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Determinates and consequences of crimeprevention measuresLarry L. ThomasAtlanta University

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Recommended CitationThomas, Larry L., "Determinates and consequences of crime prevention measures" (1987). ETD Collection for AUC Robert W.Woodruff Library. Paper 1260.

DETERMINATES AND CONSEQUENCES OF CRIME PREVENTION MEASURES

A THESIS

SUBMITTED TO THE FACULTY OF ATLANTA UNIVERSITY

IN PARTIAL FULFULLMENT OF THE REQUIREMENTS FOR

THE DEGREE OF MASTER OF ARTS

IN CRIMINAL JUSTICE ADMINISTRATION

BY

LARRY L. THOMAS

DEPARTMENT OF CRIMINAL JUSTICE ADMINISTRATION

ATLANTA, GEORGIA

MAY, 1987

I V

*.-.. ^*r

TABLE OF CONTENTS

PAGE

Acknowledgements

Table of Contents

List of Tables v

CHAPTER 1

INTRODUCTION 1

Statement of the Problem 2

Significance of the Study 2

Sources of Data 3

Limitations of the Study 3

Organization of the Study 3

CHAPTER 2

REVIEW OF LITERATURE 4

Preventive Measures 7

CHAPTER 3

CONCEPTUAL FRAMEWORK, MEASUREMENT OF

VARIABLES AND METHODOLOGY H

Measurement of Variables 11

Measurement of Background

Characteristics 13

Measurement of Preventive Measures.... 13

Measurement of Fear of Crime 13

in

PAGE

CHAPTER 4

DATA ANALYSIS 20

Descriptive Analysis 20

Preventive Measure 24

Fear of Crime 25

Analytical Procedures 25

Fear of Crime and Preventive

Measures 27

Fear of Crime During The Day 29

Fear of Crime During The Night 29

Pearson Product Momentum

Correlation 28

Multiple Linear Regression Analysis... 16

CHAPTER 5

SUMMARY, CONCLUSION, AND IMPLICATIONS 36

BIBLIOGRAPHY 38

IV

LIST OF TABLES

Page

Table 3.1 Reliability of Prevention Item 14

Table 4.1 Frequency Distribution of Sample

Respondents 21

Table 4.2 Pearson Correlation Coefficients 28

Table 4.3 Standard Metric Regression Coefficients

for Each Equation Model to Measure Fear

of Crime During the Day 32

Table 4.4 Standard Metric Regression Coefficients

for Each Equation Model to Measure Fear

of Crime During the Night 34

ACKNOWLEDGEMENTS

This is to thank Dr. K. S. Murty, Ms. Sylvia Daniel and

Ms. Diana Moore for their assistance without whose support

this study could not have been completed. Most of all many

thanks, love, and appreciation to my Grandmother, Mrs.

Lillie M. Bolden.

ABSTRACT

CRIMINAL JUSTICE ADMINISTRATION

B.S. SAVANNAH STATE COLLEGE, 1977THOMAS, LARRY L.

DETERMINES AND CONSEQUENCES OF CRIME PREVENTION MEASURES

Advisor: Dr. K. S. Murty

Thesis Dated: May, 1987

The fear of crime is a meaningful reality in the lives

of people apart from the fluctuation in victimization rates

and is not necessarily based on either personal experience

or empirical data. Some would agree that the fear of crime

is not significant because such fear bears little if any

direct relationship to the incidence of victimization.

This thesis measures the relationship between crime

preventive measures and selected background characteristics

(socio-demographic), and between the fear of crime and

preventive measures. We found sex to be the most significant

background characteristic associated with the fear of crime;

i.e. females fear crime much more than do males. House light

and neighborhood watch were the two preventive measures that

had a significant direct association with the fear of crime.

CHAPTER I

INTRODUCTION

This study is designed to examine the fear of crime.

The fear of crime is measured in terms of the degree of

safety the respondents perceive in this study feel in

their neighborhoods during the day and during the night.

There has been an increasing interest in the fear of crime

and its relationship to the quality of life in the last

seven years. This thesis is concerned with various kinds

of people who use different kinds of crime prevention

measures in response to their fear of crime; and, whether

these measures lessen their fear of crime. Do differentials

in age, education, sex, size of household and social-economic

class influence the use of different crime preventive

measures? What degree of personal safety do these measures

provide?

Some scholars, Braugart (1980) and Skogan (1977), argue

that the increase in the fear of crime is not related to

crime prevention measures used. This study addresses the

relationship of crime preventive measures to certain back

ground characteristics and the fear of crime.

-1-

-2-

STATEMENT OF THE PROBLEM

The purpose of the study is to test two hypotheses:

(1) there is a significant relationship between crime

preventive measures and background characteristics (age,

education, sex, size of household, and social-economic

class); (2) there is a direct relationship between the

fear of crime and crime preventive measures used (police

watch, mail postponement, neighbor mail watch, neighbor

hood house check, house light).

