Abstract
Despite being a widely researched topic, there is a lack of consensus across studies in how fear of crime is measured. Scholars have often used perceived safety or risk as proxies for fear of crime, without acknowledging that these may be distinct constructs (which warrant their own study), and thus may not be adequate measures of emotional fear of crime. The current study examines the prevalence, frequency, and magnitude of “fear” estimated by a measure of emotional fear compared to measures of perceived safety and risk. In addition to comparing these measures, and providing a replication of work that to date has only been conducted in the UK, the current study specifically explores the implications of using different proxies of fear of crime for testing the broken windows thesis.
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Notes
However, Ferraro (1995) notes later that the effects of age on fear are rendered non-significant when controlling for other factors (p. 70–71).
As part of that research, these surveys were identified through database searches for studies of fear of crime and victimization. To be eligible for inclusion, the studies must have tested the relationship between victimization and fear of crime (i.e. at least reported the correlation between the two). However, that did not have to be the main focus of the study, it was simply necessary for the published results to statistically examine the relationship between fear and victimization in some manner. Several broad search terms were used so that studies using any measures of “fear” would be found. For instance, one keyword combination searched for studies including the word “survey” and “victim” (or “victimization”). Thus the results included surveys that used measures of perceived safety, perceived risk, and worry about crime/victimization as the search strategy did not require use of the term “fear of crime.” For full details, see Yang and Hinkle (2012).
All interviewers were trained by an experienced survey researcher prior to the beginning of the project. Additionally, the survey was first piloted in a city removed from the study. All interviewers were required to satisfactorily complete pilot shifts before calling respondents in the study sample.
Specifically, the database used was Powerfinder—a comprehensive reverse directory of phone numbers containing address information collected and sold by INFOUSA. For Redlands and Colton the PowerFinder database was supplemented with a list of phone numbers on the study blocks provided by each city’s water department. It is important to note that like all recent telephone survey research, some households could be excluded from possible inclusion due to lacking landlines as cell phone numbers are not included in such reverse directory databases. Unlisted numbers would be excluded as well. However, the threat of bias here is no greater than in other recent phone survey research on fear of crime (see footnote 9), and thus should not pose a threat in making comparisons to such work.
For each street block in the study, all of the households/businesses present in the database were entered into an SPSS file (one file per street block). A random number variable was created in each file, and the cases were then sorted in ascending order on this variable. Numbers were then called until all were exhausted or the target of ten completed surveys was reached. The limit of ten was set as study blocks had to have a minimum of only seven households/businesses present in the telephone database to be eligible for inclusion in the study. Knowing that there would be some streets where only three to five surveys were likely to be completed, it was decided to set the maximum for one street block to ten to avoid oversampling certain blocks and biasing the data. Furthermore, power analyses showed high levels of statistical power with only 3.5 respondents per block (or complete details on the survey methodology, see Weisburd, Hinkle, Famega, & Ready, 2012).
Every attempt was made to obtain completed surveys from valid phone numbers. Specifically, an address/phone number was only dropped from the study under three conditions. First, if the person was irate or threatening on the first contact. Second, if the person verbally refused to take the survey on two separate calls (at least 2 days apart). Third, if there was no answer after five attempts on different days/times.
Business owners/managers were chosen as it was presumed that they would be more invested in the street than other workers. In other words, they were assumed to be more similar to people who lived on the blocks compared to workers and were chosen to keep the sampling logic consistent. The goal was to survey people who would presumably be familiar with the social and physical environment of that street block, and thus residents and owners/managers were deemed the best fits with the reasoning being that those groups would presumably be more attached to/involved with the area than a general employee.
The cooperation rate excludes cases that were coded as chronic no answer/busy/answering machine (n = 307) and cases where there was a language (not an English or Spanish speaker) or cognitive barrier (N = 59) from the denominator.
Specifically, a study using random digit dialing in the state of Kentucky had a response rate of 27.5 % (Rader, May, & Goodrum, 2007), a study of fear in Dallas neighborhoods had a response rate of 33.4 % (Ferguson & Mindel, 2007), and a study by Xu, Fiedler, and Flaming (2005) achieved a response rate of 60 %, and a study in Philadelphia had a response rate of 77 % (Wyant, 2008). It is worth noting that the Xu et al. data were collected by a police department (and respondents may be less likely to refuse a survey collected directly by the police) and the Wyant study involved a $10 monetary reward for respondents to encourage participation.
At the beginning of the survey the respondents’ street blocks were clearly defined to them as the street they lived on between the two intersections that defined the boundaries of the street block for the study.
The perceived social disorder measure includes fist fights, people loitering or being disorderly, public drinking, drunk or high in public, panhandlers, vandalism, people making too much noise late at night/early morning, gambling in the street, drug sales, and prostitution.
The perceived physical disorder measure includes broken windows, graffiti, abandoned or boarded-up buildings, vacant lots, abandoned cars, litter, street or sidewalks in need of repair, and areas in need of better lighting.
The crime scale includes all reports of the following completed or attempted offenses: arson, assault/battery, auto theft, burglary, carjacking, grand theft, petty theft, rape, and robbery.
As perceived risk scores were averaged across items, the scores were rounded to the nearest whole number to categorize respondents into one of the four response option categories for this table. For example, an average score between 1 and 1.49 was rounded down to 1 (very unlikely), a score between 1.5 and 2.49 was rounded up/down to 2 (unlikely) and so on.
The ordinal regression models use the logit link function. Table 7 provides the test of parallel lines results and the non-significant findings support use of the logit link function.
Analyses were also conducted using logistic regressions for the perceived risk and safety measures (by collapsing those variables into two levels) to allow for easier comparison to the fear model presented in Table 6. Results were substantively similar with one exception; gender was no longer a significant predictor of perceived safety. This was due to a reduction in variation from collapsing the ordinal measure and losing the gender difference in “very unsafe” responses compared to “somewhat unsafe.” Therefore, it was decided to present the ordinal regression models as they represent the perceived safety and risk measures as they were designed, which is important given the focus of this paper on comparing alternative measurement schemes.
In the current study the “fear” items were all asked very early in the survey, before any questions related to perceived crime/disorder or victimization experiences. The victimization questions were asked near the end of the survey. The full survey instrument is too lengthy to include in this article, but is available on the NCJRS website as an appendix in the final grant report to the National Institute of Justice (Weisburd et al., 2012) at the following URL: https://www.ncjrs.gov/pdffiles1/nij/grants/239971.pdf.
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Acknowledgments
This research was supported in part by grant no. 2007-IJ-CX-0047 from the National Institute of Justice.
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Hinkle, J.C. Emotional Fear of Crime vs. Perceived Safety and Risk: Implications for Measuring “Fear” and Testing the Broken Windows Thesis. Am J Crim Just 40, 147–168 (2015). https://doi.org/10.1007/s12103-014-9243-9
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DOI: https://doi.org/10.1007/s12103-014-9243-9