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Journal of Quantitative Criminology

, Volume 34, Issue 2, pp 367–396 | Cite as

Using Longitudinal Self-Report Data to Study the Age–Crime Relationship

  • Jaeok KimEmail author
  • Shawn D. Bushway
Original Paper

Abstract

Objectives

Given the growing reliance on longitudinal self-report data for making causal inferences about crime, it is essential to investigate whether the within-individual change in criminal involvement exists and is not a measurement artifact driven by attrition or survey fatigue—a very real possibility first identified by Lauritsen (Soc Forces 77(1):127–154, 1998) using the National Youth Survey (NYS). The current study examines whether the same threats to the validity of within-individual change in criminal involvement exist in the National Longitudinal Survey of Youth 1997 cohort (NLSY97).

Methods

We first estimate cohort-specific growth curve models of general crime, arrest, and substance use, and then test the difference between the age–crime curves of adjacent cohorts. We take a general approach to test cohort differences in the growth curve models, which advances the existing method separately modeling for each pair of adjacent cohorts. To explore the sources of cohort differences, we also estimate separate growth curve models by individual crime item and by demographic group.

Results

We document non-standard cohort differences between the age–crime curves of adjacent cohort pairs that are consistent with the findings of Lauritsen (1998) on measures of self-reported offending. However, the size of the cohort effects in the NLSY97 is substantially smaller than those in the NYS. We also found that the cohort effects were only evident in some of the survey items. Moreover, we did not identify any similar cohort issues in the longitudinal measure of arrest.

Conclusions

The findings of cohort effects localized in a certain crime items and demographic groups may mitigate concerns over the limited validity of longitudinal self-report data. We discuss how the survey techniques used in the NLSY97 might explain our findings and suggest an area of future study to explicate remaining cohort differences.

