Skip to main content
SpringerLink
Log in

Something Old, Something New: Revisiting Competing Hypotheses of the Victimization-Offending Relationship Among Adolescents

  • Original Paper
  • Published:
Journal of Quantitative Criminology Aims and scope Submit manuscript

Abstract

This study revisits a familiar question regarding the relationship between victimization and offending. Using longitudinal data on middle- and high-school students, the study examines competing arguments regarding the relationship between victimization and offending embedded within the “dynamic causal” and “population heterogeneity” perspectives. The analysis begins with models that estimate the longitudinal relationship between victimization and offending without accounting for the influence of time-stable individual heterogeneity. Next, the victimization-offending relationship is reconsidered after the effects of time-stable sources of heterogeneity, and time-varying covariates are controlled. While the initial results without controls for population heterogeneity are in line with much prior research and indicate a positive link between victimization and offending, results from models that control for time-stable individual differences suggest something new: a negative, reciprocal relationship between victimization and offending. These latter results are most consistent with the notion that the oft-reported victimization-offending link is driven by a combination of dynamic causal and population heterogeneity factors. Implications of these findings for theory and future research focusing on the victimization-offending nexus are discussed.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

Notes

  1. However, it is worth noting that the Schreck et al. (2006) study does control for one particularly prominent source of population heterogeneity, low self-control.

  2. While we primarily focus on these two possibilities, other hypotheses may be relevant as well. Indeed, as we discuss in the conclusion section, there may be a number of interactive possibilities that deserve serious attention in subsequent research.

  3. The response rate in the RSVP is generally consistent with other studies of students that employ active parental consent (see Ellickson and Hawes 1989; Esbensen et al. 1996). Nevertheless, the response rate obtained in the RSVP study calls into question whether the obtained sample is generalizable to the targeted population of adolescents. Past research suggests that active parental consent procedures often produce samples that are biased on racial characteristics (Kearney et al. 1983). However, comparing demographic characteristics from our sample to Kentucky Department of Education enrollment data for the 65 schools in our sample, we find that the racial composition of our sample is fairly close to the KDE population data. In Year 1 our sample percentage nonwhite is 9.55%, while the corresponding figure from the KDE data (which includes all kids in the selected schools, not just 7th graders) is 10.18%. In contrast, our sample does appear to under-represent males, with about 45.5% of the Year 1 respondents being male, compared to 51.9% for the KDE data. Given known gender patterning of delinquent behavior and victimization, we suspect that non-response may understate the prevalence and overall variability of victimization, delinquent behavior and perhaps other “anti-social” factors. Without explicit data on the non-responders, however, we cannot know with any certainty the extent to which they differ from responders.

  4. For the specific variables used in our analysis, the proportion of cases with missing data ranges between 8 and 25.6%. But, when item non-response is considered across all variables simultaneously, it is the case that about 49% of the individuals in the sample have missing values on at least one variable for at least one of the measurement occasions. Consequently, the use of the listwise deletion method for missing data would result in a serious data loss. On the other hand, FIML uses all available information from complete cases as well as the cases with missing data on some (but not all) variables to derive parameter estimates. The inclusion of the information from the partially complete cases contributes to knowledge of the underlying marginal distribution of variables with missing data and thereby can reduce the bias introduced by listwise deletion of cases with missing data, while also improving the efficiency of the estimates. Greater detail on the computation of FIML as well as the advantages and limitations of FIML relative to other missing data procedures are discussed in Allison (2002), Arbuckle (1996), Wothke (2000) and Enders and Bandalos (2001).

  5. Using Mplus, we computed two exploratory factor analyses on these items. In the first analysis, we treat the items as continuous variables. In the second, the items were specified as ordinal variables. Results from both analyses are similar and indicate that the items load on a single factor (e.g., in the latter specification, loadings range between .55 and .98 on a first factor with an eigenvalue of 4.1).

  6. An exploratory factor analyses with these items specified as ordinal categorical indicators indicates that all items load highly on a single factor (loadings range: .72–.96; eigenvalue = 9.6).

  7. In supplemental analyses available from the first author, we utilized “variety” indices that tap the number of different types of crime (rather than a sum of scores across the types) for both the offending and victimization variables. Results from those analyses are substantively identical to those reported below.

  8. Zumbo et al. (2007) find that the traditional computation of alpha yields a negatively biased estimate of reliability when it is applied to ordinal (or binary) items. In contrast, an “ordinal” alpha reliability coefficient derived from a polychoric (or tetrachoric, for binary data) correlation matrix is found to be more accurate. For the multi-item indices utilized in our analysis, we report both the traditional Cronbach’s alpha as well as the ordinal alpha recommended by Zumbo et al.

