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Accounting for Projection Bias in Models of Delinquent Peer Influence: The Utility and Limits of Latent Variable Approaches

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Abstract

Objectives

Projection effects have been shown to bias respondent perceptions of peer delinquency, but network data required to measure peer delinquency directly are unavailable in most existing datasets. Some researchers have therefore attempted to adjust perceived peer behavior measures for bias via latent variable modeling techniques. The present study tested whether such adjustments render perceived peer coefficients equal to direct peer coefficients, using original data collected from 538 young adults (269 dyads).

Methods

After first replicating projection effects in our own data and examining the degree to which measures of personal, perceived peer, and direct peer violence represent empirically distinct constructs, we compared coefficients derived from two alternative models of personal violence. The first model included an error-adjusted latent measure of perceived peer violence as a predictor, whereas the second substituted a latent measure of directly-assessed, peer-reported violence.

Results

Results suggest that personal, perceived peer, and direct peer measures each reflect fundamentally separate constructs, but call into question whether latent variable techniques used by prior researchers to correct for respondent bias are capable of rendering perceived peer coefficients equal to direct peer coefficients.

Conclusions

Research cannot bypass the collection of direct peer delinquency measures via latent variable modeling adjustments to perceived peer measures, nor should models of deviance view perceived peer and direct peer measures as alternative measures of the same underlying construct. Rather, theories of peer influence should elaborate and test models that simultaneously include both peer measures and, further, should attempt to identify those factors that account for currently unexplained variance in perceptions of peer behavior.

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Notes

  1. While some studies “have used a residual score approach to compute discrepancy scores” rather than a difference-score approach, some research has used both methods simultaneously, “yielding discrepancy scores that were nearly identical (i.e., r’s > .95) to one another and producing an identical pattern of results” (Prinstein and Wang 2005:297–298).

  2. Models run via fully-weighted least squares estimation failed to converge, likely as a result of our models’ complexity and limited sample size.

  3. One reviewer suggested that we consider an alternative strategy involving transforming our indicator items to reduce skew and estimating our models via the traditional maximum likelihood algorithm. We therefore ran an alternative set of models in which we (a) computed the natural log of each of our skewed indicator items; (b) computed traditional Pearson correlations; and (c) estimated all measurement and structural equation models via the maximum likelihood algorithm. Substantive results (available upon request) were identical to those presented herein, with one minor exception, noted below, that does not change our conclusions in any way. Because conclusions remained the same, and for the reasons discussed in “Estimation Method”, we present DWLS results using polychoric correlations.

  4. For purposes of comparison, the three-factor model presented in Fig. 2 fit the data better than did (a) a one-factor model (Δχ2 = 525.10, df = 7, p < .05); (b) a two-factor model in which perceived and direct peer behavior loaded on one factor (Δχ2 = 322.98, df = 6, p < .05); (c) a two-factor model in which personal behavior and perceived peer behavior loaded on one factor (Δχ2 = 182.81, df = 6, p < .05); and (d) a three-factor model omitting error correlation estimates of projection (Δχ2 = 39.90, df = 4, p < .05). Respectively, in comparison to the Fig. 2 model’s RMSEA of .026, the above alternatives yielded poorer fits of .191, .153, .115, and .058.

  5. The corresponding coefficient from our maximum-likelihood model did achieve statistical significance, but the gap between Figs. 3 and 4 coefficients when using maximum-likelihood estimation was similar to the gap presented in Figs. 3 and 4. Thus, conclusions about the research questions that we discuss at the outset were identical to those drawn from DWLS estimates, whose standard errors are likely to be more valid in light of the methodological literature we cite in “Estimation Method”.

  6. Given that our alternative models have used the same sample with different measures of peer violence rather than the same measures of peer violence with different samples, it is not appropriate to compare the coefficients of alternative models via the Paternoster et al. (1998) formula.

