Behavioral Analytics in Business Intelligence Systems

You did this project for me. Just need to adjust the format and make sure everything is included including the index and the missing head points as mentioned in the reference paper which are: problem background, theoretical conceptual framework, hypothesis, nature of study, significance of study. Make sure that the same points are included as reference paper. You don’t need to change the whole thing but just add the missing points and adjust it.

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Behavioral Analytics in Business Intelligence Systems
Name of student
Student’s ID Number
Course Name
Date
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Behavioral Analytics in Business Intelligence Systems
Introduction
Background and Rationale
One of the more recent trends has been companies’ use of state-of-the-art Business
Intelligence Systems (BIS) to acquire their strategic objectives as envisaged. Over the years,
digitalization helped manage and coordinate all business operations. However, digitization has
also exposed shipping companies to cyber-attack threats; the criminals who commit cybercrimes
can use BIS to check and learn about the user data, flows, and behavior characteristics to gain
details that stand out and prevent security breaches (Ajala et al., 2024). We can overcome these
challenges through data analysis, machine learning, and statistics, which form the bedrock of
behavioral analysis and preventive measures in information security systems. The imperative of
the homeland security counter-threats drives the necessity for this analysis, which contains
elements of these improved risks. As cyber threats are becoming increasingly sophisticated,
companies must keep their vigilance high and strengthen their defense mechanism to protect
their most vital data, ensuring the continuity of their operations (Ajala et al., 2024). More
corporations are relying on the dominant position of advanced analytics to prevent any threat
from becoming a reality, and by gaining such an approach, they are likely to support their
cybersecurity posture. Thus, this approach is essential for shielding BIS against various oblique
AI-straight cache challenges because it is a very dynamic place, and it always gets more complex
with time.
Problem Statement
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Even though constant improvements are taking place in cybersecurity technologies to
eradicate cyber threats, cyber villains have gotten super bright and complex, which is why the
security of the BI systems must be continually checked and guarded. These systems, being an
inherent part of strategic decision-making, handle prodigious quantities of confidential
information. Thus, such makes them the most probable targets in a cyber-attack. With the
emergence of new threats, this gap keeps widening, and conventional cyber security tools are
often no longer meeting the requirements, emphasizing a severe issue of the existence of BI
systems and accelerating the necessity for its protection (Kochhar et al., 2023). The central issue
concerns the effectiveness of the existing cyber security methods, including behavioral analytics,
to deliver this task through advanced threat detection and mitigation. Behavioral analytics that
look into user activity patterns and detect suspiciously harmful determinants are powerful but
need to be entirely guarded in this data-driven world (Morris et al., 2022). Also, its success may
even fail because the diet of the attacks can be customizable to bias the view and to explore and
gain access to weaknesses in gigantic databases; this leads to the biggest problem in setting
oneself up for comprehensive security of BI systems.
Therefore, it is especially noteworthy to emphasize here that cyberspace is where the
danger does not stay still; instead, it is a living and evolving being. While cyber-terrorists
develop new strategies such as artificial intelligence assaults or complicated persistence threats
(APTs), the means utilized for detecting and preventing such threats should also grow. Thus, it
becomes necessary to create new cybersecurity systems and discover new methods to replace the
old systems, which are the most significant security issues (Isakov et al., 2024). We can
implement these systems by developing analytics with a predictive capability that monitors the
systems and detects possible breach attacks before they happen or by utilizing quantum
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cryptography that ensures data transfer safety.
Purpose Statement
The reason for carrying out this research is to critically analyze the usefulness of
behavioral analytics in solving the problem of advanced threats to businesses through wellprotected IT systems. The research will systematically assess the current mechanism of setting
behavioral analytics to establish the reality whereby this approach effectively safeguards BI
systems against the evolution of cyber threats. The study will also investigate the scope for the
development and implementation of recently discovered innovative strategies and technologies
that could overhaul existing behavioral analytics approaches, a move that would improve the
security of BI systems against cyber-attacks. Thus, this paper will help identify effective
methods, and the recommendations will allow us to improve all our security and cyber defense
tools used to prevent complex cyber-attacks.
Objectives
1. To assess the effectiveness of behavioral analytics in detecting sophisticated cyber threats
within BI systems.
Analytics of behavior views the patterns in the user behavior to determine the
irregularities that are a marker of potential security threats. The objective aims to assess how
successful existing behavioral analytics systems are and how they capture these technical
intricacies. Those tactics, such as digital training and actual information verification, are
intended to estimate these tools’ precision, credibility, and promptness in the acknowledged
identification and neutralization of threats before the devastating damage can occur. The
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behavioral analytics effectiveness determinant is a fundamental rationale for the intention of an
improved BIS security framework.
