Journal of Quantitative Criminology

, Volume 30, Issue 4, pp 709–730 | Cite as

Investigating the Functional Form of the Self-control–Delinquency Relationship in a Sample of Serious Young Offenders

Original Paper

Abstract

Objective

This work further examines the functional form of the self-control–delinquency relationship as an extension of recent work by Mears et al. (J Quant Criminol, 2013). Given the importance of the authors’ conclusions regarding the nonlinear relationship between these two variables and the recognition that there are some potential limitations in the sample and assumptions required for the analytic methods used, we apply both similar and alternative techniques with a data set comprised of serious youthful offenders to determine whether key findings can be replicated.

Methods

Data from the Pathways to Desistance study, which comprise extensive individual and social history interviews with 1,354 offenders over multiple waves spread out over 84 months, is utilized in this analysis. These data are well-suited to investigating the questions of interest as the target population comprises youth with offending histories that are more extensive than those likely to be found in general surveys of adolescents. The analyses consider the self-control–delinquency relationship in an alternative sample with the previously used Generalized Propensity Score (GPS) procedure, which requires strong assumptions, as well as nonparametric regression which requires far weaker assumptions to consider the functional form of the self-control–delinquency relationship.

Results

The results generally show that the identified functional form of the self-control–delinquency relationship seems to be at least partly dependent on aspects of the modeling of dose–response associated with GPS procedures. When nonparametric general additive models are used with the same data, the relationship between self-control and delinquency seems to be approximately linear.

Conclusions

Identifying functional form relationships has importance for many criminological theories, but it is a task that requires that the balance of model assumptions to exploratory data analysis falls toward the latter. Nonparametric approaches to such questions may be a necessary first step in learning about the nature of mechanisms presumed to be at work in important explanations for crime and criminality.

Keywords

Functional form General theory of crime Generalized Propensity Score Nonparametric models 

Notes

Acknowledgments

This study employs data from Mulvey, Edward P. Research on Pathways to Desistance (Maricopa County, AZ and Philadelphia County, PA): Subject Measures, 2000–2010. ICPSR29961-v2. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2013-01-07. doi: 10.3886/ICPSR29961.v2. We wish to thank Carol Schubert for her assistance in compiling and transferring the data and members of the Pathways to Desistance research group for helpful comments on an early concept paper and Ray Paternoster and the anonymous reviewers for useful feedback on later versions of this work. All interpretations and conclusions reached in this work are those of the authors. Additionally, a portion of this article uses data from ADD Health, a program project designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a Grant P01-HD31921 from the Eunice Kennedy Shriver National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due to Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from ADD Health should contact ADD Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (addhealth@unc.edu). No direct support was received from Grant P01-HD31921 for this analysis.

