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In Search of the Intermittent Offender: A Theoretical and Statistical Journey

In Search of the Intermittent Offender: A Theoretical and Statistical Journey. Megan C. Kurlychek , Ph.D. Assistant Professor Shawn Bushway , Ph.D. Associate Professor School of Criminal Justice University at Albany. Goals.

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In Search of the Intermittent Offender: A Theoretical and Statistical Journey

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  1. In Search of the Intermittent Offender: A Theoretical and Statistical Journey Megan C. Kurlychek, Ph.D. Assistant Professor Shawn Bushway, Ph.D. Associate Professor School of Criminal Justice University at Albany

  2. Goals • Describe population of individual trajectories underlying age crime curve • Identify process of desistance • Is intermittency real? • How do these different models reflect/impact practice?

  3. Starting Point • Lifecourse criminologists care about individual lifecourse trajectory/criminal career • Descriptive: Age Crime Curve Debate • What is the underlying distribution that determines the Age- Crime Curve • Explanatory: Thornberry 1987: • “The manner in which reciprocal effects and developmental changes are interwoven in the interactional model can be explicated by the concept of behavioral trajectories.(p. 882)

  4. What Has Happened Since? • Panel models • Growth Curve Models (GCM) HLM • Group-based Trajectories Model (GTM) Proc Traj • Generalized Mixture Models (GMM) Mplus • Much annoying banter about which model is “Right”

  5. Bushway, S., G. Sweeten, P. Nieuwbeerta (2009) Measuring Long Term Individual Trajectories of Offending Using Multiple Methods. Journal of Quantitative Criminology 25:259–286

  6. What Did We Do? • Compared individual trajectories from three models: • 1) Individual time series for every person • 2) Growth Curve model (HLM) • 3) Group Trajectory model (Traj)

  7. Criminal Career and Life Course Study (CCLS) Sample: • 4.615 persons convicted in 1977 • 4% random sample of all persons convicted in 1977 • Oversample of persons convicted for serious offenses, undersample of persons convicted for traffic incidents • 500 women (10%) • 20% non-native (Surinam, Indonesia) • Mean age in 1977: 27 years; youngest: 12; oldest 79 • Data from year of birth until 2003: for most over 50 years.

  8. CCLS Data For all persons we have information on: • Full criminal conviction histories (Rap sheets) • Timing, type of offense, type of sentence, incarceration. • Life course events: • Various types: marriage, divorce, children, moving, death (GBA & Central Bureau Heraldry) – incl. Exact timing. • Cause of death (CBS) • Data = conviction for periods not dead or incarcerated

  9. Average Curves: Raw Data & ITM

  10. Job 2: Compare Best estimates of Individual paths

  11. Desistors • An individual who has a period where offending probability is statistically greater than zero, followed by at least 5 years when probability of offending is statistically indistinguishable from zero.

  12. Comparison of Desistors MODEL Desistors (% of sample) ITM 60.8% GCM 27.5% GTM 36.4% • ITM more flexible, better captures change (but with error).

  13. Conclusion • Lots of “up and down” • Could be noise • Could be intermittency • Can’t tell with conviction data – even with 50 years! • Need another approach - recidivism/survival models?

  14. In Search of the Intermittent Offender: A Theoretical and Statistical Journey Megan C. Kurlychek, Ph.D. Assistant Professor Shawn Bushway, Ph.D. Associate Professor School of Criminal Justice University at Albany

  15. Criminal Career Research • Traditional Question: • “When does a criminal career start and when does it end.” • Traditional Answer (Blumstein 1986)

  16. Instantaneous Desistance • Go immediately to zero • Very consistent with parole/probation models • Pragmatic • Fits qualitative work: Going (and staying) straight (Maruna)

  17. Hazards • Probability that you are going to offend in this period given that you have not offended yet • Used in latest round of reentry models • When does ex-offender “look like” non offender in terms of offending

  18. Test of desistance using hazards Barnett et. al. (1989)

  19. Barnett Modification • Starting point • Active career • Ending point (instantaneous desistance) • A few people restart career (Intermittency)

  20. Theoretical Intermittency • Matza (1964) • Drift: Offenders “flirt” with criminal activity. • Horney, Osgood and Rowe (1995): • “local-life circumstance” • “Relapse” • ZIP Parameter in Trajectory Models

  21. Alternative: Glide Path Desistance as a process: “glide” path towards zero ( Bushway et al. 2001, Laub and Sampson 2001)

  22. Theoretical Glide Path • Differential Association Theory/Social Learning Theory • Social Control Theory “Social bonds do not arise intact and full-grown but develop over time like a pension plan funded by regular contributions” Laub, Nagin and Sampson (1998)

  23. In Hazard Model • Both can explain FAT Tail • People still at high(er) risk after many years • BUT – Glide Path should be smooth declining hazard rate • Intermittency – bumpy declining hazard

  24. Our Data • Crime Control Effects of Sentencing in Essex County New Jersey, 1978-1997. • Judge questionnaires completed by 18 judges in Essex County NJ on cases sentenced in 1976-77. • Follow up information was collected through 1997 • New Jersey Offender Based Transaction System Computerized Criminal History • New Jersey Department of Corrections Offender based Correctional Information System • US Department of Justice Interstate Identification Index

  25. Sample and Methods • All offenders with probation or short jail sentences (n=661) • Follow for 20 years • Apply parametric survival time distributions and employ graphical comparisons and goodness of fit statistics

  26. Measures • Dependent Variable: New arrest • Independent Variables: • Age of offender • Prior Probations and Violations • Race, Gender, Type/Seriousness of Offense, Judge’s perception of risk

  27. Three Distributions • Exponential • Assumes constant rate of offending • Hazard drops fast • High rate offenders – everyone who hasn’t desisted offends quickly • Weibull • Smoothly declining hazard rate • Lognormal • Allows hazard rate to go up and down

  28. Three Distributions • Exponential = Original Criminal Career • Weibull = Glide path • Lognormal = Intermittency

  29. Goodness of Fit Tests

  30. Why the Lognormal • “Upswing” in the beginning • OR • Fat Tail (intermittency)

  31. Models t0 to t5

  32. Weibull Frailty Model

  33. High and Low Risk Offenders

  34. Conclusions • Glide path looks more realistic than strict intermittency • People experience reduced risk as they last longer on parole • But, don’t go to zero very quickly • Desistance takes time

  35. Next Steps • Multi-Event Hazard • What happens after arrest? • For people who have not offended for 5 years? • Intermittency: should start offending again at a regular rate • Glide path: should continue to decrease in offending rate

  36. Policy Implications/Questions • Most people don’t desist “instantaneously” • Declining risk • Recidivate or not mentality may miss declining risk • Is it feasible to tolerate “less” offending? • Do current practices implicitly acknowledge reality? • Do changes in other behavior (work/housing/family) serve as proxy for “declining hazard”

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