  9. The survey items and descriptive statistics for all the measures employed in the analyses are presented in “Appendix A”.

  10. An additional benefit of using the structural equation modeling approach is that many SEM software packages (including Mplus, which we utilize) include the “full-information” maximum likelihood (FIML) estimation methods which, as noted, effectively include (rather than exclude) cases with missing data on some analysis variables. These FIML methods have long been known to produce unbiased parameter estimates under the assumption that data are missing at random (MAR). In contrast, listwise deletion of missing data yields unbiased estimates only under the much stronger assumption that data are missing completely at random (MCAR). Consequently, the FIML approach is widely regarded as a superior approach to handling missing data than more traditional approaches such as listwise deletion (e.g., see Enders and Bandalos 2001; Wothke 2000). Another approach to handling missing that has nearly as good statistical properties as FIML is multiple imputation (see Rubin 1987; Allison 2002). As a supplement to our use of FIML estimation, we also utilized the missing data imputation procedures available in Stata version 11 to create 5 imputed datasets. We then re-estimated the full models reported in Table 3 using each of these imputed datasets. As expected, the average parameter estimates from those supplemental analyses yield findings that are numerically very close, and substantively identical, to those reported in Table 3.

  11. All reported results reflect maximum likelihood parameter estimates with robust standard errors that are adjusted for non-normality and the clustering of individuals within schools.

  12. Standard chi-square difference tests are not appropriate when applied to model fit chi-square statistics obtained from maximum likelihood estimators that are robust to non-normality and clustering. Therefore, we utilize the Satorra-Bentler scaled chi-square difference test, which was developed to address this concern. Detail on this test can be found in Satorra (2000) and useful advice on its practical application can be found on the MPlus website at: http://www.statmodel.com/chidiff.shtml.

References

  • Agnew R (2001) Building on the foundation of general strain theory: specifying the types of strain most likely to lead to crime and delinquency. J Res Crime Delinq 38:319–361

    Article  Google Scholar 

  • Agnew R (2002) Experienced, vicarious, and anticipated strain: an exploratory study on physical victimization and delinquency. Justice Q 19:603–632

    Article  Google Scholar 

  • Agnew R, Brezina T, Wright JP, Cullen FT (2002) Strain, personality traits, and delinquency: extending general strain theory. Criminology 40:43–72

    Article  Google Scholar 

  • Allison PD (2000) Inferring causal order from panel data. Paper prepared for presentation at the ninth international conference on panel data, 22 June, Geneva, Switzerland

  • Allison PD (2002) Missing data. Sage, Thousand Oaks

    Google Scholar 

  • Allison PD (2005) Fixed effects regression methods for longitudinal data using SAS. SAS Institute, Inc, Cary

    Google Scholar 

  • Anderson E (1999) Code of the streets: decency, violence, and the moral life of the inner city. W.W. Norton & Company, New York

    Google Scholar 

  • Arbuckle JL (1996) Full information estimation in the presence of incomplete data. In: Marcoulides GA, Schumacker RE (eds) Advanced structural equation modeling. Lawrence Erlbaum Associates, Inc, Mahwah, pp 243–277

    Google Scholar 

  • Arellano M, Bond S (1991) Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev Econ Stud 58:277–297

    Article  Google Scholar 

  • Bankston WB, Thompson CY, Jenkins QAL, Forsyth CJ (1990) The influence of fear of crime, gender and southern culture on carrying firearms for protection: a causal model. Sociol Q 31:287–305

    Article  Google Scholar 

  • Baron SW (2004) General strain, street youth and crime: a test of Agnew’s revised theory. Criminology 42:457–483

    Article  Google Scholar 

  • Baron SW, Hartnagel TF (1998) Street youth and criminal violence. J Res Crime Delinq 35:166–192

    Article  Google Scholar 

  • Baron SW, Forde DR, Kennedy LW (2001) Rough justice: street youth and violence. J Interpers Violence 16:662–678

    Article  Google Scholar 

  • Black D (1983) Crime as social control. Am Soc Rev 48:34–45

    Article  Google Scholar 

  • Brame R, Bushway SD, Paternoster R (1999) On the use of panel research designs and random effects models to investigate state and dynamic theories of criminal offending. Criminology 37:599–641

