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The Importance of Data Analysis in Research

Studying data is amongst the everyday  chores  of researchers. It’s not a big deal for them to go through hundreds of pages per day to extract useful information from it. However, recent times have seen a massive jump in the  amount  of data available. While it’s certainly good news for researchers to get their hands on more data that could result in better studies, it’s also no less than a headache.

Thankfully, the rising  trend  of  data science  in the past years has also meant a sharp rise in data analysis  techniques . These tools and techniques save a lot of time in hefty processes a researcher has to go through and allow them to finish the work of days in minutes!

As a famous saying goes,

“Information is the  oil of the 21st century , and analytics is the combustion engine.”

 –  Peter Sondergaard , senior vice president, Gartner Research.

So, if you’re also a researcher or just curious about the most important data analysis techniques in research, this article is for you. Make sure you give it a thorough read, as I’ll be dropping some very important points throughout the article.

What is the Importance of Data Analysis in Research?

Data analysis is important in research because it makes studying data a lot simpler and more accurate. It helps the researchers straightforwardly interpret the data so that researchers don’t leave anything out that could help them derive insights from it.

Data analysis is a way to study and analyze huge amounts of data. Research often includes going through heaps of data, which is getting more and more for the researchers to handle with every passing minute.

Hence, data analysis knowledge is a huge edge for researchers in the current era, making them very efficient and productive.

What is Data Analysis?

Data analysis is the process of analyzing data in various formats. Even though data is  abundant  nowadays, it’s available in different forms and scattered over various sources. Data analysis helps to clean and transform all this data into a consistent form so it can be effectively studied.

Once the data is  cleaned ,  transformed , and ready to use, it can do wonders. Not only does it contain a variety of useful information, studying the data collectively results in uncovering very minor patterns and details that would otherwise have been ignored.

So, you can see why it has such a huge role to play in research. Research is all about studying patterns and trends, followed by making a hypothesis and proving them. All this is supported by appropriate data.

Further in the article, we’ll see some of the most important types of data analysis that you should be aware of as a researcher so you can put them to use.

The Role of Data Analytics at The Senior Management Level

The Role of Data Analytics at The Senior Management Level

From small and medium-sized businesses to Fortune 500 conglomerates, the success of a modern business is now increasingly tied to how the company implements its data infrastructure and data-based decision-making. According

The Decision-Making Model Explained (In Plain Terms)

The Decision-Making Model Explained (In Plain Terms)

Any form of the systematic decision-making process is better enhanced with data. But making sense of big data or even small data analysis when venturing into a decision-making process might

13 Reasons Why Data Is Important in Decision Making

13 Reasons Why Data Is Important in Decision Making

Data is important in decision making process, and that is the new golden rule in the business world. Businesses are always trying to find the balance of cutting costs while

Types of Data Analysis: Qualitative Vs Quantitative

Looking at it from a broader perspective, data analysis boils down to two major types. Namely,  qualitative data analysis and  quantitative data  analysis. While the latter deals with the numerical data, comprising of numbers, the former comes in the non-text form. It can be anything such as summaries, images, symbols, and so on.

Both types have different methods to deal with them and we’ll be taking a look at both of them so you can use whatever suits your requirements.

Qualitative Data Analysis

As mentioned before, qualitative data comprises non-text-based data, and it can be either in the form of text or images. So, how do we analyze such data? Before we start, here are a few common tips first that you should always use before applying any techniques.

Now, let’s move ahead and see where the qualitative data analysis techniques come in. Even though there are a lot of professional ways to achieve this, here are some of them that you’ll need to know as a beginner.

Narrative Analysis

If your research is based upon collecting some answers from people in interviews or other scenarios, this might be one of the best analysis techniques for you.  The narrative analysis  helps to analyze the narratives of various people, which is available in textual form. The stories, experiences, and other answers from respondents are used to power the analysis.

The important thing to note here is that the data has to be available in the form of text only. Narrative analysis cannot be performed on other data types such as images.

Content Analysis

Content analysis  is amongst the most used methods in analyzing quantitative data. This method doesn’t put a restriction on the form of data. You can use any kind of data here, whether it’s in the form of images, text, or even real-life items.

