Journal of Youth and Adolescence

, Volume 48, Issue 8, pp 1469–1493 | Cite as

Social Media Use Subgroups Differentially Predict Psychosocial Well-Being During Early Adolescence

  • Anna VannucciEmail author
  • Christine McCauley Ohannessian
Empirical Research


Despite the salience of the social media context to psychosocial development, little is known about social media use patterns and how they relate to psychological and social functioning over time during early adolescence. This longitudinal study, therefore, identified subgroups of early adolescents based on their social media use and examined whether these subgroups predicted psychosocial functioning. Adolescents (N = 1205; 11–14 years; 51% female; 51% white) completed surveys at baseline and a six-month follow-up. There were three social media use subgroups at baseline: high overall social media use (8%); high Instagram/Snapchat use (53%); and low overall social media use (39%). The high social media use subgroup predicted higher depressive symptoms, panic disorder symptoms, delinquent behaviors, family conflict, as well as lower family and friend support, than the High-Instagram/Snapchat and low social media use subgroups. The high Instagram/Snapchat use subgroup predicted higher delinquent behaviors and school avoidance than the low social media use subgroup, but also higher close friendship competence and friend support as compared to both the high social media use and low social media use subgroups. Social media use patterns appear to differentially predict psychosocial adjustment during early adolescence, with high social media use being the most problematic and patterns of high Instagram/Snapchat use and low social media use having distinct developmental tradeoffs.


Social media Adolescence Internalizing problems Externalizing problems Social functioning 



We would like to thank all of the school partners and adolescents who participated in this study. We also would like to acknowledge Rhiannon Smith and the PANDA (Predictors of Anxiety and Depression during Adolescence) Project staff, especially Sonja Gagnon, Courtney Lincoln, and Emily Simpson, for their unmatched dedication to the implementation of this study.

Authors’ Contributions

AV participated in the study design, coordinated the implementation of the study, conceived of the manuscript objectives and hypotheses, performed the statistical analysis, and drafted the manuscript; CO conceived of the study, participated in its design and coordination, and helped to draft the manuscript. Both authors read and approved the final manuscript.


This research was supported by the Alvord Foundation (PI: Ohannessian).

Data Sharing and Declaration

This manuscript’s data will not be deposited, but syntax and output for analyses are available from the corresponding author upon reasonable request.

Compliance with Ethical Standards

Conflict of Interest

The authors declare that they have no conflict of interest.

Ethical Approval

The Connecticut Children’s Institutional Review Board approved all study procedures (16-072-COMM). The study was conducted in accordance with the ethical standards established by the Helsinki Declaration as revised 1989 and the American Psychological Association.

Informed Consent

Parents were mailed a letter inviting their child to participate in the study. Informed parental consent was obtained passively, such that parents who did not want their adolescent(s) to participate in the study contacted the research team directly. Adolescents provided written assent prior to data collection.


