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Predictors of fear of crime in older adults
Anxiety Disorders 18 (2004) 385–396
Predictors of fear of crime in older adults Ron Acierno*, Alyssa A. Rheingold, Heidi S. Resnick, Dean G. Kilpatrick National Crime Victims Research and Treatment Center, Medical University of South Carolina, 165 Cannon Street, PO Box 250852, Charleston, SC 29425, USA Received 10 September 2002; accepted 21 September 2002
Abstract Very little is known about factors that predict fear of crime in older adults. Indeed, the topic itself remains a source of controversy, with early studies indicating higher levels of crime fear with age, and new, more methodologically rigorous studies demonstrating the opposite trend. The present exploratory investigation included 106 older adults and assessed the relationship between demographic variables, interpersonal violence, psychopathology, and fear of crime. In addition, this study built on previous research in that specific feared outcomes (e.g., hospitalization) were also considered. Initial findings indicate that being female, non-Caucasian, having depressive symptoms, and reporting social isolation are predictive of general fear of crime ratings. Different predictor sets were noted for fear of crime against person and fear of crime against property. Reported perceptions of negative crime outcomes were associated with being female, non-Caucasian, and having low income. # 2003 Elsevier Science Inc. All rights reserved. Keywords: Fear; Crime; Older adults; Predictors
1. Introduction Recent research (Ferraro & LeGrange, 1992) reveals that older adults do not fear crime at levels higher than younger adults. Thus, the often noted ‘‘crime fear * Corresponding author. Tel.: þ1-843-792-2945; fax: þ1-843-792-3388. E-mail address: [email protected] (R. Acierno).
0887-6185/$ – see front matter # 2003 Elsevier Science Inc. All rights reserved. doi:10.1016/S0887-6185(03)00012-4
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R. Acierno et al. / Anxiety Disorders 18 (2004) 385–396
paradox’’ in which the least likely group to be victimized (i.e., the elderly; Clemente & Kleinman, 1976; Kennedy & Silverman, 1985; Lewis & Salem, 1986; Moeller, 1989; Ortega & Myles, 1987) displays the greatest level of fear simply does not exist. Nonetheless, a large number of older adults do report significant and distressing crime-related fears. Unfortunately, factors that predict fear of crime in the elderly are largely unknown. Inconsistencies and gaps in knowledge in this area result, in large part, from definitional variance associated with the term ‘‘fear of crime.’’ Earlier studies either used a single item to assess fear, or poorly operationalized the construct of fear of crime. Specifically, these investigations confounded concern about crime (i.e., a ‘‘feeling’’ about the expected outcome, should the event occur) with perceived risk of being victimized (i.e., an event likelihood estimate; Ferraro & LeGrange, 1987). Thus, the term ‘‘fear’’ has been used generally and often refers to expected risk, rather than perceived outcome. Moreover, studies that do separate fear of crime from perceived risk of crime have not specified precisely what it is an individual may fear. That is, the expected negative outcomes that contribute to one’s fear of crime have not been addressed. This is problematic because it may well be the outcome of a crime (e.g., injury) that is feared, and not the crime per se. Another area of relevant consideration is fear as a function of specific crime types. Along these lines, Ferraro and LeGrange (1992) identified two distinct subtypes of crime fears: that of personal crime and that of property crime. This distinction is relevant because factors associated with crime fear may vary with crime type. Thus far, studies of risk factors predicting heightened fear of crime in older adults are few, and have ignored this distinction. Current studies tend to focus primarily on gender (e.g., Ferraro & LeGrange, 1992). In younger adults, general fear of crime is related to gender, income, ethnicity, previous victimization history, social isolation, and current psychological functioning (Bazargan, 1994; Brillon, 1987; Clemente & Kleinman, 1976; Kennedy & Silverman, 1985; Orzek, 1985). To date, no investigation has examined fear of crime in terms of specific expectations or predicted consequences of a victimization event, such as injury, hospitalization, or lost freedom, and no investigation has identified specific risk factors for fear of property and person crime subtypes. Purposes of the present study were twofold: first, hypothesized factors that predict increased fear of crime scores were evaluated for both property and personal crime; second, variables that predicted higher levels of expected specific negative outcomes from crime were identified. For both crime types, we hypothesized that being female, of a minority ethnic group, and of lower income would increase both fear of crime, and negative outcome perceptions. Because fear of crime and perceived outcomes may well vary with one’s personal history of victimization, we hypothesized that having been the victim of severe physical, sexual, or emotional abuse or suffering from depression or post-traumatic stress disorder would also exacerbate both fear of crime and specific expectations of negative crime outcomes.
