HISPANIC PERCEPTIONS OF POLICE IN LAS VEGAS

“HIGH CRIME” NEIGHBORHOODS

By

Christopher Jo Percy

Bachelor of Arts –– Sociology

University of Nevada Las Vegas

2012

A Professional Paper submitted in partial fulfillment

of the requirements for the

Master of Arts –– Sociology

Department of Sociology

College of Liberal Arts

The Graduate College

University of Nevada Las Vegas

December 2014

ABSTRACT:

This paper utilizes survey data drawn from the Las Vegas

Metropolitan Police Department (LVMPD) and University of

Nevada Las Vegas Smart Policing Initiative (SPI) to explore

Hispanic perceptions of neighborhood disorder as well as

Hispanic perceptions of the police. The literature on

perceptions of police suggests that those who perceive

higher degrees of neighborhood disorder generally hold lower

perceptions of the police and police effectiveness. The

literature also suggests that Hispanics, as a group,

generally hold favorable attitudes towards the police. Most

of the literature sampled homogenous neighborhoods and

indicates demographic factors such as age, time in

residence, and education impact resident perceptions of

police. This analysis breaks down the studied Hispanic

population between English-speaking and Spanish-speaking to

focus on their specific perceptions of neighborhood disorder

iii

utilizing a scale of broken windows characteristics, quality

of life in neighborhood, and satisfaction with the police.

Additionally, I analyze whether being born in the United

States versus elsewhere has an impact on neighborhood and

perceptions of police.

LIST OF TABLES AND FIGURES

Figure 1. Crime Categories and Incident Codes used in Crime Ranking 14

Table 1 Distribution of Education Levels Among Respondents15

Table 2 Marital Status of Respondents16

Table 3 Employment status of Respondents17

iv

Table 4 Bivariate Relationships of Race/Ethnicity and the Independent Variables 24

Table 5 Bivariate Analysis of Race/Ethnicity Compared to Broken Windows 26

Table 6 OLS Linear Regression of Disorder Index (0-16) with Cultural Factors, Treatment Area and Controls. 27

Table 7 Logistic Regression predicting Neighborhood Quality of Life as “good” with Cultural Factors, Treatment Area, Neighborhood Disorderand Control Variables 29

Table 8 Logistic Regression predicting Perception of Crime as a problem in Neighborhood with Cultural Factors, Treatment Area, Neighborhood Disorder and Control Variables 31

Table 9 Logistic Regression predicting Perceptions of Policewith Cultural Factors, Treatment Area, Neighborhood Disorderand Control Variables 33

v

INTRODUCTION:

In 2012, The University of Nevada Las Vegas took part in

assisting the Metropolitan Las Vegas Police Department by

performing a survey of randomly sampled Las Vegas “high

crime” neighborhoods. High crime neighborhoods are those

defined by Metro as having the most violent calls for

service (CFS), disorder (CFS), and property crimes. The

saturation policing initiative then used a “matched pairs”

methodology to pair neighborhoods with similar crime

patterns. One neighborhood received treatment, the other

served as a control. The methodology for selecting “high

crime” neighborhoods is outlined in the DATA and METHODS

section below.

The survey was done in waves. Half of the surveys were

completed in control neighborhoods that did not receive

saturation policing, the other half were completed in

neighborhoods that received saturation policing. Contrary to

existing literature that shows blacks disproportionately

vi

represented in high crime neighborhoods (Barkan, Steven, &

Cohn, 2005; Correl et al., 2002; Decker & Smith, 1980; Hahn,

1971; Jefferis et al., 1997; Peek, Alston & Lowe, 1978;

Preist & Carter, 1999; Reisig & Parks, 2000; Rice & Piquero,

2005; Sampson & Raudenbush, 2004; 2005; Skogan, 1978;

Weitzer, 1999; Weitzer, 2000a; 2000b; Weitzer & Tuch, 2002),

those self-identifying as Hispanics are disproportionately

represented (51.6%).

While much work has been done in the fields of sociology and

criminology that explore resident perceptions of police

(Barkan & Cohn, 2005; Batson & Monnat, 2013; Brown &

Benedict, 2002; Cao, Frank, & Cullen, 1996; Carr & Keating,

2007; Chandek, 1999; Chermak, McGarrell & Weiss, 2001;

Cheurprakobkit & Bartsch, 1999; Cheurprakobkot, 2000; Decker

& Smith, 1980; Dunham & Alpert, 1988; Garcia & Liquin, 2005;

Hahn, 1971; Jesilow & Meyer, 2001; Jesilow, Meyer, &

Namazzi, 1995; Kaminski & Jefferis, 1998; Lasley, 1994;

Peek, Alston & Lowe, 1978; Peek, Lowe & Alston, 1981; Percy,

1986; Preist & Carter, 1999; Quillian & Pager, 2001; Reisig

vii

& Correia, 1997; Reisig & Giacomazzi, 1998; Reisig & Parks,

2000; Reitzel, Rice & Piquero, 2004; Rennison, 2007; Rice &

Piquero, 2005; Sampson & Bartusch, 1998; Sampson &

Raudenbush, 2004; 2005; Shaw et al., 1998; Skogan, 1978;

Skogan, 1989; Skogan, 1996; Sullivan, Dunham & Alpert, 1987;

Tuch & Weitzer, 1997; Weitzer, 1999;2000a;2000b; Weitzer &

Tuch, 2002; White & Menke, 1982; Wilson & Kelling, 1982;

Zevitz & Rettammel, 1990), very few reference the Hispanic

population at all (Carr, Napolitano & Keating, 2007;

Cheurorakobkit & Bartsc, 1999; Cheurprakobkit, 2000; Dunham

& Alpert, 1988; Garcia & Liquin, 2005; Jesilow & Meyer,

2001; Lasley, 1994; Reitzel & Piquero, 2004; Rennison, 2007;

Rice & Piquero, 2005; Sullivan, Dunham & Alpert, 1987;

Sampson & Raudenbush, 2005; Tuch & Weitzer, 1997). Even

fewer focus on the Hispanic population as a target of their

analysis (Cheurprakobkit, 2000; Garcia & Liquin, 2005;

Reitzel & Piquero, 2004; Rennison, 2007).

Through analysis of the Smart Policing Initiative (SPI)

survey data, I add to the existing literature by expanding

viii

our knowledge of Hispanic perceptions of police. The

information provided comes from neighborhoods identified by

the Las Vegas Metropolitan Police Department as “high crime”

areas. These are lower-income, heterogeneous neighborhoods

targeted by police to receive saturation policing in an

effort to reduce crime levels. It can be assumed that most

of the residents surveyed will fit similar socio-demographic

mold since those with the means are more likely to move out

of “high crime” environments (Chermak, McGarrell & Weiss,

2001; Dunham & Alpert, 1998; Garcia & Liquin, 2005; Geiss &

Ross, 1998; Hahn, 1971; Preist & Carter, 1999; Reisig &

Giacomazza, 1998; Sampson & Bartush, 1998; Sampson &

Raudenbush, 2004; 2005; Wilson & Kelling, 1982). Since much

of the previous research focused on differences in class and

race from the perspective of homogenous neighborhood

contexts (Carr, Napolitano & Keating, 2007; Cheurorakobkit &

Bartsc, 1999; Cheurprakobkit, 2000; Dunham & Alpert, 1988;

Garcia & Liquin, 2005; Jesilow & Meyer, 2001; Lasley, 1994;

Reitzel & Piquero, 2004; Rennison, 2007; Rice & Piquero,

2005; Sullivan, Dunham & Alpert, 1987; Sampson & Raudenbush,

ix

2005; Tuch & Weitzer, 1997; Weitzer, 1999), this inquiry

highlights perspectives of the Hispanic population within

low-income, high-crime, heterogeneous environments.

Research Questions

What are the Hispanic perceptions of police in Las Vegas

“high crime” neighborhoods? Do the views of the Hispanic

population(s) in this sample reflect the trends expressed in

the literature? Is there a significant difference between

the test and control samples with regard to the Smart

Policing Initiative? Is race/ethnicity the most telling

factor with regard to resident perceptions of police and

neighborhood quality or does neighborhood disorder have an

impact? Does the language the survey was taken in factor

into the analysis of resident perceptions of the police and

neighborhood quality?

Hypotheses

Based on the literature, I am inclined to these hypotheses.

H1 a). I expect that Hispanics in Las Vegas will report

x

lower levels of neighborhood disorder than non-

Hispanics.

b). I expect Hispanics will be more likely than non-

Hispanics to report quality of life in

neighborhood as “good.”

c). I expect Hispanics will report lower perceptions

of crime than non-Hispanics.

d). I expect Hispanics will be more likely than non-

Hispanics to perceive the quality of Metro as

“good.”

