Journal of Quantitative Criminology

, Volume 18, Issue 3, pp 213–237 | Cite as

Identifying Unit-Dependency and Time-Specificity in Longitudinal Analysis: A Graphical Methodology

  • Laura Dugan
Article

Abstract

Longitudinal analysis in criminology and other social sciences has become an important research tool because it allows us to draw conclusions from observing how multiple units change over time. Unfortunately, its results are more vulnerable to potential influences of unusual observational units or periods of time. Current leverage diagnostics are designed for cross-sectional analysis and are fallible when applied to longitudinal models. This article introduces a graphical diagnostic methodology to systematically examine the sensitivity of longitudinal results to extreme observational units and periods of time—unit-dependency and time-specificity. Further the article illustrates its use with an example testing policy effects on black and white female victimization of intimate partner homicide. Results are displayed in an easily understood graph that provides a snapshot of the results' time-specific patterns and robustness to unit-dependency. Currently, comparable tests for panel analysis are tedious and cumbersome. With this new illuminating methodology, researchers and policy-makers can easily decide whether a time-specific or unit-dependent pattern is consequential.

longitudinal analysis observation dependency outliers spousal homicide time specific effects graphical diagnostics 

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REFERENCES

  1. Avakame, E. F. (1999). Females' labor force participation and intimate femicide: An empirical assessment of the backlash hypothesis. Violence and Victims 14: 277-292.Google Scholar
  2. Belsley, D. A., Kuh, E., and Welsh, R. E. (1980). Regression Diagnostics Identifying Influential Data and Sources of Collinearity, Wiley, New York, NY.Google Scholar
  3. Black, D. A., and Nagin, D. S. (1998). Do right-to-carry laws deter violent crime? Journal of Legal Studies 27: 209-219.Google Scholar
  4. Conaway, M. R., and Lohr, S. L. (1994). A longitudinal analysis of factors associated with reporting violent crimes to the police. Journal of Quantitative Criminology 10: 23-39.Google Scholar
  5. Dugan, L. (1999). The Impact of Policies, Programs, and Other Exposure Reducing Factors on Intimate Partner Homicide. Doctoral Dissertation. Carnegie Mellon University.Google Scholar
  6. Dugan, L., Nagin, D. S., and Rosenfeld, R. (2000). Exposure reduction or backlash? The effects of domestic violence resources on intimate partner homicide. Report to the National Institute of Justice.Google Scholar
  7. Dugan, L., Nagin, D. S., and Rosenfeld, R. (1999). Explaining the decline in intimate partner homicide. Homicide Studies 3: 187-214.Google Scholar
  8. Duggan, M. (2000). More Guns, More Crime, Working Paper 7967. NBER Working Paper Series. National Bureau of Economic Research.Google Scholar
  9. Federal Bureau of Investigation. (1998). Supplementary Homicide Reports 1976-1996. Machine readable files and documentation obtained directly from the Uniform Crime Reporting program.Google Scholar
  10. Gartner, R., and McCarthy, B. (1991). The social distribution of femicide in urban Canada, 1921-1988. Law &; Society Review 25: 287-311.Google Scholar
  11. Haapasalo, J., Tremblay, R. E., Boulerice, B., and Vitaro, F. (2000). Relative advantages of person-and variable-based approaches for predicting problem behaviors for kindergarten assessments. Journal of Quantitative Criminology 16: 145-168.Google Scholar
  12. House Ways and Means Committee. (1996). 1996 Green Book, US Government Printing Office, Washington, DC.Google Scholar
  13. Kposowa, A. J., Singh, G. K., and Breault, K. D. (1994). The effects of marital status and social isolation on adult male homicides in the United States: Evidence from the National Longitudinal Mortality Study. Journal of Quantitative Criminology 10: 277-289.Google Scholar
