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Morrissy Sus&n B. 

The Influences of Campus Characteristics on College 
Crime Rates. AIR 1993 Annual Forum Paper. 
May 93 

23p. ; Paper presented at the Annual Forum of the 
Association for Institutional Research (33rd, 
Chicago, IL, May 16-19, 1993). 
Reports - Research/Technical (1A3) — 
Speeches/Conference Papers (150) 

MFOl/PCOl Plus Postage. 

^Campuses; ^Colleges; Community Characteristics; 
^Crine; Crime Prevention; Economic Factors; Higher 
Education; ^Ins ti tutional Characteristics ; 
Institutional Research; Law Enforcement; -^Models; 
School Location; School Security; School Vandalism; 
Security Personnel; Statistical Analysis; Stealing; 
Violence 
''^AIR Forum 



ABSTRACT 

A study of campus characteristics and crime rates 
used an economic theory of criminal choice to develop an explanatory 
model of campus crime. The model considered combinations of 
opportunities, incentives, and costs found on collegt; campuses that 
may affect criminal choice. The components included location, 
accessibility, deterrents and wealth of the higher education 
campuses. National data on campus crimes and questionnaires sent to 
institutional research offices and campus police departments provided 
the data necessary to define the components of the model. The model 
and the components were analyzed using multiple regression analysis. 
The full model was found to define a significant, positive relation 
and to explain approximately 29 percent of the variance in campus 
crime rates. In particular significant positive relationships were 
found between the level of deterrents and campus crime rates, and the 
level of public transportation and campus crime rates. However, there 
was no significant relation between location and campus crime rates 
which suggests that no higher education institution can consider 
itself immune to crime. After analysis of the individual components, 
a revised model was developed that explained 31 percent of the 
variance in campus crime rates. (Contains 17 references.) 
(Author/ JB) 



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THE INFLUEN'."ES OF CAMPUS CHARACTERISTICS 
ON COLL'iGE CRIME RATES 



By 



Susan B. Horriss 
Assistant Director for Financial Affairs 
Indiana Commission for Higher Education 
101 West Ohio, Suite 550 
Indianapolis, IN A6204 
(317) 232-1900 



ERIC 



Presented at the 
Association for Institutional Research 
33rd Annual Forum 
Chicago, Illinois 



Offc* of Edoc«lion«i Rcaearch and im(yovem«ni 

EDUCATIONAL RESOURCES INFORMATION 
y CENTER (ERIC) 

u Thii document hat b««n reproduced as 
r«C*<v*<j from th« p«fton or oratnitMUon 
originating it 

□ Minor Changes hav« b«*n mad« to rmpfove 
'•pfoduction Qualify 

• Poinit of vfw Of oCMOiont ttaiAd m thitdocu 
mjnl do not ntcesMnly r«ortMn( offKini 
OCRI pocitton or policy 



May 18, 1993 



"PERMISSION TO REPRODUCE THIS 
MATERIAL HAS SKEN GRANTED BY 



BEST copy fiMLE- 



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TO THE EDUCATIONAL RESOURCES 
INFORMATION CENTER (ERIC)." 



for Management Research, Policy Analysis, and Planning 



This paper was presented at the Thirty-Third 
Annual Forum of the Association for Institutional 
Research held at the Chicago Marriott Downtown, 
Chicago, Illinois, May 16-19, 1993. This paper 
was reviewed by the AIR Forum I>ublications 
Committee and was judged to be of high quality 
and of interest to others concerned with the 
research of higher education. It has therefore 
been selected to be included in the ERIC Collection 
of Forum Papers. 



Jean Endo 

Chair and Editor 

Forum Publications 

Editorial Advisory Committee 



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The Influences of Campus Characteristics 
on College Crime Rates 



ABSTRACT 

This paper presents the results of a study that utilized an economic theory 
of criminal choice to develop an explanatory model of campus crime. The model 
considered combinations of opportunities, incentives, and costs found on 
college campuses that may affect criminal choice. The components included 
location, accessibility, deterrents and wealth of the higher education 
campuses* National data on campus crimes and questionnaires sent to 
institutional research offices and campus police departments provided the data 
necessary to define the components of the model. The model and the components 
were analyzed using multiple regression analysis. The full model was found to 
define a significant, positive relation and to explain approximately 29 percent 
(adjusted R square) of the variance in campus crime rates. After analysis of 
the individual components, a revised model was developed that explained 31 
percent of the variance in campus crime rates. 



