Hostname: page-component-78c5997874-fbnjt Total loading time: 0 Render date: 2024-11-05T16:39:44.010Z Has data issue: false hasContentIssue false
  • English
  • Français

The decomposed affiliation exposure model: A network approach to segregating peer influences from crowds and organized sports

Published online by Cambridge University Press:  30 July 2013

KAYO FUJIMOTO ,
PENG WANG  and
THOMAS W. VALENTE
KAYO FUJIMOTO
Affiliation:
Division of Health Promotion and Behavioral Sciences, School of Public Health, University of Texas Health Science Center at Houston, Houston, TX, USA (e-mail: [email protected])
PENG WANG
Affiliation:
Melbourne School of Psychological Sciences, The University of Melbourne, Australia
THOMAS W. VALENTE
Affiliation:
Institute for Prevention Research, Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA
Rights & Permissions [Opens in a new window]

Abstract

Core share and HTML view are not available for this content. However, as you have access to this content, a full PDF is available via the ‘Save PDF’ action button.

Self-identification with peer crowds (jocks, popular kids, druggies, etc.) has an important influence on adolescent substance use behavior. However, little is known about the impact of the shared nature of crowd identification on different stages of adolescent drinking behavior, or the way crowd identification interacts with participation in school-sponsored sports activities. This study examines drinking influences from (1) peers with shared crowd identities, and (2) peers who jointly participate in organized sports at their school (activity members). This study introduces a new network analytic approach that can disentangle the effects of crowd identification and sports participation on individual behavior. Using survey data from adolescents in five high schools in a predominantly Hispanic/Latino district (N = 1,707), this paper examines the association between social influences and each stage of drinking behavior (intention to drink, lifetime, past-month, and binge drinking) by conducting an ordinal regression analysis. The results show that both shared identities and joint participation were associated with all stages of drinking, controlling for friends' influence. Additionally, shared identification overlapped with joint participation was associated with more frequent drinking. Related policy implications are discussed.

Keywords

adolescent alcohol useaffiliation exposure modelpeer influencetwo-mode affiliation networkmultiplex networksorganized sports participationcrowd identification
Type
Research Article
Information
Network Science , Volume 1 , Issue 2 , August 2013 , pp. 154 - 169
Creative Commons
Creative Common License - CCCreative Common License - BYCreative Common License - NCCreative Common License - SA
The online version of this article is published within an Open Access environment subject to the conditions of the Creative Commons Attribution-NonCommercial-ShareAlike licence <http://creativecommons.org/licenses/by-nc-sa/3.0/>. The written permission of Cambridge University Press must be obtained for commercial re-use.
Copyright
Copyright © Cambridge University Press 2013