  1. Arnett, J. J. (2014). Identity development from adolescence to emerging adulthood: what we know and (especially) what we don’t know. In K. C. McLean & M. Syed (Eds.), The Oxford handbook of identity development (pp. 53–79). Oxford, UK: Oxford University Press.Google Scholar
  2. Asparouhov, T., & Muthén, B. (2014). Auxiliary variables in mixture modeling: using the BCH method in Mplus to estimate a distal outcome model and an arbitrary secondary model. Mplus Web Notes, 21(2), 1–22.Google Scholar
  3. Bandura, A. (1977). Social learning theory. New York, NY: General Learning Press.Google Scholar
  4. Barry, C. T., Sidoti, C. L., Briggs, S. M., Reiter, S. R., & Lindsey, R. A. (2017). Adolescent social media use and mental health from adolescent and parent perspectives. Journal of Adolescence, 61, 1–11.CrossRefGoogle Scholar
  5. Birmaher, B., Brent, D. A., Chiappetta, L., Bridge, J., Monga, S., & Baugher, M. (1999). Psychometric properties of the screen for child anxiety related emotional disorders: a replication study. Journal of the American Academy of Child & Adolescent Psychiatry, 38(10), 1230–1236.CrossRefGoogle Scholar
  6. Bolck, A., Croon, M., & Hagenaars, J. (2004). Estimating latent structure models with categorical variables: one-step versus three-step estimators. Political Analysis, 12(1), 3–27.CrossRefGoogle Scholar
  7. Boyd, D. (2010). Social network sites as networked publics: affordances, dynamics, and implications. In Z. Papacharissi Ed., Networked self: identity, community, and culture on social network sites (pp. 39–58). Milton Park, UK: Routledge.Google Scholar
  8. Bray, B. C., Lanza, S. T., & Tan, X. (2015). Eliminating bias in classify-analyze approaches for latent class analysis. Structural Equation Modeling: A Multidisciplinary Journal, 22(1), 1–11.CrossRefGoogle Scholar
  9. Caballero, A., Granberg, R., & Tseng, K. Y. (2016). Mechanisms contributing to prefrontal cortex maturation during adolescence. Neuroscience & Biobehavioral Reviews, 70, 4–12.CrossRefGoogle Scholar
  10. Canty-Mitchell, J., & Zimet, G. D. (2000). Psychometric properties of the multidimensional scale of perceived social support in urban adolescents. American Journal of Community Psychology, 28(3), 391–400.CrossRefPubMedGoogle Scholar
  11. Carvalho, J., Francisco, R., & Relvas, A. P. (2015). Family functioning and information and communication technologies: how do they relate? A literature review. Computers in Human Behavior, 45, 99–108.CrossRefGoogle Scholar
  12. Casey, B. J. (2015). Beyond simple models of self-control to circuit-based accounts of adolescent behavior. Annual Review of Psychology, 66, 295–319.CrossRefPubMedGoogle Scholar
  13. Chan, J. (2017). Racial identity in online spaces: social media’s impact on students of color. Journal of Student Affairs Research and Practice, 54(2), 163–174.CrossRefGoogle Scholar
  14. Dhir, A., Yossatorn, Y., Kaur, P., & Chen, S. (2018). Online social media fatigue and psychological wellbeing—a study of compulsive use, fear of missing out, fatigue, anxiety and depression. International Journal of Information Management, 40, 141–152.CrossRefGoogle Scholar
  15. Dworkin, J., Rudi, J. H., & Hessel, H. (2018). The state of family research and social media. Journal of Family Theory and Review, 10, 796–813.CrossRefGoogle Scholar
  16. Eccles, J. S., & Roeser, R. W. (2011). Schools as developmental contexts during adolescence. Journal of Research on Adolescence, 21(1), 225–241.CrossRefGoogle Scholar
  17. Eleuteri, S., Saladino, V., & Verrastro, V. (2017). Identity, relationships, sexuality, and risky behaviors of adolescents in the context of social media. Sexual and Relationship Therapy, 32(3-4), 354–365.CrossRefGoogle Scholar
  18. Evans-Polce, R. J., Vasilenko, S. A., & Lanza, S. (2015) Changes in gender and racial/ethnic disparities in rates of cigarette use, regular heavy episodic drinking, and marijuana use: Ages 14 to 32. Addictive Behaviors, 41, 218–222.Google Scholar
  19. Fardouly, J., & Vartanian, L. R. (2016). Social media and body image concerns: current research and future directions. Current Opinion in Psychology, 9, 1–5.CrossRefGoogle Scholar
  20. Faulstich, M. E., Carey, M., Ruggiero, L., Enyart, P., & Gresham, F. (1986). Assessment of depression in childhood and adolescence: an evaluation of the center for epidemiological studies depression scale for children (CES-DC). American Journal of Psychiatry, 143(8), 1024–1027.CrossRefPubMedGoogle Scholar