[H2] a). I expect that residents living in

neighborhoods that experienced police saturation

will report higher perceptions of crime than those

in control groups.

b). I expect that residents in test neighborhoods will

report higher levels of neighborhood disorder than

those in control groups.

c). I expect that residents in test neighborhoods will

report lower overall perceptions of the police

xi

than those in control groups.

[H3] a). I expect to show differences in outcomes by

language of origin, such that residents who

completed the survey in Spanish will report higher

perceptions of crime than English-speaking

residents.

b). I expect that Spanish-speaking respondents will

report lower levels of neighborhood disorder than

English speaking residents.

c). I expect that Spanish-speaking respondents will

report higher overall perceptions of the police

than English speaking residents.

LITERATURE:

An interest in resident perceptions of police was largely

motivated by the riots that occurred in northern cities

towards the end of the civil rights movement. Researchers

began to speculate that negative perceptions of police

complicated interactions between citizens and police (Decker

xii

& Smith, 1980; Hahn, 1971; Peek et al, 1981; Skogan, 1978;

White & Menke, 1982; Kelling & Moore, 1988). Reluctance to

turn to the police, and a lacking faith in the police as a

whole, may provide the potential for civil unrest (Brown &

Benedict, 2002). In an effort to repair perceptions of

police, agencies began to move from the “reform era” into an

era marked by an emphasis on community policing (Kelling &

Moore, 1988). To date, many believe that exploring resident

perceptions of police will provide police agencies with the

information required to improve agency practices and better

meet the needs of the communities that they serve (Barkan &

Cohn, 2005; Batson & Monnat, 2013; Brown & Benedict, 2002;

Cao, Frank, & Cullen, 1996; Carr & Keating, 2007; Chandek,

1999; Chermak, McGarrell & Weiss, 2001; Cheurprakobkit &

Bartsch, 1999; Cheurprakobkot, 2000; Decker & Smith, 1980;

Dunham & Alpert, 1988; Garcia & Liquin, 2005; Hahn, 1971;

Jesilow & Meyer, 2001; Jesilow, Meyer, & Namazzi, 1995;

Kaminski & Jefferis, 1998; Lasley, 1994; Peek, Alston &

Lowe, 1978; Peek, Lowe & Alston, 1981; Percy, 1986; Preist &

Carter, 1999; Quillian & Pager, 2001; Reisig & Correia,

xiii

1997; Reisig & Giacomazzi, 1998; Reisig & Parks, 2000;

Reitzel, Rice & Piquero, 2004; Rennison, 2007; Rice &

Piquero, 2005; Sampson & Bartusch, 1998; Sampson &

Raudenbush, 2004; 2005; Shaw et al., 1998; Skogan, 1978;

Skogan, 1989; Skogan, 1996; Sullivan, Dunham & Alpert, 1987;

Tuch & Weitzer, 1997; Weitzer, 1999; 2000a; 2000b; Weitzer &

Tuch, 2002; White & Menke, 1982; Wilson & Kelling, 1982;

Zevitz & Rettammel, 1990).

A survey of over four decades of sociological inquiry into

resident perceptions of police and policing activities shows

that the field is marked with inconsistency. Many studies

focus heavily on the analysis of blacks and whites (Barkan,

Steven, & Cohn, 2005; Correl et al., 2002; Decker & Smith,

1980; Hahn, 1971; Peek, Alston & Lowe, 1978; Preist &

Carter, 1999; Reisig & Parks, 2000; Rice & Piquero, 2005;

Sampson & Raudenbush, 2004; 2005; Skogan, 1978; Weitzer,

1999; Weitzer, 2000a; 2000b; Weitzer & Tuch, 2002) leaving

out any differences that may be shown by other ethnic

minority groups. Hispanics do not appear in this literature

xiv

on perceptions of police until 1987 (Dunham & Alpert, 1987)

and when they do, they seem to be used as a comparison group

to break the dichotomy of black vs. white studies (Carr,

Napolitano & Keating, 2007; Cheurorakobkit & Bartsch, 1999;

Cheurprakobkit, 2000; Dunham & Alpert, 1988; Garcia &

Liquin, 2005; Jesilow & Meyer, 2001; Lasley, 1994; Reitzel &

Piquero, 2004; Rennison, 2007; Rice & Piquero, 2005;

Sullivan, Dunham & Alpert, 1987; Sampson & Raudenbush, 2005;

Tuch & Weitzer, 1997). Hispanics were and are commonly

referred to simply as “other” groups, or lumped into the

heading of “non-black,”(Lasley, 1994; Rice & Piquero, 2005;

Rice & Giacomazzi, 1998; Sampson & Raudenbush, 2005;

Weitzer, 2000b).

Garcia notes, “Hispanics constitute a separate group,

distinct from both African Americans and Whites. According

to the U.S. Census Bureau (2000), Hispanics represented the

largest minority group in the U.S. (Garcia & Liquin, 2005).”

According to data from the 2010 census, that group is

growing rapidly. “The Hispanic population increased by 15.2

xv

million between 2000 and 2010, accounting for over half of

the 27.3 million increase in the total population of the

United States. Between 2000 and 2010, the Hispanic

population grew by 43 percent, which was four times the

growth in the total population at 10 percent (Ennis, Rios-

Vargas & Albert, 2011).” Inquiry into Hispanic perceptions

of neighborhoods and police would seem even more relevant in

this context. This is especially true in Nevada, which was

one of the fastest growing states overall in 2010 (Mackun &

Wilson, 2011). Cities, such as Las Vegas, NV, which tied

Raleigh, NC as the third fastest growing in the country,

should benefit strongly from a deeper understanding of the

Hispanic population (Mackun & Wilson, 2011).

Race/Ethnicity, Age, and Other Factors

Race/ethnicity plays prominently in the perception

literature (Barkan, Steven, & Cohn, 2005; Carr, Napolitano &

Keating, 2007; Cheurprakobkit, 2000; Cheurprakobkit &

Bartsch, 1999; Correl et al., 2002; Decker & Smith, 1980;

Dunham & Alpert, 1988; Garcia & Liquin, 2005; Hahn, 1971;

xvi

Jesilow & Meyer, 2001; Lasley, 1994; Peek, Alston & Lowe,

1978; Preist & Carter, 1999; Reisig & Parks, 2000; Reitzel &

Piquero, 2004; Rennison, 2007; Rice & Piquero, 2005; Sampson

& Raudenbush, 2004; 2005; Skogan, 1978; Sullivan, Dunham &

Alpert, 1987; Tuch & Weitzer, 1997; Weitzer, 1999; Weitzer,

2000a; 2000b; Weitzer & Tuch, 2002). Nearly all of these

articles summarize similar findings for blacks & whites with

regard to perceptions of police. Whites tend to view police

more favorably. Blacks, regardless of racial composition of

the samples tend towards a negative view of the police. Of

the studies that focus specifically on Hispanics

(Cheurprakobkit, 2000; Garcia & Liquin, 2005; Reitzel &

Piquero, 2004; Rennison, 2007), Cheurprakobkit found that,

“Spanish-speaking Hispanics felt more satisfied with police

performance than Whites and English-speaking Hispanics

(Cheurprakobkit, 2000).” Garcia and Liquin reported that

Hispanics had the lowest overall perceptions of police in

their sample (Garcia & Liquin, 2005). Reitzel, Rice, &

Piquero found Hispanics were more likely than non-Hispanics

to believe that the practice of profiling was widespread,

xvii

and that they had been profiled (Reitzel, Rice & Piquero,

2004), which can be interpreted as a negative stance towards

police. Rennison found that robbery against Hispanics is

less likely to be reported to police, but simple assault was

more likely to be reported than similar crimes against non-

Hispanic Asians or Whites (Rennison, 2007). Clearly there is

a lot of work left to do in the field of perceptions of

police with regard to the Hispanic population.

Age

Age is commonly positively associated with perceptions of

police (Carr, Napolitano & Keating, 2007; Percy, 1986;

Reisig & Correia, 1997; Reisig & Giacomazza, 1998; Sampson &

Raudenbush, 2005; Sullivan, Dunham & Alpert, 1987). Sampson

and Raudenbush (2005), found that older residents tend to

perceive less neighborhood disorder.

Education

Education is positively linked (Reisig & Giacomazza, 1998),

negatively linked (Percy, 1986), and shows no significance

xviii

(Jesilow, Meyer & Namazzi, 1995), according to the

literature. I would expect education to have little impact

on the results of these tests with regard to the SPI survey

because the survey focuses on “high-crime,” low-income

areas. I would guess the majority of survey respondents are

not college educated, based on the sociological link between

education and class attainment.

