Kingdom of Saudi ArabiaMinistry of EducationUniversity of Ha

Kingdom of Saudi Arabia
Ministry of Education
University of Hail
College of Nursing
‫ة‬ ‫عودي‬ ‫س‬ ‫ال‬ ‫ية‬ ‫عرب‬ ‫ال‬ ‫كة‬ ‫ل‬ ‫مم‬ ‫ال‬
‫يم‬ ‫ل‬ ‫ع‬ ‫ت‬ ‫ال‬ ‫وزارة‬
‫ل‬ ‫ـائ‬‫ح‬ ‫عـة‬ ‫جامـ‬
‫ض‬ ‫تمري‬ ‫ال‬ ‫ية‬ ‫ل‬ ‫ك‬
Advanced Research in Nursing (NURS 513)
Guidelines To Write Research Proposal
Research Proposal Guidelines
The ProposalProcess
Your proposal is due on week 15. The maximum word limit of
the proposal is 4,000 +_ 5% excluding the title (cover) page,
references and appendices. Avoid repetition and be concise.
The exact format of each student’s proposal is likely to vary in
content, style and presentation.The example below provides a
possible structural framework for your proposal and should be
used as a guide only. It is important to bear in mind that
different approaches may necessitate differences in structure.
Overall, ensure that deviations from a ‘traditional’ structure are
clearly explained and justified. Title (Cover page)
This is a mandatory inclusion and should contain the following
information.
Title of project
Author Name
Proposal Submitted for the Master of Science in Nursing
College of Nursing. Hail University
Month and Year of Submission

The title (cover) page and page numbers should be exactly per
APA style
Proposal should be written in future tense, except for the
literature review section.
Introduction
Introduce your topic of interest for the reader. Provide
information on the context and rationale for the topic you chose
to study and why it is important. Describe the broad foundations
of your proposal, including some references to existing
literature and/or empirically observable situations. In other
words, the introduction needs to provide sufficient background
for readers to understand where your proposal for research, a
process improvement or policy initiative is coming
from.Background
This section provides a description of the topic being studied
(global to within the Saudi Arabian context). It provides a
justification of its importance and relevance in terms of existing
trends, reports, theory, research and practice.
Tips on drafting your background
· Indicate the general scope of your proposal project, but do not
go into so much detail that later sections (purpose/literature
review etc.) become irrelevant
· Clarify what the problem is or knowledge gap is that your
project is responding to.
Review of Literature
Overall, this section should demonstrate wide reading in the
immediate area and related theoretical areas and a discussion of
underlying theories and supporting evidence. It is recommended
that you include information on your search strategy (e.g.
keywords, databases and inclusion and exclusion criteria and
final results of your search). Provide a range of years used in
the search and a rationale. Essentially, this section should be a
comprehensive summary and a critical review of existing
literature, which identifies strengths, weaknesses and omissions

within the reviewed literature, and there should be accurate and
complete referencing.
In summary, the literature review is to examine the relevant
literature to identify the research areas which are pertinent to
your subject. A further purpose is to identify a conceptual
framework that is suitable to guide your study.
Tips on drafting your Literature Review
· Categorize the literature into recognizable topic clusters and
begin each with a sub heading. Look for trends and themes and
then synthesize, a) the various positions that are relevant to
your project, b) that build on conclusions that lead to your
project, or c) demonstrate gaps in the literature
· Avoid starting each paragraph with “Smith says X, Jones says
Y”. You should be synthesizing the information you find into
specific concepts / topics. The meaning of synthesis is to report
sources in an organized and clear way. To do this, all of the
relevant studies need to be combined to demonstrate concepts /
topics.
· Avoid including all the studies on the subject; include relevant
studies only for the synthesis process. Stick to those pieces of
the literature directly relevant to your narrowed subject
(research question or statement of a problem).
· You should fight the temptation to strongly express your
opinions about the literature you find. Look at both sides of an
argument and be nonjudgmental in your appraisal.
· Conceptualize the research problem, drawing on the relevant
literature.
Conceptual / Theoretical Framework: Research Proposal
The conceptual / theoretical framework; please read your text
books and the link provided on the PowerPoint to fully
understand the purpose of using a conceptual / theoretical
framework as part of your Capstone Project. If you use a non-
nursing theory for your conceptual framework, for example, if
you use the PDSA for process improvement, you need to write a

paragraph on a suitable nursing theory that aligns with your
project. It is very important that you study this section so that
you have a very clear understanding of the conceptual /
theoretical framework. Include the name of the creator of the
framework, their discipline (nursing, sociology etc), a
description of framework, and the rationale/relevance for
selecting the framework you chose. Methodology
The overall purpose for this section is to discuss the methods
adopted and to provide a clear rationale for this choice. The
method section for your research has a number of subsections
which should include the following
· Aim and objectives: a clear statement of aim and objectives
and where appropriate hypothesis(es) and/or research questions
(use PICO format)
· Philosophical Stance: consider the methodology of choice and
in short few sentences explain the theoretical underpinnings of
the methodology of choice (where did this worldview/lens come
from originally)
· Research design: describe and provide justification for the
type of methodology adopted and discuss its appropriateness to
the research problem / question (use PICO format) /objectives
or hypothesis(es)
· Sample: identify and describe the population from which the
sample will be drawn. Make explicit reference to the way in
which the sample will be selected and the composition of the
sample (i.e. size and characteristics)
· Instruments: describe instruments used to collect data (e.g.
questionnaires, interview schedule, observation forms). It is
also important to discuss the steps undertaken to ensure the
rigor of your proposal. Include that you will have tool
professionally translated into Arabic for use.
· Ethical issues: Include a discussion of the ethical issues
relevant to your research design and procedure and the steps
taken to ensure compliance with ethical guidelines for example,
the governing authority that may grant approval, for instance
the Saudi Ministry of Health.

