In this week’s assignment, you will explore the different types of

In this week’s assignment, you will explore the different types
of graphs used to visualize data. Results from both Excel and
SPSS should be copied and pasted into a Word document for
submission.
Use the provided datasets for building one of each of the four
chart types below. For each chart, select a variable from the
provided dataset with a measurement level that is best
visualized by that chart type. Use APA style to label each chart.
Each graph must contain a narrative description of what it
represents and an interpretation of the image.
Pie chart
Bar chart
Scatterplot
Histogram
Length: 4 to 6 pages not including title page or reference page
References: Include a minimum of 2 scholarly resources. Be
sure to reference Excel and SPSS as they are resources for this
assignment, although not scholarly.
Your paper should demonstrate thoughtful consideration of the
ideas and concepts presented in the course and provide new
thoughts and insights relating directly to this topic. Your
response should reflect scholarly writing and current APA
standards. Be sure to adhere to Northcentral University's
Academic Integrity Policy.
Analyze Numerical Summaries of Data Using Excel and SPSS
In this week’s assignment, you will answer the following
questions using both Excel and SPSS software. Results from
these programs should be copied and pasted into a Word
document for submission.
For Questions 1 and 2 you will need to input the data provided
below into both Excel and SPSS. For Questions 3 and 4 you will
use the dataset files provided in the resources for this week

(Descriptive statistics, n.d.-a, n.d.-b).
1. Suppose that a quality assurance manager took a random
sample from a thread-cutting machine. The sample consisted of
18 bolts and the manager tested their tensile strengths. Results
from the sample, in tons of force required for breakage, are
given below:
2.20 1.95 2.15 2.08 1.85 1.92
2.23 2.19 1.98 2.07 2.24 2.31
1.96 2.30 2.27 1.89 2.01 1.93
a. Use Excel to calculate the mean, median, and standard
deviation for these data.
b. Use SPSS to calculate the mean, median, and standard
c. Use SPSS to create a histogram of these data.
d. Interpret these results and explain any differences you find
between the two software tools (Hint. The results should be
identical).
2. A manager of a food manufacturing company wants to
estimate the percentage of fat in one of its salad dressings. A
sample of 20 bottles was taken and the results are given below.
15.88 19.88 21.16 20.37 22.77 20.65
18.60 18.91 21.77 21.64 18.62 18.41
20.15 17.07 19.91 21.07 16.49 21.21
17.98 20.22
d. Interpret these results and explain whether or not it is honest
for the manufacturer to state that the fat content is 20%?
Explain your answer.
3. A manager is worried that her employees are not engaged at
work. She finds an employee engagement survey and
administers it to her workers. The individual scores are
provided in the dataset file.

deviation for the employee engagement data.
deviation for the employee engagement data.
d. Next, conduct an analysis of the “age” and “gender” variables
using the appropriate measures of central tendency and
dispersion. Use SPSS only. Also, create an appropriate graphic
for each variable.
d. Interpret the results for employee engagement, age, and
gender. Identify what you believe the next step should be in this
analysis.
4. A shop supervisor wants to understand his workers better. He
hires a consultant who tells him that he should ask employees
questions related to their lives if they feel comfortable
answering these questions. Please use SPSS to analyze the
following variables, create appropriate figures and charts, and
explain the results of each variable. Do not compare the
variables in this assignment.
a. Age
b. Model of car
c. Rent or own their home
d. Hobbies
e. How happy they are (on a five-point scale)
Length: 4 to 6 pages
References: Include a minimum of 3 resources (two of these
will be Excel and SPSS).
Your assignment should demonstrate thoughtful consideration
of the ideas and concepts presented in the course and provide
new thoughts and insights relating directly to this topic. Your
response should reflect scholarly writing and current APA
standards. Be sure to adhere to Northcentral University's
Academic Integrity Policy.

Variable Questions and Data Privacy
Latrice Jones
Northcentral University
11/26/2021
Variable Questions and Data Privacy
Variables must vary
Variables are essential part of quantitative research, thus
understanding fundamental articulation on variables such as
different types of variables, operationalization and measurement
and, scales of variables underpin in an effective platform for
variables comprehension. In particularly, “variables must vary”
posit a significant implications in variables understanding ,
accommodating fundamental variability features in a
quantitative research and comprehension of variables as
essential part in a quantitative research. This statement
(“variables must vary.”) implies that different units, or
participants in a must indicate differences in the variable. For
example in one study, gender may be variable demonstrating
difference unit or participant. Significantly, one study, focusing
on married women will great gender as a non-variable in a
study, while married women form a significant variable for the
study. Therefore, the statements (“variables must vary.”) are
fundamental, demonstrating that different units and participants

must indicate different variable in a research study.
Levels of variables measurement
Significantly, understanding the operationalization and
measurement of variables are essential strategies for effective
data quantification and decision-making in a research study.
Scales of variables measurement is fundamental because it
provide effective articulation on the relationship between
variables and conclusion in a research study for practical
implications and conclusion. In a comprehensive understanding
of the variables, there are four different level of variables
measurement that requires effective articulation and description
to effectively operationalize and measure variables in a
consistent and accurate manner in a research study. Notably,
variable measurement levels including nominal measurement,
ordinal level, interval and ratio variables. Nominal level of
variable measurement describes one fundamental basic measure
that contains two or more mutually inclusive and exclusive
categories that cannot be ordered. For instance, a list of states
in India Himachal Pradesh, Uttaranchal, Maharashtra are
nominal variables that require specific rile for effective
ordering and ranking. Accommodating rules such as
alphabetical order would provide an effective platform for data
ordering and ranking States in India in alphabetical order.
Similarly, gender, accommodating male, female and third
gender is vital examples of nominal variables measurement.
Consequently, ordinal variable describes level of variable
measurements in which two or more categories like in nominal
measurement of variables. However, ordinal variable can be
ranked, accommodating the ranking capabilities in
differentiating categories in such variables. Noteworthy,
ranking in ordinal variables measurements level do not contain
numerical value; hence can only be measure in terms of greater
or lesser than. For example asking a student who often they read
in day would provide significant example of ordinal variables
measurement levels. For instance, frequently, sometimes, yes or
no are vital examples of ordinal measurement of variables.