2. To identify the limitations and challenges faced by behavioral analytics in this context.
Behavioral analytics solutions alongside them have numerous benefits, including
improving customer experience and personalization of products and services. Still, they are not
immune to inherent limitations and challenges affecting their efficiency. Given this goal, we aim
to discover the barriers that prevent secure data, integration problems with the already used
systems, and the general need for specialists and performance issues. The research project is
developed by applying a thorough approach, which would allow us to systematize behavioral
analytics problems and create a unified viewpoint of existing shortcomings; this knowledge
becomes essential when designing strategies that will help overcome the mentioned barriers and
ensure these technologies’ robust implementation and efficient performance.
3. To propose strategic improvements to enhance the capability of behavioral analytics
tools.
Based on the outcomes from the first objectives, the next phase is geared towards
suggestions on a behavioral analysis approach. Possible amendments include utilizing
sophisticated machine learning applications that would provide more efficient data management
strategies, extended user behavior modeling, and quantum cryptography that will allow us to
improve security. These recommendations are intended to help the organization systematically
improve cybersecurity practices so that BI systems always stay clear of various threats.
Research Questions
1. How effective is behavioral analytics in identifying threats in BI systems?
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2. What are the primary limitations of current behavioral analytics approaches in the context
of BI system security?
3. What improvements can be implemented to enhance the efficacy of behavioral analytics
for cybersecurity in BI environments?
4. How can behavioral analytics be utilized to detect and mitigate cyber threats in BI
systems?
Data Collection
The data collection for this research involves two main components, which include
simulating cybersecurity attacks on BI systems using virtual exercises and collecting accurate
data through actual cybersecurity incidents in real life. We will create simulation environments
in real-life crises that display realistic attacks. Also, we will observe how behavioral analysis
strategies would detect and react to these security threats. These defined assessments will affirm
security flaws and determine the validity of various security countermeasures. Additionally, we
intend to gather data from real-life cybersecurity incidents like logs and reports, which will help
us scrutinize how behavioral analytics functions in a natural environment and increase the
authenticity of our research.
Simulations of Security Breaches: Subject to scenario-based testing, behavioral analytics will
be used to find the gaps in BI systems’ security and the solutions to mitigate the impact of those
threats. These simulations will be holistic and will mimic real-world situations with the use of
test data and scenarios. We seek to test whether the behavioral analytics is up to tackling the
security issues alertly by (providing the tests with the correct environment).
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Conducting Virtual Exercises: Virtual exercises will be conducted to emulate the cybersecurity
warnings on a BI level. Scenarios will be created that will imitate cyber-attack reality and thus
allow us to witness how operationally dependent analysts can prevent and react to an attack. We
will build a testing environment with the help of Metasploit or our simulation software to
reproduce the situations in which we can try ourselves as the attacker or a defender, proving we
can learn how to defend the systems. By conducting trials in these managed testing
environments, we plan to detect potential vulnerabilities, assess the efficiency of current security
measures, and suggest modifications. The technique allows a wholesome evaluation based on
computer simulations, not on the data obtained from real-world attacks.
Data Analysis
In this research project, we will focus on data analysis using a quantitative methodology,
which provides a detailed analysis of the effectiveness of behavioral analytics in identifying and
preventing a sophisticated threat within business intelligence systems. Such analysis is designed
to be as aligned with the research objectives as possible, utilizing simulation data to directly
deliver practical quantifiable data about the implementation and limitations of behavioral
analytics systems (Mughal, 2022). Thus, we will use Metasploit, a modern and undoubtedly
reliable tool widely used by cybersecurity specialists, to make these simulations possible. As
Metasploit encourages development in terms of cyber-attacks, it provides a robust platform for
testing and evaluating the behavioral analytics that work effectively. We will avoid potential
biases or inconsistencies in the outcomes of our simulation with the help of a tool that the
academic community has widely accepted and employed.