· Procedure: Report each step clearly so that it can be replicated
by another researcher
· Data analysis: in the case of quantitative research this will
incorporate a statement of the statistical tests used or that you
are going to use, for what purpose and their justification.
Similarly, for qualitative research it is important to provide
justification for the analysis procedure how are you going to
make sense of your findings and present the findings
· Work Plan : Describe the proposed plan for managing the flow
of work on the project, indicate in the work plan the sequence
of tasks to be performed, the anticipated length of time required
for their completion.References
All references cited in the text use APA (American
Psychological Association) 6th manual for putting down your
references. Appendices
These could include, a questionnaire, interview guiding
questions, letters, and consent form. Project binding
regulations
· Ensure that your write-up answers all the set guidelines.
· “Times New Roman”- font size 12 is to be used for writing
· Give 1.5 line space
· Set margins all sides (Top, Bottom, left and right) 1”
· Total number of slides for presentation should not exceed 20.
· Remember the word limit is 4,000 words. At the beginning of
the process you may think that 4,000 words is a lot but as you
proceed you will probably be surprised at the amount you want
and need to say. Avoid repetition and be concise. Ensure that
the latter sections of the dissertation receive as much attention
as the initial sections.
Assessment Process
Your proposal is graded by course instructor who will decide
on the final grade. The project is worth up to 15 % of the total
marks for the course. The presentation is worth up to 20% of the
total marks for the moduleResearch Misconduct
“Research misconduct means fabrication, falsification,
plagiarism, or other practices that seriously deviate from those

that are commonly accepted within the research community for
proposing, conducting, or reporting research. It does not include
honest error or honest differences in interpretations or
judgments of data”. Maintaining the integrity of the assignment
and yourself is paramount at all stages of the proposal process
and is your responsibility.
1
1 [Date]
2 [Date]
Table of Contents
1.0 Introduction
...............................................................................................
.................3
2.0 Descriptive analysis
...............................................................................................
....3
2.1

Gender....................................................................................
................................4
2.2 Age
...............................................................................................
..........................5
2.3 Children
...............................................................................................
...................6
2.4 State
...............................................................................................
........................7
2.5 Salary
...............................................................................................
......................8
2.6 Respondents Opinion
...........................................................................................12
3.0 Regression analytics
...............................................................................................
.13
3.1 Correlation between Gender and environmental policy
opinion............................14
3.2 Correlation between age differences and environmental
policy opinions .............14
3.3 Correlation between number of children and environmental
policy opinion ..........15
3.4 Correlation between salary and environmental policy

opinion ..............................16
4.0 Managerial interpretation and implications
...............................................................17
5.0 Conclusion
...............................................................................................
................18
References
...............................................................................................
.....................20
3 [Date]
1.0 Introduction
This report analyses the opinion of respondents from 10 states
in the USA in respect to
environmental policy. The data was collected from 399
respondents, and the analyses is
carried out using Microsoft Excel tools. The analysis is based
on a number of research in
the past. First, study by Sundstrom and McCright (2014) based
on multivariate order

logistic regression indicates that women are more likely to be
concerned with
environmental issues than men in Sweden. Torgler et al. (2008)
in-depth study on the
‘Differences in preferences towards the environment’ found out
that age and
environmental preferences have negative correlation. Moreover,
Wynes and Nicholas
(2017) argued that having fewer children can help fight
environmental changes. While
Mair et al. (2019) argue that high salary represents a good but
not sufficient step towards
dealing with environmental issues. Thus, based on these, this
study sought to find out the
following:
a) Are women more likely to support environmental policy in
the USA than men?
b) Is there a negative correlation between Age differences and
environmental policy
opinion in the USA?
c) Is there a correlation between number of Children and
environmental policy
opinion in the USA?

d) Is there a correlation between Salary differences and
environment policy opinion
in the USA?
2.0 Descriptive analysis
According to Trochim and Donnelly (2001), descriptive
statistics entails explanation of
data in terms what it is and what it shows. It is about describing
or summarizing large
4 [Date]
volume of data into meaningful form for decision making
purposes (Van Der Aalst, 2016).
However, this statistics is not useful in making conclusions past
the data analysed
(Statistics.laerd.com, 2018). But, it is important to perform a
descriptive analysis so as to
understand the data gathered in this research.
2.1 Gender
Analysis of Gender is carried out in this report based on the
available data consisting of
399 respondents. As show in Table 1 below, Excel COUNTIF

function was used to
summarize this data in terms of Gender, and then the data was
converted into percentage
using Excel division function. The findings shows that out of
the 399 respondents, 59%
of them are Female (assuming 2 represented Female) and the
rest (41%) are Male
(assuming that 1 represented Male) as shown in Figure 1 below;
Figure 1: Illustration of respondents’ Gender by Number and
Percentage (Source;
Author).
165
234
41%
59%
0%
10%
20%
30%
40%

50%
60%
70%
0
50
100
150
200
250
1=Male 2=Female
Respondents by Gender
Count Percentage
5 [Date]
Table 1: Computation of number and percentage of respondents’
Gender (Source;
Author).

2.2 Age
The Age is summarized in terms of Young, Middle-aged, and
Elderly. As shown in Figure
2 below, 55% of the respondents were Middle-aged, 24% of the
respondents were Elderly
and rest (22%) of them were Young.
Figure 2: Illustration of the number and percentage of
respondents by Age (Source;
Author).
87
218
94
22%
55%
24%
0%
10%
20%
30%

40%
50%
60%
0
50
100
150
200
250
Young Middle-aged Elderly
Respondents by Age
Count Percentage
6 [Date]
Table 2: Computation of the number and percentage of
respondents by Age (Source;
Author).

2.3 Children
Analysis using Excel COUNTIF function shows that the
majority (42%) of the respondents
had 2 children. This is followed by 31% who said they had no
child. 17% of them said
they had 1 child and only 10% of the respondents had 3 children
as shown in Figure 3
below;
Figure 3: Respondents by number of children (Source; Author).
124
67
167
41
31%
17%
42%
10%
0%
5%
10%
15%

20%
25%
30%
35%
40%
45%
0
20
40
60
80
100
120
140
160
180
0 1 2 3
Respondents by children
Count Percentage
7 [Date]
Table 3: Computation of respondents in terms of number of
children (Source; Author).
2.4 State

Analysing of the State of the respondents using the COUNTIF
function seems to indicate
that the respondents were relative balanced across the 10 States
of the USA as shown
in Figure 4 below. The top 4 State by number of respondents
were Texas (12%), New
York and Michigan with 11.5% respectively, and then Florida at
number 4 with 10.3% of
the respondents. The State with least number of respondents is
Minnesota with only 7.8%
of them.
Figure 4: Illustration of Respondents by State (Source; Author).
48
33
38
41
46 46
33
44
39
31
12.0%