    Article  Google Scholar 

  • Broidy LM, Daday JK, Crandall CS, Sklar DP, Jost PF (2006) Exploring the demographic, structural, and behavioral overlap among homicide offenders and victims. Homicide Stud 10:155–180

    Article  Google Scholar 

  • Burrow JD, Apel R (2008) Youth behavior, school structure, and student risk of victimization. Justice Q 25:349–380

    Article  Google Scholar 

  • Campbell Augustine M, Wilcox P, Ousey GC, Clayton RR (2002) Opportunity theory and adolescent school-based victimization. Violence Vict 17:233–253

    Article  Google Scholar 

  • Cao L, Cullen FT, Link BG (1997) The social determinants of gun ownership: self-protection in an urban environment. Criminology 35:629–658

    Article  Google Scholar 

  • Chesney-Lind M (1997) The female offender: girls, women and crime. Sage, Thousand Oaks

    Google Scholar 

  • Cook PJ (1986) The demand and supply of criminal opportunities. Crime Justice Annu Rev Res 7:1–28

    Article  Google Scholar 

  • Cullen FT, Unnever JD, Hartman JL, Turner MG, Agnew R (2008) Gender, bullying victimization, and juvenile delinquency: a test of general strain theory. Vict Offenders 3:346–364

    Article  Google Scholar 

  • Daigle LE, Cullen FT, Wright JP (2007) Gender difference in the predictors of juvenile delinquency: assessing the generality/specificity debate. Youth Violence Juv Justice 5:254–286

    Article  Google Scholar 

  • Daigle LE, Beaver KM, Hartman JL (2008) A life-course approach to the study of victimization and offending behaviors. Vict Offenders 3:365–390

    Article  Google Scholar 

  • Decker SH, Curry GD (2000) Addressing key features of gang membership: measuring the involvement of young members. J Crim Justice 28:473–482

    Article  Google Scholar 

  • Eitle D, Turner RJ (2002) Exposure to community violence and young adult crime: the effects of witnessing violence, traumatic victimization, and other stressful life events. J Res Crime Delinq 39:124–237

    Article  Google Scholar 

  • Ellickson PL, Hawes J (1989) An assessment of active versus passive methods for obtaining parental consent. Eval Rev 13:45–55

    Article  Google Scholar 

  • Enders CK, Bandalos DL (2001) The relative performance of full information maximum likelihood estimation for missing data in structural equation models. Struct Equ Modeling 8:430–457

    Article  Google Scholar 

  • Esbensen F-A, Deschenes EP, Vogel RE, West J, Arboit K, Harris L (1996) Active parental consent in school-based research: an examination of ethical and methodological considerations. Evaluation Review 20:737–753

    Article  Google Scholar 

  • Fagan J, Piper ES, Cheng Y-T (1987) Contributions of victimization to delinquency in inner cities. J Crim Law Criminol 78:586–613

    Article  Google Scholar 

  • Felson RB, Tedeschi JT (eds) (1993) Aggression and violence: social interactionist perspectives. American Psychological Association, Washington, DC

    Google Scholar 

  • Ferraro KF (1995) Fear of crime: interpreting victimization risk. SUNY Press, Albany

    Google Scholar 

  • Finkel SE (1995) Causal analysis with panel data. Sage, Thousand Oaks

    Google Scholar 

  • Fisher BS, Sloan JL, Cullen FT, Chunmeng L (1998) Crime in the ivory tower: the level and sources of student victimization. Criminology 26:671–710

    Article  Google Scholar 

  • Forde DR, Kennedy LW (1997) Risky lifestyles, routine activities, and the general theory of crime. Justice Q 14:265–294

    Article  Google Scholar 

  • Gottfredson MR, Hirschi T (1990) A general theory of crime. Stanford University Press, Stanford, CA

    Google Scholar 

  • Hay C, Evans MM (2006) Violent victimization and involvement in delinquency: examining predictions from general strain theory. J Crim Justice 34:261–274

    Article  Google Scholar 

  • Hindelang MJ, Gottfredson MR, Garofalo J (1978) Victims of personal crime: an empirical foundation for a theory of personal victimization. Ballinger, Cambridge

    Google Scholar 

  • Hsiao C (2003) Analysis of panel data, 2nd edn. University of Cambridge Press, Cambridge

    Book  Google Scholar 

  • Jacques S, Wright R (2008) The victimization-termination link. Criminology 46:1009–1038

    Article  Google Scholar 

  • Jensen GF, Brownfield D (1986) Gender, lifestyles, and victimization: beyond routine activity. Violence Vict 1:85–99