References

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

    Article  Google Scholar 

  • Akers RL (2009) Social learning and social structure: a general theory of crime and deviance. Transactional Publishers, Brunswick

    Google Scholar 

  • Akers RL, Lee G (1996) A longitudinal test of social learning theory: adolescent smoking. J Drug Issues 26:317–343

    Google Scholar 

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

    Article  Google Scholar 

  • Babakus E, Ferguson CE, Jöreskog KG (1987) The sensitivity of confirmatory maximum likelihood factor analysis to violations of measurement scale and distributional assumptions. J Mark Res 24:222–228

    Article  Google Scholar 

  • Boman JH, Stogner JM, Miller BL, Griffin OH, Krohn MD (2012) On the operational validity of perceptual peer delinquency: exploring projection and elements contained in perceptions. J Res Crime Delinquency 49:601–621

    Article  Google Scholar 

  • Browne MW (1984) Asymptotically distribution-free methods for the analysis of covariance structures. Br J Math Stat Psychol 37:62–83

    Article  Google Scholar 

  • Burgess RL, Akers RL (1966) A differential association-reinforcement theory of criminal behavior. Soc Probl 14:128–147

    Article  Google Scholar 

  • Campbell DT, Fiske DW (1959) Convergent and discriminant validation by the multitrait-multimethod matrix. Psychol Bull 56:81–105

    Article  Google Scholar 

  • Caspi A, Begg D, Dickson N, Harrington H, Langley J, Moffitt TE et al (1997) Personality differences predict health-risk behaviors in young adulthood: evidence from a longitudinal study. J Pers Soc Psychol 73:1052–1063

    Article  Google Scholar 

  • Caspi A, Moffitt TE, Silva PA, Stouthamer-Loeber M, Krueger RF, Schmutte PS (2006) Are some people crime-prone? Replications of the personality-crime relationship across countries, genders, races, and methods. Criminology 32:163–196

    Article  Google Scholar 

  • Côté S, Tremblay RE, Nagin D, Zoccolillo M, Vitaro F (2002) The development of impulsivity, fearfulness, and helpfulness during childhood: patterns of consistency and change in the trajectories of boys and girls. J Child Psychol Psychiatry 43:609–618

    Article  Google Scholar 

  • Dawes RM (1989) Statistical criteria for a truly false consensus effect. J Exp Soc Psychol 25:1–17

    Article  Google Scholar 

  • Einhorn HJ (1986) Accepting error to make less error. J Pers Assess 50:387–395

    Article  Google Scholar 

  • Elliott DS, Huizinga D, Ageton SS (1985) Explaining delinquency and drug use. Sage, Beverly Hills

    Google Scholar 

  • Evans TD, Cullen FT, Burton VS, Dunaway RG, Benson ML (1997) The social consequences of self-control: testing the general theory of crime. Criminology 35:475–504

    Article  Google Scholar 

  • Geiser C, Lockhart G (2012) A comparison of four approaches to account for method effects in latent state-trait analyses. Psychol Methods 17:255–283

    Article  Google Scholar 

  • Glueck S, Glueck E (1950) Unravelling juvenile delinquency. Harvard University Press, Cambridge

    Google Scholar 

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

    Google Scholar 

  • Grasmick HG, Tittle CR, Bursik RJ, Arneklev BJ (1993) Testing the core empirical implications of Gottfredson and Hirschi’s general theory of crime. J Res Crime Delinquency 30:5–29

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Holmes DS (1978) Projection as a defense mechanism. Psychol Bull 85:677–688

    Article  Google Scholar 

  • Holtz R, Miller N (1985) Assumed similarity and opinion certainty. J Pers Soc Psychol 48:890–898

    Article  Google Scholar 

  • Huizinga D, Elliott DS (1986) Reassessing the reliability and validity of self-report delinquency measures. J Quant Criminol 2:293–327

    Google Scholar 

  • Hymel S (1986) Interpretations of peer behavior: affective bias in childhood and adolescence. Child Dev 57:431–445

    Article  Google Scholar 

  • Iannotti RJ, Bush PJ (1992) Perceived vs. actual friends’ use of alcohol, cigarettes, marijuana, and cocaine: which has the most influence? J Youth Adolesc 21:375–389

    Article  Google Scholar 

  • Jöreskog KG, Sörbom D (1993a) LISREL 8 user’s reference guide. Scientific Software International, Inc., Chicago

    Google Scholar 

  • Jöreskog KG, Sörbom D (1993b) PRELIS 2 user’s reference guide. Scientific Software International, Inc., Chicago