8.3%
9.5%
10.3%
11.5% 11.5%
8.3%
11.0%
9.8%
7.8%
0.0%
2.0%
4.0%
6.0%
8.0%
10.0%
12.0%
14.0%
0
10
20

30
40
50
60
Te
xa
s
Ca
lifo
rn
ia
Illi
no
is
Flo
rid
a
Ne
w
Yo
rk
M

ich
iga
n
Ar
izo
na
Oh
io
Vir
gin
ia
M
inn
es
ot
a
Respondents by State
Count Percentage
8 [Date]

Table 4: Computation of respondents by State (source; author).
2.5 Salary
Analysis of Salary is done using Microsoft Excel Pivot Tables
as shown in Figure 5, 6 and
7 below. Figure 5 indicates the average Salary of respondents in
respect to their Age
group. The findings indicate that Middle-aged respondents are
the most paid, with an
average Salary of $94,398.46, followed by Elderly respondents
with an average salary of
$74,110.15. Young people are the least paid, with only an
average salary of $45,434.79,
9 [Date]
Figure 5: Illustration of respondents’ average salary by age
(Source; Author).
Figure 6 below illustrates the average salary by gender among
the 399 respondents. The
insights indicates that Male (represented by 1) are the most
paid, with an average salary

of $80,733.20 compared to Female who are paid have an
average salary of $77,679.78.
Figure 6: Illustration of average salary by gender (Source;
Author).
Also, Figure 7 illustrates the average salary by State among the
respondents. The
findings clearly shows that respondents from Texas are the most
paid, with an average
10 [Date]
of $84,970.65. They are followed by New York and Illinois,
with $83,897.46 and
$82,723.76 respectively. The least paid respondents come from
Virginia State with only
an average salary of $70,860.49.
Figure 7: Illustration of average salary by State (Source;
Author).
Moreover, Figure is shows an Histogram of the Salary of the
respondents, which was
developed based on the ‘Histogram’ tool under the ‘Data
Analysis’ tool box of Microsoft

Excel. The Figure shows that salary of the respondents is
skewed to the left and majority
of respondents earn between $72,062 and $86,741.
Figure 8: Illustration of respondents’ salary using Histogram
(Source; Author).
54 48
40 38
31 26 26 25 24 23 21
10 8 6 5 4 4 4 1 1
0.00%
20.00%
40.00%
60.00%
80.00%
100.00%
120.00%
0
10
20
30
40
50
60
86
74
0.8

42
11
79
40
1.5
26
32
50
04
4.2
63
16
94
08
0.1
57
89
72
06
2.2
10
53

42
70
4.9
47
37
10
14
19
.47
37
10
87
58
.78
95
64
72
2.8
94
74
57
38
3.5

78
95
11
60
98
.10
53
13
07
76
.73
68
13
81
16
.05
26
12
34
37
.42
11

14
54
55
.36
84
28
02
6.3
15
79
35
36
5.6
31
58
15
27
94
.68
42
20
68
7

M
or
e
Fr
eq
ue
nc
y
Bin
Histogram
Frequency Cumulative %
11 [Date]
Table 5: Data used in the creation of the Histogram (Source;
Author).
Lastly, the salary of the respondents was subjected to
‘Descriptive Statistics’ under the
‘Data Analysis’ tool box of Microsoft Excel. The summary of
the finding is shown in Table
6 below. The data confirms the Histogram finding that the

salary is skewed to the left
since it has a positive Skewness of 0.30602321 (Kim, 2013).
However, the negative
Kurtosis of 0.218380955 indicates that the respondents’ salary
is less extreme than
12 [Date]
anticipated under normal distribution (Bhatt, 2015). Moreover,
the summary shows that
the smallest salary is $20,687 and the highest is $169,134.
Table 6: Summary of Descriptive Statistics (Source; Author).
2.6 Respondents Opinion
Figure 9 indicates the respondents Opinion in respect to the
environmental policy. The
findings indicate that majority (22%) of the respondents
Strongly agree to it, followed by
21% who also agree to it. However, 21% of the respondent
disagree about the policy and
20% strongly disagree about it.

13 [Date]
Figure 9: Illustration of respondents by Opinion (Source;
Author).
Table 7: Computation of respondents Opinion (Source; Author).
3.0 Regression analytics
Regression analytics entails inferential statistics that is carried
out with the view of finding
solutions to research questions (Trochim and Donnelly, 2001).
They establish the
correlation between variables, and unlike descriptive statistics,
they are capable of
providing conclusions beyond the data (Statistics.laerd.com,
2018). That is, they can be
used to predict future outcomes. The following regression
analysis were carried out to
find answers to the questions developed in this study.
78
85
68

82 86
20%
21%
17%
21% 22%
0%
5%
10%
15%
20%
25%
0
10
20
30
40
50
60
70
80
90
100
Strongly
disagree

Disagree Neutral Agree Strongly agree
Respondents Opionion
Count Percentage
14 [Date]
3.1 Correlation between Gender and environmental policy
opinion
Findings summarized in Figure 10 below done using Excel
‘Regression’ tool under the
‘Data Analysis’ tool box shows there is a negative correlation
between Gender and
Environmental Policy opinion. R Squared shows that 78.15% of
the variation in
environmental policy opinion can be explained by Gender.
Figure 10: Illustration Gender Line of fit plot (Source; Author)
3.2 Correlation between age differences and environmental
policy opinions
Figure 11 below shows also that there is insignificant positive
correlation between age

differences and environmental policy opinion. R Squared shows
that less than 1% of the
changes in environmental policy opinions that can be explained
by Age differences.
y = -0.1615x + 3.2887
R² = 0.0031
y = -0.1615x + 3.2887
R² = 0.7815
0
1
2
3
4
5
6
0 0.5 1 1.5 2 2.5
O
pi
ni
on
Gender
Gender Line Fit Plot

Opinion
Predicted Opinion
Linear (Opinion)
Linear (Predicted Opinion)
15 [Date]
Figure 11: Illustration of Age Line Fit plot (Source; Author).
3.3 Correlation between number of children and environmental
policy opinion
The findings summarized in Figure 12 below indicates that there
is relatively significant
negative correlation between number of children and the
environmental policy opinion in
the USA. R Squared shows 27.1% of the variation in
environmental policy opinion is
affected by number of children.
Figure 12: Illustration of Children line fit plot (Source; Author).
y = 0.0004x + 3.0187

