Neighborhood Family Structure and the Risk of Personal Victimization

  • Robert J. Sampson
Part of the Research in Criminology book series (RESEARCH CRIM.)


Income inequality is the cornerstone of sociological theories focusing on strain and relative deprivation (see Blau & Blau, 1982; Rosenfeld, Chapter 7, this volume). Not surprisingly, then, a great deal of contemporary ecological research has focused on the relationship between community economic factors and crime. In particular, a recurrent issue addressed in recent articles has been the relative effects of economic inequality and racial composition on urban crime-rates (especially Bailey, 1984; Blau & Blau, 1982; Messner, 1982, 1983; Williams, 1984; Rosenfeld, Chapter 7, this volume).


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  1. 1.
    For additional details of the NCS design and collection procedures see Garofalo and Hindelang (1977).Google Scholar
  2. 3.
    See Sampson, Castellano & Laub (1981) for a discussion of how cutting points were determined, and for additional details on methodological issues arising from analysis of the neighborhood characteristic data set.Google Scholar
  3. 4.
    Preliminary analysis also used poverty (percent of families with less than $5,000 income) as an indicator of economic status. However, the results were largely identical to those obtained when the Gini Index was utilized. This is not surprising, since the Gini and poverty are strongly positively related in this study (gamma =.85) and in others (e.g., r =.81 in Rosenfeld, 1982). Inequality is used as the main economic variable because it has received the most theoretical attention in recent years.Google Scholar
  4. 5.
    Some researchers have used log-linear models to analyze victimization data (see e.g., Cohen, Kleugel, & Land, 1981). However, this data-analytic option was rejected in favor of analysis of variance for several reasons. First, log-linear models apply to the analysis of dichotomous dependent variables, whereas the present study is an analysis of rates measured on an interval scale. Second, the phenomenon of multiple victimization is inconsistent with the victim-nonvictim dichotomy employed in log-linear analysis. Ecological theory predicts that neighborhood characteristics have an effect not just on the prevalence of victimization, but also on the incidence of victimization. It is likely, for instance, that in high risk environments some persons suffer repeated victimizations, a possibility that is masked by treating victimization as a dichotomy. Finally, previous ecological research using official data has almost always been based on rates. Thus, given the NCS data structure in conjunction with the desirability of comparing findings with prior research, analysis of variance seems most appropriate to the task.Google Scholar
  5. 6.
    Elsewhere (Sampson, 1985) I have examined the sensitivity of results to various weighting schemes employed in the analysis of victimization rates (e.g., case weights, cell weights, and an unweighted orthogonal design). In general the results were consistent regardless of weights. In the present study an unweighted design is used except in cases where there were relatively few observations per cell (e.g., three-way ANOVA in Table 2.6), in which case cell weights were employed. In a cell-weighted design the proportion of persons at each combination of levels of the independent variables are used as weights to reflect the unbalanced nature of the data.Google Scholar
  6. 9.
    For example, the NCS place of crime category, “on street, park, field,” representing about 45% of all victimizations, does not specify the neighborhood of occurrence. One may be victimized in the street in one’s own neighborhood or in the street outside of one’s neighborhood.Google Scholar
  7. 10.
    The “at or near home” category includes “at or in own dwelling; in garage or other building on property; yard, sidewalk, driveway, carport, apartment hall.” This category represents about 20% of all victimizations.Google Scholar
  8. 11.
    It is problematic to examine only “at or near home” rates because when neighborhood characteristics are simultaneously considered the large reduction in sample size tends to produce unreliable rates. See Sampson, Castellano, & Laub (1981) for a full discussion of the place of crime occurrence issue.Google Scholar
  9. 13.
    The three-way interaction, while significant, does not easily lend itself to interpretation. The theft interaction stems from a larger than expected theft risk in neighborhoods with a high level of structural density and mobility, and a low divorce rate. A disproportionately high violence rate is found in the high density, low mobility, and low divorce category.Google Scholar

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© Springer Science+Business Media New York 1986

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  • Robert J. Sampson

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