ERIC 



4 



INTRODUCTION 

Reported crime on university and college campuses ha."? increased 
dramatically over the last twenty years. During the 1980s, the Uniform Crime 
Reports (Federal Bureau of Investigation, 1990)reported that nearly 2,500 
crimes of personal violence and more than 105,000 serious property crimes were 
occurring annually on campuses. In 1990, the UCR reported more than 2,600 
violent crimes and approximately 120,000 property crimes occurred on campuses. 
Although some proportion of this increase may relate to improved reporting and 
recording of crimes on campus in recent years, concern about campus crime is 
being expressed by students and parents, and it has become an issue facing 
legislatures, regulatory agencies, and high<»r education institutions. The 
college campus is no longer perceived as a place with a special, erudite 
atmosphere protected from worldly happenings. 

Crime on campus is a complex issue that colleges and universities face on a 
daily basis. Violent crime affects the working and learning environment as 
fear and caution replace friendliness and exploration. Property crime has less 
impact on human Interactions, but can influence budget allocation, provision of 
equipment, and access to facilities. Higher education institutions must find 
ways to improve campus security, reduce crime on campus, and limit their 
exposure to liability claims. 

The purpose of this study was to expand the investigation of the relations 
of campus characteristics and campus crime rates by developing and testing a 
model of these relations. The study was the first attempt to apply an economic 
theory of criminal choice to campus crime. In addition, the study attempted to 
improve on previous studies by using consistent data on campus characteristics, 
better definitions for campus characteristics, recent data on campus crime, and 
to include a broad representation of institutions in the sample. The study 



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addressed q\ sstions about which characteristics vere significantly related to 
campus crime rates and whether groups of characteristics were related to each 
other. The study specifically looked at the relation between location and 
campus crime rates in order to confirm or reject popularly held beliefs on this 
topic. 

RELATED LITERATURE 
Given these issues and concerns , explanatory models that link the 
characteristics of university and college campuses to campus crime rates would 
allow institutions to consider different alternatives and to develop 
appropriate responses* Unfortunately, few studies have been done on the 
relation between campus characteristics and campus crime rates, and none have 
tested explanatory models. Most studies approached campus crime from one of 
three perspectives: (1) administrative or operational aspects, (2) legal and 
liability issues, and (3) studies of victims of campus crimes and programs for 
victims. 

Only two studies analyzed campus crime and attempted to identify related 
campus characteristics. McPheters (1978) conducted the first study. He used 
an econometric model to test the hypothesis that campus crime was related to 
several independent variables. The independent variables included expenditures 
on security, student density on c pus, percentage of students living in 
dormitories, campus facility data, location in an urban or rural area, and 
unemployment in the nearest city. McPheters tested the hypothesis using data 
from 38 institutions. Of the variables, the proportion of students living in 
dormitories and high unemployment levels in nearby cities were found to be 
significantly related to the campus crime rate. 

Fox and Hellman (1985) conducted a more extensive study on location and 
other possible correlates of campus crime* Data on campus characteristics were 



gathered on 222 colleges and universities and analyzed using crime rates 
calculated from 1979 campus crime data* The authors used an analysis of 
variance methodology to consider patterns of relative saf«ness of college 
campuses and campus crime by location within and outside of urban areas* The 
study concluded that location was not significantly related to the level of 
campus, although these was a difference in the mixture of violent and property 
crime by campus location. Fox and Bellman also did a correlation analysis of 
33 campus characteristics and campus crime rates. The correlation analysis 
clarified the strength and direction of the relations* The researchers then 
attempted to obtain additional information about these relations by using 
principal components analysis to identify the primary dimensions of the 
characteristics. Campus crime rates were then regressed on the primary 
components. Only two characteristics, campus size and scholastic quality, were 
identified as having a significant, positive relation with campus crime rates. 

