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A Multilevel Analysis of the Vulnerability, Disorder, and Social Integration Models of Fear of Crime

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Abstract

The current research tests three conceptual models designed to explain citizens’ fear of crime—vulnerability, disorder, and social integration. These models are assessed for differential impact across the cognitive and affective dimensions of fear of crime. The analysis reported here considers the consecutive and simultaneous influence of individual- and city-level factors using multilevel modeling techniques. Recently collected survey data for 2,599 citizens nested within 21 cities across Washington State provide the empirical evidence for the analysis. Results indicate that the disorder model is best able to explain variation in both the cognitive and affective dimensions of citizens’ fear of crime across cities. The vulnerability and social integration models explain significantly less variation. Further, the vulnerability model lacks directional consistency across the observed dimensions of fear. Societal implications of the research findings are discussed.

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Notes

  1. Rountree and Land (1996a) explored measures associated with each model of fear of crime; however, a systematic comparison of the models was not the focus of their study. Further, their measures of disorder and social integration were assessed at the neighborhood level, while the current study is concerned with individual-level measures of perceived disorder and social integration.

  2. While it would be ideal to make comparisons across all three dimensions of the fear of crime, the data set for the current analysis only contains information capturing the cognitive and affective dimensions.

  3. Because poorer individuals are also more likely to have experienced victimization as compared to wealthier individuals, it could be argued that social vulnerability (as captured by income) should only be related to fear of crime through the experience of past victimization. We would like to note, however, that past victimization has been argued to influence fear of crime by inciting feelings of vulnerability concerning future victimization. Thus, victimization appears to operate through vulnerability to cause feelings of fear. For further discussion, see the work of Gibson, Jihong, Lovrich, and Gaffney (2002).

  4. Survey instruments were mailed to a random sample of household addresses (extracted from local telephone directories) within each of the cities included in the analysis presented here. From the 8,836 correct addresses identified, 2,861 respondents completed and returned a survey instrument, yielding a response rate of 32.4%. Thus, caution must be taken when generalizing the survey findings.

  5. For a complete list of scale items, see Appendix A.

  6. In the absence of more direct measures of vulnerability to criminal victimization, race, age, sex, income, and education are included in the present analysis. It should be noted, however, that these items are not treated as a unified scale measuring vulnerability. For increased accuracy, future analyses should attempt to capture more direct measures of vulnerability that would be suitable for scaling as a single construct.

  7. Ideally neighborhood-level contextual factors would be used in the current analysis, but such information was unavailable. Thus, city-level contextual factors were included in their place. This raises concern over the relationship between city-level factors (e.g., crime rate) and fear experienced within smaller special regions (e.g., perceived safety in the area where respondents live). Hypothetically, one could live in an affluent, safe neighborhood nested within a dangerous city, attenuating the influence of city-level factors. In the current analysis, however, the majority of the cities are rural towns with populations of less than 10,000 residents, arguably creating a situation where city-level factors, particularly crime rates, have widespread influence over fear of crime, despite location within the town.

  8. In accordance with the 2000 U.S. Census, the current study operationalized metropolitan areas as locations with a population nucleus of at least 50,000 people. Locations that were too small to meet the requirements for a metropolitan area were considered to be rural, with populations ranging from approximately 1,000 to 29,000 residents.

  9. Ideally, theoretically interesting cross-level interactions (e.g., the potential interaction between crime rates and individual perceptions of disorder) would be specified in the current study; however, the small sample size at level two (N = 21) was not large enough for such an analysis.

  10. It should also be noted that multicollinearity diagnostics were examined for both individual- and city-level variables. Tolerances ranged from .54 to .98 and Variance Inflation Factors ranged from 1.0 to 1.8, indicating that multicollinearity was not a concern in the present analysis. Moreover, bivariate correlations for all independent variables are presented in Appendix B.

  11. Additional analyses (not shown in tabular form) indicate that the changes in the effects of race, income, and education are primarily due to the inclusion of the disorder measure. More specifically, once disorder is included in the worry of victimization model, race, income, and education exert positive effects on respondents’ worry of victimization.

  12. Due to the small number of level-two groups available for analysis (N = 21) and the consequent reduction in statistical power (estimated at .44 for a weak correlation of .20), we caution the reader that statistical significance reported at level-two of the analysis is more conservative than traditionally desired (increasing the chance of Type II error). Thus, weaker relationships at level-two may not be detected in the current analysis.

