Data analysis APA style

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Analytics Individual Case Analysis Consulting Report
Drout Advertising research Project
Attention: Ms. JamieDrout
Daniel Castillo
St. Thomas University
BUS -777-167
Prof. Rocha
05/25/24
2
Table of Contents
1. Executive Summary
2. Scope of Consulting Work and Report
3. Introduction
4. Analysis and Findings
•
5.1 Assignment(s) 1 – Review the questionnaire and classify the data collected from each
question as categorical, ordinal, interval, or ratio
•
5.2 Assignment(s) 2 – Explain how the data and subsequent analysis using business
analytics might lead to a better understanding of stereotype versus empowerment
advertising. Specifically, state some of the key insights that you would hope to answer
by analyzing the data
•
5.n Assignment(s) – Use appropriate charts to visualize the data. Summarize the data
5. Appendix/Appendices
6. Bibliography and References
3
Executive Summary
The report was information that was found behind Jaime Sprout’s questionnaire which involved a list
of questions. The questionnaire received a total of 105 responses and asked participants several
questions regarding their gender, age, highest educational level, annual income, etc. The survey’s goal
was to examine how gender stereotypes are perceived. This helps give us have a better insight on some
background data that has to do with their opinions on gender stereotypes or their level of consumption.
I will be searching for any correlations based on the answers that were given that would lead to a
certain pattern. This case analysis focuses on the importance of data analysis and how it can be applied
to find out more details about whatever topic someone wants to know about.
4
Scope of Consulting Work and Report
In this survey, 105 participants which were mostly women were asked several questions with the focus
of understanding consumer perceptions with advertising strategies. This report will give people a better
insight on the information that was given on the survey using bar charts, pivot tables, etc. This will
make it easier to process the information and simpler to compare numbers between he questions that
were asked. Visualizing the data also makes it easier to answer questions that anyone has towards the
survey and come to any conclusions.
5
Introduction
The answers were given by a total of 105 people, 86 of them were female and 19 of them were male.
The age ranges from the individuals that participated were from 19 to 68, but the average age of
individuals that were asked questions in the survey were in their early 20s. The survey asked
participants a total of ten questions from what their furthest level of education was to how much they
spend on beauty and hygiene products. They also asked the participants what their annual income was
and how big of a role do advertisements play when it comes to reinforcing gender stereotypes. This
survey had a lot of information because of all the questions that were asked which made it easy to
visualize the data, making it possible to do this report. We will be looking at all the data that was
collected from the survey and I will be talking about the findings. After analyzing the data, it can be
confirmed that most of the participants regardless of gender thought that the count of ad frequency was
thought to be influential.
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Analysis and Findings
5.1
Categorial: Gender, Education, Reinforcing, Transform
Ordinal: Spending, Ad Frequency, Stereotype, Empowerment
Interval: Age
Relation: Income
5.2
Data analysis is very critical when it comes to making information easier to process for anyone and
answers hard questions that experts might not be able to answer without dissecting the data first. These
insights lead to directly improved decision making from any organizations perspective and can lead to
better efficiency since you learn more with the data that is given through the survey. The data from this
survey may lead to a better understanding of stereotype versus empowerment advertising because of all
the specific data that was received while doing this survey which helps to show whether it correlates.
Some key insights I would hope to find would be what the mean and the total sum for the answers that
were given during the survey. Averages, medians, ranges are all examples of things I hope to find.
The pivot table above gives us a better insight on the sum of both ad frequency and stereotype and
manages to separate them between male and female. It shows how the total of ad frequency for females
were 4034 while for male it was 739 for a grand total of 4773. The total sum of stereotype was 4555
with 3891 being from the female side while for male it was 664.
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This pie chart above shows the sum of ad frequency and separates it by the respondent’s level of
education. This visualizing form of data shows how the ones that had the most ad frequency sum were
the ones that completed some undergraduate courses.
5.n
The chart below can be used to compare between both genders, and which one spend more with
hygiene products.
Gender Spending
60
50
40
30
Total
20
10
0
Drastic
Influential
Limited
Drastic
Influential
Female
Limited
Trivial
Male
The chart below can be used to compare between both genders and their incomes, and which one
spends more when it comes to hygiene products.
Income Spending
60
50
40
30
20
Total
10
0
Drastic
Influential
Female
Limited
Drastic
Influential
Limited
Male
Trivial
8
The pivotable below gives the total sum of ad frequency between their genders and separates them by
drastic, influential, limited, or even trivial. This table shows how most of the ad frequency sum came
from the influential category and the least was limited for both females and males.
Row Labels
Female
Drastic
Influential
Limited
Male
Drastic
Influential
Limited
Trivial
Grand Total
# of Ad Frequency
86
32
52
2
19
3
10
3
3
105
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Report
This case analysis made it a lot easier to process the information that was given with the survey and
visualize the data. This method of data analytics also makes it easier to compare whatever information
is needed and allows us to find answers to questions that may be challenging to find without
visualizing the data first. It was discovered that the women spent an average of $750 on hygiene
products while for men it was less that $250. This shows how crucial data visualization is as we
managed to compare between both groups and find out what both the average was and by how much
the difference was.
The average price that is spent on hygiene products in general between all participants regardless of
their gender was $650 while the median was $400. Another interesting thing I discovered while doing
this case study was how about 60 percent of the respondents had $30,000 or less of annual income.
This was an interesting number when discovering that there are different levels of education between
the respondents with almost 70 percent of them having completed at least their bachelors.
The pie chart in the case analysis shows how the more educated the participants were, the less likely
they were seeing advertisements. This can be shown when looking at the chart and discovering how
the ones with the highest levels of education (J.D, Doctorates) are the ones with the smallest sums.
About 93 percent of the respondents felt that advertisements had either a drastic or influential role
when it comes to reinforcing stereotypes and gender roles. This case study also helped me discover
how the younger range of respondents felt that advertisements used empowerment and reinforced
traditional roles as well.
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Recommendations
One recommendation I would give towards the survey would be to have more men next time or at least
even out the number of participants by gender. I think it would’ve been more fair and less bias by
having the same number of males and females to conduct the survey as the results would be based on a
more balanced out experiment. People could easily make an argument on the results of the survey
being one sided because there were more females than males which is why if you even it out on both
sides then it will result in a fairer conclusion. Another recommendation that I would give is making
sure that the survey does a better job asking people of different ages questions. This survey asked
participants of different ages the ten questions, but the average age was around 29 years old. This
shows that most of the respondents were around their early to mid 20s so while the survey did a decent
job asking people of different ages questions, they allowed mostly people of a certain age group to
participate. I think this could also be seen as a sort of bias because people of different age ranges tend
to have different mindsets so a better way to conduct the survey would be to ensure that the age groups
are more even to result in a more fair and average result.
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Conclusion
Overall, the findings that I managed to make while completing this case study did not show me any
sort of evidence on if gender is important when it comes to a subject feeling empowerment from
advertisements and turn them into stereotypes. One thing that I did discover was that gender could
affect how a subject could view gender stereotypes with the advertisements. The women group had a
higher percentage of ads reinforcing gender stereotypes than the men group. I felt that this survey
could’ve changed a few factors to produce fairer results when you consider the difference in
participants being a majority women and the average age being around the 20s.
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Appendices
13
References
Morling , L. (2023, July 4). How to create a data visualization in Excel (plus types) | indeed.com.
https://www.indeed.com/career-advice/career-development/create-data-visualization-in-excel