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At-Risk High School Students in the “Gaining Early Awareness and Readiness” Program (GEAR UP): Academic and Behavioral Outcomes

The purpose of this study was to assess the effects of a GEAR UP intervention with at-risk high school students at a large urban high school in Florida. The goals of the GEAR UP program were to improve academic performance, decrease behavior-related problems, and reduce truancy and absenteeism. The study design consisted of a three-group comparison of the 447 students in GEAR UP: the No Participation Group, the Low Participation Group, and the High Participation Group. Participation levels were calculated for each category of activity (academic, behavior-related, and social), and propensity scoring was used to match the groups on sociodemographic characteristics and other differentiating variables. Results indicated that race (i.e., African American) and sex (i.e., female) are associated with high participation in program activities. Also, students who spent a substantial amount of time on academic activities improved their GPAs over a semester, and students who took advantage of behavior-related services and participated in social activities significantly reduced disciplinary referrals (p < .05). Suggestions for program refinement resulting from the study are discussed.

Editors' Strategic Implications: These findings should be helpful to educators, practitioners, and researchers as they attempt to understand which program components contribute to observed outcomes, how these components affect at-risk students, and what groups of students require more intense intervention. The GEAR UP results are promising but await replication and long-term follow-up.

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References

  1. Brier, N. (1995). Predicting antisocial behavior in youngsters displaying poor academic achievement: A review of risk factors. Journal of Developmental and Behavioral Pediatrics, 16, 271–275.

    PubMed  Article  CAS  Google Scholar 

  2. Bryant, A. L., Schulenberg, J., Bachman, J. G., O’Malley, P. M., & Johnston, L. D. (2000). Understanding the links among school misbehavior, academic achievement and cigarette use during adolescence: A national panel study of adolescents. Prevention Science, 1, 71–87.

    PubMed  Article  CAS  Google Scholar 

  3. Braddock, J. H. (1990). Tracking: Implications for student race-ethnic subgroups. Baltimore, MD: John Hopkins University, Center for Research for the Effective Schooling of Disadvantaged Children.

    Google Scholar 

  4. Cairns, R. B., Cairns, B. D., & Neckerman, H. J. (1989). Early school dropout: Configurations and determinants. Child Development, 60, 1437–1452.

    PubMed  Article  CAS  Google Scholar 

  5. Cochran, W. G. (1965). The planning of observational studies in human populations. Journal of the Royal Statistical Society, 128, 134–155.

    Google Scholar 

  6. Cook, T. D., Church, M. B., Ajanaku, S., & Shadish, W. R. (1996). The development of educational aspirations and expectations among inner-city boys. Child Development, 6, 3368–3385.

    Article  Google Scholar 

  7. Crooks, D. L. (1995). American children at risk: Poverty and its consequences for children's health, growth, and school achievement. Yearbook of Physical Anthropology, 38, 57–86.

    Article  Google Scholar 

  8. Curtis, B., Livingstone, D. W., & Smaller, H. (1992). Stacking the deck: The streaming of working-class kids in Ontario schools. Toronto, ON: Our Schools/Our Selves Education Foundation.

    Google Scholar 

  9. Dryfoos, J. G. (1990). Adolescents at risk: Prevalence and prevention. New York: Oxford University Press.

    Google Scholar 

  10. Duncan, G. J., Brooks-Gunn, J., Yeung, W. J., & Smith, J. R. (1998). How much does childhood poverty affect the life chances of children? American Sociological Review, 63, 406–423.

    Article  Google Scholar 

  11. Ellickson, P. L., & McGuigan, K. A. (2000). Early predictors of adolescence violence. American Journal of Public Health, 90, 566–572.

    PubMed  CAS  Article  Google Scholar 

  12. Foster, E. M. (2003). Propensity score matching: An illustrative analysis of dose response. Medical Care, 41, 1183–1192.