Sex

Demographic identification of sex does not play a prominent

role in the literature. Where it is found (Cao, Frank, &

Cullen, 1996; Rice & Piquero, 2005) females are linked

positively to perceptions of police, while males tend

towards negative perceptions.

Media and Perceptions

I include this in the literature discussion because there is

a field of inquiry that pertains to media portrayals of the

police and their effect on perceptions of the police.

(Jesilow & Meyer, 2001; Kaminski & Jefferis, 1998; Lasley,

xix

1994; Tuch & Weitzer, 1997; Weitzer, 1999) show that while

all races seem to show an increase in negative perceptions

of police following the spread of an incident of police

brutality in the media, Hispanics are more effected than

Whites, and generally less effected than Blacks. It should

be noted for the purposes of this study that the SPI survey

was performed prior to the well-publicized events in

Ferguson, MO regarding the shooting of Michael Brown. I do

not expect that any publicized event is having an effect on

this data.

THEORY

Conceptualizations about police perception that center

around neighborhood characterizations emerge in the

literature after the “broken windows” work of Kelling &

Wilson in 1982. The theoretical framework of broken windows

is posited around the notion that visual signs of physical

decay signal a decline in informal social controls (Kelling

& Wilson, 1982). The decline in social control paired with

increasing visual signs of physical decay and disorder, then

xx

invites more negative behavior to the neighborhood (Kelling

& Wilson, 1982). Once reified in the literature, measures of

neighborhood characteristics become prominent in the

perception literature (Batson & Monnat, 2013; Cao, Frank &

Cullen, 1996; Chermak, McGarrell & Weiss, 2001; Garcia &

Liquin, 2005; Geis & Ross, 1998; Quillian & Pager, 2001;

Reisig & Giacomazza, 1998; Reisig & Parks, 2000; Sampson &

Bartusch, 1998; Sampson & Raudenbush, 2004; 2005; Skogan,

1978; 1989; Weitzer, 1999; 2000a; 2000b; Weitzer & Tuch,

2002). It is important to note that many of these studies do

not indicate “broken windows” variables specifically.

However, the ones that do tend to show a positive

correlation between the presence of physical decay and the

perceived rate of crime or disorder (Cao, Frank, & Cullen;

Geis & Ross, 1998; Preist & Carter, 1999, Quillian & Pager,

2001; Reisig & Giacommazza, 1998; Sampson & Raudenbush,

2004; 2005). Additionally, these studies show that there is

a negative association between physical decay and resident

perceptions of police.

xxi

DATA and METHODS:

I utilize data gained from the survey portion of the SPI,

(n=1005). Dependent on missing data, corrected sample size

for various questions ranges from n=932 – n=996. The data

comprises tested respondents (n=498) and control respondents

(n=507) in neighborhoods identified by the Las Vegas

Metropolitan Police Department (LVMPD) as “high crime”

neighborhoods. The project employed a matched-pairs

experimental design to test the effectiveness of saturation

police in 24 designated high-crime hotspots in the Las Vegas

metropolitan area. The high-crime hotspots were compiled by

using LVMPD Area Command crime information. Geographically,

LVMPD is divided into 8 area commands across the Las Vegas

Metropolitan area. Each Area Command provided the three

highest trouble areas in their command jurisdiction, thus

generating a list of 24 high-crime areas across the valley.

This method ensured that the study was geographically

distributed across the metro region. Each of the 24 areas

represents an approximate 1-mile radius. Once the 24 areas

were defined by street name boundaries, Calls for Service

xxii

(CFS) data were used to identify three levels of offenses to

rank the areas from highest to lowest crime based on their

cumulative number of crimes. Table 1 displays the crime

categories and their respective incident codes used in our

ranking of the 24 high-crime hotspots.

Figure 1. Crime Categories and Incident Codes used in Crime Ranking

Violent CFS Disorder CFS Property CrimesRobbery, Assault, Homicide, Sexual Assault, Illegal Shooting, Person with a gun, Person with a knife, etc.

Prowler, Drunk, Reckless Driver, Fight, Suspicious Person, W anted Suspect, Narcotics, Destruction of property, and other disturbances.

Burglary, attempted burglary, Auto burglary, stolen motor vehicle, and attempted stolen motor vehicle

Codes: 407, 407G, 407Z, 413, 413A, 413B, 413 G, 415, 415A, 415B, 415C, 415D, 415G, 415Z, 420, 420G, 420Z, 426, 426Z, 434, 434G.

Codes: 403, 408, 410, 416, 416A, 416B, 416F, 416G, 416S, 416V, 425, 425A, 425B, 425G, 425H, 440, 441, 441G, 441V, 441Z, 446.

Codes: 406, 406V, 406Z, 411, 411Z.

With a ranking of the 24 high-crime hotspots, a matched-

pairs method was used to pair two areas with very similar

crime statistics. Among the pairs, one area received

saturation treatment and the other area served as the

control group. Thus, the two highest-crime hotspots were

matched together so that one will receive saturation

treatment and the other will be the control hotspot.

xxiii

Description of the sample

Hispanics make up the majority of respondents surveyed

(51.6%), a 10% increase over those who identified as “other

(41.6%)” when asked to choose which race/ethnicity they

identified with from the selections of: White (36.4%),

African American (14.6%), Asian or Asian American (3.6%),

American Indian or Native American (1.1%), Native Hawaiian

or Pacific Islander (2.7%), or Other (41.6%). A second

question regarding race/ethnicity asked, “do you consider

yourself to be Spanish/Hispanic/Latino?” Respondents were

given the opportunity to select: “No, not

Spanish/Hispanic/Latino; Yes, Mexican; Yes, Puerto Rican;

Yes, El Salvadorian; Yes, other Spanish/Hispanic/Latino.”

The responses to this second question were recoded into a

variable isolating respondents identifying as Hispanic and

analysis was completed using this recoded variable as it

contained a larger sampling of Hispanics than the previous

race/ethnicity question (51.6%). Of the Hispanic population,

17.3% of respondents took the survey in Spanish.

xxiv

Education levels of the sample population are best viewed

from this chart:

Table 1: Distribution of Education Levels Among Respondents

Education LevelRace/Ethnicity

NoDiploma

HSGrad

or GED

SomeCollege

Associates

Degree

Bachelors

Degree

Graduate

DegreeWhite 10.5% 26.9% 34.4% 11.1% 11.8% 5.3%

AfricanAmerican

14.0% 34.9% 29.5% 10.1% 7.8% 3.9%

AsianAmerican

10.0% 33.3% 16.7% 16.7% 16.7% 6.7%

AmericanIndian/Native

American

30.0% 10.0% 40.0% 10.0% 10.0% 0.0%

NativeHawaiian/PacificIslander

12.5% 29.2% 20.8% 12.5% 12.5% 12.5%

**Hispanic 29.7% 46.8% 14.3% 3.9% 2.5% 1.0%

**Hispanic calculated from q19 – Recoded into dichotomous Hispanic =1,

Other=0

Of all tested groups, we see that the Hispanic population

ranks lower than any other racial/ethnic category for

college attendance. The percentage of Hispanics is lower

xxv

than every other race/ethnic group identified in every

category from “some college – graduate degree,” and this is

consistent in comparison with every other race/ethnicity

identified.

Table 2: Marital Status of Respondents

What is your current marital status?

Race/Ethnicity

Married

Single Divorced

Widowed

Separated

Cohab.

White 32.1% 33.6% 16.0% 9.0% 2.2% 7.1%

AfricanAmerican

24.6% 45.4% 6.9% 9.2% 1.5% 12.3%

AsianAmerican

36.7% 43.3% 13.3% 0.0% 3.3% 3.3%

AmericanIndian/Native

American

11.1% 66.7% 11.1% 0.0% 0.0% 11.1%

NativeHawaiian/PacificIslander

43.5% 21.7% 8.7% 0.0% 0.0% 26.1%

**Hispanic 47.1% 28.7% 10.6% 5.5% 3.6% 8.6%

xxvi

**Hispanic calculated from q19 – Recoded into dichotomous Hispanic =1,

Other=0

The Hispanic population is the most likely of all groups to

be married (47.1%). A higher percentage of American Indians

or Native Americans are single than any other group (66.7%).

Whites lead in percentages of divorce (16.0%). African

Americans, Whites and Hispanics identify as widowed. The

percentages for African Americans and Whites are similar

(9.2%/9.0%), both race/ethnicities are nearly double

Hispanics (5.5%). There are low percentages reported for

separated amongst Whites, African Americans, Asian Americans

and Hispanics, with none reported for the Native groups.

More than a quarter of the Native Hawaiian/Pacific Islander

demographic live with a partner (26.1%).