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2. Method 2.1. Participants Participants were 106 older adults aged 55–85 years (x ¼ 66:5, S:D: ¼ 8:5) residing in a southeastern suburban region. Of the total, 47 comprised a victim over-sample referred by local police departments and 59 were randomly selected from local telephone directories. The victim over-sample assured that a sufficient number of crime victims were available to assess the impact of these events on fear of crime ratings. The present study was part of a larger study to evaluate whether or not older adults could be interviewed about victimization events over the telephone, and interview context (in person vs. telephone) was randomly assigned. Of the 47 police referred participants, 22 were interviewed in person and 25 via telephone. Of the 59 randomly selected participants, 28 were interviewed in person and 31 via telephone. No differences in crime reporting were evident between telephone and in person sub-samples. Sixty-eight participants were women, 38 were men. Of the total sample, 67 were White, non-Hispanic, 37 were African American, non-Hispanic, one was Native American, and one refused to disclose. Among police referred participants, 19 filed reports for physical assault, 12 filed reports for verbal assault or intimidation, 8 reports involved a ‘‘domestic dispute,’’ 4 participants filed reports for vandalism or burglary resulting in fear for their safety, and the remaining 4 reported other crimes in which they feared for their safety, but were not assaulted, verbally abused, or robbed. 2.2. Measures A highly structured interview, modified slightly for older adults from that used in the National Womens Study (Kilpatrick, Edmonds, & Seymour, 1992), was used to collect information about a variety of topics, including demographic characteristics, fear of crime, interpersonal violence experiences, post-traumatic stress disorder (PTSD), and major depressive disorder (MDD). 2.2.1. Demographic variables Gender, race, and income data were collected from each participant. Race was categorized in terms of Caucasian/minority status because the sample size precluded further categorical breakdowns and the majority of minority participants were of one race (African American). Income was reported in increments of $10,000 up to $50,000þ. 2.2.2. Fear of crime and prediction of crime outcomes The fear of crime measure was identical to that refined and validated by Ferraro and LeGrange (1992) and measured fear of 10 specific crimes on a 10-point scale. The following preface statement was read to each participant, and specific
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Table 1 Fear of crime scores Mean (S.D.) Individual item scores Question: First, rate your fear of . . . 1. being approached on street by a beggar 2. fear of being cheated 3. fear of having someone break into your home while you are away 4. fear of someone breaking into your home while you are there 5. fear of being raped 6. fear of being murdered 7. fear of being attacked by someone w/weapon 8. Fear of having car stolen 9. Fear of being robbed or mugged 10. Fear of property damage Total scores by subgroup Male Caucasian Minority Female Caucasian Minority Fear of crime against person subscale score Male Caucasian Minority Female Caucasian Minority Fear of crime against property Male Caucasian Minority Female Caucasian Minority
n
2.5 (2.8) 2.8 (2.8) 3.3 (3.0)
105 105 104
3.6 (3.4)
105
2.8 2.6 3.4 2.7 3.5 3.1
105 105 105 105 104 104
(3.2) (3.1) (3.3) (2.8) (3.3) (2.9)
19.6 (9.3) 28.0 (18.3)
28 9
32.0 (24.4) 39.96 (29.2)
39 28
6.5 (3.3) 11.2 (8.6)
28 9
14.3 (13.0) 16.2 (14.2)
39 28
11.8 (7.1) 15.8 (10.7)
28 9
15.0 (11.9) 19.8 (14.8)
39 28
questions are given in Table 1: ‘‘At one time or another, most of us have experienced fear about becoming the victim of crime. Some crimes probably frighten you more than others. We are interested in how afraid people are in everyday life of being a victim of different kinds of crimes. Please rate your fear on a scale of 1 to 10 where 1 means you are not afraid at all and 10 means you are very afraid. First, rate your fear of . . .’’ In addition to the total summed score, Ferraro and LaGrange also identify two factor sub-scores: fear of crime against one’s person, and fear of property crime. These factors had aceptable
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reliability and were divided as follows: (a) fear of crime against one’s person defined by Items 4, 5, 6, and 7; (b) fear of property crime defined by Items 2, 3, 8, 9, 10. (Ferraro and LaGrange’s data did not support inclusion of Item 1 on either subscale). Consequently, the total score, and subscores were used in this study. A measure of perceptions of negative outcomes of crime was devised for this study. Crime outcomes referred to specific expected consequences of direct victimization. Participants were asked: ‘‘If you were to be attacked or robbed, do you think any of the following would probably happen?’’ Each of the eight outcome items (e.g., ‘‘I would probably have to go to the emergency room for treatment’’) was then read aloud to the participant. See Table 2 for outcome questions. 2.2.3. Interpersonal violence variables Sexual assault, physical assault, and emotional abuse were assessed. Sexual assault included forced or coerced vaginal or anal penetration by a penis or other object, or oral penetration by a penis. Physical assault was noted when participants reported being attacked by another person with a weapon or without a weapon but with the intent to seriously injure or kill. Emotional abuse was defined as verbal attacks by others that resulted in strong feelings of threat, intimidation, humiliation, or worthlessness. Also included in this category were episodes in which participants were harassed or coerced into doing something against their will.
Table 2 Fear of crime outcome scores Crime outcome prediction
Percentage endorsing (N ¼ 106)
I I I I I I I I
71.3 48.8 30.5 38.1 63.9 15.8 54.8 34.0
would would would would would would would would
probably probably probably probably probably probably probably probably
have to go to the emergency room for treatment have to stay in the hospital overnight be afraid to leave my home be afraid to leave my home after dark be nervous or jumpy for months afterward have problems in my personal relationships feel depressed or sad not be able to pay all my bills
Fear of crime predicted outcome total score
Mean (S.D.)
n
Male Caucasian Minority
1.5 (1.3) 3.6 (2.0)
28 10
Female Caucasian Minority
3.5 (2.6) 4.0 (2.6)
39 29
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2.2.4. Psychopathology variables Current major depressive disorder and PTSD were measured via a structured clinical interview based on DSM-IV and modified from the National Womens Study (NWS; Kilpatrick et al., 1992). 2.3. Procedure Participants were either randomly selected from the local population, or referred by local police departments for participation in the study. For randomly selected participants, a random number generator was used to derive numbers that corresponded to the page number and listing number of participants from the telephone directory. Each randomly generated number was called by a project assistant, and the person answering the phone was asked if there were any individuals age 55 and over residing in the home and available for interview. If more than one individual was available, the most recent birthday method was used to select a single respondent. The project was described, consent to participate was read aloud over the phone, and agreement for participation was obtained. Subsequently, a time convenient for respondents was arranged. In-person interviews took place in respondents’ homes. Interviews followed the format of the NWS in that victimization and psychopathology modules incorporated skip-outs so that only questions relevant to each participant’s reported experiences and symptom presentation were asked. Because some participants were necessarily asked fewer questions than others, interview lengths varied considerably from 20 to 153 min (x ¼ 53 min, S:D: ¼ 20 min). In order to control for non-specific interviewer characteristics, interviewers administered approximately equal numbers of telephone and inperson interviews to police referred and randomly selected participants. Interviewers were required to achieve 90% accuracy relative to ‘‘gold standard’’ practice interviews prior to interviewing study participants for data collection. Two steps were taken in order to assure that participants could answer questions freely and without fear of negative consequence. First, interviewers asked whether participants could answer questions in relative privacy. If they were unable to answer privately, the interview was rescheduled. Second, the interview was structured nearly exclusively with closed-ended questions that required simple ‘‘yes’’ or ‘‘no’’ answers. Thus, if respondents were overheard, nothing they were saying would place them at risk. Participant’s received a $20 check as compensation for their time. The field period for the study was January 2000 to December 2001. 2.4. Participant protection The sensitive nature of the information collected from participants required strong steps to protect confidentiality. Therefore, in order to protect participant data from subpoena and other threats to privacy, a Certificate of Confidentiality
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was applied for and obtained from the National Institutes of Health. In addition, participant names were matched with arbitrarily assigned identification numbers and data files referenced by these numbers were stored on a secure computer.
