Final thesis!!

Warrantless Search
and Seizures
Greg Keogh
Dr. Verrill
Senior Thesis
St. Gregory’s University
May 7th, 2015

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Abstract
In this study, the research explores the factors that are important predictors for the
amount of illegal items found, if any, during traffic stop searches. Using scholarly
research and studies, such as Stoughton’s research into the effectiveness of “the search
incident to arrest exception”, many different factors which contribute to law enforcement
officials being forced to conduct searches and seizures were investigated in order to find
how the frequency of police searches impacts the amount of evidence found. Some of the
factors investigated include the many exceptions to the warrant requirement, such as the
consent search exception, the amount of illegal items found, and was there consent given
to conduct a search. Using quantitative research, the relationship between both
independent and controlled variables and illegal items found, if any, during traffic stop
searches is investigated. The variables were obtained from the Inter-university
Consortium for Political and Social Research (ICPSR) dataset (20020), which includes
63,237 events of police-public contact for the period of July 1st to December 31st 2005.
The Police-Public Contact Survey (PPCS) was designed by the Bureau of Justice
Statistics (BJS) to document contacts and interactions between the police and the public.

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Table of Contents
Abstract...........................................................................................................................................................................ii
Chapter 1: Introduction..........................................................................................................................................1
Background of the Problem.........................................................................................................................1
Statement of the Problem..............................................................................................................................1
Purpose of the Study.........................................................................................................................................2
Theoretical Framework..................................................................................................................................2
Scope of the Study..............................................................................................................................................3
Definition of Terms...........................................................................................................................................3
Summary.................................................................................................................................................................4
Chapter 2: Literature Review .............................................................................................................................5
Hypothesis Statements.....................................................................................................................................7
Chapter 3: Methodology.......................................................................................................................................8
Data Collection....................................................................................................................................................8
Unit of Analysis...................................................................................................................................................9
Measurements......................................................................................................................................................9
Dependent Variable ......................................................................................................................................9
Independent Variables.................................................................................................................................9
Control Variables........................................................................................................................................10
Limitations..........................................................................................................................................................10
Chapter 4: Results .................................................................................................................................................12
Logistical Modeling.......................................................................................................................................12
Summary Statistics.........................................................................................................................................13
Analysis of the Results.................................................................................................................................16
Testing the Hypotheses................................................................................................................................20
Chapter 5: Summary and Conclusion..........................................................................................................28
Summary..............................................................................................................................................................28
Limitations..........................................................................................................................................................28
Recommendations for Future Research ............................................................................................29
Conclusions........................................................................................................................................................29
Bibliography.............................................................................................................................................................31

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Chapter 1: Introduction
Background of the Problem
Police searches and seizures are a necessary exercise in the ongoing pursuit of
crime and justice within our society. Therefore, officers are delegated with the authority
to conduct investigations, make arrests, perform searches and seizures, and when
necessary, use lethal force in the line of duty. However, this power must be exercised
within the boundaries of the law. Therefore, the warrant requirement is used within the
United States justice system as the chief means of balancing the need for efficient and
effective law enforcement, against the need to protect the rights of individual citizens to
be secure against unreasonable searches and seizures. The warrants procedure is preferred
because it places responsibility for deciding the delicate question of probable cause with
a neutral and detached judicial officer and also helps to protect the Fourth Amendment
rights of citizens. However, situations often arise, in which the officers are not presents
with enough time or do not have the means to obtain a justifiable search warrant. To
make sure that the delicate balance between citizens Fourth Amendment rights and the
law is maintained, the courts have made various exceptions to the warrant requirements.
One of these many exceptions that is investigated during this research is the consent
exception.
Statement of the Problem
This research addresses the effectiveness of the consent search exception and how
this exception also serves as a potential benefit to those that consents to a search, as it
may prevent a more time-consuming and intrusive police intervention. The Constitution

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of the United States of America, under the Fourth Amendment, protects the citizens from
unreasonable searches and seizures by government officials. These rights force officers to
consistently follow a clear standard when it comes to privacy and the idea of innocent
until proven guilty. The level of protection available under the Fourth Amendment in
each particular case is dependent on the nature of the detention or arrest, the
physiognomies of the place searched, and the circumstances under which the search takes
place.
Purpose of the Study
The purpose of this study is to research and explore the factors that are important
predictors for the amount of illegal items found, if any, during traffic stop searches.
Using quantitative research, the relationship between independent variables, controlled
variables, and the illegal items found variable is investigated.
Theoretical Framework
The social learning theory is the most relatable theory and best possible choice for
this research because it proposes that both lawful and unlawful behaviors are acquired,
maintained, or changed by the same process of interaction with others. The variance lies
in the unlawful or deviant sway or balance of the social influences such as reinforcement,
values and attitudes, and imitation. For example, many criminals have begun to use their
Fourth Amendment rights and refuse to give consent to a police search, even though a
search may be inevitable. This is most often because of their negative social influences.