R² = 2E-05
y = 0.0004x + 3.0187
R² = 0.0054
0
1
2
3
4
5
6
0 10 20 30 40 50 60 70
O
pi
ni
on
Age
Age Line Fit Plot
Opinion
Predicted Opinion
Linear (Opinion)

y = -0.0459x + 3.0928
R² = 0.0011
y = -0.0459x + 3.0928
R² = 0.271
0
1
2
3
4
5
6
0 0.5 1 1.5 2 2.5 3 3.5
O
pi
ni
on
Children
Children Line Fit Plot
Opinion
Predicted Opinion

Linear (Opinion)
16 [Date]
3.4 Correlation between salary and environmental policy
opinion
Figure 13 shows that there is insignificant positive correlation
between salary and
environmental policy opinion in the USA. R Squared indicates
less than 1% of changes
in environmental policy opinion can be explained by changes in
salary.
Figure 13: Illustration of Salary line fit plot (Source; Author).
y = 2E-07x + 3.0138
R² = 2E-05
y = 2E-07x + 3.0138
R² = 0.0052
0
1

2
3
4
5
6
$0 $50,000 $100,000 $150,000 $200,000
O
pi
ni
on
Salary
Salary Line Fit Plot
Opinion
Predicted Opinion
Linear (Opinion)
17 [Date]

Overall summary of the regression output discussed above is
shown below;
Figure 14: Summary of Regression output (Source; Author).
4.0 Managerial interpretation and implications
The finding of this report has establish that there is a disparity
between the existing
literature and the current situation in the USA. First, it is
interesting to note that there is
insignificant correlation between age differences and
environmental policy opinion in the
USA, contrary to the findings of Torgler et al. (2008), who had
argued that a negative
correlation existed between these two variables. Secondly, it is
also important to note that
there is no correlation between salary and environmental policy
opinion in the USA,
contrary to Mair et al. (2019) findings that suggested high
salary could influence
environmental concerns. The implication of these two findings
is that increasing the salary
of people in organisation cannot lead to improved support for
environment policies.

18 [Date]
Secondly, age is not important in making policy about
environment and therefore no
attention is needed as far as this is concerned.
However, the findings establish that Gender has a negative
correlation with environmental
policies, with an R Squared of 78.15%. Moreover, descriptive
statistics indicated that
majority (59%) of the respondents were Female. Thus, this
suggests that Male (41%) are
more likely to be concerned with environment policy than
women, contrary to the findings
of Sundstrom and McCright (2014), who claimed a positive
correlation existed between
women and environmental concerns. The implications is that
women should be educated
more on the need to engage in environmental policy so as to
enhance environment
protection and conservations.
Moreover, the findings indicated that a negative correlation
exists between number of

children and environmental policy opinion. This is consistent
with findings of Wynes and
Nicholas (2017) that suggested fewer children can enhance
environmental protection.
The implication of this is that USA and organisations can
promote environment policies if
they encourage people to have fewer children.
5.0 Conclusion
This report has analysed how Gender, Age, Number of children
and Salary differences
affects environmental policies in the USA. The findings indicate
that both Age and Salary
did not have any effect on environmental policy opinion.
However, the findings indicated
that Gender affected negatively environmental concerns,
especially women. A negative
correlation is also found in respect to Number of children. It is
therefore concluded that
USA and organisations in this country can promote
environmental policy by encouraging
19 [Date]

people to have fewer children and educating women more about
environment and how it
affects them and people across the globe.
20 [Date]
References
Bhatt, R. (2015) What is the meaning of negative coefficient of
kurtosis obtained in my
specific AFM sample? [online]. Available at:
https://www.researchgate.net/post/What_is _the_meaning_of_ne
gative_coefficient_of
_kurtosis_obtained_in_my_specific_AFM_sample (Accessed
11th July 2019).
Kim, H. Y. (2013). Statistical notes for clinical researchers:
assessing normal distribution
(2) using skewness and kurtosis. Restorative dentistry &
endodontics, 38(1), 52-54.
Mair, S., Druckman, A., & Jackson, T. (2019) Higher Wages for
Sustainable

Development? Employment and Carbon Effects of Paying a
Living Wage in Global
Apparel Supply Chains. Ecological economics, 159, 11-23.
Statistics.laerd.com, (2018) Descriptive and inferential
statistics. [online]. Available at:
https://statistics.laerd.com/statistical-guides/descriptive-
inferential-statistics.php
(Accessed 11th July 2019).
Sundström, A., & McCright, A. M. (2014) Gender differences in
environmental concern
among Swedish citizens and politicians. Environmental Politics,
23(6), 1082-1095.
Torgler, B., Garcia-Valiñas, M. A., & Macintyre, A. (2008)
Differences in preferences
towards the environment: The impact of a gender, age and
parental effect.
Trochim, W. M., & Donnelly, J. P. (2001). Research methods
knowledge base (Vol. 2).
Cincinnati, OH: Atomic Dog Publishing.
Van Der Aalst, W. (2016). Data science in action. In Process
Mining (pp. 3-23). Springer,
Berlin, Heidelberg.

21 [Date]
Wynes, S., & Nicholas, K. A. (2017) The climate mitigation
gap: education and
government recommendations miss the most effective individual
actions. Environmental Research Letters, 12(7), 074024.
Individual Report
This assignment requires you to build and estimate a model
using Microsoft Excel or specialist
software to address policy/business/management related
questions. Please upload both your report
file on Moodle.
Specifics
You are required to find a management decision problem in any
company. Using Excel (or specialised
software), conduct an analytics for the problem, and interpret
the results. You are required to submit a
technical report (1,500 words) that discusses the managerial

problem, the results, their implications and
makes recommendations for improvement.
Your report will be marked according to the following criteria:
problem description;
modelling and solution; discussion on the result; managerial
recommendations.
The grade components will be approximately distributed as
follows:
Introduction (5%)
Descriptive analysis (20%)
Regression analytics (25%)
Managerial interpretations and implications (40%)
Conclusion (10%)
Introduction
In this section, you will identify your research question - based
on the Excel Data you are
assigned. It is important to give reasons for why you think it’s
interesting to explore such
question.
Descriptive analysis:
Import the raw data to the data platform of your chosen
software (e.g., MS excel), make sure it
imported correctly, identify the main tables and relationships

between, produce relevant
visualizations.
Regression analysis:
Using appropriate regression models to address the managerial
problem that you have identified in the
Introduction.
II. Steps to be taken
1. Please go to the module webpage in Unihub;
2. Go to the section named “ Individual Assignment”;
3. Upload the zip file to your laptop;
4. Open the file name “Assignment of Excel file” to individual
student;
5. Identify the data file that is associated with your student ID.
6. Open the Excel file that you find in step 5. For example, if
your student ID is
associated with Data 2, then you will work on Data 2.