3. Results 3.1. Data analytic plan In order to identify factors important to predicting fear of crime and perceptions of crime outcomes, bivariate correlations were obtained and analyzed for all study variables. Specifically, the total fear of crime score, the fear of crime against persons and crime against property subscale scores, and the perceptions of crime outcome total score were computed for each participant. The perceptions of outcome score was derived by summing the number of crime-related negative predictions endorsed by participants. The statistical significance of the relationship between these scores and potential predictors of fear, including gender, race, income, previous victimization history, psychopathology, and number of social contacts was considered at the liberal alpha <.10 level. Variables that were significantly correlated with each of the four fear indexes were included as predictors for that fear index in hierarchical multiple regression analyses. Hence, the predictor set for each regression analysis on each fear index was potentially different. Bonferonni corrections were not applied to correlational analyses when determining significance because we wished to adopt a liberal entrance criterion for inclusion in regression equations, thereby limiting Type II error, which we felt appropriate for this exploratory study. For all regression analyses, the first step involved demographic variables, the second involved interpersonal violence variables (if any), and the third included psychopathology and social isolation variables (if any). Table 1 provides mean scores for the total sample at the individual item level, as well as mean fear of crime total scores and subscale scores. Table 2 gives the proportion of participants endorsing each negative crime outcome, as well as the total summed score for these ratings across participants. Table 3 provides the overall correlation matrix for study variables, and Tables 4–7 provide subsequent multiple regression statistics. Fear of crime total score was significantly correlated with the following four variables, which were included in the regression equation: gender, race, depression, and social isolation. Demographic variables entered on the first step were race and gender, and accounted for 11% of the variance in total fear of crime scores. Adding depression and social isolation significantly enhanced predictive power of the regression equation, increasing the overall variance accounted for to 16%. The change in R2 refers to the combined effects of all variables in each step, thus the combination of being female and of minority racial status was associated with higher scores, as was the combination of being depressed and socially isolated.
392
Table 3 Correlation fear sum 1
2
3
4
5
6
7
8
9
10
11
12
13
1. Fear Sum
–
2. Fear person
–
.91** (n ¼ 104) –
3. Fear property 4. Outcome
–
–
.92** (n ¼ 104) .70** (n ¼ 104) –
–
–
–
.56** (n ¼ 104) .54** (n ¼ 104) .49** (n ¼ 104) –
5. Gender
–
–
–
–
.28** (n ¼ 104) .30** (n ¼ 104) .17* (n ¼ 104) .32** (n ¼ 106) –
6. Race
–
–
–
–
–
.21* (n ¼ 104) .16* (n ¼ 104) .21* (n ¼ 104) .24** (n ¼ 106) .16* (n ¼ 106) –
7. Income
–
–
–
–
–
–
.04 (n ¼ 97) .07 (n ¼ 97) .02 (n ¼97) .26** (n ¼ 99 ) .14 (n ¼ 99 ) .54** (n ¼ 99) –
8. Sexual assault 9. Phys ever
–
–
–
–
–
–
–
.04 (n ¼ 104) .08 (n ¼ 104) .03 (n ¼ 104) .22* (n ¼ 106) .21* (n ¼ 106) .07 (n ¼ 106) .15 (n ¼ 99) –
–
–
–
–
–
–
–
–
.12 (n ¼ 104) .09 (n ¼ 104) .11 (n ¼ 104) .08* (n ¼ 106) .13 (n ¼ 106) .05 (n ¼ 106) .00 (n ¼ 99) .18* (n ¼ 106) –
10. Emtever
–
–
–
–
–
–
–
–
–
.12 (n ¼ 104) .04 (n ¼ 104) .13* (n ¼ 104) .01 (n ¼ 106) .07 (n ¼ 106) .17* (n ¼ 106) .12 (n ¼ 99) .12 (n ¼ 106) .11 (n ¼ 106) –
11. Mdcrita
–
–
–
–
–
–
–
–
–
–
.19* (n ¼ 104) .29** (n ¼ 104) .09 (n ¼ 104) .10 (n ¼ 106) .17* (n ¼ 106) .12 (n ¼ 106) .12 (n ¼ 99) .46* (n ¼ 106) .02 (n ¼ 106) .04 (n ¼ 106) –
12. PTSD
–
–
–
–
–
–
–
–
–
–
–
.03 (n ¼ 104) .05 (n ¼ 104) .07 (n ¼ 104) .25* (n ¼ 106) .08 (n ¼ 106) .03 (n ¼ 106) .18* (n ¼ 99) .35** (n ¼ 106) .16 (n ¼ 106) .07 (n ¼ 106) .25** (n ¼ 106) –
13. Social
–
–
–
–
–
–
–
–
–
–
–
–
.20* (n ¼ 104) .20* (n ¼ 104) .14* (n ¼ 104) .09 (n ¼ 106) .19* (n ¼ 106) .07 (n ¼ 106) .08 (n ¼ 99) .05 (n ¼ 106) .14 (n ¼ 106) .03 (n ¼ 106) .11 (n ¼ 106) .13 (n ¼ 106) –
*
Correlation is significant at the .05 level (one-tailed). Correlation is significant at the .01 level (one-tailed).
**
R. Acierno et al. / Anxiety Disorders 18 (2004) 385–396
Subscale
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393
Table 4 Regression of significantly correlated variables on fear of crime total score Variable
B
Step 1 Race Gender Step 2 Number of social contacts Major depression
S.E. B 9.99 9.11
4.64 4.74
1.42 10.61
.87 5.98
b .21* .19 .16 .17
R2 ¼ :11 for step 1; DR2 ¼ :05 for step 2 (P < :05); final model R2 ¼ :16 (P < :01). * P < :05. Table 5 Regression of significantly correlated variables on fear of crime against person subscale score b
Variable
B
S.E. B
Step 1 Race Gender
3.93 5.06
2.23 2.33
.16 .21*
Step 2 Number of social contacts Major depression
.65 8.03
.43 2.94
.14 .25**
R2 ¼ :10 for step 1; DR2 ¼ :09 for step 2 (P < :01); final model R2 ¼ :19 (P < :01). * P < :05. ** P < :01.