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Scope of the Study
This study will be conducted by using quantitative research of an already existing
data set. As the individual conducting this particular study is using secondary data and
not conducting the original research, there will be some limitations and challenges met
throughout the study. For example, there may be minimal or missing information and
data for certain variables of particular interest, such as the “Driver Consented to Vehicle
Search” variable. Another limitation is that the individual conducting this particular study
can only use the cases that have a response for the “Illegal Items Found” variable, as it is
the dependent variable of this study.
Definition of Terms
4th amendment - Part of the Bill of Rights that protects the citizens of the United States by
prohibiting unreasonable searches and seizures from government officials and
requiring any warrant to be judicially authorized and supported by probable cause.
Consent – An agreement or to give permission for something to happen.
Search – An attempt to find something illegal by looking or otherwise seeking carefully
and thoroughly through a property or individual.
Search Warrant - A judicial document that authorizes a government official to search a
private property or an individual in order to obtain evidence for presentation in
criminal prosecutions.
Warrant Exception – A situation in which a search and seizure may be conducted without
the possession of a search warrant.

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Summary
This study will research the effectiveness of the warrant exceptions, in particular
the consent search exception, and also aim to identify specific factors that may prove to
be important predictors for the amount of illegal items found, if any, during traffic stop
searches.

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Chapter 2: Literature Review
Many situations often arise, in which law enforcement officers may not be
presented with enough time or just simply do not have the resources available to obtain a
justifiable search warrant. To make sure that the delicate balance between citizens Fourth
Amendment rights and the law is maintained, the courts have and continue to create
various exceptions to the warrant requirements, which allows for warrantless searches.
Some of these exceptions include when the search conducted after arrest is lawful, when
the motor vehicle being searched was deemed capable of being able to evade the scene
and destroy any evidence before a warrant may be obtained, when any evidence obtained
was in plain view, when consent is given by the suspect, or when an emergency arises
(Mullenbach).
There are many different factors that can contribute to law enforcement officials
being forced to conduct searches and seizures, without a search warrant. . Research
shows that in 2009, law enforcement officers carried out over 13.5 million searches were
carried out without a warrant, under the “search incident to arrest” exception alone
(Stoughton). Some of the factors that can contribute to this high level of warrantless
searches may include, but are not limited to, the many exceptions to the warrant
requirement, such as the search incident to arrest exception and the consent search
exception, the area of society in which the search takes place, the general object of the
search and seizure, the criminal focus of the search, did the driver of a traffic stop consent
to a vehicle search, did a suspect consent to a personal search, was there a search of the
surrounding area conducted after an arrest to ensure the safety of officers and to ensure
that no evidence is destroyed.

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Mullenbach’s research suggests that the amount of warrantless entries into private
residences to prevent the destruction of criminal evidence and for the protection of the
officers are increasing under the newly emerging exceptions to the warrant requirement
of the fourth amendment, such as the search incident to arrest exception. However many
of these entries can only be considered valid if law enforcement officers believe that
evidence is threatened with imminent destruction by nature, the defendant, or third parties
(Mullenbach). This leads us to believe that the more searches incident to arrest are made,
the less criminal evidence is destroyed.
Another scenario that can lead to the search and seizure of criminal evidence,
without the use of a warrant requirement, is the consent exception. Law enforcement
officers may request consent to search an individual, a premises, or motor vehicle, at any
time or place, providing that consent is voluntary. The U.S Supreme Court has approved
the consent exception to the warrant requirement for two main reasons; to encourage
citizens to cooperate with law enforcement and to potentially benefit an individual that
consents to a search, as it may prevent a more time-consuming and intrusive police
intervention. Research suggests that consent searches, under the consent search
exception, have become a common means to the war on drugs in the United States, as one
of the most frequent uses of this exception occurs between law enforcement officers and
automobile drivers (Davis).

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Hypothesis Statements
H1: As the police gave a reason for the stop (V35) increases, the chances of
illegal items found (V56) also increases.
H2: As the police asked to search the vehicle (V49) increases, the amount of
illegal items found during traffic stop and search (V56) decreases.
H3: As the police asked to search the driver (V51) increases, the amount of illegal
items found during traffic stop and search (V56) decreases.
H4: As the driver consents to a vehicle search (V53) increases, the amount of
H5: As the driver consents to a driver search (V54) increases, the amount of
H6: As the police conducted a search of the vehicle (V50) increases, the amount
of illegal items found during traffic stop and search (V56) decreases.
H7: As the police conducted a search of the driver (V52) increases, the amount of
H8: As the frequency of illegal items found during traffic stop and search (V56)
increases, the chances of an arrest being made during contact (V61) increases.

