Journal of Quantitative Criminology

, Volume 30, Issue 3, pp 373–387 | Cite as

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

Original Paper



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.


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.


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.


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.


Egocentric Sensitivity Peer influence Social networks 



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


  1. Agnew R (1991) The interactive effects of peer variables on delinquency. Criminology 29:47–72CrossRefGoogle Scholar
  2. Akers R (2009) Social learning and social structure: a general theory of crime and deviance. Northeastern University Press, LebanonGoogle Scholar
  3. Aseltine RH (1995) A reconsideration of parental and peer influences on adolescent deviance. J Health Soc Behav 36:103–121CrossRefGoogle 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 57:907–927CrossRefGoogle Scholar
  5. Boman JH, Stogner JM, Miller BL, Griffin OH, Krohn MD (2011) On the operational validity of perceptual measures. J Res Crime Delinq 49:601–621CrossRefGoogle Scholar
  6. Cohen AK (1955) Delinquent boys: the culture of the gang. The Free Press, New YorkGoogle 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 48:187–220CrossRefGoogle Scholar
  8. Elwert F, Winship C (2008) Endogenous selection bias. Department of Sociology, University of Wisconsin-Madison (unpublished manuscript)Google Scholar
  9. Feld S (1982) Social structural determinants of similarity. Am Sociol Rev 47:797–801CrossRefGoogle Scholar
  10. Glueck S, Glueck E (1950) Unraveling juvenile delinquency. CommonwealthGoogle Scholar
  11. Gottfredson MR, Hirschi T (1990) A general theory of crime. Stanford University Press, Palo AltoGoogle Scholar
  12. Haynie D (2001) Delinquent peers revisited: does network structure matter? Am J Sociol 106:1013–1057CrossRefGoogle Scholar
  13. Haynie D (2002) Friendship networks and delinquency: the relative nature of peer delinquency. J Quant Criminol 18:99–134CrossRefGoogle Scholar
  14. Haynie D, Osgood DW (2005) Reconsidering peers and delinquency: how do peers matter? Soc Forces 84:1109–1130CrossRefGoogle Scholar
  15. Jussim L, Osgood DW (1989) Influence and similarity among friends: an integrated model applied to incarcerated adolescents. Soc Psychol Q 52:98–112CrossRefGoogle Scholar
  16. Kreager DA (2007) When it’s good to be ‘bad’: violence and adolescent peer acceptance. Criminology 45:893–923CrossRefGoogle Scholar
  17. Mcgloin JM (2009) Delinquency balance: revisiting peer influence. Criminology 47:439–477CrossRefGoogle Scholar
  18. Mcgloin JM, Shermer L (2008) Self-control and deviant peer network structure. J Res Crime Delinq 46:35–72CrossRefGoogle Scholar
  19. McPherson M, Smith-Lovin L, Cook JM (2001) Birds of a feather: homophily in social networks. Annu Rev Sociol 27:415–444Google Scholar
  20. Megens KCIM, Weerman FM (2010) Attitudes, delinquency and peers: the role of social norms in attitude-behaviour inconsistency. Eur J Criminol 7:299–316CrossRefGoogle 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 46:353–376CrossRefGoogle 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, CambridgeGoogle Scholar
  24. Piquero NL, Gover AR, MacDonald JM, Piquero AR (2005) The influence of delinquent peers on delinquency: does gender matter? Youth Soc 36:251–275Google Scholar
  25. Pratt TC, Cullen FT (2000) The empirical status of Gottfredson and Hirschi’s general theory of crime: a meta-analysis. Criminology 38:931–964Google 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 36:91–115CrossRefGoogle Scholar
  28. Rosenbaum PR, Rubin DB, Apr N (1983) The central role of the propensity score in observational studies for causal effects. Biometrika 70:41–55CrossRefGoogle Scholar
  29. Rosenthal R (1979) The ‘file drawer problem’ and tolerance for null results. Psychol Bull 86:638–641Google Scholar
  30. Schaefer DR (2010) A configurational approach to homophily using lattice visualization. Connections 31:21–40Google Scholar
  31. Schaefer DR (2012) Homophily through nonreciprocity: results of an experiment. Soc Forces 90:1271–1295CrossRefGoogle Scholar
  32. Shalizi CR, Thomas AC (2011) Homophily and contagion are generically confounded in observational social network studies. Sociol Methods Res 40:211–239CrossRefGoogle Scholar
  33. Short JF Jr, Strodtbeck FL (1965) Group process and gang delinquency. University of Chicago, ChicagoGoogle Scholar
  34. Snijders TAB (2001) The statistical evaluation of social network dynamics. Sociol Methodol 31:361–395CrossRefGoogle Scholar
  35. Stigler SM (1999) Statistics on the table: the history of statistical concepts and methods. Harvard University Press, CambridgeGoogle Scholar
  36. Sutherland EH (1947) Principles of criminology, 4th edn. Lippincott, PhiladelphiaGoogle 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,
  38. VanderWeele TJ (2011) Sensitivity analysis for contagion effects in social networks. Sociol Methods Res 40:240–255CrossRefGoogle Scholar
  39. Vanderweele TJ, Arah OA (2011) Bias formulas for sensitivity analysis of unmeasured confounding for general outcomes, treatments, and confounders. Epidemiology 22:42–52CrossRefGoogle Scholar
  40. Weerman FM (2011) Delinquent peers in context: a longitudinal network analysis of selection and influence effects. Criminology 49:253–286CrossRefGoogle Scholar
  41. Weerman FM, Smeenk WH (2005) Peer similarity in delinquency for different types of friends: a comparison using two measurement methods. Criminology 43:499–523CrossRefGoogle Scholar
  42. Warr M (2002) Companions in crime: the social aspects of criminal conduct. Cambridge University Press, CambridgeCrossRefGoogle 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 27:251–273CrossRefGoogle 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 159–180CrossRefGoogle 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):334–356Google Scholar
  46. Young JTN, Barnes JC, Meldrum R, Weerman FM (2011) Assessing and explaining misperceptions of peer delinquency. Criminology 49:599–630CrossRefGoogle 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)Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  1. 1.School of Criminology and Criminal JusticeArizona State UniversityPhoenixUSA

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