data analysis example essay

15 Data Analysis Examples

Data analysis is the process of inspecting, cleaning, transforming, and modeling data to discover useful information, derive conclusions, and support decision-making (Upton & Brawn, 2023).

It encompasses a variety of techniques from statistics, mathematics, and computer science to interpret complex data structures and extract meaningful insights (Bekes & Kezdi, 2021).

We use data analysis to generate useful insights from data that can help in our decision-making and strategic planning in various realms. For example:

• It can help businesses to develop a better understanding of market trends and customer preferences to inform marketing strategies.
• We can develop a modeled understanding of risks and prevent issues before they escalate into larger problems.
• Data analysis may reveal hidden or not easily identifiable insights and trends, empowering you to enrich your knowledge base and anticipate future needs (Naeem et al., 2020; Upton & Brawn, 2023).

Below are some common ways that data analysis is conducted.

Data Analysis Examples

1. Sales Trend Analysis This type of data analysis involves assessing sales data over various periods to identify trends and patterns. For instance, a retail company might monitor its quarterly sales data to identify peak buying times or popular products (Bihani & Patil, 2014). Such analysis allows businesses to adjust their sales strategies, inventory management, and marketing efforts to align with customer demands and seasonal trends, thereby enhancing profitability and operational efficiency (Kohavi, Rothleder & Simoudis, 2002).

2. Customer Segmentation In this data analysis example, businesses compartmentalize their customer base into different groups based on specific criteria such as purchasing behavior, demographics, or preferences (Kohavi, Rothleder & Simoudis, 2002). For example, an online shopping platform might segment its customers into categories like frequent buyers, seasonal shoppers, or budget buyers. This analysis helps tailor marketing campaigns and product offerings to appeal to each group specifically, enabling improved customer engagement and business growth.

3. Social Media Sentiment Analysis This is a popular use of data analysis in the digital age. Companies harness big data from social media platforms to analyze public sentiment towards their products or brand. By examining comments, likes, shares, and other interactions, they can gauge overall satisfaction and identify areas for improvement. This kind of scrutiny can significantly impact a business’s online reputation management and influence its marketing and public relations strategies.

4. Forecasting and Predictive Analysis Businesses often use data analysis to predict future trends or outcomes. For instance, an airline company might analyze past data on seat bookings, flight timings, and passenger preferences to forecast future travel trends. This predictive analysis allows the airline to optimize its flight schedules, plan for peak travel periods, and set competitive ticket prices, ultimately contributing to improved customer satisfaction and increased revenues.

5. Operational Efficiency Analysis This form of data analysis is focused on optimizing internal processes within an organization. For example, a manufacturing company might analyze data regarding machine performance, maintenance schedules, and production output to identify bottlenecks or inefficiencies (Bihani & Patil, 2014). By addressing these issues, the company can streamline its operations, improve productivity, and reduce costs, signifying the importance of data analysis in achieving operational excellence.

6. Risk Assessment Analysis This type of data analysis helps businesses identify potential risks that could adversely impact their operations or profits. An insurance company, for instance, might analyze customer data and historical claim information to estimate future claim risks. This supports more accurate premium setting and helps in proactively managing any potential financial hazards, underscoring the role of data analysis in sound risk management.

7. Recruitment and Talent Management Analysis In this example of data analysis, human resources departments scrutinize data concerning employee performance, retention rates, and skill sets. For example, a technology firm might conduct analysis to identify the skills and experience most prevalent among its top-performing employees (Chang, Wang & Hawamdeh, 2019). This enables the company to attract and retain high-caliber talent, tailor training programs, and improve overall workforce effectiveness.

8. Supply Chain Optimization Analysis This form of data analysis aims to enhance the efficiency of a business’s supply chain. For instance, a grocery store might examine sales data, warehouse inventory levels, and supplier delivery times to ensure the right products are in stock at the right time (Chang, Wang & Hawamdeh, 2019). This can reduce warehousing costs, minimize stockouts or overstocks, and increase customer satisfaction, marking data analysis’s role in streamlining supply chains.

9. Web Analytics In this digital age, businesses invest in data analysis to optimize their online presence and functionality. An ecommerce business, for example, might analyze website traffic data, bounce rates, conversion rates, and user engagement metrics. This analysis can guide website redesign, enhance user experience, and boost conversion rates, reflecting the importance of data analysis in digital marketing and web optimization.

10. Medical and Healthcare Analysis Data analysis plays a crucial role in the healthcare sector. A hospital might analyze patient data, disease patterns, treatment outcomes, and so forth. This can support evidence-based treatment plans, inform research on healthcare trends, and contribute to policy development (Islam et al., 2018). It can also enhance patient care by identifying efficient treatment paths and reducing hospitalization time, underlining the significance of data analysis in the medical field.

11. Fraud Detection Analysis In the financial and banking sector, data analysis plays a paramount role in identifying and mitigating fraudulent activities. Banks might analyze transaction data, account activity, and user behavior trends to detect abnormal patterns indicative of fraud. By alerting the concerned authorities about the suspicious activity, such analysis can prevent financial losses and protect customer assets, illustrating data analysis’s importance in ensuring financial security.

12. Energy Consumption Analysis Utilities and energy companies often use data analysis to optimize their energy distribution and consumption. By evaluating data on customer usage patterns, peak demand times, and grid performance, companies can enhance energy efficiency, optimize their grid operations, and develop more customer-centric services. It shows how data analysis can contribute to a more sustainable and efficient use of resources.

13. Market Research Analysis Many businesses rely on data analysis to gauge market dynamics and consumer behaviors. A cosmetic brand, for example, might analyze sales data, consumer feedback, and competitor information. Such analysis can provide useful insights about consumer preferences, popular trends, and competitive strategies, facilitating the development of products that align with market demands, showcasing how data analysis can drive business innovation.

14. Quality Control Analysis Manufacturing industries often use data analysis in their quality control processes. They may monitor operational data, machine performance, and product fault reports. By identifying causes of defects or inefficiencies, these industries can improve product quality, enhance manufacturing processes, and reduce waste, demonstrating the decisive role of data analysis in maintaining high-quality standards.

15. Economic and Policy Analysis Government agencies and think tanks utilize data analysis to inform policy decisions and societal strategies. They might analyze data relating to employment rates, GDP, public health, or educational attainment. These insights can inform policy development, assess the impact of existing policies, and guide strategies for societal improvement. This reveals that data analysis is a key tool in managing social and economic progression.

For more General Examples of Analysis, See Here

Data analysis, encompassing activities such as trend spotting, risk assessment, predictive modeling, customer segmentation, and much more, proves to be an indispensable tool in various fields.

From optimizing operations and making informed decisions to understanding customer behavior and predicting future trends, its applications are diverse and far-reaching. Through meticulous examination of relevant data and astute interpretation of patterns, businesses and organizations can extract actionable insights, enhance their strategic planning, and bolster their competitive advantage.

Furthermore, with the current growth in digital technology, the potency of data analysis in enhancing operational efficiency, facilitating innovation, and driving economic growth cannot be overstated. Therefore, mastery of data analysis techniques and methodologies is critical for anyone seeking to harness the full potential of their data.

Ultimately, data analysis seeks to turn raw data into valuable knowledge, enabling organizations and individuals to thrive in today’s data-driven world.

Bekes, G., & Kezdi, G. (2021). Data Analysis for Business, Economics, and Policy . Cambridge University Press.

Bihani, P., & Patil, S. T. (2014). A comparative study of data analysis techniques.  International journal of emerging trends & technology in computer science ,  3 (2), 95-101.

Chang, H. C., Wang, C. Y., & Hawamdeh, S. (2019). Emerging trends in data analytics and knowledge management job market: extending KSA framework.  Journal of Knowledge Management ,  23 (4), 664-686. doi: https://doi.org/10.1108/JKM-02-2018-0088

Islam, M. S., Hasan, M. M., Wang, X., Germack, H. D., & Noor-E-Alam, M. (2018, May). A systematic review on healthcare analytics: application and theoretical perspective of data mining. In  Healthcare  (Vol. 6, No. 2, p. 54). doi: https://doi.org/10.3390/healthcare6020054

Kohavi, R., Rothleder, N. J., & Simoudis, E. (2002). Emerging trends in business analytics .  Communications of the ACM ,  45 (8), 45-48.

Naeem, M., Jamal, T., Diaz-Martinez, J., Butt, S. A., Montesano, N., Tariq, M. I., … & De-La-Hoz-Valdiris, E. (2022). Trends and future perspective challenges in big data. In  Advances in Intelligent Data Analysis and Applications: Proceeding of the Sixth Euro-China Conference on Intelligent Data Analysis and Applications, 15–18 October 2019, Arad, Romania  (pp. 309-325). Springer Singapore.