    Google Scholar 

  • Kearney KA, Hopkins RH, Mauss AL, Weisheit RA (1983) Sample bias resulting from a requirement for written parental consent. Public Opin Q 47:96–102

    Article  Google Scholar 

  • Kubrin CE, Weitzer R (2003) Retaliatory homicide: concentrated disadvantage and neighborhood culture. Soc Probl 50:157–180

    Article  Google Scholar 

  • Lasley JR (1989) Drinking routines/lifestyles and predatory victimization: a causal analysis. Justice Q 6:529–542

    Article  Google Scholar 

  • Lauritsen JL, Davis Quinet KF (1995) Repeat victimization among adolescents and young adults. J Quant Criminol 11:143–166

    Article  Google Scholar 

  • Lauritsen JL, Laub JH (2007) Understanding the link between victimization and offending: new reflections on an old idea. Crime Prev Stud 22:55–75

    Google Scholar 

  • Lauritsen JL, Sampson RJ, Laub JH (1991) The link between offending and victimization among adolescents. Criminology 29:265–292

    Article  Google Scholar 

  • Lauritsen JL, Laub JH, Sampson RJ (1992) Conventional and delinquent activities: implications for the prevention of violent victimization among adolescents. Violence Vict 7:91–108

    Google Scholar 

  • Luckenbill DF (1977) Criminal homicide as situated transaction. Soc Probl 25:176–186

    Article  Google Scholar 

  • Menard S, Huizinga D (2001) Repeat victimization in a high-risk neighborhood sample of adolescents. Youth Soc 32:447–472

    Article  Google Scholar 

  • Miller J (1998) Up it up: gender and the accomplishment of street robbery. Criminology 36:37–66

    Article  Google Scholar 

  • Moriarty LJ, Parsons-Pollard N (2008) Role reversals in the life-course: a systematic review. Vict Offenders 3:331–345

    Article  Google Scholar 

  • Mundlak Y (1978) On the pooling of time series and cross sectional data. Econometrica 56:69–86

    Article  Google Scholar 

  • Mustaine EE, Tewksbury R (1998a) Specifying the role of alcohol in predatory victimization. Deviant Behav 19:173–200

    Article  Google Scholar 

  • Mustaine EE, Tewksbury R (1998b) Predicting risks of larceny theft victimization: a routine activity analysis using refined lifestyle measures. Criminology 36:829–857

    Article  Google Scholar 

  • Muthén B (1994) Multilevel covariance structure analysis. Sociol Methods Res 22:376–398

    Article  Google Scholar 

  • 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 45:313–354

    Article  Google Scholar 

  • Ousey GC, Wilcox P, Brummel S (2008) Déjà vu all over again: investigating temporal continuity of adolescent victimization. J Quant Criminol 24:307–335

    Article  Google Scholar 

  • Peterson D, Taylor TJ, Esbensen F-A (2004) Gang membership and violent victimization. Justice Q 21:793–815

    Article  Google Scholar 

  • Piquero AR, MacDonald J, Dobrin A, Daigle LF, Cullen FT (2005) Self control, violent offending, and homicide victimization: assessing the general theory of crime. J Quant Criminol 21:55–71

    Article  Google Scholar 

  • Sampson RJ, Lauritsen JL (1990) Deviant lifestyles, proximity to crime and the offender-victim link in personal violence. J Res Crime Delinq 27:110–139

    Article  Google Scholar 

  • Satorra A (2000) Scaled and adjusted restricted tests in multi-sample analysis of moment structures. In: Heijmans RDH, Pollock DSG, Satorra A (eds) Innovations in multivariate statistical analysis. A Festschrift for Heinz Neudecker. Kluwer, London, pp 233–247

    Google Scholar 

  • Schreck CJ (1999) Criminal victimization and low self-control: an extension and test of a general theory of crime. Justice Q 16:633–654

    Article  Google Scholar 

  • Schreck CJ, Mitchell Miller J, Gibson CL (2003) Trouble in the school yard: a study of the risk factors of victimization at school. Crime Delinq 49:460–484

    Article  Google Scholar 

  • Schreck CJ, Stewart EA, Fisher BS (2006) Self-control, victimization, and their influence on risky lifestyles: a longitudinal analysis using panel data. J Quant Criminol 22:319–340

    Article  Google Scholar 

  • Siegel JA, Williams LM (2003) The relationship between child sexual abuse and female delinquency and crime: a prospective study. J Res Crime Delinq 40:71–94

    Article  Google Scholar 

  • Singer SI (1986) Victims of serious violence and their criminal behavior: subcultural theory and beyond. Violence Vict 1:61–70