SIGNIFICANCE OF THE STUDY

The study enables us to identify: (1) the segments

of the population that fear crime the most; (2) the most

commonly used crime preventive measures by groups who fear

crime; (3) the relationships between the fear of crime and

the preventive measures used. These findings will provide

law enforcement officials with a knowledge of the communit

ies' fears of crime and what community memberships do about

these fears. This knowledge will enable the police to

intergrate (and articulate) their crime preventive measures

with community members' fear of crime and their deterring

strategies. Consequently, police community relations could

be strengthened.

-3-

SOURCES OF DATA

The survey data for this thesis, were extracted from

a large data set on Research On Minorities: Race and Crime

In Atlanta and Washington, D.C. (ICPSR 8459). This study

contains data from 621 black interviewees from four neighbor

hoods in each of the two cities: (1) middle income-high

crime: (2) middle income-low crime; (3) low income-high

crime; and (4) low income-low crime neighborhoods. A

complete documentation of this data may be found elsewhere

(Debro et al., 1982). Debro's study examines the relation

ship of the extent of criminal occurrence to differential

community income levels.

LIMITATION OF THE STUDY

This study is a cross-sectional survey that covers

only one period in time. Therefore, it cannot measure the

lag effect or change within a given variable over a period

of time. Secondly, the study population was black and

drawn from four neighborhood types within two cities. This

limits the general ability of the findings to metropolitan

black populations.

ORGANIZATION OF THE THESIS

This thesis consists of five chapters: (1) Introduction;

(2) Review of Literature; (3) Conceptual Framework and

Methodology; (4) Data Analysis; and (5) Summary, Conclusion,

and Implication.

CHAPTER II

REVIEW OF LITERATURE

This review is restricted to the studies that utilize

the independent variables selected for this thesis.

Fear of Crime By Age - Lee (1982) examined the growing

fear of crime among the general public by using a national

public opinion survey. He used a discriminant analysis to

find those variables that best measure the fear of crime.

Age was found to be one of the most important variables

discriminating between fearful and non-fearful respondents.

Although less likely to be victims of most types of crime

the elderly and women are more likely to experience high

levels of fear than younger people and men. [Antunes

et al., 1977; Balkin, 1979; Baumer, 1978; Braungart et al.,

1980; Clemente, Kleiman, 1977; Dubau et al., 1979; Garofelo,

1981; Garafelo and Lach, 1978; Lee, 1982; Yin, 1980.]

According to Clemente and Kleiman (1976) approximately

40 percent of the people in the age group 18 to 41 years

and 50 percent of those who are older than 50 years feared

walking alone at night in their neighborhoods. Garofelo

(1979) confirmed that 63 percent of those 65 years and older

expressed similar fears. The national crime survey of 1981,

found that the 65 and older age group was the least

victimized age category.

-4-

-5-

Fear Of Crime By Education - Earlier studies find a

differentiation in the fear of crime by education. Clemente

and Kleiman (1976) found that the fear of crime is usually

correlated inversely with educational level. He reported

that 37 percent of the respondents with high school educa

tions fear crime. Forty-four percent of those attending

high school expressed a fear of crime. Forty-three percent

of those who dropped out of high school feared crime.

Hindleng (1974) observed similar patterns to Clemente and

Kleiman. He claimed that the relationship between previous

victimization is relatively weak among educated groups

compared to uneducated groups.

Fear Of Crime By Sex - Previous data indicate that the

victimization rate for women is low. However females usually

fear crime substantially more than men Debro (1982). Ennis

(1967); Conklin (1975); and Handleng (1974) showed that

females feared crime substantially more than men. Yin (1985)

and Baumer (1978) found that women fear crime more than men

because they, women, realize they are targets of sexual

assaults. Further, it has been found that women living

alone are more fearful of crime than women living in house

holds with others. Women are also more fearful of their

neighborhoods than are men (Baumer, 1978). Yin (1981)

found several reasons why women fear crime more than men:

-6-

(1) women believe more than men that there is a higher

increase in crime rates in their neighborhoods than any

other places; (2) women have a greater tendency to be

afraid of certain areas in metropolitan cities; (3) women

are more likely to perceive the crime situation as worsen

ing. (4) women are more likely to believe that other

people have limited their activities because of crime;

(5) women are more likely to worry about crime; and (6)

women see themselves as physically weaker than their male

counterparts.

Fear of Crime by Size of Household - Although there

are no extant studies on the association of fear of crime

and the size of household, there is an indirect indication

that the fear of crime may be associated with household

size. For example, Gubrum (1979) found that elderly persons

living alone, living away from friends of the same age, and

living without locally supportive relationships feared crime

more than those living with others and family members who

enjoyed supportive relationships. Hahan (1980) found

similar results among those elderly people who live alone.

Richards and Title (1981) suggested that living alone is

a significant source for fear of crime among women.

Braungart et al., (1980) reported that people living alone

are especially fearful of crime and that the most fearful

groups living alone were middle-aged and elderly females.

For men a greater fear was expressed by elderly males.

-7-

They also reported that young females and elderly males

living with others were fearful of crime.

Fear of Crime By Social-Economic Status - Reiss (1979)

found an intensive fear of crime among lower-income groups.