III. Description of the EXCEL data files:
Data 1:
The file P02_03.xlsx contains data from a survey about
consumer behavior.
Data 2:
The file P02_07.xlsx includes data on 204 employees at the
(fictional) company Beta Technologies.
Data 3:
The data Lasagna Triers.xlsx is related to the buying behavior
of customers.
Data 4:
Catalog Marketing.xlsx contains recent data on 1000 HyTex
customers.

Data 5:
The file P03_63.xlsx contains financial data on 85 U.S.
companies in the Computer and Electronic
Product Manufacturing sector (NAICS code 334) with 2009
earnings before taxes of at least $10,000.
Each of these companies listed R&D (research and
development) expenses on its income statement.
Data 6:
The file P10_05.xlsx contains information about the human
resources.
Data 7:
P11_14.xlsx This is data for Business Week’s top U.S. MBA
programs in the MBA Data sheet of the file.
Data 8: The file P11_44.xlsx lists the test scores and
performance ratings for a randomly selected group
of employees. It also lists their seniority (months with the
company) at the time of the performance
rating.
IV. Some suggestions on how to proceed with the assignment

The best way to think about this assignment is to consider it a
simplified version of your master
dissertation which you are going to do soon! As such, you can
apply knowledge acquired from the
Dissertation Module, and any other modules to work on the
assignment. In what follows, I would like to
provide you with some suggestions on how to explore the
EXCEL data to write the report.
Let’s assume you are given a data that have information about
HR. In this data you have variables such
as age, gender, work experience, education, salary, tenure and
so on.
1. How to define a problem or research question?
It is sufficient to define just one research question. In this
example, you can propose a question such as “
Does there exist a link between gender and wage differential?”.
In other words, you want to study
whether female employees and male employees have the same
salary level if they have the same level of
education, work experience….
It is also important to elaborate on why you think this is an
interesting and relevant question. To do so,
it’s a good idea to incorporate some existing academic
references that address the link between gender
and salary.
2. How to do the data analysis

2.1. Descriptive analysis
You can report some measure of central tendency such as mean,
median, standard deviation, frequency
of the main variables in the Excel file;
2.2. Regression analysis
In this example, salary would be the dependent variable. You
then can run the regression using the
techniques we talk in class.
3. Interpretation of the results
It’s important to provide some discussion on your findings. For
example, if you find that the coefficient
for men is positive i.e., men have higher salary than women.
You can explain why this might be the case.
Also, it’s good idea to make references to academic studies that
may or may not support your results.
4. Recommendation
What would be the managerial implications for your results?
For example, if men have higher salary
then women, what would you like to recommend either from the
government’s policy perspective, and/or

from the firm’s HR practices perspective.
Source
Mergent Online
This lists publicly traded USA companies from Mergent's
database with NAICS code 334 (Computer and Electronic
Product Manufacturing) that have earnings before taxes in 2009
greater than $10,000 and reported R&D expenses (or a similar
line item) in their income statement.
DataUS Publicly Traded Companies with NAICS code
334Financial SummaryProfitability RatiosLiquidity
IndicatorsCompanyAs of DateTotal
RevenueR&DEBITDAOperating IncomeNet IncomeTotal
AssetsCurrent AssetsTotal LiabilitiesCurrent LiabilitiesLong
Term DebtStockholders' EquityROA % (Net)ROE % (Net)ROI
% (Operating)EBITDA Margin %Calculated Tax Rate %Quick
RatioCurrent RatioNet Current Assets % TA3Com
Corp.5/29/091,316,978,000179,979,000178,492,000100,692,000
114,725,0001,815,357,000944,913,000703,182,000510,453,000
152,000,0001,112,175,0006.4110.927.7413.5522.131.371.8523.
93Agilent Technologies,
Inc.10/31/094,481,000,000642,000,000178,000,00047,000,000-
31,000,0007,612,000,0003,961,000,0005,106,000,0001,123,000,
0002,904,000,0002,506,000,000-0.41-
1.220.933.97542.862.753.5337.28American Science &
Engineering
Inc3/31/09218,367,00018,070,00047,754,00042,024,00028,353,
000268,287,000242,116,00086,221,00073,096,0008,377,000182
,066,00011.2616.2922.8221.8735.121.953.3163AML
Communications
Inc.3/31/0913,288,0001,932,0002,148,000882,000959,00016,43
1,0008,301,0002,903,0002,309,000594,00013,528,0005.777.416
.0616.1628.911.713.636.47Analog Devices,
Inc.10/31/092,014,908,000446,980,000425,294,000284,817,000