  21. Fuster, H., Chamarro, A., & Oberst, U. (2017). Fear of missing out, online social networking and mobile phone addiction: a latent profile approach. Aloma: Revista de Psicologia, Ciències de l’Educació i de l’Esport, 35(1), 23–30.Google Scholar
  22. Galica, V. L., Vannucci, A., Flannery, K. M., & Ohannessian, C. M. (2017). Social media use and conduct problems in emerging adults. Cyberpsychology, Behavior, and Social Networking, 20(7), 448–452.CrossRefGoogle Scholar
  23. Gault-Sherman, M. (2013). The gender gap in delinquency: does SES matter? Deviant Behavior, 34(4), 255–273.CrossRefGoogle Scholar
  24. Gerwin, R. L., Kaliebe, K., & Daigle, M. (2018). The interplay between digital media use and development. Child and Adolescent Psychiatric Clinics of North America, 27(2), 345–355.CrossRefPubMedGoogle Scholar
  25. Glover, J., & Fritsch, S. L. (2017). KidsAnxiety and social media: a review. Child and Adolescent Psychiatric Clinics of North America, 27, 171–182.CrossRefPubMedGoogle Scholar
  26. Goodman, E., Adler, N. E., Kawachi, I., Frazier, A. L., Huang, B., & Colditz, G. A. (2001). Adolescents’ perceptions of social status: development and evaluation of a new indicator. Pediatrics, 108(2), e31.CrossRefPubMedGoogle Scholar
  27. Hankin, B. L., Mermelstein, R., & Roesch, L. (2007). Sex differences in adolescent depression: stress exposure and reactivity models. Child Development, 78(1), 279–295.CrossRefGoogle Scholar
  28. Hammen, C. (2006). Stress generation in depression: reflections on origins, research, and future directions. Journal of Clinical Psychology, 62(9), 1065–1082.CrossRefPubMedGoogle Scholar
  29. Hargittai, E., & Hsieh, Y. P. (2010). Predictors and consequences of differentiated practices on social network sites. Information, Communication & Society, 13(4), 515–536. Scholar
  30. Herrenkohl, T. I., Kosterman, R., Hawkins, J. D., & Mason, W. A. (2009). Effects of growth in family conflict in adolescence on adult depressive symptoms: mediating and moderating effects of stress and school bonding. Journal of Adolescent Health, 44(2), 146–152.CrossRefPubMedGoogle Scholar
  31. Hoge, E., Bickham, D., & Cantor, J. (2017). Digital media, anxiety, and depression in children. Pediatrics, 140(Suppl. 2), S76–S80.CrossRefPubMedGoogle Scholar
  32. Karahanna, E., Xin, Xu,S., Xu, Y., & Zhang, N. (2018). The needs-affordances-features perspective for the use of social media. MIS Quartlerly, 42(3), 737–756.CrossRefGoogle Scholar
  33. Katz, E., Blumler, J. G., & Gurevitch, M. (1973). Uses and gratifications research. The Public Opionion Quarterly, 37(4), 509–523.CrossRefGoogle Scholar
  34. Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukophadhyay, T., & Scherlis, W. (1998). Internet paradox: a social technology that reduces social involvement and psychological well-being? American Psychologist, 53(9), 1017.CrossRefPubMedGoogle Scholar
  35. Lazarsfeld, P. F., & Henry, N. W. (1968). Latent structure analysis. Boston, MA: Houghton Mifflin.Google Scholar
  36. Lee, A. R., Son, S.-M., & Kim, K. K. (2016). Information and communication technology overload and social networking service fatigue: a stress perspective. Computers in Human Behavior, 55, 51–61.CrossRefGoogle Scholar
  37. Lenhart, A., Anderson, M., Duggan, M., & Perrin, A. (2015). Teens, technology, and friendships. Washington, DC: Pew Research Center.
  38. Masyn, K. E. (2013). Latent class analysis and finite mixture modeling. In: K. E. Masyn and T. Little (Eds.), Oxford handbook of quantitative methods in psychology (pp. 551–611). Oxford, UK: Oxford University Press.Google Scholar
  39. McFarland, L. A., & Ployhart, R. E. (2015). Social media: a contextual framework to guide research and practice. Journal of Applied Psychology, 100(6), 1653–1677.CrossRefPubMedGoogle Scholar
  40. McLaughlin, K. A., & King, K. (2015). Developmental trajectories of anxiety and depression in early adolescence. Journal of Abnormal Child Psychology, 43(2), 311–323.PubMedCentralCrossRefPubMedGoogle Scholar
  41. Miech, R. A, Johnston, L. D, O’Malley, P. M, Bachman, J. G, Schulenberg, J. E., & Patrick, M. E. (2017). Monitoring the Future national survey results on drug use, 1975–2016: Volume I, Secondary school students. Ann Arbor: Institute for Social Research, The University of Michigan.