    PubMed  Article  Google Scholar 

  13. Imbens, G. W. (2000). The role of propensity score in estimating dose-response functions. Biometrica, 87, 706–710.

    Article  Google Scholar 

  14. Ensminger, M. E., & Slusarcick, A. L. (1992). Paths to high school graduation or dropout: A longitudinal study of a first-grade cohort. Sociology of Education, 65, 95–113.

    Article  Google Scholar 

  15. Fachola, O. S. (1999). After-school programs and student misbehavior. Education Digest, 65, 62–65.

    Google Scholar 

  16. Hearn, J. C. (1991). Academic and nonacademic influences on the college destinations of 1980 high school graduates. Sociology of Education, 64, 158–171.

    Article  Google Scholar 

  17. Higher Education Amendments of 1998. Pub. L. No. 105-244.

  18. Holden, G. W., Nelson, P. B., Velasquez, J., & Ritchie K. L. (1993). Cognitive, psychological, and reported sexual behavior differences between pregnant and nonpregnant adolescents. Adolescence, 28, 557–572.

    PubMed  CAS  Google Scholar 

  19. Huberty, C. J., & Morris, J. D. (1989). Multivariate analysis versus multiple univariate analyses. Psychological Bulletin, 105, 302–308.

    Article  Google Scholar 

  20. Hullsiek, K. H., & Louis, T. A. (2002). Propensity score modeling strategies for the causal analysis of observational data. Biostatistics, 2, 179–193.

    Article  Google Scholar 

  21. Jencks, C. (1979). Who gets ahead? The determinants of economic success in America. New York: Basic Books.

    Google Scholar 

  22. Lipsey, M. W., & Derzon, J. H. (1998). Predictors of violent or serious delinquency in adolescence and early adulthood: A synthesis of longitudinal research. In R. Loeber & D. P. Farrington (Eds.), Serious and violent juvenile offenders: Risk factors and successful interventions (pp. 86–105). Thousand Oaks, CA: Sage.

    Google Scholar 

  23. Marshall, N. L., Coll, C. G., Marx, F., McCartney, K., Keefe, N., & Ruh, J. (1997). After-school time and children's behavior-related adjustment. Merrill-Palmer Quarterly, 43, 497–514.

    Google Scholar 

  24. McCaffrey, D. F., Ridgeway, G., & Morral, A. R. (2004). Propensity score estimation with boosted regression for evaluating causal effects in observational studies. Psychological Methods, 9, 403–425.

    PubMed  Article  Google Scholar 

  25. McNamara, K. (2000). Outcomes associated with service involvement among disengaged youth. Journal of Drug Education, 30, 229–245.

    PubMed  Article  CAS  Google Scholar 

  26. Newcomb, M. D., Abbott, R., D., Catalano, R. F., Hawkins, J. D., Battin-Pearson, S., & Hill, K. (2002). Mediational and deviance theories of late high school failure: Process roles of structural strains, academic competence, and general vs. specific problem behaviors. Journal of Counseling Psychology, 49, 171–186.

    Article  Google Scholar 

  27. Oakes, J., & Lipton, M. (1996). Developing alternatives to tracking and grading. In L. I. Rendon & R. O. Hope (Eds.), Educating a new majority: Transforming America's educational system for diversity (pp. 168–200). San Francisco: Jossey-Bass.

    Google Scholar 

  28. Parelius, A. P., & Parelius R. J. (1978). The sociology of education. Englewood Cliffs, NJ: Prentice Hall.

    Google Scholar 

  29. Parke, R. D., & Buriel, R. (1998). Socialization in the family: Ethnic and ecological perspectives. In W. Damon & N. Eisenberg (Eds.), Handbook of child psychology: Social, emotional, and personality development (pp. 463–552). New York: Wiley.

    Google Scholar 

  30. Pong, S. (1997). Family structure, school context, and eighth-grade math and reading achievement. Journal of Marriage and the Family, 59, 734–746.