References

Agneessens, F., & Roose, H. (2008). Local structural patterns and attribute characteristics in 2-mode networks: p* models to map choices of theatre events. Journal of Mathematical Sociology, 32, 204–237.Google Scholar
Audrey, S., Holliday, J., & Campbell, R. (2008). Commitment and compatibility: Teachers' perspectives on the implementation of an effective school-based, peer-led smoking intervention. Health Education Journal, 67 (2), 74–90.Google Scholar
Barber, B. L., Stone, M., Hunt, J., & Eccles, J. S. (2005). Benefits of activity participation: The roles of identity affirmation and peer group norm sharing. In Mahoney, J. L., Larson, R., & Eccles, J. S. (Eds.), Organized activities as contexts of development: Extracurricular activities, after school and community programs (pp. 185–209). Mahwah, NJ: Lawrence Erlbaum Assoc Inc.Google Scholar
Brown, B. B. (1990). Peer groups and peer cultures. In Feldman, S. S., & Elliott, G. R. (Eds.), At the threshold: The developing adolescent (pp. 171–196). Cambridge, MA: Harvard University Press.Google Scholar
Brown, B. B., Eicher, S. A., & Petrie, S. (1986). The importance of peer group (“crowd”) affiliation in adolescence. Journal of Adolescence, 9 (1), 73–96.Google Scholar
Brown, B. B., & Lohr, M. J. (1987). Peer group affiliation and adolescent self-esteem: An integration of ego identity and symbolic interaction theories. Journal of Personality and Social Psychology, 52 (1), 47–55.CrossRefGoogle ScholarPubMed
Brown, B. B., Lohr, M. J., & Trujillo, C. (1990). Multiple crowds and multiple life styles: Adolescents' perceptions of peer-group stereotypes. In Muuss, R. E. (Ed.), Adolescent behavior and society (4th ed.) (pp. 30–36). New York: McGraw-Hill Publishing Company.Google Scholar
Campbell, R., Starkey, F., Holliday, J., Audrey, S., Bloor, M., Parry-Langdon, N.. . . Moore, L. (2008). An informal school-based peer-led intervention for smoking prevention in adolescence (ASSIST): A cluster randomized trial. The Lancet, 371, 1595–1602.CrossRefGoogle Scholar
Coleman, J. S. (1961). The adolescent society. New York: Free Press.Google Scholar
Coleman, J. C. (1974). Relationships in adolescence. London: Routledge & Kegan Paul.Google Scholar
Coleman, J. C. (1980). Friendship and peer group acceptance in adolescence. In Adelson, J. (Ed.), Handbook of adolescent psychology (pp. 408–431). New York: John Wiley.Google Scholar
Costanzo, P. R., & Shaw, M. E. (1966). Conformity as a function of age level. Child Development, 37, 967–975.Google Scholar
Daraganova, G., & Robins, G. (2013). Autologistic actor attribute models. In Lusher, D., Koskinen, J., & Robins, G. L. (Eds.), Exponential random graph models for social networks: Theories, methods and applications. Cambridge, MA: Cambridge University Press.Google Scholar
Darling, N. (2005). Participation in extracurricular activities and adolescent adjustment: Cross-sectional and longitudinal findings. Journal of Youth and Adolescence, 34 (5), 493–505.Google Scholar
Eccles, J. S., & Barber, B. L. (1999). Student council, volunteering, basketball, or marching band: What kind of extracurricular involvement matters? Journal of Adolescent Research, 14 (1), 10–43.Google Scholar
Eccles, J. S., Barber, B. L., Stone, M., & Hunt, J. (2003). Extracurricular activities and adolescent development. Journal of Social Issues, 59 (4), 865–889.Google Scholar
Eder, D. (1985). The cycle of popularity: Interpersonal relations among female adolescents. Sociology of Education, 58 (3), 154–165.Google Scholar
Erikson, E. H. (1968). Identity, youth, and crisis. New York: Norton.Google Scholar
Frank, O., & Strauss, D. (1986). Markov graphs. Journal of the American Statistical Association, 81, 832–842.Google Scholar
Fujimoto, K. (2012). Using mixed-mode networks to disentangle multiple sources of social influence. In Yang, S. J., Greenberg, A. M. & Endsley, M. (Eds.), Social Computing, Behavioral–Cultural Modeling and Prediction, Lecture Notes in Computer Science, Vol. 7227 (pp. 214–221), Berlin/Heidelberg: Springer.Google Scholar