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Chapter 3: Methodology
This research explores the factors that are important predictors for the amount of
illegal items found, if any, during traffic stop searches. Using quantitative research, the
relationship between both independent and controlled variables and illegal items found, if
any, during traffic stop searches is investigated.
Data Collection
The variables were obtained from the Inter-university Consortium for Political
and Social Research (ICPSR) dataset (20020), which includes 63,944 events of police-
public contact for the period of July 1st to December 31st 2005. The Police-Public Contact
Survey (PPCS) was designed by the Bureau of Justice Statistics (BJS) to document
contacts and interactions between the police and the public. The PPCS was conducted for
the BJS during the last six months of 2005 by the U.S Census Bureau, as a supplement to
the National Crime Victimization Survey (NCVS). The NCVS sample consisted of
80,238 individuals, however about 20% of this sample, or 16,294 individuals, were
excluded from the PPCS as non-interviews or as proxy interviews. The ICPSR dataset
has 129 variables, including variables that measure the sex of the respondent, the race of
the respondent, the reason for police contact, did the police ask for consent to search the
vehicle, did the driver consent to a personal search, was there illegal items found during
the traffic stop, and what type of evidence was found.

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Unit of Analysis
The unit of analysis of the research is the 63,944 respondents aged 16 and older
that took part in the Police-Public Contact Survey (PPCS), which was designed by the
Bureau of Justice Statistics (BJS) to document contacts and interactions between the
police and the public) during the last six months of 2005. However, a subset of data (63
interactions) was used for the purpose of this study due to the large amount of missing
data for the dependent variable: Illegal items found during traffic stop and search.
Measurements
Dependent Variable
The dependent variable is illegal items found during traffic stop and search
(V56), if any, during traffic stops leading to searches and is found in the
“Vehicle/Personal Search” group of variables. In the ICPSR dataset, the variable has 2
categories: no = 0 and yes = 1
Independent Variables
Firstly, this research attempts to answer what influence the frequency of traffic
stops have on the success of illegal items being found during a resulting search of the
individual and/or the vehicle? Also, this research attempts to answer what influence the
frequency of traffic stops, leading to “consent searches”, have on the success of illegal
items being found during a resulting search of the individual and/or the vehicle? To
answer these questions, eight different independent variables will be evaluated to
measure the success of traffic stop;

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Police Gave Reason for the Stop (V35): No = 0 and Yes = 1
Police Asked to search The Vehicle (V49): No = 0 and Yes = 1
Police Asked to Search Driver (V51): No = 0 and Yes = 1
Driver Consented to Vehicle Search (V53): No = 0 and Yes = 1
Driver Consented to Search of the Driver (V54): No = 0 and Yes = 1
Police Conducted Search of the Vehicle (V50): No = 0 and Yes = 1
Police Conducted Search of Driver (V52): No = 0 and Yes = 1
Arrested During Contact - Traffic (V61): No = 0 and Yes = 1
Control Variables
There are two main factors in the literature that are proven to affect the amount of
illegal items found, if any, during traffic stops leading to searches. These variables are
controlled for; however they are not the variables of interest in this study. These two
variables are the age of the respondent, and the race of the respondent.
Age of Respondent (AGECAT6): 16 = 16-19, 20 = 20-29, 30 = 30-39, 40 = 40-
49, 50 = 50-59, 60 = 60 or older
Race of respondent (V5V6): White non-Hispanic = 1, Black non-Hispanic = 2,
Hispanic = 3, other non-Hispanic = 4
Limitations
The first challenge that was met was, as an already existing dataset was chosen
for this particular research and study, there was no information or research into one
specific variable, ’criminal evidence destroyed’’. For this reason, one particular
hypothesis that was of a certain interest to the individual conducting the research could
not be created and therefore was not able to be tested. The second challenge that was met
was the lack of investigation into warrantless searches during traffic stops and accidents.
There were only very few variables that were readily available to help conduct more

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extensive research into warrantless searches and seizures and the many exceptions
involved; these variables are police asked to search the vehicle, police asked to search
driver, driver consented to vehicle search, and driver consented to personal search. All of
which fall under the consent exception to the warrant requirement. Finally, the biggest
problem that was met was, as I was using an already existing data set, most of the 63,237
respondents had missing data that was important for this particular study. For this reason,
a subset of data, comprising of 63 interactions, was needed to be made in order to get
more accurate results.