  42. Moreno, M. A., & Uhls, Y. T. (2019). Applying an affordances approach and a developmental lens to approach adolescent social media use. Digital Health, 5, 1–6.CrossRefGoogle Scholar
  43. Muris, P., Merckelbach, H., Ollendick, T., King, N., & Bogie, N. (2002). Three traditional and three new childhood anxiety questionnaires: their reliability and validity in a normal adolescent sample. Behaviour Research and Therapy, 40(7), 753–772.CrossRefGoogle Scholar
  44. Muthen, L. K., & Muthen, B. O. (1998). Mplus user’s guide: statistical analysis with latent variables. 7th Edn. Los Angeles, CA: Muthen & Muthen. 2017.Google Scholar
  45. Nelson, E. E., Jarcho, J. M., & Guyer, A. E. (2016). Social re-orientation and brain development: an expanded and updated view. Developmental Cognitive Neuroscience, 17, 118–172.CrossRefPubMedGoogle Scholar
  46. Nesi, J., Choukas-Bradley, S., & Prinstein, M. J. (2018a). Transformation of adolescent peer relations in the social media context: Part 1—a theoretical framework and application to dyadic peer relationships. Clinical Child and Family Psychology Review, 21(3), 267–294.PubMedCentralCrossRefPubMedGoogle Scholar
  47. Nesi, J., Choukas-Bradley, S., & Prinstein, M. J. (2018b). Transformation of adolescent peer relations in the social media context: Part 2—application to peer group processes and future directions for research. Clinical Child and Family Psychology Review, 21(3), 295–319.PubMedCentralCrossRefPubMedGoogle Scholar
  48. Nesi, J., Miller, A. B., & Prinstein, M. J. (2017). Adolescents’ depressive symptoms and subsequent technology-based interpersonal behaviors: a multi-wave study. Journal of Applied Developmental Psychology, 51, 12–19.PubMedCentralCrossRefPubMedGoogle Scholar
  49. Nesi, J., & Prinstein, M. J. (2015). Using social media for social comparison and feedback-seeking: gender and popularity moderate associations with depressive symptoms. Journal of Abnormal Child Psychology, 43(8), 1427–1438.PubMedCentralCrossRefPubMedGoogle Scholar
  50. Nesi, J., & Prinstein, M. J. (2018). In search of likes: longitudinal associations between adolescents’ digital status seeking and health-risk behaviors. Journal of Clinical Child & Adolescent Psychology, 8, 1–9. [Epub ahead of print].Google Scholar
  51. Nesi, J., Rothenberg, W. A., Hussong, A. M., & Jackson, K. M. (2017). Friends’ alcohol-related social networking site activity predicts escalations in adolescent drinking: mediation by peer norms. Journal of Adolescent Health, 60(6), 641–647.CrossRefPubMedGoogle Scholar
  52. Nylund, K. L., Asparouhov, T., & Muthén, B. O. (2007). Deciding on the number of classes in latent class analysis and growth mixture modeling: a Monte Carlo simulation study. Structural Equation Modeling, 14(4), 535–569.CrossRefGoogle Scholar
  53. O’Keeffe, G. S.(2016). Social media: challenges and concerns for families. Pediatric Clinics, 63(5), 841–849.PubMedGoogle Scholar
  54. Obar, J. A., & Wildman, S. S. (2015). Social media definition and the governance challenge: An introduction to the special issue. Telecommunications Policy, 39(9), 745–750.CrossRefGoogle Scholar
  55. Ohannessian, C. M. (2009). Does technology use moderate the relationship between parental alcoholism and adolescent alcohol and cigarette use? Addictive Behaviors, 34(6–7), 606–609.PubMedCentralCrossRefPubMedGoogle Scholar
  56. Padilla-Walker, L. M., Coyne, S. M., & Fraser, A. M. (2012). Getting a high‐speed family connection: Associations between family media use and family connection. Family Relations, 61(3), 426–440.CrossRefGoogle Scholar
  57. Petersen, I. T., Bates, J. E., Dodge, K. A., Lansford, J. E., & Pettit, G. S. (2015). Describing and predicting developmental profiles of externalizing problems from childhood to adulthood. Development and Psychopathology, 27(3), 791–818.CrossRefPubMedGoogle Scholar
  58. Pew Research Center (2013). Teens, social media, and privacy. Pew Research Center.
  59. Pew Research Center (2018a). Teens, social media, and technology 2018. Pew Research Center.
  60. Pew Research Center (2018b). Teens’ social media habits and experiences. Pew Research Center.
  61. Pittman, M., & Reich, B. (2016). Social media and loneliness: why an instagram picture may be worth more than a thousand Twitter words. Computers in Human Behavior, 62, 155–167.CrossRefGoogle Scholar