Upton, G., & Brawn, D. (2023). Data Analysis: A Gentle Introduction for Future Data Scientists . Oxford: Oxford University Press.

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Data analysis write-ups

What should a data-analysis write-up look like.

Writing up the results of a data analysis is not a skill that anyone is born with. It requires practice and, at least in the beginning, a bit of guidance.

Organization

When writing your report, organization will set you free. A good outline is: 1) overview of the problem, 2) your data and modeling approach, 3) the results of your data analysis (plots, numbers, etc), and 4) your substantive conclusions.

1) Overview Describe the problem. What substantive question are you trying to address? This needn’t be long, but it should be clear.

2) Data and model What data did you use to address the question, and how did you do it? When describing your approach, be specific. For example:

• Don’t say, “I ran a regression” when you instead can say, “I fit a linear regression model to predict price that included a house’s size and neighborhood as predictors.”
• Justify important features of your modeling approach. For example: “Neighborhood was included as a categorical predictor in the model because Figure 2 indicated clear differences in price across the neighborhoods.”

Sometimes your Data and Model section will contain plots or tables, and sometimes it won’t. If you feel that a plot helps the reader understand the problem or data set itself—as opposed to your results—then go ahead and include it. A great example here is Tables 1 and 2 in the main paper on the PREDIMED study . These tables help the reader understand some important properties of the data and approach, but not the results of the study itself.

3) Results In your results section, include any figures and tables necessary to make your case. Label them (Figure 1, 2, etc), give them informative captions, and refer to them in the text by their numbered labels where you discuss them. Typical things to include here may include: pictures of the data; pictures and tables that show the fitted model; tables of model coefficients and summaries.

4) Conclusion What did you learn from the analysis? What is the answer, if any, to the question you set out to address?

General advice

Make the sections as short or long as they need to be. For example, a conclusions section is often pretty short, while a results section is usually a bit longer.

It’s OK to use the first person to avoid awkward or bizarre sentence constructions, but try to do so sparingly.

Do not include computer code unless explicitly called for. Note: model outputs do not count as computer code. Outputs should be used as evidence in your results section (ideally formatted in a nice way). By code, I mean the sequence of commands you used to process the data and produce the outputs.

When in doubt, use shorter words and sentences.

A very common way for reports to go wrong is when the writer simply narrates the thought process he or she followed: :First I did this, but it didn’t work. Then I did something else, and I found A, B, and C. I wasn’t really sure what to make of B, but C was interesting, so I followed up with D and E. Then having done this…” Do not do this. The desire for specificity is admirable, but the overall effect is one of amateurism. Follow the recommended outline above.

Here’s a good example of a write-up for an analysis of a few relatively simple problems. Because the problems are so straightforward, there’s not much of a need for an outline of the kind described above. Nonetheless, the spirit of these guidelines is clearly in evidence. Notice the clear exposition, the labeled figures and tables that are referred to in the text, and the careful integration of visual and numerical evidence into the overall argument. This is one worth emulating.

Business growth

Business tips

What is data analysis? Examples and how to get started

Even with years of professional experience working with data, the term "data analysis" still sets off a panic button in my soul. And yes, when it comes to serious data analysis for your business, you'll eventually want data scientists on your side. But if you're just getting started, no panic attacks are required.

Table of contents:

Quick review: What is data analysis?

Why is data analysis important, types of data analysis (with examples), data analysis process: how to get started, frequently asked questions.

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Data analysis is the process of examining, filtering, adapting, and modeling data to help solve problems. Data analysis helps determine what is and isn't working, so you can make the changes needed to achieve your business goals. 

Keep in mind that data analysis includes analyzing both quantitative data (e.g., profits and sales) and qualitative data (e.g., surveys and case studies) to paint the whole picture. Here are two simple examples (of a nuanced topic) to show you what I mean.

An example of quantitative data analysis is an online jewelry store owner using inventory data to forecast and improve reordering accuracy. The owner looks at their sales from the past six months and sees that, on average, they sold 210 gold pieces and 105 silver pieces per month, but they only had 100 gold pieces and 100 silver pieces in stock. By collecting and analyzing inventory data on these SKUs, they're forecasting to improve reordering accuracy. The next time they order inventory, they order twice as many gold pieces as silver to meet customer demand.

An example of qualitative data analysis is a fitness studio owner collecting customer feedback to improve class offerings. The studio owner sends out an open-ended survey asking customers what types of exercises they enjoy the most. The owner then performs qualitative content analysis to identify the most frequently suggested exercises and incorporates these into future workout classes.

Here's why it's worth implementing data analysis for your business:

Understand your target audience: You might think you know how to best target your audience, but are your assumptions backed by data? Data analysis can help answer questions like, "What demographics define my target audience?" or "What is my audience motivated by?"

Inform decisions: You don't need to toss and turn over a decision when the data points clearly to the answer. For instance, a restaurant could analyze which dishes on the menu are selling the most, helping them decide which ones to keep and which ones to change.

Adjust budgets: Similarly, data analysis can highlight areas in your business that are performing well and are worth investing more in, as well as areas that aren't generating enough revenue and should be cut. For example, a B2B software company might discover their product for enterprises is thriving while their small business solution lags behind. This discovery could prompt them to allocate more budget toward the enterprise product, resulting in better resource utilization.

Identify and solve problems: Let's say a cell phone manufacturer notices data showing a lot of customers returning a certain model. When they investigate, they find that model also happens to have the highest number of crashes. Once they identify and solve the technical issue, they can reduce the number of returns.

There are five main types of data analysis—with increasingly scary-sounding names. Each one serves a different purpose, so take a look to see which makes the most sense for your situation. It's ok if you can't pronounce the one you choose. 

Text analysis: What is happening?

Text analysis, AKA data mining , involves pulling insights from large amounts of unstructured, text-based data sources : emails, social media, support tickets, reviews, and so on. You would use text analysis when the volume of data is too large to sift through manually. 

Here are a few methods used to perform text analysis, to give you a sense of how it's different from a human reading through the text: 

Word frequency identifies the most frequently used words. For example, a restaurant monitors social media mentions and measures the frequency of positive and negative keywords like "delicious" or "expensive" to determine how customers feel about their experience. 

Language detection indicates the language of text. For example, a global software company may use language detection on support tickets to connect customers with the appropriate agent. 

Keyword extraction automatically identifies the most used terms. For example, instead of sifting through thousands of reviews, a popular brand uses a keyword extractor to summarize the words or phrases that are most relevant. 

Because text analysis is based on words, not numbers, it's a bit more subjective. Words can have multiple meanings, of course, and Gen Z makes things even tougher with constant coinage. Natural language processing (NLP) software will help you get the most accurate text analysis, but it's rarely as objective as numerical analysis. 

Statistical analysis: What happened?

Statistical analysis pulls past data to identify meaningful trends. Two primary categories of statistical analysis exist: descriptive and inferential.

Descriptive analysis

Descriptive analysis looks at numerical data and calculations to determine what happened in a business. Companies use descriptive analysis to determine customer satisfaction , track campaigns, generate reports, and evaluate performance. 

Here are a few methods used to perform descriptive analysis: 

Measures of frequency identify how frequently an event occurs. For example, a popular coffee chain sends out a survey asking customers what their favorite holiday drink is and uses measures of frequency to determine how often a particular drink is selected. 

Measures of central tendency use mean, median, and mode to identify results. For example, a dating app company might use measures of central tendency to determine the average age of its users.

Measures of dispersion measure how data is distributed across a range. For example, HR may use measures of dispersion to determine what salary to offer in a given field. 

Inferential analysis

Inferential analysis uses a sample of data to draw conclusions about a much larger population. This type of analysis is used when the population you're interested in analyzing is very large. 

Here are a few methods used when performing inferential analysis: 

Hypothesis testing identifies which variables impact a particular topic. For example, a business uses hypothesis testing to determine if increased sales were the result of a specific marketing campaign. 

Confidence intervals indicates how accurate an estimate is. For example, a company using market research to survey customers about a new product may want to determine how confident they are that the individuals surveyed make up their target market. 

Regression analysis shows the effect of independent variables on a dependent variable. For example, a rental car company may use regression analysis to determine the relationship between wait times and number of bad reviews. 

Diagnostic analysis: Why did it happen?

Diagnostic analysis, also referred to as root cause analysis, uncovers the causes of certain events or results. 