247,772,0003,404,294,0002,490,636,000875,145,000386,613,00
0379,626,0002,529,149,0007.6510.0410.7221.1116.822.436.446
1.8Anaren
Inc6/30/09166,905,00012,986,00022,427,00014,027,0009,859,0
00237,055,000127,031,00076,110,00024,819,00040,000,000160
,945,0004.826.327.7613.4427.6835.1243.12Apple
Inc9/26/0936,537,000,0001,333,000,0008,280,000,0007,658,000
,0005,704,000,00053,851,000,00036,265,000,00026,019,000,00
019,282,000,00027,832,000,00012.2423.4131.4322.6628.560.54
1.8831.54Applied Signal Technology,
Inc.10/31/09202,615,00014,482,00029,231,00022,870,00014,52
9,000171,000,000113,514,00031,975,00026,329,0002,500,0001
39,025,0008.8311.0216.9314.4337.081.944.3150.99ARGON ST
Inc9/30/09366,076,0008,990,00043,356,00036,195,00023,691,0
00380,025,000174,717,00060,447,00056,856,00013,000319,578
,0006.587.7411.8311.8434.522.813.0731.01Astro-Med,
Inc.1/31/0971,783,3384,884,7675,983,1424,626,2312,963,98762
,155,25048,022,66210,683,7657,903,65351,471,4854.775.869.1
58.3435.252.566.0864.55Biosynergy,
Inc.4/30/0912,639,63690,5721,739,0381,337,369943,9377,402,6
415,611,9561,743,7021,414,5195,658,93912.5623.888.1514.327
8.51Biotel,
Inc.6/30/0912,639,6361,601,5251,739,0381,337,369943,9377,40
2,6415,611,9561,743,7021,414,5195,658,93913.7418.2225.3613
.7630.422.383.9756.7Blue Coat Systems
Inc4/30/09444,745,00076,680,00017,273,0002,568,000-
8,508,000562,293,000218,078,000280,684,000151,434,00076,3
47,000281,609,000-1.79-
3.190.843.881465.651.261.4411.85Broadcom
Corp.12/31/094,490,323,0001,534,918,00089,034,00056,072,00
065,261,0005,127,242,0002,914,332,0001,235,396,0001,148,35
0,0003,891,846,0001.371.741.51.989.61.662.5434.44Cabot
Microelectronics
Corp9/30/09291,372,00048,150,00038,240,00016,023,00011,18
7,000515,144,000316,852,00044,415,00039,536,0001,308,0004
70,729,0002.252.473.5213.1232.76.418.0153.83Cemtrex

Inc9/30/096,967,9925,535299,540266,244155,0101,743,4821,65
4,1191,655,1961,264,676390,52088,2867.79AvgEqty<061.154.3
2.171.031.3122.34Cirrus Logic,
Inc.3/28/09174,642,00044,315,00010,553,0003,216,0003,475,00
0209,496,000155,148,00036,568,00028,240,0002,077,000172,9
28,0001.371.681.546.0443.561.595.4960.58Cisco Systems,
Inc.7/25/0936,117,000,0005,208,000,0007,727,000,0007,322,00
0,0006,134,000,00068,128,000,00044,177,000,00029,481,000,0
0013,655,000,00010,295,000,00038,647,000,0009.716.8516.282
1.3920.270.653.2444.8ClearOne Communications
Inc6/30/0935,700,0007,541,0003,615,0002,776,0002,227,00040,
853,00028,315,00012,367,00010,634,00028,486,0005.237.359.1
610.1330.881.392.6643.28Comtech Telecommunications
Corp.7/31/09586,372,00050,010,00097,022,00076,927,00049,55
8,000938,671,000689,051,000309,542,00092,526,000200,000,0
00629,129,0006.239.2511.1716.5535.226.117.4563.55Corning,
Inc.12/31/095,395,000,000563,000,0001,354,000,000391,000,00
02,008,000,00021,295,000,0005,521,000,0005,752,000,0001,53
9,000,0001,930,000,00015,543,000,0009.913.852.425.1-
14.832.143.5918.7CPI International
Inc10/2/09332,876,00010,520,00051,021,00039,979,00023,466,
000458,254,000155,206,000284,701,00062,826,000194,922,000
173,553,0005.0914.8310.8615.33-0.941.132.4720.16Cree,
Inc.6/28/09567,255,00071,363,000123,819,00030,590,00030,32
5,0001,404,567,000629,436,000179,819,000128,681,0001,224,7
48,0002.242.572.5921.8322.734.054.8935.65Cubic
Corp9/30/091,016,657,0008,173,000101,207,00084,708,00055,6
86,000756,315,000584,858,000335,470,000269,665,00020,570,
000420,845,0007.9713.7519.559.9534.671.762.1741.67Cyberon
ics,
Inc.4/24/09143,600,97919,732,94131,789,04118,989,70826,720,
620112,043,628104,533,90287,631,78417,645,24062,339,00024
,411,84421.58583.2119.3822.142.665.015.9277.55Daktronics
Inc.5/2/09581,931,00021,619,00066,364,00042,617,00026,428,0
00324,876,000206,973,000112,965,000102,430,00023,000211,9
11,0008.413.1621.1511.434.331.32.0232.18Dell

Inc1/30/0961,101,000,000665,000,0004,186,000,0003,190,000,0
002,478,000,00026,500,000,00020,151,000,00022,229,000,0001
4,859,000,0001,898,000,0004,271,000,0009.1961.3559.86.8525.
4511.3619.97Digi International,
Inc.9/30/09165,928,00026,381,0009,907,0003,070,0004,083,000
258,948,000122,655,00029,362,00016,534,000229,586,0001.54
1.771.335.974.654.087.4240.98Dionex
Corp.6/30/09385,048,00028,979,00091,957,00084,247,00055,45
5,000329,984,000206,222,000107,570,00077,999,000222,414,0
0016.7926.4638.223.8833.411.82.6438.86Dolby Laboratories
Inc9/25/09719,503,00071,557,000374,589,000363,666,00 0242,9
91,0001,581,315,000900,838,000240,207,000156,584,0005,825,
0001,341,108,00016.720.3930.352.0634.213.035.7547.07Dynatr
onics
Corp.6/30/0932,406,891993,338820,321707,405103,32417,087,
28912,003,06810,667,5407,785,8812,881,6596,419,7490.581.63
4.772.5344.230.641.5424.68e.Digital
Corp.3/31/0911,055,732518,6493,536,3773,699,9432,933,9864,
477,6704,451,0321,197,8071,173,8073,279,863109.91372.6823
4.9531.9912.563.333.7973.19Emulex
Corporation6/28/09378,222,000129,795,00025,530,0001,277,00
07,544,000658,918,000406,553,00089,474,00052,240,000569,4
44,0001.111.320.226.75-34.576.627.7853.77Encision
Inc.3/31/0912,789,2931,138,677432,210207,272159,8174,970,0
893,889,5481,709,2521,709,2523,260,8373.225.165.933.380.79
2.2843.87Extreme Networks,
Inc.6/28/09335,559,00058,176,000 7,976,000859,0002,815,0002
46,637,000116,441,000105,127,00093,027,000141,510,0000.92
1.510.462.3846.760.971.259.49F5 Networks,
Inc.9/30/09653,079,000103,664,000158,055,000121,924,00091,
535,0001,068,645,000468,182,000269,625,000223,014,000799,
020,0009.1212.0716.0724.230.470.982.122.94Globecomm
Systems
Inc.6/30/09170,161,0002,392,0009,164,0003,958,0003,299,0001
91,539,000109,865,00036,727,00035,221,000154,812,0001.722.
172.615.3926.562.543.1238.97GSI Technology