Ennis (1979) arrived at similar conclusions by examining the

National Victimization Survey conducted in 1966. He reported

that fear of walking alone, in..neighborhoods at night was

greater among low-income members than among high-income

groups. Louis Harris (1974) found that 31 percent of the

people with less than $3000.00 earnings per year reported

the fear of crime was a major social problem. On the other

hand, Lee (1982) suggested that fear of crime did not vary

significantly by income.

PREVENTIVE MEASURES

The literature available on strategies to prevent crime

can be grouped into two catagories: (1) how the community

protects itself from crime; and (2) how individuals protect

themselves from crime.

Crime Preventive Strategies By Communities - One popu

lar strategy adopted by several communities is neighborhood

watch; e.g., a strategy whereby neighbors watch each other's

house and property for any signs of illegal activity. The

Presidential Commission of 1967 explicitly noted the need

for citizen involvement both in improving the performance

of the Criminal Justice System and reducing the circumstances

-8-

and situations in which crimes are most likely to be commit

ted. Citizens involvement in this type of strategy is to

prevent their own victimization through various personal

protective measures; e.g., to protect property, and lessen

burguiary through preventive measures. Neighborhood Watch

limits unlawful access by creating both physical and psycho

logical barriers to offenders.

Protective escorting and block watching are two other

strategies of community crime prevention. [Leavakas, 1980;

Levin, 1980; Pennell, 1978; Percy, 1979; Rosenpraub and

Harlow, 1980.] Leavakas and Herz (1982) observed that most

citizens become involved in neighborhood anti-crime activit

ies not because of fear of crime but as a extension of their

general tendency toward community based voluntary action.

Wright and Mayer (1981) identified four crime neighborhood

control tactics: (1) neglect and copy strategy; (2) pre

ceptive strategy? (3) intergration strategy; and (4) recon

struction strategy. They argued that any crime control

system that is not some form of social adaptation will not

work.

Louis and Maxfield (1980) surveyed residents in four

Chicago neighborhoods to determine the relationship between

fear of crime and crime rate. They found that citizens •

perceptions of dangerous areas in their neighborhoods

matched the crime rates obtained in these areas.

-9-

They argued however that citizens1 perceptions of crime are

shaped not so much by the neighborhood conditions reflected

in the official crime reports but whether or not area members

had a community feeling. They feared crime less if they per

ceived themselves to belong to a close knit community.

Citizen involvement in crime prevention programs has

gained much attention, not only from the community but from

the Presidential Commission of Neighborhood Watch in 1967.

Other community anti-crime programs include the National

Sheriffs Association, Neighborhood Love Programs, Associat

ed Federation of Women Clubs, Helping Hands Programs, and

several youth preventions programs (Roebuck, 1985).

Crime Preventive Strategies By Individuals - Most of

the crime preventive strategies that individuals adopt are

primarily aimed to ensure home and personal safety. Some

individual crime prevention strategies follow: making

homes appear occupied when occupants are away; locking up

gasoline tanks, placing serial numbers on household items

and personal equipment (e.g. farm machinery, placing coded

buts of confetti into beams of hay bails for identification

purposes); security alarms, monitors, watch dogs, National

Rural Crime Prevention registers NCRC 26 and NCRC 27; Davis

and Proctor (1980); Crime Prevention Indicators System 1981;

Carter and Bealiea (1982). Still other individual preventive

measures include: dead bolt locks, secure windows, security

-10-

for sliding glass and other problem doors, auxilliary locks

for all doors, self analysis for home security, burglary

alarms system, night lighting for the home, and pad locks

for out buildings Roebuck (1985) .

Although the above studies offer a great deal of infor

mation about the relationship between the fear of crime and

people's background characteristics, none of these studies

systhesize these components into a meaningful casual model.

The fear of crime may be the covariant of preventive measures

adopted by those with differential characteristics.

This review of the literature indicates that one's

background characteristics influences one's fear of crime,

and that different kinds of people use different kinds of

preventive measures. This survey found no attempt to estab

lish a possible relationship between people's crime preven

tive measures and their fear of crime. This literature

review suggests two tasks: (1) to determine if the observed

relationship between individuals characteristics and fear of

crime hold true for the data set under study and (2) to

examine the.-relationship, if any, between preventive measures

and the fear of crime.

CHAPTER III

CONCEPTUAL FRAMEWORK

MEASUREMENT OF VARIABLES AND METHODOLOGY

The conceptual model for this thesis is presented in

Figure 3.1. The available information on fear of crime, as

revealed in Chapter II, indicates that one's background char

acteristics determine one's fear of crime. We also find

that differential groups use a wide variety of crime pre

ventive measures.

This study examines the relationship of respondents'

age, educational level, sex, size of household and social-

economic class to the degree of safety they feel during the

night and during the day. The study also examines the

relationship between preventive measures and the respondents

fear of crime. These measures are namely police watch,

mail postponement, neighbor mail watch, neighborhood house

check and house light.

MEASUREMENT OF VARIABLES

In order to maintain clarity, this section is organized

into three subsections: (1) measurement of background char

acteristics; (2) measurement of preventive measures; and

(3) measurement of fear of crime.