Here are a few methods used to perform diagnostic analysis: 

Time-series analysis analyzes data collected over a period of time. A retail store may use time-series analysis to determine that sales increase between October and December every year. 

Data drilling uses business intelligence (BI) to show a more detailed view of data. For example, a business owner could use data drilling to see a detailed view of sales by state to determine if certain regions are driving increased sales.

Correlation analysis determines the strength of the relationship between variables. For example, a local ice cream shop may determine that as the temperature in the area rises, so do ice cream sales. 

Predictive analysis: What is likely to happen?

Predictive analysis aims to anticipate future developments and events. By analyzing past data, companies can predict future scenarios and make strategic decisions.  

Here are a few methods used to perform predictive analysis: 

Machine learning uses AI and algorithms to predict outcomes. For example, search engines employ machine learning to recommend products to online shoppers that they are likely to buy based on their browsing history. 

Decision trees map out possible courses of action and outcomes. For example, a business may use a decision tree when deciding whether to downsize or expand. 

Prescriptive analysis: What action should we take?

The highest level of analysis, prescriptive analysis, aims to find the best action plan. Typically, AI tools model different outcomes to predict the best approach. While these tools serve to provide insight, they don't replace human consideration, so always use your human brain before going with the conclusion of your prescriptive analysis. Otherwise, your GPS might drive you into a lake.

Here are a few methods used to perform prescriptive analysis: 

Lead scoring is used in sales departments to assign values to leads based on their perceived interest. For example, a sales team uses lead scoring to rank leads on a scale of 1-100 depending on the actions they take (e.g., opening an email or downloading an eBook). They then prioritize the leads that are most likely to convert. 

Algorithms are used in technology to perform specific tasks. For example, banks use prescriptive algorithms to monitor customers' spending and recommend that they deactivate their credit card if fraud is suspected. 

The actual analysis is just one step in a much bigger process of using data to move your business forward. Here's a quick look at all the steps you need to take to make sure you're making informed decisions. 

Data decision

As with almost any project, the first step is to determine what problem you're trying to solve through data analysis. 

Make sure you get specific here. For example, a food delivery service may want to understand why customers are canceling their subscriptions. But to enable the most effective data analysis, they should pose a more targeted question, such as "How can we reduce customer churn without raising costs?" 

These questions will help you determine your KPIs and what type(s) of data analysis you'll conduct , so spend time honing the question—otherwise your analysis won't provide the actionable insights you want.

Data collection

Next, collect the required data from both internal and external sources. 

Internal data comes from within your business (think CRM software, internal reports, and archives), and helps you understand your business and processes.

External data originates from outside of the company (surveys, questionnaires, public data) and helps you understand your industry and your customers. 

You'll rely heavily on software for this part of the process. Your analytics or business dashboard tool, along with reports from any other internal tools like CRMs , will give you the internal data. For external data, you'll use survey apps and other data collection tools to get the information you need.

Data cleaning

Data can be seriously misleading if it's not clean. So before you analyze, make sure you review the data you collected.  Depending on the type of data you have, cleanup will look different, but it might include: 

Removing unnecessary information 

Addressing structural errors like misspellings

Deleting duplicates

Trimming whitespace

Human checking for accuracy 

You can use your spreadsheet's cleanup suggestions to quickly and effectively clean data, but a human review is always important.

Data analysis

Now that you've compiled and cleaned the data, use one or more of the above types of data analysis to find relationships, patterns, and trends. 

Data analysis tools can speed up the data analysis process and remove the risk of inevitable human error. Here are some examples.

Spreadsheets sort, filter, analyze, and visualize data. 

Business intelligence platforms model data and create dashboards. 

Structured query language (SQL) tools manage and extract data in relational databases. 

Data interpretation

After you analyze the data, you'll need to go back to the original question you posed and draw conclusions from your findings. Here are some common pitfalls to avoid:

Correlation vs. causation: Just because two variables are associated doesn't mean they're necessarily related or dependent on one another. 

Confirmation bias: This occurs when you interpret data in a way that confirms your own preconceived notions. To avoid this, have multiple people interpret the data. 

Small sample size: If your sample size is too small or doesn't represent the demographics of your customers, you may get misleading results. If you run into this, consider widening your sample size to give you a more accurate representation. 

Data visualization

Last but not least, visualizing the data in the form of graphs, maps, reports, charts, and dashboards can help you explain your findings to decision-makers and stakeholders. While it's not absolutely necessary, it will help tell the story of your data in a way that everyone in the business can understand and make decisions based on. 

Automate your data collection

Data doesn't live in one place. To make sure data is where it needs to be—and isn't duplicative or conflicting—make sure all your apps talk to each other. Zapier automates the process of moving data from one place to another, so you can focus on the work that matters to move your business forward.

Need a quick summary or still have a few nagging data analysis questions? I'm here for you.

What are the five types of data analysis?

The five types of data analysis are text analysis, statistical analysis, diagnostic analysis, predictive analysis, and prescriptive analysis. Each type offers a unique lens for understanding data: text analysis provides insights into text-based content, statistical analysis focuses on numerical trends, diagnostic analysis looks into problem causes, predictive analysis deals with what may happen in the future, and prescriptive analysis gives actionable recommendations.

What is the data analysis process?

The data analysis process involves data decision, collection, cleaning, analysis, interpretation, and visualization. Every stage comes together to transform raw data into meaningful insights. Decision determines what data to collect, collection gathers the relevant information, cleaning ensures accuracy, analysis uncovers patterns, interpretation assigns meaning, and visualization presents the insights.

What is the main purpose of data analysis?

In business, the main purpose of data analysis is to uncover patterns, trends, and anomalies, and then use that information to make decisions, solve problems, and reach your business goals.

Related reading: 

How to get started with data collection and analytics at your business

How to conduct your own market research survey

Automatically find and match related data across apps

How to build an analysis assistant with ChatGPT

What can the ChatGPT data analysis chatbot do?

This article was originally published in October 2022 and has since been updated with contributions from Cecilia Gillen. The most recent update was in September 2023.

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Shea is a content writer currently living in Charlotte, North Carolina. After graduating with a degree in Marketing from East Carolina University, she joined the digital marketing industry focusing on content and social media. In her free time, you can find Shea visiting her local farmers market, attending a country music concert, or planning her next adventure.

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Sat / act prep online guides and tips, 5 steps to write a great analytical essay.

General Education

Do you need to write an analytical essay for school? What sets this kind of essay apart from other types, and what must you include when you write your own analytical essay? In this guide, we break down the process of writing an analytical essay by explaining the key factors your essay needs to have, providing you with an outline to help you structure your essay, and analyzing a complete analytical essay example so you can see what a finished essay looks like.

What Is an Analytical Essay?

Before you begin writing an analytical essay, you must know what this type of essay is and what it includes. Analytical essays analyze something, often (but not always) a piece of writing or a film.

An analytical essay is more than just a synopsis of the issue though; in this type of essay you need to go beyond surface-level analysis and look at what the key arguments/points of this issue are and why. If you’re writing an analytical essay about a piece of writing, you’ll look into how the text was written and why the author chose to write it that way. Instead of summarizing, an analytical essay typically takes a narrower focus and looks at areas such as major themes in the work, how the author constructed and supported their argument, how the essay used literary devices to enhance its messages, etc.

While you certainly want people to agree with what you’ve written, unlike with persuasive and argumentative essays, your main purpose when writing an analytical essay isn’t to try to convert readers to your side of the issue. Therefore, you won’t be using strong persuasive language like you would in those essay types. Rather, your goal is to have enough analysis and examples that the strength of your argument is clear to readers.

Besides typical essay components like an introduction and conclusion, a good analytical essay will include:

• A thesis that states your main argument
• Analysis that relates back to your thesis and supports it
• Examples to support your analysis and allow a more in-depth look at the issue

In the rest of this article, we’ll explain how to include each of these in your analytical essay.

How to Structure Your Analytical Essay

Analytical essays are structured similarly to many other essays you’ve written, with an introduction (including a thesis), several body paragraphs, and a conclusion. Below is an outline you can follow when structuring your essay, and in the next section we go into more detail on how to write an analytical essay.