Here, an important application is when you know the questions you need to know the answers to. Upon getting the answers, you can perform this method to perform analysis to them, followed by extracting insights from it to be used in your research. It’s a full-fledged method and a lot of analytical  studies  are based solely on this.

Grounded Theory

Grounded theory  is used when the researchers want to know the reason behind the occurrence of a certain event. They may have to go through a lot of different  use cases  and comparing them to each other while following this approach. It’s an iterative approach and the explanations keep on being modified or re-created till the researchers end up on a suitable conclusion that satisfies their specific conditions.

So, make sure you employ this method if you need to have certain qualitative data at hand and you need to know the reason why something happened, based on that data.

Discourse Analysis

Discourse analysis  is quite similar to narrative analysis in the sense that it also uses interactions with people for the analysis purpose. The only difference is that the focal point here is different. Instead of analyzing the narrative, the researchers focus on the context in which the conversation is happening.

The complete background of the person being questioned, including his everyday environment, is used to perform the research.

Quantitative Analysis

Quantitative analysis involves any kind of analysis that’s being done on numbers. From the most basic analysis techniques to the most advanced ones, quantitative analysis techniques comprise a huge range of techniques. No matter what level of research you need to do, if it’s based on numerical data, you’ll always have efficient analysis methods to use.

There are two broad ways here;  Descriptive statistics  and  inferential analysis . 

However, before applying the analysis methods on numerical data, there are a few pre-processing steps that need to be done. These steps are used to make the data ‘ready’ for applying the analysis methods.

Make sure you don’t miss these steps, or you will end up drawing biased conclusions from the data analysis. IF you want to know why data is the key in data analysis and problem-solving, feel free to check out this article here . Now, about the steps for PRE-PROCESSING THE QUANTITATIVE DATA .

Descriptive Statistics

Descriptive statistics  is the most basic step that researchers can use to draw conclusions from data. It helps to find patterns and helps the data ‘speak’. Let’s see some of the most common data analysis techniques used to perform descriptive statistics .

Mean is nothing but the average of the total data available at hand. The formula is simple and tells what average value to expect throughout the data.

The median is the middle value available in the data. It lets the researchers estimate where the mid-point of the data is. It’s important to note that the data needs to be sorted to find the median from it.

The mode is simply the most frequently occurring data in the dataset. For example, if you’re studying the ages of students in a particular class, the model will be the age of most students in the class.

  • Standard Deviation

Numerical data is always spread over a wide range and finding out how much the data is spread is quite important. Standard deviation is what lets us achieve this. It tells us how much an average data point is far from the average.

Related Article: The Best Programming Language for Statistics

Inferential Analysis

Inferential statistics  point towards the techniques used to predict future occurrences of data. These methods help draw relationships between data and once it’s done, predicting future data becomes possible.

  • Correlation

Correlation  s the measure of the relationship between two numerical variables. It measures the degree of their relation, whether it is causal or not. 

For example, the age and height of a person are highly correlated. If the age of a person increases, height is also likely to increase. This is called a positive correlation.

A negative correlation means that upon increasing one variable, the other one decreases. An example would be the relationship between the age and maturity of a random person.

Regression  aims to find the mathematical relationship between a set of variables. While the correlation was a statistical measure, regression is a mathematical measure that can be measured in the form of variables. Once the relationship between variables is formed, one variable can be used to predict the other variable.

This method has a huge application when it comes to predicting future data. If your research is based upon calculating future occurrences of some data based on past data and then testing it, make sure you use this method.

A Summary of Data Analysis Methods

Now that we’re done with some of the most common methods for both quantitative and qualitative data, let’s summarize them in a tabular form so you would have something to take home in the end.

Before we close the article, I’d like to strongly recommend you to check out some interesting related topics:

That’s it! We have seen why data analysis is such an important tool when it comes to research and how it saves a huge lot of time for the researchers, making them not only efficient but more productive as well.

Moreover, the article covers some of the most important data analysis techniques that one needs to know for research purposes in today’s age. We’ve gone through the analysis methods for both quantitative and qualitative data in a basic way so it might be easy to understand for beginners.

Emidio Amadebai

As an IT Engineer, who is passionate about learning and sharing. I have worked and learned quite a bit from Data Engineers, Data Analysts, Business Analysts, and Key Decision Makers almost for the past 5 years. Interested in learning more about Data Science and How to leverage it for better decision-making in my business and hopefully help you do the same in yours.

