Quantitative Research
Quantitative Research
Rachel McBride
Bellevue University
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Quantitative Research
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Quantitative Research
Qualitative research is a deep dive into something specific, such as a situation that is being
studied or looking for an answer to a question. An example would be why people behave in a
certain way in certain situations. The data is gathered through the use of “focus groups,
individual depth interviews (IDIs), case studies, ethnography, grounded theory, action research,
and observation” (Cooper & Schindler, 2011). Focus groups are small groups of people that are
interviewed about a thing or event. Sometimes they are shown a video or test a product and are
asked questions as a group to gain feedback. IDIs are interviews seeking detailed responses from
individuals. Case studies are in depth reviews of all aspects of an event, situation, person, etc., to
include interviews, document analysis, and observations. Ethnography is observation and
informal interviewing of a subject outside of the lab. Grounded theory is the technique of
analyzing data while it is being collected. Action research is hands on, real time research of
trying numerous possible solutions until successful. Observation is the collection of data through
monitoring visually and physically.
Qualitative research methodologies gather data from many sources, to include:

People

Organizations or institutions

Texts

Settings and Environments

Objects, artifacts, media products

Events and happenings (Cooper & Schindler, 2011)
Once the qualitative research data is gathered, it must be analyzed. Analysis is the
examination and assessment of the data collected. Analysis can be contextual or independent,
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depending on the basis for the research. The data is analyzed through “content analysis of
written or recorded materials drawn from person expressions by participants, behavioral
observations, and debriefing of observers, as well as study of artifacts and trace evidence from
the physical environment” (Cooper & Schindler, 2011).
Quantitative research is analysis of numerical measurements, such as statistics. The data
being gathered is usually specific in nature, such as how many people drive blue cars.
Quantitative research answers the questions “how much, how often, how many, when, and who”
(Cooper & Schindler, 2011).
Both types of research have methodologies for gathering and analyzing data, but qualitative
research typically requires more preparation and forethought to ensure the right questions are
being asked in the right manner of data collection to answer the question at hand. The nature of
quantitative research is clear cut, and the data being sought is usually known ahead of time, it is
just the numbers that need to be researched.
Variance
Variance is “a measure of score dispersion about the mean; calculated as the squared
deviation scores from the data distribution’s mean; the greater the dispersion of score, the greater
the variance in the data set.” (Cooper & Schindler, 2011, p.731). In other words, variance is a
measure of the average distance between the data points within a data set and the mean (or
average) of that data set. The variance is calculated by finding the sum of the squares of the
standard deviation, which is the average amount the data points differ from the mean of the data
set.
Variance is more than just finding an average; it determines the total spread of the values in a
data set. Variance is helpful when analyzing the distribution of data, which provides aspects to
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decide the likelihood of a distribution occurring, thus determining if the sample or population is
following a normal distribution or if the variance just a fluke.
There should always be a degree of variance because there will be some difference between
the measures captured. So by measuring the spread of the data points, you can then understand
how data diverges from the normal distribution.
ANOVA is a statistical method that stands for analysis of variance. Variance can be used
where I work to compare the productivity and efficiency of the different regions we support. If
one region shows more productivity for IT customer support, we can capture their processes and
procedures for adoption in the other regions.
ANOVA can also be used to when trying to validate root cause. Root cause is important for
preventing and fixing problems within the IT environment. Changing some of the variables
within the situation and comparing can determine the best course of action.
ANOVA can also be used for process improvement, by measuring variance of the current
process against proposed changes to improve the process. If there is more than one proposed
solution, measuring variance can show which the best solution is by comparing the data.
Standard Deviation
Standard deviation is an important statistic in many business areas, especially for
performance output. Whether it is people or equipment being measured, you determine what the
values are for each thing being measured. For a manager, you could be measuring the
productivity of each of your employees. Once you figure out with the standard deviation is, you
can see what the “norm” is and determine what your outlier threshold should be. For employee
productivity, if the average employee is producing X, and the standard deviation for your
employees is Y, then anyone operating above X+Y is doing an outstanding job and anyone doing
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below X-Y should be talked to about making improvements. You would want to reduce