Measurement of background characteristics

Education is measured by the amount of schooling

reported by the respondents. Is there a correlation

-11-

-12-

between these two variables and if so what is the signific

ance of this correlation: For example do college graduates

fear crime differentially than do high school graduates?

Do these two groups use different types of preventive

measures? Do these two memberships vary in the degree of

safety they feel when using a given preventive measure?

Educational level categories are grouped as follows:

(1) enrolled in high school; (2) dropped out of grade

school; (3) grade school only; (4) dropped out of high

school; (5) high school -vocational; (6) vocational- no

high school; (7) zero to two years of college; (8) two-

three years of college; (9) college graduate; (10) graduate

or professional; (11) obtained GED; (12) never attended

school.

Age in this research is the number of respondents'

chronological years. Also it is a correlate of fear of

crime. The age groups utilized follows: (1) 15-18; (2)

19-25; (3) 26-64; (4) 65+.

Sex is the gender of respondents. Do females differ

significantially in fear of crime from their male counter

parts? For quantitative purposes this variable is coded as

(1) for male; and (2) for female.

Size of Household is the number of persons in a dwel

ling unit. Also household size correlated with fear of

crime.

-13-

MEASUREMENT OF PREVENTIVE MEASURES

Five preventive measures were selected for this analy

sis: police watch, mail postponement, neighbor mail watch,

neighborhood house check, and house light. Respondents were

required to answer "yes or "no" to each of the preventive

measures. We measured for the relationship, if any, be

tween preventive measures and background characteristics

on one hand; and, between preventive measures and fear of

crime on the other. Table 3.1 shows that the data on pre

ventive measures is fairly reliable (A=.61).

Social Economic Class is measured by a self-rating

variable from the following list of options: (1) lower;

(2) middle; and (3) upper.

MEASUREMENT OF FEAR OF CRIME

Fear of crime as defined in this research is the

degree of safety that respondents feel during the day or

during the night by age, education, sex, size of household

and social-economic class. The specific questions included

to measure the fear of crime among respondents were:

(A) how safe do you feel during the day: and (B) how safe

do you feel during the night: The rating involved a four

point scale from "very safe to "very unsafe."

ANALYTICAL PROCEDURES

In the analysis of the data two techniques are utilized:

(1) Pearson's Product Momentum Correlation; and

Variable

Niwhpr

V28

V29

V30

V31

V32

V33

Scale Mean

If Item

Deleted

7.385

7.441

8.029

7.767

7.647

8.037

Scale Varience

If Item

Deleted

1.9143

1.8684

1.5713

1.4356

1.5643

1.7884

Corrected Item

Total

Correlation

.26120

.23366

.48263

.47968

.37706

.26698

Squared

Multiple

Cdirrelation

.12007

.12587

.26916

.30210

.22161

.09595

Alpha

If Item

Deleted

.60459

.61465

.51922

.51269

.56293

.60509

-14i

Alpha - .61773Standardized Item Alpha - .61055

Item Alpha - .61055

-15-

(2) Multiple Linear Regression Analysis.

A brief description of each of these techniques follows.

Pearson's Product Momentum Correlation

Correlation is an inferential statistical measure

which measures the magnitude and direction of association

between any two variables. The magnitude of association

can be: (1) independent and dependent, (2) independent and

independent, or (3) dependent and dependent variables. The

direction of association between the variables is another

advantage of the correlation analysis, and can be positive

or inverse. Inverse is a negative association, while di

rect is a positive association. In explaining the vari

ables, the correlation coefficients help to measure the

degree of association or the strength of the relationship

between one pair of variables. The correlation coefficients

were obtained from the Pearson's Correlation Computer Pro

gram. Pearson's Product Momentum Correlation is a statis

tical measure of the amount of spread around the linear

least squared reduction. This correlation is commonly

used in studying the relationship between severity and

certainty of the coefficient it may be concluded that the

larger the magnitude the better the cohesiveness. If the

coefficient equals zero there is no correlation between the

two variables. The formula for computing Pearson's

Coefficient is:

-16-

(» - I) <y - y)

Qy - y)2J \l <*2> <y2)

Operationally, the correlation coefficient consist of

the ratio of the co-variation to the square root of the

product of the variation in x and the variation in y. The

square of the coefficient (r2) can be defined as the coef

ficient of determination or proportion of variance that is

explained.

MULTIPLE LINEAR REGRESSION ANALYSIS

A multiple regression analysis utilizes more than one

independent variable to predict the value of the dependent

variable. Regression analysis is used to measure the impact

of independent variables on dependent variables. In regres

sion analysis one can have a number, of independent variables,

but only one dependent variable in a given equation.

Regression techniques are of several types: Simple, Multi

ple, and Step-Wise Regression. A regression can be either

linear or nonlinear. A linear regression is one in which

the data inclines to fall along a straight plane. For the

present study multiple linear regression was used.