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A Step-by-Step Guide to the Data Analysis Process

Like any scientific discipline, data analysis follows a rigorous step-by-step process. Each stage requires different skills and know-how. To get meaningful insights, though, it’s important to understand the process as a whole. An underlying framework is invaluable for producing results that stand up to scrutiny.

In this post, we’ll explore the main steps in the data analysis process. This will cover how to define your goal, collect data, and carry out an analysis. Where applicable, we’ll also use examples and highlight a few tools to make the journey easier. When you’re done, you’ll have a much better understanding of the basics. This will help you tweak the process to fit your own needs.

Here are the steps we’ll take you through:

  • Defining the question
  • Collecting the data
  • Cleaning the data
  • Analyzing the data
  • Sharing your results
  • Embracing failure

On popular request, we’ve also developed a video based on this article. Scroll further along this article to watch that.

Ready? Let’s get started with step one.

1. Step one: Defining the question

The first step in any data analysis process is to define your objective. In data analytics jargon, this is sometimes called the ‘problem statement’.

Defining your objective means coming up with a hypothesis and figuring how to test it. Start by asking: What business problem am I trying to solve? While this might sound straightforward, it can be trickier than it seems. For instance, your organization’s senior management might pose an issue, such as: “Why are we losing customers?” It’s possible, though, that this doesn’t get to the core of the problem. A data analyst’s job is to understand the business and its goals in enough depth that they can frame the problem the right way.

Let’s say you work for a fictional company called TopNotch Learning. TopNotch creates custom training software for its clients. While it is excellent at securing new clients, it has much lower repeat business. As such, your question might not be, “Why are we losing customers?” but, “Which factors are negatively impacting the customer experience?” or better yet: “How can we boost customer retention while minimizing costs?”

Now you’ve defined a problem, you need to determine which sources of data will best help you solve it. This is where your business acumen comes in again. For instance, perhaps you’ve noticed that the sales process for new clients is very slick, but that the production team is inefficient. Knowing this, you could hypothesize that the sales process wins lots of new clients, but the subsequent customer experience is lacking. Could this be why customers don’t come back? Which sources of data will help you answer this question?

Tools to help define your objective

Defining your objective is mostly about soft skills, business knowledge, and lateral thinking. But you’ll also need to keep track of business metrics and key performance indicators (KPIs). Monthly reports can allow you to track problem points in the business. Some KPI dashboards come with a fee, like Databox and DashThis . However, you’ll also find open-source software like Grafana , Freeboard , and Dashbuilder . These are great for producing simple dashboards, both at the beginning and the end of the data analysis process.

2. Step two: Collecting the data

Once you’ve established your objective, you’ll need to create a strategy for collecting and aggregating the appropriate data. A key part of this is determining which data you need. This might be quantitative (numeric) data, e.g. sales figures, or qualitative (descriptive) data, such as customer reviews. All data fit into one of three categories: first-party, second-party, and third-party data. Let’s explore each one.

What is first-party data?

First-party data are data that you, or your company, have directly collected from customers. It might come in the form of transactional tracking data or information from your company’s customer relationship management (CRM) system. Whatever its source, first-party data is usually structured and organized in a clear, defined way. Other sources of first-party data might include customer satisfaction surveys, focus groups, interviews, or direct observation.

What is second-party data?

To enrich your analysis, you might want to secure a secondary data source. Second-party data is the first-party data of other organizations. This might be available directly from the company or through a private marketplace. The main benefit of second-party data is that they are usually structured, and although they will be less relevant than first-party data, they also tend to be quite reliable. Examples of second-party data include website, app or social media activity, like online purchase histories, or shipping data.

What is third-party data?

Third-party data is data that has been collected and aggregated from numerous sources by a third-party organization. Often (though not always) third-party data contains a vast amount of unstructured data points (big data). Many organizations collect big data to create industry reports or to conduct market research. The research and advisory firm Gartner is a good real-world example of an organization that collects big data and sells it on to other companies. Open data repositories and government portals are also sources of third-party data .