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Chapter 4: Results
Logistical Modeling
In this research, the illegal items found during traffic stop and search variable is a
binary response ranging from 0 (no) to 1 (yes). This indexing is a latent, but continuous
descriptor of the response. A logistical model can be used for the data and is knows as a
proportional odds model. When the assumption of a linear relationship between the
dependent and the independent variables is relaxed, it is still possible to make an
assumption about the function of the dependent and independent variables. In an ordered
logistical model, the likelihood function for the conditional density of Y1,…,Yn (the
dependent variables) given X1,…,Xn (the independent variables), is a function of the
unknown parameters 1. There are three ordered logistical regression models being
estimated;
Pr(the amount of illegal items found (V56), if any, during traffic stops leading to
searches) = f(ß0 + ß1(Police Gave Reason For The Stop (V35)) + ß2) (police
Asked To Search The Vehicle (V49)) + ß3(Police Asked to Search Driver (V51)) +
+ ß4(Arrested During Contact - Traffic (V61)) + ß5(Age of Respondent
(AGECAT6)) + ß6(Race Of Respondent (V5V6))
searches) = f(ß0 + ß1(Police Gave Reason For The Stop (V35)) + ß2(Driver
Consented to Vehicle Search (V53)) + ß3(Driver Consented to Driver Search
(V54)) + ß4(Arrested During Contact - Traffic (V61)) + ß5(Age of Respondent

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searches) = f(ß0 + ß1(Police Gave Reason For The Stop (V35)) + ß2(Driver
Conducted Search of Vehicle (V50)) + ß3(Police Conducted Search of Driver
(V52)) + ß4(Arrested During Contact - Traffic (V61)) + ß5(Age of Respondent
Summary Statistics
There are several summary statistics that provide a description of the variables
used in the model. Table 1 shows the frequency distribution and percentages for the
illegal items found during traffic stop and search variable. The unit of analysis of the
research is the 63,944 respondents aged 16 and older that took part in the Police-Public
Contact Survey (PPCS), which was designed by the Bureau of Justice Statistics (BJS) to
document contacts and interactions between the police and the public) during the last six
months of 2005. However, a subset of data (63 interactions) was used for the purpose of
this study due to the large amount of missing data for the dependent variable: Illegal
items found during traffic stop and search. Furthermore, from this study one could
conclude that less than 10% of incidents involve an officer stopping and searching an
individual or vehicle, which resulted in the officer finding illegal items.

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Table 1 Frequency Distribution and Percentages for Illegal Items Found During Traffic Stop Search
Illegal Items Found During Traffic Stop Search Frequency Percentage
0. No 57 90.48%
1. Yes 6 9.52%
N = 63 100.00%
The second is the descriptive statistics (Table 2) for each of the variables. The
summary statistics provide a different view of the variables used to predict the perceived
amount of illegal items found during stop and searches. For example, the police asked to
conduct a search of the vehicle variable (V49) has a mean of .67, which suggested that
although some officers ask to conduct a search of the vehicle when reasonable cause
presented itself, this practice is not excessive.

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Table 2 Descriptive Statistics for All the Variables
Variables
Cases
(n) M SD Min Max
Dependent Variable
1. Illegal Items Found During Traffic Stop and
Search (V56) 63 0.10 0.30 0.00 1.00
Independent Variables
2. Police Gave Reason For The Stop (V35) 61 0.93 0.25 0.00 1.00
3. Police Asked To Search The Vehicle (V49) 63 0.67 0.48 0.00 1.00
4. Police Asked To Search The Driver (V51) 63 0.41 0.50 0.00 1.00
5. Driver Consented To Vehicle Search (V53) 62 0.63 0.49 0.00 1.00
6. Driver Consented To Search Of The Driver
(V54) 60 0.38 0.49 0.00 1.00
7. Police Conducted Search of The Vehicle
(V50) 62 0.90 0.3 0.00 1.00
8. Police Conducted Search of The Driver
(V52) 63 0.63 0.49 0.00 1.00
9. Arrested During Contact - Traffic (V61) 63 0.30 0.46 0.00 1.00
Controlled Variables
10. Age Of Respondent - 6 Groups
(AGECAT6) 63 25.94 10.34 16.00 50.00
11. Race Of Respondent (V5V6) 63 1.71 0.92 1.00 4.00

