological need satisfaction and frustration? 2. What is the within-sample stability of adolescents’ profile membership? 3. What is the within-person stability of adolescents’ profile membership? 4. Does profile membership differ by adolescents’ school, age, gender, and ethnicity? 5. What is the relationship between adolescents’ latent profile membership and theoretically informed covariates (i.e., habitual smartphone use, FoMO, and parent/peer attachment)? 6. Does habitual smartphone use predict adolescents’ profile transitions over time? 7. What is the relationship between adolescents’ profile membership and their mental health (depression, anxiety, stress)? While an emerging body of work has begun to examine adolescent basic need satisfaction in relation to attachment (e.g., La Guardia & Patrick, 2008) and smartphone engagement (e.g., Sheldon et al., 2011), this work has been largely variable-centered (i.e., focused on the clustering of variables, such as factor analysis to measure constructs from subscales), and thus fails to recognize the distinct patterns with which features of basic psychological need satisfaction may 25 be experienced at the individual level. Indeed, a recent study by Li and colleagues (2020) was the first to examine the relationship between technology engagement and overall basic psychological need satisfaction and frustration using a person-centered approach (i.e., Latent Profile Analysis). Specifically, this study identified four profiles of overall need satisfaction and frustration among Chinese undergraduate students that were associated with distinct levels of social network site addiction and well-being. Findings from this work indicated that individuals in profiles with higher need frustration were more likely to experience social network site addiction – thus providing a strong rational for examining the relationship between need frustration and other aspects of the socio-technological context (e.g., smartphone engagement and fear of missing out). Moreover, Li and colleagues (2020) were the first to identify a profile of individuals who experienced both need satisfaction and frustration simultaneously – supporting the need to investigate both aspects of need satisfaction within a person-centered approach. This doctoral study builds on this work by investigating both online basic need satisfaction and overall need satisfaction and frustration among adolescents’ using a longitudinal person-centered approach. In this way, this study investigates distinct patterns of online and overall basic psychological need satisfaction and frustration that underly the contemporary experience of development and well-being in adolescence as it unfolds over time. Whereas variable-centered approaches examine the relationships between “averaged” parameters across individuals, person-centered approaches examine possible subgroups characterized by different sets of parameters (Morin et al., 2018), and allow for the investigation of how these various aspects of basic psychological needs (i.e., online satisfaction, overall satisfaction, and overall frustration) co-occur within individuals. Thus, the primary objective of this work is to identify distinct profiles of basic need satisfaction and frustration in adolescence (RQ 1). Based on the findings from Li and colleagues (2020), it is 26 hypothesized that at least three profiles of basic psychological need satisfaction and frustration will be identified and that these profiles will be characterized by overall high and/or low basic psychological need satisfaction and frustration. 2.5.2 Longitudinal Similarity and Stability of Profiles The second research question was focused on examining the similarity and stability of these profiles over time. That is, via the adoption of a longitudinal perspective, this work assesses the extent to which the identified profiles, and adolescents’ membership in these profiles, is similar and stable over a one-year period. This involves identifying distinct profiles of adolescents based on their levels of online and overall need satisfaction and frustration at each time point and then testing the similarity of the profile solutions across time points. In other words, once a solution is found at each time point, the similarity of these profiles will be tested with a series of statistical tests similar to those used in measurement invariance testing. These tests examine what is referred to as “within-sample stability” and include determining whether each time point includes a similar number of profiles (i.e., configural similarity) that are characterized by the same shape (i.e., structural similarity) across both time points (Gillet et al., 2017). These two types of within-sample stability (i.e., configural and structural similarity) are the most important as they allow for the identification of inter-individual differences (e.g., they allow for comparisons between individuals’ profile membership over time). Following this, two additional types of within-sample stability) are tested. That is, the degree to which the profiles remain similar to each other (i.e., dispersion similarity), and whether the size of the profiles (i.e., distributional similarity) remains unchanged is examined over time (Gillet et al., 2017). When support for these two types of within-sample stability is found, it suggests that the profiles reflect meaningful configurations of characteristics, which may be generalizable to other samples. 27 Beyond the within-sample stability, the third research question focused on exploring how individual adolescents’ profile membership changes over time (i.e., whether adolescents stay in the same profile group over time; Kam et al., 2013). This is referred to as “within-person stability” and includes investigating how adolescents’ transition between profiles over time (e.g., being in Profile 1 and Time 1 and transitioning into Profile 2 at Time 2). This form of stability is independent from within-sample stability. Given that basic psychological need satisfaction is theorized to be relatively stable over time (Deci & Ryan, 2017), it is expected that most adolescents will remain in the same profile across time points. However, online need satisfaction has yet to be examined longitudinally, and may be more prone to fluctuations in technology use (Li et al., 2020; Shen et al., 2013), so it is possible that adolescents in profiles distinguished by their level of online need satisfaction may be more likely to transition between profiles over time. 2.5.3 Covariates of Need Satisfaction Profiles Another aim of this dissertation was to assess how individual characteristics (i.e., age, gender, parent/peer attachment, FoMO, and smartphone engagement) relate to adolescents’ need satisfaction profiles (RQ 4 and RQ 5). In terms of socio-demographic factors, previous work has found that age is slightly correlated with basic psychological need satisfaction across the lifespan with peaks in competence and relatedness satisfaction in early adulthood (Lataster et al., 2022), therefore it is expected that there will be no significant differences in terms of adolescents’ age across profiles. Moreover, SDT assumes that basic psychological needs are universal and equivalent across gender groups and ethnicity (Deci & Ryan, 2000), therefore it is expected that adolescents’ profile membership will not vary by gender or ethnicity. In terms of theoretically informed covariates, because need satisfaction is critical to secure attachment relationships (La 28 Guardia, 2008), it is hypothesized that secure parent and peer relationships will positively relate to need satisfaction such that having higher attachment security in parent/peer relationships will predict membership in profiles with higher need satisfaction (and less frustration) (RQ 4). Moreover, given that both FoMO and habitual smartphone engagement are theorized to lead to basic need frustration (Przybylski et al., 2013; Sheldon et al., 2011), it is expected that higher levels of FoMO and/or habitual smartphone use will be associated with membership in profiles with higher need frustration (and less satisfaction) (RQ 5). Additionally, given the cyclical process whereby habitual patterns of smartphone use may contribute to thwarted basic psychological needs which, in turn, can lead to increased smartphone engagement as a means of overcoming thwarted needs, it is hypothesized that habitual smartphone use will predict adolescents’ profile transitions over time (RQ 6). That is, higher levels of habitual smartphone use will predict transitions into profiles with lower need satisfaction (and higher frustration) at the second time point. This hypothesis is further supported by the understanding that habitual patterns of smartphone use may be exacerbated over time, due to addictive features of these technologies. 2.5.4 Need Satisfaction Profiles and Mental Health A final objective of this dissertation was to document the relationship between adolescents’ basic psychological need satisfaction and their mental health (RQ 7). Several studies have linked need frustration with negative experiences of mental health, including stress (Campbell et al., 2017; Weinstein & Ryan, 2011), depression (e.g., Brenning & Soenens, 2017; Verstuyf et al., 2013), and anxiety (Ng et al., 2012). Thus, the relationship between these negative experiences of mental health and adolescents’ distinct profiles of need satisfaction and frustration were examined in this study to further understand how these experiences co-occur at 29 the individual level.? In line with the findings from cross-sectional and variable-centered work, it is hypothesized that profiles with lower basic need satisfaction (and higher need frustration) will be associated with higher levels of depression, anxiety, and stress. 30 Chapter 3: Methods This doctoral research was conducted amidst the global COVID-19 pandemic (2021-2022) – a period of drastic changes in the lives of Canadian youth. During this time, government efforts to control the spread of COVID-19 included limiting physical social interactions through periodic school closures and the cancelation of extracurricular activities. While this context was challenging for most Canadians, the effects of this decreased socialization may have been particularly detrimental for adolescents, for whom the need for social connection is developmentally rooted and exacerbated (Brown & Larson, 2009). Indeed, recent research has indicated that the COVID-19 global pandemic has led to an increase in mental health concerns among children and adolescents in Canada. For example, findings from the Canadian Perspectives Survey indicated that only 42% of youth ages 15-24 reported having excellent or very good mental health in the time of COVID-19, compared to 62% in 2018 (Findlay & Arim, 2022). Additionally, a study conducted in Ontario, Canada, found that the population-level demand for mental health care for those aged 3-17 increased above expected levels through the fall of 2020 and continued to rise until early 2021 (Saunders et al., 2022). Moreover, alongside the increasing concerns around adolescents’ mental health in the time of COVID-19, as youth relied on technologies like smartphones to maintain and foster relationships with others, many worried about increased rates of problematic smartphone use among adolescents – an established risk factor for increased loneliness, depression, anxiety, and stress in the time of the pandemic (Rogers et al., 2021). However, despite these concerns around excessive use, smartphones played a pivotal role in fostering adolescents’ feelings of social connection during the pandemic (Ellis et al., 2020; Parent et al., 2021), and technology made it possible for daily life to continue amidst social distancing restrictions (e.g., by allowing for remote schooling and after-school programs). 31 This dissertation study is part of a larger ongoing program of research investigating adolescents’ technology engagement and well-being in British Columbia (B.C.), Canada. For this study, data was collected at two time points: Time 1, between April 27th and May 3rd, and 2021; and Time 2, between February 15th and March 1st, 2022. Participants were recruited from two secondary schools across two public school districts. One school was located in a large urban population centre, while the other was located in a medium suburban population centre (Statistics Canada, 2017). At Time 1, each school was operating under COVID-19 restrictions, which included mask wearing and physical distancing and classes were taking place part of the time online and part of the time in-person. Both schools had adopted a “Quarter System” wherein courses were condensed over a 10-week period. B.C. residents were asked to limit their social gatherings to only include their immediate household, plus six “safe guests” whom they included in their social bubble. Individuals were encouraged to avoid all non-essential travel, wear masks in all public places, and no large gatherings were to take place (BC Centre for Disease Control, 2021). At Time 2 of data collection, vaccines were made available to adolescents and each school was back to full-time in-person instruction (though students were still required to wear masks at all times when indoors; BC Centre for Disease Control, 2021). Despite the move back to full-time in-person instruction, there remained no extra-curricular activities, no school assemblies, and students were asked to social distance when possible. Moreover, both schools had changed to a “Semester System” where courses were taught over 5 months. 32 3.1 Methodological framework This study employed a longitudinal multi-cohort design in which self-report questionnaire data was collected online from participants from two high schools (grades 8-12) in British Columbia at two points approximately one year apart. 3.3 Procedure Ethical approval for this study was obtained from the Behavioural Review Ethics Board (BREB) of the University of British Columbia and the participating School Districts. School principals and administrators were contacted about the study via email and details of the study were discussed in follow-up meetings over Zoom, Microsoft Teams, or in person. Information about the study was then distributed to parents/guardians and students via email (see Appendix A), including an introductory video describing the study and its procedures, as well as a copy of the parent consent (see Appendix B) and student assent forms (see Appendix C). A meeting was also set up with the teachers from each school to explain the study and its procedures and answer any questions. To assist with obtaining a representative sample within each school, this study utilized a passive parental consent procedure (Pokorny et al., 2001; Shaw et al., 2015). Active informed assent was also obtained from participants prior to their participation. All students attending either school were invited to take part in the study provided that their parent(s) or guardian(s) did not oppose their participation. As a recruitment incentive, all students from each participating school were entered into a draw for a chance to win a smartphone. On the day of data collection, participants were directed to a survey site (www.Qualtrics.com) where an anonymous questionnaire was accessed through their personal electronic devices (e.g., smartphones). The questionnaire was administered on a school-wide basis during class time while participants were either physically or virtually present and took 35 33 to 60 minutes to complete. Data collection was supervised by teachers, and research assistants who, due to COVID-19, were available to answer questions about the survey via Microsoft Teams, and/or text message, instead of in-person. Students whose parents did not want them participating in the study were asked to quietly work at their desk while the others completed the questionnaire. 3.2 Participants The initial sample was comprised of 1789 high school students (n(urban school) = 956; n(suburban school) = 1203) between the ages of 11 and 18 (grades 8 –12). In order to study changes over time, only students who participated in the study at both time points were included in the final sample (n = 875; see Table 1 for a summary of participant characteristics). All participants in this final sample owned a smartphone. 