  14. Levitt, S. D. (1996). The effect of prison population size on crime rates: Evidence from prison overcrowding litigation. The Quarterly Journal of Economics 111: 319-351.Google Scholar
  15. Liao, T. F. (1994). Interpreting Probability Models Logit, Probit, and Other Generalized Linear Models. Sage Publications, Thousand Oaks, CA.Google Scholar
  16. Loeber, R., and Le Blanc, M. (1990). Toward a developmental criminology. Crime and Justice 12: 375-473.Google Scholar
  17. Long, J. S. (1997). Regression Models for Categorical and Limited Dependent Variables. Sage Publications, Thousand Oaks, CA.Google Scholar
  18. Lott, J. R. (1998). The concealed-handgun debate. Journal of Legal Studies 27: 221-243.Google Scholar
  19. Lott, J. R., and Mustard, D. B. (1997). Crime, deterrence, and right-to-carry concealed handguns. Journal of Legal Studies 26: 1-68.Google Scholar
  20. Ludwig, J. (1998). Concealed-gun-carrying laws and violent crime: Evidence from State Panel Data. International Review of Law and Economics 18: 239-254.Google Scholar
  21. Maddala, G. S. (1983). Limited-Dependent and Qualitative Variables in Econometrics. Cambridge Press, New York.Google Scholar
  22. Maltz, M. D., and Mullany, J. M. (2000). Visualizing lives: New pathways for analyzing life course trajectories. Journal of Quantitative Criminology 16: 255-281.Google Scholar
  23. Maughan, B., Pickles, A., Rowe, R., Costello, E. J., and Angold, A. (2000). Developmental trajectories of aggressive and non-aggressive conduct problems. Journal of Quantitative Criminology 16: 199-221.Google Scholar
  24. McCord J. (2000). Longitudinal Analysis: An introduction to the special issue. Journal of Quantitative Criminology 16: 113-115.Google Scholar
  25. McCullagh P., and Nelder, J. A. (1989). Generalized Linear Models, Chapman and Hall, London.Google Scholar
  26. McFarland, J. M., Campbell, J. C., Wilt, S., Sachs, C. J., Ulrich, Y., and Xu, X. (1999). Stalking and Intimate Partner Femicide. Homicide Studies 3: 300-316.Google Scholar
  27. Moffitt, T. E. (1993). Adolescence-limited and life-course-persistent antisocial behavior: A developmental taxonomy. Psychological Review 100: 674-701.Google Scholar
  28. Nagin, D. S., Farrington, D. P., and Moffitt, T. E. (1995). Life-course trajectories of different types of offenders. Criminology 33: 111-139.Google Scholar
  29. Nagin, D. S., and Land, K. C. (1993). Age, criminal careers, and population heterogeneity: specification and estimation of a nonparametric, mixed Poisson Model. Criminology 31: 327-362.Google Scholar
  30. Nagin, D. S., and Tremblay, R. E. (1999). Trajectories of boys' physical aggression, opposition, and hyperactivity on the path to physically violent and nonviolent juvenile delinquency. Child Development 70: 1181-1196.Google Scholar
  31. Paternoster, R. (1987). The deterrent effect of the perceived certainty and severity of punishment: A review of the evidence and issues. Justice Quarterly 4: 173-217.Google Scholar
  32. Radford, J., and Russell, D. (1994). Femicide: The politics of woman killing. The Australian and New Zealand Journal of Criminology 27: 210.Google Scholar
  33. US Bureau of the Census (1993). 1990 Census of the Population. Social and Economic Characteristics. USGPO, Washington, DC. (State volumes.)Google Scholar
  34. US Bureau of the Census (1981). 1980 Census of the Population. Volume 1, Characteristics of the Population. USGPO, Washington, DC. (State volumes.)Google Scholar
  35. US Bureau of the Census (1973). Census of the Population, 1970. Volume 1, Characteristics of the Population. USGPO, Washington, DC. (State volumes.)Google Scholar

Copyright information

© Plenum Publishing Corporation 2002

Authors and Affiliations

  • Laura Dugan
    • 1
    • 2
  1. 1.Department of Criminology and Criminal JusticeUniversity of Maryland
  2. 2.National Consortium on Violence ResearchCarnegie Mellon UniversityPittsburgh

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