Of those responding to the question (n=991), 76.9% of the

sample population rent their home. Males make up 51.3% of

the overall population, though the Hispanic population

xxvii

(n=488) is 52.3% female. Median age of the sample population

is 42. The median length of time at their current address is

4 years.

Employment status of the sample population:

Table 3: Employment status of Respondents

Which of the following best describes your current employment or labor force status?

Race/Ethnicity Fu

ll Time

Part Time

Unemp.

Looking

Not

Looking

FT

Student

Home

Worker

Retired

Other

Refuse

Answer

White 42.9%

9.8% 8.8% 3.5% 1.6% 3.8% 24.3%

5.0% 0.3%

AfricanAmerica

n

30.8%

12.3%

8.8% 4.6% 5.4% 2.3% 13.1%

10.8%

2.3%

AsianAmerica

n

40.6%

9.4% 18.5%

0.0% 3.1% 6.3% 28.1%

0.0% 3.1%

Am.Indian/

Native

40.0%

30.0%

9.4% 0.0% 0.0% 10.0%

0.0% 20.0%

0.0%

Hawaiian/

Islander

54.5%

13.6%

4.5% 4.5% 9.1% 4.5% 9.1% 0.0% 0.0%

**Hispanic

51.2%

14.6%

11.7%

1.0% 4.0% 11.5%

4.2% 1.9% 0.0%

**Hispanic calculated from q19 – Recoded into dichotomous Hispanic =1,

Other=0

xxviii

The majority of the Hispanic and Hawaiian Islander

populations are employed full time (51.2%/54.5%). The

American Indian/Native American group has the highest

percentage of all races in any kind of employment

(FT+PT=70%). Whites and Asian Americans boast the highest

percentages of retired people (24.3%/28.1%) respectively.

Native Hawaiian/Pacific Islanders report the highest

percentage of full time students (9.1%).

Research Questions

What are the Hispanic perceptions of police in Las Vegas

“high crime” neighborhoods? Do the views of the Hispanic

population(s) in this sample reflect the trends expressed in

the literature? Is there a significant difference between

the test and control samples with regard to the Smart

Policing Initiative? Is race/ethnicity the most telling

factor with regard to resident perceptions of police and

neighborhood quality or does neighborhood disorder have an

impact? Does the language the survey was taken in factor

xxix

into the analysis of resident perceptions of the police and

neighborhood quality?

Hypotheses

My research is guided by the following hypotheses.

H1 a). I expect that Hispanics in Las Vegas will report

lower levels of neighborhood disorder than non-

Hispanics.

b). I expect Hispanics will be more likely than non-

Hispanics to report quality of life in

neighborhood as “good.”

c). I expect Hispanics will report lower perceptions

of crime than non-Hispanics.

d). I expect Hispanics will be more likely than non-

Hispanics to perceive the quality of Metro as

“good.”

[H2] a). I expect that residents living in

neighborhoods that experienced police saturation

will report higher perceptions of crime than those

xxx

in control groups.

b). I expect that residents in test neighborhoods will

report higher levels of neighborhood disorder than

those in control groups.

c). I expect that residents in test neighborhoods will

report lower overall perceptions of the police

than those in control groups.

[H3] a). I expect to show differences in outcomes by

language of origin, such that residents who

completed the survey in Spanish will report higher

perceptions of crime than English-speaking

residents.

b). I expect that Spanish-speaking respondents will

report lower levels of neighborhood disorder than

English speaking residents.

c). I expect that Spanish-speaking respondents will

report higher overall perceptions of the police

than English speaking residents.

xxxi

Dependent Variables

This paper uses four dependent variables; perceptions of

crime, perceptions of neighborhood disorder, perception of

neighborhood quality of life, and perceptions of the police.

Perceptions of crime are taken from survey question three

that asks respondents, “Generally speaking, how would you

rate crime as a problem in your neighborhood?” Respondents

were offered the following choices: 1. Very big problem, 2.

Somewhat of a problem, 3. Not much of a problem, and 4. No

problem at all. The selections were recoded into a

dichotomous variable where “not much of a problem” and “no

problem at all” were coded as “0” and “somewhat of a

problem” and “very big problem” were coded as “1”. This

coding is used specifically for the regression analysis. The

original codings, as stated below, are used for the index

comparison of the dependent variables against the disorder

index. This is explained below and represented in Table 6.

Perceptions of neighborhood disorder are measured as an

index created from the neighborhood disorder variables in

xxxii

question 7. Ordinary least squares regression (OLS) analysis

is particular useful for showing strength and direction of a

relationship between ordinal and interval/ratio variables. I

analyze perceptions of neighborhood disorder through the use

of “broken windows” theory of Kelling & Wilson (1982). The

SPI survey utilized a series of questions assessing

respondent perceptions of neighborhood disorder that

pertained directly to broken windows measures. Respondents

were asked to rate the following questions: Are there any

homes or building with broken windows on your block; Are

there any homes, other buildings or other places on your

block which [that] have graffiti on them; are there any

abandoned or boarded up homes or buildings on your block;

Are there any vacant lots on your block; Are there any

abandoned cars on the street on your block; Are there areas

on your block where litter is a problem; Are there areas on

your block where the street or sidewalk needs repairs; Are

there areas on your block that need better lighting?

Respondents were given the following choices for response:

None; A few; Many; Don’t Know; or Refused to Answer. The

xxxiii

responses were first recoded into ordinal variables, “None,”

was recoded as “0”, “A few=1” and “Many =2.” “Don’t Know,”

and, “Refused to Answer,” were recoded as “Missing.” For

ordinary least squares analysis, the broken windows

variables were then combined into an index to be treated as

a continuous interval ratio variable (possibility of 0-16).

The index shows an adequate goodness of fit with a

Cronbach’s Alpha of 0.850 and a Cronbach’s Alpha based

solely on the standardized items of 0.852 for the 8 items in

the index.

Perceptions of neighborhood quality of life are taken from

question one of the survey that asks respondents to rate the

overall quality of life in their neighborhood. A response of

“1” or “2” was recoded into “1” indicating a respondent that

rated the overall quality of life in their neighborhood as

“good.” A response of “3” or “4” was coded as “0” indicating

a respondent that rated the overall quality of life in their

neighborhood as “not good.”

xxxiv

Perceptions of police are taken from question nine of the

survey. Question nine asks, “Overall, do you think the [Las

Vegas Metro] police are doing…,” respondents could select: A

very good job, a good job, a fair job, a poor job, a very

poor job, don’t know, or refused. The responses were recoded

with “A very good job, A good job,” and, “A fair job,”

recoded as “1,” “A poor job,” and, “A very poor job,”

recoded as “0,” and all other responses coded as system

missing. In this regard, any positive response became a “1,”

all negative responses a “0,” and all other regarded as

missing.

Independent Variables

The variable for “Hispanic” is coded from question 19 that

asks respondents, “Do you consider yourself to be

Spanish/Hispanic/Latino?” The majority of survey respondents

identified as “Hispanic,” 32% self-identified as “Mexican,”

0.9% identify as Puerto Rican, 3.8% identified as El

Salvadorian, 14.8% selected “other Spanish/Hispanic/Latino,”

and .1% show as an unidentified category. Most likely, the

xxxv

unidentified category is a result of data entry error. The

remaining 48.3% of respondents identified as “not

Spanish/Hispanic/Latino.” All sub-categories of this

variable in the affirmative were coded as “1.” The response,

“No, not Spanish/Hispanic/Latino,” was coded as “0.”

Nativity is assessed from question 21. All responses linked

to being born in the United States were combined and coded

as “1,” where the response, “in another country outside of

the U.S.,” was coded as “0.” A category was created for

Spanish-speaking respondents based on whether or not the

survey was taken in Spanish. If the survey was taken in

Spanish, it received the code of “1,” where surveys taken in

English received the code “0.” Areas that received treatment

were combined and coded as “1,” under the variable name

“treatment area.” Those areas that did not receive

saturation policing were coded as “0.”

I utilize broken windows questions (q7a-7h) to assess

perceptions of neighborhood disorder. Each component has

been recoded to a dichotomous variable for bivariate

xxxvi

analysis, “A few,” and “many” combined=1, “none”=0. For

logistic regression analysis, the dichotomous coding of each

disorder variable was used as independent variables. This is

reflected in the Logistic Regression Analysis Tables below

(Tables 7,8, & 9).