The method of multiple linear regression extends the

idea of simple regression with one independent

variable. Multiple linear regression deals with more than

one independent variable. Multiple regression allows the

FIGURE 3.1

Conceptual Model

Age

Education

Sex , .Size of Household

Social Economic Status

V-2B Past Police WatchV-29 Past Mail Postponement

V-30 Neighborhood Watchv-31 Neighborhood Mail WatchV-32 Past Neighborhood House Check

V-33 Past House Light

V-41 Safe Alone During the DayV-42 Safe Alone During the Might]

Background

Characteristics

Preventive Measures

Fear of Crime

-18-

researcher to predict the effect of two or more independent

variables xl, x2..., xk on the dependent variable y. The

fundamental regression equation is:

yl = a + bl + bk x k

Where,

yl = predicted scores of the dependent variable

xl, ..., xk = the scores of the independent variables

X X / • • • X&

a = intercept constant

bl, ..., bk = regression coefficients for the

independent variables xl, ..., xk

The above general regression model helps to develop a

series of models that should be used to analyze the concept

ual relationships among the variables in this study.

The symbolic form of these specific equations are given

below:

Fi = ao + blV28 + e (1)

Fi = ao + blV28 + b2V29 + e (2)

Fi = ao + blV28 + b2V29 + b3V30 (3)

Fi = ao + blV28 + b2V29 + b3V30 + b4V31 + e (4)

Fi = ao + blV28 + b2V29 + b3V30 + b4V31 + b5V32 + (5)

Fi = ao + blV28 + b2V29 + b3V30 + b4V31 + b5V32 +b6V33

+ e (6)

Fi = ao + blV28 + b2V29 + b3V30 + b4V31 + b5V32 + b6V33

+ b7V200 + e (7)

-19-

Fi = ao + blV28 + b2V29 + b3V30 + b4V31 + b5V32 + b6V33 +

b7V200 + b8V260 + e (8)

Fi = ao + blV28 + b2V29 + b3V30 + 4bV31 + 5bV32 + 6V33 +

b7V200 + b8V260 + b9V261 + e (9)

Fi = ao + lbV28 + 2bV29 + 3bV30 + b4V31 + b5V32 + b6V33 +

b7V200 + b8V260 + b9V261 +blOV295 (10)

Fi = ao +blV28 + b2V29 + b3V30 + b4V31 + b5V32 + b6V33 +

b7V200 + b8V260 + b9V261 + blOV295 + bllV325 (11)

Where,

Fi = Fear of crime (during the day if i = 1 and during

the night if i = 2)

V28 = Past Police Watch

V29 = Past Mail Postponement

V30 = Past Neighborhood Watch

V31 = Past Neighbor Mail Watch

V32 = Past Neighborhood House Check

V33 = Past House Light

V200 = Amount Of Schooling

V260 = Respondent Age

V261 = Respondent Sex

V295 = Total Number In Household

CHAPTER IV

DATA ANALYSIS

For the sake of clarity, this chapter is divided into

two sections: descriptive and analytical procedures. The

descriptive part of the analysis focused on the sample

distribution according to the variables selected for the

study. The analytical procedures assess the possible re

lationship between the presumed dependent and the presumed

independent variables.

DESCRIPTIVE ANALYSIS

Table 4.1 provides the frequency distribution and

corresponding percentages for the sampled respondents on

selected variables. The following patterns are observed:

Age - nearly forty-six percent of the respondents are

in the age group of 26-64 years, followed by twenty-one

percent between 19-25 years and nineteen percent between

15-18 years. Though the elderly comprise only thirteen

percent of the total sample they make up a large enough

group to compare with the other age categories. Only eight

percent of the respondents did not report their age.

Education - this variable has thirteen options varying

from never attended school to college and above. Nearly

twenty-eight percent of the respondents did not complete

high school; forty-two percent has completed high school

but did not complete college, and thirteen percent had

-20-

-21-

TABLB 4.1

FREQUENCY DISTRIBUTION OF SANPLBD RBSPONDBNTS_____—~ ——— •• —••———— — —•••- — —— —— — —— _*•_.»_ — — — —. — — — — — — —-• — — — ••••»«.,— — — .

Variable Nuaber Percent

(N=621)

Respondents Age

rV260)

15-18

19-25

26-64

65+

DK/NA

118

129

288

78

8

19.0

20.8

46.4

12.6

1.3

Respondents

Education

(V200)

Enrolled In High School 90 14.5

Drooped Out Grade 31 5.0Grade School Only 32 5.2Dropped Out High School 99 15.9High School Only 131 21.1High School-Vocation 44 7.1Vocation No High School 4 0.6Zero to Two College 56 9.0Two-Three College 33 5.3College Graduate 34 5.5Graduate or Professional 50 8.1Obtained GBD 1 °-2Never Attended School 3 0.5

DK/NA 11 1-8

41.2

57.5

1.3

Respondents Sex

(V261)

Male

Female

DK/NA

Respondents Household

Size

(V295)

1

2

3

4

5

6

7

256

357

8

78

148

107

97

70

46

30

12.6

23.8

17.2

15.6

11.3

7.4

4.8

Table 4.1 (Continued)

-22-

Variable Nuaber Percent

(N=621)

8

9

10

11

12

13

14

15

99

DK/NA

Fear of Criae

(V17)

Not A Problem

Soaewhat A Problem

Big Problem

DK/NA

Past Police Watch

(V28)