Fear of crime against one’s person was significantly related to the same set of predictors as was total score. Race and gender accounted for 10% of the variance on the first step, whereas depression and social isolation accounted for an additional 9% of the variance. Fear of property crime was correlated with gender, race, a history of emotional abuse, and social isolation. These demographic Table 6 Regression of significantly correlated variables on fear of crime against property subscale score Variable Step 1 Race Gender Step 2 Ever emotional abuse Step 3 Number of social contacts
B
S.E. B
b
4.39 3.10
2.44 2.47
.18 .13
3.81
3.28
.11
.58
.46
.13
R2 ¼ :06 for step 1; DR2 ¼ :01 for step 2 (P ¼ :22); DR2 ¼ :02 for step 3 (P ¼ :20); final model R2 ¼ :09 (P < :01).
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Table 7 Regression of significantly correlated variables on fear of crime outcomes total score Variable
B
S.E. B
b
Step 1 Race Gender Household income
.96 1.19 .01
.58 .50 .14
.19 .23* .08
.97
.93
.11
1.40
.76
.18
Step 2 Sexual assault Step 3 PTSD
R2 ¼ :16 for step 1; DR2 ¼ :02 for step 2 (P ¼ :11); DR2 ¼ :03 for step 3 (P ¼ :07); final model R2 ¼ :21 (P < :01). * P < :05.
variables accounted for 6% of the variance in fear of property crime. R2 was not significantly enhanced by the step two addition of emotional abuse history or the step three addition of social isolation. Finally, crime outcome scores were significantly correlated with gender, race, income, sexual assault, and PTSD. These variables were subsequently entered into a regression equation. The three demographic factors accounted for 16% of the variance in crime outcome prediction scores, indicating that being female, non-White, and poor increased negative predictions of crime outcome. Increases in R2 to 18% (step 2 variable sexual assault) and 21% (step 3 variable PTSD) were not significant on each subsequent step. However, post hoc analysis indicated that the increase in R2 from step 1 to step 3 (16–21%) was significant at the P < :05 level.
4. Discussion This study attempted to identify factors important in predicting both fear of crime and perceptions of specific negative crime outcome in an elderly sample. Previous research has focused on comparing fear of crime estimates between older and younger adults, with no study of specific negative outcome estimates. This is problematic because crime fear very likely varies as a function of these expected outcomes. Findings indicate that gender, race, depressive status, and social isolation all contributed to participants’ general ‘‘fear of crime’’ score, with greater scores linked to being female, non-Caucasian, depressed, and socially isolated. Different pictures emerged with respect to predicting fear of crime against one’s person and fear of property crime. Fully a fifth of the variance in fear of crime against one’s person was accounted for by gender, race, depression, and social isolation while
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less than a tenth of the variance in property crime was accounted for by race and gender, and no predictive power was evidenced for other factors in the analysis (i.e., emotional abuse and social isolation). Women reported higher fear of crime than men. This finding is consistent across age groups (Ferraro & LeGrange, 1992). Both younger and older adult minority individuals also have higher rates of fear than Caucasian individuals (see also Bazargan, 1994). It appears that several factors assessed in the current study accounted for some, but not all of the variance in fear of crime. Other variables, not assessed here, may account for the other 80% of variance. Environmental factors such as amount of social support, type of housing, amount of television viewed per day, or other trauma experiences such as being a witness to community violence may also play a role in fear of crime ratings. For example, in a study on fear of crime in an urban African American elderly sample, Bazargan (1994) found that self-reported health status, pervious victimization history, television exposure, length of residence and type of house, to name a few, were significant predictors of fear of crime. Even though depression and social isolation accounted for a proportion of the variance for fear of crime against person, they did not account for any variance associated with fear of crime against property. This may be because crime against person is a more intimate experience by nature, and therefore fear of such a crime may be more affected by individual functioning such as depressive symptoms and social isolation. Whereas, fear of property crime may, in part, be more related to external factors such as place or type of residence. Mean fear of crime scores for specific crimes in this study were less than those found in Ferraro and LeGrange’s (1992) sample. Among the specific types of crime, Ferraro and LeGrange found higher ratings for being approached by a beggar and having a car stolen in older adults than any other age groups. The mean scores for fear of being approached by a beggar and having a car stolen in our study, however, were lower than Ferraro and LeGrange’s scores. Our results are more consistent with the position that older adults are not likely to experience high fear of being victimized. Our findings that older adults had generally low scores for other specific fears were consistent with Ferraro and LeGrange; for example, older adults in our sample also reported low fear of sexual assault. Surprisingly, history of victimization did not aid in predicting negative crime outcome. This lack of a relationship was not simply due to low incidence of assault in our participants, as we included a recent victim over-sample. However, our victimization variable captured a combination of adults, some of whom experienced a recent crime, and some of whom experienced a crime in the distant past. To test the possibility that length of time since a crime occurred affected prediction of negative crime outcome, regression analyses were re-calculated using recent crime events (past age 55) as predictor variables. Once again, assault experiences did not affect predictions of crime fears or outcome expectations. Finally, a significant proportion of the score for perceived outcomes of crime was accounted for by the expected predictors of race, gender, and income. History
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of sexual assault and PTSD were associated with perceived negative outcomes in simple correlational analysis, but did not significantly enhance predictive power in regression analysis. Interestingly, fear of personal and property crime, and crime outcome expectations were driven by different predictors. In addition, regression equations indicated that the predictor set was more strongly related to fear of crime against one’s person relative to fear of crime against property. This supports the idea that these dependent variables do, in fact, measure different constructs. In summary, our findings indicate that a myriad of factors may play a role in fear of crime estimates and crime outcome predictions in older adults. It appears that demographic variables and certain psychopathology predict fears related to crimes of person and crimes of property and in different ways. However, this was an initial exploratory study beginning to investigate this area in the elderly. There are limited psychometrics available on the measures that were used. Future research is warranted to examine these constructs and their effect on the wellbeing of older adults.