Keywords

Longitudinal self-report Panel survey NLSY97 Growth curve model Cohort effect 

References

  1. Alexander CS, Somerfield MR, Ensminger ME, Johnson KE, Kim YJ (1993) Consistency of adolescents’ self-report of sexual behavior in a longitudinal Study. J Youth Adolesc 22(5):455–471. doi: 10.1007/bf01537710 CrossRefGoogle Scholar
  2. Antecol H, Bedard K (2007) Does single parenthood increase the probability of teenage promiscuity, substance use, and crime? J Popul Econ 20(1):55–71. doi: 10.1007/s00148-005-0019-x CrossRefGoogle Scholar
  3. Apel R, Kaukinen C (2008) On the relationship between family structure and antisocial behavior: parental cohabitation and blended households. Criminology 46(1):35–70. doi: 10.1111/j.1745-9125.2008.00107.x CrossRefGoogle Scholar
  4. Apel R, Bushway S, Brame R, Haviland AM, Nagin DS, Paternoster R (2007) Unpacking the relationship between adolescent employment and antisocial behavior: a matched samples comparison. Criminology 45(1):67–97. doi: 10.1111/j.1745-9125.2007.00072.x CrossRefGoogle Scholar
  5. Apel R, Bushway S, Paternoster R, Brame R, Sweeten G (2008) Using state child labor laws to identify the causal effect of youth employment on deviant behavior and academic achievement. J Quant Criminol 24(4):337–362. doi: 10.1007/s10940-008-9055-5 CrossRefGoogle Scholar
  6. Averdijk M (2010) Individuals’ victimization patterns over time. Doctoral dissertation. Vrije University, AmsterdamGoogle Scholar
  7. Bauman KE, Ennett ST (1994a) Peer influence on adolescent drug use. Am Psychol 49(9):820–822. doi: 10.1037/0003-066X.49.9.820 CrossRefGoogle Scholar
  8. Bauman KE, Ennett ST (1994b) Tobacco use by black and white adolescents: the validity of self-reports. Am J Public Health 84(3):394–398CrossRefGoogle Scholar
  9. Baumer EP, Wolff KT (2014) Evaluating contemporary crime drop(s) in America, New York City, and many other places. Justice Q 31(1):5–38CrossRefGoogle Scholar
  10. Blumstein A, Cohen J, Farrington DP (1988a) Criminal career reserach: its value for criminology. Criminology 26(1):1–35. doi: 10.1111/j.1745-9125.1988.tb00829.x CrossRefGoogle Scholar
  11. Blumstein A, Cohen J, Farrington DP (1988b) Longitudinal and criminal career research: further clarifications. Criminology 26(1):57–74. doi: 10.1111/j.1745-9125.1988.tb00831.x CrossRefGoogle Scholar
  12. Bosick SJ (2009) Operationalizing crime over the life course. Crime Delinq 55(3):472–496. doi: 10.1177/0011128707307223 CrossRefGoogle Scholar
  13. Bowling A (2005) Mode of questionnaire administration can have serious effects on data quality. J Public Health 27(3):281–291. doi: 10.1093/pubmed/fdi031 CrossRefGoogle Scholar
  14. Brame R, Piquero AR (2003) Selective attrition and the age–crime relationship. J Quant Criminol 19(2):107–127. doi: 10.1023/A:1023009919637 CrossRefGoogle Scholar
  15. Brame R, Bushway SD, Paternoster R, Turner MG (2014) Demographic patterns of cumulative arrest prevalence by ages 18 and 23. Crime Delinq 60(3):471–486CrossRefGoogle Scholar
  16. Brener ND, Billy J, Grady WR (2003) Assessment of factors affecting the validity of self-reported health-risk behavior among adolescents: evidence from the scientific literature. J Adolesc Health 33(6):436–457. doi: 10.1016/S1054-139X(03)00052-1 CrossRefGoogle Scholar
  17. Das M, Toepoel V, Van Soest A (2007) Can I use a panel? Panel conditioning and attrition bias in panel surveys. CentER discussion paper series no. 2007-56. Tilburg University, Center for Economic ResearchGoogle Scholar
  18. Durant LE, Carey MP (2000) Self-administered questionnaires versus face-to-face interviews in assessing sexual behavior in young women. Arch Sex Behav 29(4):309–322. doi: 10.1023/a:1001930202526 CrossRefGoogle Scholar
  19. Elliot DS (1995) Lies, damn lies and arrest statistics. Paper presented at the American Society of Criminology Annual Meetings, BostonGoogle Scholar
  20. Farrington DP (1986) Age and crime. Crime Justice 7:189–250CrossRefGoogle Scholar
  21. Federal Bureau of Investigation (2003) Table 1. Crime in the United States, by volume and rated, 1984–2003. Retrieve from https://www.fbi.gov/about-us/cjis/ucr/crime-in-the-u.s/2003
  22. Garson GD (2013) Chapter 1. Fundamentals of hierarchical lineal (multilevel) modeling. In: Garson GD (ed) Hierarchical linear modeling. SAGE Publications, Thousand Oaks, pp 1–25Google Scholar
  23. Gottfredson M, Hirschi T (1986) The true value of lambda would appear to be zero: an essay on career criminals, criminal careers, selective incapacitation, cohort studies, and related topics. Criminology 24(2):213–234. doi: 10.1111/j.1745-9125.1986.tb01494.x CrossRefGoogle Scholar