34 Table 1 Characteristics of Participants Participants Urban School (n = 370) Suburban School (n = 505) Total (n = 875) Age in years at Time 1 (mean, standard deviation) 14.78 (1.28) 14.66 (1.15) 14.71 (1.21) Grade at Time 1 Grade 8 111 (30.0%) 132 (26.1%) 243 (27.8%) Grade 9 76 (20.5%) 147 (29.1%) 223 (25.5%) Grade 10 94 (25.4%) 138 (27.3%) 232 (26.5%) Grade 11 85 (23.0%) 67 (13.27%) 152 (17.4%) Grade 12 1 (0.3%) 5 (1.0%) 6 (0.7%) Grade at Time 2 Grade 8 2 (0.6%) 1 (0.2%) 3 (0.3%) Grade 9 108 (29.2%) 130 (25.7%) 238 (27.2%) Grade 10 79 (21.4%) 157 (31.1%) 236 (27.0%) Grade 11 96 (26.0%) 146 (28.9%) 242 (27.7%) Grade 12 80 (21.6%) 65 (12.9%) 145 (15.6%) Gendera Boy 196 (53.0%) 256 (50.7%) 452 (51.7%) Girl 160 (43.2%) 222 (44.0%) 382 (43.7%) Non-binary 8 (2.2%) 5 (1.0%) 13 (1.5%) Transgender 5 (1.4%) 1 (0.2%) 6 (0.7%) Two-spirit 1 (0.3%) 1 (0.2%) 2 (0.2%) Not sure/I don’t know 8 (2.2%) 11 (2.2%) 19 (2.2%) Ethnicityb Black 7 (1.9%) 15 (3.0%) 22 (2.5%) East Asian 162 (43.8%) 15 (3.0%) 177 (20.2%) Indigenous 3 (0.8%) 16 (3.2%) 19 (2.2%) 35 Latin American 12 (3.2%) 6 (1.2%) 18 (2.1%) South Asian 16 (4.3%) 285(56.4%) 301 (34.4%) Southeast Asian 141 (38.1%) 39 (7.7%) 180 (20.6%) West Asian 2 (0.5%) 4 (.8%) 6 (0.7%) White 45 (12.2%) 52 (10.3%) 97 (11.1%) Language spoken at home English 161 (43.5%) 203 (40.2%) 364 (41.6%) Not English 206 (55.7%) 286 (56.6%) 492 (56.2%) aSome participants selected more than one gender identity. bSome participants selected more than one ethnic identification. 36 3.4 Measures 3.4.1 Demographics Participants were asked to self-report their school, age, grade, gender, ethnicity, and language(s) spoken at home. Specifically, participants were asked to select whether they identified as a “girl”, a “boy”, “something else (for example, non-binary, transgendered)”, or “Not sure/I don’t know”. Participants who selected “something else” were asked to list their gender identity. In terms of ethnicity data, participants were asked to select the ethnicity with which they most identified. The options for ethnicity were developed to mirror the demographics of the lower mainland of British Columbia (i.e., where data was collected). Lastly, participants were asked to select whether or not they spoke a language other than English most of the time at home. 3.4.2 Parent and peer attachment In order to assess participants’ attachment to parents and peers, a shortened version of the Inventory of Parent and Peer Attachment was used (IPPA-SV; Raja et al., 1992; see Appendix D). This measure includes two sections that target both parent attachment and peer attachment (12-items each) with subsequent sections (4-items each) that assess three distinct components of parent/peer attachment: parent/peer communication (e.g., “If my parents know something is bothering me, they ask me,” “My friends encourage me to talk about my difficulties”), parent/peer trust (e.g., “My parents respect my feelings,” “When I am angry about something my friends try to be understanding”), and parent/peer alienation (e.g., “I don’t get much attention at home,” “I feel alone or apart when I am with my friends”). Items are rated on a 4-point Likert scale ranging from 1 (Never true for me) to 4 (Always true for me). Parent and peer attachment scores for each individual were computed by summing the trust and communication raw scores 37 and subtracting from this sum of the alienation raw score (Armsden & Greenberg, 1987). See Table 2 for reliability of the measures. 3.4.3 Fear of missing out (FoMO) FoMO was measured using the Fear of Missing Out scale (Przybylski et al., 2013; see Appendix E). The scale is comprised of 10-items rated on a 5-point Likert scale, ranging from 1 (Never) to 5 (All of the time). Sample items include “I get worried when I find out my friends are having fun without me,” “It bothers me when I miss an opportunity to hang out with friends,” and “I fear my friends have more rewarding experiences than me.” A composite variable was created by calculating the average score across all items. See Table 2 for reliability information. 3.4.4 Habitual Smartphone Use Adolescents’ level of habitual smartphone engagement was measured using the Absent-Minded Subscale of the Smartphone Usage Questionnaire (SUQ-A; Marty-Dugas et al., 2018; see Appendix F). This measure was selected since it assesses absent-minded and extrinsically motivated smartphone use without over-pathologizing these behaviours within a medical addiction model. Adolescents were asked to respond to 7 items using a 6-point Likert Scale from 1 (Never) to 6 (Almost Always). The SUQ-A was designed to assess the frequency with which individuals absent-mindedly engaged with their smartphones and included items such as “How often do you find yourself checking your phone without realizing why you did it?” and “How often do you lose track of time while using your phone?” A composite variable was created by calculating the average score across all items. See Table 2 for reliability information. 3.4.5 Overall Need Satisfaction and Frustration Adolescents’ overall satisfaction and frustration of the basic psychological needs for autonomy, competence, and relatedness was assessed using the Basic Psychological Need Satisfaction and 38 Frustration Scale – For Children (BPNSNF; Chen et al., 2015; van der Kaap-Deeder et al., 2015; see Appendix G). Translations of this scale have been previously validated for use with adolescents in Italian (Liga et al., 2020) and in Spanish (Rodríguez-Meirinhos et al., 2019). The BPNSFS-child version consists of 24 items divided into six subscales representing the satisfaction and the frustration of each basic psychological need: autonomy satisfaction (e.g., “I feel free to choose which activities I do”), autonomy frustration (e.g., “I feel pressured to do too many things”), relatedness satisfaction (e.g., “The people that I like, also like me”), relatedness frustration (e.g., “I feel excluded from the group I want to be a part of”), competence satisfaction (e.g., “I can do things well”), and competence frustration (e.g., “I often have doubts about whether I’m good at things”). Adolescents rated each item on a 5 point-Likert scale ranging from 1 (Completely untrue) to 5 (Completely true). Composite variables were created by calculating the average score across items for each subscale. See Table 2 for reliability information. 3.4.7 Online Need Satisfaction Adolescents’ online satisfaction of their basic psychological needs for autonomy, competence, and relatedness was assessed using the Online Intrinsic Need Satisfaction Scale (OINS; Reinecke et al., 2014; see Appendix H). This scale was originally conceptualized to assess basic psychological need satisfaction through Facebook. For the purpose of this study, “When I use Facebook,” was changed to “When I use my device,” to assess adolescents’ need satisfaction through smartphone engagement. This scale consists of 12 items divided into three subscales representing the satisfaction of each basic psychological need: autonomy satisfaction (e.g., “When I use my device, I feel free to be who I am”), competence satisfaction (e.g., “When I use my device, I feel like I have something to contribute”), and relatedness satisfaction (e.g., “When 39 I use my device, I feel a strong connection between myself and other people”). Adolescents rated each item on a 5-point Likert scale ranging from 1 (Not at all true) to 5 (Very true). Composite variables were created by calculating the average score across items for each subscale. See Table 2 for reliability information. 3.4.6 Mental Health To assess their mental health, adolescents completed the Depression, Anxiety, and Stress Scale (DASS- 21; Lovibond & Lovibond, 1995; see Appendix I). The DASS- 21 consisted of 3 subscales: depression (7 items; e.g., “I couldn’t seem to experience any positive feeling at all”), anxiety (7 items; e.g., “I was aware of dryness of my mouth” ), and stress (7 items; e.g., “I found it hard to wind down”), that are rated on a 4-point Likert scale, ranging from 0 (Did not apply to me at all) to 3 (Applied to me very much or most of the time). As proposed by Lovibond and Lovibond (1995), sum scores were computed by adding up the scores on the items per subscale and multiplying them by 2. While this is a dimensional measure of mental health, Lovibond and Lovibond suggest transforming scores into severity ratings. Thus, these scores were then transformed into mental health severity ratings according to the following cut-offs (Lovibond & Lovibond, 1995): scores = 0-14 are labeled as 1 (Normal), Scores = 15-18 are labeled as 2 (Mild), Scores = 19-25 are labeled as 3 (Moderate), Scores = 26-33 are labeled as 4 (Severe), Scores > 34 are labeled as 5 (Extremely Severe). This measure has been shown to have good psychometric properties (Zanon et al., 2021). 40 Table 2 Descriptive Statistics of Measures Variable n M SD Range # items Cronbach’s  Time 1 Habitual Smartphone Use 824 4.00 1.16 1-7 7 .87 Fear of Missing Out (FoMO) 767 2.24 0.80 1-5 10 .88 Parent Attachment 719 14.03 7.50 -8-28 12 .89 Peer Attachment 712 15.12 6.03 -8-28 12 .83 Overall Autonomy Satisfaction 797 3.52 0.77 1-5 4 .78 Overall Competence Satisfaction 794 3.31 0.87 1-5 4 .85 Overall Relatedness Satisfaction 795 3.62 0.79 1-5 4 .76 Overall Autonomy Frustration 796 2.26 0.81 1-5 4 .74 Overall Competence Frustration 793 2.79 1.09 1-5 4 .86 Overall Relatedness Frustration 794 1.72 0.73 1-5 4 .71 Online Autonomy Satisfaction 835 2.89 0.87 1-5 4 .72 Online Competence Satisfaction 831 2.43 0.82 1-5 4 .72 Online Relatedness Satisfaction 829 2.42 0.92 1-5 4 .83 Time 2 Overall Autonomy Satisfaction 784 3.46 0.85 1-5 4 .82 Overall Competence Satisfaction 782 3.32 0.88 1-5 4 .85 Overall Relatedness Satisfaction 782 3.56 0.85 1-5 4 .79 Overall Autonomy Frustration 782 2.35 0.83 1-5 4 .78 Overall Competence Frustration 782 2.81 1.08 1-5 4 .85 Overall Relatedness Frustration 782 1.81 0.80 1-5 4 .78 Online Autonomy Satisfaction 841 2.82 0.86 1-5 4 .73 Online Competence Satisfaction 840 2.40 0.79 1-5 4 .72 Online Relatedness Satisfaction 840 2.39 0.91 1-5 4 .84 Depression 796 3.94 0.92 2-5 7 .91 41 Anxiety 797 4.42 0.79 1-5 7 .84 Stress 797 3.14 1.34 1-5 7 .87 Note. SD = Standard Deviation of the mean. 42 3.5 Analyses 3.5.1 Preliminary Analyses Descriptive statistics of the measures are displayed in Table 2. The data was checked for outliers and normality tests were conducted for all variables using SPSS (IBM Corp, 2021). These tests confirmed the normality of the data. The final sample had 10.5% missing data which was Missing Completely at Random (MCAR; Rubin, 1976). All subsequent analyses were conducted in Mplus 8 (Muthén & Muthén, 2017) using robust Maximum Likelihood (MLR) estimator, which handles any item level missing or non-normality of the data. Following this, preliminary factor analyses were conducted to verify the psychometric properties of the latent profile indicators used in this dissertation. Factor scores (estimated in standardized units with M = 0, SD = 1) were saved from these preliminary measurement models and used as inputs for the main analyses. By giving less weight to the items that are subject to higher levels of measurement error, factor scores provide some control for measurement error (Skrondal & Laake, 2001), and preserve the nature of the measurement model (i.e., measurement invariance), better than scale scores (Morin et al., 2015). To ensure that the measures used at both time points remained fully comparable, these factors scores were saved from longitudinally invariant measurement models (Millsap, 2011). Correlations for all variables (including these factor scores) used in the present research are reported in Tables 3 and 4. 43 Table 3 Correlations between Variables of Interest 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 1. Overall AS T1 2. Overall AF T1 -.430 3. Overall RS T1 .715 -.320 4. Overall RF T1 -.391 .738 -.528 5. Overall CS T1 .817 -.313 .704 -.360 6. Overall CF T1 -.400 .712 -.294 .683 -.488 7. Online AS T1 .284 .033 .315 .002 .255 -.013 8. Online CS T1 .236 .126 .294 .145 .293 .053 .909 9. Online RS T1 .178 .111 .276 .131 .176 .080 .922 .960 10. Overall AS T2 .590 -.328 .374 -.281 .511 -.324 .223 .138 .104 11. Overall AF T2 -.385 .662 -.342 .496 -.299 .517 .038 .119 .133 -.294 12. Overall RS T2 .402 -.256 .584 -.462 .427 -.322 .287 .220 .218 .728 -.251 13. Overall RF T2 -.236 .414 -.313 .687 -.265 .502 .037 .154 .180 -.187 .703 -.455 14. Overall CS T2 .560 -.310 .481 -.343 .777 -.452 .232 .234 .139 .810 -.279 .713 15. Overall CF T2 -.339 .505 -.291 .502 -.433 .784 -.011 .035 .082 -.244 .778 -.240 16. Online AS T2 .148 .052 .177 .035 .140 -.013 .646 .596 .582 .230 .179 .232 17. Online CS T2 .136 .061 .192 .099 .229 -.025 .609 .672 .667 .136 .210 .155 18. Online RS T2 .074 .077 .193 .113 .099 .052 .583 .682 .704 .109 .192 .212 19. Habitual Smartphone Use .038 -.010 .046 -.017 .023 .009 .053 .033 .036 -.014 -.008 -.001 20. FoMO .006 .007 .028 -.025 -.002 .071 .013 -.008 -.009 -.013 -.017 .004 21. Parent Attachment .035 -.039 .018 -.022 .019 -.057 .009 .014 .01 .077 .005 .062 22. Friend Attachment .020 -.041 .047 -.056 .027 -.023 .027 .033 .027 .059 .038 .088 23. Stress -.012 .019 .001 .001 -.064 .058 -.034 -.051 -.039 0.00 -.023 -.016 24. Anxiety -.011 .050 -.004 .039 -.063 .065 -.005 -.015 -.005 -.029 .031 -.056 25. Depression -.012 -.008 -.001 -.016 -.030 .011 -.024 -.045 -.041 -.016 -.044 -.031 44 Table 4 Correlations between Variables of Interest (Continued) 13. 14. 15. 16. 17. 18. 19. 20. 21. 22. 23. 24. 14. Overall CS T2 -.292 15. Overall CF T2 .674 -.387 16. Online AS T2 .176 .182 .099 17. Online CS T2 .270 .199 .068 .887 18. Online RS T2 .249 .100 .110 .862 .939 19. Habitual Smartphone Use -.013 -.006 .012 .039 .030 .013 20. FoMO -.046 -.021 .062 -.024 -.033 -.042 .379 21. Parent Attachment .026 .055 -.014 .064 .058 .053 -.235 -.319 22. Friend Attachment .014 .059 .029 .003 .004 .012 -.069 -.179 .380 23. Stress -.020 -.062 .030 -.051 -.068 -.057 .293 .324 -.371 -.222 24. Anxiety .048 -.071 .045 -.037 -.041 -.047 .227 .189 -.277 -.165 .681 25. Depression -.032 -.037 -.012 -.019 -.030 -.043 .244 .274 -.403 -.262 .724 .609 Note. Bolded coefficients represent p < .05. Overall and Online Need Satisfaction and Frustration (AS= Autonomy Satisfaction, CS=Competence Satisfaction, RS=Relatedness Satisfaction, AF=Autonomy Frustration, CF=Competence Frustration, RF=Relatedness Frustration) are measurement invariant factor scores (T1=Time 1, T2=Time 2). Predictors (Habitual Smartphone Use, FoMO, Parent Attachment, Peer Attachment) are from Time 1. Outcomes (Stress, Anxiety, Depression) are from Time 2. 45 3.5.3 Preliminary Measurement Models for Latent Profile Indicators All factor scores were estimated by confirmatory factor analysis in Mplus 8 (Muthén & Muthén, 2017) using robust Maximum Likelihood (MLR) estimator, to handle any non-normality of the data. Confirmatory factor analytic models (CFA) of the overall and online need satisfaction and frustration scales were first estimated at each time point. First, a model with three correlated factors (overall basic need satisfaction, overall basic need frustration, and online need satisfaction) was estimated at each time point. Then, a model with nine correlated factors (overall autonomy satisfaction, overall competence satisfaction, overall relatedness satisfaction, overall autonomy frustration, overall competence frustration, overall competence frustration, online autonomy satisfaction, online competence satisfaction, and online relatedness satisfaction) was estimated at each time point. The model with nine-correlated factors showed superior fit to that of the three-correlated factors and was retained for longitudinal measurement invariance testing. See Table 5 for information about the model fit criteria and Table 6 for the standardized factor loadings. To ensure that the measurement models operated in the same manner across time points, measurement invariance testing was conducted on the nine-factor model (Millsap, 2011; Morin et al., 2018): (1) configural invariance, (2) weak invariance (loadings), (3) strong invariance (loadings and thresholds), (4) strict invariance (loadings, thresholds, and uniquenesses), (5) invariance of the latent variances-covariances (loadings, thresholds, uniquenesses, and latent variances-covariances), and (6) latent means invariance (loadings, thresholds, uniquenesses, latent variances-covariances, and latent means). Comparative model fit indices were used to describe the fit of the alternative models (Hu & Bentler, 1999), this included the comparative fit 46 index (CFI), the Tucker-Lewis index (TLI), the root mean square error of approximation (RMSEA) and its 90% confidence interval, and the chi-square difference tests. Values greater than .90 for the CFI and TLI indicate adequate model fit, although values greater than .95 are preferable. Values smaller than .08 or .06 for the RMSEA respectively support acceptable and excellent model fit. Like the chi square, chi square difference tests present a known sensitivity to sample size and minor model misspecifications such that recent studies suggest complementing this information with changes in CFI and RMSEA in the context of tests of measurement invariance (e.g., Chen, 2007). A ∆CFI/TLI of -.010 or less and a ∆RMSEA of -.015 or less between a more restricted model and the previous one supports the invariance hypothesis. Factor scores (estimated in standardized units with M = 0, SD = 1) were saved from the factor mean invariant model and used as inputs for the Latent Profile Analyses. 