    Google Scholar 

  • Judd CM, Johnson JT (1981) Attitudes, polarization, and diagnosticity: exploring the effect of affect. J Pers Soc Psychol 41:26–36

    Article  Google Scholar 

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

    Google Scholar 

  • Kandel D (1978) Homopily, selection, and socialization in adolescent friendships. Am J Sociol 84:427–436

    Article  Google Scholar 

  • Kandel D (1980) Drug and drinking behavior among youth. Ann Rev Sociol 6:235–285

    Article  Google Scholar 

  • Kandel D (1996) The parental and peer contexts of adolescent deviance: an algebra of interpersonal influences. J Drug Issues 26:289–315

    Google Scholar 

  • Katz D, Allport F (1931) Students’ attitudes. Craftsman Press, Syracuse

    Google Scholar 

  • Kernis MH (1984) Need for uniqueness, self-schemas, and thought as moderators of the false-consensus effect. J Exp Soc Psychol 20:350–362

    Article  Google Scholar 

  • Knecht A, Snijders T, Baerveldt C, Steglich CEG, Werner R (2010) Friendship and delinquency: selection and influence processes in early adolescence. Soc Dev 19:494–514

    Article  Google Scholar 

  • Krueger J, Clement RW (1994) The truly false consensus effect: an ineradicable and egocentric bias in social perception. J Pers Soc Psychol 67:596–610

    Article  Google Scholar 

  • Loehlin JC (1992) Latent variable models: an introduction to factor, path, and structural analysis. Lawrence Erlbaum Associates, Hillsdale

    Google Scholar 

  • Lowenkamp CT, Cullen FT, Pratt TC (2003) Replicating Sampson and Groves’s test of social-disorganization theory: revisiting a criminological classic. J Res Crime Delinquency 40:351–373

    Article  Google Scholar 

  • Marks G, Miller N (1987) Ten years of research on the false-consensus effect: an empirical and theoretical review. Psychol Bull 102:72–90

    Article  Google Scholar 

  • Matsueda RL, Anderson K (1998) The dynamics of delinquent peers and delinquent behavior. Criminology 36:269–308

    Article  Google Scholar 

  • McDougall PT, Hymel S (2007) Same-gender versus cross-gender friendship conceptions. Merrill-Palmer Q 53:347–380

    Article  Google Scholar 

  • McGloin JM, Shermer LON (2009) Self-control and deviant peer network structure. J Res Crime Delinquency 46(1):35–72

    Article  Google Scholar 

  • Meldrum RC, Young JTN, Weerman FM (2009) Reconsidering the effect of self-control and delinquent peers: implications of measurement for theoretical significance. J Res Crime Delinquency 46:353–376

    Article  Google Scholar 

  • Mullen B, Atkins JL, Champion DS, Edwards C, Hardy D, Story JE, Vanderklok M (1985) The false consensus effect: a meta-analysis of 155 hypothesis tests. J Exp Soc Psychol 21:262–283

    Article  Google Scholar 

  • Muthén B (1984) A general structural equation model with dichotomous, ordered categorical, and continuous latent variable indicators. Psychometrika 49:115–132

    Article  Google Scholar 

  • Newcomb TM (1961) The acquaintance process. Holt, Rinehart, and Winston, New York

  • Oliva EM, Keyes M, Iacono WG, McGue M (2012) Adolescent substance use groups: antecedent and concurrent personality differences in a longitudinal study. J Pers 80:769–793

    Article  Google Scholar 

  • Osgood DW, Schreck CJ (2007) A new method for studying the extent, stability, and predictors of individual specialization in violence. Criminology 45:273–311

    Article  Google Scholar 

  • Osgood DW, Wilson JK, O’Malley PM, Bachman JG, Johnson LD (1996) Routine activities and individual deviant behavior. Am Sociol Rev 61:635–655

    Article  Google Scholar 

  • Paternoster R, Brame R, Mazerolle P, Piquero A (1998) Using the correct statistical test for the equality of regression coefficients. Criminology 36:859–866

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Prinstein MJ, Wang SS (2005) False consensus and adolescent peer contagion: examining discrepancies between perceptions and actual reported levels of friends’ deviant and health risk behaviors. J Abnorm Child Psychol 33:293–306