Inc3/31/0962,108,0005,737,00012,788,00011,524,0009,2 89,000
92,673,00067,371,0007,968,0007,617,00084,705,00010.2611.48
14.2420.5927.922.58.8464.48GT Solar International
Inc3/31/09541,027,00018,323,000142,643,000143,544,00087,96
8,000737,423,000632,016,000655,518,000574,344,00081,905,0
0013.15101.38165.4226.3738.130.291.17.82Hewlett-Packard
Co10/31/09114,552,000,0002,819,000,00014,186,000,00010,13
6,000,0007,660,000,000114,799,000,00052,539,000,00074,282,
000,00043,003,000,00013,980,000,00040,517,000,0006.7219.28
17.9212.3818.640.841.228.31Irvine Sensors
Corp.9/27/0911,536,2002,266,7004,328,300-
10,254,400914,8005,851,6002,901,60010,554,9009,172,900-
4,703,3006.38AvgEqty<0-2374.1837.527.910.260.32-
107.17Iteris
Inc3/31/0969,359,0004,030,0006,554,0005,707,00010,474,0007
6,066,00033,231,00020,708,00012,631,0005,274,00055,358,000
14.7821.2510.049.45-102.321.822.6327.08IXYS
Corp.3/31/09273,552,00019,931,00016,930,000-26,000-
3,349,000252,832,000185,220,00074,340,00034,303,00022,017,
000178,492,000-1.23-1.77-0.016.19193.972.75.459.69Key
Tronic
Corp.6/27/09184,924,0002,266,0003,378,0001,783,0001,063,00
077,755,00061,055,00026,641,00023,611,0002,412,00051,114,0
001.212.133.111.8310.91.082.5948.16Linear Technology
Corp.6/28/09968,498,000185,843,000484,310,000412,076,0003
13,510,0001,421,529,0001,089,251,0001,688,131,000125,341,0
001,405,644,000-
266,602,00020.92AvgEqty<034.3650.0122.972.498.6967.81Mag
neTek,
Inc.6/28/0998,221,0003,522,0007,189,0006,146,0003,283,00084
,080,00043,816,00095,371,00012,040,0004,000-
11,291,0003.7535.5766.467.3220.932.493.6437.79Maxim
Integrated Products,
Inc.6/27/091,646,015,000521,628,000227,119,00017,378,00010,
455,0003,081,775,0001,584,951,000487,310,000268,776,0002,5
94,465,0000.310.370.6113.869.84.175.942.71Medtronic,

Inc.4/24/0914,599,000,0001,355,000,0003,041,000,0003,019,00
0,0002,169,000,00023,661,000,0007,460,000,00010,810,000,00
03,147,000,0006,772,000,00012,851,000,0009.4917.8415.6720.
8316.381.42.3718.23Mercury Computer Systems,
Inc.6/30/09188,939,00042,372,00016,564,0007,747,000-
1,262,000219,372,000146,105,00074,335,00065,389,0002,0001
45,037,000-0.45-0.873.448.771.361.162.2336.79Mesa
Laboratories,
Inc.3/31/0921,536,000636,0007,893,0007,608,0004,790,00029,6
14,00018,593,0002,012,0001,484,00027,602,00017.3718.6629.6
436.6537.749.0512.5357.77Mettler-Toledo International,
Inc.12/31/091,728,853,00089,685,000284,313, 000282,631,0001
72,593,0001,718,787,000646,107,0001,007,649,000494,675,000
203,590,000711,138,00010.2128.4228.8116.4523.210.81.318.81
Microchip Technology,
Inc.3/31/09903,297,000115,524,000322,274,000239,762,000248
,820,0002,421,439,0001,742,789,0001,430,673,000155,645,000
1,149,184,000990,766,00010.0924.5511.0835.68-
4.883.4411.265.55MIPS Technologies,
Inc.6/30/0970,193,00021,496,00021,117,00020,833,000-
9,438,00067,885,00048,534,00041,224,00017,870,0007,813,000
26,661,000-8.56-26.3737.5130.0840.82.642.7245.17MTS
Systems
Corp.10/3/09408,881,00016,322,00036,951,00024,594,00017,39
4,000386,914,000285,558,000182,949,000167,992,000203,965,
0004.358.3710.179.0427.231.31.730.39Multi-Fineline
Electronix
Inc9/30/09764,432,0005,505,00096,807,00057,399,00046,068,0
00525,930,000348,563,000166,942,000146,527,00010,852,0003
58,988,0009.0913.7716.8812.6617.791.842.3838.41National
Semiconductor
Corp.5/31/091,460,400,000308,900,000295,700,000183,200,000
73,300,0001,963,300,0001,087,200,0001,786,300,000275,600,0
001,227,400,000177,000,0003.5138.5711.4820.2535.482.83.944
1.34Netapp,
Inc.4/24/093,406,393,000498,495,000184,622,00047,175,00086,