47 Table 5 Goodness-of-Fit Statistics of the Preliminary Measurement Models of LPA Indicators Description χ² (df) CFI TLI RMSEA 90% CI ∆CFI ∆RMSEA ∆ χ² (df) TIME 1 Three correlated factors 2337.55 (591) 0.826 0.815 0.059 .057-.062 N/A N/A N/A Nine correlated factors 1194.86 (558) 0.937 0.929 0.037 .034-.040 N/A N/A N/A TIME 2 Three correlated factors 2645.00 (591) 0.799 0.786 0.064 .061-.066 N/A N/A N/A Nine correlated factors 1269.20 (558) 0.930 0.922 0.039 .037-.042 N/A N/A N/A Longitudinal Measurement Invariance Configural invariance 4531.39 (2331) 0.906 0.897 0.033 .031-.031 N/A N/A N/A Metric invariance 4565.91 (2358) 0.906 0.898 0.033 .031-.032 0.000 0.000 34.51 (27) Scalar Invariance 4611.36 (2385) 0.905 0.898 0.033 .031-.033 -0.001 0.000 45.45 (27)* Residual Invariance 4645.41 (2410) 0.905 0.900 0.032 .031-.034 0.000 -0.001 34.06 (35) Factor Invariance/covariance 4672.00 (2430) 0.904 0.900 0.032 .031-.034 -0.001 0.000 26.59 (20) Factor mean invariance 4707.63 (2439) 0.903 0.899 0.033 .031-.034 -0.001 0.001 35.63 (9)*** Note. *** p < .001, * p < .05 48 Table 6 Standardized Factor Loadings for Online and Offline Need Satisfaction and Frustration Time 1 Time 2  S.E.  S.E. F1BNAS BY F2BNAS BY T1BN1 0.54 0.03 T2BN1 0.64 0.03 T1BN7 0.77 0.03 T2BN7 0.82 0.02 T1BN13 0.80 0.02 T2BN13 0.81 0.02 T1BN19 0.64 0.03 T2BN19 0.69 0.03 F1BNAF BY F2BNAF BY T1BN2 0.46 0.04 T2BN2 0.58 0.04 T1BN8 0.76 0.03 T2BN8 0.74 0.03 T1BN14 0.73 0.03 T2BN14 0.75 0.03 T1BN20 0.60 0.04 T2BN20 0.68 0.03 F1BNRS BY F2BNRS BY T1BN3 0.53 0.04 T2BN3 0.62 0.03 T1BN9 0.80 0.02 T2BN9 0.81 0.02 T1BN15 0.82 0.02 T2BN15 0.83 0.02 T1BN21 0.56 0.04 T2BN21 0.60 0.03 F1BNRF BY F2BNRF BY T1BN4 0.55 0.04 T2BN4 0.62 0.04 T1BN10 0.63 0.04 T2BN10 0.75 0.03 T1BN16 0.69 0.03 T2BN16 0.76 0.03 T1BN22 0.67 0.03 T2BN22 0.66 0.03 F1BNCS BY F2BNCS BY T1BN5 0.79 0.02 T2BN5 0.79 0.02 T1BN11 0.80 0.02 T2BN11 0.82 0.02 T1BN17 0.76 0.02 T2BN17 0.77 0.02 T1BN23 0.71 0.02 T2BN23 0.69 0.03 F1BNCF BY F2BNCF BY T1BN6 0.72 0.02 T2BN6 0.76 0.02 T1BN12 0.83 0.02 T2BN12 0.80 0.02 T1BN18 0.81 0.02 T2BN18 0.79 0.02 T1BN24 0.75 0.02 T2BN24 0.73 0.03 49 F1ONA BY F2ONA BY T1ON1 0.67 0.03 T2ON1 0.61 0.03 T1ON5 0.55 0.03 T2ON5 0.59 0.03 T1ON8 0.60 0.03 T2ON8 0.60 0.03 T1ON11 0.69 0.03 T2ON11 0.75 0.02 F1ONC BY F2ONC BY T1ON2 0.62 0.03 T2ON2 0.57 0.03 T1ON4 0.71 0.02 T2ON4 0.68 0.03 T1ON7 0.68 0.03 T2ON7 0.70 0.02 T1ON10 0.52 0.03 T2ON10 0.56 0.03 F1ONR BY F2ONR BY T1ON3 0.68 0.03 T2ON3 0.71 0.02 T1ON6 0.79 0.02 T2ON6 0.78 0.02 T1ON9 0.75 0.02 T2ON9 0.75 0.02 T1ON12 0.76 0.02 T2ON12 0.78 0.02 Note. Time 1 Factors: F1BNAS = Overall Autonomy Satisfaction, F1BNCS = Overall Competence Satisfaction, F1BNRS = Overall Relatedness Satisfaction, F1BNAF = Overall Autonomy Frustration, F1BNCF = Overall Competence Frustration, F1BNRF = Overall Relatedness Need Frustration, F1ONA = Online Autonomy Satisfaction, F1ONC = Online Competence Need Satisfaction, F1ONR = Online Relatedness Satisfaction. Time 2 Factors: F2BNAS = Overall Autonomy Satisfaction, F2BNCS = Overall Competence Satisfaction, F2BNRS = Overall Relatedness Satisfaction, F2BNAF = Overall Autonomy Frustration, F2BNCF = Overall Competence Frustration, F2BNRF = Overall Relatedness Need Frustration, F2ONA = Online Autonomy Satisfaction, F2ONC = Online Competence Need Satisfaction, F2ONR = Online Relatedness Satisfaction. 50 Table 7 Standardized Factor Correlations at Time 1 1. 2. 3. 4. 5. 6. 7. 8. 1. Overall Autonomy Satisfaction 2. Overall Autonomy Frustration -.370 3. Overall Relatedness Satisfaction .630 -.259 4. Overall Relatedness Frustration -.330 .659 -.476 5. Overall Competence Satisfaction .750 -.255 .636 -.307 6. Overall Competence Frustration -.342 .635 -.239 .620 -.438 7. Online Autonomy Satisfaction .241 .027 .257 -.021 .227 -.008 8. Online Relatedness Satisfaction .160 .085 .250 .102 .151 .064 .885 9. Online Competence Satisfaction .220 .107 .241 .138 .275 .056 .916 .964 51 Table 8 Standardized Factor Correlations at Time 2 1. 2. 3. 4. 5. 6. 7. 8. 1. Overall Autonomy Satisfaction 2. Overall Autonomy Frustration -.186 3. Overall Relatedness Satisfaction .616 -.404 4. Overall Relatedness Frustration -.216 .658 -.231 5. Overall Competence Satisfaction .705 -.178 .589 -.330 6. Overall Competence Frustration -.274 .670 -.144 .772 -.240 7. Online Autonomy Satisfaction .153 .222 .197 .124 .064 .128 8. Online Relatedness Satisfaction .211 .106 .277 .198 .089 .147 .965 9. Online Competence Satisfaction .158 .199 .180 .163 .091 .201 .924 .966 52 3.5.3 Latent Profile Transition Analyses (LPTA) Latent Profile Analysis (LPA) was first estimated separately at each time point using the nine basic need satisfaction and frustration factors as profile indicators in order to ensure that the same number of profiles was extracted at each time point. Following this, a longitudinal LPA model was estimated to test the similarity of the profile measurement model over time. The most similar longitudinal solution was then converted into a Latent Transition Analysis to examine participants’ transitions between profiles over time. First, for each time point, solutions including 1–7 latent profiles in which the means and variances of the factors were freely estimated were examined (Diallo et al., 2016; Morin et al., 2011; Peugh & Fan, 2013). To avoid local maximum, all LPA were conducted using 5000 random sets of start values, 1000 iterations, and retained the 200 best solutions for final stage optimization (Hipp & Bauer, 2006). To help decide on the optimal number of profiles, the theoretical conformity and meaningfulness, as well as the statistical adequacy of the alternative solutions were considered (Hipp & Bauer, 2006; Marsh et al., 2009). A series of statistical indices were also considered to help guide this decision about the optimal number of profiles (McLachlan & Peel, 2000): (i) The Akaïke Information Criterion (AIC), (ii) the Consistent AIC (CAIC), (iii) the Bayesian Information Criterion (BIC), (iv) the sample-size Adjusted BIC (ABIC), (v) the standard and adjusted Lo, Mendell, and Rubin’s (2001) Likelihood Ratio Tests (LMR/aLMR; because these two tests typically yield the same conclusion, only the aLMR is reported), and (vi) the Bootstrap Likelihood Ratio Test (BLRT). A lower AIC, CAIC, BIC and ABIC value suggests a better-fitting model. A statistically significant aLMR or BLRT supports the estimated model when compared to a model including one fewer profiles. Simulation studies support the efficacy of the CAIC, BIC, ABIC, and BLRT, but not that of the AIC and aLMR 53 (e.g., Diallo et al., 2016; Peugh & Fan, 2013), so these last two are not reported. It should also be noted that these tests remain heavily influenced by sample size (Marsh et al., 2009), so that with sufficiently large samples, they often keep on suggesting the addition of profiles without reaching a minimum. When this happens, Morin and colleagues (2011) recommend using an elbow plot (i.e., a graphical display) to suggest the optimal number of profiles. In these plots, the point after which the slope flattens suggests the optimal number of profiles. Finally, the entropy (ranging from 0 to 1) indicates the precision with which the cases are classified into the various profiles (a.k.a., classification reliability), with high values indicating a more precise and reliable classification. Entropy is not used to guide model selection (Lubke & Muthén, 2007). In preparation for the Latent Transition Analysis, once the optimal number of profiles had been selected at each time point, the two retained time-specific LPA solutions were integrated into a single longitudinal LPA model allowing for systematic longitudinal tests of profile similarity. These tests were conducted following the sequential strategy proposed by Morin et al. (2016) for tests of profile similarity across multiple groups and recently optimized by Morin & Litalien (2019) for the longitudinal context. The first step examined whether the same number of profiles were identified at each time point (i.e., configural similarity) and corresponded to the previously described time-specific LPA. A longitudinal LPA was then estimated from the model of configural similarity, to which equality constraints were progressively integrated. In the second step, the structural similarity of the profiles was verified by including equality constraints across time points on the means of the profile indicators (i.e., the basic need satisfaction and frustration factors) to test whether the profiles retained the same global shape over time. Once this form of similarity was confirmed, the third step verified whether the within-profile variability remained stable across time points by testing the dispersion similarity of the profiles 54 by including equality constraints on the variances of the profile indicators across time points. Fourth, to ascertain whether the relative size of the profiles remained the same over time, the distributional similarity of the profiles was tested by constraining the class probabilities to equality across time points. The fit of these models was then compared using the aforementioned information criteria, and Morin and colleagues (2016) suggest that at least two indices out of the CAIC, BIC, and ABIC should be lower for the more “similar” model for the hypothesis of profile similarity to be supported. Following this, the most similar model was converted to a LTA model (Collins & Lanza, 2009), in order to more systematically investigate within-person stability and transitions in profile membership (Morin & Litalien, 2019). Following Morin and Litalien’s (2019) recommendations, all LTA were estimated using the manual auxiliary 3-step approach described by Asparouhov and Muthén (2013). 3.5.4 Predictors and outcomes of profile membership Multinomial logistic regressions were conducted to test the relations between the predictors estimated at Time 1 (school, age, gender, ethnicity, habitual smartphone use, FoMO, and parent and peer attachment) and the likelihood of membership into the various profiles at Time 1. To facilitate interpretations, odds ratios (OR) are reported to reflect changes in the likelihood of membership in a target profile versus a comparison profile for each unit increase in the predictor. Outcomes were also incorporated into the final LTA solution. In these analyses, Time 2 measures of the outcomes (stress, anxiety, and depression) were specified as associated with the profiles estimated at Time 2. The MODEL CONSTRAINT command of Mplus was used to systematically test mean-level differences across pairs of profiles using the multivariate delta method (Kam et al., 2013). 55 Chapter 4: Results 4.1 Latent Profile Analyses The fit indices associated with the time-specific LPA are reported in Table 9 (elbow plots are reported in Figs. 1 and 2). For all models presented, the best loglikelihood has been replicated (i.e., 19/200 times at Time 1; 200/200 at Time 2). Across time points, the statistical indicators kept on suggesting the addition of profiles without converging on any specific solutions. As such, elbow plots were used to obtain additional guidance. These plots revealed an inflection point corresponding to the three-profile solution, at Time 1, and two-inflection points, corresponding to the two-profile and four-profile solution at Time 2. Solutions including two to five profiles were thus more carefully considered and examined theoretically. Importantly, this examination revealed that up to four profiles, each increase resulted in the addition of a meaningful profile to the solution at both time points (e.g., the profile with online and overall need satisfaction and frustration). In contrast, adding a fifth profile only resulted in the arbitrary division of an existing profile into smaller ones (e.g., the profile with below average need satisfaction was divided into two profiles with the same pattern). On the basis of these theoretical considerations, the four-profile solution was retained at both time waves, supporting its configural similarity. This solution provides a good level of classification accuracy, with an entropy value of .896 at Time 1 and .931 at Time 2. The results from the longitudinal LPA analyses further support the structural, dispersion, and distributional similarity of the solution (based on the observation of lower values of the BIC and CAIC, Morin et al., 2016; see Table 10). Thus, this model was retained for interpretation and for the next stages of the analyses. This final model is illustrated in Fig. 3 and within-profile means are reported in Table 11. 56 Table 9 Results from the Latent Profile Analysis Models Estimated Separately at Each Time Point Number classes LL #fp Scaling CAIC BIC ABIC Entropy BLRT VLMR Time 1 (N = 824 ) 1 -6504.097 18 0.978 13078.681 13129.049 13071.888 N/A N/A N/A 2 -5702.970 28 1.268 11515.586 11593.937 11505.020 0.852 <.001 <.001 3 -5106.506 38 1.217 10361.817 10468.150 10347.476 0.929 <.001 <0.001 4 -4779.531 48 1.371 9747.027 9881.343 9728.913 0.896 <.001 <0.001 5 -4526.013 58 2.212 9279.150 9441.447 9257.261 0.889 <.001 0.713 6 -4301.988 68 1.954 8870.259 9060.540 8844.597 0.899 <.001 0.377 7 -4098.081 78 1.719 8501.604 8719.868 8472.169 0.916 0.001 0.326 Time 2 (N = 824) 1 -7515.712 18 1.008 15101.911 14552.28 14495.118 N/A N/A N/A 2 -6280.190 28 1.179 12670.026 12748.376 12659.458 0.912 <.001 <0.001 3 -5813.835 38 1.363 11776.475 11882.809 11762.135 0.925 <.001 0.001 4 -5388.574 48 1.492 10965.113 11099.428 10946.998 0.931 <.001 0.050 5 -5158.768 58 1.509 10544.660 10706.959 10522.772 0.908 <.001 0.060 6 -4910.487 68 1.528 10087.257 10277.538 10061.595 0.905 <.001 0.031 7 -4742.078 78 1.543 9789.598 10007.862 9760.163 0.920 <.001 0.311 Note. LL: model loglikelihood; #fp: number of free parameters; scaling: scaling correction factor associated with robust maximum likelihood estimates; AIC: Akaïke information criteria; CAIC: constant AIC; BIC: Bayesian information criteria; ABIC: sample size adjusted BIC; aLMR: adjusted Lo-Mendel-Rubin likelihood ratio test; BLRT: bootstrap likelihood ratio test. 57 Table 10 Results from the Latent Profile Transition Analysis LL #fp scaling AIC BIC Entropy CAIC Final Latent Profile Analyses Time 1 -4779.531 48 1.371 9655.063 9881.343 0.896 9747.027 Time 2 -5388.574 48 1.492 10873.148 11099.428 0.931 10965.113 Longitudinal LPA Configural Similarity -17120.490 146 1.297 34532.980 3532.980 0.900 34816.513 Structural Similarity -17186.74 111 1.387 34595.480 35125.419 0.893 34811.043 Dispersion Similarity -17215.729 76 1.547 34583.458 34946.299 0.892 34731.051 Distribution Similarity -17217.004 73 1.595 34580.008 34928.526 0.892 34721.775 Latent Transition Analysis -2081.917 15 1.000 4193.833 4217.810 0.847 4222.964 LTA with Predictors Demographic Predictors -1982.400 33 1.002 4030.799 4187.275 0.848 4094.886 All Predictors -1496.421 45 1.026 3082.841 3286.534 0.852 3170.232 Predicting Transitions -1488.734 57 0.993 3091.467 3349.478 0.855 3202.162 Outcomes -3860.668 72 1.124 7865.336 8191.243 0.851 8005.161 Note. LL: Model Loglikelihood; #fp: Number of free parameters; Scaling: scaling factor associated with MLR loglikelihood estimates; AIC: Akaïke Information Criteria; CAIC: Constant AIC; BIC: Bayesian Information Criteria. 58 Figure 2 Elbow Plot of the Value of the Information Criteria for Solutions Including Different Number of Latent Profiles (Time 1) 8000900010000110001200013000140001 2 3 4 5 6 7AICBICSABIC 59 Figure 3 Elbow Plot of the Value of the Information Criteria for Solutions Including Different Number of Latent Profiles (Time 2) 90001000011000120001300014000150001 2 3 4 5 6 7AICBICSABIC 60 Figure 4 Longitudinal 4-Profile Solution Note. The profile indicators are estimated from factor scores with mean of 0 and a standard deviation of 1; Profile 1: Unsatisfied and Frustrated; Profile 2: Satisfied and Frustrated; Profile 3: Online Satisfied and Frustrated; Profile 4: Overall Satisfied and Unfrustrated. -1.5-1-0.500.511.5Profile 1 Profile 2 Profile 3 Profile 4Online AS Online CS Online RS Overall AS Overall CS Overall RS Overall AF Overall CF Overall RF 61 Table 11 Detailed Results from the Final Longitudinal Latent Profile Analytic Solution (Distributional Similarity) at Time 1 and 2 Profile 1 (31.4%) Profile 2 (14.1%) Profile 3 (29.4%) Profile 4 (25.2%) “Unsatisfied and Frustrated” “Satisfied and Frustrated” “Online Satisfied and Frustrated” “Overall Satisfied and Unfrustrated” Mean Mean Mean Mean Online Autonomy Satisfaction -0.780 1.446 0.324 -0.193 Online Competence Satisfaction -0.826 1.536 0.424 -0.279 Online Relatedness Satisfaction -0.805 1.507 0.433 -0.266 Overall Autonomy Satisfaction -0.531 0.446 -0.266 0.736 Overall Competence Satisfaction -0.568 0.438 -0.225 0.779 Overall Relatedness Satisfaction -0.576 0.467 -0.152 0.695 Overall Autonomy Frustration 0.156 0.213 0.362 -0.820 Overall Competence Frustration 0.302 0.166 0.324 -0.862 Overall Relatedness Frustration 0.148 0.239 0.375 -0.793 Variance Variance Variance Variance Online Autonomy Satisfaction 0.297 0.256 0.140 0.572 Online Competence Satisfaction 0.253 0.270 0.149 0.520 Online Relatedness Satisfaction 0.228 0.276 0.148 0.435 Overall Autonomy Satisfaction 0.674 0.726 0.435 0.433 Overall Competence Satisfaction 0.614 0.868 0.448 0.407 Overall Relatedness Satisfaction 0.759 0.728 0.443 0.282 Overall Autonomy Frustration 0.667 0.975 0.524 0.188 Overall Competence Frustration 0.726 1.010 0.524 0.251 Overall Relatedness Frustration 0.645 1.157 0.598 0.086 Note. All variances were non-negative and confidence intervals did not cover zero, indicating variability within each profile. The profile indicators are estimated from factor scores with mean of 0 and a standard deviation of 1. Profile 1: Unsatisfied and Frustrated; Profile 2: Satisfied and Frustrated; Profile 3: Online Satisfied and Frustrated; Profile 4: Overall Satisfied and Unfrustrated. 