Control Variables

Age is treated as a continuous interval/ratio variable

calculated from the year the respondent was born,

(median=42). Gender is dichotomously coded, male=1,

female=0. Socioeconomic status (SES) is assessed from level

of education, an ordinal variable. Less than High School was

coded as “0,” high school diploma/GED as “1,” some college

as “2,” associates degree as “3,” bachelors degree as “4,”

and graduate or professional degree was coded as “5.” Length

of time at address was a continuous interval/ratio variable

reported in months, (median=48). This variable was recoded

to be a categorical (ordinal) variable where 0-12 months was

coded as “0,” 13-35 months coded as “1,” and 36+ was

combined as “2.”

xxxvii

Analysis

Bivariate analysis is beneficial for showing relationships

between dichotomous and nominal, “discrete,” variables. It

allows us to visualize whether or not variables of this type

are related in a statistically significant way. For the

analysis of this data, it is particularly useful to employ

the Chi Square test to assess whether or not comparisons of

the dependent and independent variables show any level of

statistical significance. Moreover, Chi Square shows how

this particular data differ from what we would expect to see

in a particular frequency distribution, if there are indeed

differences.

To test the stated hypotheses, I first run a bivariate

analysis of race/ethnicity (q20race), and the Hispanic

variable (Hispanic_q19) as independent variables against

Overall quality of life in the Las Vegas Valley

(LasVegas_Quality); quality of life in neighborhood

(Neighborhood_Quality); overall perception of Metro

xxxviii

(Metro_Overall); and perception of crime (crime_perception).

Results in Table 4 of the results section.

To assess race/ethnic perceptions of broken windows

characteristics, I run a bivariate analysis of

race/ethnicity against dichotomous codings of the questions

relation to broken windows. “None” is recoded as “0,” with

any presence of the various characteristics recoded together

as “1”. Results are presented in Table 5.

I first run the dependent and control variables against the

disorder index. Results of the OLS regression are shown in

Table 6 below. Logistic regression follows between each of

the dependent variables: neighborhood quality of life,

perception of crime as a problem, perception of the quality

of metro as good in comparison to each of the independent

and control variables. The results of the logistic

regression analyses are presented in Tables 7, 8, and 9.

RESULTS:

xxxix

The following section contains all of the analytic Tables as

well as brief descriptions of the information gathered in

each Table. The hypothesis testing follows the Tables.

Table 4:

Bivariate Relationships of Race/Ethnicity and the

Independent Variables

NeighborhoodQuality“Good”

CrimePerception“Problem”

Metro Overall“Positive”

Race/Ethnicity

N=885 N=882 N=856

White 237/32074.1%

203/32163.2%

283/31390.4%

AfricanAmerican

102/12879.7%

70/12655.6%

99/11883.9%

AsianAmerican

26/3281.3%

15/3246.9%

31/31100%

Am.Indian/Native

8/1080.0%

5/1050.0%

8/988.9%

Hawaiian/Islander

21/2487.5%

11/2445.8%

17/2470.8%

X2/df/Sig. 43.174,5, .000

13.863,5, .017

14.902,5, .011

N=491 N=489 N=482**Hispanic 295/491

60.1%321/48965.6%

415/48286.1%

X2/df/Sig. 36.991,1, .000

3.162,1, .075

1.904,1, .168

Table 4 indicates that the Chi Square statistic (X2) for

race/ethnicity (43.174, df=5) and Hispanic (36.991, df=1)

xl

are both below the expected usual threshold value of 0.05

(0.000), therefore statistically significant. The X2 for

perception of crime (13.863, df=5) and overall perceptions

of Metro (14.902, df=5) show that race/ethnicity is a

significant factor for both categories respectively

(0.017/0.011). No other test variables show statistical

significance for respondents identifying as Hispanic: Las

Vegas Quality (X2=2.375, df=1, 0.123), perception of crime

(X2=3.162, df=1, 0.075), or Metro overall quality (X2=1.904,

df=1, 0.168). Race/ethnicity is significant for every

dependent variable except perceptions of Las Vegas overall,

while identifying as Hispanic is only significant for

neighborhood level perceptions.

Cramer’s V, a measure of the magnitude or the strength of

the comparison between the racial/ethnic categories as they

relate to neighborhood quality as good, crime as a problem,

and Metro as positive report as 0.198/0.058/0.046

respectively. With regard to the Hispanic population

specifically, only the first relationship is statistically

xli

significant. With other racial/ethnic categories, Cramer’s V

statistics are 0.221/0.125/0.132 respectively. With all

racial/ethnic groups represented, we see a statistically

significant relationship, though this is not a strong

relationship as Cramer’s V is reported in a scale of 0-1.

Table 5:

Bivariate Analysis of Race/Ethnicity Compared to Broken

Windows

Broken

Windows

Graffiti

Abandone

d Homes

Broken

Sidewalk

s

Broken

Streetli

ghts

Race/Ethnicity

N=849 N=868 N=864 N=869 N=868

White 140/30645.8%

159/31151.1%

153/31249.0%

157/31450.0%

182/31557.8%

AfricanAmerican

51/12341.5%

64/12850.0%

54/12842.2%

53/12841.4%

73/12657.9%

AsianAmerican

5/3116.1%

7/3221.9%

9/3129.0%

8/3225.0%

14/3145.2%

Am.Indian/ Native

3/1030.0%

7/1070.0%

4/1040%

5/955.6%

4/944.4%

xlii

Hawaiian/Islander

8/2433.3%

5/2420.8%

6/2425.0%

7/2330.4%

13/2454.2%

X2/df/Sig. 28.597/5/.000*

38.885/5/.000*

22.917/5/.000*

27.941/5/.000*

33.493/5/.000*

**Hispanic

N=473269/47356.9%

N=482298/48261.8%

N=478274/47857.3%

N=480291/48060.6%

N=478345/47872.2%

X2/df/Sig. 24.878/1/.000*

22.177/1/.000*

19.118/1/.000*

26.127/1/.000*

25.467/1/.000*

**Hispanic calculated from q19 – Recoded into dichotomous Hispanic =1, Other=0Chi Square of 0.000 less than expected threshold value 0.05, (Statistically Significant)

Table 5 shows the number of respondents and corresponding

percentage values of each race/ethnic group that responded

in the affirmative to the presence of each of the listed

broken windows characteristics. Vacant lots, abandoned cars,

and litter were removed from the Table because they were not

statistically significant, (X2 values p > 0.05). Cramer’s V

for the race/ethnic categories are as follows: broken

windows (0.135), graffiti (0.209), abandoned homes (0.127),

broken sidewalks (0.135), broken streetlights (0.141). When

the Hispanic population is highlighted specifically and

compared to non-Hispanics the strength of relationship

xliii

changes slightly for each: broken windows (0.177), graffiti

(0.202), abandoned homes (0.146), broken sidewalks (0.169),

and broken streetlights (0.200). Again, these are

statistically significant relationships, though they are not

incredibly strong relationships.

Table 6: OLS Linear Regression of Disorder Index (0-16)

with Cultural Factors, Treatment Area and Controls.

(N=888) Model 1 Model 2 Model 3Cultural FactorsHispanic 1.713*** 1.721*** 1.299***Nativity 0.096 0.095 -0.015Spanish -0.603 -0.686 -0.531

TreatmentReceived Saturation Policing -0.250

-0.387

ControlsAge -0.035***Gender -0.637*Education 0.038Length of Time at Address

0.342*R-Square0.038 0.039 0.060

α = .05, p ≤ 0.05*, p ≤ 0.01**, p ≤ 0.001***

xliv

Table 6 shows that Hispanics perceive greater neighborhood

disorder than non-Hispanics. There is not a significant

difference between treatment areas and control areas in

their perceptions of neighborhood disorder. When age,

gender, education, and length of time at address are

included in the analysis, the significant difference between

Hispanics and non-Hispanics decreases slightly from 1.71 to

1.29. Age, tested as the continuous variable, is

statistically significant, indicating that as age increases

ones perceptions of neighborhood disorder decreases. Yet,

the longer one has lived at their current residence, their

perceptions of neighborhood disorder increase. As predicted,

education is not a statistically significant factor in this

analysis. With regard to the R-Square statistic, Model 1

explains 3.8% of the variation within the sample, Model 2

explains 3.9% and Model 3 explains 6.0% of the variation

within the sample. The models explain more variation as

factors are added to the model. Adding the test variable for

neighborhoods receiving saturation policing improves the

variation explained by 1/10th of percent. The addition of

xlv

demographic control variables allows model 3 to explain an

additional 2.1% of the variation in the sample.

xlvi

Table 7: Logistic Regression predicting Neighborhood Quality of Life as “good” with Cultural Factors, Treatment Area, Neighborhood Disorder and Control Variables.