    Article  Google Scholar 

  • Rebellon, CJ (2006) Do adolescents engage in delinquency to attract the social attention of peers? An extension and longitudinal test of the social reinforcement hypothesis. J Res Crime Delinq 43:387–411

    Google Scholar 

  • Rebellon, CJ (2012) Differential association and substance use: assessing the roles of discriminant validity, socialization, and selection in traditional empirical tests. Eur J Criminol 9:74–97

    Google Scholar 

  • Rebellon, CJ, Waldman, I (2003) Deconstructing “force and fraud”: an empirical assessment of the generality of crime. J Quant Criminol 19:303–331

    Google Scholar 

  • Roberts BW, DelVecchio WF (2000) The rank-order consistency of personality traits from childhood to old age: a quantitative review of longitudinal studies. Psychol Bull 126:3–25

    Article  Google Scholar 

  • Ross L, Greene D, House P (1977) The “false consensus effect”: an egocentric bias in social perception and attribution processes. J Exp Soc Psychol 13:279–301

    Article  Google Scholar 

  • Sampson RJ (1999) Techniques of research neutralization. Theor Criminol 3:438–451

    Article  Google Scholar 

  • Saris WE, Aalberts C (2003) Different explanations for correlated disturbance terms in MTMM studies. Struct Equ Model A Multidiscip J 10:193–213

    Article  Google Scholar 

  • Sherman SJ, Presson CC, Chassin L, Corty E, Olshavsky R (1983) The false consensus effect in estimates of smoking prevalence: underlying mechanisms. Pers Soc Psychol Bull 9:197–207

    Article  Google Scholar 

  • Sherman SJ, Presson CC, Chassin L (1984) Mechanisms underlying the false consensus effect: the special role of threats to the self. Pers Soc Psychol Bull 10:127–138

    Article  Google Scholar 

  • Sutherland EH (1947) The principles of criminology. Lippincott, Philadelphia

    Google Scholar 

  • Tangney JP, Baumeister RF, Boone AL (2008) High self-control predicts good adjustment, less pathology, better grades, and interpersonal success. J Pers 72:271–324

    Article  Google Scholar 

  • Thornberry TP (1987) Toward an interactional theory of delinquency. Criminology 25:863–891

    Article  Google Scholar 

  • Urberg KA, Shyu S, Liang J (1990) Peer influence in adolescent cigarette smoking. Addict Behav 15:247–255

    Article  Google Scholar 

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

    Book  Google Scholar 

  • Weerman FM (2011) Delinquent peers in context: a longitudinal network analysis of selection and influence effects. Criminology 49:1745–9125

    Article  Google Scholar 

  • 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 

  • Wiecko FM (2010) Research note: assessing the validity of college samples: are students really that different? J Crim Justice 38:1186–1190

    Article  Google Scholar 

  • Wills TA, Dishion TJ (2004) Temperament and adolescent substance use: a transactional analysis of emerging self-control. J Clin Child Adolesc Psychol 33:69–81

    Article  Google Scholar 

  • Wolfson S (2000) Students’ estimates of the prevalence of drug use: evidence for a false consensus effect. Psychol Addict Behav 14:295–298

    Article  Google Scholar 

  • Young JT, Barnes JC, Meldrum RC, Weerman FM (2011) Assessing and explaining misperception of peer delinquency. Criminology 49:599–630

    Article  Google Scholar 

  • Zhang L, Messner SF (2000) The effects of alternative measures of delinquent peers on self-reported delinquency. J Res Crime Delinquency 37:323–337

    Article  Google Scholar 

Download references

Acknowledgments

We thank Robert Agnew, Tim Brezina, Ginger Lockhart, Michelle E. Manasse and Jacob T. N. Young for suggestions on a prior version of this manuscript. Preparation of this manuscript was funded in part by NIH Training Grant #T32 MH 018387. A previous version of this manuscript was presented at the 2012 Annual Meetings of the American Society of Criminology.

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Correspondence to Cesar J. Rebellon.

Appendix

Appendix

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Table 2 Measurement model

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Rebellon, C.J., Modecki, K.L. Accounting for Projection Bias in Models of Delinquent Peer Influence: The Utility and Limits of Latent Variable Approaches. J Quant Criminol 30, 163–186 (2014). https://doi.org/10.1007/s10940-013-9199-9

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