62 4.1.1 Longitudinally Invariant Latent Profiles Profile 1 was characterized by low levels of online and overall need satisfaction accompanied by moderate feelings of overall need frustration, with slightly higher levels of competence frustration. This “Unsatisfied and Frustrated” profile corresponded to 31.4% of the sample. Profile 2 presented high levels of online and overall need satisfaction, including slightly higher online competence and relatedness need satisfaction, accompanied by moderate levels of overall need frustration, with slightly higher levels of autonomy and relatedness frustration. This “Satisfied and Frustrated” profile characterized 14.1% of participants. Profile 3 presented moderately high levels of online need satisfaction (with slightly lower autonomy need satisfaction) accompanied by slightly below average levels of overall need satisfaction (with slightly lower autonomy need satisfaction and slightly more relatedness need satisfaction) and moderately high levels of overall need frustration. This “Online Satisfied and Frustrated” profile characterized 29.4% of participants. Finally, Profile 4 presented below average online need satisfaction (with slightly higher online autonomy satisfaction) accompanied by high levels of overall need satisfaction (with slightly lower relatedness satisfaction) and low levels of overall need frustration (with slightly lower levels of overall competence frustration). This “Overall Satisfied and Unfrustrated” profile characterized 25.2% of the sample. 4.2 Latent Transitions This final model of distributional similarity was converted to a Latent Transition Analysis (LTA) using the manual auxiliary 3-step approach (Asparouhov & Muthén, 2014; Morin & Litalien, 2017). The transition probabilities from this LTA are reported in Table 12. These results show that membership into Profiles 1 (Unsatisfied and Frustrated), and 4 (Overall Satisfied and Unfrustrated) were mostly stable over time (i.e., 80.8% of those in Profile 1 stayed in Profile 1, 63 and 77.9% of those in Profile 4 stayed in Profile 4). As such, transitions were less common for participants initially corresponding to these profiles, with some of the members form Profile 1 (Unsatisfied and Frustrated) transitioning into Profile 3 (Online Satisfied and Frustrated; 13.2%), and some members of Profile 4 (Overall Satisfied and Unfrustrated) transitioning into Profile 1 (Unsatisfied and Frustrated; 11.2%). In contrast, membership in Profile 2 (Satisfied and Frustrated) and Profile 3 (Online Satisfied and Frustrated) displayed a higher level of instability over time (i.e., only 41.8% of those initially in Profile 2 stayed in Profile 2, and 53.2% of those in Profile 3 stayed in Profile 3). When transitions occurred for participants originally in Profile 2 (Satisfied and Frustrated), they mainly involved Profile 3 (Online Satisfied and Frustrated; 32.2%), although some members of Profile 2 also transitioned to Profile 4 (Overall Satisfied and Unfrustrated; 18.6%). For those originally in Profile 3 (Online Satisfied and Frustrated), transitions mainly involved Profile 1 (Unsatisfied and Frustrated; 33.4%), with some also transitioning into Profile 2 (Satisfied and Frustrated; 9.8%). 64 Table 12 Latent Transition Probabilities for Final Model Time 2 Profiles Time 1 Profiles Profile 1 Profile 2 Profile 3 Profile 4 Profile 1 0.808 0.013 0.132 0.047 Profile 2 0.074 0.418 0.322 0.186 Profile 3 0.334 0.098 0.532 0.037 Profile 4 0.112 0.038 0.070 0.779 Note. Profile 1: Unsatisfied and Frustrated; Profile 2: Satisfied and Frustrated; Profile 3: Online Satisfied and Frustrated; Profile 4: Overall Satisfied and Unfrustrated. 65 4.3 Covariates of Profile Membership Covariates were added to the LTA model of distributional similarity following the manual auxiliary 3-step approach (Asparouhov & Muthén, 2014; Morin & Litalien, 2017). Specifically, school, gender, age, ethnicity, habitual smartphone use, fear of missing out, and parent and peer attachment security at Time 1 were added as covariates of profile membership at Time 1. Moreover, habitual smartphone use at Time 1 was added as a predictor of profile transitions from Time 1 to Time 2. Model fit indices of the Latent Profile Transition Analysis are presented in Table 10. The results from the multinomial logistic regressions estimated for the covariates of the Time 1 profiles are reported in Table 13. Given that there are four profiles, the results are presented as six pairwise comparisons. For example, in column 1 of Table 13, the effect of each covariate is presented as the likelihood of being in Profile 1 when compared to the likelihood of being in Profile 4 (Satisfied and Unfrustrated). Profile 4 is used as the primary comparison class as this class represents the most “well-adjusted” group. The results of the multinomial logistic regression of the effect of habitual smartphone use on transitions between profiles are presented in Table 14. Once again, the results are presented as six pairwise comparisons to illustrate the differences between the four profiles. Here, the effect of habitual smartphone use on profile transitions is presented as the likelihood (as logit that can be transformed to probability or odds) of moving to one profile when compared to another given participants’ profile membership at Time 1. For example, in column 2 of Table 14, the effect of habitual smartphone on changing profiles is presented as the likelihood of transitioning from Profile 1 to Profile 2 compared to the likelihood of transitioning from Profile 1 to Profile 4. Participants’ school and age were not significantly associated with the likelihood of membership into the various profiles. Similarly, few associations were noted between the 66 likelihood of membership into the various profiles and participants’ ethnicity. That said, a few significant associations were observed. Specifically, adolescents who identified as Southeast Asian were less likely (OR = 0.37) to be in Profile 2 (Satisfied and Frustrated) than in Profile 3 (Online Satisfied and Frustrated), and more likely (OR = 2.94) to be in Profile 1 (Unsatisfied and Frustrated) than in Profile 2 (Satisfied and Frustrated). Moreover, several significant associations were found between participants gender and profile membership. Indeed, girls were more likely (OR = 2.17) to be in Profile 1 (Unsatisfied and Frustrated) than in Profile 4 (Satisfied and Unfrustrated), and more likely (OR = 3.39) to be in Profile 1 than in Profile 2 (Satisfied and Frustrated). Contrarily, girls were less likely (OR = 0.37) to be in Profile 2 (Satisfied and Frustrated) than in Profile 3 (Online Satisfied and Frustrated). Beyond the demographic predictors, participants’ levels of FoMO, habitual smartphone use, and parent and peer attachment were significantly associated with the likelihood of their membership into the various profiles. These variables were converted into z-scores (M = 0, SD = 1) to aid with interpretation of the findings. For each standard deviation increase in FoMO, the odds increased by a factor of 2.70 to be in Profile 2 (Satisfied and Frustrated) and by a factor of 2.45 be in Profile 3 (Online Satisfied and Frustrated), than to be in Profile 4 (Overall Satisfied and Unfrustrated). Moreover, participants were less likely (OR = 0.56) to be in Profile 1 (Unsatisfied and Frustrated) than Profile 3 (Online Satisfied and Frustrated), and less likely (OR = 0.51) to be in Profile 1 (Unsatisfied and Frustrated) than in Profile 2 (Satisfied and Frustrated). Moreover, for each standard deviation increase in habitual smartphone use, the odds of participants being in Profile 2 (Satisfied and Frustrated) increased by a factor of 1.63, compared to being in Profile 4 (Overall Satisfied and Unfrustrated). By contrast, a standard deviation increase in habitual smartphone use was also associated with being less likely (OR = 0.71) to be 67 in Profile 1 (Unsatisfied and Frustrated) compared to Profile 3 (Online Satisfied and Frustrated Profile 2 (Satisfied and Frustrated) and less likely (OR = 0.54) to be in Profile 1 (Unsatisfied and Frustrated) compared to Profile 2 (Satisfied and Frustrated). Parent and peer attachment security were also significant predictors of profile membership. Indeed, for each standard deviation increase in parent attachment security, participants were less likely (OR = 0.43) to be in Profile 1 (Unsatisfied and Frustrated), less likely (OR = 0.55) to be in Profile 2 (Satisfied and Frustrated), and less likely (OR = 0.55) to be in Profile 3 (Online Satisfied and Frustrated), when compared to Profile 4 (Overall Satisfied and Unfrustrated). Moreover, many significant associations were found between profile membership and peer attachment security. Specifically, a standard deviation increase in peer attachment security was associated with being less likely (OR = 0.33) to be in Profile 1 (Unsatisfied and Frustrated), and less likely (OR = 0.47) to be in Profile 3 (Online Satisfied and Frustrated), when compared to Profile 4 (Overall Satisfied and Unfrustrated). Similarly, participants were less likely (OR = 0.71) to be in Profile 1 (Unsatisfied and Frustrated) than in Profile 3 (Online Satisfied and Frustrated) and less likely (OR = 0.35) to be in Profile 1 (Unsatisfied and Frustrated) than in Profile 2 (Satisfied and Frustrated). Lastly, for each standard deviation increase in peer attachment security, the odds of participants being in Profile 2 (Satisfied and Frustrated) increased by a factor of 2.03, compared to being in Profile 3 (Online Satisfied and Frustrated). 4.4 Predictors of Profile Transitions Results indicated that habitual smartphone use was a significant predictor of transitions between profiles (see Table 14 for results from the Multinomial Logistic Regression). Specifically, as illustrated in the first column and row of Table 14, for every standard deviation increase in habitual smartphone use, those in Profile 1 (Unsatisfied and Frustrated) at Time 1 were more 68 likely to stay in Profile 1 (OR = 2.55) than to transition into Profile 4 (Overall Satisfied and Frustrated). Similarly, as illustrated in the second column of the first row of Table 14, participants in Profile 1 (Unsatisfied and Frustrated) at Time 1 were more likely (OR = 7.88) to transition into Profile 2 (Satisfied and Frustrated) at Time 2, when compared to the odds of transitioning into Profile 4 (Overall Satisfied and Unfrustrated). Additionally, the coefficients in third column of the first row of Table 14 illustrate that participants in Profile 1 (Unsatisfied and Frustrated) at Time 1 were more likely (OR = 3.65) to transition into Profile 3 (Online Satisfied and Frustrated), when compared to the odds of transitioning into Profile 4 (Overall Satisfied and Unfrustrated). Contrarily, the coefficients represented in column four and row six of Table 14, indicate that those initially in Profile 1 (Unsatisfied and Frustrated) were less likely (OR = 0.32) to remain in Profile 1, when compared to the likelihood of transitioning into Profile 2 (Satisfied and Frustrated). 69 Table 13 Results of the Multinomial Logistic Regression for Covariates of Profile Membership Profile 1 vs. Profile 4 Profile 2 vs. Profile 4 Profile 3 vs. Profile 4 Covariates Coef. SE OR Coef. SE OR Coef. SE OR School -0.72 0.37 0.49 -0.18 0.43 0.84 -0.31 0.36 0.74 Age 0.13 0.11 1.14 -0.11 0.14 0.90 0.07 0.11 1.08 Girl (vs. others) 0.78 0.31 2.17 -0.45 0.37 0.64 0.56 0.30 1.75 East Asian 0.33 0.42 1.39 -0.19 0.48 0.83 0.54 0.43 1.71 South Asian -0.22 0.41 0.81 -0.12 0.37 0.89 0.20 0.37 1.22 Southeast Asian 0.65 0.41 1.92 -0.43 0.49 0.65 0.59 0.42 1.80 FoMO 0.32 0.20 1.38 0.99 0.20 2.70 0.90 0.18 2.45 Habitual Smartphone Use -0.13 0.17 0.88 0.49 0.20 1.63 0.21 0.16 1.24 Parent Attachment Security -0.84 0.20 0.43 -0.60 0.22 0.55 -0.60 0.18 0.55 Peer Attachment Security -1.10 0.18 0.33 -0.04 0.19 0.96 -0.75 0.17 0.47 Profile 1 vs. Profile 3 Profile 2 vs. Profile 3 Profile 1 vs. Profile 2 Covariates Coef. SE OR Coef. SE OR Coef. SE OR School -0.41 0.33 0.66 0.13 0.40 1.16 -0.54 0.43 0.58 Age 0.06 0.10 1.06 -0.18 0.12 0.84 0.24 0.12 1.27 Girl (vs. others) 0.22 0.24 1.24 -1.00 0.31 0.37 1.22 0.33 3.39 East Asian -0.21 0.35 0.81 -0.72 0.43 0.47 0.52 0.43 1.68 South Asian -0.42 0.36 0.66 -0.32 0.35 0.69 -0.10 0.40 0.91 Southeast Asian 0.07 0.34 1.07 -1.01 0.45 0.37 1.08 0.45 2.94 FoMO -0.58 0.14 0.56 0.10 0.15 1.13 -0.67 0.17 0.51 Habitual Smartphone Use -0.35 0.13 0.71 0.28 0.18 1.26 -0.62 0.19 0.54 Parent Attachment Security -0.23 0.14 0.79 0.00 0.16 1.00 -0.24 0.19 0.79 Peer Attachment Security -0.35 0.14 0.71 0.71 0.16 2.03 -1.06 0.19 0.35 Note. Bolded coefficients represent p < .05. SE: standard error of the coefficient; OR: odds ratio; the coefficients and OR reflects the effects of the predictors on the likelihood of membership into the first listed profile relative to the second listed profile; Gender: Girl=1, Other = 0; Profile 1: Unsatisfied and Frustrated; Profile 2: Satisfied and Frustrated; Profile 3: Online Satisfied and Frustrated; Profile 4: Overall Satisfied and Unfrustrated. 70 Table 14 Results from the Multinomial Logistic Regression for the Effect of Habitual Smartphone Use on Profile Transitions Note. Bolded coefficients represent p < .05. SE: standard error of the coefficient; OR: odds ratio; the coefficients and OR reflects the effects of habitual smartphone use on the likelihood of membership into the first listed profile relative to the second listed profile based on profile membership at Time 1; Profile 1: Unsatisfied and Frustrated; Profile 2: Satisfied and Frustrated; Profile 3: Online Satisfied and Frustrated; Profile 4: Overall Satisfied and Unfrustrated. Time 2 Profile Profile 1 vs. Profile 4 Profile 2 vs. Profile 4 Profile 3 vs. Profile 4 Time 1 Profile Coef. SE OR Coef. SE OR Coef. SE OR Profile 1 0.94 0.35 2.55 2.07 0.61 7.88 1.30 0.38 3.65 Profile 2 0.72 1.01 2.05 0.60 0.38 1.82 0.69 0.36 1.98 Profile 3 1.09 1.12 2.96 1.24 1.13 3.45 1.26 1.13 3.52 Profile 4 -0.38 0.25 0.68 0.05 0.32 1.05 -0.02 0.25 0.98 Profile 1 vs. Profile 3 Profile 2 vs. Profile 3 Profile 1 vs. Profile 2 Time 1 Profile Coef. SE OR Coef. SE OR Coef. SE OR Profile 1 -0.36 0.22 0.70 0.77 0.53 2.16 -1.13 0.50 0.32 Profile 2 0.03 0.96 1.03 -0.09 0.22 0.92 0.12 0.93 1.13 Profile 3 -0.17 0.21 0.84 -0.02 0.26 0.98 -0.15 0.28 0.86 Profile 4 -0.36 0.31 0.70 0.07 0.37 1.07 -0.43 0.37 0.65 71 Table 15 Associations between Profile Membership and Mental Health Profile 1 Profile 2 Profile 3 Profile 4 Summary of significant differences, comparing profiles M [CI] M [CI] M [CI] M [CI] Stress 3.76 [3.56; 3.96] 3.76 [3.07; 4.44] 3.99 [3.77; 4.20] 1.87 [1.72; 2.02] 1=2=3>4 Anxiety 4.82 [4.74; 4.91] 4.73 [4.52; 4.95] 4.84 [4.76; 4.92] 3.67 [3.50; 3.84] 1=2=3>4 Depression 4.43 [4.25; 4.60] 4.09 [3.61; 4.56] 4.42 [4.22; 4.61] 3.10 [3.05; 3.15] 1=2=3>4 Note. M: Mean; CI: 95% Confidence Interval. Profile 1: Unsatisfied and Frustrated; Profile 2: Satisfied and Frustrated; Profile 3: Online Satisfied and Frustrated; Profile 4: Overall Satisfied and Unfrustrated. 72 4.5 Profile Membership and Mental Health Mean-level statically significant differences in stress, anxiety, and depression were found across pairs of profiles using the multivariate delta method (Kam et al., 2016, Raykov and Marcoulides, 2004). The within-profile means (and 95% confidence intervals) of depression, anxiety, and stress are reported in Table 15. Specifically, membership in Profile 1 (Unsatisfied and Frustrated), Profile 2 (Satisfied and Frustrated), and Profile 3 (Online Satisfied and Frustrated) were associated with statistically significantly higher mean levels of stress, anxiety, and depression when compared to membership in Profile 4 (Satisfied and Unfrustrated). Across these first three profiles, participants displayed an average level of severe stress (M=3.76-3.99), extremely severe anxiety (M=4.73-4.84), and severe depression (M=4.09-4.43) according to the cut-offs proposed by Lovibond and Lovibond (1995). By contrast, those in Profile 4 (Satisfied ad Unfrustrated) showed mild stress (M=1.87), severe anxiety (M=3.67), and moderate depression (M=3.10). 