OMCBPTUAL FRAMEWORK 
Given the lack of previous explanatory research on campus crime, a review 
of the various theoretical frameworks for considering ases of criminal 
behavior indicated five main perspectives (Nettler, 1984; Pepinsky, 1980^ 
Schafer, 1977): biological, psychological, cultural, social, and economic 
explanations. Consideration of these frameworks led to the selection of the 
economic theory for the development of a model of the relation between campus 
characteristics and campus crime rates. The economic explanation of criminal 
behavior views human behavior as rational. Economic choice theory proposes 
that "all individuals, criminals and non-criminals, respond to incentives? and 
if the costs and benefits associated with action change, the agent's choices 
are also likely to change ... the decision to commit an illegal act is reach 
via an egocentric cost-benefit analysis" (Heineke, 1978, p. 2). Taylor (1978) 



4 

indicated that with the economic theory, a model of criminal behavior can be 
described in ways similar to normal economic behavior with little reference to 
psychological theories. An economic model provides for fairly direct empirical 
testing. Shortcomings of this model include the assumption that all criminals 
exhibit economically rational behavior and an equal weighting of all components 
that make up the decision. 

Usin this theory, the study developed an explanatory model of campus crime 
that consf.^ered combinations of opportunities, incentives, and costs found on 
colleges that may affect criminal choice. The first component to be considered 
for the model was location. Although the studies by McPheters (1978) and Fox 
and Hellman (1985) did not find location significantly related to campus crime 
rates, location continues to be perceived as a factor in campus crime. 
Examples of this are found in Powell (1981), Smii:h (1988, 1989), and Bromley 
and Territo (1990). Within the economic framework. Hakim (1981) perceived 
location to be a prime opportunity factor in the crime decision. The criminal 
evaluates the net benefits of various sites and selection can be affected by 
transportation costs, familiarity with the environment, and possibility of 
recognition as an outsider. Therefore, the location of a campus may be defined 
by the surrounding area's crime rate. This definition differed from previous 
studies which considered location as an urban-rural dichotomy and might explain 
why location was not related to campus crime rates. 

Accessibility of a campus to criminals was considered to be an opportunity 
and incentive factor for inclusion in the model. Visibility of the 
institution, such as a large, well-known campus, may attract criminals because 
of increased awareness of the campus and the areas associated with the facility 
(Pepinsky, 1980; Reiss, 1970). In addition, freedom of movement on and around 
the campus via heavily trafficked streets or mass transportation can increase 



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attractiveness of a campus for criminal choice* As indicated previously! Hakim 
(1981) included accessibility as part of the criminal decision making process. 

The wealth or resources of the campus nay increase incentives for property 
crime which makes up nearly 98 percent of all campus crime* Studies by Cohen 
and Felson (1979| 1980) supported that prosperity relates to an increase in 
theft because there were more goods to steal and more things left unguarded* 
Vealth also provides the campus with the ability to increase the number of 
deterrents to crime^ such as che number of police and the use of alarm systems. 

Deterrents to crime are the preventive measures implemented by campuses in 
order to limit criminal activity. A higher level of deterrent on a campus 
implies that decisions were made to invest in deterrents due to increased 
crime, greater demand for police services , and/or efforts to reduce the 
potential for liability claims. This scenario reflects the current climate 
toward crime at most higher education institutions. 

The proposed economic model of criminal choice has four components and 
hypothesizes that the crime rates in the area surrounding a college or 
university campus, alone and in combination with the accessibility of the 
campus, level of deterrents, and the wealth of the campus explains the campus 
crime rates. Figure 1, Explanatory Model, provides a diagram of these 
relations. 




Wttlth 



FiQurt 1 



Explinitory Model 



9 



5 



6 

Five conceptual propositions vere derived from this model: 

1. Location is positively related to campus crime rates; 

2. Location is positively related to the level of deterrents on a campus 
and the level of accessibility of a campus; 

3. The level of accessibility of the campus is positively related to 
campus crime rates; 

4. The level of deterrents is positively related to campus crime rates; 
and, 

5. The level of wealth of a campus is positively related to the levels of 
deterrents and campus crime rates. 

RESEARCH DESIGN AND METHODOLOGY 
Operational definitions vere developed for the components of the model. 
The criterion variable In the study was campus crime rate. This rate was the 
ratio of the total number of campus crimes to the campus population scaled by 
1,000. To ensure commonality of the crime data used in the study, campus crime 
information reported by higher education institutions and published in the 
Federal Bureau of Investigation's Uniform Crime Reports ( UCR ) provided the data 
on campus crimes* 

Campus population was defined to be more encompassing than in previous 
studies. The intent was to reflect the number of people that are on campus 
frequently. This study included unduplicated annual student headcount, 
faculty, and non-faculty employees in calculating the campus population. 