Fujimoto, K., Chou, C.-P., & Valente, T. W. (2011). The network autocorrelation model using two-mode network data: Affiliation exposure and biasness in ρ. Social Networks, 33 (3), 231–243.Google Scholar
Fujimoto, K., Unger, J., & Valente, T. W. (2012). Network method of measuring affiliation-based peer influence: Assessing the influences on teammates smokers on adolescent smoking. Child Development, 83 (2), 442–451.Google Scholar
Fujimoto, K. & Valente, T. W. (in press). Alcohol peer influence from participating in organized school activities among U. S. adolescents: A network approach. Health Psychology. doi: 10.1037/a0029466Google Scholar
Gavin, L. A., & Furman, W. (1989). Age differences in adolescents' perceptions of their peer groups. Developmental Psychology, 25 (5), 827–834.Google Scholar
Handcock, M. S., Hunter, D. R., Butts, C. T., Goodreau, S. M., & Morris, M. (2003). Statnet: Software tools for the statistical modeling of network data. Retrieved from http://statnetproject.org.Google Scholar
Hansen, D. M., Larson, R. W., & Dworkin, J. B. (2003). What adolescents learn in organized youth activities: A survey of self-reported developmental experiences. Journal of Research on Adolescence, 13 (1), 25–55.Google Scholar
Harris, K. M. (1999). The health status and risk behavior of adolescents in immigrant families. In Hernandez, D. (Ed.), Children of immigrants: Health, adjustment, and public assistance (pp. 286–347). Washington, DC: National Academy Press.Google Scholar
Jessor, R. (1984). Adolescent development and behavioral health. In Matarazzo, J. D., Weiss, S. M., Herd, J. A., Miller, N. E., & Weiss, S. M. (Eds.), Behavioral health: A handbook of health enhancement and disease prevention. New York: John Wiley and Sons.Google Scholar
La Greca, A. M., Prinstein, M. J., & Fetter, M. D. (2001). Adolescent peer crowd affiliation: Linkages with health risk behaviors and close friendships. Journal of Pediatric Psychology of Addictive Behaviors, 26 (3), 131–143.Google Scholar
Mahoney, J. L. (2000). School extracurricular activity participation as a moderator in the development of antisocial patterns. Child Development, 71 (2), 502–516.Google Scholar
Mahoney, J. L., & Cairns, R. B. (1997). Do extracurricular activities protect against early school dropout? Developmental Psychology, 33 (2), 241–253.Google Scholar
Melnick, M. J., Miller, K. E., Sabo, D. F., Farrell, M. P., & Barnes, G. M. (2001). Tobacco use among high school athletes and nonathletes: Results of the 1997 Youth Risk Behavior Survey. Adolescence, 36 (144), 727–747.Google Scholar
Miller, K. E., Farrell, M. P., Barnes, G. M., Melnick, M. J., & Sabo, D. (2005). Gender/racial differences in jock identity, dating, and adolescent sexual risk. Journal of Youth and Adolescence, 34 (2), 123–136.Google Scholar
Miller, K. E., Hoffman, J. H., Barnes, G. M., & Farrell, M. P. (2003). Jocks, gender, race, and adolescent problem drinking. Journal of Drug Education, 33 (4), 445–462.Google Scholar
Mosbach, P., & Leventhal, H. (1988). Peer group identification and smoking: Implications for intervention. Journal of Abnormal Psychology, 97 (2), 238–245.Google Scholar
Newman, P. R., & Newman, B. M. (1976). Early adolescence and its conflict: Group identity versus alienation. Adolescence, 11, 261–274.Google Scholar
Pate, R. R., Trost, S. G., Levin, S., & Dowda, M. (2000). Sports participation and health-related behaviors among US youth. Archives of Pediatrics and Adolescent Medicine, 154 (9), 904–911.Google Scholar
Pattison, P., & Wasserman, S. (1999). Logit models and logistic regressions for social networks, II. Multivariate relationships. British Journal of Mathematical and Statistical Psychology, 52, 169–193.Google Scholar
Robins, G. L., Pattison, P., & Elliott, P. (2001). Network models for social influence processes. Psychometrika, 66, 161–190.Google Scholar
Robins, G. L., Pattison, P., Kalish, Y., & Lusher, D. (2007). An introduction to exponential random graph (p*) models for social networks. Social Networks, 29, 173–191.Google Scholar