  13. It should also be noted that separate bivariate analyses of crime trends (operationalized as the difference in violent and property crime rates between the years 2000 and 2002) were conducted to determine their potential influence on perceived risk and worry of victimization. These analyses were conducted separately due to the relatively small number of level-two groups (N = 21). The results of these tests indicated that neither violent nor property crime trends influenced the outcome measures.

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Acknowledgments

We would like to thank Nicholas P. Lovrich, Michael J. Gaffney, and the Division of Governmental Studies and Services for providing the primary data analyzed herein. This research was supported, in part, by Project Safe Neighborhoods contract F03-68303004. Please direct correspondence to Noelle E. Fearn, Department of Sociology and Criminal Justice, Saint Louis University, 3500 Lindell Blvd., 211 Fitzgerald Hall, St. Louis, MO 63103.

Author information

Authors and Affiliations

  1. College of Criminal Justice, Sam Houston State University, Huntsville, TX, USA

    Travis W. Franklin & Cortney A. Franklin

  2. Department of Sociology and Criminal Justice, Saint Louis University, St. Louis, MO, USA

    Noelle E. Fearn

Authors
  1. Travis W. Franklin
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  3. Noelle E. Fearn
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Correspondence to Noelle E. Fearn.

Appendices

Appendix A: Scale Items

Perceived Risk

  1. 1.

    How safe would you feel walking alone during the day in the area where you live?

    • (1) Very safe (2) Safe (3) Neither safe nor unsafe (4) Unsafe (5) Very unsafe

  2. 2.

    How safe would you feel walking alone in the area where you live at night?

    • (1) Very safe (2) Safe (3) Neither safe nor unsafe (4) Unsafe (5) Very unsafe

Worry of Victimization

How much do you worry about each of the following situations? Do you worry very frequently, somewhat frequently, seldom, or never about:

  1. 1.

    Yourself or someone in your family getting sexually assaulted

  2. 2.

    Being attacked while driving your car

  3. 3.

    Getting mugged

  4. 4.

    Getting beaten up, knifed, or shot

  5. 5.

    Getting murdered

  6. 6.

    Getting burglarized while someone is at home

  7. 7.

    Getting burglarized while no one is at home

Neighborhood Disorder

Using the answer key below, please write the number from the Answer Key that most accurately describes the extent of these problems in the neighborhood where you live.

Answer Key: (1) No Problem (2) Uncertain (3) A Problem (4) A Serious Problem

  1. 1.

    Vandalism

  2. 2.

    Groups of teenagers or others hanging out and harassing people

  3. 3.

    Garbage and litter

  4. 4.

    Traffic problems

  5. 5.

    People drinking to excess in public

  6. 6.

    Dogs running at large

  7. 7.

    Youth gangs are present

  8. 8.

    Noise

Social Integration

  1. 1.

    Would you describe the area where you live as a place where people mostly help one another or a place where people mostly go their own way?

    • (1) People help one another (2) People go their own way

  2. 2.

    Do you feel the area where you live is more of a “real home” or more “just a place to live”?

    • (1) Real home (2) Just a place to live

  3. 3.

    How often do you talk with your neighbors?

    • (1) Daily (2) 1–3 times a week (3) 1–3 times a month (4) Less than once a month

  4. 4.

    When you do a favor for a neighbor, can you trust the neighbor to return the favor?

    • (1) Always (2) Nearly always (3) Most of the time (4) Sometimes (5) Seldom

Appendix B: Bivariate Correlation Matrixes

Individual-Level Bivariate Correlation Matrix

 

Race

Gender

Age

Income

Education

Disorder

Social integration

Race

–

      

Gender

.02

–

     

Age

.06*

.07*

–

    

Income

.08*

.01

−.16*

–

   

Education

.03

.09*

−.09*

.31*

–

  

Disorder

−.08*

−.09*

−.12*

−.16*

−.13*

–

 

Social integration

.09*

.05*

.13*

.11*

.09*

.24*

–

  1. * p < .05

City-Level Bivariate Correlation Matrix

 

Violent crime

Property crime

Unemployment

Urbanism

Violent crime

–

   

Property crime

.60*

–

  

Unemployment

.27

.36

–

 

Urbanism

.35

.12

−.15

–

  1. * p < .05

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Franklin, T.W., Franklin, C.A. & Fearn, N.E. A Multilevel Analysis of the Vulnerability, Disorder, and Social Integration Models of Fear of Crime. Soc Just Res 21, 204–227 (2008). https://doi.org/10.1007/s11211-008-0069-9

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