Instead of defining location by an urban-rural dichotomy, this study used 
the crime rate of the community within which the higher education institution 
is located and the crime rate of the neighborhood of the campus. The first 
indicator was calculated from data available in the UCR. Because no crime data 
are available for the area specifically surrounding a campus, the latter 



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indicator vas derived from the perceptions of the neighborhood crime rate by 
campus police. To further test locationi an indicator vas included for city 
population* 

Indicators of the level of deterrents on a campus reflected measures of 
policing capacity* One indicator vas a labor force measure^ the ratio of 
full-time police officers to the campus population scaled by liOOO. Another 
indicator, the ratio of annual operating expenditures for the police department 
to the campus population, measured the level of resources supporting policing 
efforts. A third indicator addressed the level of deterrents by providing 
measures of the level of police involvement in outreach activities and of the 
use of security technology. Since campuses are increasing the level of 
deterrents as crime rates are going up, this component should be positively 
related to campus crime rates. 

Accessibility and visibility o£ the campus to criminals vas addressed 
through indicators that incorporated the following measures: square footage of 
the campus physical facility per campus population, campus population per acre, 
accessibility to automobiles, availability of public transportation, and 
percentage of residential students. 

Indicators of campus vealth attempted to measure institutional aspects of 
wealth as veil as that of the campus population, primarily students and 
faculty. Cost of tuition vas used as a reflection of resources available to 
students and because it was significantly correlated with campus crime rates in 
Fox and Bellman's study (1985). The ratio of total university operating 
expenditures to campus population scaled by 1,000 vas used as another indicator 
of campus vealth. Other indicators of resources also included the percentage 
of applicants for admission not accepted, the percentage of faculty holding a 
terminal degree, and the ratio of faculty to students. These indicators 

11 



8 



addressed the concept that more competitive and high quality colleges and 
universities tend to be veil funded. 

Figure 2, Explanatory Model with Indicators, presents the full model, 
including the cawi'^us characteristics or indicators and anticipated direction of 
relations, tested in the study. 



CM, 



l«fl./M«. •••• itiifl«Mt« 



Surrounding Area 
Crime Ratti 



Atctitibllity 



z 



Campul Crime 
Rattt 



Dtttrrents 





Figure 2 

Explanatory Model with Indicators 

In order to gather the data necessary to define campus characteristics, 
questionnaires were mailed to institutional research offices and campus police 
or security departments at the higher education institutions that reported 
crime statistics in the FBI's Uniform Crime Reports * This population included 
392 institutions from 42 states. Public and private colleges, universities, 
community colleges and technical schools were part of the population. 
Institutions that are solely medical or health science facilities were excluded 
from the study because of their distinctive characteristics. The resulting 
population of 370 institutions equaled the sample for this study. 



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s 



9 

Tvo distinct questionnaires were used. The institutional research office 
questionnaire requested descriptive data, such as numbers of faculty, staff, 
and students; percentage of faculty with terminal degrees; percentage of 
residential students; percentage of admission applications accepted; cost of 
tuition and fees; annual expenditures; gross square footage of campus 
buildings; and numbers^^f acres. To facilitate completion and to ensure 
consistency of data^^f inition, specific sources of data were identified by 
report and line numbers for most items on the survey. These sources, the EEO-6 
report and the IPEDS IC and Finance Reports, are standard reports required by 
federal agencies. 

The questionnaire sent to the campus police or security department gathered 
data for both factual components and belief indicators relating to policing 
capacity and campus location. Information on campus accessibility to mass 
transportation services, level of campus outreach services, extent of use of 
security technology, and scope of responsibility was requested through the use 
of closed questions with the format of a forced-choice checklist. Respondents 
were asked to check all of the choices that applied to their campus. The use 
of this format allowed the development of an overall score for one deterrent 
variable. For tvo belief indicators, accessibility of campus to automobile 
traffic and perception of neighborhood crime rates^ a Likert*like rating scale 
vas used. Respondents selected the level that most described their campus in 
these areas. Other questions requested information on the number and type of 
departmental staff members and annual operating expenditures of the department. 