References Bazargan, M. (1994). The effects of health, environmental, and socio-psychological variables on fear of crime and its consequences among urban black elderly individuals. International Journal on Aging and Human Development, 38, 99–115. Brillon, Y. (1987). Victimization and fear of crime among the elderly. Toronto: Butterworths. Clemente, F., & Kleinman, M. D. (1976). Fear of crime among the aged. The Gerontologist, 16, 207–210. Ferraro, K. F., & LeGrange, R. L. (1987). The elderly’s fear of crime: a critical examination of the research. Research on Aging, 9, 372–391. Ferraro, K. F., & LeGrange, R. L. (1992). Are older people most afraid of crime? Reconsidering age differences in fear of victimization. Journal of Gerontology: Social Sciences, 47, S233–S244. Kennedy, L. W., & Silverman, R. A. (1985). Significant others and fear of crime among the elderly. International Journal on Aging and Human Development, 20, 241–256. Kilpatrick, D. G., Edmonds, C. N., & Seymour, A. K. (1992). Rape in America: a report to the nation. Arlington, VA: National Victim Center and Medical University of South Carolina. Lewis, D. A., & Salem, G. (1986). Fear of crime: incivility and the production of a social problem. New Brunswick, NJ: Transaction. Moeller, G. L. (1989). Fear of criminal victimization: the effect of neighborhood racial composition. Sociological Inquiry, 59, 208–221. Ortega, S. L., & Myles, J. L. (1987). Race and gender effects on the fear of crime: an interactive model with age. Criminology, 25, 133–152. Orzek, A. M. (1985). Treatment strategies for fear of crime among the elderly. Clinical Gerontologist, 4, 17–29.
Predictors of fear of crime in older adults Ron Acierno*, Alyssa A. Rheingold, Heidi S. Resnick, Dean G. Kilpatrick National Crime Victims Research and Treatment Center, Medical University of South Carolina, 165 Cannon Street, PO Box 250852, Charleston, SC 29425, USA Received 10 September 2002; accepted 21 September 2002
Abstract Very little is known about factors that predict fear of crime in older adults. Indeed, the topic itself remains a source of controversy, with early studies indicating higher levels of crime fear with age, and new, more methodologically rigorous studies demonstrating the opposite trend. The present exploratory investigation included 106 older adults and assessed the relationship between demographic variables, interpersonal violence, psychopathology, and fear of crime. In addition, this study built on previous research in that specific feared outcomes (e.g., hospitalization) were also considered. Initial findings indicate that being female, non-Caucasian, having depressive symptoms, and reporting social isolation are predictive of general fear of crime ratings. Different predictor sets were noted for fear of crime against person and fear of crime against property. Reported perceptions of negative crime outcomes were associated with being female, non-Caucasian, and having low income. # 2003 Elsevier Science Inc. All rights reserved. Keywords: Fear; Crime; Older adults; Predictors
1. Introduction Recent research (Ferraro & LeGrange, 1992) reveals that older adults do not fear crime at levels higher than younger adults. Thus, the often noted ‘‘crime fear * Corresponding author. Tel.: þ1-843-792-2945; fax: þ1-843-792-3388. E-mail address: [email protected] (R. Acierno).
0887-6185/$ – see front matter # 2003 Elsevier Science Inc. All rights reserved. doi:10.1016/S0887-6185(03)00012-4
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paradox’’ in which the least likely group to be victimized (i.e., the elderly; Clemente & Kleinman, 1976; Kennedy & Silverman, 1985; Lewis & Salem, 1986; Moeller, 1989; Ortega & Myles, 1987) displays the greatest level of fear simply does not exist. Nonetheless, a large number of older adults do report significant and distressing crime-related fears. Unfortunately, factors that predict fear of crime in the elderly are largely unknown. Inconsistencies and gaps in knowledge in this area result, in large part, from definitional variance associated with the term ‘‘fear of crime.’’ Earlier studies either used a single item to assess fear, or poorly operationalized the construct of fear of crime. Specifically, these investigations confounded concern about crime (i.e., a ‘‘feeling’’ about the expected outcome, should the event occur) with perceived risk of being victimized (i.e., an event likelihood estimate; Ferraro & LeGrange, 1987). Thus, the term ‘‘fear’’ has been used generally and often refers to expected risk, rather than perceived outcome. Moreover, studies that do separate fear of crime from perceived risk of crime have not specified precisely what it is an individual may fear. That is, the expected negative outcomes that contribute to one’s fear of crime have not been addressed. This is problematic because it may well be the outcome of a crime (e.g., injury) that is feared, and not the crime per se. Another area of relevant consideration is fear as a function of specific crime types. Along these lines, Ferraro and LeGrange (1992) identified two distinct subtypes of crime fears: that of personal crime and that of property crime. This distinction is relevant because factors associated with crime fear may vary with crime type. Thus far, studies of risk factors predicting heightened fear of crime in older adults are few, and have ignored this distinction. Current studies tend to focus primarily on gender (e.g., Ferraro & LeGrange, 1992). In younger adults, general fear of crime is related to gender, income, ethnicity, previous victimization history, social isolation, and current psychological functioning (Bazargan, 1994; Brillon, 1987; Clemente & Kleinman, 1976; Kennedy & Silverman, 1985; Orzek, 1985). To date, no investigation has examined fear of crime in terms of specific expectations or predicted consequences of a victimization event, such as injury, hospitalization, or lost freedom, and no investigation has identified specific risk factors for fear of property and person crime subtypes. Purposes of the present study were twofold: first, hypothesized factors that predict increased fear of crime scores were evaluated for both property and personal crime; second, variables that predicted higher levels of expected specific negative outcomes from crime were identified. For both crime types, we hypothesized that being female, of a minority ethnic group, and of lower income would increase both fear of crime, and negative outcome perceptions. Because fear of crime and perceived outcomes may well vary with one’s personal history of victimization, we hypothesized that having been the victim of severe physical, sexual, or emotional abuse or suffering from depression or post-traumatic stress disorder would also exacerbate both fear of crime and specific expectations of negative crime outcomes.