Yes

No

DK/NA

Past Mai 1 Postponement

(V29)

Yes

No

DK/NA

Neighborhood Watch

14

4

4

1

2

1

1

1

0

15

420

125

64

12

79

526

16

110

475

35

2.3

0.6

0.6

0.2

0.3

0.2

0.2

0.2

0.3

2.4

67.6

20.1

10.3

1.9

12.7

84.7

2.6

17.7

76.5

5.6

Yes

No

DK/NA

Neighbor Mail Watch

464

140

17

74.7

22.5

2.7

Yes

No

DK/NA

292

282

47

47.0

45.4

7.6

-23-

Table 4.1 (Continued)

.. * Nuaber PercentVariable »*u-ber ^^

Past

House Check

(V32)

007 38•2

Ye9 364 58.6No "ZZ 3 9

DK/NA zu

Past House Light

(V33)

v . 467 75-212S 20.3

No 27 4-3DK/NA z'

Safe Alone During

The Day

(V41)nn 9

436 *Very Safe * 21.4Reasonably Safe J« 3#1

Somewhat Unsafe 1» 2'6

Very Unsafe *)> 2.7

DK/NA 17

Safe Alone During

The Night

(V42)

«>in 33.8Very Safe ^1 26.4Reasonably Safe |" 18-0Sonewhat Unsafe ||^ 18 0

Very Unsafe *■)* 3 7DK/NA c*

-24-

graduated from college. Nearly fifteen percent of the

respondents are still in high school. Nearly two percent

did not answer the question.

Sex - the sample has a slight over representation of

females (fifty-eight percent) over males (forty-one percent),

A few respondents (8) were reluctant to provide information

on their gender.

Size of Household - some households are as large as

10-15 members. But, nearly one-third of the sample consists

of 2-3 members followed by those with 4-5 members. The no

response rate is larger for this variable (15 respondents)

than compared to age and sex.

PREVENTIVE MEASURES

Preventive measures adopted by respondents in the past

are measured in terms of six individual variables: (1)

police watch, (2) mail postponement; (3) neighborhood watch;

(4) neighborhood mail watch; (5) neighborhood house check;

and (6) house light. Of there variables house light (75

percent) and neighborhood watch (74.4 percent) were the

most common measures opted by respondents. Next in order

were neighbor mail watch, neighborhood house check, mail

postponement and then police watch. Police watch was per

ceived to be insignificant.

-25-

FEAR OF CRIME

The fear of crime is often a confusing variable to

measure at operational levels. The study collected data

on fear of crime via the following three questions:

1. "Was fear of crime a problem in the community?"

Approximately 30 percent responded from somewhat a

problem to a big problem.

2. "Were you safe alone during the day in your community?"

While 91 percent said that they were very safe to rea

sonably safe, 6 percent indicated somewhat to very

unsafe.

3. "Were you safe alone during the night in your com

munity?" As expected more people expressed feeling

unsafe during the night (61 percent) that during the

day (36 percent). From the reliability and validity

analysis it is concluded that the third question,

"were you safe alone during the night in your com

munity?" is more valid as well as reliable (Alpha=.73)

then the first (Alpha=.62) and second (Alpha=.43)

questions.

ANALYTICAL PROCEDURES

Correlation Analysis the Pearson's Product Momentum

Correlation Coefficients were computed between independent

and dependent variables and tested at the significance

level of 5 percent. The results of this analysis are

-26-

discussed under two headings: (1) Correlation between

fear of crime and background characteristics and (2) cor

relation between fear of crime and preventive measures.

Correlation Between Fear of Crime and Background

Characteristics

Age is negatively correlated with all fear of crime

variables and significantly safe alone during the night;

that is, as an individual advances in age he or she tends

to fear crime more, and this fear increases during the night.

This is expected because as age increases the perceived

vulnerability to being victimized increases. Several

studies support this relationship. (For Example, see

Clemente and Kleiman, 1976). Education is intensely as

sociated with "feeling safe alone during the day and night."

That is, as educational level increases the fear of crime

decreases. Hindeleang, (1974) provided an explanation for

less fear of crime among educated than uneducated persons.

He found that educated people do not permit victimization

to totally influence their perception of criminal activity

in the community. We suggest that educated persons tend

to be more affluent than uneducated persons, and conse

quently feel more capable than uneducated people to replace

victimization losses.

The significant positive relationship between sex

and fear of crime clearly indicates that females fear

-27-

crime at a higher rate than their male counterparts (Table

4.2) .

The relationship between the fear of crime on house

hold size is significant. Thus, the smaller the household

size the higher is the fear of crime. The elderly tend to

feel more vulnerability to victimization when they live

alone. Braungart et al., (1980) also reported that people

living alone are more fearful of crime that those living

with others.

FEAR OF CRIME AND PREVENTIVE MEASURES

The direction of the correlations between preventive

measures and fear of crime is negative. This inverse

relationship is observed consistently between fear of crime

and all preventive measures. Thus, the larger the adoption

of various preventive measures the less will be the fear of

crime. However, only two of the four preventive measures

are found to be significant: (1) police watch and (2)

neighborhood check.

