Abstract
Life-course research has benefited recently from the development and application of advanced statistical methods, most notably group-based trajectory modeling (GBTM). These methods have allowed life-course researchers to assess taxonomies of offending and identify distinct offender trajectories. Guided by this methodological framework, this chapter offers the most comprehensive trajectory analyses to date among the Cambridge Study in Delinquent Development males from ages 10 to 56. Specifically, trajectory solutions are identified across a series of age ranges (e.g., ages 10–16, ages 10–24, ages 10–32, ages 10–40, ages 10–48, and ages 10–56). Subsequent analyses focus on comparing the stability in trajectory solutions across age ranges and the ability of childhood risk factors to differentiate trajectory groups generally and among Cambridge Study in Delinquent Development males who evince the most childhood risk specifically.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Notes
- 1.
It is important to note that the percentages described here for the trajectory estimations for the age 10–40 period is slightly different from the trajectory estimations for the same age range described in Piquero et al. (2007). The reason is because the current analyses were based on 404 males (excluding males who were not at risk because they emigrated), while the analyses in Piquero et al. (2007) were based on the full, original sample of 411 men. Importantly, however, the high rate chronic trajectory that is found in the current study as well as in Piquero et al. (2007) is comprised of the exact same eight men.
- 2.
It is interesting to note that there is an increase in the proportion of the sample (∼3%) that is in the high rate chronic trajectory. The most likely reason for this shift of persons across groups is because, as more time (ages) is (are) incorporated into the model, particularly late middle age/older adulthood, the prevalence and frequency of offending is declining generally for all trajectories so the high rate chronics in the latest age band trajectory (ages 10–56) do not look as “high rate” in terms of frequency compared to the very low rate chronics as the comparisons suggest in earlier age band trajectories. Thus, some switching, particularly among these two chronic offending trajectories, is apparent.
- 3.
The two summated indexes are from Piquero et al. (2007), who used data from age 8–10 interviews with the boy, his parent(s), his teacher(s), and other records. Twenty-seven risk factors in total were measured prior to most criminal involvement including individual (12 items, α = .57) and environmental (15 items, α = .77) domains. Individual risk factors included (1) low junior school attainment, (2) daring disposition, (3) small height, (4) low nonverbal intelligence, (5) nervous/withdrawn boy, (6) high extraversion of boy, (7) high neuroticism of boy, (8) psychomotor impulsivity, (9) dishonest, (10) unpopular, (11) troublesome, and (12) lacks concentration/restless. Environmental risk factors included (1) harsh attitude/discipline of parents, (2) teen mother at birth of her first child, (3) behavior problems of siblings, (4) criminal record of a parent, (5) delinquent older sibling, (6) large family size, (7) poor housing, (8) low family income, (9) parental disharmony, (10) neurotic/depressed father, (11) neurotic/depressed mother, (12) low socioeconomic status, (13) separated parents, (14) poor supervision, and (15) high-delinquency-rate school. Coding for all risk factors was dichotomous: 1 = “ok” and 2 = “bad”; higher scores indicate presence of the risk factor.
- 4.
To be sure, these analyses are not designed to comment on our (or the field’s) ability to predict the future life course of any specific individual from their early childhood experiences. There are, of course, many cautions that must be exercised when predicting future criminal behavior, but there is good knowledge to suggest that subjective predictions (even among judges; Gottfredson 1999) are fraught with much more failure than objective, instrument-based risk assessments. More generally, these analyses do suggest that, in the aggregate, children who exhibit very high risk should be targeted for early, evidence-based prevention efforts.
References
Blumstein, A., Cohen, J., Roth, J. A., & Visher, C. A. (Eds.). (1986). Criminal careers and “career criminals”. Washington, DC: National Academy Press.
Eggleston, E. P., Laub, J. H., & Sampson, R. J. (2004). Methodological sensitivities to latent class analysis of long-term criminal trajectories. Journal of Quantitative Criminology, 20, 1–26.
Gottfredson, D. M. (1999). Effects of judges’ sentencing decisions on criminal careers. Research in Brief. Washington, DC: National Institute of Justice.
Gottfredson, M., & Hirschi, T. (1988). Science, public policy, and the career paradigm. Criminology, 26, 37–55.
Jones, B. L., Nagin, D. S., & Roeder, K. (2001). A SAS procedure based on mixture models for estimating developmental trajectories. Sociological Methods & Research, 29, 374–393.
Laub, J. H., & Sampson, R. J. (2003). Shared beginnings, divergent lives: Delinquent boys to age 70. Cambridge, MA: Harvard University Press.
Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review, 100, 674–701.
Nagin, D. S. (2010). Group-based trajectory modeling: An overview. In A. R. Piquero & D. Weisburd (Eds.), Handbook of quantitative criminology (pp. 53–67). New York: Springer.
Patterson, G. R., DeBaryshe, B. D., & Ramsey, E. (1989). A developmental perspective on antisocial behavior. American Psychologist, 44, 329–335.
Piquero, A. R. (2008). Taking stock of developmental trajectories of criminal activity over the life course. In A. M. Lieberman (Ed.), The long view of crime: A synthesis of longitudinal research (pp. 23–78). Washington, DC: Springer.
Wolfgang, M. E., Figlio, R. M., & Sellin, T. (1972). Delinquency in a birth cohort. Chicago: University of Chicago Press.
Nagin, D. S., Farrington, D. P., & Moffitt, T. E. (1995). Life-course trajectories of different types of offenders. Criminology, 33, 111–139.
Piquero, A. R., Farrington, D. P., & Blumstein, A. (2007). Key Issues in Criminal Career Research: New Analyses of the Cambridge Study in Delinquent Development. Cambridge: Cambridge University Press.
Piquero, A. R., Sullivan, C. J., & Farrington, D. P. (2010d). Assessing differences between short-term high-rate offenders and long-term low-rate offenders. Criminal Justice and Behaviour, 37, 1309–1329.
Author information
Authors and Affiliations
Rights and permissions
Copyright information
© 2013 Springer Science+Business Media New York
About this chapter
Cite this chapter
Farrington, D.P., Piquero, A.R., Jennings, W.G. (2013). Trajectories of Offending to Age 56. In: Offending from Childhood to Late Middle Age. SpringerBriefs in Criminology. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6105-0_5
Download citation
DOI: https://doi.org/10.1007/978-1-4614-6105-0_5
Published:
Publisher Name: Springer, New York, NY
Print ISBN: 978-1-4614-6104-3
Online ISBN: 978-1-4614-6105-0
eBook Packages: Humanities, Social Sciences and LawSocial Sciences (R0)