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2. Method 2.1. Participants Participants were 106 older adults aged 55–85 years (x ¼ 66:5, S:D: ¼ 8:5) residing in a southeastern suburban region. Of the total, 47 comprised a victim over-sample referred by local police departments and 59 were randomly selected from local telephone directories. The victim over-sample assured that a sufficient number of crime victims were available to assess the impact of these events on fear of crime ratings. The present study was part of a larger study to evaluate whether or not older adults could be interviewed about victimization events over the telephone, and interview context (in person vs. telephone) was randomly assigned. Of the 47 police referred participants, 22 were interviewed in person and 25 via telephone. Of the 59 randomly selected participants, 28 were interviewed in person and 31 via telephone. No differences in crime reporting were evident between telephone and in person sub-samples. Sixty-eight participants were women, 38 were men. Of the total sample, 67 were White, non-Hispanic, 37 were African American, non-Hispanic, one was Native American, and one refused to disclose. Among police referred participants, 19 filed reports for physical assault, 12 filed reports for verbal assault or intimidation, 8 reports involved a ‘‘domestic dispute,’’ 4 participants filed reports for vandalism or burglary resulting in fear for their safety, and the remaining 4 reported other crimes in which they feared for their safety, but were not assaulted, verbally abused, or robbed. 2.2. Measures A highly structured interview, modified slightly for older adults from that used in the National Womens Study (Kilpatrick, Edmonds, & Seymour, 1992), was used to collect information about a variety of topics, including demographic characteristics, fear of crime, interpersonal violence experiences, post-traumatic stress disorder (PTSD), and major depressive disorder (MDD). 2.2.1. Demographic variables Gender, race, and income data were collected from each participant. Race was categorized in terms of Caucasian/minority status because the sample size precluded further categorical breakdowns and the majority of minority participants were of one race (African American). Income was reported in increments of $10,000 up to $50,000þ. 2.2.2. Fear of crime and prediction of crime outcomes The fear of crime measure was identical to that refined and validated by Ferraro and LeGrange (1992) and measured fear of 10 specific crimes on a 10-point scale. The following preface statement was read to each participant, and specific
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Table 1 Fear of crime scores Mean (S.D.) Individual item scores Question: First, rate your fear of . . . 1. being approached on street by a beggar 2. fear of being cheated 3. fear of having someone break into your home while you are away 4. fear of someone breaking into your home while you are there 5. fear of being raped 6. fear of being murdered 7. fear of being attacked by someone w/weapon 8. Fear of having car stolen 9. Fear of being robbed or mugged 10. Fear of property damage Total scores by subgroup Male Caucasian Minority Female Caucasian Minority Fear of crime against person subscale score Male Caucasian Minority Female Caucasian Minority Fear of crime against property Male Caucasian Minority Female Caucasian Minority
n
2.5 (2.8) 2.8 (2.8) 3.3 (3.0)
105 105 104
3.6 (3.4)
105
2.8 2.6 3.4 2.7 3.5 3.1
105 105 105 105 104 104
(3.2) (3.1) (3.3) (2.8) (3.3) (2.9)
19.6 (9.3) 28.0 (18.3)
28 9
32.0 (24.4) 39.96 (29.2)
39 28
6.5 (3.3) 11.2 (8.6)
28 9
14.3 (13.0) 16.2 (14.2)
39 28
11.8 (7.1) 15.8 (10.7)
28 9
15.0 (11.9) 19.8 (14.8)
39 28
questions are given in Table 1: ‘‘At one time or another, most of us have experienced fear about becoming the victim of crime. Some crimes probably frighten you more than others. We are interested in how afraid people are in everyday life of being a victim of different kinds of crimes. Please rate your fear on a scale of 1 to 10 where 1 means you are not afraid at all and 10 means you are very afraid. First, rate your fear of . . .’’ In addition to the total summed score, Ferraro and LaGrange also identify two factor sub-scores: fear of crime against one’s person, and fear of property crime. These factors had aceptable
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reliability and were divided as follows: (a) fear of crime against one’s person defined by Items 4, 5, 6, and 7; (b) fear of property crime defined by Items 2, 3, 8, 9, 10. (Ferraro and LaGrange’s data did not support inclusion of Item 1 on either subscale). Consequently, the total score, and subscores were used in this study. A measure of perceptions of negative outcomes of crime was devised for this study. Crime outcomes referred to specific expected consequences of direct victimization. Participants were asked: ‘‘If you were to be attacked or robbed, do you think any of the following would probably happen?’’ Each of the eight outcome items (e.g., ‘‘I would probably have to go to the emergency room for treatment’’) was then read aloud to the participant. See Table 2 for outcome questions. 2.2.3. Interpersonal violence variables Sexual assault, physical assault, and emotional abuse were assessed. Sexual assault included forced or coerced vaginal or anal penetration by a penis or other object, or oral penetration by a penis. Physical assault was noted when participants reported being attacked by another person with a weapon or without a weapon but with the intent to seriously injure or kill. Emotional abuse was defined as verbal attacks by others that resulted in strong feelings of threat, intimidation, humiliation, or worthlessness. Also included in this category were episodes in which participants were harassed or coerced into doing something against their will.
Table 2 Fear of crime outcome scores Crime outcome prediction
Percentage endorsing (N ¼ 106)
I I I I I I I I
71.3 48.8 30.5 38.1 63.9 15.8 54.8 34.0
would would would would would would would would
probably probably probably probably probably probably probably probably
have to go to the emergency room for treatment have to stay in the hospital overnight be afraid to leave my home be afraid to leave my home after dark be nervous or jumpy for months afterward have problems in my personal relationships feel depressed or sad not be able to pay all my bills
Fear of crime predicted outcome total score
Mean (S.D.)
n
Male Caucasian Minority
1.5 (1.3) 3.6 (2.0)
28 10
Female Caucasian Minority
3.5 (2.6) 4.0 (2.6)
39 29
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2.2.4. Psychopathology variables Current major depressive disorder and PTSD were measured via a structured clinical interview based on DSM-IV and modified from the National Womens Study (NWS; Kilpatrick et al., 1992). 2.3. Procedure Participants were either randomly selected from the local population, or referred by local police departments for participation in the study. For randomly selected participants, a random number generator was used to derive numbers that corresponded to the page number and listing number of participants from the telephone directory. Each randomly generated number was called by a project assistant, and the person answering the phone was asked if there were any individuals age 55 and over residing in the home and available for interview. If more than one individual was available, the most recent birthday method was used to select a single respondent. The project was described, consent to participate was read aloud over the phone, and agreement for participation was obtained. Subsequently, a time convenient for respondents was arranged. In-person interviews took place in respondents’ homes. Interviews followed the format of the NWS in that victimization and psychopathology modules incorporated skip-outs so that only questions relevant to each participant’s reported experiences and symptom presentation were asked. Because some participants were necessarily asked fewer questions than others, interview lengths varied considerably from 20 to 153 min (x ¼ 53 min, S:D: ¼ 20 min). In order to control for non-specific interviewer characteristics, interviewers administered approximately equal numbers of telephone and inperson interviews to police referred and randomly selected participants. Interviewers were required to achieve 90% accuracy relative to ‘‘gold standard’’ practice interviews prior to interviewing study participants for data collection. Two steps were taken in order to assure that participants could answer questions freely and without fear of negative consequence. First, interviewers asked whether participants could answer questions in relative privacy. If they were unable to answer privately, the interview was rescheduled. Second, the interview was structured nearly exclusively with closed-ended questions that required simple ‘‘yes’’ or ‘‘no’’ answers. Thus, if respondents were overheard, nothing they were saying would place them at risk. Participant’s received a $20 check as compensation for their time. The field period for the study was January 2000 to December 2001. 2.4. Participant protection The sensitive nature of the information collected from participants required strong steps to protect confidentiality. Therefore, in order to protect participant data from subpoena and other threats to privacy, a Certificate of Confidentiality
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was applied for and obtained from the National Institutes of Health. In addition, participant names were matched with arbitrarily assigned identification numbers and data files referenced by these numbers were stored on a secure computer.