The validity of both questionnaires vas evaluated through reviev by 
experts, five directors of institutional research or five police chiefs. These 
experts performed informal content and face validity checks on indicators and 
evaluated the format of the questionnaire. Revisions vert made folloving the 



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review. Prior to distribution, a second face validity check vas conducted on 
the police department survey* The experts rated the survey using a scale 
ranging from 1 (poor coverage) to 5 (excellent) coverage* The result vas an 
average score of 4«4. When asked about consistency of the instruments, both 
teams of experts indicated that the format of the questionnaire, such as use of 
checklists and specification of sources, would promote consistency in 
responses. 

Distribution of the questionnaires and data collection occurred over the 
period from December 1991 to April 1992. A total of 257 institutional research 
questionnaires (70 percent response) and 310 campus police questionnaires (84 
percent response) were received. This response resulted in 241 sets matched by 
institution for a response rate of 65 percent. All matched-set questionnaires 
were reviewed for completeness of information. If data were missing, the 
respondent vas contacted to get the required information. After this process 

vas finished, there were 238 usable matched sets and a final response rate of 

2 

64 percent. A Chi-square (X ) goodness-of-f it test was performed on the 
matched set to determine if there was a difference between the population and 

the sample in the distribution among types of institutions. The null 

2 2 
hypothesis vas tested. The X equaled 2.81. With two degrees of freedom, X 

must be 5.59 to be significant at the .05 level. Hence, it was inferred that 

the sample vas adequately representative of the population under study. 

The study did have several limitations. First, given the ex post facto 

nature of the study, no causal relations could be established; only possible 

explanations vere developed. Second, most higher education institutions do not 

report crime data to the FBI. Approximately ten percent of all colleges and 

universities submitted data. This fact limited the population available and 

affected the representativeness and generalizability of the study. Third, the 

t4 



11 

best crime data available from the UCR vere used^ but may not accurately 
reflect the true level of crime on a campus. Only crimes reported to campus 
police are included in the UCR and many crimes, such as acquaintance rape, go 
unreported. A Tovsen State University study found a high level of unreported 
crime on campuses (Cockey» Sherrill, & Cave, 1989)* Definitional and 
jurisdictional problems also may occur in reporting campus crime. Thus, the 
true crime rate for campuses is likely to be higher than shovn in the study. 

FINDINGS 

The null hypotheses testing the full model and the conceptual propositions 
vere evaluated using multiple regression. Because the study vas investigating 
influences or explanation, the significance level for all tests vas established 
at 0.05. 

The hypothesis for testing the full economic model (shovn previously in 
Figure 2) stated that no significant relation exists between the predictor 
variables and campus crime rates. The multiple regression analysis shoved that 
the model defined a significant relation betveen the campus characteristics and 
campus crime rates. The null hypothesis vas rejected (g < 0.05). The R square 
value indicated that approximately 34 percent of the variance in campus crime 
rates can be explained through the model. The level of explanation dropped to 
29 percent, a 14 percent decline in explanatory capabilityi vhen adjusted for 
the degrees of freedom. 

The study next investigated the relations among the components of the model 
and betveen the components and the campus crime rates. This investigation vas 
done by testing hypotheses developed from the conceptual propositions. The 
first hypothesis proposed that location is not positively related to catnpus 
crime rates* The result of the analysis confirmed the null hypothesis (£ « 
•32). Even using more precise definitions of location, the analysis supported 



15 



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findings in other studies about location and offered more evidence to 
contradict this long-standing myth. The lack of relation suggests that higher 
education institutions should be careful not to imply levels of safety in 
descriptions of location. The analysis also shoved that location does not 
explain a significant amount of variance in campus crime rates » having R square 
and adjusted R square of 0.01 and 0.00 respectively. 