The scenarios development part will cover threats from different sides: very modern
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sophisticated cyber-attacks, including advanced persistent threats (APTs), phishing attacks,
ransomware, and many other types of malware. The simulations will be based on each
simulation, testing specific aspects of the behavioral analytics features, such as its ability to
detect abnormal users, identify patterns showing an imminent threat to cyber-security, and
promote immediate action to stop the threat. The mentioned approach is through approach
because the research methodology is aligned directly with the intentions, which gives a complete
assessment of the behavioral analytics system’s functionality under real scenarios (Cascavilla et
al., 2022). In each simulator, we will measure critical factors that focus on efficiency and
effectiveness. Measures that aim to detect the rate include the detection rate, defined as the
number of simulated attacks that successfully identify the behavioral analytics system, and false
positives, which are cases where a normal user’s behavior is incorrectly flagged as a threat.
Further, false negatives will be identified and attributed to cases where the security
system does not succeed in detecting threatening acts. Response time, the time the system takes
to detect and react to attacks, and resource utilization, as the computational and network
resources consumed by the behavioral analytics system during the simulations, including the
usage of both attack and defense, will also be tracked. The obtained data will undergo the quality
passing of the statistical analysis methods to enumerate the exact performance of behavioral
analytics. Descriptive statistics is used most preferably to give details, such as mean and median,
showing central trends and standard deviation about variance, which will aid in the computation
of detection rates, false positives, false negatives, response times, and resource utilization (Suša
Vugec et al., 2020). The statistics will show the average performance trends, indicate the
presence of any abnormalities or conflicting data, and provide a better understanding of the
overall performance. In this study, we will use inferential statistics to estimate the population of
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potential cyber-attacks using the observed cyber-attacks as our sample from the simulations
carried out. Among others, we will apply methods like the null hypothesis and confidence
intervals to assess and recognize the statistical significance of our data concerning the topic. The
subject of this section will be the comparison of our behavioral analytics system to a thresholdbased detection strategy that usually only detects simple attacks to show that the latter can be
much better than the former. The assumptions of the simulation and the situation it was applied
to will be used to come up with possible real-world problems and put the system’s attribute
consideration in a broader context.
Adding machine learning models, especially neural networks and decision trees, to the
simulation data introduces more precision in evaluating reliability. These graphs will help detect
the hidden complicated patterns and relationships in the data that cannot be noticed through
traditional analytics methods. By training the models with mock attack scenarios, the behavioral
analytics system can develop a higher accuracy and stability in prediction. The machine learning
models will further be tested using the data held out for validation after being trained with the
chosen portion of simulation data (Gołębiowska et al., 2021). Through such an approach, the
models are not just fitted to the training data but can also be generalized for the general case not
seen during the attack training. The metrics used regarding the models will be precision, recall,
and F1 score, which will aim to gauge the models’ capabilities in detecting and combating cyber
threats.
The accuracy of the given data will determine the performance, the number of false
positives, and the number of false negatives. Detection efficacy is represented by the
combination of the few missed events (false negatives) and meager numbers of false alarms
(false positives). Stratification is going to be utilized in terms of measurements against a baseline
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patron threshold, as well as against the alternative cybersecurity tactics, to assess the results
comparatively. By analysis, the significant flaws and difficulties of BRC will be determined with
the help of false positives, false negatives, and resource utilization. Fast rates of false positives
and negatives will show that some systems are not as efficient as they should be.
In contrast, excessive resource consumption will be seen as inefficiency in other system
parts. These revelations are primarily to guide the specific upgrades to the system. Based on the
discovered barriers, we will then consider strategic rectifications that would boost the functional
capacity of behavioral analytics. The inclusions can support the algorithm by improving
abnormal behavior detection, providing valuable files, and amalgamating data streams. The
applicability of different proposed changes will be tested in subsequent simulations to see which
results are better. The efficiency of behavioral analytics in combating cyber threats will be
evaluated by considering response time and the accuracy of automatic mitigation actions
supervised by the system and related to them (Isakov et al., 2024). Rapid reaction times and
successful proof of concept of neutralizing the simulated incidents will prove the system’s ability
to detect and respond to the response time.
When the results are reliable and valid, the exact techniques will be introduced for
performing simulations and acquiring the data. The methods will be standardized to enable
replication and credibility of outcomes. The simulation runtime (Metasploit) will be configured
and beta-tested to ensure it can replicate the actual attack scenarios in the real scenario. Machine
learning models will be validated using a cross-validated approach to safeguard their generalized
and robust nature. However, peer review is also essential and will be a part of the methodology
and results verification through peer review. The method, a quantitative research approach based
on the simulations performed with the Metasploit tool, offers a robust and well-defined
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framework that will allow for a complete assessment of the effectiveness of behavioral analytics
in cybersecurity (Basholli et al., 2024). Thus, this way of researching postulates the
magnification of the subjective aspect of the investigation to ensure that the info collected is
objective, measurable, and relevant. The study will provide the company with significant
knowledge concerning the positive sides and pitfalls of emissions, suggesting future progression
and betterment of the security shield of BI protocols from high-quality hackers.