variation in productivity though for better planning and estimating. How much productivity will
you get out of a certain number of employees would be a good input number when planning for
income and resourcing. For example, if employee productivity were as follows:
Employee 1 – 15 documents per day
Employee 2 – 12 documents per day
Employee 3 – 10 documents per day
Employee 4 – 11 documents per day
Employee 5 – 7 documents per day
Mean – 11
Standard Deviation – 2.9155
Target productivity range per employee – (11 – 2.9155) to (11 + 2.9155) = 8 to 14 documents
per day. For the employee that is only producing 7, the manager should discuss the target range
with them and ways they can improve.
Another business use for standard deviation is quality. You want to ensure your products are
of a good quality, so you determine the mean or average rating of quality for your product. You
determine what the standard deviation is so you know what your quality range should be. For
any extreme outliers, especially on the lower end, you need to determine a threshold that you do
not want to go below, and remove those from the data set. Extreme outliers will affect standard
deviation.
For salaries, standard deviation can be useful, but only if you are collecting the salary data on
people with similar educations, job duties and skill sets. HR can use the calculations to
determine a salary range for a new position they are hiring for. If there are 5 people already on
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the team, HR can determine the average and the standard variation. The range is what they can
use for the job posting. For example, if the employees’ salaries were as follows:
Employee 1 - $25,000
Employee 2 - $32,000
Employee 3 - $28,000
Employee 4 - $30,000
Employee 5 - $27,000
Mean - $28,400
Standard Deviation - $2701.85
Salary range for new position – (28400 - 2701.85) to (28400 + 2701.85) = $25698.15 to
$31101.85
Applying Quantitative Research Methods
Quantitative research is analysis of numerical measurements, such as statistics. The data
being gathered is usually specific in nature, such as how many people drive blue cars.
Quantitative research answers the questions “how much, how often, how many, when, and who”
(Cooper & Schindler, 2011).
Quantitative research is based models, which can be descriptive or prescriptive. Descriptive
models identify correlations between variables, such as do service desk calls increase on given
days or before holidays. Descriptive models can also provide insight to how many calls are
answered by how many technicians, and what is the average wait time. This kind of modeling
can be used to scale staffing for a service desk. Prescriptive models focus on providing an
answer based on costing to ensure the best return on investment (Brandimarte, 2011, p. 68).
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In my current organization, quantitative research methods could be applied for a variety of
reasons. As an Information Technology Service Management (ITSM) consultant, we gather a lot
of information pertaining to the “as is” and “to be” statuses of our customer. Once we have the
data, we must perform analysis to determine the best route to the destination, as well as help
define what that destination will look like. A major part of consulting is advising the ITSM
process owners and managers on the best courses of action that should be taken. The decisions
made should be based on actually data and analysis, which is the result of quantitative research.
As part of an IT shop, there is an asset management piece that must be considered. The
customer has to order hardware and software to meet the demands of their customer, the end
user. Quantitative research methodologies can be used to determine how many of each hardware
type is needed now, how much should be kept on hand as spares, and at what threshold more
hardware should be ordered.
One of the activities we are responsible for is to identity risks and issues, the likelihood the
events will occur, and how to mitigate. Quantitative research methods can be used to determine
predictability, which can be useful for risk management.
Another activity we perform in support of our customer is helping them to determine
customer satisfaction regarding the end users. Quantitative research methods using a sampling
of the end user community can provide the data needed to determine if there are any customer
satisfaction issues that need to be addressed.
One of the process areas we support is Problem Management. Problem Management is the
process area responsible for finding root causes of incidents (service calls) in an effort to prevent
future incidents. Sometimes the fix is to replace equipment or systems, which can be costly.
Quantitative research methods can be used to establish the cost of repeated incidents, and
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possible subsequent down time, the cost of repairing, and the cost of replacing. Once the data
has been captured, analysis can be done to determine the best decision for the customer.
Quantitative research has a plethora of applications in all industries. Finding the right
methods and models to suit your organization will provide invaluable insight from making better
business decisions to choosing more profitable options.
Quantitative Research
References
Brandimarte, P. (2011). Quantitative methods: An introduction for business management.
Hoboken, NJ: John Wiley & Sons, Inc.
Cooper, D., & Schindler, P. (2011). Business research methods. (11th ed., pp. 160-161). New
York, NY: McGraw-Hill Irwin.
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