Although the above analysis provides valuable infor

mation regarding the association of fear of crime with the

respondent's background characteristics and preventive mea

sures adopted (in the past), it does not distinguish bet

ween the dependent and independent variables. Such infor

mation is essential to test the conceptual model of the

study because one's choice of adopting or not adopting a

-28-

TABLB 4.2

PEARSON CORRELATION COEFFICIENTS

Preventive Measures

Respondents Age

(V260)

Respondents Education

(V200)

Respondents Sex

.0659* (V261)

Respondents Size

of Household

(V295)

Past Police Watch

(V28)

Past Mail Postponement

(V29)

Past Neighborhood Watch

(V30)

Past Neighbor Mail Watch

.0284* (V31)

Past Neighborhood

* House Check

(V32)

Past House Light

(V33)

Prevent

V17

-.0569

.1164*

.1024

-.0901

.0653*

-.0233

.0398

-.0957*

-.0957

-.0252

-.0854*

V41

.0275

-.0568

.1975*

-.0022

.0239

.0377

-.0019

-.0066

-.0066

.0024

V42

. 1189

-.1390

-.0659

.0286

.0077

.0412

. 1004

.0722*

.0739*

Note: *Significant at least .05 level.

y_17 Fear of crime as a problem in the community

V-41 Safe alone during the dayV-42 Safe alone during the nightPrevent-Preventive Measure Index

-29-

preventive measure depends on one's own background; and,

one's fear of crime is the outcome of his or her adoption

of various preventive measures. Therefore, the regression

analysis is employed.

REGRESSION ANALYSIS

The regression analysis was conducted separately for

fear of crime during the day and during the night. For

each analysis ten regression equations were tested. The

first six models tested the relative contribution of each

additional preventive measure and fear of crime. The fol

lowing three models measured the relationship of all pre

ventive measures (plus a single background characteristic

age, sex, or household size) to fear of crime. Finally,

model ten includes all variables in the model.

FEAR OF CRIME DURING THE DAY

The regression results of the chosen independent

variables of fear of crime during the day are provided in

Table 4.3 where it is observed that neighborhood watch has

as relatively larger relationship to fear of crime than the

other preventive measures. Secondly, these preventive mea

sures could explain a larger proportion of variance when

sex was included in the model than when age and education

were included. Thus, the predictability of preventive mea

sures increases more significantly when they interact with

the gender of the respondents than when they interact with

-30-

age or education. Interestingly, the predictability did

not increase even when age and education were included in

the model along with sex (equation 10). Thus, on the whole

neighborhood watch among preventive measures and sex among

background characteristics are significant variables in the

prediction of fear of crime.

FEAR OF CRIME DURING THE NIGHT

The predictability of the independent variable is

higher for fear of crime during the night than during the

day (Table 4.4). While the inclusion of education in the

model increased the ability to explain variance in fear

of crime during the night be one percent, age increased

it by two percent and sex by eight percent. Thus sex

continued to be a dominant background variable in explain

ing fear of crime during the night as well.

Summing up the regression results, this indicates

that preventive measures per se do not have a significant

relationship to fear of crime. But the predictability of

the preventive measures increases significantly when they

interact with sex rather than with education, age, or

household size. Therefore, sex plays a significant role

in measuring the relationship of preventive measures to

the fear of crime. In short, as the number of preventive

measures increase the degree of the fear of crime decrease

significantly. This is more the case for females than for

-31-

males, regardless of age, education, household size or

socio-economic class.

-32-

TABLB 4.3

STANDARD MBTRIC RBGRBSSION COBFFICIBNTS FOR BACH BQUATIONNODIl TO MBASURB FBAR OF CRIMB DURING THB DAY

V-41

Independent Model Raw

Beta

Standard

Beta

R2

V-29

V-28

Intercept

V-30

V-28

V-29Intercept

V-31

V-28

V-29

V-30

Intercept

V-32

V-29

V-30

V-28

V-31Intercept

V-33

V-28

' V-31V-29

V-32

V-30

Intercept

V-200

V-33

V-28

V-31

V-29

V-32

V-30

Intercept

.04336

.06824

1.17506*

.02948

.06637

.03880

1.15104*

-.06941

.07096

.04539

.06648

1.18962*

.00040

.04601

.06563

.06984

-.07080

1.18718*

-.01053

.06968

-.07013

.04723

-.00040

.06822

1.19392*

-.01610

-.00387

.06796

-.07134

.01912

-.00061

.05355

1.37551*

.02556

.03488

.01783

.03393

.02288

-.05143

.03627

.02676

.00029

.02713

.03970

.03570

-.05246

-.00063

-.63561

-.05196

.03784

.00029

.04127

-.07585

-.00052

.03474

-.05385

.01127

-.00044

.03239

.44192 .00250

.33011 .00281

.42987 .00489

.34338 .00490

.28740 .00493

.50848 .01015

-33-

Table 4.3 (Continued)