  62. Primack, B. A., Shensa, A., Escobar-Viera, C. G., Barrett, E. L., Sidani, J. E., Colditz, J. B., & James, A. E. (2017). Use of multiple social media platforms and symptoms of depression and anxiety: a nationally-representative study among US young adults. Computers in Human Behavior, 69, 1–9.CrossRefGoogle Scholar
  63. Prinstein, M. J., Boergers, J., & Vernberg, E. M. (2001). Overt and relational aggression in adolescents: social-psychological adjustment of aggressors and victims. Journal of Clinical Child Psychology, 30(4), 479–491.CrossRefPubMedPubMedCentralGoogle Scholar
  64. Renninger, B. J. (2015). “Where I can be myself… where I can speak my mind”: networked counterpublics in a polymedia environment. New Media & Society, 17(9), 1513–1529.CrossRefGoogle Scholar
  65. Rideout, V. J. (2012). Social media, social life: How teens view their digital lives. San Francisco, CA: Common Sense Media.Google Scholar
  66. Rose, A. J., & Rudolph, K. D. (2006). A review of sex differences in peer relationship processes: potential trade-offs for the emotional and behavioral development of girls and boys. Psychological Bulletin, 132(1), 98–131.PubMedCentralCrossRefPubMedGoogle Scholar
  67. Rudolph, K. D., Flynn, M., & Abaied, J. L. (2008). A developmental perspective on interpersonal theories of depression. In J. R. Z. Abela & B. L. Hankin (Eds.), Handbook of depression in children and adolescents (pp. 79–102). New York, NY: Guilford Press.Google Scholar
  68. Saunders, J. F., & Eaton, A. A. (2018). Snaps, selfies, and shares: how three popular social media platforms contribute to the sociocultural model of disordered eating among young women. Cyberpsychology, Behavior, and Social Networking, 21(6), 343–354.CrossRefGoogle Scholar
  69. Scherer, C. W., & Cho, H. (2003). A social network contagion theory of risk perception. Risk Analysis: An International Journal, 23(2), 261–267.CrossRefGoogle Scholar
  70. Scott, C. F., Bay-Cheng, L. Y., Prince, M. A., Nochajski, T. H., & Collins, R. L. (2017). Time spent online: latent profile analyses of emerging adults’ social media use. Computers in Human Behavior, 75, 311–319.CrossRefGoogle Scholar
  71. Seabrook, E. M., Kern, M. L., & Rickard, N. S. (2016). Social networking sites, depression, and anxiety: a systematic review. JMIR Mental Health, 3(4), e50.PubMedCentralCrossRefPubMedGoogle Scholar
  72. Shapiro, L. A. S., & Margolin, G. (2014). Growing up wired: social networking sites and adolescent psychosocial development. Clinical Child and Family Psychology Review, 17(1), 1–18.PubMedCentralCrossRefGoogle Scholar
  73. Sherman, L. E., Payton, A. A., Hernandez, L. M., Greenfield, P. M., & Dapretto, M. (2016). The power of the like in adolescence: effects of peer influence on neural and behavioral responses to social media. Psychological Science, 27(7), 1027–1035.PubMedCentralCrossRefPubMedGoogle Scholar
  74. Shifflet-Chila, E. D., Harold, R. D., Fitton, V. A., & Ahmedani, B. K. (2016). Adolescent and family development: autonomy and identity in the digital age. Children and Youth Services Review, 70, 364–368.CrossRefGoogle Scholar
  75. Shulman, E. P., Smith, A. R., Silva, K., Icenogle, G., Duell, N., Chein, J., & Steinberg, L. (2016). The dual systems model: review, reappraisal, and reaffirmation. Developmental Cognitive Neuroscience, 17, 103–117.CrossRefPubMedGoogle Scholar
  76. Strasburger, V. C. (2007). Super-peer theory. In J. J. Arnett (Ed.), Encyclopedia of children, adolescents, and the media. Thousand Oaks, CA: Sage Publications, Inc.Google Scholar
  77. Subrahmanyam, K., & Greenfield, P. (2008). Online communication and adolescent relationships. The Future of Children, 18(1), 119–146.CrossRefPubMedGoogle Scholar
  78. Sundar, S. S., & Limperos, A. M. (2013). Uses and grats 2.0: new gratifications for new media. Journal of Broadcasting & Electronic Media, 57(4), 504–525.CrossRefGoogle Scholar
  79. Tottenham, N., & Galván, A. (2016). Stress and the adolescent brain: amygdala-prefrontal cortex circuitry and ventral striatum as developmental targets. Neuroscience & Biobehavioral Reviews, 70, 217–227.CrossRefGoogle Scholar
  80. U.S. Census Bureau. (2016). County Population by Characteristics: 2010–2017.