545,0005,472,819,0003,438,784,0003,810,473,0001,679,325,00
01,265,000,0001,662,346,0001.825.161.975.42-
93.061.162.0532.15Netezza
Corp1/31/09187,769,00032,557,00016,594,00012,589,00031,51
9,000258,859,000180,763,00082,237,00067,433,000176,622,00
013.7420.088.028.84-95.782.172.6843.78Notify Technology
Corp.9/30/096,032,2571,917,614163,81263,78770,6852,679,249
2,416,5303,814,5913,670,7986,543-
1,135,3423.2AvgEqty<0AvgInvCap<02.720.650.66-46.81NVE
Corp3/31/0923,372,2691,218,57213,729,53013,251,5909,782,89
542,566,4408,826,418998,869998,86941,567,57125.9726.7736.
2758.7432.195.258.8418.39OSI Systems,
Inc.6/30/09590,361,00036,862,00035,427,00019,527,00011,152,
000474,828,000323,243,000198,828,000135,635,00039,803,000
276,000,0002.274.035.74632.511.022.3839.51OYO Geospace
Corp.9/30/0992,860,0008,062,0008,004,0003,621,0001,760,000
141,482,00095,742,00022,824,00012,900,0008,820,000118,658,
0001.171.492.728.6246.842.557.4258.55Pericom Semiconductor
Corp.6/27/09128,645,00016,697,0009,711,0003,017,0006,087,0
00246,058,000161,378,00033,850,00026,002,0001,610,000212,
208,0002.562.911.447.5527.412.386.2155.02Qualcomm,
Inc.9/27/0910,416,000,0002,440,000,0002,545,000,0002,226,00
0,0001,592,000,00027,445,000,00012,570,000,0007,129,000,00
02,813,000,00020,316,000,0006.148.3411.6224.4323.121.214.4
735.55RF Industries
Ltd.10/31/0914,213,0451,050,3981,145,917906,140655,96716,5
98,20015,769,6561,344,718973,18815,253,4823.824.185.788.06
40.3410.2416.289.15Risk (George) Industries
Inc4/30/098,822,00079,000342,0001,485,000516,00026,843,000
25,724,000744,000658,00026,099,0001.871.935.543.8844.818.9
539.0993.38Scientific Industries,
Inc.6/30/095,989,100452,600450,700396,200319,5004,831,0003
,903,600920,500920,5003,910,5006.658.3810.397.5320.91.684.
2461.75SeaChange International
Inc.1/31/09201,836,00043,042,00018,494,00010,194,0009,974,0
00233,983,000140,814,00061,747,00051,265,000172,236,0004.

45.896.029.165.082.122.7538.27Semtech
Corp.1/25/09294,820,00041,405,00047,704,00041,891,00037,52
1,000420,795,000313,702,00042,775,00033,815,000378,020,00
09.2110.3511.5616.1818.755.189.2866.51Sigma Designs,
Inc.1/31/09209,160,00043,558,00035,517,00025,619,00026,423,
000330,947,000193,810,00025,697,00018,481,000305,250,0007
.468.147.8916.9815.636.5810.4952.98Skyworks
Solution
s,
Inc.10/2/09802,577,000123,996,000119,921,00071,703,00093,2
89,0001,355,326,000590,127,000250,197,000196,995,00047,11
6,0001,105,129,0007.229.136.0814.94-41.892.43329.01Super
Micro Computer
Inc6/30/09505,609,00034,514,00026,906,00023,253,00016,107,
000283,135,000220,018,000104,513,00089,031,0009,741,00017
8,622,0005.889.7513.255.3229.351.32.4746.26Supertex,
Inc.3/28/0978,810,00014,553,00015,954,00013,994,00012,545,0
00190,304,00095,220,00022,728,00017,889,000167,576,0006.8
47.888.7920.2420.431.815.3240.64Synergetics USA
Inc7/31/0952,965,0002,998,0004,185,0003,125,0001,595,00058,
080,00025,358,00019,950,00011,948,0006,079,00038,130,0002.
744.286.197.932.70.782.1223.09Technical Communications
Corp.9/26/097,751,8581,888,953582,429423,137942,8089,322,2
768,982,7692,609,6182,609,6186,712,65811.7915.266.857.51-

103.672.233.4468.36Tel Instrument Electronics
Corp.3/31/0913,075,9422,948,356510,317323,626196,2377,505,
3896,142,7412,901,8692,858,6264,603,5202.684.456.733.953.7
31.182.1543.76Telular
Corp.9/30/0947,194,0004,783,0003,139,0002,031,0001,866,000
40,325,00033,569,0004,903,0004,903,00035,422,0004.234.945.
376.652.775.26.8571.09Torotel,
Inc.4/30/097,205,000331,000462,000360,000303,0003,026,0002
,056,0001,057,000469,000588,0001,969,00010.4317.2514.726.4
12.594.3852.45Varian Medical Systems,
Inc.10/2/092,214,060,000147,375,000518,755,000474,146,0003
19,022,0002,308,248,0001,672,451,000996,465,000842,320,000
23,394,0001,311,783,00014.6526.8438.6123.4330.161.351.9935
.96Varian,
Inc.10/2/09806,744,00056,425,00078,683,00058,983,00038,620,
000944,187,000559,847,000305,916,000249,656,00012,500,000
638,271,0004.196.329.369.7534.711.482.2432.85ViaSat,
Inc.4/3/09628,179,00029,622,00064,045,00044,287,00038,331,0
00622,942,000338,824,000164,194,000135,434,000458,748,000
6.428.7410.110.215.021.682.532.65Western Digital
Corp.7/3/097,453,000,000509,000,000998,000,000519,000,0004
70,000,0005,291,000,0003,230,000,0002,099,000,0001,525,000,
000400,000,0003,192,000,0009.115.7114.8513.396.191.782.123
2.22White Electronic Designs
Corp.9/30/0962,559,0004,408,0006,573,0003,773,0002,029,000

111,597,00096,193,0009,707,0008,518,000101,890,0001.82.013
.7310.5128.98.7211.2978.56Xilinx,
Inc.3/28/091,825,184,000355,392,000540,507,000429,518,0003
75,640,0002,825,515,0001,752,468,0001,087,615,000233,066,0
00690,125,0001,737,900,00012.6322.0916.8929.6124.65.57.525
3.77Zoll Medical
Corp.9/27/09385,185,00039,474,00027,874,00011,614,0009,564
,000371,847,000230,119,00091,289,00073,489,000280,558,000
2.673.54.257.2428.531.793.1342.12

Kingdom of Saudi ArabiaMinistry of EducationUniversity of Ha

Empfohlen

Empfohlen

Weitere ähnliche Inhalte

Ähnlich wie Kingdom of Saudi ArabiaMinistry of EducationUniversity of Ha

Ähnlich wie Kingdom of Saudi ArabiaMinistry of EducationUniversity of Ha (14)

Mehr von JospehStull43

Mehr von JospehStull43 (20)

Kürzlich hochgeladen

Kürzlich hochgeladen (20)

Kingdom of Saudi ArabiaMinistry of EducationUniversity of Ha