Skip to main content

A Sensitivity Analysis of Egocentric Measures of Peer Delinquency to Latent Homophily: A Research Note

Abstract

Objectives

Egocentric measures of peer delinquency, obtained through a census of a social network, have become the preferred operationalization for examining the relationships between social influence and delinquency. Studies regressing ego’s delinquency on the delinquency of nominated friend/s (i.e. alter/s) conclude that a statistically significant coefficient provides evidence of social influence. However, the inferences drawn from these studies may be biased by the introduction of artificial statistical dependence as a consequence of using social network data in a regression framework. Recent work (Shalizi and Thomas Sociol Methods Res 40:211–239, 2011) shows that latent homophily, or unmeasured confounding of observables, may lead to nonzero estimates of social influence, even if there is no causal significance. To examine this possibility, sensitivity analyses have been created (e.g. VanderWeele and Arah Epidemiology 22:42–52, 2011; VanderWeele Sociol Methods Res 40:240–255, 2011) to determine the robustness of an estimated coefficient to latent homophily.

Methods

In this research note, I examine the robustness of estimates for social influence from two articles (Haynie Am J Sociol 106:1013–1057, 2001; Meldrum et al. J Res Crime Delinq 46:353–376, 2009) using egocentric measures of peer delinquency.

Results

Findings indicate that for large, precise point estimates, highly improbable conditions are needed to explain away the effects of social influence. However, less precise point estimates (i.e. large standard errors) are more sensitive to latent homophily.

Conclusions

The analyses indicate that studies using egocentric measures should conduct sensitivity tests, particularly when the estimated effect is weak and/or has a relatively large standard error. Scripts written in the free programming language R (R Core Team R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, 2012) are provided for researchers to conduct such analyses.

This is a preview of subscription content, access via your institution.

Fig. 1
Fig. 2
Fig. 3

Notes

  1. 1.

    Perceptual measures use self-reports from a respondent regarding the delinquency of his/her peers and are also referred to as “indirect” or “subjective” measures. Egocentric measures use self-reported delinquency from an individual's friends nominated through a roster and are also referred to as “direct” or “objective” measures.

  2. 2.

    For a list of publications using AddHealth see: http://www.cpc.unc.edu/projects/addhealth/publications.

References

  1. Agnew R (1991) The interactive effects of peer variables on delinquency. Criminology 29:47–72

    Article  Google Scholar 

  2. Akers R (2009) Social learning and social structure: a general theory of crime and deviance. Northeastern University Press, Lebanon

    Google Scholar 

  3. Aseltine RH (1995) A reconsideration of parental and peer influences on adolescent deviance. J Health Soc Behav 36:103–121

    Article  Google Scholar 

  4. Beaver KM, Gibson CL, Turner MG, DeLisi M, Vaughn MG, Holand A (2009) Stability of delinquent peer associations: a biosocial test of warr’s sticky-friends hypothesis. Crime Delinq 57:907–927

    Article  Google Scholar 

  5. Boman JH, Stogner JM, Miller BL, Griffin OH, Krohn MD (2011) On the operational validity of perceptual measures. J Res Crime Delinq 49:601–621

    Article  Google Scholar 

  6. Cohen AK (1955) Delinquent boys: the culture of the gang. The Free Press, New York

    Google Scholar 

  7. Dijkstra JK, Lindenberg S, Veenstra R, Steglich C, Isaacs J, Card NA, Hodges EVE (2010) Influence and selection processes in weapon carrying during adolescence: the roles of status, aggression, and vulnerability. Criminology 48:187–220

    Article  Google Scholar 

  8. Elwert F, Winship C (2008) Endogenous selection bias. Department of Sociology, University of Wisconsin-Madison (unpublished manuscript)

  9. Feld S (1982) Social structural determinants of similarity. Am Sociol Rev 47:797–801

    Article  Google Scholar 

  10. Glueck S, Glueck E (1950) Unraveling juvenile delinquency. Commonwealth

  11. Gottfredson MR, Hirschi T (1990) A general theory of crime. Stanford University Press, Palo Alto

    Google Scholar 

  12. Haynie D (2001) Delinquent peers revisited: does network structure matter? Am J Sociol 106:1013–1057

    Article  Google Scholar 

  13. Haynie D (2002) Friendship networks and delinquency: the relative nature of peer delinquency. J Quant Criminol 18:99–134

    Article  Google Scholar 

  14. Haynie D, Osgood DW (2005) Reconsidering peers and delinquency: how do peers matter? Soc Forces 84:1109–1130

    Article  Google Scholar 

  15. Jussim L, Osgood DW (1989) Influence and similarity among friends: an integrated model applied to incarcerated adolescents. Soc Psychol Q 52:98–112

    Article  Google Scholar 

  16. Kreager DA (2007) When it’s good to be ‘bad’: violence and adolescent peer acceptance. Criminology 45:893–923

    Article  Google Scholar 

  17. Mcgloin JM (2009) Delinquency balance: revisiting peer influence. Criminology 47:439–477

    Article  Google Scholar 

  18. Mcgloin JM, Shermer L (2008) Self-control and deviant peer network structure. J Res Crime Delinq 46:35–72

    Article  Google Scholar 

  19. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27:415–444

    Google Scholar 

  20. Megens KCIM, Weerman FM (2010) Attitudes, delinquency and peers: the role of social norms in attitude-behaviour inconsistency. Eur J Criminol 7:299–316

