Journal of Experimental Criminology

, Volume 6, Issue 3, pp 263–292 | Cite as

The correspondence of family features with problem, aggressive, criminal, and violent behavior: a meta-analysis

  • James H. Derzon


Family features and characteristics are often identified as central to the development of antisocial behavior and are thus attractive targets for risk-focused preventive intervention. Using meta-analytic techniques, we examined the covariation between 21 family constructs with the current or later display of problem, aggressive, criminal, or violent behaviors. The 80 mean relationships, based on 3,124 correlations from 233 reports of 119 longitudinal studies, discussed in this paper are generally moderate, with a grand mean across outcomes of \( \overline {{r_{x,y}}} = .15 \). Family constructs were most predictive of problem behaviors, \( \overline {{r_{x,y}}} = .21 \). Predictors measured earlier in life were significantly stronger in 12 relationships and significantly weaker in 18 relationships. These findings are discussed with reference to Rutter’s (American Journal of Orthopsychiatry 57:316–331, 1987) conceptualization of protective mechanisms which suggests that if family factors warrant the attention they have engendered, then it is through their interaction with other developmental and situational factors.


Aggressive behavior Anti-social behavior Criminal behavior Family factors Meta-analysis Prediction Problem behavior Review Violent behavior 



I am indebted to my friend and colleague Dr. Mark Lipsey for all the opportunities he has extended me over the years and the chance to stretch my thinking and skills on this and other projects. I am also particularly indebted to Dr. Ali Habibi. A meta-analysis requires a tremendous amount of work, and this one was no exception. Without Dr. Habibi’s assistance, I’d still be coding. I would also like to thank my anonymous reviewers for their helpful and thoughtful suggestions and give special thanks to my good friend Dr. Anthony Petrosino for his thoughtful additions to the text and for encouraging me publish this work.