References

  1. Agnew R (2005) Why do criminals offend? A general theory of crime and delinquency. Roxbury, Los AngelesGoogle Scholar
  2. Berk R (2004) Regression analysis: a constructive critique. Sage, Thousand OaksGoogle Scholar
  3. Bia M, Mattei A (2008) A Stata package for the estimation of the dose–response function through adjustment for the generalized propensity score. Stata J 8:354–373Google Scholar
  4. Bia M, Flores-Lagunes A, Mattei A (2013) A Stata package for the application of semiparametric estimators of dose–response functions. CEPS/INSTEAD working paper. http://ideas.repec.org/p/irs/cepswp/2013-07.html
  5. Caliendo M, Kopeinig A (2008) Some practical guidance for the implementation of propensity score matching. J Econ Surv 22:31–72CrossRefGoogle Scholar
  6. Cleveland W (1979) Robust locally weighted regression and smoothing scatterplots. J Am Stat Assoc 74:829–836CrossRefGoogle Scholar
  7. Cook RD, Weisberg S (1999) Applied regression including computing and graphics. Wiley, New YorkCrossRefGoogle Scholar
  8. DeLisi M, Hochstetler A, Murphy D (2003) Self-control behind bars: a validation study of the Grasmick et al. scale. Justice Q 20:241–263CrossRefGoogle Scholar
  9. Feldman SS, Weinberger DA (1994) Self-restraint as a mediator of family influences on boys’ delinquent behavior: a longitudinal study. Child Dev 65:195–211CrossRefGoogle Scholar
  10. Finifter BM (1975) Replication and extension of social research through secondary analysis. Soc Sci Inf 14:119–153CrossRefGoogle Scholar
  11. Flores CA, Flores-Lagunes A, Gonzalez A, Neumann TC (2012) Estimating the effects of length of exposure to instruction in a training program: the case of job corps. Rev Econ Stat 4:153–171CrossRefGoogle Scholar
  12. Fox J (2000a) Nonparametric simple regression: smoothing scatterplots. Sage, Thousand OaksGoogle Scholar
  13. Fox J (2000b) Multiple and generalized nonparametric regression. Sage, Thousand OaksGoogle Scholar
  14. Fox J (2008) Applied regression analysis and generalized linear models. Sage, Thousand OaksGoogle Scholar
  15. Goode E (2008) Out of control: an introduction to the general theory of crime. In: Goode E (ed) Out of control: assessing the general theory of crime. Stanford University Press, Palo Alto, pp 1–25Google Scholar
  16. Gottfredson MR (2006) The empirical status of control theory in criminology. In: Cullen FT, Wright JP, Blevins KR (eds) Taking stock: the status of criminological theory—advances in criminological theory, vol 15. Transaction, New Brunswick, pp 77–100Google Scholar
  17. Gottfredson M, Hirschi T (1990) A general theory of crime. Stanford University Press, Palo AltoGoogle Scholar
  18. Grasmick HG, Tittle CR, Bursik RJ, Arneklev BJ (1993) Testing the core empirical implications of Gottfredson and Hirschi’s general theory of crime. J Res Crime Delinq 30:5–29CrossRefGoogle Scholar
  19. Guo S, Fraser MW (2010) Propensity score analysis: statistical methods and applications. Sage, Thousand Oaks, CAGoogle Scholar
  20. Hastie TJ, Tibshirani RJ (1990) Generalized additive models. Chapman & Hall/CRC, Boca RatonGoogle Scholar
  21. Hay C, Forrest W (2008) Self-control theory and the concept of opportunity: the case for a more systematic union. Criminology 46:1039–1072CrossRefGoogle Scholar
  22. Hedström P (2005) Dissecting the social: on the principles of analytic sociology. Cambridge University Press, New YorkCrossRefGoogle Scholar
  23. Hirano K, Imbens GW (2004) The propensity score with continuous treatments. In: Gelman A, Meng XL (eds) Applied Bayesian modeling and causal inference from incomplete data perspectives. Wiley, West Sussex, pp 77–84Google Scholar
  24. Hirschi T, Gottfredson MR (1993) Commentary: testing the general theory of crime. J Res Crime Delinq 30:47–54CrossRefGoogle Scholar
  25. Hirschi T, Gottfredson MR (2008) Critiquing the critics: the authors respond. In: Goode E (ed) Out of control: assessing the general theory of crime. Stanford University Press, Palo Alto, pp 217–231Google Scholar
  26. Imai K, van Dyke D (2004) Causal inference with generaltreatment regimes: generalizing the propensity score. J Am Stat Assoc 99:854–866Google Scholar
  27. Imbens GW, Rubin DB, Sacerdote BI (2001) Estimating the effect of unearned income on labor earnings, savings, and consumption: evidence from a survey of lottery players. Am Econ Rev 91:778–794CrossRefGoogle Scholar