73 Chapter 5: Discussion This dissertation used Latent Profile Transition Analysis to provide a contemporary examination of adolescents’ development and well-being while considering features of the current socio-technological context (e.g., smartphone engagement and fear of missing out) through the lens of Self-Determination Theory. While previous work has examined the relationship between smartphone engagement, FoMO, and well-being in adolescence, most of this work has been variable-centered and thus fails to account for the nuances of these experiences. As such, this work employed a person-centered approach (i.e., Latent Profile Transition Analysis) to investigate individual-level differences in the developmental and motivational factors underpinning adolescents’ well-being in the contemporary socio-technological context. Specifically, this study identified groups of adolescents with distinct levels of online and overall basic psychological need satisfaction and frustration using Latent Profile Analysis across two time points. Following this, Latent Transition Analysis was used to investigate the stability of these profiles, as well as adolescents’ transitions between profiles over time. Intra and inter-personal factors (i.e., school, age, gender, ethnicity, fear of missing out, habitual smartphone use, and parent/peer attachment) were then examined as covariates of adolescents’ profile membership. Additionally, to better understand the role of smartphone engagement in contributing to need satisfaction and frustration over time, habitual smartphone use was examined as a predictor of adolescents’ transitions between profiles. Finally, adolescents’ mental health (i.e., stress, anxiety, and depression) was examined in relation to their profile membership. The following sections provide a detailed discussion of the results. 74 5.1 Adolescents’ Online and Overall Basic Psychological Need and Frustration Profiles This study is the first to identify distinct profiles of online and overall basic need satisfaction and frustration and thus provides empirical support for the heterogeneity of adolescents’ need satisfaction in the contemporary socio-technological context. Indeed, four profiles of adolescents’ online and overall basic psychological need satisfaction and frustration were identified at both time points: (1) “Unsatisfied and Frustrated”; (2) “Satisfied and Frustrated” (3) “Online Satisfied and Frustrated”; (4) “Overall Satisfied and Unfrustrated”. These profiles illustrated clear patterns in how adolescents’ online need satisfaction, overall need satisfaction, and overall need frustration tend to co-occur at the individual level. The largest variations in each profile occurred between types of basic psychological need satisfaction or frustration (i.e., relative levels of online need satisfaction, overall need satisfaction, and overall need frustration), rather than between levels of each basic psychological need (i.e., autonomy, competence, and relatedness). This is in line with the theorizing of Deci & Ryan (2017) who posit that the three basic psychological needs for autonomy, competence and relatedness are closely related, and the findings of Li and colleagues (2020) who identified four profiles characterised by different levels of overall need satisfaction and frustration among Chinese undergraduate students. The investigation of profiles of both online and overall need satisfaction and frustration within one model in this study provided a unique opportunity to uncover the nuanced and diverse ways with which these various experiences of basic psychological needs co-occurred in adolescence. Indeed, findings indicated that profiles with higher online need satisfaction (which was measured in relation to smartphone use) also featured higher levels of overall need frustration – thus illustrating the complex ways in which smartphone engagement might contribute to adolescents’ well-being. Notably, while Vansteenkiste and Ryan (2013) claimed that need frustration involves 75 low need satisfaction in general, the identification of two profiles characterized by both need satisfaction and frustration (i.e., Profile 2; Satisfied and Frustrated, and Profile 3; Online Satisfied and Frustrated) suggests that high need satisfaction and need frustration may in fact be present at the same time. Indeed, the finding of a profile characterized by both overall need satisfaction and frustration is consistent with the work of Li and colleagues (2020). According to Li and colleagues (2020), students in the satisfied and frustrated profile may be exposed to both supportive and thwarting environments since, according to Self-Determination theory, need supportive environments primarily contribute to need satisfaction and need thwarting environments primarily elicit need frustration. While this may also be true for adolescents in this study, the observed pattern of online need satisfaction and overall need frustration co-occurrence suggests that the online context may instead be responsible for this experience (i.e., online environments may be simultaneously contributing to adolescents’ need satisfaction and frustration). For example, online environments provide opportunities for adolescents to exercise a sense of agency and volition in what they choose to engage with (supporting their need for autonomy), to connect with close others (supporting their need for relatedness), and to experience a sense of mastery through receiving positive feedback (supporting their need for competence). However, online environments can also feel controlling due to addictive features and social pressures around self-presentation (undermining adolescents’ need for autonomy), contribute to feelings of social exclusion and disconnection through constant exposure to what others are doing (undermining their need for relatedness), and lead to harsh social comparisons with the embellished self-presentations of their peers (undermining their need for competence). Thus, online environments can simultaneously contribute to adolescents’ need satisfaction and frustration – explaining the observation of two profiles featuring high online need satisfaction 76 and overall need frustration in this study. Alternatively, it is also possible that adolescents who experience more overall frustration of their basic psychological needs might turn to online contexts to satisfy their needs – also explaining the presence of profiles with more online need satisfaction and overall need frustration. This theorizing aligns with notions of compensatory internet usage theory (Kardefelt-Winther, 2014), which posits that adolescents may turn online to overcome deficits in overall need satisfaction. The next few paragraphs will describe each of the profiles identified in this study in greater detail. The first and largest profile (31.4%) was labelled “Unsatisfied and Frustrated” since adolescents’ in this profile had below average levels of online and overall need satisfaction and above average levels of need frustration. Previous research has shown that deficits in basic psychological need satisfaction, and the presence of need frustration, are associated with ill-being (e.g., Cordeiro et al., 2016; Liu et al., 2016). Thus, this first and largest profile, containing about a third of the sample, represented the most maladjusted subgroup of adolescents. The fact that this profile included such a large group of adolescents may be partly attributed to the data being collected during the COVID-19 pandemic – a time during which adolescents’ experienced feelings of social disconnection (Parent et al., 2021) and negative mental health (Ellis et al., 2020; Saunders et al., 2022). Future work is needed to better understand the trajectory of this group beyond the COVID-19 context and to examine how they might compare to those who weren’t in high school during the pandemic. Though across all profiles levels of online satisfaction, overall satisfaction, and overall frustration of the needs for autonomy, competence and relatedness were very similar, participants in this profile displayed relatively higher levels of overall competence need frustration. This heightened competence need frustration may be explained by the deficits in online and overall need satisfaction experienced by the adolescents in 77 this group – as they may have experienced a sense of failure in meeting their basic psychological needs. The second and smallest profile (14.1%) was labelled “Satisfied and Frustrated” since adolescents in this profile had above average online and overall need satisfaction and frustration. Specifically, adolescents in this profile had very high online need satisfaction, moderately high overall need satisfaction, and somewhat high need frustration. Given the high levels of need satisfaction in this profile, according to Self-Determination Theory (Deci & Ryan, 2017), adolescents in this group should be mostly well-adjusted, despite the presence of above average need frustration. The presence of both online and overall need satisfaction with need frustration in this profile exemplifies how these constructs do not operate in opposition but rather reflect distinct experiences of basic psychological needs. Indeed, as described above, this finding contrasts with the theorizing of Vansteenkiste and Ryan (2013) that need frustration involves a deficit in need satisfaction – instead suggesting that it is possible to experience both satisfaction and frustration of basic psychological needs simultaneously. This finding builds on the work of Li and colleagues (2020) who identified a profile with high need satisfaction and frustration in Chinese undergraduate students, to also include the presence of online need satisfaction. Notably, the pattern of online need satisfaction and overall need frustration co-occurrence observed in this study suggests that need frustration may be a necessary antecedent of online need satisfaction – whether this be due to frustrating characteristics of online environments or to overall need frustration pushing adolescents to satisfy needs online. Moreover, this profile displayed the highest level of online need satisfaction which may explain their above average levels of overall need satisfaction. That is, previous work has shown that online need satisfaction is associated with more positive affect experienced online (Shen et al., 2013), which may 78 subsequently result in higher overall need satisfaction. Alternatively, according to notions of the “rich-get-richer” hypothesis (Kraut et al., 2002), adolescents with higher overall need satisfaction may also experience more basic need satisfaction online. From this perspective, the benefits of higher overall need satisfaction might transfer into the online context such that the “rich-get-richer.” However, this online need satisfaction may come at the cost of higher need frustration, explaining the higher need frustration also characteristic of this profile. The third and second largest profile (29.4%) was labelled “Online Satisfied and Frustrated” since adolescents’ in this profile showed moderately high levels of online need satisfaction and overall need frustration with below average levels of overall need satisfaction. This profile was unique in the presence of online, but not overall, need satisfaction, suggesting that online and overall basic psychological need satisfaction represent distinct constructs and experiences – and that the presence of online need satisfaction does not necessitate overall need satisfaction. While previous work has investigated online and overall need satisfaction (e.g., Liu et al., 2016; Shen et al., 2013), this is the first study to investigate how these experiences co-occur at the individual-level. Thus, this finding has important implications because it suggests that these are two distinct constructs. Moreover, as in Profile 2, the presence of above average online need satisfaction in this profile was accompanied by above average overall need frustration. While previous work has identified online need satisfaction as a precursor to positive affect online, and to well-being through contributing to overall need satisfaction (Shen et al., 2013; Wang et al., 2015), little is known about how online need satisfaction uniquely contributes to overall positive adjustment and well-being. Thus, adolescents in this profile are likely to be somewhat less well-adjusted than those in Profile 2, who also displayed higher levels of overall need satisfaction (i.e., a known precursor to positive development and well-being: Deci & Ryan, 79 2017). Additionally, adolescents in this profile displayed the highest levels of overall need frustration, thus emphasizing the relationship between online need satisfaction and overall need frustration, along with the potentially protective function of overall need satisfaction. Notably, adolescents in this profile showed higher overall need frustration than those in Profile 1 “Unsatisfied and Frustrated”, despite the later having the lowest levels of online and overall need satisfaction – thus clearly illustrating the link between online need satisfaction and overall need frustration. Further, the fact that this profile was almost the same size as the largest and most maladjusted profile (i.e., Profile 1; Unsatisfied and Frustrated), may also be partly attributed to the context of the COVID-19 pandemic. Indeed, studies have found that adolescents’ smartphone engagement increased during the COVID-19 pandemic as they turned to technology to connect with others (Ellis et al., 2020; Parent et al., 2021). Given that previous work has established a link between technology engagement and online need satisfaction (Liu et al., 2016; Okur & Ozekes, 2020; Shen et al., 2013), it is likely that the popularity of this profile is partly due to an increased use of smartphones to meet basic psychological needs during the pandemic. Lastly, it is also important to note that adolescents in this profile displayed relatively lower autonomy than relatedness and competence need satisfaction online, suggesting that this need may be more difficult to meet online (i.e., via smartphone engagement). Similarly, adolescents in this profile displayed lower overall autonomy than relatedness need satisfaction. This supports the notion that being higher in online need satisfaction may be associated with less autonomy need satisfaction due to addictive features of smartphones and/or social pressures for self-presentation. Alternatively, it is possible that relatedness need satisfaction was relatively higher than autonomy need satisfaction for adolescents in this profile because online relatedness need 80 satisfaction was providing some support for overall relatedness need satisfaction – particularly in the context of COVID-19. The fourth and second smallest profile (25.2%) was labelled “Overall Satisfied and Unfrustrated” since adolescents’ in this profile had high levels of overall need satisfaction, below average levels of online need satisfaction, and very low levels of need frustration. According to Self-Determination Theory (Deci & Ryan, 2017), this profile included the most well-adjusted adolescents as high overall need satisfaction and low frustration is associated with positive development and well-being. Specifically, this profile displayed the highest levels of overall need satisfaction combined with the lowest level of overall need frustration – suggesting that it should include the most well-adjusted adolescents with the highest levels of well-being. Importantly, these four profiles, characterized by the same structure, within-profile variability, and size were identified at both time points. This suggests that the nature of these profiles can be expected to generalize over time among different groups of adolescents. That said, as mentioned above, it is possible that the characteristics of these profiles were impacted by the context of the COVID-19 pandemic. For example, three of the four profiles displayed above average levels of overall need frustration – a known precursor to feelings of passivity and ill-being (Deci & Ryan, 2017). While these profiles displayed within-sample stability over time (i.e., they were the same at each time point), it is possible that the dominance of profiles with higher overall need frustration was a consequence of government-imposed restrictions on social gatherings during COVID-19, which may have contributed to adolescents’ need frustration. That said, there were major differences in the context of the pandemic, and associated restrictions (e.g., policies on masks and social gatherings) between Time 1 and Time 2 of data collection for this study, and these were not reflected in differences between the profiles. 