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Analysis of the Results
The first analysis is a correlation table (Table 3), which shows the correlation of
each variable with each of the other variables. A positive correlation tells us that
increasing values of one of the variables is associated with increasing values of the other
variable or decreasing values of one of the variables is associated with decreasing values
of the other variable, in other words, the variables are moving in the same direction.
However, a negative correlation tells us that increasing values of one of the variables is
associated with decreasing values of the other variable. If any of the predictor variables
are highly correlated with each other, than there may be a collinearity problem and
additional diagnostics would need to be performed to determine the severity of the
problem.
Although none of the correlation coefficients exceed 0.80, the analysis does
produce a couple of note-worthy relationships. First, there is a moderately positive
correlation (.64 and statistically significant at .10) between the police asked to search the
driver variable (V51) and the police conducted search of the driver variable (V52). This
suggests that as the amount of police searches on vehicle drivers increases, the police
asking to search the driver of the vehicle also increases. The same positive relationship
(.32 and statistically significant at .10) exists between the police asked to search the
vehicle variable (V49) and the police asked to search the driver variable (V51). This
makes senses because both the search of the vehicle and the search of the vehicle driver
come hand-in-hand as part of an officer’s routine during police stops.

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Table 3. Pearson's Pairwise Correlations (N = 61)
Variables 1 2 3 4 5 6 7 8 9 10 11
1. Illegal Items Found During Traffic Stop and Search
(V56) 1
2. Police Gave Reason For The Stop (V35) -.20 1
3. Police Asked To Search The Vehicle (V49) -.23* .11 1
4. Police Asked To Search The Driver (V51) -.16 -.04 .32* 1
5. Driver Consented To Vehicle Search (V53) -14 .21* .68* .31* 1
6. Driver Consented To Search Of The Driver (V54) -.07 -.06 .24* .76* .32* 1
7. Police Conducted Search of The Vehicle (V50) .11 .13 .34*
-
.29* .31* -.32* 1
8. Police Conducted Search of The Driver (V52) .02 -.07 .09 .64* -.04 .58* -.25* 1
9. Arrested During Contact - Traffic (V61) .14 .16 -.12 .01
-
.24* -.01 -.03 -.21* 1
10. Age Of Respondent - 6 Groups (AGECAT6) -.09 -.08 -.10 -.15 -.12 -.13 .09 -.38* -.04 1
11. Race Of Respondent (V5V6) -.08 .06 -.00 .02 .04 -.06 .00 .02 .17 -.09 1
* p < 0.10

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Second, there is a negative, statistically significant (at the .05 level) relationship
between the amount of illegal items found variable (V56) and the police asked to search
the vehicle variable (V49), and also the amount of illegal items found variable (V56),
and the police asked to search the driver variable (V51). This suggests that as an officer
asks a vehicle driver for consent to either search the car or the driver, there is a decrease
in the amount of evidence found during the search. Another interpretation is that vehicle
drivers generally do not give consent if they have nothing illegal to hide.
The final interesting relationship is that there is a negative, statistically significant
(at the .05 level) relationship between the age of the respondent variable (AGECAT6)
and the police conducted search of the driver variable (V52). This negative relationship
between the two variables suggests that as the age of the vehicle driver increases, the
chance that an officer will conduct a search of the driver decreases.
The goal of this research is to identify the factors that contribute to illegal items
found during traffic stop search. Tables 4-6 reports the results for the logistical model
that looks at the determinants of illegal items found during traffic stop search. The model
includes the explanatory and the control variables outlined above. The unit of analysis is
the respondent of the survey. In other words, the results of the model indicate the effects
of the independent variables on the probability of illegal items found during traffic stop
search.

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Testing the Hypotheses
This research seeks to explore the relationship between the amount of illegal
items found during traffic stop and searches and multiple diverse contributing factors,
such as the reason for the stop in the first place and consent given from the driver to
search the vehicle. As a result, there were eight hypotheses that were tested.

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H1: As the police gave a reason for the stop (V35) increases, the chances of
illegal items found (V56) also increases.
H2: As the police asked to search the vehicle (V49) increases, the amount of
H3: As the police asked to search the driver (V51) increases, the amount of illegal
items found during traffic stop and search (V56) decreases.
H4: As the driver consents to a vehicle search (V53) increases, the amount of
H5: As the driver consents to a driver search (V54) increases, the amount of
H6: As the police conducted a search of the vehicle (V50) increases, the amount
of illegal items found during traffic stop and search (V56) decreases.
H7: As the police conducted a search of the driver (V52) increases, the amount of
H8: As the frequency of illegal items found during traffic stop and search (V56)
increases, the chances of an arrest being made during contact (V61) increases.
To test these hypotheses, the correlation and logistical regression model are used.
The first of the eight hypotheses assumes that as the frequency of law enforcement
officers giving a reason for an initial stop increase, then the chances of finding illegal
items also increases. The variable “Illegal Items Found during Traffic Stop Search (V56)”
is coded no = 0 and yes = 1, and the “Police Gave Reason for the Stop (V35)” variable is
coded no = 0 and yes = 1. Table 3, the correlation table, shows that “Police Gave Reason

