Evaluation of the Effects of the Aban Aya Youth Project in Reducing Violence among African American Adolescent Males Using Latent Class Growth Mixture Modeling Techniques
Restricted accessResearch articleFirst published online April, 2005
Evaluation of the Effects of the Aban Aya Youth Project in Reducing Violence among African American Adolescent Males Using Latent Class Growth Mixture Modeling Techniques
This study employs growth mixture modeling techniques to evaluate the preventive effects of the Aban Aya Youth Project in reducing the rate of growth of violence among African American adolescent males (N = 552). Results suggest three distinct classes of participants: high risk (34%), medium risk (54%), and low risk (12%) based on both the participants’ initial violence scores and their growth of violence over time. Results further show significant effects (almost 3 times as large as the effect found in the regular one-class analysis) for the high-risk class but not for the medium- or low-risk classes.
August, G. J., S. S. Lee, M. L. Bloomquist, G. M. Realmuto, and J. M. Hektner. 2003. Dissemination of an evidence-based prevention innovation for aggressive children living in culturally diverse urban neighborhood: The Early Risers Effectiveness Study. Prevention Science4:271-286.
2.
Bauer, Daniel J., and Patrick J. Curran. 2003. Distributional assumptions of growth mixture models: Implications for overextraction of latent trajectory classes. Psychological Methods8:338-363.
3.
Brame, B., D. S. Nagin, and R. E. Tremblay. 2001. Developmental trajectories of physical aggression from school entry to late adolescence. Journal of Child Psychology and Psychiatry and Allied Disciplines42:503-512.
4.
Colder, C. R., R. T. Campbell, E. Ruel, J. L. Richardson, and B. R. Flay. 2002. A finite mixture model of growth trajectories of adolescent alcohol use: Predictors and consequences. Journal of Consulting and Clinical Psychology70:975-986.
5.
Colder, C. R., P. Mehta, K. Balanda, R. T. Campbell, K. P. Mayhew, W. R. Stanton, M. A. Pentz, & B. R. Flay. 2001. Identifying trajectories of adolescent smoking: An application of latent growth mixture model. Health Psychology20:127-135.
6.
Duncan, T. E., S. E. Duncan, L. A. Strycker, F. Li, and A. Alpert. 1999. An introduction to latent variable growth curve modeling: Concepts, issues, and applications. Mahwah, NJ: Lawrence Erlbaum.
7.
D’Unger, A. V., K. C. Land, P. L. McCall, and D. S. Nagin. 1998. How many latent class of delinquent/criminal careers? Results from mixed poisson regression analyses. American Journal of Sociology103:1593-1630.
8.
Flay, B. R., S. Graumlich, E. Segawa, J. L. Burns, M. Y. Holliday, and Aban Aya Investigators. 2004. Effects of two prevention programs on high-risk behaviors among African-American youth: A randomized trial. Archives of Pediatric & Adolescent Medicine158:377-384.
9.
Graham, J., B. R. Flay, C. A. Johnson, W. B. Hansen, and L. M. Collins. 1984. Group comparability: A multi attribute utility measurement approach to the use of random assignment with small numbers of aggregated units. Evaluation Review8:247-260.
10.
Hagenaars, J. A., and A. L. McCutcheon. 2002. Applied latent class analysis. New York: Cambridge University Press.
11.
Hedeker, D. 2000. A fully semi-parametric mixed-effects regression model for categorical outcomes. Presented at the annual meeting of the American Statistical Association, Indianapolis, IN.
12.
Moffitt, T. E.1993. Adolescent-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review100:674-701.
13.
Muthen, B. O., C. H. Brown, K. Masyn, B. Jo, S. T. Khoo, C. C. Yang, C. P. Wang, S. G. Kellam, J. B. Carlin, and J. Liao. 2002. General growth mixture modeling for randomized preventive interventions. Biostatistics3:459-475.
14.
Muthen, B. O., and L. K. Muthen. 2000. Integrating person-centered and variable-centered analyses: Growth mixture modeling with latent trajectory classes. Alcoholism, Clinical, and Experimental Research24:882-891.
15.
Muthen, B. O.2001. Mplus user’s guide. Version 2. Los Angeles: Muthen & Muthen.
16.
Nagin, D. S.1999. Analyzing developmental trajectories: A semiparametric, group-based approach. Psychological Methods4:139-157.
17.
Nagin, D. S., D. P. Farrington, and T. E. Moffitt. 1995. Life-course trajectories of different types of offenders. Criminology33:111-139.
18.
Nagin, D. S., and K. C. Land. 1993. Age, criminal career, and population heterogeneity: Specification and estimation of nonparametric, mixed Poisson model. Criminology31:327-362.
19.
Nagin, D. S., and R. Tremblay. 1999. Trajectories of boys’ physical aggression, opposition, and hyperactivity on the path to physically violent and nonviolent juvenile delinquency. Child Development70:1181-1196.
20.
Ngwe, J. E., C. L. Qi, B. R. Flay, and E. Segawa. 2004. Violence prevention among African American adolescent males: A test of mediation mechanisms. American Journal of Health Behavior28 (suppl. 1): S24-S37.
21.
Raudenbush, S. W., and A. S. Bryk. 2002. Hierarchical linear models: Applications and data analysis methods. 2nd ed.Thousand Oaks, CA: Sage.
22.
Schwartz, G.1978. Estimating the dimension of a model. Annals of Statistics6:461-464.
23.
Simons, R. L., C. Wu, R. D. Conger, and F. O. Lorenz. 1994. Two routes to delinquency: Differences between early and late starters in the impact of parenting and deviant peers. Criminology32:247-275.