  1. Agnew, R. (1992). Foundation for a general strain theory of crime and delinquency. Criminology, 30(1), 47–87.CrossRefGoogle Scholar
  2. Becker, B. J., & Hedges, L. V. (1989). Synthesizing research on organizational participation. Research on the Sociology of Organizations, 7, 203–231.Google Scholar
  3. Burgess, R., & Akers, R. L. (1966). A differential association-reinforcement theory of criminal behavior. Social Problems, 14, 363–383.CrossRefGoogle Scholar
  4. Caldwell, B.M., & Bradley, R. H. (1984). Home Observation for Measurement of the Environment. Little Rock, Ark.: University of Arkansas.Google Scholar
  5. Coddington, R. D. (1984). Measuring the stressfulness of a child's environment. In Humphrey, J. S. (Ed.) Stress in Childhood (pp. 3–18). New York: AMS PressGoogle Scholar
  6. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale: Earlbaum.Google Scholar
  7. Cohen, J., & Cohen, P. (1983). Applied multiple regression/correlation analysis for the behavioral sciences (2nd ed.). Hillsdale: Lawrence Erlbaum Associates.Google Scholar
  8. Cordray, D. S. (1990). Strengthening causal interpretations of nonexperimental data: The role of meta-analysis. In L. Secherst, E. Perrin, & J. Bunker (Eds.), Research methodology: Strengthening causal interpretations of nonexperimental data (Publication No. PHS 90–3454) (pp. 151–172). Rockville: U.S. Department of Health and Human Services.Google Scholar
  9. Derzon, J. H. (1996). A meta-analysis of the efficacy of various antecedent behaviors, characteristics, and experiences for predicting later violent behavior (Doctoral dissertation, Claremont Graduate School, 1996). Dissertation Abstracts International, 57, 748.Google Scholar
  10. Derzon, J. H., Springer, F., Sale, L., & Brounstein, P. (2005). Estimating intervention effectiveness: Synthetic projection of field evaluation results. The Journal of Primary Prevention, 26, 321–343.CrossRefGoogle Scholar
  11. Einhorn, H. J., & Hogarth, R. M. (1986). Judging probable cause. Psychological Bulletin, 99, 3–19.CrossRefGoogle Scholar
  12. Gottfredson, M., & Hirschi, T. (1990). A general theory of crime. Stanford: Stanford University Press.Google Scholar
  13. Hedges, L. V., & Olkin, I. (1985). Statistical methods for meta-analysis. NY: Academic Press.Google Scholar
  14. Hirschi, T. (1969). Causes of delinquency. University of California Press.Google Scholar
  15. Hunter, J. E., & Schmidt, F. L. (1990a). Methods of meta-analysis: Correcting error and bias in research findings. Newbury Park: Sage.Google Scholar
  16. Hunter, J. E., & Schmidt, F. L. (1990b). Dichotomizing continuous variables: The implications for meta-analysis. The Journal of Applied Psychology, 75, 334–349.CrossRefGoogle Scholar
  17. Kanner, A. D., Coyne, I. C., Schaefer, C., & Lazarus, R. S. (1981). Comparison of two modes ofstress measurement: Daily hassles and uplifts versus major life events. Journal of BehavioralMedicine, 4, 1–39.Google Scholar
  18. Kazdin, A. E., Kraemer, H. C., Kessler, R. C., Kupfer, D. J., & Offord, D. R. (1997). Contributions of risk-factor research to developmental psychopathology. Clinical Psychology Review, 17, 375–406.CrossRefGoogle Scholar
  19. Laub, J., Sampson, R., & Sweeten, G. A. (2006). Assessing Sampson and Laub’s life course theory of crime, chapter 11. In F. Cullen, J. P. Wright, & K. Blevens (Eds.), Taking stock. The status of criminological theory. Advances in criminological theory volume 15. New Brunswick: Transaction Press.Google Scholar
  20. Lipsey, M. W., & Derzon, J. H. (1998). Predictors of serious delinquency in adolescence and early adulthood: A synthesis of longitudinal research. In R. Loeber & D. P. Farrington (Eds.), Serious and violent offenders: Risk factors and successful interventions (pp. 86–105). Thousand Oaks: Sage.Google Scholar
  21. Lipsey, M. W., & Wilson, D. B. (2000). Practical meta-analysis. In L. Bickman & D. Rog (Eds.), Applied social research methods series. Thousand Oaks: Sage.Google Scholar
  22. Merton, R. K. (1938). Social structure and anomie. American Sociological Review, 3, 672–682.CrossRefGoogle Scholar
  23. Payton, M. E., Greenstone, M. H., & Schenker, N. (2003). Overlapping confidence intervals or standard error intervals: What do they mean in terms of statistical significance? 6pp. Journal of Insect Science, 3:34, Available online: Accessed 11/23/2009.
  24. Piquero, A. R., Farrington, D. P., Welsh, B. C., Tremblay, R., & Jennings, W. G. (2009). Effects of early family/parenting training programs on antisocial behavior and delinquency. Journal of Experimental Criminology, 5, 83–120.CrossRefGoogle Scholar
  25. Raudenbush, S. W. (1994). Random effects models. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 301–321). New York: Russell Sage.Google Scholar
  26. Rosenthal, R. (1994). Statistically describing and combining studies. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 231–244). New York: Russell Sage.Google Scholar
  27. Rutter, M. (1987). Psychosocial resilience and protective mechanisms. The American Journal of Orthopsychiatry, 57, 316–331.CrossRefGoogle Scholar
  28. Schenker, N., & Gentleman, J. F. (2001). On judging the significance of differences by examining the overlap between confidence intervals. American Statistician, 55, 182–186.CrossRefGoogle Scholar
  29. Shadish, W. R., & Haddock, C. K. (1994). Combining estimates of effect size. In H. Cooper & L. V. Hedges (Eds.), The handbook of research synthesis (pp. 301–321). New York: Russell Sage.Google Scholar
  30. White, P. A. (1990). Ideas about causation in philosophy and psychology. Psychological Bulletin, 108, 3–18.CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Battelle Centers for Public Health Research and EvaluationArlingtonUSA

Personalised recommendations