3. Results 3.1. Data analytic plan In order to identify factors important to predicting fear of crime and perceptions of crime outcomes, bivariate correlations were obtained and analyzed for all study variables. Specifically, the total fear of crime score, the fear of crime against persons and crime against property subscale scores, and the perceptions of crime outcome total score were computed for each participant. The perceptions of outcome score was derived by summing the number of crime-related negative predictions endorsed by participants. The statistical significance of the relationship between these scores and potential predictors of fear, including gender, race, income, previous victimization history, psychopathology, and number of social contacts was considered at the liberal alpha <.10 level. Variables that were significantly correlated with each of the four fear indexes were included as predictors for that fear index in hierarchical multiple regression analyses. Hence, the predictor set for each regression analysis on each fear index was potentially different. Bonferonni corrections were not applied to correlational analyses when determining significance because we wished to adopt a liberal entrance criterion for inclusion in regression equations, thereby limiting Type II error, which we felt appropriate for this exploratory study. For all regression analyses, the first step involved demographic variables, the second involved interpersonal violence variables (if any), and the third included psychopathology and social isolation variables (if any). Table 1 provides mean scores for the total sample at the individual item level, as well as mean fear of crime total scores and subscale scores. Table 2 gives the proportion of participants endorsing each negative crime outcome, as well as the total summed score for these ratings across participants. Table 3 provides the overall correlation matrix for study variables, and Tables 4–7 provide subsequent multiple regression statistics. Fear of crime total score was significantly correlated with the following four variables, which were included in the regression equation: gender, race, depression, and social isolation. Demographic variables entered on the first step were race and gender, and accounted for 11% of the variance in total fear of crime scores. Adding depression and social isolation significantly enhanced predictive power of the regression equation, increasing the overall variance accounted for to 16%. The change in R2 refers to the combined effects of all variables in each step, thus the combination of being female and of minority racial status was associated with higher scores, as was the combination of being depressed and socially isolated.
392
Table 3 Correlation fear sum 1
2
3
4
5
6
7
8
9
10
11
12
13
1. Fear Sum
–
2. Fear person
–
.91** (n ¼ 104) –
3. Fear property 4. Outcome
–
–
.92** (n ¼ 104) .70** (n ¼ 104) –
–
–
–
.56** (n ¼ 104) .54** (n ¼ 104) .49** (n ¼ 104) –
5. Gender
–
–
–
–
.28** (n ¼ 104) .30** (n ¼ 104) .17* (n ¼ 104) .32** (n ¼ 106) –
6. Race
–
–
–
–
–
.21* (n ¼ 104) .16* (n ¼ 104) .21* (n ¼ 104) .24** (n ¼ 106) .16* (n ¼ 106) –
7. Income
–
–
–
–
–
–
.04 (n ¼ 97) .07 (n ¼ 97) .02 (n ¼97) .26** (n ¼ 99 ) .14 (n ¼ 99 ) .54** (n ¼ 99) –
8. Sexual assault 9. Phys ever
–
–
–
–
–
–
–
.04 (n ¼ 104) .08 (n ¼ 104) .03 (n ¼ 104) .22* (n ¼ 106) .21* (n ¼ 106) .07 (n ¼ 106) .15 (n ¼ 99) –
–
–
–
–
–
–
–
–
.12 (n ¼ 104) .09 (n ¼ 104) .11 (n ¼ 104) .08* (n ¼ 106) .13 (n ¼ 106) .05 (n ¼ 106) .00 (n ¼ 99) .18* (n ¼ 106) –
10. Emtever
–
–
–
–
–
–
–
–
–
.12 (n ¼ 104) .04 (n ¼ 104) .13* (n ¼ 104) .01 (n ¼ 106) .07 (n ¼ 106) .17* (n ¼ 106) .12 (n ¼ 99) .12 (n ¼ 106) .11 (n ¼ 106) –
11. Mdcrita
–
–
–
–
–
–
–
–
–
–
.19* (n ¼ 104) .29** (n ¼ 104) .09 (n ¼ 104) .10 (n ¼ 106) .17* (n ¼ 106) .12 (n ¼ 106) .12 (n ¼ 99) .46* (n ¼ 106) .02 (n ¼ 106) .04 (n ¼ 106) –
12. PTSD
–
–
–
–
–
–
–
–
–
–
–
.03 (n ¼ 104) .05 (n ¼ 104) .07 (n ¼ 104) .25* (n ¼ 106) .08 (n ¼ 106) .03 (n ¼ 106) .18* (n ¼ 99) .35** (n ¼ 106) .16 (n ¼ 106) .07 (n ¼ 106) .25** (n ¼ 106) –
13. Social
–
–
–
–
–
–
–
–
–
–
–
–
.20* (n ¼ 104) .20* (n ¼ 104) .14* (n ¼ 104) .09 (n ¼ 106) .19* (n ¼ 106) .07 (n ¼ 106) .08 (n ¼ 99) .05 (n ¼ 106) .14 (n ¼ 106) .03 (n ¼ 106) .11 (n ¼ 106) .13 (n ¼ 106) –
*
Correlation is significant at the .05 level (one-tailed). Correlation is significant at the .01 level (one-tailed).
**
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Subscale
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Table 4 Regression of significantly correlated variables on fear of crime total score Variable
B
Step 1 Race Gender Step 2 Number of social contacts Major depression
S.E. B 9.99 9.11
4.64 4.74
1.42 10.61
.87 5.98
b .21* .19 .16 .17
R2 ¼ :11 for step 1; DR2 ¼ :05 for step 2 (P < :05); final model R2 ¼ :16 (P < :01). * P < :05. Table 5 Regression of significantly correlated variables on fear of crime against person subscale score b
Variable
B
S.E. B
Step 1 Race Gender
3.93 5.06
2.23 2.33
.16 .21*
Step 2 Number of social contacts Major depression
.65 8.03
.43 2.94
.14 .25**
R2 ¼ :10 for step 1; DR2 ¼ :09 for step 2 (P < :01); final model R2 ¼ :19 (P < :01). * P < :05. ** P < :01.