Introduction

Your introduction will begin with some sort of attention-grabbing sentence to get your audience interested, then you’ll give a few sentences setting up the topic so that readers have some context, and you’ll end with your thesis statement. Your introduction will include:

• Brief background information explaining the issue/text
• Your thesis

Body Paragraphs

Your analytical essay will typically have three or four body paragraphs, each covering a different point of analysis. Begin each body paragraph with a sentence that sets up the main point you’ll be discussing. Then you’ll give some analysis on that point, backing it up with evidence to support your claim. Continue analyzing and giving evidence for your analysis until you’re out of strong points for the topic. At the end of each body paragraph, you may choose to have a transition sentence that sets up what the next paragraph will be about, but this isn’t required. Body paragraphs will include:

• Introductory sentence explaining what you’ll cover in the paragraph (sort of like a mini-thesis)
• Analysis point
• Evidence (either passages from the text or data/facts) that supports the analysis
• (Repeat analysis and evidence until you run out of examples)

You won’t be making any new points in your conclusion; at this point you’re just reiterating key points you’ve already made and wrapping things up. Begin by rephrasing your thesis and summarizing the main points you made in the essay. Someone who reads just your conclusion should be able to come away with a basic idea of what your essay was about and how it was structured. After this, you may choose to make some final concluding thoughts, potentially by connecting your essay topic to larger issues to show why it’s important. A conclusion will include:

• Paraphrase of thesis
• Summary of key points of analysis
• Final concluding thought(s)

5 Steps for Writing an Analytical Essay

Follow these five tips to break down writing an analytical essay into manageable steps. By the end, you’ll have a fully-crafted analytical essay with both in-depth analysis and enough evidence to support your argument. All of these steps use the completed analytical essay in the next section as an example.

#1: Pick a Topic

You may have already had a topic assigned to you, and if that’s the case, you can skip this step. However, if you haven’t, or if the topic you’ve been assigned is broad enough that you still need to narrow it down, then you’ll need to decide on a topic for yourself. Choosing the right topic can mean the difference between an analytical essay that’s easy to research (and gets you a good grade) and one that takes hours just to find a few decent points to analyze

Before you decide on an analytical essay topic, do a bit of research to make sure you have enough examples to support your analysis. If you choose a topic that’s too narrow, you’ll struggle to find enough to write about.

For example, say your teacher assigns you to write an analytical essay about the theme in John Steinbeck’s The Grapes of Wrath of exposing injustices against migrants. For it to be an analytical essay, you can’t just recount the injustices characters in the book faced; that’s only a summary and doesn’t include analysis. You need to choose a topic that allows you to analyze the theme. One of the best ways to explore a theme is to analyze how the author made his/her argument. One example here is that Steinbeck used literary devices in the intercalary chapters (short chapters that didn’t relate to the plot or contain the main characters of the book) to show what life was like for migrants as a whole during the Dust Bowl.

You could write about how Steinbeck used literary devices throughout the whole book, but, in the essay below, I chose to just focus on the intercalary chapters since they gave me enough examples. Having a narrower focus will nearly always result in a tighter and more convincing essay (and can make compiling examples less overwhelming).

#2: Write a Thesis Statement

Your thesis statement is the most important sentence of your essay; a reader should be able to read just your thesis and understand what the entire essay is about and what you’ll be analyzing. When you begin writing, remember that each sentence in your analytical essay should relate back to your thesis

In the analytical essay example below, the thesis is the final sentence of the first paragraph (the traditional spot for it). The thesis is: “In The Grapes of Wrath’s intercalary chapters, John Steinbeck employs a variety of literary devices and stylistic choices to better expose the injustices committed against migrants in the 1930s.” So what will this essay analyze? How Steinbeck used literary devices in the intercalary chapters to show how rough migrants could have it. Crystal clear.

#3: Do Research to Find Your Main Points

This is where you determine the bulk of your analysis--the information that makes your essay an analytical essay. My preferred method is to list every idea that I can think of, then research each of those and use the three or four strongest ones for your essay. Weaker points may be those that don’t relate back to the thesis, that you don’t have much analysis to discuss, or that you can’t find good examples for. A good rule of thumb is to have one body paragraph per main point

This essay has four main points, each of which analyzes a different literary device Steinbeck uses to better illustrate how difficult life was for migrants during the Dust Bowl. The four literary devices and their impact on the book are:

• Lack of individual names in intercalary chapters to illustrate the scope of the problem
• Parallels to the Bible to induce sympathy for the migrants
• Non-showy, often grammatically-incorrect language so the migrants are more realistic and relatable to readers
• Nature-related metaphors to affect the mood of the writing and reflect the plight of the migrants

#4: Find Excerpts or Evidence to Support Your Analysis

Now that you have your main points, you need to back them up. If you’re writing a paper about a text or film, use passages/clips from it as your main source of evidence. If you’re writing about something else, your evidence can come from a variety of sources, such as surveys, experiments, quotes from knowledgeable sources etc. Any evidence that would work for a regular research paper works here.

In this example, I quoted multiple passages from The Grapes of Wrath  in each paragraph to support my argument. You should be able to back up every claim you make with evidence in order to have a strong essay.

#5: Put It All Together

Now it's time to begin writing your essay, if you haven’t already. Create an introductory paragraph that ends with the thesis, make a body paragraph for each of your main points, including both analysis and evidence to back up your claims, and wrap it all up with a conclusion that recaps your thesis and main points and potentially explains the big picture importance of the topic.

Analytical Essay Example + Analysis

So that you can see for yourself what a completed analytical essay looks like, here’s an essay I wrote back in my high school days. It’s followed by analysis of how I structured my essay, what its strengths are, and how it could be improved.

One way Steinbeck illustrates the connections all migrant people possessed and the struggles they faced is by refraining from using specific titles and names in his intercalary chapters. While The Grapes of Wrath focuses on the Joad family, the intercalary chapters show that all migrants share the same struggles and triumphs as the Joads. No individual names are used in these chapters; instead the people are referred to as part of a group. Steinbeck writes, “Frantic men pounded on the doors of the doctors; and the doctors were busy.  And sad men left word at country stores for the coroner to send a car,” (555). By using generic terms, Steinbeck shows how the migrants are all linked because they have gone through the same experiences. The grievances committed against one family were committed against thousands of other families; the abuse extends far beyond what the Joads experienced. The Grapes of Wrath frequently refers to the importance of coming together; how, when people connect with others their power and influence multiplies immensely. Throughout the novel, the goal of the migrants, the key to their triumph, has been to unite. While their plans are repeatedly frustrated by the government and police, Steinbeck’s intercalary chapters provide a way for the migrants to relate to one another because they have encountered the same experiences. Hundreds of thousands of migrants fled to the promised land of California, but Steinbeck was aware that numbers alone were impersonal and lacked the passion he desired to spread. Steinbeck created the intercalary chapters to show the massive numbers of people suffering, and he created the Joad family to evoke compassion from readers.  Because readers come to sympathize with the Joads, they become more sensitive to the struggles of migrants in general. However, John Steinbeck frequently made clear that the Joads were not an isolated incident; they were not unique. Their struggles and triumphs were part of something greater. Refraining from specific names in his intercalary chapters allows Steinbeck to show the vastness of the atrocities committed against migrants.

Steinbeck also creates significant parallels to the Bible in his intercalary chapters in order to enhance his writing and characters. By using simple sentences and stylized writing, Steinbeck evokes Biblical passages. The migrants despair, “No work till spring. No work,” (556).  Short, direct sentences help to better convey the desperateness of the migrants’ situation. Throughout his novel, John Steinbeck makes connections to the Bible through his characters and storyline. Jim Casy’s allusions to Christ and the cycle of drought and flooding are clear biblical references.  By choosing to relate The Grapes of Wrath to the Bible, Steinbeck’s characters become greater than themselves. Starving migrants become more than destitute vagrants; they are now the chosen people escaping to the promised land. When a forgotten man dies alone and unnoticed, it becomes a tragedy. Steinbeck writes, “If [the migrants] were shot at, they did not run, but splashed sullenly away; and if they were hit, they sank tiredly in the mud,” (556). Injustices committed against the migrants become greater because they are seen as children of God through Steinbeck’s choice of language. Referencing the Bible strengthens Steinbeck’s novel and purpose: to create understanding for the dispossessed.  It is easy for people to feel disdain for shabby vagabonds, but connecting them to such a fundamental aspect of Christianity induces sympathy from readers who might have otherwise disregarded the migrants as so many other people did.