α = .05, p ≤ 0.05*, p ≤ 0.01**, p ≤ 0.001*** Model 1 Model 2 Model 3 Model 4Model 5

HISPANIC 0.422*** 0.444*** 0.444*** 0.627* 0.630*Cultural FactorsNativity 0.981 0.969 0.931 0.907Spanish 0.827 0.688 0.506** 0.437**Treatment AreaReceived Saturation Policing 0.591*** 0.528*** 0.559**Neighborhood DisorderBroken Windows 0.411*** 0.419***Graffiti 0.519** 0.501**Abandoned Houses 0.880 0.895Vacant Lots 0.886 0.872Abandoned Cars 0.893 0.897Litter 0.672* 0.647*Broken Sidewalks 0.870 0.886Broken Lights 0.690 0.761Control VariablesAge (Continuous) 0.993Gender 1.906***Education(Less Than High School)Diploma/GED 0.806Some College 0.451Associates 0.505Bachelors 0.531

47

Graduate Degree 0.535Length of Time at Address(0-12)13-35 1.03336+ 1.399(N=803) R-Squared: 0.037 0.038 0.051 0.190 0.210

48

Table 7 shows the results from a logistic regression analysis

(reported in odds ratios) predicting “Good” neighborhood quality

of life. Looking at Table 7, we see that Hispanics are 58% less

likely than non-Hispanics to report their neighborhood as “Good”.

This ethnic pattern remains consistent across all models even

after adding control variables. In Model 3, I added the

treatment/control variable to the model. I show that residents in

treatment areas have lower odds than those in non-treatment areas

of reporting their neighborhood quality of life as good (41%

lower odds). Model 4, controls for neighborhood disorder with the

addition of eight neighborhood disorder measures. Based on the

results presented in Model 4 we see that three of the eight

measures are significant predictors of neighborhood quality of

life. Residents reporting “broken windows” in their neighborhood

are 59% less likely to report their quality of life as “Good”

compared to those not reporting visible broken windows. Those

reporting graffiti are 48% less likely than those who do not

report graffiti to see neighborhood overall quality as good.

Those reporting litter in their neighborhood are 33% less likely

than those not reporting litter to view their neighborhood

49

quality as good. The ethnic difference between Hispanics and non-

Hispanics remains even after controlling for neighborhood

disorder. Interestingly, the addition of neighborhood disorder

variables renders native language statistically significant. When

these variables are added, we see that Spanish speakers are half

as likely as non-Spanish speakers to report neighborhood quality

as good (0.506). In Model 5, I include the demographic control

variables to the analysis. Gender is the only control variable

that has a statistically significant effect on the test

population. Males are 90% more likely than females to report

neighborhood quality as good. The r-squared statistics is

valuable here. We see that adding neighborhood disorder

characteristics in Model 4 results in a 14% jump in the amount of

variation explained by the model. Controlling for demographic

factors improves the model by only 2%.

50

Table 8: Logistic Regression predicting Perception of Crime as a problem in Neighborhood with Cultural Factors, Treatment Area, Neighborhood Disorder and Control Variables

α = .05, p ≤ 0.05*, p ≤ 0.01**, p ≤ 0.001*** Model 1 Model 2 Model 3 Model 4Model 5

HISPANIC 1.381* 1.675** 1.664** 1.102 0.996Cultural FactorsNativity 1.097 1.096 1.129 1.117Spanish 0.647* 0.688 0.866 0.932Treatment AreaReceived Saturation Policing 1.207 1.248 1.244Neighborhood DisorderBroken Windows 1.560* 1.569*Graffiti 2.741*** 2.713***Abandoned Houses 1.245 1.247Vacant Lots 1.283 1.274Abandoned Cars 0.968 0.947Litter 1.398 1.432Broken Sidewalks 1.342 1.322Broken Lights 1.636* 1.606*Control VariablesAge (Continuous) 0.995Gender 0.907Education(Less Than High School)Diploma/GED 1.182Some College 1.807Associates 1.396Bachelors 1.263

51

Graduate Degree 1.256Length of Time at Address(0-12 months)13-35 Months 0.97436+ 0.523**(N=801): R-Squared: 0.006 0.013 0.014 0.199 0.204

52

Referencing Table 8, we see that the Hispanic population is 38%

more likely than the non-Hispanic population to report that crime

is a problem. When we include the cultural control variables of

Nativity and Spanish, we see that the Hispanic population becomes

nearly 70% more likely (67.5%) than non-Hispanics to report that

crime is a problem in their neighborhood. Those who took the

survey in Spanish are 0.647 times less likely (35.3%) than

English speaking respondents to report crime as a problem.

Controlling for treatment, results in a 1% change for Hispanics

over non-Hispanics, though this not statistically significant.

When the control variables for neighborhood disorder are added to

the regression in model 4, we see that racial/ethnic difference

is no longer statistically significant. Additionally, there is

again a large jump in the R-Squared statistic. Models 1, 2, & 3

together explain about 1.4% of the variation. However, when we

include neighborhood disorder in model 4, the model now explains

almost 20% (19.9) of the variation.

53

Graffiti is the most relevant neighborhood disorder indicator.

Those reporting graffiti are 174% more likely than those who do

not report graffiti to view crime as a problem in their

neighborhood (2.741). Those who report “broken windows” are 56%

more likely than those who do not to see crime as a problem in

their neighborhood. Controlling for demographic factors, we see

that length of time of residence within a neighborhood makes a

difference only once the respondents have resided in the

neighborhood longer than 3 years. Those who have lived in the

neighborhood the longest are 52% more likely than those who are

new to the neighborhood to report crime as a problem.

54

Table 9: Logistic Regression predicting Perceptions of Police with Cultural Factors, Treatment Area, Neighborhood Disorder and Control Variables

α = .05, p ≤ 0.05*, p ≤ 0.01**, p ≤ 0.001*** Model 1 Model 2 Model 3 Model 4Model 5

HISPANIC 0.784 0.494** 0.491** 0.632 0.832Cultural FactorsNativity 0.424** 0.410** 0.383** 0.370***Spanish 1.188 0.946 0.827 0.822Treatment AreaReceived Saturation Policing 0.499** 0.441*** 0.445**Neighborhood DisorderBroken Windows 0.779 0.755Graffiti 0.629 0.661Abandoned Houses 0.873 0.901Vacant Lots 0.702 0.666Abandoned Cars 0.789 0.761Litter 0.805 0.874Broken Sidewalks 0.612 0.576Broken Lights 0.932 0.947Control VariablesAge (Continuous) 1.015Gender 0.919Education(Less Than High School)Diploma/GED 1.146Some College 1.022Associates 2.117Bachelors 1.653

55

Graduate Degree 2.900Length of Time at Address(0-12 months)(13-35 months) 0.937(36 +) 1.126(N=776) R-Squared: 0.002 0.017 0.028 0.072 0.086

56

Hispanics are nearly half as likely as non-Hispanics to

report the overall quality of Metro as “good (0.494 and

0.491).” However, this is only statistically significant for

the models that take into account cultural factors (0.494)

and whether the neighborhood received the saturation

policing treatment (0.491). Race/ethnicity is not

statistically significant with regard to opinions of police

when it is the only factor analyzed. Additionally, it is no

longer significant when we control for neighborhood disorder

factors or demographic variables.

Those born in the U.S. remain approximately 40% less likely

than those born elsewhere to rate the quality of the Metro

police department as “good.” Respondents in treatment areas

are approximately half as likely as those in non-treatment

areas to rate the quality of Metro as “good.” This remains

consistent when controlling for neighborhood disorder as

well as demographics.

57

Again, looking at the R-Squared statistic for each model

shows us that the factors with the most impact in any of the

logistic regression models are the neighborhood disorder

characteristics. With regard to resident perceptions of

police, addition of the neighborhood disorder

characteristics results in a 5.0% increase in the amount of

variation explained by the model.

Hypotheses

H1 a). I expected that Hispanics in Las Vegas would report

lower levels of neighborhood disorder than non-Hispanics.

b).I expected Hispanics would be more likely than non-

Hispanics to report quality of life in neighborhood as

“good.” c). I expected Hispanics would report lower

perceptions of crime than non-Hispanics. d). I expected

Hispanics would be more likely to report that they perceive

the police as doing a “good” job than Non-Hispanics.

If we look at Table 6, we see that being Hispanic is

positively correlated with perceptions of neighborhood

58

disorder. Part “a” of the hypothesis predicted the opposite.

Table 7 shows that Hispanics are less likely than non-

Hispanics to report the overall quality of life in their

neighborhood as “Good,” this is exactly the opposite of part

(b). Additionally, Table 8 shows that Hispanics are more

likely than non-Hispanics to report that crime is a problem,

again this is exactly the opposite of part (c). We can see

from Table 9 that Hispanic respondents are less likely than

non-Hispanic respondents to view the quality of Metro as

“good,” the direct opposite of part (d). We can see from the

analysis that there is no support for the first hypothesis.