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for the Stop” is- 0.20 in relation to “Illegal Items Found during Traffic Stop Search” and
it is not statistically significant at .10. Table 4, the logistical regression model, shows that
“Police Gave Reason for the Stop” is – 2.39 and it is statistically significant at 0.10.
Table 5, the logistical regression model, shows that “Police Gave Reason for the Stop” is
– 2.73 and it is statistically significant at 0.10. Table 6, the logistical regression model,
shows that “Police Gave Reason for the Stop” is –3.11 and it is statistically significant at
0.10. Therefore, the results indicate a negative relationship between the variable “Police
Gave Reason for the Stop” and the “Illegal Items Found during Traffic Stop Search”
variable. The variable is also statistical significant on all three of the logistical regression
models, which indicates that there is a strong relationship between the variables, however
the relationship is in the opposite direction of the first hypothesis, “as the police gave a
reason for the stop increases, the chances of illegal items found also increases.”
The second hypothesis adopts that as the frequency of law enforcement officers
who ask to search a vehicle when making a stop increases, then the chances of finding
illegal items decreases. Again, the variable “Illegal Items Found during Traffic Stop
Search (V56)” is coded no = 0 and yes = 1, and the “Police Asked to Search the Vehicle
(V49)” variable is also coded no = 0 and yes = 1. Table 3, the correlation table, shows
that “Police Asked to Search the Vehicle” is – .23 in relation to “Illegal Items Found
during Traffic Stop Search” and it is statistically significant at .10. Table 4, the logistical
regression model, shows that “Police Asked to Search the Vehicle” is -1.81 and it is not
statistically significant at 0.10. Therefore, the results indicate a negative relationship
between the variable “Police Asked to Search the Vehicle” and the “Illegal Items Found
during Traffic Stop Search” variable. The results indicate a negative relationship between

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the variables and the relationship is statistically significant, which shows support for the
hypothesis, “as the police asked to search the vehicle increases, the amount of illegal
items found during traffic stop and search decreases”, because the correlation table is a
one-on-one relationship, whereas the logistical regression model takes into account all the
variables.
The third hypothesis assumes that as the frequency of law enforcement officers
who ask to search the driver when making a stop increases, then the chances of finding
illegal items decreases. Once again, the variable “Illegal Items Found during Traffic Stop
Search (V56)” is coded no = 0 and yes = 1, and the “Police Asked to Search Driver
(V51)” variable is also coded no = 0 and yes = 1. Table 3, the correlation table, shows
that “Police Asked to Search Driver” is – .16 in relation to “Illegal Items Found during
Traffic Stop Search” and it is not statistically significant at .10. Table 4, the logistical
regression model, shows that “Police Asked to Search Driver” is -1.08 and it is not
statistically significant at 0.10. Therefore, the results indicate a negative relationship
between the variable “Police Asked to Search Driver” and the “Illegal Items Found
during Traffic Stop Search” variable. The variables are not statistical significant which
suggests that there is no support for the third hypothesis, “as the police asked to search
driver increases, the amount of illegal items found during traffic stop and search
decreases.”
The fourth hypothesis assumes that as the driver consents to a vehicle search
increases, the amount of illegal items found during the stop and search decreases. Again,
the variable “Illegal Items Found during Traffic Stop Search (V56)” is coded no = 0 and
yes = 1, and the “Driver Consented to Vehicle Search (V53)” variable is also coded no =

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0 and yes = 1. Table 3, the correlation table, shows that “Driver Consented to Vehicle
Search” is - .14 in relation to “Illegal Items Found during Traffic Stop Search” and it is
not statistically significant at .10. Table 5, the logistical regression model, shows that
“Driver Consented to Vehicle Search” is .63 and it is not statistically significant at 0.10.
Therefore, the results indicate a mixed relationship between the variable “Driver
Consented to Vehicle Search” and the “Illegal Items Found during Traffic Stop Search”
variable. The variable is not statistical significant on the logistical regression model,
which does not support the fourth hypothesis, “as the driver consents to a vehicle search,
the chances of illegal items found decreases.”
The fifth hypothesis aims to believe that as the driver consents to a vehicle search
increases, the amount of illegal items found during the stop decreases. The variable
“Illegal Items Found during Traffic Stop Search (V56)” is coded no = 0 and yes = 1, and
the “Driver Consented to a Driver Search (V54)” variable is also coded no = 0 and yes =
1. Table 3, the correlation table, shows that “Driver Consented to Driver Search” is - .07
in relation to “Illegal Items Found during Traffic Stop Search” and it is not statistically
significant at .10. Table 5, the logistical regression model, shows that “Driver Consented
to Vehicle Search” is -.86 and it is not statistically significant at 0.10. Therefore, the
results indicate a negative relationship between the variable “Driver Consented to Driver
Search” and the “Illegal Items Found during Traffic Stop Search” variable. The variable
is not statistical significant on the logistical regression model which does not support the
fifth hypothesis, “as the driver consents to a driver search, the chances of illegal items
found decreases.”