  28. Kluve J, Schneider H, Uhlendorff A, Zhao Z (2012) Evaluating continuous training programmes by using the generalized propensity score. J R Stat Soc A 175:587–617CrossRefGoogle Scholar
  29. Knight GP, Little M, Losoya SH, Mulvey EP (2004) The self-report of offending among serious juvenile offenders cross-gender, cross-ethnic/race measurement equivalence. Youth Violence Juv Justice 2:273–295CrossRefGoogle Scholar
  30. Kochanska G, Knaack A (2003) Effortful control as a personality characteristic of young children: antecedents, correlates, and consequences. J Pers 71:1087–1112CrossRefGoogle Scholar
  31. Kuhn TS (1970) The structure of scientific revolutions. University of Chicago Press, ChicagoGoogle Scholar
  32. Lieberson S (1985) Making it count: the improvement of social research and theory. University of California Press, BerkeleyGoogle Scholar
  33. MacCallum RC, Zhang S, Preacher KJ, Rucker DD (2002) On the practice of dichotomization of quantitative variables. Psychol Methods 7:19–40CrossRefGoogle Scholar
  34. Manski CF (2003) Identification problems in the social sciences and everyday life. South Econ J 70:11–21CrossRefGoogle Scholar
  35. Manski CF (2007) Identification for prediction and decision. Harvard University Press, CambridgeGoogle Scholar
  36. Mears D, Cochran J, Beaver K (2013) Self-control theory and nonlinear effects on offending. J Quant Criminol 29:447–476Google Scholar
  37. Mulvey EP (2012) Research on Pathways to Desistance [Maricopa County, AZ and Philadelphia County, PA]: subject measures, 2000–2010. ICPSR29961-v1. Inter-university consortium for political and social research [distributor], Ann Arbor, MI, 20 Aug 2012. doi: 10.3886/ICPSR29961.v1
  38. Mulvey EP, Steinberg L, Fagan J, Cauffman E, Piquero AR, Chassin L (2004) Theory and research on desistance from antisocial activity among serious adolescent offenders. Youth Violence Juv Justice 2:213–236CrossRefGoogle Scholar
  39. Mulvey EP, Steinberg L, Piquero AR, Besana M, Fagan J, Schubert C, Cauffman E (2010) Trajectories of desistance and continuity in antisocial behavior following court adjudication among serious adolescent offenders. Dev Psychopathol 22:453–475CrossRefGoogle Scholar
  40. Osgood D, Schreck C (2007) A new method for studying the extent, stability, and predictors of individual, specialization in violence. Criminology 45:273–312CrossRefGoogle Scholar
  41. Ousey GC, Wilcox P (2007) The interaction of antisocial propensity and life-course varying predictors of delinquent behavior: differences by method of estimation and implications for theory. Criminology 45:313–354CrossRefGoogle Scholar
  42. Piquero A (2008) Measuring self-control. In: Goode E (ed) Out of control: assessing the general theory of crime. Stanford University Press, Palo Alto, pp 26–37Google Scholar
  43. Posner MI, Rothbart MK (2000) Developing mechanisms of self-regulation. Dev Psychopathol 12:427–441CrossRefGoogle Scholar
  44. Pratt TC, Cullen FT (2000) The empirical status of Gottfredson and Hirschi’s general theory of crime: a meta-analysis. Criminology 38:931–964CrossRefGoogle Scholar
  45. Rosenbaum PR (1984) The consequences of adjustment for a concomitant variable that has been affected by the treatment. J R Stat Soc 147:656–666Google Scholar
  46. Rosenbaum PR (1995) Observational studies. Springer-Verlag, New YorkGoogle Scholar
  47. Schubert CA, Mulvey EP, Steinberg L, Cauffman E, Losoya S, Hecker T, Chassin L (2004) Operational lessons from the Pathways to Desistance Project. Youth Violence Juv Justice 2:237–255CrossRefGoogle Scholar
  48. Shadish W, Cook T, Campbell D (2002) Experimental and quasi-experimental design for generalized causal inference. Houghton Mifflin, BostonGoogle Scholar
  49. Weinberger DA, Schwartz GE (1990) Distress and restraint as superordinate dimensions of self-reported adjustment: a typological perspective. J Pers 58:381–417CrossRefGoogle Scholar
  50. Wikström POH (2008) In search of causes and explanations of crime. In: King R, Wincump E (eds) Doing research on crime and justice. Oxford University Press, New York, pp 117–139Google Scholar
  51. Wood SN (2006) Generalized additive models: an introduction with R. Chapman & Hall/CRC, Boca RatonGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2014

Authors and Affiliations

  1. 1.University of CincinnatiCincinnatiUSA
  2. 2.University of MarylandCollege ParkUSA

Personalised recommendations