How do we establish reliability and validity?
The obtained results should be correctly interpreted, and it is indispensable to make
simulations using uniform protocols and collect data, keeping in mind repeatability and stability.
The Metasploit simulation platform will be adjustable and ready to go after tests, which means it
will be accurate to realistic attack scenarios (Basholli et al., 2024). Machine learning algorithms
will be validated by performing cross-validation to confirm how accurate the models are and that
they will efficiently generalize. Furthermore, the research methodology will be reviewed by
peers to confirm their scientific reliability and validity. Hence, the selected stringent method is
intended to give the regrettable but required reaction so that the research results are reliable,
credible, and valuable in cybersecurity.
Conclusion
In conclusion, this study has shown that behavioral analytics is an effective tool in
dealing with the general belief that only exhaustive and complex programs help identify and
tackle intricate stakes in BI systems. The literature analysis showed cybersecurity’s priority in the
new digital era and the role of behavioral data in security improvement processes. The gaps and
difficulties, as follows in the literature, emphasized the importance of research and innovation as
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areas of improvement. Such theoretical frameworks as data analysis techniques, machine
learning models, or statistical approaches give a good beginning for BI systems understanding in
the behavioral analysis way. The research used mixed methods with a mixture of quantitative
and qualitative insights gathered from cybersecurity experts. As a part of this approach, I intend
to develop a prosthetics device that will allow amputee patients to regain their physical
independence. Data collection, such as test runs of incident response drills and real-world data
acquisition, will yield essential information on the performance of behavioral analytics under
different circumstances.
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References
Ajala, O. A., Arinze, C. A., Ofodile, O. C., Okoye, C. C., & Daraojimba, O. D. (2024).
Reviewing advancements in privacy-enhancing technologies for big data analytics in an
era of increased surveillance. World Journal of Advanced Engineering Technology and
Sciences, 11(1), 294-300.
Basholli, F., Mema, B., & Basholli, A. (2024). Training of information technology
personnel through simulations for protection against cyber attacks. Engineering
Applications, 3(1), 45-58.
Kochhar, S. K., Bhatia, A., & Tomer, N. (2023). Using Deep Learning and Big Data Analytics
for Managing Cyber-Attacks. In New Approaches to Data Analytics and Internet of
Things Through Digital Twin (pp. 146-178). IGI Global.
Morris, D., Madzudzo, G., & Garcia-Perez, A. (2020). Cybersecurity threats in the auto industry:
Tensions in the knowledge environment. Technological Forecasting and Social Change,
157, 120102.
Isakov, A., Urozov, F., Abduzhapporov, S., & Isokova, M. (2024). ENHANCING
CYBERSECURITY: PROTECTING DATA IN THE DIGITAL AGE. Innovations in
Science and Technologies, 1(1), 40-49.
Mughal, A. A. (2022). Building and Securing the Modern Security Operations Center (SOC).
International Journal of Business Intelligence and Big Data Analytics, 5(1), 1-15.
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Cascavilla, G., Tamburri, D. A., & Van Den Heuvel, W. J. (2021). Cybercrime threat
intelligence: A systematic multi-vocal literature review. Computers & Security, 105,
102258.
Suša Vugec, D., Bosilj Vukšić, V., Pejić Bach, M., Jaklič, J., & Indihar Štemberger, M. (2020).
Business intelligence and organizational performance: The role of alignment with
business process management. Business process management journal, 26(6), 1709-1730.
Gołębiowska, A., Jakubczak, W., Prokopowicz, D., & Jakubczak, R. (2021). Cybersecurity of
business intelligence analytics is based on processing large sets of information using
sentiment analysis and Big Data. European Research Studies Journal, 24(4).
DISSERTATION RESEARCH PROSPECTUS
A prospectus is used to evaluate the viability and feasibility of a study. Thus, it is
evaluated to ensure clarity, conciseness, and manageability of the study’s research questions.
Additionally, the design method’s appropriateness and alignment in relation to the study’s
purpose are ensured. Finally, the feasibility of the candidate to complete the study using their
respective skillset, available resources, and allotted time frame will be considered. Overall, the
prospectus identifies the evidence-based problem the proposed study will address and the
significance of the problem. It further elaborates on the purpose of the study, the questions and
hypotheses that will guide the study, and the methods that will be used to answer the specified
questions and analyze the collected data.