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The sixth hypothesis adopts that as the police conduct a search of the vehicle
increases, the amount of illegal items found during the stop decreases. The variable
the “Police Conducted Search of Vehicle (V50)” variable is also coded no = 0 and yes =
1. Table 3, the correlation table, shows that “Police Conducted Search of the Vehicle” is
.11 in relation to “Illegal Items Found during Traffic Stop Search” and it is not
statistically significant at .10. Table 6, the logistical regression model, shows that “Police
Conducted Search of Vehicle” is dropped because when Police Conducted Search of
Vehicle (V50) = 1, it predicts failure perfectly. Therefore, the results indicate a positive
relationship between the variable “Police Conducted Search of Vehicle” and the “Illegal
Items Found during Traffic Stop Search” variable. The variable is not statistical
significant on the logistical regression model which does not support the sixth hypothesis,
“as the police conducted search of the vehicle increases, the chances of illegal items
found decreases.”
The seventh hypothesis aims to believe that as the police conduct a search of the
driver increases, the amount of illegal items found during the stop decreases. The variable
the “Police Conducted Search of Driver (V52)” variable is also coded no = 0 and yes =
1. Table 3, the correlation table, shows that “Police Conducted Search of Driver” is .02
in relation to “Illegal Items Found during Traffic Stop Search” and it is not statistically
significant at .10. Table 6, the logistical regression model, shows that “Police Conducted
Search of Driver” is -2.03 and it is not statistically significant at 0.10. Therefore, the
results indicate a mixed relationship between the variable “Police Conducted Search of

26
Driver” and the “Illegal Items Found during Traffic Stop Search” variable. The variable
is not statistical significant on the logistical regression model which does not support the
seventh hypothesis, “as the police conducted a search of the driver increases, the chances
of illegal items found decreases.”
The eighth and final hypothesis assumes that as the frequency of illegal items
found during a traffic stop and search increases, then the chances of an arrest being made
during contact increases. Again, the variable “Illegal Items Found during Traffic Stop
Search (V56)” is coded no = 0 and yes = 1, and the “Arrest during Contact (V61)”
variable is also coded no = 0 and yes = 1. Table 3, the correlation table, shows that
“Arrest during Contact” is .14 in relation to “Illegal Items Found during Traffic Stop
Search” and it is not statistically significant at .10. Table 4, the logistical regression
model, shows that “Arrest during Contact” is .24 and it is not statistically significant at
0.10. Table 5, the logistical regression model, shows that “Arrest during Contact” is
dropped because when Arrest during Contact (V61) = 0, it predicts failure perfectly.
Table 6, the logistical regression model, shows that “Arrest during Contact” is 1.01 and
it is not statistically significant at 0.10. Therefore, the results indicate a positive
relationship between the variable “Police Gave Reason for the Stop” and the “Arrest
during Contact” variable. The variable is not statistical significant on all three of the
logistical regression models which shows no support for the eight hypothesis, “as the
police gave a reason for the stop increases, then the chances of an arrest being made
during contact increases.”

28
Chapter 5: Summary and Conclusion
Summary
The goal of this research was to explore contributing factors that are important
predictors for the amount of illegal items, if any, found during traffic stop searches. Some
of the factors investigated are included in the many exceptions to the warrant
requirement, such as the search incident to arrest exception, and the consent search
exception. Using quantitative research, the relationship between both independent and
controlled variables and the dependent variable, the amount of illegal items found during
traffic stop searches, was investigated. By running the logistical regression model, this
research found only the first two of the eight possible relationships between the
dependent variable and the independent variables to be statistically significant, as seen in
chapter 4, which helps to prove one of the two particular hypothesis; “as the police asked
to search the vehicle increases, the amount of illegal items found during traffic stop and
search decreases.” The other relationship that was statistically significant was from the
first hypothesis, “as the police gave a reason for the stop (V35) increases, the chances of
illegal items found (V56) also increases”. However, the relationship was in the opposite
direction of the first hypothesis, and therefore cannot be proved correct.
Limitations
There were only some minor challenges and limitations that were faced during
this research and study. The first of these limitations that was met was as an already
existing dataset was chosen, there was no particular data and research for one specific
variable, ’criminal evidence destroyed’’. For this reason, one particular hypothesis, that