[H2] a). I expected that residents living in neighborhoods

that experienced police saturation, would be more likely to

perceive crime as a problem than those in control groups, b)

would report higher levels of neighborhood disorder than

those in control groups, and c) would be less likely to

perceive the police are doing a good job than those in

control groups.

59

Logistic regression (Table 8) shows that respondents in test

neighborhoods are more likely than those in non-treatment

areas to report crime is a problem, this is consistent with

part (a) of the second hypothesis. Linear regression shows

that there is a negative correlation between respondents in

test neighborhoods and their assessment of neighborhood

disorder, inconsistent with part (b) of the hypothesis.

Viewing Table 9, we can see that those in test neighborhoods

were less likely than those in non-test neighborhoods to

respond that the overall quality of Metro was “good,” this

is also consistent with the hypothesis H2(c). This lends

partial support to the second hypothesis. A negative

correlation with neighborhood disorder was not expected.

[H3] a) I expected to show differences in outcomes by

language of origin, such that residents who completed the

survey in Spanish would report higher perceptions of crime

than English-speaking residents, b) lower levels of

neighborhood disorder than English speaking residents, and

c) be more likely than English speaking residents to report

60

the quality of Metro as “good.”

Differences in language were not much of a factor in the

analysis. Where language was statistically significant is

represented in Table 7 and Table 8. Spanish-speaking

respondents were less likely to view crime as a problem in

their neighborhood than non-Spanish speaking respondents.

There was no longer statistical significance when treatment

area was controlled for or demographic control variables

were introduced. If we view Table 6, we see a negative

correlation, though this is not statistically significant.

Language is not a statistically significant factor with

regard to opinions of police. This suggests that there is no

support for the third hypothesis. Language is not

statistically significant with regard to neighborhood

disorder, perceptions of crime, or perceptions of police.

DISCUSSION & CONCLUSION:

This study is limited because there was no direct question

that specifically assessed SES status. Class struggles are

61

similar for all races, and likely shape perceptions

similarly. Though addition of an SES measure would have

enriched the analysis. For analysis of SES, level of

education obtained was substituted for an SES measure.

Education was not found to be statistically significant in

any of the analysis (α = .05, p > 0.05). Future studies

regarding the Hispanic population may benefit from the

addition of SES measures.

Overall, the data is well suited for analysis of perceptions

of police, quality of life in the neighborhood, and

perceptions of neighborhood disorder. It should be noted

that while the Hispanic population is well represented by

this data, making up better than 50% of the study

population, the Hispanic population illustrated by this data

resides in low-income, “high-crime” areas and should not be

assumed to represent Hispanic perceptions across classes.

Hispanic populations in higher income, low crime

environments may exhibit different characteristics.

62

It is important to note that respondents identifying as

“other” made up 41.6% of the sample population when asked to

choose which race/ethnicity they identified with when

Hispanic or Latino options were not available. However, when

a question was presented that allowed respondents to

identify as Hispanic or Latino, 51.6% of respondents chose

an option in this category. A limitation of this study is

that there is not a racial or ethnic category made available

to respondents for every country of origin or every

conceivable ethnic identity. It is conceivable that

providing additional options for racial or ethnic identity

amongst countries considered to be Latin or Central American

countries could have yielded an even higher percentage of

respondents that identified as Hispanic. Even with an

expanded number of ethnicities to choose from, some of the

population that took the survey in Spanish fell into

categories for White, Black, and Native American. This

suggests that American views on race/ethnicity are not

matched to overall ideas about race/ethnicity in the global

63

community and they may be ill suited for assessing

indigenous populations as well.

The study adds to the literature on perceptions of police by

focusing on the Hispanic population. Future studies might

approach a more diverse Hispanic population by focusing on

factors of socio-economic status, venturing out of the

studied high crime areas. Further, more complex methods of

statistical analysis may be able to flesh out more in-depth

conclusions from the existing SPI data file. The Hispanic

population remains one of the fastest growing populations in

the United States and it is imperative that social

scientists in every area make an effort to add this

demographic as a focal point of inquiry.

In line with suggested changes to policing from the

literature, perceptions of police may be improved through

efforts to diversify staffing. The closer the demographics

of the department match the demographics of the population

they serve, the more likely the community is to cooperate

64

with policing efforts. The use of integrated teams also

shows promise in improving the efficacy of policing in

diverse environments. A continued effort to focus on the

needs of the community, paying close attention to the

varying needs of culturally diverse residents, is likely to

improve relations between residents and the police.

This study has several implications for law enforcement and

community planners. First, the fact that the hypotheses,

developed from the literature, were not supported indicates

that sample populations in high-crime environments in Las

Vegas do not match the studied populations from the rest of

the literature pertaining to perceptions of police. This

study is the first of its kind in Las Vegas, and the first,

in comparison with the past studies, to isolate high-crime

environments as a focal point for analysis. That high-crime

neighborhoods in Las Vegas do not exhibit similar

homogeneity to other study samples may be indicative that

Las Vegas differs with regard to other cities with regard to

racial segregation. Many neighborhoods, at all ends of the

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socio-economic spectrum tend to demonstrate a diverse

racial/ethnic mix. It will be necessary to conduct similar

studies in cities across the United States to see whether

this is something isolated to Las Vegas. Many of the

previous studies seem to have been conducted in the Eastern

part of the country. Performing similar studies in other

areas would help to determine whether this is an isolated

feature of Las Vegas a broader regional characteristic of

Western cities overall.

Second, the change in variation demonstrated by the addition

of neighborhood disorder characteristics provides strong

support for the previous work of Kelling and Wilson. When

broken windows variables are added to the models,

race/ethnicity is either drastically reduced in significance

or no longer statistically significant at. This indicates

that efforts to improve neighborhoods by reducing clutter,

graffiti, and damage to buildings and property are the

factors that have the largest impact on individual

perceptions of crime and the police. Additionally, they have

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a large impact on how a respondent views the quality of

their neighborhood. These factors are far more important

than race/ethnicity or any of the demographic variables

included in the models. It may be prudent to revisit

previous studies and incorporate neighborhood disorder

variables to see if racial/ethnic differences continue to be

significant.

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APPENDIX A: Survey

NEIGHBORHOOD #_________________DATE_____________________

I would like to speak with a member of this household who isat least 18 years old. If there are multiple members over the age of 18, I would like to speak to the resident who most recently celebrated a birthday.

I’m here on behalf of a research project sponsored by the Sociology Department at the University of Nevada Las Vegas. We’re interviewing residents about neighborhood issues, suchas quality of life, crime, and disorder. We would really appreciate your participation in our survey. It should onlytake about 15 minutes. Your participation in this survey is strictly voluntary. You may choose not to take part at all. If you decide to participate in this survey, you may stop at any time and may skip any questions that you are notcomforTable answering. All answers are strictly confidential and used only for research purposes. Your namewill not be attached to any research reports.

LET ME BEGIN BY ASKING YOU SOME QUESTIONS ABOUT YOUR NEIGHBORHOOD.

[QUALITY OF LIFE / NEIGHBORHOOD]

1. Please rate the overall quality of life in your neighborhood today.

1- Very good2- Fairly good3- Not very good4- Not at all good

2. If you could live where you want, would you…

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1 – Stay at your current address2 – Move from your current address to another Las Vegas Valley location3 – Move to another location in Nevada4 – Move outside of Nevada

3. Generally speaking, how would you rate crime as a problem in your neighborhood?

1- Very big problem2- Somewhat of a problem3- Not much of a problem4- No problem at all

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4. Please indicate how much of each type of activity, as far as you can tell, seems to be taking place in your neighborhood.

Not Very Often

Somewhat Often

Very Often All the Time

Vandalism, suchas, graffiti, slashing tires)Disorderly Behavior, such as rowdy, unsupervised teens.Car Break-insHome Break-insDomestic Assaults (in homes)Assaults outside of homesGang activityDrug activitySexual AssaultsRobbery

5. How safe do you feel when walking alone at night on your block?

Very safe................1

Somewhat safe............2

Somewhat unsafe..........3

Very unsafe..............4

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DON’T KNOW...............-8

REFUSED..................-9

6. Overall, how physically safe from crime do you feel in your neighborhood?

1- Very safe2- Somewhat safe3- Not very safe4- Not safe at all

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7. Ok, now I’m going to ask you some questions about thephysical conditions of your block. For each question please respond with none, a few or many.

NONE

A FEW

MANY

DON’T KNOW

REFUSED

7a. Are there any homes or buildings with broken windows on your block?

1 2 3 -8 -9

7b. Are there any homes, other buildings or other places on your block which have graffiti on them?