81 5.1.1 Adolescents’ Profile Membership over Time While the structure, variability, and size of the four profiles were the same at both time points, there were several within-person changes between profiles at Time 1 and Time 2 (i.e., many adolescents changed profiles over time). Overall, membership in the most and least well-adjusted profiles remained mostly stable across time points. That is, those who were in Profiles 1 (Unsatisfied and Frustrated), and 4 (Overall Satisfied and Unfrustrated) at Time 1 also tended to be in those profiles at Time 2 (i.e., 80.8% of those in Profile 1 stayed in Profile 1, and 77.9% of those in Profile 4 stayed in Profile 4). This suggests that those at the extremes of basic psychological need satisfaction and frustration were the least likely to transition into different profiles across time. While this bodes well for those in the most well-adjusted profile (i.e., Profile 4; Overall Satisfied and Unfrustrated), this is particularly concerning for those in the least well-adjusted profile (Unsatisfied and Frustrated), as this group is most likely to experience negative outcomes (Deci & Ryan, 2017). However, while transitions from these two profiles were less common, they did still occur. For example, 13.2% of those in Profile 1 (Unsatisfied and Frustrated) at Time 1, transitioned into Profile 3 (Online Satisfied and Frustrated) at Time 2. This group appeared to gain some online need satisfaction over time, suggesting that they may experience some improvements in terms of their positive adjustment and well-being. Moreover, 11.2% of those in Profile 4 (Satisfied and Unfrustrated) at Time 1 transitioned into Profile 1 (Unsatisfied and Frustrated) at Time 2. This group appeared to experience much less satisfaction and much more frustration over time – thus placing them at higher risk for negative outcomes. By contrast, adolescents in Profiles 2 (Satisfied and Frustrated) and 3 (Online Satisfied and Frustrated) were less likely to remain in those profiles over time. Notably, these two profiles were distinguished by higher online need satisfaction, suggesting that this type of need 82 satisfaction may be less stable over time. Specifically, while 41.8% of those in Profile 2 (Satisfied and Frustrated) at Time 1 stayed in Profile 2 at Time 2, 32,2% transitioned into Profile 3 (Online Satisfied and Frustrated) and 18.6% transitioned into Profile 4 (Satisfied and Unfrustrated). Notably, those who transitioned from Profile 2 (Satisfied and Frustrated) to Profile 3 (Online Satisfied and Frustrated) appeared to experience less need satisfaction over time. In fact, while adolescents in this group experienced a decrease in both online and overall need satisfaction over time, their online need satisfaction remained above average while their overall need satisfaction dropped below average. Thus, for this group, it is possible that the very high levels of online need satisfaction in Profile 2 were driving their overall need satisfaction at Time 1, which decreased over time. Conversely, those who were in Profile 2 (Satisfied and Frustrated) at Time 1 and transitioned into Profile 4 (Satisfied and Unfrustrated) at Time 2 seemed to become more well-adjusted over time, as they experienced less overall need frustration. Given the presence of high online need satisfaction at Time 1 for this group, it is possible that these adolescents moved from meeting their basic psychological needs online during COVID-19 restrictions to meeting these needs in-person when many COVID-19 restrictions were lifted at Time 2 – thus accounting for their decreased online need satisfaction and frustration over time. Similarly, while just over half (53.2%) of adolescents in Profile 3 (Online Satisfied and Frustrated) at Time 1 stayed in Profile 3 at Time 2, 32.2% transitioned into Profile 2 (Satisfied and Frustrated), and appeared to experience more need satisfaction over time. While this group continued to experience overall need frustration, they also experienced higher online and overall need satisfaction at Time 2. Thus, it is possible that adolescents in this group got better at meeting their basic psychological needs online over the course of the pandemic, and that this translated into higher overall need satisfaction over time. Alternatively, it is possible that these 83 adolescents adapted to meeting their needs in-person, in addition to online, once many of the COVID-19 restrictions were lifted at Time 2 – and thus experienced higher online and overall need satisfaction over time. Moreover, 13.2% of adolescents’ who were in Profile 3 (Online Satisfied and Frustrated) at Time 1 transitioned into Profile 1 (Unsatisfied and Frustrated) at Time 2. Unlike those who transitioned into Profile 2, adolescents in this group appeared to become less well-adjusted over time as they experienced less online and overall need satisfaction. Indeed, adolescents in this group went from having moderately high to having below average online need satisfaction. It is possible that these adolescents were able to meet some of their basic psychological needs online during the earlier phases of the COVID-19 pandemic at Time 1, but that this became insufficient over time. Indeed, studies have found severe and prolonged negative impacts of the pandemic on adolescents’ mental health and well-being (see Samji et al., 2022 for systematic review), which may help explain this transition pattern. Importantly, adolescents in this group may be at a heightened risk for maladaptive outcomes since they appeared to become more maladjusted over time. 5.2 Predicting Adolescents’ Need Profiles 5.2.1 The Relationship between Gender, Ethnicity, and Adolescents’ Profile Membership Findings from this study indicated that adolescents’ school and age did not significantly vary according to their profile membership. However, there were significant differences between profiles in terms of adolescents’ self-reported gender and ethnicity. Notably, findings indicated that adolescents in Profile 2 (Satisfied and Frustrated) were significantly less likely to identify as girls or report being of Southeast Asian ethnicity than those in Profile 1 (Unsatisfied and Unfrustrated) and those in Profile 3 (Online Satisfied and Frustrated). Given that Profile 2 is distinguished by very high online need satisfaction, it is possible that this difference is due to 84 gender and cultural differences in technology engagement. For example, previous work has found that boys are more likely to engage in video games (Leonhardt & Overå, 2021), and that gaming is associated with basic psychological need satisfaction online (see Ryan & Deci, 2017 for a review). Seeing as this profile showed the highest level of online need satisfaction, this profile may consist of more boys who experience online need satisfaction through gaming. Moreover, the fact that this profile included significantly less adolescents’ who identified as Southeast Asian may be attributed to gendered cultural expectations around smartphone use. For example, findings from United Nations Children’s Fund (2019) indicate significant gender inequities in terms of mobile phone ownership and access for girls in Cambodia and the Philippines. This gender inequity may reflect cultural norms around smartphone engagement for Southeast Asian girls making them less likely to turn online to satisfy their needs. Results from this study also indicated that participants in the least well-adjusted profile (i.e., Profile 1; Unsatisfied and Frustrated) were significantly more likely to identify as girls. While according to Self-Determination Theory (Deci & Ryan, 2017), basic psychological need satisfaction should be universally experienced across genders, this finding is consistent with previous work indicating that adolescent girls are at heightened risk for experiencing maladaptive outcomes, such as negative mental health (Carlén et al., 2022), as well as findings during COVID-19 that girls reported higher levels of worry, concern, and fear about the pandemic (Ellis et al., 2020) – which may have contributed to their lack of need satisfaction in this study. 5.2.2 The Relationship between Fear of Missing Out and Adolescents’ Profile Membership Findings from this study indicated that adolescents higher in FoMO were more likely to be in profiles with higher online need satisfaction and overall need frustration (i.e., Profile 2 and Profile 3). Importantly, this finding is in direct opposition to the conceptualization of FoMO as a 85 deficit in relatedness need satisfaction (e.g., Przybylski et al., 2013). Specifically, according to this conceptualization of FoMO, adolescents higher in FoMO would be more likely to be in profiles with lower need satisfaction (i.e., Profile 1; Unsatisfied and Frustrated) – since it is theorized that their higher levels of FoMO would be the result of deficits in basic psychological needs. However, findings from this study indicate that FoMO was instead associated with both online and overall need satisfaction – such that adolescents’ higher in FoMO were more likely to be in profiles with above average need satisfaction. Thus, this finding better aligns with recent conceptualizations of FoMO within an attachment theory perspective, which posit that FoMO represents a pervasive fear of disruption to attachment relationships (Parent et al., 2021; Throuvala et al., 2019). From this perspective, to experience FoMO one must perceive that they have a close relationship which could be disrupted – thus necessitating some degree of relatedness need satisfaction. In the case of those in Profiles 2 (Satisfied and Frustrated) and 3 (Online Satisfied and Frustrated), their high levels of relatedness need satisfaction suggests that they have strong existing relationships (either online or both online and overall) which they could fear being disrupted. In addition, it is important to note that adolescents’ levels of FoMO did not influence their likelihood of membership into the profile with both online and overall need satisfaction (Profile 2; Satisfied and Frustrated) and that of the profile with only online need satisfaction (Profile 3; Online Satisfied and Frustrated). This provides further support that FoMO does not simply result from deficits in relatedness need satisfaction, as it was associated with profiles with both high online and overall need satisfaction. However, the two profiles associated with higher levels of FoMO also exhibited higher levels of overall need frustration – suggesting that FoMO also contributed to frustrating adolescents’ basic psychological needs. This is not surprising 86 given that FoMO includes a pervasive apprehension that one is being socially excluded or left out, which can threaten adolescents’ experiences of autonomy, competence, and relatedness. In this way, findings suggest that adolescents higher in FoMO are more likely to experience basic psychological need satisfaction and frustration simultaneously, as some degree of relatedness need satisfaction is a necessary antecedent of FoMO, and FoMO can contribute to basic need frustration. Moreover, the fact that FoMO was associated with profiles with higher online need satisfaction specifically contributes to emerging evidence on the link between FoMO and smartphone and social media use. Indeed, FoMO may be exacerbated by smartphone and social media use since it provides constant access to information about of all the things that one may be missing out on. Thus, the experience of FoMO may be amplified for those who experience satisfaction of their needs for autonomy, competence, and relatedness online as they may be more connected to online communities – which increases their exposure to information about close others. 5.2.3 The Relationship between Habitual Smartphone Use and Adolescents’ Profile Membership Similar patterns in the likelihood of profile membership were observed for adolescents’ higher in habitual smartphone, such that they tended to be more likely to be in Profile 2 (Satisfied and Frustrated) than in Profile 1 (Unsatisfied and Frustrated) or in Profile 4 (Satisfied and Unfrustrated), and more likely to be in Profile 3 (Online Satisfied and Frustrated) than in Profile 1 (Unsatisfied and Frustrated). Taken together, these findings suggest that being higher in habitual smartphone use was associated with membership in profiles with higher online need satisfaction. This finding is consistent with previous work which has demonstrated a link between technology engagement and online need satisfaction (Liu et al., 2016; Okur & Ozekes, 87 2020; Shen et al., 2013). Importantly, while previous work in this area has focused on internet use more broadly, findings from this study suggest a similar relationship for habitual smartphone use. Specifically, findings suggest that adolescents’ who engage in more habitual use of their smartphones tend to experience more basic psychological need satisfaction online. By examining smartphone engagement in terms of habitual smartphone use, which encompasses addictive features of device use without over-pathologizing them within a medical addiction-model, this finding provides meaningful insights into the relationship between smartphone engagement and well-being that extend beyond comorbidities in symptomology (e.g., the relationship between anxiety and problematic smartphone use/addiction). Indeed, findings from this study suggest that adolescents’ habitual use of smartphones may provide some benefit in terms of their online and overall need satisfaction, which may hold important implications for their positive development and well-being. Notably, unlike for FoMO, there were no significant differences in terms of the effect of habitual smartphone use on the likelihood of being in Profile 3 (Online Satisfied and Frustrated) compared to being in Profile 4 (Satisfied and Unfrustrated). This suggests that being higher in habitual smartphone use was not a significant risk factor to membership in the most well-adjusted profile (i.e., those in the most well-adjusted profiles had similar levels of habitual smartphone use). Rather, this finding suggests that habitual smartphone use may be a normative feature of adolescents’ daily lives. This has important implications for the contemporary moral panic around smartphone use, as it suggests that habitual smartphone use is not a significant risk factor to adolescents’ need satisfaction. That said, while habitual smartphone engagement was associated with profiles which displayed higher levels of need satisfaction, these profiles also featured higher levels of need frustration – thus illustrating the complexity of the relationship between smartphone engagement and well-being in adolescence. This link between habitual 88 smartphone use and need frustration suggests that addictive features of smartphone use, which can feel controlling (e.g., Osatuyi & Turel, 2018), may be threatening their autonomy need satisfaction. Thus, taken together, findings from this study suggest that habitual smartphone use may simultaneously contribute to adolescents’ basic psychological need satisfaction and frustration by connecting them to online environments that support their needs for autonomy, competence, and relatedness, but also include addictive features that may contribute to their need frustration. Alternatively, adolescents who experience more overall need frustration may be more likely to turn online to meet their basic psychological needs. Moreover, beyond the relationship between habitual smartphone use and adolescents’ profile membership, this study also examined the impact of habitual smartphone use on adolescents’ transitions between profiles over time. Findings revealed that habitual smartphone use only significantly predicted the likelihood of adolescents’ transitioning from Profile 1 (Unsatisfied and Frustrated) at Time 1. Specifically, higher habitual smartphone use was associated with staying in Profile 1 or transitioning into Profile 2 (Satisfied and Frustrated) or into Profile 3 (Online Satisfied and Frustrated), when compared to the likelihood of transitioning into Profile 4 (Satisfied and Unfrustrated). Moreover, with higher habitual device use, the likelihood of staying in Profile 1 was less than that of transitioning into Profile 2. Overall, this pattern suggests that habitual smartphone use was associated with an increased likelihood of transitioning into profiles with more need satisfaction, and similar levels of need frustration, over time. Thus, while habitual smartphone use only predicted transitions into profiles with similarly high levels of overall need frustration, the finding that these profiles also had higher levels of need satisfaction has important implications for understanding the relationship between 89 smartphone engagement and well-being. Indeed, this finding suggests that habitual smartphone use plays an adaptive role in contributing to adolescents’ need satisfaction over time. In addition, there were no significant differences in terms of the effect of habitual smartphone use on predicting transitions into Profile 2 (Satisfied and Frustrated), which had higher levels of online and overall need satisfaction, and Profile 3 (Online Satisfied and Frustrated), which only had higher levels of online need satisfaction. As mentioned above, this may be explained by the fact that the very high level of online need satisfaction in Profile 2 (Satisfied and Frustrated) may be contributing to the overall need satisfaction observed in this profile (i.e., the very high level of online need satisfaction may be translating into overall need satisfaction). In this case, habitual device use may predict transitions into this profile because higher rates of smartphone use are contributing to higher online need satisfaction by connecting adolescents to online environments which support their basic psychological needs which, in turn, contribute to their overall need satisfaction. Thus, in contrast to a moralizing literature on smartphone use, this finding highlights a potentially beneficial effect of habitual smartphone engagement in adolescence. 