    Google Scholar 

  • Skogan WG, Maxfield MG (1981) Coping with crime: individual and neighborhood reactions. Sage, Beverly Hills

    Google Scholar 

  • Spano R, Freilich JD, Bolland J (2008) Gang membership, gun carrying, and employment: applying routine activities theory to explain violent victimization among inner-city, minority youth living in extreme poverty. Justice Q 25:381–410

    Article  Google Scholar 

  • Sparks RF (1982) Research on victims of crime. Government Printing Office, Washington, DC

    Google Scholar 

  • Stewart EA, Schreck CJ, Simons RL (2006) I ain’t gonna let no one disrespect me: does the code of the street reduce or increase violent victimization among African American adolescents? J Res Crime Delinq 43:427–458

    Article  Google Scholar 

  • Teague R, Mazerolle P, Legosz M, Sanderson J (2008) Linking childhood abuse and adult offending: examining mediating factors and gendered relationships. Justice Q 25:313–348

    Article  Google Scholar 

  • Von Hentig H (1948) The criminal and his victim: studies in the sociobiology of crime. Schocken Books, New York

    Google Scholar 

  • Widom CS (1989) Does violence beget violence? A critical examination of the literature. Psychol Bull 106:3–28

    Article  Google Scholar 

  • Wilcox Rountree P, Land KC (1996) Burglary victimization, perceptions of crime risk, and routine activities: a multilevel analysis across Seattle neighborhoods and census tracts. J Res Crime Delinq 33:146–180

    Google Scholar 

  • Wilcox P, May DC, Roberts SD (2006) Student weapon possession and the “fear and victimization hypothesis”: unraveling the temporal order. Justice Q 23:502–529

    Article  Google Scholar 

  • Windle M (1994) Substance use, risky behaviors, and victimization among a US national adolescent sample. Addiction 89:175–182

    Article  Google Scholar 

  • Wolfgang ME (1958) Patterns in criminal homicide. Wiley, New York

    Google Scholar 

  • Wooldridge JM (2002) Econometric analysis of cross section and panel data. MIT Press, Cambridge

    Google Scholar 

  • Wothke W (2000) Longitudinal and multi-group modeling with missing data. In: Little TD, Schnabel KU, Baumert J (eds) Modeling longitudinal and multilevel data: practical issues, applied approaches and specific examples. Lawrence Erlbaum Associates, Mahwah

    Google Scholar 

  • Wright RT, Decker SH (1997) Armed robbers in action: stickups and street culture. Northeastern University Press, Boston

    Google Scholar 

  • Wright BRE, Caspi A, Moffitt TE, Silva PA (2001) The effects of social ties on crime vary by criminal propensity: a life-course model of interdependence. Criminology 39:321–352

    Article  Google Scholar 

  • Zumbo BD, Gadermann AM, Zeisser C (2007) Ordinal versions of coefficients alpha and theta for Likert rating scales. J Appl Stat Methods 6:21–29

    Google Scholar 

Download references

Acknowledgments

This research was sponsored, in part, by grant DA-11317 (Richard R. Clayton, PI) from the National Institute on Drug Abuse. The authors would like to thank Richard R. Clayton, Scott A. Hunt, Kimberly Reeder, Michelle Campbell Augustine, Shayne Jones, Staci Roberts, and Jon Paul Bryan for their contributions to the Rural Substance abuse and Violence Project, which provides the data analyzed here.

Author information

Authors and Affiliations

  1. Department of Sociology, College of William & Mary, Williamsburg, VA, 23187, USA

    Graham C. Ousey

  2. School of Criminal Justice, University of Cincinnati, Cincinnati, OH, USA

    Pamela Wilcox & Bonnie S. Fisher

Authors
  1. Graham C. Ousey
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Pamela Wilcox
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Bonnie S. Fisher
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Graham C. Ousey.

Appendices

Appendix A

See Table 5.

Table 5 Description of measures with means, standard deviations and reliability coefficients
Full size table

Appendix B

See Table 6.

Table 6 Performance of estimators of longitudinal victimization-offending relationship in repeated simulations
Full size table

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ousey, G.C., Wilcox, P. & Fisher, B.S. Something Old, Something New: Revisiting Competing Hypotheses of the Victimization-Offending Relationship Among Adolescents. J Quant Criminol 27, 53–84 (2011). https://doi.org/10.1007/s10940-010-9099-1

Download citation

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s10940-010-9099-1

Keywords

Search

Navigation