1 2 3 -8 -9

7c. Are there any abandoned or boarded up homes or buildings on your block?

1 2 3 -8 -9

7d. Are there any vacant lots on your block? 1 2 3 -8 -9

7e. Are there any abandoned cars on the street on your block?

1 2 3 -8 -9

7f. Are there areas on your block where litter is a problem?

1 2 3 -8 -9

7g Are there areas on your block where the street or sidewalk needs repairs?

1 2 3 -8 -9

7h. Are there areas on your block that needbetter lighting?

1 2 3 -8 -9

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8. Ok, now I’m going to ask you some questions about the Las Vegas Metropolitan Police Dept.

[READ QUESTION, THEN RESPONSE OPTIONS:

During the past 2 months… (OR IN THE PAST 60 DAYS)

Once a month or less, a few times a month, a few times a week, everyday, not at all]

ONCE A MONTH OR LESS

A FEW TIMES A MONTH

A FEW TIMES A WEEK

EVERYDAY

NOT AT ALL

DON’T KNOW

REFUSED

8a. How often have you seen [METRO] police officers in your neighborhood? [PROBE: DOING ANYTHING]

2 3 4 5 1 -8 -9

8b. How often have you seen the [METRO] police talking topeople in your neighborhood?

2 3 4 5 1 -8 -9

8c. How often have you seen the [METRO] police searching people in your neighborhood?

2 3 4 5 1 -8 -9

8d. How often have you seen the [METRO] police arresting people in your neighborhood?

2 3 4 5 1 -8 -9

8e. How often have you called the [METRO] police to report about something in your neighborhood?

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9. Overall, do you think the [LAS VEGAS METRO] police are doing……

A very good job..........1

A good job...............2

A fair job...............3

A poor job...............4

A very poor job..........5

DON’T KNOW...............-8

REFUSED..................-9

10. Please tell me if you strongly agree, agree, disagree, or strongly disagree with the following statements about the [LAS VEGAS METRO] police.

STRONGLY AGREE

AGREE

DISAGREE

STRONGLY DISAGREE

DON’T KNOW

REFUSED

10a. I have a lot of respect for the[METRO] police.

1 2 3 4 -8 -9

10b. On the whole [METRO] police officers are honest.

1 2 3 4 -8 -9

10c. I feel proud of the [METRO] police.

1 2 3 4 -8 -9

10d. I am very supportive of the 1 2 3 4 -8 -9

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[METRO] police.

10e. The [METRO] police treat peoplefairly.

1 2 3 4 -8 -9

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11. How likely is it that you would call the police if each of the following situations happened tomorrow: Do you think it is very likely, likely, unlikely or very unlikely.

VERY LIKELY

LIKELY

UNLIKELY

VERY UNLIKELY

DON’T KNOW

REFUSED

11a. You have a complaint againstsomeone causing problems on your block?

1 2 3 4 -8 -9

11b. You have an emergency situation?

1 2 3 4 -8 -9

11c. You see suspicious activity on your block?

1 2 3 4 -8 -9

The following questions are for descriptive (statistical) purposes.

12. First, in what year were you born? __________________

13. How many years have you lived at your current address? If less than a year, enter the number of months; if more than a year, round up.________________

14. How many years have you lived in Las Vegas, total?__________________________

15. Please rate the overall quality of life in the Las Vegas Valley today.

a. Very good

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b. Fairly goodc. Not very goodd. Not at all good

16. What is your current marital status?

1 – Married2 – Single3 – Divorced4 – Widowed3 – Separated4 Living with a partner

17. Which of the following best describes your currentemployment or labor force status? (CHOOSE ONLY ONE)

1 – Work full-time 2 – Work part-time3 – Unemployed, looking for work4 – Unemployed, not looking for work 5 – A full-time student 6 – A homemaker 7 – Retired 8 – Other9 – Refuse to answer

18. What is the highest level of education you have completed?

1 – 0-11 years, no diploma2 – High school graduate (including GED)3 – Some college, no degree4 – Associate Degree5 – Bachelor’s Degree6 – Graduate or professional degree

19. Do you consider yourself to be Spanish/Hispanic/Latino?

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1 – No, not Spanish/Hispanic/Latino2 – Yes, Mexican3 – Yes, Puerto Rican4 – Yes, El Salvadorian5 – Yes, other Spanish/Hispanic/Latino

20. With which racial group do you identify yourself? [ALLOW MULTIPLE RESPONSES]

1 – White/Anglo2 – African American3 – Asian or Asian American4 – American Indian or Native American5 – Native Hawaiian or Pacific Islander5 – Other

21. Were you born…

1 – In Las Vegas2 – In Nevada but not in Las Vegas3 – In the US but not in Nevada4 – In another country outside of the U.S.

22. Are you...

1 – Male2 – Female

23. Do you have children under the age of 18 living in your home?

1 – YES2 – NO

24. Do you own or rent your current home?

1 – Own2 – Rent3 – Other

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25. Have you or any member of your household been a victim of a crime in the past….

a. 60 days? Yes……....................1

No.......................0

DON’T KNOW...............-8REFUSED..................-9

a. 6 months?Yes……....................1

No.......................0

DON’T KNOW...............-8

REFUSED..................-9

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Christopher J. PercyUniversity of Nevada-Las Vegas, Department of Sociology4505 Maryland Parkway, Box 455033, Las Vegas, NV 89084percyc@unlv.nevada.edu, or Chrispercy75@gmail.com

Education

Present Ph.D. Student, University of Nevada-Las Vegas, Las Vegas, Nevada

2012 Bachelor of Arts in Sociology, Cum LaudeUniversity of Nevada-Las Vegas, Las Vegas, Nevada

2010 Associate of Arts Oregon Transfer, High Academic Honors

Treasure Valley Community College, Ontario, Oregon

Research and Teaching Interests

Environment, Critical Theory, Demography, Quantitative Methods, Academic Success/Leadership

Honors and Awards

2012 Undergraduate Commencement Speaker – UNLV Fall Commencement

2012 Alpha Kappa Delta Sociology Honor Society

2012 Golden Key International Honor Society

2012 National Society of Leadership and Success

2010 Orlowski Candidate/Orlowski Scholarship AwardDistinguished International Officer Candidate

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2010 NASA National Community College Aerospace Scholar

2010 All USA Academic Team/Coca-Cola Bronze Scholar

2010 Regional President, Alumni AssociationRocky Mountain Cascade Region of Phi Theta Kappa• Founded in July of 2010 – Founding Member

2009-2010 Regional President, Rocky Mountain Cascade RegionPhi Theta Kappa• Distinguished Regional Officer Team – 2010• Regional Milestone Award for Most Improved

Region

2009 Certificate of CompletionPhi Theta Kappa Leadership Development Cohort

2009 Phi Theta Kappa Honors Scholar – Honors Institute Completion

Presentations

2014 “Everyday Life in a Las Vegas ‘High Crime’ Environment.” Working paper presented at the annual meeting of the Pacific Sociological Association, Portland, OR. March 2014.

2012 “Access Through Membership: How non-profit organizations are improving opportunities for future success regardless of race, gender, and class.” Undergraduate roundTable discussion at annual meeting of the Pacific Sociological Association, San Diego, CA. March 2012.

2010 Welcome Speaker for the “Diversity Symposium.” A joint project between the Diversity Council and the Omicron Phi Chapter of Phi Theta Kappa modeled

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after the Medical Teams International “Real Life Exhibit”.

2010 Presenter. Regional Honors in Action Conference atMt. Hood Community College, a weekend long training session for chapter advisors and officersfor the Phi Theta Kappa chapters at 23 community colleges across Idaho, Oregon, and Utah.

2010 Presentation on “Hands of Hope Northwest.”Lead chapter in assembly of “hope kits” containingneeded sanitary items.

2009 Presenter. Tillamook Training Toolkit at TillamookCommunity College, a weekend long training sessionfor chapter advisors and officers for the Phi Theta Kappa chapters at 23 community colleges across Idaho, Oregon, and Utah.

2009 Keynote Speaker. Fall Induction Ceremony for Omicron Phi Chapter of Phi Theta Kappa. Treasure Valley Community College

Research and Internship Experience

2012 Smart Policing Initiative Graduate Assistant, Department of Sociology

2011 Independent research on group involvement in collegiate environments and the role of membershipas a benefit to completion guided by Dr. Parker

2009-2010 Administrative Assistant for the Dean of Leadership DevelopmentPhi Theta Kappa Leadership Development Program.

2008-2009 Teaching Assistant, Biology Department, TVCC

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Professional Affiliations

2014 Pacific Sociological Association

2011 Pacific Sociological Association

2011 Southwest Social Science Association

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