29
was of certain interest to the individual conducting the research, could not of been
created, tested, and investigated. The second limitation that challenged this research was
the lack of investigation into warrantless searches during traffic stops and accidents.
There were only very few variables that were readily available to help conduct more
extensive research into warrantless searches and seizures and the many exceptions
involved. The third limitation that was met was, again as this research used an already
existing data set, most of the 63,237 respondents had missing data that was important for
this particular study. For this reason, a subset of data, comprising of 63 interactions, was
needed to be made in order to get more accurate results.
Recommendations for Future Research
The biggest recommendation for future research would most definitely be to find
a stronger data set that is not missing research and data on certain variables that are
important for this particular field of study. In the future, a stronger data set, with all the
required research and data into searches and seizures, would make it much easier to run
the data and to test certain hypotheses with more than 63 police/public interactions.
Another recommendation for future research would also be to find more extensive
research into warrantless searches and seizures and the many other warrant requirement
exceptions, in particular the “plain view” exception and the “motor vehicle” exception.
Conclusions
The process of conducting my own personal research into a particular subject of
interest has proven to be a lot easier and much more enjoyable than it was at the
beginning of my senior year. This was simply because with the help and encouragement
of Dr. Verrill, I was able to gain a much better understanding of the process of

30
conducting scholarly research. Also, as the data set used was missing a lot of research
and data, it proved to be quite difficult to get most the variables to be run correctly
through the correlation analysis and the logistical regression model. Therefore, I found it
extremely satisfying when many of the variables ran smoothly through statistical analysis
and proved the hypotheses to be either true or false.

31
Bibliography
Davis, Wade V. "Warrantless Entry of a Residence: Exigent Circumstances.” Tennessee
Bar Journal 50.3 (2014): 26-28. EbscoHost. Web. 21 Sept. 2014.
Mullenbach, Linda H. "Warrantless Residential Searches to Prevent the Destruction of
Evidence: A Need For Strict Standards." Journal of Criminal Law &
Criminology 70.2 (1979): 255-69. EbscoHost. Web. 21 Sept. 2014.
Schott, Richard G., JD. "The Supreme Court Reexamines Search Incident to Lawful
Arrest." FBI Law Enforcement Bulletin 78.7 (2009): 22-31.LIRNSearch. Web.
12 Sept. 2014.
Stoughton, Seth W. "Modern Police Practices: Arizona V, Gant Illusory Restrictions of
Vehicle Search Incident to Arrest." Virginia Law Review 97.7 (2011): 1727-
773. Ebsco Host. Web. 12 Sept. 2014.
U.S. Dept. of Justice, Bureau of Justice Statistics. "Police-Public Contact Survey, 2005
[United States] (ICPSR 20020)." ICPSR. N.p., 2005. Web. 4 Sept. 2014.

32
Greg Keogh
1900 W. McArthur St. #18 | Shawnee, OK 74804
405-695-8949 keogh.greg@yahoo.ie
EDUCATION
St. Gregory’s University – Shawnee,OK
Bachelorsof Science in Criminal Justice, summa cumlaude
Expected to graduate in May 2015
GPA: 3.7
President’s List
Dean’s List
NAIA Academic All-American: 2013, 2014
August 2011 – present
Colaiste Ide College – Dublin, Ireland
Association Football
September 2010 – May 2011
EXPERIENCE
Grill and Fry Cook, Compass Group
St. Gregory’s University, OK
 Prepared and maintained food and drinks for students
 Promoted Compass Group by informing students of
certain catering events
 Oversaw levels of cleanliness and sanitation
August 2011 - present
Vegetable Harvester, JetViewFarms
Dublin, Ireland
 Harvested fruits and vegetables by hand
 Graded and packaged products for customer satisfaction
 Worked long hours in hot weather conditions
 Completed all jobs in a hygienic and timely manner
May 2009 – August 2011
ORGANIZATIONS AND CLUBS
Captain, Men’s Soccer Team
St. Gregory’s University, Shawnee,OK
 SGU Men’s Soccer,Team Captain: 2014
 Men’s soccer SAC,Second Team, All-Conference:
2013, 2014
 Capital One Athletic/Academic All-American: 2013,
2014
August 2011 – May 2015

33
REFERENCES
Kovoor Pieris, managing boss, Compass Group
Phone number: 405-535-1693
Andrew Rundell, men’s head soccer coach
Phone number: 405-919-5737
Martin Flynn, owner and manager, Jet View Farms
Dub, IRE
Phone Number: 083-315-4800

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