Interval is vital level of data measurements that accommodates
significant articulation on numerical value and continuum
measurement that requires effective articulation. Specifically,
Interval variables are variables that have a numerical value, and
are measured on a continuum, accommodating equal interval
between values and items. For instance a temperature measured
in Celsius or Fahrenheit is a vital example of interval level of
variable measurements. For instance, the difference between 20
degrees Celsius and 30 degrees Celsius is equal to the different
between 30 degrees Celsius and 40 degrees Celsius. Similarly,
the example, 1 dollar to 2 dollars is the same interval as 88
dollars to 89 dollars gives another significant example in
interval level of variable measurements. Finally, the ratio
variables measures numerical value and continuum, however the
ratio values are in absolute zero. This zero on the measurements
scale indicating no value of the variable or the unit or items
measured is absent at level zero. Some significant examples of
ratio variables level of measurement include height, weight,
currency, mass, among others. Thus, scales of measurement of
variables are nominal, ordinal, interval and ratio,
accommodating categories, numerical value, and order among
other issues in variables instances of variable measurement
level.
Discreet and Continuous Variables
Understanding data accommodates quantitative and qualitative
data in a study. In statistical analysis, accommodate quantitative
data provide significant attention to discrete and continuous
variables that posits fundamental characteristics. Discrete
variables are variable assuming a finite number of isolated
values. In contrast, Continuous variables define variables that
assume infinite number of different values. According to the
author, understanding discrete and continuous variables
accommodates meaning, specific numbers range, classification,
assumption and representation (Allen 2017). On one hand,
discrete variables constitute complete range of numbers, while
continuous variable define an incomplete rage of number.

Similarly, discrete variables obtains value through counting
while, continuous variable obtain values through measuring.
Discrete variables assume distinct or separate values. In
contrast continuous variables assume any value between the two
values. Finally, in classification, discrete variables describe
non-overlapping classification, while continuous variables
identify overlapping classifications. For example, the discrete
variable is describable with possible values such as 1, 2, 3…
while continuous variable can define how far a ball thrown
upwards will take to settle, accommodate measurement accuracy
for actual value determination. Thus, understanding the
comparison between discrete and continuous variables provide a
vital platform for decision-making in data usage and
operationalization.
In calculation, discrete variables are insignificant information
that gives less accurate outcomes for decision-making and
process improvement. Accommodating accuracy in using
discrete variables requires repeated measurements (Berkman, &
Reise, 2012) Therefore, dependability on probability of the
actual magnitude depends on the accuracy of the values.
Level of measurement for:
Career field (e.g., accountant, production manager,). This is
Nominal level of variable measurement because it describes one
fundamental basic measure that contains two or more mutually
inclusive and exclusive categories that cannot be ordered.
Temperature in Fahrenheit. This is Interval variables
measurement of variables because there are variables that have
a numerical value, and are measured on a continuum,
accommodating equal interval between values and items.
A job satisfaction survey measured as “disagree, neutral, agree”
is an example of ordinal variable describes level of variable
measurements because there are two or more categories like in
nominal measurement of variables.
Finally, an example, Total sales for a firm is an example of, the
ratio variables because it measures numerical value and
continuum; however the ratio values are in absolute zero.

Types of a variable
The number of workers in each department of a large
organization describes discrete variable because these are
variables are variable assuming a finite number of isolated
values.
The dollars of revenue earned during a fiscal year is discrete
variable accommodating variables have variable assuming a
finite number of isolated values.
The number of software licenses available to employees in a
firm is an example of discrete variables because, variables have
variable assuming a finite number of isolated values
Finally, the average annual salary of middle managers of an
organization demonstrates continuous variable because the
variables that assume infinite number of different values
Data Privacy
Data privacy is a fundamental concern for business organization
and researchers that require pragmatic articulation to ensure
availability, integrity and confidentiality of data. Specifically,
creating a significant platform for effective data collection,
processing, analysis and transmission, security privacy is a vital
consideration in data management. In a research scenario,
accommodating techniques such as information security
practices and data privacy and confidential through a robust
data protection strategies such as access control , authentication
and data transmission and sharing polices provide an enhanced
platform for data privacy. Notably, organizations dealing with
customers’ data for decision-making require a robust platform
for a secure data protection platform. Specifically, embracing
data privacy principles including fair, lawful and transparency,
accuracy, storage limitation, integrity and confidentiality and
accountability are vital for effective data privacy concepts
(Medine, and Murthy, 2020). Therefore, Data privacy is a vital
concern that requires real-time solution and technology-based
strategies for a secure and safety data management systems.

References
Allen, M. (2017). Variables, Continuous. In: The SAGE
Encyclopedia of Communication and Research methods. SAGE
Publications
Berkman, E.T., & Reise, S.P. (2012). A Conceptual Guide to
Statistics Using SPSS. Thousand Oaks, California, United
States: SAGE Publications, Inc.
Medine, D., and Murthy, D. (2020). New Approaches to Data
Protection and
Privacy.https://www.cgap.org/sites/default/files/publi cations/20
20_01_Focus_Note_Making_Data_Work_for_Poor_0.pdf

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