The simple, uneducated dialogue Steinbeck employs also helps to create a more honest and meaningful representation of the migrants, and it makes the migrants more relatable to readers. Steinbeck chooses to accurately represent the language of the migrants in order to more clearly illustrate their lives and make them seem more like real paper than just characters in a book. The migrants lament, “They ain’t gonna be no kinda work for three months,” (555). There are multiple grammatical errors in that single sentence, but it vividly conveys the despair the migrants felt better than a technically perfect sentence would. The Grapes of Wrath is intended to show the severe difficulties facing the migrants so Steinbeck employs a clear, pragmatic style of writing.  Steinbeck shows the harsh, truthful realities of the migrants’ lives and he would be hypocritical if he chose to give the migrants a more refined voice and not portray them with all their shortcomings. The depiction of the migrants as imperfect through their language also makes them easier to relate to. Steinbeck’s primary audience was the middle class, the less affluent of society. Repeatedly in The Grapes of Wrath , the wealthy make it obvious that they scorn the plight of the migrants. The wealthy, not bad luck or natural disasters, were the prominent cause of the suffering of migrant families such as the Joads. Thus, Steinbeck turns to the less prosperous for support in his novel. When referring to the superior living conditions barnyard animals have, the migrants remark, “Them’s horses-we’re men,” (556).  The perfect simplicity of this quote expresses the absurdness of the migrants’ situation better than any flowery expression could.

In The Grapes of Wrath , John Steinbeck uses metaphors, particularly about nature, in order to illustrate the mood and the overall plight of migrants. Throughout most of the book, the land is described as dusty, barren, and dead. Towards the end, however; floods come and the landscape begins to change. At the end of chapter twenty-nine, Steinbeck describes a hill after the floods saying, “Tiny points of grass came through the earth, and in a few days the hills were pale green with the beginning year,” (556). This description offers a stark contrast from the earlier passages which were filled with despair and destruction. Steinbeck’s tone from the beginning of the chapter changes drastically. Early in the chapter, Steinbeck had used heavy imagery in order to convey the destruction caused by the rain, “The streams and the little rivers edged up to the bank sides and worked at willows and tree roots, bent the willows deep in the current, cut out the roots of cottonwoods and brought down the trees,” (553). However, at the end of the chapter the rain has caused new life to grow in California. The new grass becomes a metaphor representing hope. When the migrants are at a loss over how they will survive the winter, the grass offers reassurance. The story of the migrants in the intercalary chapters parallels that of the Joads. At the end of the novel, the family is breaking apart and has been forced to flee their home. However, both the book and final intercalary chapter end on a hopeful note after so much suffering has occurred. The grass metaphor strengthens Steinbeck’s message because it offers a tangible example of hope. Through his language Steinbeck’s themes become apparent at the end of the novel. Steinbeck affirms that persistence, even when problems appear insurmountable, leads to success. These metaphors help to strengthen Steinbeck’s themes in The Grapes of Wrath because they provide a more memorable way to recall important messages.

John Steinbeck’s language choices help to intensify his writing in his intercalary chapters and allow him to more clearly show how difficult life for migrants could be. Refraining from using specific names and terms allows Steinbeck to show that many thousands of migrants suffered through the same wrongs. Imitating the style of the Bible strengthens Steinbeck’s characters and connects them to the Bible, perhaps the most famous book in history. When Steinbeck writes in the imperfect dialogue of the migrants, he creates a more accurate portrayal and makes the migrants easier to relate to for a less affluent audience. Metaphors, particularly relating to nature, strengthen the themes in The Grapes of Wrath by enhancing the mood Steinbeck wants readers to feel at different points in the book. Overall, the intercalary chapters that Steinbeck includes improve his novel by making it more memorable and reinforcing the themes Steinbeck embraces throughout the novel. Exemplary stylistic devices further persuade readers of John Steinbeck’s personal beliefs. Steinbeck wrote The Grapes of Wrath to bring to light cruelties against migrants, and by using literary devices effectively, he continuously reminds readers of his purpose. Steinbeck’s impressive language choices in his intercalary chapters advance the entire novel and help to create a classic work of literature that people still are able to relate to today. 

This essay sticks pretty closely to the standard analytical essay outline. It starts with an introduction, where I chose to use a quote to start off the essay. (This became my favorite way to start essays in high school because, if I wasn’t sure what to say, I could outsource the work and find a quote that related to what I’d be writing about.) The quote in this essay doesn’t relate to the themes I’m discussing quite as much as it could, but it’s still a slightly different way to start an essay and can intrigue readers. I then give a bit of background on The Grapes of Wrath and its themes before ending the intro paragraph with my thesis: that Steinbeck used literary devices in intercalary chapters to show how rough migrants had it.

Each of my four body paragraphs is formatted in roughly the same way: an intro sentence that explains what I’ll be discussing, analysis of that main point, and at least two quotes from the book as evidence.

My conclusion restates my thesis, summarizes each of four points I discussed in my body paragraphs, and ends the essay by briefly discussing how Steinbeck’s writing helped introduce a world of readers to the injustices migrants experienced during the dust bowl.

What does this analytical essay example do well? For starters, it contains everything that a strong analytical essay should, and it makes that easy to find. The thesis clearly lays out what the essay will be about, the first sentence of each of the body paragraph introduces the topic it’ll cover, and the conclusion neatly recaps all the main points. Within each of the body paragraphs, there’s analysis along with multiple excerpts from the book in order to add legitimacy to my points.

Additionally, the essay does a good job of taking an in-depth look at the issue introduced in the thesis. Four ways Steinbeck used literary devices are discussed, and for each of the examples are given and analysis is provided so readers can understand why Steinbeck included those devices and how they helped shaped how readers viewed migrants and their plight.

Where could this essay be improved? I believe the weakest body paragraph is the third one, the one that discusses how Steinbeck used plain, grammatically incorrect language to both accurately depict the migrants and make them more relatable to readers. The paragraph tries to touch on both of those reasons and ends up being somewhat unfocused as a result. It would have been better for it to focus on just one of those reasons (likely how it made the migrants more relatable) in order to be clearer and more effective. It’s a good example of how adding more ideas to an essay often doesn’t make it better if they don’t work with the rest of what you’re writing. This essay also could explain the excerpts that are included more and how they relate to the points being made. Sometimes they’re just dropped in the essay with the expectation that the readers will make the connection between the example and the analysis. This is perhaps especially true in the second body paragraph, the one that discusses similarities to Biblical passages. Additional analysis of the quotes would have strengthened it.

Summary: How to Write an Analytical Essay

What is an analytical essay? A critical analytical essay analyzes a topic, often a text or film. The analysis paper uses evidence to support the argument, such as excerpts from the piece of writing. All analytical papers include a thesis, analysis of the topic, and evidence to support that analysis.

When developing an analytical essay outline and writing your essay, follow these five steps:

Reading analytical essay examples can also give you a better sense of how to structure your essay and what to include in it.

What's Next?

Learning about different writing styles in school? There are four main writing styles, and it's important to understand each of them. Learn about them in our guide to writing styles , complete with examples.

Writing a research paper for school but not sure what to write about? Our guide to research paper topics has over 100 topics in ten categories so you can be sure to find the perfect topic for you.

Literary devices can both be used to enhance your writing and communication. Check out this list of 31 literary devices to learn more !

Christine graduated from Michigan State University with degrees in Environmental Biology and Geography and received her Master's from Duke University. In high school she scored in the 99th percentile on the SAT and was named a National Merit Finalist. She has taught English and biology in several countries.

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Data Analysis Essays

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Importance of Data Analysis Essay

The data analysis process will take place after all the necessary information is obtained and structured appropriately. This will be a basis for the initial stage of the mentioned process – primary data processing. It is important to analyze the results of each study as soon as possible after its completion. So far, the researcher’s memory can suggest those details that, for some reason, are not fixed but are of interest for understanding the essence of the matter. When processing the collected data, it may turn out that they are either insufficient or contradictory and therefore do not give grounds for final conclusions.

In this case, the study must be continued with the required additions. After collecting information from various sources, it is necessary to understand what exactly is needed for the initial analysis of needs in accordance with the task at hand. In most cases, it is advisable to start processing with the compilation of tables (pivot tables) of the data obtained (Simplilearn, 2021). For both manual and computer processing, the initial data is most often entered into the original pivot table. Recently, computer processing has become the predominant form of mathematical and statistical processing.

The second stage is mathematical data processing, which implies a complex preparation. In order to determine the methods of mathematical and statistical processing, first of all, it is important to assess the nature of the distribution for all the parameters used. For parameters that are normally distributed or close to normal, parametric statistics methods can be used, which in many cases are more powerful than nonparametric statistical methods (Ali & Bhaskar, 2016). The advantage of the latter is that they allow testing statistical hypotheses regardless of the shape of the distribution.

One of the most common tasks in data processing is assessing the reliability of differences between two or more series of values. There are a number of ways in mathematical statistics to solve it. The computer version of data processing has become the most widespread today. Many statistical applications have procedures for evaluating the differences between the parameters of the same sample or different samples (Tyagi, 2020). With fully computerized processing of the material, it is not difficult to use the appropriate procedure at the right time and assess the differences of interest.