Tools to help you collect data

Once you’ve devised a data strategy (i.e. you’ve identified which data you need, and how best to go about collecting them) there are many tools you can use to help you. One thing you’ll need, regardless of industry or area of expertise, is a data management platform (DMP). A DMP is a piece of software that allows you to identify and aggregate data from numerous sources, before manipulating them, segmenting them, and so on. There are many DMPs available. Some well-known enterprise DMPs include Salesforce DMP , SAS , and the data integration platform, Xplenty . If you want to play around, you can also try some open-source platforms like Pimcore or D:Swarm .

Want to learn more about what data analytics is and the process a data analyst follows? We cover this topic (and more) in our free introductory short course for beginners. Check out tutorial one: An introduction to data analytics .

3. Step three: Cleaning the data

Once you’ve collected your data, the next step is to get it ready for analysis. This means cleaning, or ‘scrubbing’ it, and is crucial in making sure that you’re working with high-quality data . Key data cleaning tasks include:

  • Removing major errors, duplicates, and outliers —all of which are inevitable problems when aggregating data from numerous sources.
  • Removing unwanted data points —extracting irrelevant observations that have no bearing on your intended analysis.
  • Bringing structure to your data —general ‘housekeeping’, i.e. fixing typos or layout issues, which will help you map and manipulate your data more easily.
  • Filling in major gaps —as you’re tidying up, you might notice that important data are missing. Once you’ve identified gaps, you can go about filling them.

A good data analyst will spend around 70-90% of their time cleaning their data. This might sound excessive. But focusing on the wrong data points (or analyzing erroneous data) will severely impact your results. It might even send you back to square one…so don’t rush it! You’ll find a step-by-step guide to data cleaning here . You may be interested in this introductory tutorial to data cleaning, hosted by Dr. Humera Noor Minhas.

Carrying out an exploratory analysis

Another thing many data analysts do (alongside cleaning data) is to carry out an exploratory analysis. This helps identify initial trends and characteristics, and can even refine your hypothesis. Let’s use our fictional learning company as an example again. Carrying out an exploratory analysis, perhaps you notice a correlation between how much TopNotch Learning’s clients pay and how quickly they move on to new suppliers. This might suggest that a low-quality customer experience (the assumption in your initial hypothesis) is actually less of an issue than cost. You might, therefore, take this into account.

Tools to help you clean your data

Cleaning datasets manually—especially large ones—can be daunting. Luckily, there are many tools available to streamline the process. Open-source tools, such as OpenRefine , are excellent for basic data cleaning, as well as high-level exploration. However, free tools offer limited functionality for very large datasets. Python libraries (e.g. Pandas) and some R packages are better suited for heavy data scrubbing. You will, of course, need to be familiar with the languages. Alternatively, enterprise tools are also available. For example, Data Ladder , which is one of the highest-rated data-matching tools in the industry. There are many more. Why not see which free data cleaning tools you can find to play around with?

4. Step four: Analyzing the data

Finally, you’ve cleaned your data. Now comes the fun bit—analyzing it! The type of data analysis you carry out largely depends on what your goal is. But there are many techniques available. Univariate or bivariate analysis, time-series analysis, and regression analysis are just a few you might have heard of. More important than the different types, though, is how you apply them. This depends on what insights you’re hoping to gain. Broadly speaking, all types of data analysis fit into one of the following four categories.

Descriptive analysis

Descriptive analysis identifies what has already happened . It is a common first step that companies carry out before proceeding with deeper explorations. As an example, let’s refer back to our fictional learning provider once more. TopNotch Learning might use descriptive analytics to analyze course completion rates for their customers. Or they might identify how many users access their products during a particular period. Perhaps they’ll use it to measure sales figures over the last five years. While the company might not draw firm conclusions from any of these insights, summarizing and describing the data will help them to determine how to proceed.

Learn more: What is descriptive analytics?

Diagnostic analysis

Diagnostic analytics focuses on understanding why something has happened . It is literally the diagnosis of a problem, just as a doctor uses a patient’s symptoms to diagnose a disease. Remember TopNotch Learning’s business problem? ‘Which factors are negatively impacting the customer experience?’ A diagnostic analysis would help answer this. For instance, it could help the company draw correlations between the issue (struggling to gain repeat business) and factors that might be causing it (e.g. project costs, speed of delivery, customer sector, etc.) Let’s imagine that, using diagnostic analytics, TopNotch realizes its clients in the retail sector are departing at a faster rate than other clients. This might suggest that they’re losing customers because they lack expertise in this sector. And that’s a useful insight!