  81. Uhls, Y. T., Ellison, N. B., & Subrahmanyam, K. (2017). Benefits and costs of social media in adolescence. Pediatrics, 140(Supplement 2), S67–S70.CrossRefPubMedGoogle Scholar
  82. Utz, S., Muscanell, N., & Khalid, C. (2015). Snapchat elicits more jealousy than Facebook: a comparison of Snapchat and Facebook use. Cyberpsychology, Behavior, and Social Networking, 18(3), 141–146.CrossRefGoogle Scholar
  83. Valkenburg, P. M., & Peter, J. (2011). Online communication among adolescents: an integrated model of its attraction, opportunities, and risks. Journal of Adolescent Health, 48(2), 121–127.CrossRefPubMedGoogle Scholar
  84. van Der Schuur, W. A., Baumgartner, S. E., Sumter, S. R., & Valkenburg, P. M. (2015). The consequences of media multitasking for youth: a review. Computers in Human Behavior, 53, 204–215.CrossRefGoogle Scholar
  85. Vannucci, A., Ohannessian, C. M., & Gagnon, S. (2018). Use of multiple social media platforms in relation to psychological functioning in emerging adults. Emerging Adulthood. [ahead of print].Google Scholar
  86. Vannucci, A. & Ohannessian, C. M. (2018a). Psychometric properties of the brief loss of control over eating scale (LOCES-B) in early adolescents. International Journal of Eating Disorders, 51(5), 459–464.Google Scholar
  87. Vannucci, A., & Ohannessian, C. M. (2018b). Self-competence and depressive symptom trajectories during adolescence. Journal of Abnormal Child Psychology, 46(5), 1089–1109.Google Scholar
  88. Wang, R., Yang, F., Zheng, S., & Sundar, S. S. (2016). Why do we pin? New gratifications explain unique activities in Pinterest. Social Media & Society, 2(3), 1–9.Google Scholar
  89. Weissman, M. M., Orvaschel, H., & Padian, N. (1980). Children’s symptom and social functioning self-report scales comparison of mothers’ and children’s reports. The Journal of Nervous and Mental Disease, 168(12), 736–740.CrossRefPubMedGoogle Scholar
  90. Wheeler, L. (1966). Toward a theory of behavioral contagion. Psychological Review, 73(2), 179–192.CrossRefGoogle Scholar
  91. Wichstraum, L. (1995). Harter’s self-perception profile for adolescents: reliability, validity, and evaluation of the question format. Journal of Personality Assessment, 65(1), 100–116.CrossRefGoogle Scholar
  92. Wills, T. A., Simons, J. S., Sussman, S., & Knight, R. (2016). Emotional self-control and dysregulation: a dual-process analysis of pathways to externalizing/internalizing symptomatology and positive well-being in younger adolescents. Drug and Alcohol Dependence, 163, S37–S45.PubMedCentralCrossRefPubMedGoogle Scholar
  93. Wothke, W. (2000). Longitudinal and multigroup modeling with missing data. In T. D. Little, K. U. Schnabel & J. Baumert (Eds.), Modeling longitudinal and multilevel data: practical issues, applied approaches, and specific examples (pp. 219–240). Mahwah, NJ: Lawrence Erlbaum.Google Scholar
  94. Yang, C. C., & Lee, Y. (2018). Interactants and activities on Facebook, Instagram, and Twitter: Associations between social media use and social adjustment to college. Applied Developmental Science.
  95. Zawawi, B. F., Al Abri, M. H., & Dabbah, N. (2017). Affordance analysis of Google+features: advancing teaching and learning in higher education. Journal of Educational Multimedia and Hypermedia, 26(4), 425–443.Google Scholar
  96. Zhao, Y., Liu, J., Tang, J., & Zhu, Q. (2013). Conceptualizing perceived affordances in social media interaction design. Aslib Proceedings, 65(3), 289–303.CrossRefGoogle Scholar
  97. Zimet, G. D., Dahlem, N. W., Zimet, S. G., & Farley, G. K. (1988). The multidimensional scale of perceived social support. Journal of Personality Assessment, 52(1), 30–41.CrossRefGoogle Scholar
  98. Zimmer-Gembeck, M. J., & Collins, W. A. (2008). Autonomy development during adolescence. In G. Adams & M. Berzonsky (Eds.), Blackwell handbook of adolescence (pp. 175–204). Hoboken, NJ: Wiley-Blackwell.Google Scholar
  99. Zimmer-Gembeck, M. J., & Skinner, E. A. (2011). The development of coping across childhood and adolescence: an integrative review and critique of research. International Journal of Behavioral Development, 35(1), 1–17.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  1. 1.Center for Behavioral Health ResearchConnecticut Children’s Medical CenterHartfordUSA
  2. 2.Department of Pediatrics and PsychiatryUniversity of Connecticut School of MedicineFarmingtonUSA

Personalised recommendations