Prior to completion of the prospectus, a preliminary literature review must be conducted
to familiarize oneself with published literature currently available on a given topic. Additionally,
the literature review will assist the researcher in identifying possible gaps in the available
research. As the prospectus is a plan for research, the future verb tense will be used. Once the
research is complete, the tenses of verbs must be changed to the past tense.
(Please delete this informational page before submitting the document.)
TITLE OF DISSERTATION HERE CENTERED WITH UPPERCASE LETTERS
UPPERCASE BOLDFACE LETTERS APPEARING AS AN
INVERTED PYRAMID
Doctoral Dissertation
Submitted to the
College of [College Name]
University
In Partial Fulfillment of the Requirements for the Degree of
Doctor of [Program Title]
Concentration: [Concentration Title if Applicable]
by
[Doctoral Candidate’s Full Name]
[Month, Year of Completion]
Copyright © [Full Name], [Year]. All rights reserved.
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Table of Contents
CHAPTER ONE: INTRODUCTION
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Problem Background
1
Problem Statement
1
Purpose of the Study
2
Theoretical and Conceptual Framework
2
Research Questions
2
Hypotheses
3
Nature of the Study
3
Research Methods
3
Data Collection
3
Data Analysis
4
Definitions of the Terms
4
Significance of the Study
4
References
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CHAPTER ONE: INTRODUCTION
Chapter One of the dissertation introduces the study problem, background of the problem,
and identifies the framework of the research. In the introduction to this chapter, provide context
of the chosen topic, clarity of scope in how this research will engage with the topic, and an
overview of the structure of the dissertation and chapter flow. The following sections are
required at minimum, but additional sections can be included if deemed appropriate.
Problem Background
The problem background will provide information regarding the central problem of
inquiry the research seeks to address. A focused, timely, and relevant problem, as supported by
the current scholarship, is required. In this section, verify the existence of this problem through
evidence-based research, the extent to which it spans, who or what it impacts, and how it has
been presented within current scholarship. Objectively identify what the research will achieve
within the context of this centralized problem and the continued inquiry into the problem that is
needed.
Problem Statement
Based on the context of the central problem identified in the problem background section,
this section indicates specific problems that this research addresses. These problem statements
form the basis of the research question(s) posed. Each problem statement should be evidencebased and contextualized within the current scholarship. Problem statements are written as single
sentences but should be followed by discussion on the origins and potential impact of each
problem as it connects to the aforementioned centralized problem. Add a maximum of three
problem statements in this section. The format of the problem statements should follow:
Problem Statement 1: Identify the specific problem.
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Include discussion in paragraph format with APA citations.
Purpose of the Study
Based on the context of the centralized and specific problems indicated above, this
section will explain, in general terms, the purpose or reason for this study. Identification of the
research method and design, direction, objective, and overarching goal of the study is
incorporated and expanded upon in this section. This section begins with the statement: The
purpose of this study is…
Theoretical and Conceptual Framework
This section explains the theoretical or conceptual foundation of the research and its
credibility in the context of previous studies and/or commonly accepted theoretical
understandings. A conceptual framework identifies the expected outcome(s) of the research
through identification of the variables and how they connect or impact one another. A theoretical
framework identifies, compares, and synthesizes prevalent theories established in literature that
support the basis of the study and its theoretical context.
Research Questions
In this section, identify the research question(s) that has guided the research. This
question(s) should align with the problem statement(s) and ensure appropriate collection of data
to achieve the purpose of the research. Research questions are formatted as simple interrogative
sentences, address one issue per question, and cannot be answered with a yes or no response. In
addition to listing the question(s), explain in subsequent paragraphs how the answers to the
research question(s) will cumulatively lead to the achievement of the purpose of the research. As
noted in the problem section, the research questions must be directly aligned with the problem
statements and study purpose. The format of a research question should be incorporated as
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follows:
Research Question 1: What is your research question?
Incorporate information regarding how this question will lead to the achievement of the
aforementioned purpose.
Hypotheses
This section is optional and will be dependent upon the type of study conducted (verify
with the Dissertation Chair). Hypotheses primarily identify the expected outcomes of a
quantitative or mixed methods study and must be directly aligned with the research question(s).
Nature of the Study
This section provides an overview of the study’s method, design, and data collection and
analysis methodology. Note that this section is more general, as the doctoral candidate will
expand upon the details of the research methodology in Chapter Three. The following
subsections are required at minimum, but additional subsections may be included if deemed
appropriate.