Predictive analysis

Predictive analysis allows you to identify future trends based on historical data . In business, predictive analysis is commonly used to forecast future growth, for example. But it doesn’t stop there. Predictive analysis has grown increasingly sophisticated in recent years. The speedy evolution of machine learning allows organizations to make surprisingly accurate forecasts. Take the insurance industry. Insurance providers commonly use past data to predict which customer groups are more likely to get into accidents. As a result, they’ll hike up customer insurance premiums for those groups. Likewise, the retail industry often uses transaction data to predict where future trends lie, or to determine seasonal buying habits to inform their strategies. These are just a few simple examples, but the untapped potential of predictive analysis is pretty compelling.

Prescriptive analysis

Prescriptive analysis allows you to make recommendations for the future. This is the final step in the analytics part of the process. It’s also the most complex. This is because it incorporates aspects of all the other analyses we’ve described. A great example of prescriptive analytics is the algorithms that guide Google’s self-driving cars. Every second, these algorithms make countless decisions based on past and present data, ensuring a smooth, safe ride. Prescriptive analytics also helps companies decide on new products or areas of business to invest in.

Learn more:  What are the different types of data analysis?

5. Step five: Sharing your results

You’ve finished carrying out your analyses. You have your insights. The final step of the data analytics process is to share these insights with the wider world (or at least with your organization’s stakeholders!) This is more complex than simply sharing the raw results of your work—it involves interpreting the outcomes, and presenting them in a manner that’s digestible for all types of audiences. Since you’ll often present information to decision-makers, it’s very important that the insights you present are 100% clear and unambiguous. For this reason, data analysts commonly use reports, dashboards, and interactive visualizations to support their findings.

How you interpret and present results will often influence the direction of a business. Depending on what you share, your organization might decide to restructure, to launch a high-risk product, or even to close an entire division. That’s why it’s very important to provide all the evidence that you’ve gathered, and not to cherry-pick data. Ensuring that you cover everything in a clear, concise way will prove that your conclusions are scientifically sound and based on the facts. On the flip side, it’s important to highlight any gaps in the data or to flag any insights that might be open to interpretation. Honest communication is the most important part of the process. It will help the business, while also helping you to excel at your job!

Tools for interpreting and sharing your findings

There are tons of data visualization tools available, suited to different experience levels. Popular tools requiring little or no coding skills include Google Charts , Tableau , Datawrapper , and Infogram . If you’re familiar with Python and R, there are also many data visualization libraries and packages available. For instance, check out the Python libraries Plotly , Seaborn , and Matplotlib . Whichever data visualization tools you use, make sure you polish up your presentation skills, too. Remember: Visualization is great, but communication is key!

You can learn more about storytelling with data in this free, hands-on tutorial .  We show you how to craft a compelling narrative for a real dataset, resulting in a presentation to share with key stakeholders. This is an excellent insight into what it’s really like to work as a data analyst!

6. Step six: Embrace your failures

The last ‘step’ in the data analytics process is to embrace your failures. The path we’ve described above is more of an iterative process than a one-way street. Data analytics is inherently messy, and the process you follow will be different for every project. For instance, while cleaning data, you might spot patterns that spark a whole new set of questions. This could send you back to step one (to redefine your objective). Equally, an exploratory analysis might highlight a set of data points you’d never considered using before. Or maybe you find that the results of your core analyses are misleading or erroneous. This might be caused by mistakes in the data, or human error earlier in the process.

While these pitfalls can feel like failures, don’t be disheartened if they happen. Data analysis is inherently chaotic, and mistakes occur. What’s important is to hone your ability to spot and rectify errors. If data analytics was straightforward, it might be easier, but it certainly wouldn’t be as interesting. Use the steps we’ve outlined as a framework, stay open-minded, and be creative. If you lose your way, you can refer back to the process to keep yourself on track.