  24. Gottfredson M, Hirschi T (1988) Science, public policy, and the career paradigm. Criminology 26(1):37–55. doi: 10.1111/j.1745-9125.1988.tb00830.x CrossRefGoogle Scholar
  25. Greene JA (1999) Zero tolerance: a case study of police policies and practices in New York City. Crime Delinq 45(2):171–187. doi: 10.1177/0011128799045002001 CrossRefGoogle Scholar
  26. Gribble JN, Miller HG, Rogers SM, Turner CF (1999) Interview mode and measurement of sexual behaviors: methodological issues. J Sex Res 36(1):16–24. doi: 10.1080/00224499909551963 CrossRefGoogle Scholar
  27. Halpern-Manners A, Warren JR (2012) Panel conditioning in longitudinal studies: evidence from labor force items in the current population survey. Demography 49(4):1499–1519. doi: 10.1007/s13524-012-0124-x CrossRefGoogle Scholar
  28. Hindelang MJ, Hirschi T, Weis JG (1979) Correlates of delinquency: the illusion of discrepancy between self-report and official measures. Am Sociol Rev 44(6):995–1014. doi: 10.2307/2094722 CrossRefGoogle Scholar
  29. Hirschi T, Gottfredson M (1983) Age and the explanation of crime. Am J Sociol 89(3):552–584CrossRefGoogle Scholar
  30. Huebner BM, Bynum TS (eds) (2016) The handbook of measurement issues in criminology and criminal justice. Wiley, New YorkGoogle Scholar
  31. Johnson EO, Schultz L (2005) Forward telescoping bias in reported age of onset: an example from cigarette smoking. Int J Methods Psychiatr Res 14(3):119–129CrossRefGoogle Scholar
  32. Kim J, Bushway S, Tsao HS (2016) Identifying classes of explanations for crime drop: period and cohort effects for New York State. J Quant Criminol 32(3):357–375CrossRefGoogle Scholar
  33. King RD, Massoglia M, Macmillan R (2007) The context of marriage and crime: gender, the propensity to marry, and offending in early adulthood. Criminology 45(1):33–65. doi: 10.1111/j.1745-9125.2007.00071.x CrossRefGoogle Scholar
  34. Kissinger P, Rice J, Farley T, Trim S, Jewitt K, Margavio V, Martin DH (1999) Application of computer-assisted interviews to sexual behavior research. Am J Epidemiol 149(10):950–954CrossRefGoogle Scholar
  35. Kreager DA, Matsueda RL, Erosheva EA (2010) Motherhood and criminal desistance in disadvantaged neighborhoods. Criminology 48(1):221–258. doi: 10.1111/j.1745-9125.2010.00184.x CrossRefGoogle Scholar
  36. Lauritsen JL (1998) The age–crime debate: assessing the limits of longitudinal self-report data. Soc Forces 77(1):127–154CrossRefGoogle Scholar
  37. Lauritsen JL (1999) Limitations in the use of longitudinal self-report data: a comment. Criminology 37(3):687–694. doi: 10.1111/j.1745-9125.1999.tb00500.x CrossRefGoogle Scholar
  38. Lehnen RG, Reiss AJ (1978) Some response effects of the national crime survey. Victimology 3(1–2):110–124Google Scholar
  39. Levitt SD (2004) Understanding why crime fell in the 1990s: four factors that explain the decline and six that do not. J Econ Perspect 18(1):163–190. doi: 10.1257/089533004773563485 CrossRefGoogle Scholar
  40. Liu S (2015) Is the shape of the age–crime curve invariant by sex? Evidence from a national sample with flexible non-parametric modeling. J Quant Criminol 31(1):93–123. doi: 10.1007/s10940-014-9225-6 CrossRefGoogle Scholar
  41. Marvell TB, Moody CE (1994) Prison population growth and crime reduction. J Quant Criminol 10(2):109–140. doi: 10.1007/BF02221155 CrossRefGoogle Scholar
  42. Mason KO, Mason WM, Winsborough HH, Poole WK (1973) Some methodological issues in cohort analysis of archival data. Am Sociol Rev 38(3):242–258CrossRefGoogle Scholar
  43. Miyazaki Y, Raudenbush SW (2000) Tests for linkage of multiple cohorts in an accelerated longitudinal design. Psychol Methods 5(1):44CrossRefGoogle Scholar
  44. Moore W, Pedlow S, Krishnamurty P, Wolter K (2000) National longitudinal survey of youth (NLSY97): technical sampling report. National Opinion Research Center, ChicagoGoogle Scholar
  45. O’Brien RM, Stockard J, Isaacson L (1999) The enduring effects of cohort characteristics on age-specific homicide rates, 1960–1995. Am J Sociol 104(4):1061–1095CrossRefGoogle Scholar
  46. Paternoster R, Bushway S, Brame R, Apel R (2003) The effect of teenage employment on delinquency and problem behaviors. Soc Forces 82(1):297–335. doi: 10.1353/sof.2003.0104 CrossRefGoogle Scholar
  47. Piquero AR, Brame RW (2008) Assessing the race–crime and ethnicity–crime relationship in a sample of serious adolescent delinquents. Crime Delinq 54(3):390–422CrossRefGoogle Scholar
  48. Porter SR, Whitcomb ME, Weitzer WH (2004) Multiple surveys of students and survey fatigue. New Dir Inst Res 2004(121):63–73Google Scholar