Research Methods
This subsection identifies if the study is quantitative, qualitative, or both (mixed methods)
and explains the rationale for the selection in the context of the purpose of the research. Include
the research design, justification for the design, and an overview of the implementation of the
research plan.
Data Collection
This subsection describes the primary and/or secondary data to be gathered for the study
and how and why the specific data collection method(s) will be employed. A description of the
data collection instrument(s) and method used in the research is explained and the rationale
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justified. Indicate the underlying population, sampling method, and minimum sample size to be
gathered from the target population.
Data Analysis
In this subsection, the doctoral candidate identifies the data analysis methodology chosen
for the study and presents justification for the selection. The data analysis method must be
aligned with the study method and design choice for the research. The software application to be
used for analysis should also be noted here as well as any data assumptions to be tested prior to
data analysis to address the research questions.
Definitions of the Terms
This section will be explained and required at a later point in the dissertation writing
process. Leave this section blank for now.
Significance of the Study
The significance of the study explains the potential impact of the study. Specifically, the
doctoral candidate describes, in scholarly, objective, and unbiased language, the study’s potential
contribution to the existing body of knowledge on the chosen topic and the relevant field.
Doctoral candidates can stipulate who or what may benefit from this research and the extent of
that benefit.
(Note: The doctoral candidate must use non-confirmatory language, such as: The potential
findings of the study may lead to a better understanding of the effectiveness of computer assisted
instruction; the evaluation of employee attitudes could help improve productivity.)
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References
The reference list contains all sources cited in the dissertation. There should be one-toone correspondence between the citations in text and those included in the references. The
references follow APA 7th edition and start on a new page after the significance of the study of
the prospectus.
While formatting references, ensure:
1. They are organized alphabetically.
2. A hanging indent of 0.5” is used.
3. They are double-spaced with no additional space ‘before or after.’
4. DOIs are used whenever available, and hyperlinked.
5. Database URLs are not included.
6. References are predominantly from the last five to ten years.
7. References are from credible sources.
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Prospectus Formatting Guide
Please see the following formatting guides below. Delete this page prior to submission of
prospectus.
General Guidelines
1. The prospectus text should be double-spaced with all paragraphs indented.
2. Font should be black and Times New Roman 12-point (except for tables/figures, see
below).
3. All margins should be set to one (1) inch (top, bottom, left, right), on all pages.
4. Page numbers must be at least ¾ inch from the edge of the page.
5. Align text to the left (do not use full justification).
6. The spacing should be set to zero (0) point before and after lines of text (paragraph
spacing).
7. Seriation should follow APA 7th edition guidelines.
8. Level headings should follow APA 7th edition (except for chapter titles – see deviations
from APA 7th edition below).
9. In-text citations should follow APA 7th edition guidelines. All claims and references to
ideas or conclusions found in a source must be properly cited. Results of the doctoral
candidate’s findings do not need citation support unless a direct quote from a participant
is being used. In these situations, cite the personal communication.
10. Language used in reference to people or people groups should adhere to the bias-free
guidelines stipulated by APA 7th edition.
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11. Using survey instruments developed by third parties requires the owner’s written
permission to use or proof of purchase. In this case, the permission or the proof should be
provided in an appendix separate from the one that contains the survey.
Opening Pages
1. The prospectus title should be bold, in all caps, and in an inverted-pyramid format.
2. On the Title Page itself, there should be no visible page number, though it is considered
page 1 of the entire document. Pages prior to Chapter 1 should be numbered by Roman
numerals. The pages in the main body of the prospectus should be numbered by Arabic
numerals.
3. Entries in the Table of Contents should not be bolded or italicized.
Tables and Figures
1. Tables and figures may be displayed in color to enhance clarity.
2. Contents of tables and figures must be no less than 9-point font; notes for tables and
figures must be no less than 10-point font; table/figure numbers and titles must be 12point font.
3. Tables or figures used from alternative sources require copyright permission and should
be stipulated in the note under the table/figure along with the reference.
4. Any table or figure should not be divided between two pages. If a table requires more
than one page, divide the table so that all separated portions share a common heading
row.
5. All tables and figures should have an in-text callout and should be placed as close to their
callout as possible.
Deviations from APA 7th Edition
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1. Chapter titles are to be bold, centered, and in all caps.
2. Starting with Chapter One, use arabic numerals in the top right corner.
3. References will be single-spaced.
4. To verify supplemental file requirements, please check the ProQuest Formatting
Requirements.