Fear of crime against one’s person was significantly related to the same set of predictors as was total score. Race and gender accounted for 10% of the variance on the first step, whereas depression and social isolation accounted for an additional 9% of the variance. Fear of property crime was correlated with gender, race, a history of emotional abuse, and social isolation. These demographic Table 6 Regression of significantly correlated variables on fear of crime against property subscale score Variable Step 1 Race Gender Step 2 Ever emotional abuse Step 3 Number of social contacts
B
S.E. B
b
4.39 3.10
2.44 2.47
.18 .13
3.81
3.28
.11
.58
.46
.13
R2 ¼ :06 for step 1; DR2 ¼ :01 for step 2 (P ¼ :22); DR2 ¼ :02 for step 3 (P ¼ :20); final model R2 ¼ :09 (P < :01).
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Table 7 Regression of significantly correlated variables on fear of crime outcomes total score Variable
B
S.E. B
b
Step 1 Race Gender Household income
.96 1.19 .01
.58 .50 .14
.19 .23* .08
.97
.93
.11
1.40
.76
.18
Step 2 Sexual assault Step 3 PTSD
R2 ¼ :16 for step 1; DR2 ¼ :02 for step 2 (P ¼ :11); DR2 ¼ :03 for step 3 (P ¼ :07); final model R2 ¼ :21 (P < :01). * P < :05.
variables accounted for 6% of the variance in fear of property crime. R2 was not significantly enhanced by the step two addition of emotional abuse history or the step three addition of social isolation. Finally, crime outcome scores were significantly correlated with gender, race, income, sexual assault, and PTSD. These variables were subsequently entered into a regression equation. The three demographic factors accounted for 16% of the variance in crime outcome prediction scores, indicating that being female, non-White, and poor increased negative predictions of crime outcome. Increases in R2 to 18% (step 2 variable sexual assault) and 21% (step 3 variable PTSD) were not significant on each subsequent step. However, post hoc analysis indicated that the increase in R2 from step 1 to step 3 (16–21%) was significant at the P < :05 level.
4. Discussion This study attempted to identify factors important in predicting both fear of crime and perceptions of specific negative crime outcome in an elderly sample. Previous research has focused on comparing fear of crime estimates between older and younger adults, with no study of specific negative outcome estimates. This is problematic because crime fear very likely varies as a function of these expected outcomes. Findings indicate that gender, race, depressive status, and social isolation all contributed to participants’ general ‘‘fear of crime’’ score, with greater scores linked to being female, non-Caucasian, depressed, and socially isolated. Different pictures emerged with respect to predicting fear of crime against one’s person and fear of property crime. Fully a fifth of the variance in fear of crime against one’s person was accounted for by gender, race, depression, and social isolation while
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less than a tenth of the variance in property crime was accounted for by race and gender, and no predictive power was evidenced for other factors in the analysis (i.e., emotional abuse and social isolation). Women reported higher fear of crime than men. This finding is consistent across age groups (Ferraro & LeGrange, 1992). Both younger and older adult minority individuals also have higher rates of fear than Caucasian individuals (see also Bazargan, 1994). It appears that several factors assessed in the current study accounted for some, but not all of the variance in fear of crime. Other variables, not assessed here, may account for the other 80% of variance. Environmental factors such as amount of social support, type of housing, amount of television viewed per day, or other trauma experiences such as being a witness to community violence may also play a role in fear of crime ratings. For example, in a study on fear of crime in an urban African American elderly sample, Bazargan (1994) found that self-reported health status, pervious victimization history, television exposure, length of residence and type of house, to name a few, were significant predictors of fear of crime. Even though depression and social isolation accounted for a proportion of the variance for fear of crime against person, they did not account for any variance associated with fear of crime against property. This may be because crime against person is a more intimate experience by nature, and therefore fear of such a crime may be more affected by individual functioning such as depressive symptoms and social isolation. Whereas, fear of property crime may, in part, be more related to external factors such as place or type of residence. Mean fear of crime scores for specific crimes in this study were less than those found in Ferraro and LeGrange’s (1992) sample. Among the specific types of crime, Ferraro and LeGrange found higher ratings for being approached by a beggar and having a car stolen in older adults than any other age groups. The mean scores for fear of being approached by a beggar and having a car stolen in our study, however, were lower than Ferraro and LeGrange’s scores. Our results are more consistent with the position that older adults are not likely to experience high fear of being victimized. Our findings that older adults had generally low scores for other specific fears were consistent with Ferraro and LeGrange; for example, older adults in our sample also reported low fear of sexual assault. Surprisingly, history of victimization did not aid in predicting negative crime outcome. This lack of a relationship was not simply due to low incidence of assault in our participants, as we included a recent victim over-sample. However, our victimization variable captured a combination of adults, some of whom experienced a recent crime, and some of whom experienced a crime in the distant past. To test the possibility that length of time since a crime occurred affected prediction of negative crime outcome, regression analyses were re-calculated using recent crime events (past age 55) as predictor variables. Once again, assault experiences did not affect predictions of crime fears or outcome expectations. Finally, a significant proportion of the score for perceived outcomes of crime was accounted for by the expected predictors of race, gender, and income. History
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of sexual assault and PTSD were associated with perceived negative outcomes in simple correlational analysis, but did not significantly enhance predictive power in regression analysis. Interestingly, fear of personal and property crime, and crime outcome expectations were driven by different predictors. In addition, regression equations indicated that the predictor set was more strongly related to fear of crime against one’s person relative to fear of crime against property. This supports the idea that these dependent variables do, in fact, measure different constructs. In summary, our findings indicate that a myriad of factors may play a role in fear of crime estimates and crime outcome predictions in older adults. It appears that demographic variables and certain psychopathology predict fears related to crimes of person and crimes of property and in different ways. However, this was an initial exploratory study beginning to investigate this area in the elderly. There are limited psychometrics available on the measures that were used. Future research is warranted to examine these constructs and their effect on the wellbeing of older adults.
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