    Article  Google Scholar 

  21. Meldrum RC, Young JTN, Weerman FM (2009) Peers, self-control, and crime: assessing effect size across different measures of delinquent peers. J Res Crime Delinq 46:353–376

    Article  Google Scholar 

  22. Paternoster R, McGloin JM, Nguyen H, Thomas KJ (2012) The causal impact of exposure to deviant peers: an experimental investigation. J Res Crime Delinq. doi:10.1177/0022427812444274

  23. Pearl J (2000) Causality: models, reasoning, and Inference. Cambridge University Press, Cambridge

    Google Scholar 

  24. Piquero NL, Gover AR, MacDonald JM, Piquero AR (2005) The influence of delinquent peers on delinquency: does gender matter? Youth Soc 36:251–275

    Google Scholar 

  25. Pratt TC, Cullen FT (2000) The empirical status of Gottfredson and Hirschi’s general theory of crime: a meta-analysis. Criminology 38:931–964

    Google Scholar 

  26. Rebellon CJ, Modecki KL (2013) Accounting for projection bias in models of delinquent peer influence: the utility and limits of latent variable approaches. J Quant Criminol. doi:10.1007/s10940-013-9199-9

  27. Rivera MT, Soderstrom SB, Uzzi B (2010) Dynamics of dyads in social networks: assortative, relational, and proximity mechanisms. Ann Rev Sociol 36:91–115

    Article  Google Scholar 

  28. Rosenbaum PR, Rubin DB, Apr N (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55

    Article  Google Scholar 

  29. Rosenthal R (1979) The ‘file drawer problem’ and tolerance for null results. Psychol Bull 86:638–641

    Google Scholar 

  30. Schaefer DR (2010) A configurational approach to homophily using lattice visualization. Connections 31:21–40

    Google Scholar 

  31. Schaefer DR (2012) Homophily through nonreciprocity: results of an experiment. Soc Forces 90:1271–1295

    Article  Google Scholar 

  32. Shalizi CR, Thomas AC (2011) Homophily and contagion are generically confounded in observational social network studies. Sociol Methods Res 40:211–239

    Article  Google Scholar 

  33. Short JF Jr, Strodtbeck FL (1965) Group process and gang delinquency. University of Chicago, Chicago

    Google Scholar 

  34. Snijders TAB (2001) The statistical evaluation of social network dynamics. Sociol Methodol 31:361–395

    Article  Google Scholar 

  35. Stigler SM (1999) Statistics on the table: the history of statistical concepts and methods. Harvard University Press, Cambridge

    Google Scholar 

  36. Sutherland EH (1947) Principles of criminology, 4th edn. Lippincott, Philadelphia

    Google Scholar 

  37. R Core Team (2012) R: a language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, http://www.R-project.org/

  38. VanderWeele TJ (2011) Sensitivity analysis for contagion effects in social networks. Sociol Methods Res 40:240–255

    Article  Google Scholar 

  39. Vanderweele TJ, Arah OA (2011) Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology 22:42–52

    Article  Google Scholar 

  40. Weerman FM (2011) Delinquent peers in context: a longitudinal network analysis of selection and influence effects. Criminology 49:253–286

    Article  Google Scholar 

  41. Weerman FM, Smeenk WH (2005) Peer similarity in delinquency for different types of friends: a comparison using two measurement methods. Criminology 43:499–523

    Article  Google Scholar 

  42. Warr M (2002) Companions in crime: the social aspects of criminal conduct. Cambridge University Press, Cambridge

    Book  Google Scholar 

  43. Young JTN (2011) How do they ‘end up together’? A social network analysis of self-control, homophily, and adolescent relationships. J Quant Criminol 27:251–273

    Article  Google Scholar 

  44. Young JTN, Rees C (2013) Social networks and delinquency in adolescence: implications for life-course criminology. In: Gibson C, Krohn M (eds) Handbook of life-course criminology. Springer, New York, pp 159–180

    Chapter  Google Scholar 

  45. Young JTN, Weerman FM (2013) Misperception of peer delinquency and its consequences: examining a mechanism of social influence and delinquency. Soc Probl 60(3):334–356

    Google Scholar 

  46. Young JTN, Barnes JC, Meldrum R, Weerman FM (2011) Assessing and explaining misperceptions of peer delinquency. Criminology 49:599–630

    Article  Google Scholar 

  47. Young JTN, Rebellon CJ, Barnes JC, Weerman FM (2013) Are we measuring what we think we are? A latent variable approach to the discriminant validity of personal and peer delinquency measures. Justice Q (in press)

Download references

Acknowledgments

I would like to thank Ryan Meldrum, Brooks Louton, Carter Rees, and three anonymous reviewers for comments on earlier versions of this manuscript.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Jacob T. N. Young.

Appendices

Appendix 1: R Script for Haynie (2001) Sensitivity Analysis

figurea
figureb

Appendix 2: R Script for Meldrum et al. (2009) Sensitivity Analysis

figurec
figured
figuree

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Young, J.T.N. A Sensitivity Analysis of Egocentric Measures of Peer Delinquency to Latent Homophily: A Research Note. J Quant Criminol 30, 373–387 (2014). https://doi.org/10.1007/s10940-013-9207-0

Download citation

Keywords

  • Egocentric
  • Sensitivity
  • Peer influence
  • Social networks