The following stage may be called the formulation of conclusions. The latter are statements expressing in a concise form the meaningful results of the study. They, in a thesis form, reflect the new findings that were obtained by the author. A common mistake is that the author includes in the conclusions generally accepted in science provisions – no longer needing proof. The responses to each of the objectives listed in the introduction should be reflected in the conclusions in a certain way.

The format for presenting the results after completing the task of analyzing information is of no small importance (Tyagi, 2020). The main content needs to be translated into an easy-to-read format based on their requirements. At the same time, you should provide easy access to additional background data for those who are interested and want to understand the topic more thoroughly. These basic rules apply regardless of the format of the presentation of the information.

In order to successfully solve this problem, special methods of analysis and information processing are required. Classical information technologies make it possible to efficiently store, structure and quickly retrieve information in a user-friendly form. The main strength of SPSS Statistics is the provision of a vast range of instruments that can be utilized in the framework of statistics (Allen et al., 2014). For all the complexity of modern methods of statistical analysis, which use the latest achievements of mathematical science, the SPSS program allows one to focus on the peculiarities of their application in each specific case. This program has capabilities that significantly exceed the scope of functions provided by standard business programs such as Excel.

The SPSS program provides the user with ample opportunities for statistical processing of experimental data, for the formation of databases (SPSS data files), for their modification. SPSS may be considered a complex and flexible statistical analysis tool (Allen et al., 2014). SPSS can take data from virtually any file type and use it to create tabular reports, graphs and distribution maps, descriptive statistics, and sophisticated statistical analysis.

At this point, it seems reasonable to define the sequence of the analysis using the SPSS tools. First, it is essential to draw up a questionnaire with the questions necessary for the researcher. Next, a survey is carried out. To process the received data, you need to draw up a coding table. The coding table establishes the correspondence between individual questions of the questionnaire and the variables used in the computer data processing (Allen et al., 2014). This solves the following tasks; first, a correspondence is established between the individual questions of the questionnaire and the variables. Second, a correspondence is established between the possible values of variables and code numbers.

Next, one needs to enter the data into the data editor according to the defined variables. After that, depending on the task, it is necessary to select the desired function and schedule. Then, you should analyze the subsequent tabular output of the result. All the necessary statistical functions that will be directly used in exploring and analyzing data are located in the Analysis menu. A very important analysis can be done with multiple responses; it is called the dichotomous method. This approach is used in cases when in the questionnaire for answering a question, it is proposed to mark several answer options (Allen et al., 2014).

Comparison of the means of different samples is one of the most commonly used methods of statistical analysis. In this case, the question must always be clarified whether the existing difference in mean values can be explained by statistical fluctuations or not. This method seems appropriate as the study will involve participants from all over the state, and their responses will need to be compared.

It should be stressed that SPSS is the most widely used statistical software. The main advantage of the SPSS software package, as one of the most advanced attainments in the area of automatized data analysis, is the broad coverage of modern statistical approaches. It is successfully combined with a large number of convenient visualization tools for processing results (Allen et al., 2014). The latest version gives notable possibilities not only within the scope of psychology, sociology, and biology but also in the field of medicine, which is crucial for the aims of future research. This greatly expands the applicability of the complex, which will serve as a significant basis for ensuring the validity of the study.

Ali, Z., & Bhaskar, S. B. (2016). Basic statistical tools in research and data analysis. Indian Journal of Anesthesia, 60 (9), 662–669.

Allen, P., Bennet, K., & Heritage, B. (2014). SPSS Statistics version 22: A practical guide . Cengage.

Simplilearn. (2021). What is data analysis: Methods, process and types explained . Web.

Tyagi, N. (2020). Introduction to statistical data analysis . Analytic Steps. Web.

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Writing a Good Data Analysis Report: 7 Steps

As a data analyst, you feel most comfortable when you’re alone with all the numbers and data. You’re able to analyze them with confidence and reach the results you were asked to find. But, this is not the end of the road for you. You still need to write a data analysis report explaining your findings to the laymen - your clients or coworkers.

That means you need to think about your target audience, that is the people who’ll be reading your report.

They don’t have nearly as much knowledge about data analysis as you do. So, your report needs to be straightforward and informative. The article below will help you learn how to do it. Let’s take a look at some practical tips you can apply to your data analysis report writing and the benefits of doing so.

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Data Analysis Report Writing: 7 Steps

The process of writing a data analysis report is far from simple, but you can master it quickly, with the right guidance and examples of similar reports .

This is why we've prepared a step-by-step guide that will cover everything you need to know about this process, as simply as possible. Let’s get to it.

Consider Your Audience

You are writing your report for a certain target audience, and you need to keep them in mind while writing. Depending on their level of expertise, you’ll need to adjust your report and ensure it speaks to them. So, before you go any further, ask yourself:

Who will be reading this report? How well do they understand the subject?

Let’s say you’re explaining the methodology you used to reach your conclusions and find the data in question. If the reader isn’t familiar with these tools and software, you’ll have to simplify it for them and provide additional explanations.

So, you won't be writing the same type of report for a coworker who's been on your team for years or a client who's seeing data analysis for the first time. Based on this determining factor, you'll think about:

the language and vocabulary you’re using

abbreviations and level of technicality

the depth you’ll go into to explain something

the type of visuals you’ll add

Your readers’ expertise dictates the tone of your report and you need to consider it before writing even a single word.

Draft Out the Sections

The next thing you need to do is create a draft of your data analysis report. This is just a skeleton of what your report will be once you finish. But, you need a starting point.

So, think about the sections you'll include and what each section is going to cover. Typically, your report should be divided into the following sections:

Introduction

Body (Data, Methods, Analysis, Results)

For each section, write down several short bullet points regarding the content to cover. Below, we'll discuss each section more elaborately.

Develop The Body

The body of your report is the most important section. You need to organize it into subsections and present all the information your readers will be interested in.

We suggest the following subsections.

Explain what data you used to conduct your analysis. Be specific and explain how you gathered the data, what your sample was, what tools and resources you’ve used, and how you’ve organized your data. This will give the reader a deeper understanding of your data sample and make your report more solid.

Also, explain why you choose the specific data for your sample. For instance, you may say “ The sample only includes data of the customers acquired during 2021, in the peak of the pandemic.”

Next, you need to explain what methods you’ve used to analyze the data. This simply means you need to explain why and how you choose specific methods. You also need to explain why these methods are the best fit for the goals you’ve set and the results you’re trying to reach.

Back up your methodology section with background information on each method or tool used. Explain how these resources are typically used in data analysis.

After you've explained the data and methods you've used, this next section brings those two together. The analysis section shows how you've analyzed the specific data using the specific methods. 

This means you’ll show your calculations, charts, and analyses, step by step. Add descriptions and explain each of the steps. Try making it as simple as possible so that even the most inexperienced of your readers understand every word.

This final section of the body can be considered the most important section of your report. Most of your clients will skim the rest of the report to reach this section. 

Because it’ll answer the questions you’ve all raised. It shares the results that were reached and gives the reader new findings, facts, and evidence. 

So, explain and describe the results using numbers. Then, add a written description of what each of the numbers stands for and what it means for the entire analysis. Summarize your results and finalize the report on a strong note. 

Write the Introduction

Yes, it may seem strange to write the introduction section at the end, but it’s the smartest way to do it. This section briefly explains what the report will cover. That’s why you should write it after you’ve finished writing the Body.

In your introduction, explain:

the question you’ve raised and answered with the analysis

context of the analysis and background information

short outline of the report

Simply put, you’re telling your audience what to expect.

Add a Short Conclusion

Finally, the last section of your paper is a brief conclusion. It only repeats what you described in the Body, but only points out the most important details.

It should be less than a page long and use straightforward language to deliver the most important findings. It should also include a paragraph about the implications and importance of those findings for the client, customer, business, or company that hired you.

Include Data Visualization Elements

You have all the data and numbers in your mind and find it easy to understand what the data is saying. But, to a layman or someone less experienced than yourself, it can be quite a puzzle. All the information that your data analysis has found can create a mess in the head of your reader.

So, you should simplify it by using data visualization elements.

Firstly, let’s define what are the most common and useful data visualization elements you can use in your report:

There are subcategories to each of the elements and you should explore them all to decide what will do the best job for your specific case. For instance, you'll find different types of charts including, pie charts, bar charts, area charts, or spider charts.

