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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.
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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. CrossRefPubMedPubMedCentral
Baumrind, D. (1966). Effects of authoritative parental control on child behavior. Child Development, 37(4), 887–907. CrossRef
Bernstein, B. (1977). Primary Socialization, Language and Education) (Vol. 3). London: Routledge & Kegan Paul.
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.
Cullen, J. B., Jacob, B. A., & Levitt, S. (2006). The effect of school choice on participants: evidence from randomized lotteries. Econometrica, 74(5), 1191–1230. CrossRef
Deci, E. L., & Ryan, R. M. (2000). The “What” and “Why” of goal pursuits: human needs and the self-determination of behavior. Psychological Inquiry, 11(4), 227–268. CrossRef
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.
Fletcher, A., & Bonell, C. (2013). Social network influences on smoking, drinking and drug use in secondary school: centrifugal and centripetal forces. Sociology of Health & Illness, 35(5), 699–715. CrossRef
Fryer, R. G. (2011). Financial incentives and student achievement: evidence from randomized trials. The Quarterly Journal of Economics, 126(4), 1755–1798. CrossRef
Guerrero, L. R., Dudovitz, R., Chung, P. J., Dosanjh, K. K., & Wong, M. D. (2016). Grit: a potential protective factor against substance use and other risk behaviors among latino adolescents. Academic PediDemographics of study atrics, 16(3), 275–281. CrossRef
Hendrickson, A. E., & White, P. O. (1964). Promax: a quick method for rotation to oblique simple structure. British Journal of Mathematical and Statistical Psychology, 17(1), 65–70. CrossRef
Hu, L., & Bentler, P. M. (1999). Cutoff criteria for fit indexes in covariance structure analysis: conventional criteria versus new alternatives. Structural Equation Modeling: a Multidisciplinary Journal, 6, 1–55. CrossRef
Lau, C., Wong, M., & Dudovitz, R. (2017). School disciplinary style and adolescent health. The Journal of Adolescent Health: Official Publication of the Society for Adolescent Medicine, 62(2), 136–142. CrossRef
Matheny, A. P., Wachs, T. D., Ludwig, J. L., & Phillips, K. (1995). Bringing order out of chaos: psychometric characteristics of the confusion, hubbub, and order scale. Journal of Applied Developmental Psychology, 16, 429–444. CrossRef
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.
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.
Sanbonmatsu, L., Kling, J. R., Duncan, G. J., & Brooks-Gunn, J. (2006). Neighborhoods and academic achievement: results from the moving to opportunity experiment. The Journal of Human Resources, 41(4), 649–691. CrossRef
StataCorp. (2018). Stata: Statistical Software. Stata.com. College Station, TX.
Swanson, M. C. (1989). Advancement via individual determination: project AVID. Educational Leadership, 46, 63–64.
Thapa, A., Cohen, J., Guffey, S., & Higgins-DAllesandro, A. (2013). A review of school climate research. Review of Educational Research, 83(3), 357–385. CrossRef
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.
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.
Volpp, K. G., & Asch, D. A. (2017). Make the healthy choice the easy choice: using behavioral economics to advance a culture of health. QJM: Monthly Journal of the Association of Physicians, 110(5), 271–275. PubMed
Watt, K. M., Powell, C. A., & Mendiola, I. D. (2009). Implications of one comprehensive school reform model for secondary school students underrepresented in higher education. Journal of Education for Students Placed at Risk, 9(3), 241–259. CrossRef
Weller, S. C. (2007). Cultural consensus theory: applications and frequently asked questions. Field Methods, 19(4), 339–368. CrossRef
Wentzel, K. R., & Caldwell, K. (1997). Friendships, peer acceptance, and group membership: relations to academic achievement in middle school. Child Development, 68(6), 1198–1209. PubMed
- The Social Economics of Adolescent Behavior and Measuring the Behavioral Culture of Schools
Mitchell D. Wong
Paul J. Chung
Ron D. Hays
David P. Kennedy
Joan S. Tucker
Rebecca N. Dudovitz
- Springer US