5.2.4 The Relationship between Parent and Peer Attachment and Adolescents’ Profile Membership Findings from this study also revealed significant differences for the effect of parent and peer attachment on the likelihood of adolescents’ profile membership. Overall, in line with previous work (e.g., Felton & Jowett, 2013; La Guardia et al., 2000; Patrick et al., 2007; Wei et al., 2005), adolescents’ attachment security was significantly associated with their basic psychological need satisfaction. That said, differences in the nuances of this pattern were observed between parent and peer attachment security. Specifically, parent attachment security was associated with being 90 in the most well-adjusted profile (i.e., Profile 4; Satisfied and Unfrustrated) when compared to each of the other three profiles. This finding is consistent with previous work indicating that parent attachment is an important precursor to adolescents’ positive development and well-being (La Guardia et al., 2000), and suggests that secure parent attachment relationships play an important role in adolescents’ overall basic psychological need satisfaction. Indeed, adolescents in this profile displayed moderately high overall need satisfaction accompanied by below average online need satisfaction and overall need frustration. From the perspective of compensatory internet usage theory (Kardefelt-Winther, 2014), it is possible that adolescents in this profile did not display online need satisfaction because they felt they that their needs were being met in their daily lives – and thus they did not need to turn to their smartphones to overcome deficits in their need satisfaction. A more nuanced pattern of findings was observed for peer attachment security. While similar to parent attachment, adolescents’ higher in peer attachment security were more likely to be in the most well-adjusted profile (i.e., Profile 4; Satisfied and Unfrustrated), they were also equally likely to be in Profile 2 (Satisfied and Frustrated). Additionally, adolescents with more secure peer attachment relationships were significantly more likely to be in Profile 2 (Satisfied and Frustrated) than in Profile 1 (Unsatisfied and Frustrated) or Profile 3 (Online Satisfied and Frustrated) and significantly more likely to be in Profile 3 (Online Satisfied and Frustrated) than in Profile 1 (Unsatisfied and Frustrated). Thus, overall, peer attachment security appeared to be associated with membership in profiles with high overall and/or high online need satisfaction. This is consistent with previous work identifying a link between secure peer attachment relationships in adolescence and positive adjustment and well-being (e.g., Oldfield et al., 2016). Moreover, the relationship between peer attachment security and profiles with higher online need 91 satisfaction is likely explained by these adolescents using technology to connect with their secure peer attachment targets. This also explains why this association was found for peer attachment and not parent attachment, since connecting with peers (and not parents) is one of the top motivations for smartphone engagement in adolescence (Anderson & Jiang, 2018). Taken together, these findings also have implications for understanding the relationship between FoMO and need satisfaction. Indeed, while FoMO was associated with membership into both Profiles 2 (Satisfied and Frustrated) and 3 (Online Satisfied and Frustrated), adolescents with secure peer attachment relationships were significantly more likely to be in Profile 2 than in Profile 3. Thus, FoMO and peer attachment security were differently associated with membership into these profiles. Notably, adolescents’ with and without higher peer attachment security were in profiles associated with higher levels of FoMO – suggesting that peer attachment was neither a risk nor protective factor to experiencing FoMO. This supports the conceptualization of FoMO as a unique dimension of the attachment system involving a unique fear of disruption to attachment relationships (Parent et al., 2021). 5.3 Adolescents’ Profile Membership and Mental Health Finally, in terms the relationship between adolescents’ profile membership and their mental health (i.e., stress, anxiety, depression), findings indicated that membership in Profile 4 (Satisfied and Unfrustrated) was associated with significantly better mental health when compared to that of all the other profiles. Specifically, membership into the first three profiles (i.e., Unsatisfied and Frustrated, Satisfied and Frustrated, and Online Satisfied and Frustrated) was equally associated with an average score of “severe stress,” “extremely severe anxiety,” and “severe depression,” according to the cut-offs proposed by Lovibond and Lovibond (1995), while membership in the fourth and most well-adjusted profile (Satisfied and Unfrustrated) was 92 associated with “mild stress,” “severe anxiety,” and “moderate depression.” The finding that the most well-adjusted profile experienced the best mental health is consistent with previous work linking need satisfaction and well-being (e.g., Felton & Jowett, 2013; Gagne, 2003; La Guardia et al., 2000; Reinboth & Duda, 2006). That said, while according to Self-Determination Theory (Deci & Ryan, 2017; Vansteenkiste et al., 2019), adolescents in profiles with above average levels of basic psychological need satisfaction should experience more positive development and well-being, findings from this work suggest that this does not equate to better experiences of mental health. Indeed, the finding that the least well-adjusted profile was not associated with significantly worse mental health than those with above average online need satisfaction or both online and overall need satisfaction (i.e., Profiles 2 and 3), suggests that need frustration, and not need satisfaction, is associated with negative experiences of mental health. This is in line with previous work indicating that need frustration is an important risk factor to feelings of passivity and ill-being (Deci & Ryan, 2017). This has important implications for interventions targeting adolescents’ basic psychological needs and mental health in that it suggests that these efforts should focus on reducing need frustration, rather than on increasing need satisfaction. Additionally, the lack of significant differences in mental health for the first three profiles emphasizes the importance of looking beyond pathology when investigating adolescents’ positive development and well-being – since there were notable differences in adolescents’ levels of need satisfaction between these three profiles, suggesting important differences in their development and well-being. In this way, it appears that examining profiles of adolescents’ need satisfaction and frustration provides a more nuanced investigation of adolescents’ developmental well-being. 93 Chapter 6: Conclusion 6.1 Strengths, Limitations and Directions for Future Research There are several strengths to this study, including a large ethnically and geographically diverse sample, a strong theoretical framework, and the use of a person-centered approach and longitudinal design. Specifically, the ethnic diversity of this sample, recruited from an urban and suburban high school in British Columbia, increases the generalizability of findings. Notably, the study sample was only 11.1% white, which greatly contributes to diversifying the existing work in this area. Moreover, the lack of significant differences between profile membership according to participants’ school suggests that the existence of these profiles may be generalizable to the high school experience in British Columbia more broadly. Similarly, the minimal differences between profiles based on participants’ ethnicity suggests that these profiles may be generalizable across cultures. That said, despite this diversity, participants primarily identified as Asian ethnicities, with more than half speaking a language other than English most of the time at home, which may limit the generalizability of the findings. Moreover, important cultural differences may exist in how the study constructs were experienced and understood amongst this demographic. Thus, more work is needed to better understand how culture might influence adolescents’ development and well-being in the contemporary socio-technological context – including the investigation of these profiles in different cultures. Additionally, this study is rooted in a well-established theoretical framework for understanding adolescent development and well-being (i.e., Self-Determination Theory; Deci & Ryan, 2000). Thus, implications arising from this work can build on existing knowledge of basic psychological need satisfaction to help inform intervention and prevention efforts. Further, the use person-centered analyses (i.e., Latent Profile Transition Analysis) allowed for a more nuanced examination of the experience of 94 adolescents’ development and well-being in the contemporary socio-technological context. Specifically, the investigation of profiles of online and overall need satisfaction and frustration within one model allowed for a better understanding of how these various aspects of basic psychological needs co-occur at the individual-level. Lastly, the study’s longitudinal design allowed for the examination of the within-sample and within-person stability of adolescents’ profiles over time, and how they relate to theoretically established covariates. Several limitations of this study also need to be acknowledged. First, the variables in this study were assessed based on adolescents’ self-reports, so social desirability may have influenced adolescents’ responses. Although self-reported measures are appropriate to examine intrapsychic processes and identify intrapersonal configurations of psychological variables like need satisfaction (Litalien et al., 2017), the use of more objective indicators in future work would contribute to deeper understanding. Moreover, while the variables selected for this study were rooted in a strong theoretical framework and measured using empirically developed scales with good reliability, it remains possible that other factors contributed to the relationships found. Thus, future work should investigate alternative models of the relationship between online and overall basic need satisfaction and frustration and contemporary socio-technological factors. Another potential limitation of this study is that the person-centered approaches used rely on the representativeness and size of the available sample for profile identification (Litalien et al., 2017). Thus, future studies are needed to replicate these results in other samples. In addition, the data for this study was collected during the COVID-19 pandemic, which may limit the generalizability of the findings beyond this context. Notably, three of the four profiles displayed above average levels of overall need frustration – a known risk factor to feelings of passivity and ill-being (Deci & Ryan, 2017). While these profiles showed within-sample stability over time 95 (i.e., they were the same across time points), it is possible that the heightened presence of overall need frustration was a consequence of the COVID-19 pandemic, a time during which government-imposed restrictions may have contributed to adolescents’ need frustration. That said, there were major differences in the context of the pandemic between Time 1 and Time 2 (e.g., restrictions on social gatherings, access to vaccines, mask wearing), which did not affect the structure of the profiles – thus suggesting that this may not be a major factor contributing to the makeup of each profile. Nonetheless, future work is needed to establish the uniformity/presence of these profiles in different contexts (e.g., not during a global pandemic). 6.2 Significance and Conclusion Overall, findings from this study provide rich insights into the relationship between adolescents’ smartphone engagement and well-being through the lens of Self-Determination Theory, and thus contribute to the understanding of adolescent development in the contemporary socio-technological context. Indeed, as the first study to examine adolescents’ online and overall need satisfaction and frustration simultaneously, findings from this work illustrate the nuances with which adolescents’ smartphone engagement, and related socio-contemporary factors (e.g., fear of missing out, parent/peer attachment) are associated with their experience of basic psychological needs. The identification of four distinct profiles of adolescents’ online and overall basic psychological need satisfaction and frustration present at both time points suggests that each profile represents a pattern of adolescents’ online and overall basic psychological need satisfaction and frustration that may generalize over time. In fact, the identification of four distinct profiles with varying levels of online need satisfaction, overall need satisfaction, and overall need frustration is an important contribution to work in this area – and empirically establishes these various aspects of need satisfaction as distinct constructs. Moreover, 96 membership into these profiles was differently associated with contemporary intra- and inter-personal factors (i.e., gender, ethnicity, fear of missing out, habitual smartphone use, and parent/peer attachment) – thus providing empirical support for the heterogeneity in the experience of online and overall basic psychological need satisfaction and frustration that is related to distinct individual-level risk and protective factors. As an illustration of the complexity of the relationship between these socio-technological factors and adolescents’ well-being, adolescents higher in fear of missing out and habitual smartphone use were more likely to be in profiles with more online need satisfaction and overall frustration. Notably, the finding that fear of missing out was associated with membership in profiles with higher need satisfaction is in direct opposition to popular conceptualizations of FoMO as a deficit in relatedness need satisfaction (e.g., Przybylski et al., 2013) – and thus makes an important contribution to work in this area. Further, in addition to being associated with profiles with more need frustration, higher habitual smartphone use also predicted transitions into profiles with more need satisfaction over time – thus suggesting that smartphone engagement may be an important antecedent to overcoming deficits in need satisfaction. Additionally, those higher in parent/peer attachment security were more likely to be in profiles with less need frustration, suggesting that secure attachment relationships may function as a protective factor. Finally, the finding that membership in the “Overall Satisfied and Unfrustrated” profile was associated with significantly better mental health (i.e., lower levels of stress, anxiety, and depression) when compared to the other profiles has important implications for intervention and prevention efforts targeting adolescents’ need satisfaction and mental health – as it suggests that these efforts should focus on reducing need frustration rather than increasing need satisfaction. 97 References Ainsworth, M. D. S. (1989). Attachments beyond infancy. American Psychologist, 44(4), 709–716. https://doi.org/10.1037/0003-066X.44.4.709 Ainsworth, M. D. S., Blehar, M. C., Waters, E., & Wall, S. (1978). Patterns of attachment: A psychological study of the strange situation. 