For each data visualization element, add a brief description to tell the readers what information it contains. You can also add a title to each element and create a table of contents for visual elements only.

Proofread & Edit Before Submission

All the hard work you’ve invested in writing a good data analysis report might go to waste if you don’t edit and proofread. Proofreading and editing will help you eliminate potential mistakes, but also take another objective look at your report.

First, do the editing part. It includes:

reading the whole report objectively, like you’re seeing it for the first time

leaving an open mind for changes

adding or removing information

rearranging sections

finding better words to say something

You should repeat the editing phase a couple of times until you're completely happy with the result. Once you're certain the content is all tidied up, you can move on to the proofreading stage. It includes:

finding and removing grammar and spelling mistakes

rethinking vocabulary choices

improving clarity 

improving readability

You can use an online proofreading tool to make things faster. If you really want professional help, Grab My Essay is a great choice. Their professional writers can edit and rewrite your entire report, to make sure it’s impeccable before submission.

Whatever you choose to do, proofread yourself or get some help with it, make sure your report is well-organized and completely error-free.

Benefits of Writing Well-Structured Data Analysis Reports

Yes, writing a good data analysis report is a lot of hard work. But, if you understand the benefits of writing it, you’ll be more motivated and willing to invest the time and effort. After knowing how it can help you in different segments of your professional journey, you’ll be more willing to learn how to do it.

Below are the main benefits a data analysis report brings to the table.

Improved Collaboration

When you’re writing a data analysis report, you need to be aware more than one end user is going to use it. Whether it’s your employer, customer, or coworker - you need to make sure they’re all on the same page. And when you write a data analysis report that is easy to understand and learn from, you’re creating a bridge between all these people.

Simply, all of them are given accurate data they can rely on and you’re thus removing the potential misunderstandings that can happen in communication. This improves the overall collaboration level and makes everyone more open and helpful.

Increased Efficiency

People who are reading your data analysis report need the information it contains for some reason. They might use it to do their part of the job, to make decisions, or report further to someone else. Either way, the better your report, the more efficient it'll be. And, if you rely on those people as well, you'll benefit from this increased productivity as well.

Data tells a story about a business, project, or venture. It's able to show how well you've performed, what turned out to be a great move, and what needs to be reimagined. This means that a data analysis report provides valuable insight and measurable KPIs (key performance indicators) that you’re able to use to grow and develop. 

Clear Communication

Information is key regardless of the industry you're in or the type of business you're doing. Data analysis finds that information and proves its accuracy and importance. But, if those findings and the information itself aren't communicated clearly, it's like you haven't even found them.

This is why a data analysis report is crucial. It will present the information less technically and bring it closer to the readers.

Final Thoughts

As you can see, it takes some skill and a bit more practice to write a good data analysis report. But, all the effort you invest in writing it will be worth it once the results kick in. You’ll improve the communication between you and your clients, employers, or coworkers. People will be able to understand, rely on, and use the analysis you’ve conducted.

So, don’t be afraid and start writing your first data analysis report. Just follow the 7 steps we’ve listed and use a tool such as ProWebScraper to help you with website data analysis. You’ll be surprised when you see the result of your hard work.

Jessica Fender is a business analyst and a blogger. She writes about business and data analysis, networking in this sector, and acquiring new skills. Her goal is to provide fresh and accurate information that readers can apply instantly.

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The questionnaires are closed ended and they will be self-administered. The assumption made in this exercise of data collection is that all the respondents-parents of the children are literate. The questions will be simple and short. Presenting the questionnaires in person to the parents and caregivers during social events or...

Words: 1497

Kindly note that if a source was in the previous paper and not in this final it is because the student had listed it and did not use it in citation so I have removed it. In addition, some of the sources may seem to have different names because in...

Skewness Skewness is asymmetrical in a statistical distribution, where the curve appears distorted or concentrated either to the left or to the right. Skewness can be used to define the extent to which a distribution differs from normal distribution. When a curve or distribution in a graph is classical or symmetrical,...

Despite the existence of numerous meanings, outliers are understood as data points which are far outside the standard for a population or a variable. The outlier could be an observation or a reading that is too different from other readings to the extent that it raises suspicion in regards to...

Outliers are data point(s) that deviate so much from the others that suspicions of having been generated through a different mechanism from the rest are aroused, or an indication of an error having been committed while compiling the data (Cressie, 2015). Special cases of outliers are fringeliers, which are data...

Outliers and their Causes Outliers are data points, sets of data or observations that fall far outside the normal variable population (Osborne & Overbay, 2004). Such data is inconsistent with the majority of the intended population or the variable range. It can be brought about by an experimental error or special...

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Introduction There are so many confusing aspects between the various uses of histograms and bar charts, a bar chart for one is made of different columns that are plotted in a graph (Daly, "Bourke, 2008). Histograms on the other hand, just like the bar charts have their columns uniquely plotted in...

The type of food eaten determines eating habits of individuals. 2. How DNA affects height. Variables: Independent: DNA                  Dependent: Height Hypothesis: The DNA of an individual has impact on tallness or shortness of a person. Data Analysis How Food affects Habits The appropriate data analysis techniques for the study will be use of Statistical Package for...

A comparison between the effectiveness of decision making using computer-driven data and human intuition A comparison between the effectiveness of decision making when using computer-driven data as opposed to human intuition is prone to spark controversy. A lot of things human beings used to do like keeping records, filing, accounting, mathematics...

Words: 1025

Twitter is one of the biggest information hubs of our century and things being so, a lot of information is posted and consumed within a given period of time .One of the topics that have caused a real buzz over the past few years has been the out of control...

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Primary data simply means the data from the original source with the purpose in mind. It is the kind of data that has not been distorted by a third party unlike the secondary data that can be described as the kind of data that has been collected by any other...

Words: 1463

The tourism and hotel industry in the world is booming with a potential to post even better results. However, the rising use of the internet particularly in social networking sites has the potential to help hotels attract more customers (Hanson, 2016). Before social media was invented and made popular, customers...

Words: 3293

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• How to write a literary analysis essay | A step-by-step guide

How to Write a Literary Analysis Essay | A Step-by-Step Guide

Published on January 30, 2020 by Jack Caulfield . Revised on August 14, 2023.

Literary analysis means closely studying a text, interpreting its meanings, and exploring why the author made certain choices. It can be applied to novels, short stories, plays, poems, or any other form of literary writing.

A literary analysis essay is not a rhetorical analysis , nor is it just a summary of the plot or a book review. Instead, it is a type of argumentative essay where you need to analyze elements such as the language, perspective, and structure of the text, and explain how the author uses literary devices to create effects and convey ideas.

Before beginning a literary analysis essay, it’s essential to carefully read the text and c ome up with a thesis statement to keep your essay focused. As you write, follow the standard structure of an academic essay :

• An introduction that tells the reader what your essay will focus on.
• A main body, divided into paragraphs , that builds an argument using evidence from the text.
• A conclusion that clearly states the main point that you have shown with your analysis.

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Table of contents

Step 1: reading the text and identifying literary devices, step 2: coming up with a thesis, step 3: writing a title and introduction, step 4: writing the body of the essay, step 5: writing a conclusion, other interesting articles.

The first step is to carefully read the text(s) and take initial notes. As you read, pay attention to the things that are most intriguing, surprising, or even confusing in the writing—these are things you can dig into in your analysis.

Your goal in literary analysis is not simply to explain the events described in the text, but to analyze the writing itself and discuss how the text works on a deeper level. Primarily, you’re looking out for literary devices —textual elements that writers use to convey meaning and create effects. If you’re comparing and contrasting multiple texts, you can also look for connections between different texts.

To get started with your analysis, there are several key areas that you can focus on. As you analyze each aspect of the text, try to think about how they all relate to each other. You can use highlights or notes to keep track of important passages and quotes.

Language choices

Consider what style of language the author uses. Are the sentences short and simple or more complex and poetic?

What word choices stand out as interesting or unusual? Are words used figuratively to mean something other than their literal definition? Figurative language includes things like metaphor (e.g. “her eyes were oceans”) and simile (e.g. “her eyes were like oceans”).

Also keep an eye out for imagery in the text—recurring images that create a certain atmosphere or symbolize something important. Remember that language is used in literary texts to say more than it means on the surface.

Narrative voice

Ask yourself:

• Who is telling the story?
• How are they telling it?

Is it a first-person narrator (“I”) who is personally involved in the story, or a third-person narrator who tells us about the characters from a distance?