Independent Model Raw Standard

Variable »•*• >•*•

R2

V-260

V-28

V-30

V-200

V-33

V-32

V-29

V-31♦Intercept

V-261

V-31

V-260

V-28

V-29

V-32

V-30

Intercept

V-295

V-200

V-261

V-33

V-28

V-31

V-29

V-260

V-32

V-30

Intercept

.00098

.06730

.04907

-.01677

-.01943

.00048

-.02542

-.06681

1.34081

.22815

-.04316

.00061

.02658

.04770

.01552

.01263

1.08862*

.00097

.01873

.22856

.03609

.02645

.04317

.04813

.00057

.01487

.01267

1.09139

.02786

.03440

.02968

-.07904

-.01164

-.04950

-.04950

-.04190

.16715

-.03197

.01732

.01348

.06873

.01124

.00076

.00328

.08828

.16745

.02161

.02838

.03199

.02838

.01622

.01077

.00076

47470 .01086

1.4898 .03740

1.3369 .03741

♦Significant at least .05 level.

-34-

TABLB 4.4

STANDARD MBTRIC RBGRBSSION COBFFICIBNTS FOR BACH BQUATIONMODBL TO MBASURB FBAR OF CHIMB DURING NIGHT

V-42

IndependentVariable

V-28

Intercept

V-29

V-28

Intercept

V-30

V-28

V-29

V-31

V-28

V-29

V-30

Intercept

V-32

V-29

V-30

V-28

V-31

Intercept

V-33

V-28

V-32

V-29

V-30

V-31

Intercept

V-200

V-33

V-28

V-31

V-29

V-30

Intercept

V-260

V-28

V-30

V-200

V-33

V-32

Model

1

2

3

4

5

6

7

8

Raw

Beta

.13349

-.92773*

.18309

.20839

2.11810*

.18793

.19661

-.21200

.07665

.19158

.21923

.14707

1.92199*

.25879

-.17954

.09234

.12063

-.01202

1.76659*

.07657

.12175

.25906

-.18856

.07325

-.01698

1.71839*

-.04126*

.08138

.11717

-.01885

-.26129

.03603

2. 18365*

.00851

.11117

-.00089

-.04695

-.01219

.24429

StandardBeta

.042666

-.06745

■06659

.06706

.06282

-.07811

.03538

.06122

-.08078

.11692

-.06615

.03473

.03855

.00005

.02866

.03891

.11704

-.06947

.02755

-.00783

-. 12158

.03046

.03744

-.00869

.16153

.01355

.15077

.03553

-.00337

-.13833

-.00045

.11037

F

.68809

1.01743

1.24721

1.01995

1.5507

1.3325

1.8390

2.5754

R2

.00182

.00580

.01604

.01162

.02192

.02265

.03607

.05666

Table 4.4 (Continued)

-35-

Independent Model Raw StandardVariable Beta Beta

R2

V-29

V-31

V-261

V-31

V-260

V-28

V-200

V-33

V-29

V-32

V-30

Intercept

V-295

V-200

V-261

V-33

V-28

V-31

V-29

V-260

V-32

Intercept

9

10

-.20660

.02126

.63938

.08693

.00029

-.00029

-.05224

-.05565

-.14217

.21784

-.11224

1.17143*

-.03145

-.05063

.65140

.06380

-.00068

.08620

-.12930

.00061

-.10953

1.26279*

-.07612

.00091

.29187

.04012

.13098

.00009

-.15393

-.05238

.09844

.01325

-.04222

-.05751

-.14917

.29736

-.02388

.00021

.03978

-.04764

.10883

-.04120

6.0526 .13740

5.5539 .14006

CHAPTER V

SUMMARY, CONCLUSION AND IMPLICATIONS

This study determines the relationship between three

sets of variables: (1) background characteristics; (2)

preventive measures; and (3) fear of crime. The respondents

age, educational level, sex, household size and social-

economic class were examined in relationship to the degree

of fear of crime during the day and night. In addition to

background characteristics, several preventive measures

(past police watch, past mail postponement, past neighbor

mail watch, past neighborhood house check, and past hour

light) were examined in relationship to the fear of crime.

The research indicated that preventive measures do not

have a significant relationship to the fear of crime per se

but, a relationship is found when these measures interact

with sex, but not with age, educational level, household

size and social-economic class. Preventive measures reduce

the degree of fear of crime significantly among females

than among males regardless of their age, education, house

hold size or social-economic class.

The implications of this study suggest that additional

variables may be used in similar future research. For

example, respondents for this study were black and from

only two metropolitan areas. A study is needed including

several different kinds of population groups: non-urban

-36-

-37-

blacks, other metropolitan blacks, urban whites, non-

metropolitan whites.

This study suggests that females and elderly Americans

should be educated in some way to the fact that their fears

of crime are not related to their actual victimization.

The police may use these findings to design more effective

intergrated neighborhood crime preventive strategies-

whereby they and neighborhood members cooperate in programs:

(1) to reduce the fear of crime; (2) to implement preventive

measures, and to (3) protect the citizenry. The findings

suggest that Police-Community relations programs should

improve the perceived image of the police held by the

neighborhood members. Overall the results show that fear

of crime is a variable that affects people's quality of

life.

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Some Issues