Royston, P. (2004). Multiple imputation of missing values. Stata Journal, 4 (3), 227–241.Google Scholar
Simons-Morton, B. G., & Farhat, T. (2010). Recent findings on peer group influences on adolescent smoking. Journal of Primary Prevention, 31, 191–208.Google Scholar
Snijders, T. A. B., Lomi, A. & Torló, V. J. (2013). A model for the multiplex dynamics of two-mode and one-mode networks, with an application to employment preference, friendship, and advice. Social Networks, 35, 265–276.CrossRefGoogle Scholar
Sussman, S., Dent, C. W. & McCullar, W. J. (2000). Group self-identification as a prospective predictor of drug use and violence in high-risk youth. Psychology of Addictive Behaviors, 14 (2), 192–196.Google Scholar
Sussman, S., Pokhrel, P., Ashmore, R. D., & Brown, B. B. (2007). Adolescent peer group identification and characteristics: A review of the literature. Addictive Behavior, 32 (8), 1602–1627.Google Scholar
Sussman, S., Simon, T. R., Stacy, A. W., Dent, C. W., Ritt, A., Kipke, M. D., . . . Flay, B. R. (1999). The association of group self-identification and adolescent drug use in three samples varying in risk. Journal of Applied Social Psychology, 29 (8), 1555–1581.Google Scholar
Sussman, S., Unger, J. B., & Dent, C. W. (2004). Peer group self-identification among alternative high school youth: A predictor of their psychosocial functioning five years later. International Journal of Clinical and Health Psychology, 41 (1), 9–25.Google Scholar
Valente, T. W. (1995). Network models of the diffusion of innovations. Cresskill, NJ: Hampton Press.Google Scholar
Valente, T. W. (2005). Network models and methods for studying the diffusion of innovations. In Carrington, P. J., Scott, J., & Wasserman, S. (Eds.), Models and methods in social network analysis: Structural analysis in the social sciences (pp. 98–116). Cambridge, MA: Cambridge University Press.Google Scholar
Valente, T. W. (2010). Social networks and health: Models, methods, and applications. New York: Oxford University Press.Google Scholar
Valente, T. W. (2012). Network interventions. Science, 337 (6), 49–53.Google Scholar
Valente, T. W., Fujimoto, K., Unger, J. B., Soto, D., & Meeker, D. (in press). Variations in network boundary and type: A study of adolescent peer influences. Social Networks. doi: 10.1016/j.socnet.2013.02.008Google Scholar
Verkooijen, K. T., de Vries, N. K., & Nielsen, G. A. (2007). Youth crowds and substance use: The impact of perceived group norm and multiple group identification. Psychology of Addictive Behaviors, 21 (1), 55–61.Google Scholar
Wang, P. (2013). ERGM extensions: Models for multiple networks and bipartite networks. In Lusher, D., Koskinen, J., & Robins, G. L. (Eds.), Exponential random graph models for social networks: Theories, methods and applications. New York: Cambridge University Press.Google Scholar
Wang, P., Pattison, P. E., & Robins, G. L. (2013a). Exponential random graph model specifications for bipartite networks – A dependence hierarchy. Social Networks, 35 (2), 211–222.Google Scholar
Wang, P., Robins, G. L., & Pattison, P. E., (2006). PNet: A program for simulations and estimations of exponential random graph models., Australia: Melbourne School of Psychological Sciences, The University of Melbourne. URL: http://sna.unimelb.edu.au/PNet.Google Scholar
Wang, P., Robins, G. L., Pattison, P. E., & Lazega, E. (2013b). Exponential random graph models for multilevel networks. Social Networks, 35 (1), 96–115.Google Scholar
Wasserman, S., & Pattison, P. E. (1996). Logit models and logistic regression for social networks, I. An introduction to Markov graphs and p*. Psychometrika, 6 (3), 401–425.Google Scholar
Williams, R. (2006). Generalized ordered logit/partial proportional odds models for ordinal dependent variables. The Stata Journal, 6 (1), 58–82.Google Scholar
Wichstrøm, T., & Wichstrøm, L. (2009). Does sports participation during adolescence prevent later alcohol, tobacco and cannabis use? Addiction, 104 (1), 138–149.Google Scholar