The Social Economics of Adolescent Behavior and Measuring the Behavioral Culture of Schools
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Schools are thought to have an important impact on adolescent behaviors, but the mechanisms are not well understood. We hypothesize that there are measurable constructs of peer- and teacher-related extrinsic motivations for adolescent behaviors and sought to develop measures of school culture that would capture these constructs.
We developed several survey items to assess school behavioral culture and collected self-reported data from a sample of adolescents age 14–17 attending high school in low income neighborhoods of Los Angeles. We conducted exploratory and confirmatory factor analysis to inform the creation of simple-summated multi-item scales. We also conducted a cultural consensus analysis to identify the existence of shared pattern of responses to the items among respondents within the same school.
From 1159 adolescents, six factors were identified: social culture regarding popular (Cronbach’s alpha = 0.84) and respected (alpha = 0.83) behaviors, teacher support (alpha = 0.86) and monitoring of school rules (alpha = 0.85), valued student traits (alpha = 0.67) and school order (alpha = 0.68). Cultural consensus analysis identified a shared pattern of responses to the items among respondents at 8 of the 13 schools. School academic performance, which is based on standardized test results, is strongly correlated with social culture regarding popular behaviors (Pearson’s correlation coefficient r = 0.64), monitoring of school rules (r = 0.71), and school order (r = 0.83).
The exploratory and confirmatory factor analyses did not support a single, overall factor that measures school culture. However, the six identified sub-scales might be used individually to examine school influence on academic performance and health behaviors.
KeywordsSchool culture Social networks Behavioral economics Academic performance Risky behaviors
This study was supported by a grant to Dr. Wong from the National Institute on Drug Abuse (R01DA033362). We also received support from the UCLA CTSI Healthy Neighborhoods School Initiative, which was supported by the NIH National Center for Advancing Translational Science (NCATS) UCLA CTSI (UL1TR001881). The research described in this study involved human participants and was approved by the RAND institutional review board (Protocol # 2012-0169-CR01). All procedures performed in this study involving human participants were in accordance with the ethical standards of the institutional and/or national research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. No animals were used in the study. Informed assent was obtained from all youth participants included in this study, and informed consent was obtained from a parent or legal guardian of all youth participants included in this study.
M.D.W. designed and supervised the execution of the study, analyzed the data and wrote the paper. P.J.C. assisted in the study design and collaborated in the writing and editing of the final manuscript. R.D.H. provided assistance with the data analysis and edited the manuscript. D.P.K. assisted with the study design and data analyses and edited the manuscript. J.S.T. assisted with the study design and edited the manuscript. R.N.D. assisted in the study design and collaborated in the writing of the manuscript.
Compliance with Ethical Standards
Conflict of interest
The authors declare that they have no conflict of interest.
The human subjects research review board approved all research activities (IRB#16-001512).
Informed written consent was obtained from the parents of the study participants and informed written assent was obtained from the adolescent participants.
- Bailey, J. A., Hill, K. G., Guttmannova, K., Epstein, M., Abbott, R. D., Steeger, C. M., & Skinner, M. L. (2016). Associations between parental and grandparental marijuana use and child substance use norms in a prospective, three-generation study. The Journal of Adolescent Health, 59(3), 262–268.CrossRefPubMedGoogle Scholar
- Bernstein, B. (1977). Primary Socialization, Language and Education) (Vol. 3). London: Routledge & Kegan Paul.Google Scholar
- Borgatti, S. P., Everett, M. G., & Freeman, L. C. (2014). UCINET for Windows: Software for Social Network Analysis. Encyclopedia of Social Network Analysis and Mining. Harvard, MA: Analytic Technologies.Google Scholar
- Dudovitz, R. N., Chung, P. J., Reber, S. J., Kennedy, D., Tucker, J. S., & Shoptaw, S., et al. (2018). Assessment of exposure to high-performing schools and risk of adolescent substance use: a natural experiment. JAMA Pediatrics, 2018, 1–31. published online October29.Google Scholar
- Mehan, H., Hubbard, L., Lintz, A., & Villanueva, I. (1997). In: Hall, P. (ed.) Race, ethnicity, and multiculturalism: Policy and practice. (pp. 115–149). New York, NY: Garland.Google Scholar
- Samejima, F. (1969). Estimation of latent ability using a response pattern of graded scores (Psychometric Monograph No. 17). Retrieved November 29, 2016, from http://www.psychometrika.org/journal/online/MN17.pdf.
- StataCorp. (2018). Stata: Statistical Software. Stata.com. College Station, TX.Google Scholar
- Swanson, M. C. (1989). Advancement via individual determination: project AVID. Educational Leadership, 46, 63–64.Google Scholar
- Tucker, J. S., de la Haye, K., Kennedy, D. P., Green, Jr., H. D., & Pollard, M. S. (2014). Peer influence on marijuana use in different types of friendships. Journal of Adolescent Health, 54(1), 67–73.Google Scholar
- Van Ryzin, M. J., & Roseth, C. J. (2017). Enlisting peer cooperation in the service of alcohol use prevention in middle school. Child Development, 107, 238.Google Scholar