Consider the narrator’s perspective . Is the narrator omniscient (where they know everything about all the characters and events), or do they only have partial knowledge? Are they an unreliable narrator who we are not supposed to take at face value? Authors often hint that their narrator might be giving us a distorted or dishonest version of events.

The tone of the text is also worth considering. Is the story intended to be comic, tragic, or something else? Are usually serious topics treated as funny, or vice versa ? Is the story realistic or fantastical (or somewhere in between)?

Consider how the text is structured, and how the structure relates to the story being told.

• Novels are often divided into chapters and parts.
• Poems are divided into lines, stanzas, and sometime cantos.
• Plays are divided into scenes and acts.

Think about why the author chose to divide the different parts of the text in the way they did.

There are also less formal structural elements to take into account. Does the story unfold in chronological order, or does it jump back and forth in time? Does it begin in medias res —in the middle of the action? Does the plot advance towards a clearly defined climax?

With poetry, consider how the rhyme and meter shape your understanding of the text and your impression of the tone. Try reading the poem aloud to get a sense of this.

In a play, you might consider how relationships between characters are built up through different scenes, and how the setting relates to the action. Watch out for  dramatic irony , where the audience knows some detail that the characters don’t, creating a double meaning in their words, thoughts, or actions.

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Your thesis in a literary analysis essay is the point you want to make about the text. It’s the core argument that gives your essay direction and prevents it from just being a collection of random observations about a text.

If you’re given a prompt for your essay, your thesis must answer or relate to the prompt. For example:

Essay question example

Is Franz Kafka’s “Before the Law” a religious parable?

Your thesis statement should be an answer to this question—not a simple yes or no, but a statement of why this is or isn’t the case:

Thesis statement example

Franz Kafka’s “Before the Law” is not a religious parable, but a story about bureaucratic alienation.

Sometimes you’ll be given freedom to choose your own topic; in this case, you’ll have to come up with an original thesis. Consider what stood out to you in the text; ask yourself questions about the elements that interested you, and consider how you might answer them.

Your thesis should be something arguable—that is, something that you think is true about the text, but which is not a simple matter of fact. It must be complex enough to develop through evidence and arguments across the course of your essay.

Say you’re analyzing the novel Frankenstein . You could start by asking yourself:

Your initial answer might be a surface-level description:

The character Frankenstein is portrayed negatively in Mary Shelley’s Frankenstein .

However, this statement is too simple to be an interesting thesis. After reading the text and analyzing its narrative voice and structure, you can develop the answer into a more nuanced and arguable thesis statement:

Mary Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as.

Remember that you can revise your thesis statement throughout the writing process , so it doesn’t need to be perfectly formulated at this stage. The aim is to keep you focused as you analyze the text.

Finding textual evidence

To support your thesis statement, your essay will build an argument using textual evidence —specific parts of the text that demonstrate your point. This evidence is quoted and analyzed throughout your essay to explain your argument to the reader.

It can be useful to comb through the text in search of relevant quotations before you start writing. You might not end up using everything you find, and you may have to return to the text for more evidence as you write, but collecting textual evidence from the beginning will help you to structure your arguments and assess whether they’re convincing.

To start your literary analysis paper, you’ll need two things: a good title, and an introduction.

Your title should clearly indicate what your analysis will focus on. It usually contains the name of the author and text(s) you’re analyzing. Keep it as concise and engaging as possible.

A common approach to the title is to use a relevant quote from the text, followed by a colon and then the rest of your title.

If you struggle to come up with a good title at first, don’t worry—this will be easier once you’ve begun writing the essay and have a better sense of your arguments.

“Fearful symmetry” : The violence of creation in William Blake’s “The Tyger”

The introduction

The essay introduction provides a quick overview of where your argument is going. It should include your thesis statement and a summary of the essay’s structure.

A typical structure for an introduction is to begin with a general statement about the text and author, using this to lead into your thesis statement. You might refer to a commonly held idea about the text and show how your thesis will contradict it, or zoom in on a particular device you intend to focus on.

Then you can end with a brief indication of what’s coming up in the main body of the essay. This is called signposting. It will be more elaborate in longer essays, but in a short five-paragraph essay structure, it shouldn’t be more than one sentence.

Mary Shelley’s Frankenstein is often read as a crude cautionary tale about the dangers of scientific advancement unrestrained by ethical considerations. In this reading, protagonist Victor Frankenstein is a stable representation of the callous ambition of modern science throughout the novel. This essay, however, argues that far from providing a stable image of the character, Shelley uses shifting narrative perspectives to portray Frankenstein in an increasingly negative light as the novel goes on. While he initially appears to be a naive but sympathetic idealist, after the creature’s narrative Frankenstein begins to resemble—even in his own telling—the thoughtlessly cruel figure the creature represents him as. This essay begins by exploring the positive portrayal of Frankenstein in the first volume, then moves on to the creature’s perception of him, and finally discusses the third volume’s narrative shift toward viewing Frankenstein as the creature views him.

Some students prefer to write the introduction later in the process, and it’s not a bad idea. After all, you’ll have a clearer idea of the overall shape of your arguments once you’ve begun writing them!

If you do write the introduction first, you should still return to it later to make sure it lines up with what you ended up writing, and edit as necessary.

The body of your essay is everything between the introduction and conclusion. It contains your arguments and the textual evidence that supports them.

Paragraph structure

A typical structure for a high school literary analysis essay consists of five paragraphs : the three paragraphs of the body, plus the introduction and conclusion.

Each paragraph in the main body should focus on one topic. In the five-paragraph model, try to divide your argument into three main areas of analysis, all linked to your thesis. Don’t try to include everything you can think of to say about the text—only analysis that drives your argument.

In longer essays, the same principle applies on a broader scale. For example, you might have two or three sections in your main body, each with multiple paragraphs. Within these sections, you still want to begin new paragraphs at logical moments—a turn in the argument or the introduction of a new idea.

Robert’s first encounter with Gil-Martin suggests something of his sinister power. Robert feels “a sort of invisible power that drew me towards him.” He identifies the moment of their meeting as “the beginning of a series of adventures which has puzzled myself, and will puzzle the world when I am no more in it” (p. 89). Gil-Martin’s “invisible power” seems to be at work even at this distance from the moment described; before continuing the story, Robert feels compelled to anticipate at length what readers will make of his narrative after his approaching death. With this interjection, Hogg emphasizes the fatal influence Gil-Martin exercises from his first appearance.

Topic sentences

To keep your points focused, it’s important to use a topic sentence at the beginning of each paragraph.

A good topic sentence allows a reader to see at a glance what the paragraph is about. It can introduce a new line of argument and connect or contrast it with the previous paragraph. Transition words like “however” or “moreover” are useful for creating smooth transitions:

… The story’s focus, therefore, is not upon the divine revelation that may be waiting beyond the door, but upon the mundane process of aging undergone by the man as he waits.

Nevertheless, the “radiance” that appears to stream from the door is typically treated as religious symbolism.

This topic sentence signals that the paragraph will address the question of religious symbolism, while the linking word “nevertheless” points out a contrast with the previous paragraph’s conclusion.

Using textual evidence

A key part of literary analysis is backing up your arguments with relevant evidence from the text. This involves introducing quotes from the text and explaining their significance to your point.

It’s important to contextualize quotes and explain why you’re using them; they should be properly introduced and analyzed, not treated as self-explanatory:

It isn’t always necessary to use a quote. Quoting is useful when you’re discussing the author’s language, but sometimes you’ll have to refer to plot points or structural elements that can’t be captured in a short quote.

In these cases, it’s more appropriate to paraphrase or summarize parts of the text—that is, to describe the relevant part in your own words:

The conclusion of your analysis shouldn’t introduce any new quotations or arguments. Instead, it’s about wrapping up the essay. Here, you summarize your key points and try to emphasize their significance to the reader.

A good way to approach this is to briefly summarize your key arguments, and then stress the conclusion they’ve led you to, highlighting the new perspective your thesis provides on the text as a whole:

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By tracing the depiction of Frankenstein through the novel’s three volumes, I have demonstrated how the narrative structure shifts our perception of the character. While the Frankenstein of the first volume is depicted as having innocent intentions, the second and third volumes—first in the creature’s accusatory voice, and then in his own voice—increasingly undermine him, causing him to appear alternately ridiculous and vindictive. Far from the one-dimensional villain he is often taken to be, the character of Frankenstein is compelling because of the dynamic narrative frame in which he is placed. In this frame, Frankenstein’s narrative self-presentation responds to the images of him we see from others’ perspectives. This conclusion sheds new light on the novel, foregrounding Shelley’s unique layering of narrative perspectives and its importance for the depiction of character.

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