    Article  Google Scholar 

  31. Ramey, C. T., & Ramey, S. L. (1990). Intensive educational intervention for children of poverty. Intelligence, 14, 1–9.

    Article  Google Scholar 

  32. Richmond, J. B. (1992). Poverty and the schools. Bulletin of the New York Academy of Medicine, 68, 32–45.

    PubMed  CAS  Google Scholar 

  33. Roffman, J. G., Pagano, M. E., & Hirsch, B. J. (2001). Youth functioning and experiences in inner-city after-school programs among age, gender, and race groups. Journal of Child and Family Studies, 10, 85–100.

    Article  Google Scholar 

  34. Rosenbaum, P. R., & Rubin, D. B. (1984). Reducing bias in observational studies using subclassification on the propensity score. Journal of the American Statistical Association, 79, 516–524.

    Article  Google Scholar 

  35. Rosenbaum, P. R. (1995). Observational studies. New York: Springer-Verlag.

    Google Scholar 

  36. Royse, D. (1998). Mentoring high-risk minority youth: Evaluation of the Brothers Project. Adolescence, 33, 146–158.

    Google Scholar 

  37. Rubin, D. B. (1997). Estimating causal effects from large data sets using propensity scores. Annals of Internal Medicine, 127, 757–763.

    PubMed  CAS  Google Scholar 

  38. Rubin, D. B., & Thomas, N. (1996). Matching using estimated propensity scores: Relating theory to practice. Biometrics, 52, 249–264.

    PubMed  Article  CAS  Google Scholar 

  39. Rumberger, R. W. (1987). High school dropouts: A review of issues and evidence. Review of Educational Research, 57, 101–121.

    Google Scholar 

  40. Schinke, S. P., Orlandi, M. A., & Cole, K. C. (1992). Boys and Girls Clubs in public housing developments: Prevention services for youth at risk. Journal of Community Psychology, OSAP Special Issues, 118–128.

  41. Smith, C. (1996). The link between childhood maltreatment and teenage pregnancy. Social Work Research, 20, 131–140.

    Google Scholar 

  42. Smith-Maddox, R. (1999). The social networks and resources of African American eighth graders: Evidence from the national educational longitudinal study of 1988. Adolescence, 34, 169–183.

    PubMed  CAS  Google Scholar 

  43. U.S. Department of Education (1990). National goals for education. Washington, DC: U.S. Government Printing Office.

    Google Scholar 

  44. U.S. Department of Education (2002). Audit of gaining awareness and readiness for undergraduate programs (ED-OIG/A07-A0033). Washington, DC: U.S. Department of Education, Office of the Inspector General.

    Google Scholar 

  45. Voelkl, K. E., & Fronte, M. R. (2000). Predictors of substance use at school among high school students. Journal of Educational Psychology, 92, 583–592.

    Article  Google Scholar 

  46. Weisman, S. A., & Gottfredson, D. C. (2001). Attrition from after school programs: Characteristics of students who drop out. Prevention Science, 2, 201–205.

    PubMed  Article  CAS  Google Scholar 

  47. Wentzel, K. R. (1998). Social relationships and motivation in middle school: The role of parents, teachers, and peers. Journal of Educational Psychology, 90, 202–209.

    Article  Google Scholar 

  48. Wilson, S. J., Lipsey, M. W., & Derzon, J. H. (2003). The effects of school-based intervention programs on aggressive behavior: A meta-analysis. Journal of Consulting and Clinical Psychology, 71, 136–149.

    Article  Google Scholar 

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Correspondence to Svetlana Yampolskaya Ph.D., MHC 2435.

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Yampolskaya, S., Massey, O. & Greenbaum, P. At-Risk High School Students in the “Gaining Early Awareness and Readiness” Program (GEAR UP): Academic and Behavioral Outcomes. J Primary Prevent 27, 457–475 (2006). https://doi.org/10.1007/s10935-006-0050-z

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KEY WORDS

  • at-risk high school students
  • education program
  • academic performance