  49. Raudenbush SW, Chan WS (1992) Growth curve analysis in accelerated longitudinal designs. J Res Crime Delinq 29(4):387–411. doi: 10.1177/0022427892029004001 CrossRefGoogle Scholar
  50. Raudenbush SW, Chan WS (1993) Application of a hierarchical linear model to the study of adolescent deviance in an overlapping cohort design. J Consult Clin Psychol 61(6):941CrossRefGoogle Scholar
  51. Sampson RJ, Laub JH (2005) A life-course view of the development of crime. Ann Am Acad Polit Soc Sci 602(1):12–45. doi: 10.1177/0002716205280075 CrossRefGoogle Scholar
  52. Schneider AL, Sumi D (1981) Patterns of forgetting and telescoping: an analysis of LEAA survey victimization data. Criminology 19(3):400–410CrossRefGoogle Scholar
  53. Shillington AM, Clapp JD (2000) Kicking the camel: adolescent smoking behaviors after two years. J Child Adolesc Subst Abuse 10(2):53–80CrossRefGoogle Scholar
  54. Shillington AM, Woodruff SI, Clapp JD, Reed MB, Lemus H (2012) Self-reported age of onset and telescoping for cigarettes, alcohol, and marijuana: across eight years of the National Longitudinal Survey of Youth. J Child Adolesc Subst Abuse 21(4):333–348CrossRefGoogle Scholar
  55. Shulman E, Steinberg L, Piquero A (2013) The age–crime curve in adolescence and early adulthood is not due to age differences in economic status. J Youth Adolesc 42(6):848–860. doi: 10.1007/s10964-013-9950-4 CrossRefGoogle Scholar
  56. Slocum LA, Wiley SA, Esbensen FA (2016) The importance of being satisfied: a longitudinal exploration of police contact, procedural injustice, and subsequent delinquency. Crim Justice Behav 43(1):7–26CrossRefGoogle Scholar
  57. Smith JK, Gerber AS, Orlich A (2003) Self-prophecy effects and voter turnout: an experimental replication. Polit Psychol 24(3):593–604. doi: 10.1111/0162-895X.00342 CrossRefGoogle Scholar
  58. Steffensmeier DJ, Allan EA, Harer MD, Streifel C (1989) Age and the distribution of crime. Am J Sociol 94(4):803–831CrossRefGoogle Scholar
  59. Sturgis P, Allum N, Brunton-Smith I (2009) Chapter 7. Attitudes over time: the psychology of panel conditioning. In: Lynn P (ed) Methodology of longitudinal surveys. Wiley, New York, pp 113–126CrossRefGoogle Scholar
  60. Sweeten G (2012) Scaling criminal offending. J Quant Criminol 28(3):533–557. doi: 10.1007/s10940-011-9160-8 CrossRefGoogle Scholar
  61. Thornberry TP (1989) Reflections on the advantages and disadvantages of theoretical integration. In: Messner SF, Krohn MD, Liska AE (eds) Theoretical integration in the study of deviance and crime: problems and prospects. State University of New York Press, Albany, pp 51–60Google Scholar
  62. Thornberry TP, Krohn MD (2000) The self-report method for measuring delinquency and crime. In: Duffee D, Crutchfield RD, Mastrofski S, Mazerolle L, McDowall D (eds) Criminal Justice 2000: Measurement and analysis of crime and justice, vol 4. National Institute of Justice, Washington, pp 33–83Google Scholar
  63. Toepoel V, Das M, Van Soest A (2008) Effects of design in web surveys: comparing trained and fresh respondents. Public Opinion Quarterly 72(5):985–1007. doi: 10.1093/poq/nfn060 CrossRefGoogle Scholar
  64. Tourangeau R, Smith TW (1996) Asking sensitive questions: the impact of data collection mode, question format, and question context. Public Opin Q 60(2):275–304CrossRefGoogle Scholar
  65. Tourangeau R, Yan T (2007) Sensitive questions in surveys. Psychol Bull 133(5):859–883. doi: 10.1037/0033-2909.133.5.859 CrossRefGoogle Scholar
  66. Turner CF, Ku L, Rogers SM, Lindberg LD, Pleck JH, Sonenstein FL (1998) Adolescent sexual behavior, drug use, and violence: increased reporting with computer survey technology. Science 280(5365):867–873. doi: 10.1126/science.280.5365.867 CrossRefGoogle Scholar
  67. Tyler TR, Fagan J, Geller A (2014) Street stops and police legitimacy: teachable moments in young urban men’s legal socialization. J Empir Legal Stud 11(4):751–785CrossRefGoogle Scholar
  68. Wright JP, Tibbetts SG, Daigle LE (2014) Criminals in the making: criminality across the life course. Sage Publications, LondonGoogle Scholar
  69. Zimring FE (2006) The great American crime decline. Oxford University Press, USACrossRefGoogle Scholar
  70. Zimring FE (2011) The city that became safe: New York’s lessons for urban crime and its control. Oxford University Press, New YorkCrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media New York 2017

Authors and Affiliations

  1. 1.University at AlbanyAlbanyUSA

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