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Assessment, 28(6),1531–1544. https://doi.org/10.1177/1073191119887449 119 Appendices Appendix A: Email to Parents from School Principal (with consent form attached). Dear Parent, Your child has been invited to participate in a school-wide research study conducted by Dr. Jennifer Shapka, of the University of British Columbia: https://ecps.educ.ubc.ca/person/jennifer-shapka/. The study is a four-year longitudinal study that is looking at how youth engage with technology. It will also look at the impact of technology-use on their day-to-day lives. Attached to this email, you will find a letter with more information about the study, along with a consent form. Your child will be bringing home a paper copy of these as well. If you do not wish for your child to participate in this study, you must fill out and return the consent form. It can be returned with your child to school, or you can email it directly to <email>. The school supports this research project, and we are looking forward to working with Dr. Shapka for the next four years. During this time, she will be offering workshops about kids and technology, as well as providing (anonymized) reports about the data collected from our school. Even if your child does not participate in the study, these events will be open to you. Sincerely, <School Principal> 120 Appendix B: Parental Consent Form Adolescent Technology Engagement, Self-Regulation, and the Changing Nature of Parent, Peer, and Intimate Relationships Your child has been invited to participate in a research study conducted by the University of British Columbia that will be occurring in the coming weeks. Please take a moment to review this information about the study. Principal Investigator: Dr. Jennifer Shapka Purpose: Adolescents spend an enormous amount of time online and on their mobile phones. Unfortunately, emerging research indicates that spending a disproportionate amount of time online everyday can lead to outcomes such as poor sleep, struggles with mood and anxiety, as well as strained relationships with family. The latest research suggests that this reliance on technology can’t always be helped, as adolescents form deep attachments to digital devices. These attachments might help serve the social needs and goals of adolescents, despite research supporting the relative importance of face-to-face interactions. Research Procedures: This study involves having your adolescent complete a yearly questionnaire about their mobile device use, as well as their relationships with friends, family, and romantic partners (please note that we will be asking them if they have sent or received any messages of a sexual nature). The online questionnaire will take approximately 45 minutes to complete and will be completed once a year during school time until your adolescent graduates. Your child will complete the questionnaire on their own device. A link to the questionnaire will 121 be sent to their school email address. There are no known risks associated with this study, however, should your teen feel uncomfortable, they have the right to withdraw from the study without any penalty, at any time. Your adolescent also does not have to answer any question that they are uncomfortable about. That said, if you do not wish your adolescent to participate in this study, you must return the second page of this letter, indicating this. If your child is not participating, they will simply be asked to work on their homework or read while their classmates complete the questionnaire. If you would like to view the questionnaire that your child will be filling out, please contact Dr. Jennifer Shapka (<email>; <phone number>) who will provide you with a copy of the questionnaire via mail or via email. The school also has a copy of the questionnaire available for you to look at. Confidentiality: We want to make sure your child feels safe answering questions honestly. To make sure this is the case, we make every effort to ensure confidentiality. Throughout the study your child will only be identified by a unique ID number. Their questionnaire answers will never be linked with any identifying information and we use only their ID number to link each year’s questionnaires. All the data for this study will be encrypted and stored on a secure, password protected server at UBC. Only the core research team will have access to the data. Remuneration: As a thank you for having your child participate in this study, each year, all eligible participants will have their name entered into a draw to win a new iPhone or gift card of equal value. Note that your teen’s name will still be entered into the draw even if you do not give consent. 122 Contact for concerns about the rights of research subjects: If you have any questions or concerns about your child’s rights as a research participant and/or their experiences while participating in this study, please contact the Research Participant Complaint Line in the UBC Office of Research Ethics at 604-822-8598, or if long distance e-mail RSIL@ors.ubc.ca or call toll free 1-877-822-8598. **IMPORTANT: Please return this form to either <email> if you DO NOT wish your teen to participate in any part of this study. _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ Consent: Your child’s participation in this study is entirely voluntary and you may refuse to have your child participate this study by returning this form or have them withdraw from the study at any point without jeopardy to their class standing. Your signature below indicates that you have received a copy of this consent form for your own records and that you DO NOT consent to your child’s participation in the study. Part 1 (questionnaire administered at school during class time):  I DO NOT consent to my child's participation. Name of Child (please print): ____ Your Name (please print): ______________________________________________________ Your Signature Date 123 Appendix C: Student Informed Assent Form Adolescent Technology Engagement, Self-Regulation, and the Changing Nature of Parent, Peer, and Intimate Relationships Dear Student, you, your classmates, and your parents/guardians are being invited to help us, at the University of British Columbia (UBC), learn more about how teenagers are using mobile devices, and how it influences your relationships with your friends and family. Please read the information below to learn more about our study. Person in charge of this project: Dr. Jennifer Shapka (Professor) from the Department of Educational and Counselling Psychology and Special Education is the principal investigator for this project. If you have any questions you can email them to her at <email>. You can also call Dr. Shapka at <phone number>. Why we are doing this project: We know that your mobile devices are one of the most important things you own. Unfortunately, emerging research indicates that spending a lot of time online everyday can lead to outcomes such as poor sleep, struggles with mood and anxiety, as well as strained relationships with family. The latest research suggests that this reliance on technology can’t always be helped, as adolescents form deep attachments to digital devices. These attachments might help serve your social goals, despite research supporting the relative importance of face-to-face interactions. We see you as the experts on this topic, and your answers will help inform important research, as well as help to contribute to effective programming to help teens with technology use. 124 What this project means for you: If you want to take part in this project, for up to four years, (until you graduate in Grade 12), we will ask you to fill out a 45-minute online questionnaire. We will ask you questions about your mobile device use, as well as about your relationships with your friends, family, and romantic partners (please note that we will be asking if you have sent or received any messages of a sexual nature). Given the current context of the COVID-19 pandemic, we will also be asking you to answer questions about how COVID-19 has affected your device use, schooling, and overall well-being. All questions are optional and can be skipped if you prefer not to answer. A link to the questionnaire will be sent to your school email address, and you can complete the study on your own device. As a thank you for participating in this study, each year, you will have your name entered into a draw to win a new iPhone of gift card. Note that even if you choose not to participate or your parents do not consent to your participation, you will still be entered into the draw. Your answers will be kept safe and private: We want to make sure you feel safe answering question as honestly as you can, so we will be doing all we can to make sure your answers remain confidential. To do this, for the study, you will be emailed a link to the study, but you will never have to enter your name anywhere. The data will also be encrypted and stored on a password protected server. Also, your answers will be kept private and will not be shared with your parents, friends, or teachers. Risks of participating: There are no serious risks associated with this research. If you choose not to participate in the study, you will not be penalized in any way. 125 At the end of the questionnaire, we will give you the contact information for your school counsellor, as well as other resources in BC, such as the Kids Help Line, in case you want to talk to someone about anything that came up for you while you were answering the questions. Questions or concerns: If you have any questions or concerns about your rights as a research participant and/or your experiences while participating in this study, please contact the Research Participant Complaint Line in the UBC Office of Research Ethics at 604-822-8598, or if long distance e-mail RSIL@ors.ubc.ca or call toll free 1-877-822-8598. Consent: Your decision to take part in this study is completely up to you and your parents. This means that if you do not want to take part, or if you change your mind in the middle, you can stop at any time and it will not affect your grades in any way. You can also just skip any questions that you don’t feel comfortable answering. Next year, when we come back to the school, you can decide whether you want to continue to participate. If you choose not to participate, you can simply work on your homework or read while your classmates complete the questionnaire. If you want to help us out and take part in this project, please fill out the form below: If you want to help us out and take part in this study, please complete the information below. You will then be redirected to the first page of the study questionnaire. The information on this page will be kept entirely separate from your answers on the questionnaire. 126 Appendix D: Inventory of Parent and Peer Attachment Scale 1. I tell my parents about my problems and troubles. 2. My parents help me understand myself better. 3. If my parents know something is bothering me, they ask me. 4. My parents have their own problems, so I don’t bother them with mine. 5. My parents respect my feelings. 6. When I am angry about something, my parents try to be understanding. 7. I wish I had different parents. 8. My parents accept me as I am. 9. I don’t get much attention at home. 10. I get easily upset at home. 11. Talking over my problems with my parents makes me feel ashamed or foolish. 12. I feel angry with my parents. 13. My friends encourage me to talk about my difficulties. 14. My friends are concerned about my well-being. 15. I tell my friends about my problems and troubles. 16. I like to get my friends point of view on things I’m concerned about. 17. My friends listen to what I have to say. 18. I feel my friends are good friends. 19. I wish I had different friends. 20. When I am angry about something my friends try to be understanding. 21. I get upset a lot more than my friends know about. 22. I feel alone or apart when I am with my friends. 127 23. It seems as if my friends are irritated with me. 24. Talking over my problems with my friends makes me feel ashamed or foolish. 128 Appendix E: Fear of Missing Out Scale 1. I fear others have more rewarding experiences than me. 2. I fear my friends have more rewarding experiences than me. 3. I get worried when I find out my friends are having fun without me. 4. I get anxious when I don’t know what my friends are up to. 5. It is important that I understand my friends ‘‘inside jokes’’. 6. Sometimes, I wonder if I spend too much time keeping up with what is going on. 7. It bothers me when I miss an opportunity to meet up with friends. 8. When I have a good time it is important for me to share the details online/via social media. 9. When I miss out on a planned get-together it bothers me. 10. When I go on vacation, I continue to keep tabs on what my friends are doing. 129 Appendix F: Habitual Smartphone Use Scale 1. How often do you open your phone to do one thing and wind up doing something else without realizing it? 2. How often do you check your phone while interacting with other people (for example, during a conversation)? 3. How often do you find yourself checking your phone "for no good reason" (without a purpose)? 4. How often do you check your phone out of habit? 5. How often do you find yourself checking your phone without realizing why you did it? 6. How often do you find yourself using your phone absent-mindedly (without realizing you're doing it)? 7. How often do you lose track of time while using your phone? 130 Appendix G: Basic Psychological Need Satisfaction and Frustration Scale 1. I feel free to choose which activities I do. 2. Most of the things I do, I do because I have to. 3. The people that I like, also like me. 4. I feel excluded from the group I want to be a part of. 5. I can do things well. 6. I often have doubts about whether I'm good at things. 7. I do the things I do because I really want to do them. 8. I feel forced to do many things that I actually do not want to do. 9. I feel close to the people I care about. 10. I feel that the people who are important to me are unkind to me. 11. I am good at what I do. 12. I feel disappointed in a lot of things I do. 13. I choose to do the things I do because I want to do them. 14. I feel pressured to do too many things. 15. I feel close to and connected with the people who are important to me. 16. The people I spend time with don't like me. 17. I can achieve my goals. 18. I feel insecure about what I am able to do. 19. I find the things I do really interesting. 20. I do the things I do every day because I have to, not because I want to. 21. I have warm feelings towards the people I spend time with. 22. I feel that the relationships I have with other people are easily broken. 131 23. I am good at difficult tasks. 24. I sometimes feel like a failure when I make mistakes. 132 Appendix H: Online Need Satisfaction and Frustration Scale 1. When I use my phone, I feel free to be who I am. 2. When I use my phone, I feel competent. 3. When I use my phone, I feel liked by other people. 4. When I use my phone, I feel like I have something to contribute. 5. When I use my phone, I can control what happens. 6. When I use my phone, I feel a strong connection between myself and other people. 7. When I use my phone, I feel like an expert. 8. When I use my phone, I do not feel pressure to behave in a certain way. 9. When I use my phone, I have a feeling of closeness and intimacy with other people. 10. When I use my phone, I feel like I am getting things done. 11. When I use my phone, I feel like I can freely express my opinion. 12. When I use my phone, I feel that other people care about me. 133 Appendix I: Depression Anxiety and Stress Scale 1. I found it hard to wind down. 2. I was aware of dryness of my mouth. 3. I couldn’t seem to experience any positive feeling at all. 4. I experienced breathing difficulty (for example, excessively rapid breathing, breathlessness in the absence of physical exertion). 5. I found it difficult to work up the initiative to do things. 6. I tended to over-react to situations. 7. I experienced trembling (for example, in the hands). 8. I felt that I was using a lot of nervous energy. 9. I was worried about situations in which I might panic and make a fool of myself. 10. I felt that I had nothing to look forward to. 11. I found myself getting agitated. 12. I found it difficult to relax. 13. I felt down-hearted and blue. 14. I was intolerant of anything that kept me from getting on with what I was doing. 15. I felt I was close to panic. 16. I was unable to become enthusiastic about anything. 17. I felt I wasn’t worth much as a person. 18. I felt that I was rather touchy. 19. I was aware of the action of my heart in the absence of physical exertion (for example, sense of heart rate increase, heart missing a beat). 20. I felt scared without any good reason. 134 21. I felt that life was meaningless.
Thesis/Dissertation
eng
Human Development, Learning, and Culture
Vancouver : University of British Columbia Library
Attribution-NonCommercial-NoDerivatives 4.0 International
Graduate
Shapka, Jennifer
Adolescence 2.0 : an examination of basic need satisfaction and well-being in a socio-technological world using latent profile transition analysis
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