The second hypothesis stated that location is not positively related to the 
level of deterrents or the level of accessibility on a campus. The multiple 
regression analyses of the tvo parts of the hypothesis had similar results. In 
both cases, the regression indicated the existence of veak, but significant, 
positive relations and required rejection of the null hypotheses (£ < 0.05), 
The R square and adjusted R square indicated that about eight percent of the 
variance in the level of deterrents and only three percent in the level of 
accessibility was explained by location. This result indicated that location 
had little explanatory power in relation to deterrents and accessibility* 

Accessibility was the next component of the model to be considered. The 
third hypothesis proposed that the Itivel of accessibility of the campus is not 
positively related to campus crime rates. The multiple regression analysis 
indicated that a significant, positive relation existed and prevented 
acceptance of the null hypothesis (£ < 0.05). Approximately 22 percent of the 
variance in the criterion variable was explained by adjusted R square. This 
finding suggests that campuses with higher crime rates are likely to be more 
accessible to people and various types of traffic, thus, providing greater 
opportunity for criminal access. 

The deterrent component was the next aspect of the model to be tested. The 
fourth hypothesis stated that the level of deterrents is not positively related 
to campus crime rates. The analysis showed a significant, positive relation 



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13 

and did not support the null hypothesis (£ < 0.05). Over 25 percent of the 
variance in campus crime rates was explained by the deterrents component. This 
explanation level was higher than any other component and indicated the 
importance of the component to the total model. The result implies that 
campuses having a higher crime rate will utilize more deterrents than 
institutions with lower crime rates. This finding supported the concept that 
campuses recently have been increasing deterrents in response to demand and 
that a high level of deterrents may increase safety, but also facilitates the 
discovery and reporting of campus crimes. Lower crime rates may be a future 
impact of more deterrents. 

The final hypothesis evaluated the wealth component and proposed that the 
level of wealth of a campus is not positively related to the level of 
deterrents or campus crime rates. The multiple regression analyses of the two 
parts of the hypothesis had similar results. In both cases, the 
regression indicated the existence of significant, positive relations and 
required rejection of the null hypotheses (£ < 0.05). The R square and 
adjusted R square indicated that over 17 percent of the variance in the level 
of deterrents was explained by wealth and that wealth explained more than 10 
percent of the variance in campus crime rates. The result suggests that 
wealthier campuses can provide more deterrents, but it also supports the 
concept that wealthier campuses offer more opportunities or targets for 
criminals. 

These analyses suggested that the economic model and its components 
identified a significant relation and provided some explanation of campus crime 
rates. The analyses also suggested that some parts of the model did not 
contribute significantly to the explanation and that a more efficient model 
might be developed. 

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Using the information obtained from the analyses, the location component 
was eliminated from the model. Correlation analysis using Pearson's R was 
conducted on the variables of the other components to test the hypothesis that 
none were significantly correlated with campus crime rates. The correlation 
analysis shoved that the null hypothesis could be accepted for two variables, 
accessibility to automobiles (£ « 0*14) and campus population per acre (£ » 
0.05). These variables vert eliminated from the model on that basis and a 
revised model vas formulated as sho%m in Figure 3* 



Acctssibility 



/ 



Ctmput Crimt 
Ratts 




Figurt 3 
Rtvited Explanatory Model 



The revised model with its three components having a total of 11 variables 
vas tested. The predictor variables were entered into the analysis without 
regard to order or magnitude of effect. The multiple regression analysis 
indicated that the relation continued to be significant and that approximately 
34 percent of the variance vas explained. This result vas similar to the 



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original model. Vhen the adjusted R square vas considered, the explanation 
level had changed from 29 percent in the original model to 30 percent, a slight 
improvement • 

One last analysis vas conducted on the revised model to take advantage of 
the power of step-vise multiple regression analysis and its ability to select 
and utilize variables according to the level of their contribution to the 
regression equation. This analysis indicated that approximately 33 percent of 
the variance in campus crime rates could be explained through four variables. 
While this percentage is slightly lover than either of the previous models, the 
adjusted R square increased to over 31 percent. In order of addition to the 
regression analysis, the four variables consisted of two deterrent variables, 
police department budget per campus population and level of deterrents; and two 
accessibility variables, percentage of residential students and level of public 
transportation. 

The results of the step-vise analysis implied that knovledge of these 
variables explained as much variance as the 11 variables in the revised model 
or the 16 variables of the full model. The results also can be interpreted to 
support the conceptual basis of the model. The police department budget per 
campus population might be considered as a vealth indicator rather than a 
deterrent indicator. Each component of the model then vas represented through 
the step-vise variables. 

The positive relations of both police department budget per campus 
population and level of deterrents to campus crime rates suggests that 
reduction of crime rates may not be the result of increasing budgets or 
deterrents • As mentioned previously, more and better police programs and 
deterrents may succeed in raising campus awareness. This greater level of 
avareness and staffing may lead to a safer campus in reality, but also higher 



19 



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crime statistics as more crime is discovered and reported* Given studies 
shoving high levels o£ unreported crime on campuses, improved police services 
may encourage more reported crime* In addition, the ability of an institution 
to support a higher police budget could imply a wealthier campus and its 
concomitant attractiveness as a target £or criminal activity* 

The inclusion of the two accessibility variables, percentage of residential 
students and level of public transportation, support the concept that 
familiarity and opportunity increases with access* A residential population is 
likely to have access to all parts of the campus at all hours* A higher level 
of public transportation implies a relative ease of access to campus fo:" 
students, staff, and criminals* 

RECOMMENDATIONS FOR PRACTICE 

Since a significant, positive relation was identified betveen the level of 
deterrents and campus crime rates, there may be a need to lover expectations 
held by university administrators and police departments of reducing crime 
rates by increasing the level of deterrents on campus* As stated previously, 
increased avareness may result in better reporting of crime and higher crime 
statistics* 

Another significant, positive relation vas found betveen the level of 
public transportation and campus crime rates* This relation implies that 
expansion of transportation services may increase access to the campus and 
opportunity for criminal acts* Obviously, the benefits of improved 
transportation may outveigh any potential increase in crime, but campus police 
departments might vant to pay special attention to the nevly served areas or 
during expanded hours of access* 

The lack of a significant relation betveen location and campus crime rates 
suggests that no higher education institution can consider itself immune to 

20 



17 

crime. The study evaluated location using city population, city crime rates, 
and perceptions of neighborhood crime rates by campus police. Institutions in 
high crime areas may have lov crime rates and v^ce versa. This finding, and 
those from other studies on this topic, suggests that as institutions and 
authors try to describe the campus crime situation, references should be 
eliminated to location as a warning factor or confidence building aspect. As a 
liability issue, emphasis on the rural nature of a campus as a part of safety 
information may be risky. 

The results of the study encourage higher education institutions to do self 
evaluation, to consider hov their characteristics may be in:eracting with 
campus crime and efforts to improve campus security, and to be conscious of the 
complexities of campus crime. The study does not support comparison of an 
institution to the result of any analysis because the sample was not selected 
randomly. The results can not be considered representative of higher education 
institutions and should not be generalized. 

RECOMMENDATION FOR RESEARCH 

The present study examined an economic model of campus crime in order to 
expand the investigation of the relation between campus characteristics and 
campus crime rates. It was the first attempt to apply an economic theory of 
criminal choice to campus crime. The study had several limitations, some which 
can be addressed through further research. At the time of the study, many 
institutions did not record or report crime data for their campus. This fact 
limited the population available for study. Vith the implementation of the 
Student Right-to-Knov and Campus Security Act of 1990, mote higher education 
institutions will be recording crime data. Therefore, the opportunity exists 
to test the findings of this study or to expand the study using a random sample 
of the nation's colleges and universities. The larger population also will 

?1 n 



18 

allow survey instruments to be pilot tested for better vali'iity of model 
indicators. 

Campus crime is a very complex topic, and this study took one approach and 
used one set of components. Among these components, some proved not to be 
significant to the relation under study. There may be other components that 
should be included or other variables that better define the components. There 
may be other models that include components relating to size and type of 
institution, local unemployment, type of crime, or many other possibilities. 

In addition, the study identified some specific relations that deserve 
further investigation. Time series analysis could be used to determine if over 
time the relation between deterrents and crime rates becomes negative. 

CONCLUSION 

Crime on campus is the reality that each higher education institution must 
accept. Violent crime has the attention of parents, the media, and the federal 
government. When statistics are known about even a portion of the property 
crime, trustees, administrators and funding agencies will be conscious of the 
other costs of campus crime. Few studies of campus crime have been done and 
there is much more to learn. 



22 



19 

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