In this post, we’ve covered the main steps of the data analytics process. These core steps can be amended, re-ordered and re-used as you deem fit, but they underpin every data analyst’s work:

  • Define the question —What business problem are you trying to solve? Frame it as a question to help you focus on finding a clear answer.
  • Collect data —Create a strategy for collecting data. Which data sources are most likely to help you solve your business problem?
  • Clean the data —Explore, scrub, tidy, de-dupe, and structure your data as needed. Do whatever you have to! But don’t rush…take your time!
  • Analyze the data —Carry out various analyses to obtain insights. Focus on the four types of data analysis: descriptive, diagnostic, predictive, and prescriptive.
  • Share your results —How best can you share your insights and recommendations? A combination of visualization tools and communication is key.
  • Embrace your mistakes —Mistakes happen. Learn from them. This is what transforms a good data analyst into a great one.

What next? From here, we strongly encourage you to explore the topic on your own. Get creative with the steps in the data analysis process, and see what tools you can find. As long as you stick to the core principles we’ve described, you can create a tailored technique that works for you.

To learn more, check out our free, 5-day data analytics short course . You might also be interested in the following:

  • These are the top 9 data analytics tools
  • 10 great places to find free datasets for your next project
  • How to build a data analytics portfolio

Research Ethics and Design

Writing about your data.

After you have collected and analyzed all your data, you will normally write at least three sections about your primary research:

  • Methods : How did you collect your data?
  • Results (or Findings) : What did you find?
  • Discussion : What do those findings mean?

For example, describing who, when, and how you sent out and collected your 10-question survey would be your Methods section. Describing how many people responded in particular or different ways is the Results section. Interpreting the data and making a statement about what that data means is your Discussion section. Limitations of your study need to be explored as well, but this is either incorporated into one of these sections (usually the Discussion) or has its own section.

This section is crucial to your credibility and understanding of your data. Clear descriptions will help your readers know why you did what you did and how you got your results. Here are a few points to keep in mind when writing the Methods section.

  • Who did you interview/survey/observe? Was it a specific group? “Random” people on campus? Why?
  • What did you ask, overall? Why? Avoid listing every question and instead just give a quick overview. (Direct your readers to your full instrument in an appendix instead.)
  • When and where did this occur? Did you send out a survey? How long was it online? If you did an interview, how did you set it up, where did you conduct it, and how long did it take? Similar questions apply for observations as well: where were you, when, and why?
  • How did you complete your research? If you did an interview, did you do this in-person, online, by phone? If you conducted a survey, did you do it via pen and paper or an online survey? What did you look for in your observation(s) and how did you take notes?
  • If you are using a theoretical framework to analyze your data, what is it? Why are you using it?

Do not see this list as a way to organize the section but instead as questions your Methods section should answer. You do not want this section to read like a checklist.

Note, while readers mostly want to know your findings and interpretation of the data in the following two sections, the Methods section is just as important. The more you can describe your methods, the better other researchers can understand your data and also potentially replicate your research.

Results (or Findings)

This will be where you describe your collected data (i.e. data that you have collected from your study that you have not “interpreted” yet). Like in your Methods section, you want to be clear and transparent.

  • Surveys. Avoid listing a question, then an answer, then a question, then an answer, etc. Using visuals where appropriate, report on (instead of list) the more significant parts of your survey. You should list your questions in an appendix, and you can list your full results in a table/visual there as well.
  • Interviews. Avoid listing questions and answers and having an almost dialogue form. Instead, report on the more significant parts of the interview and use quotations when necessary.
  • Observations. Describe what you saw. Again, like your interviews/surveys, avoid giving a “play-by-play” and discuss what you know are the more significant aspects.

In your Results section, you generally want to avoid “flowery” language and/or inserting too much opinion. Simply report your findings in as clear a way as possible.

In this final section is where you will give your own analysis of the data. Here is where you will make connections for the reader(s) on what your data “means.” The main difference between your Results section and the Discussion section is that this is, for all intents and purposes, your opinion (though that opinion is rooted heavily in your data). Whichever method you chose to collect your data, these suggestions will help organize your discussion section and make it clear for your reader.

  • Clear Topic Sentence(s). As you have learned throughout the semester, clear topic sentences will help set up your paragraph(s) to be easily understandable.
  • Explicit Connections . In your paragraphs, make explicit connections between your claim(s) and evidence from your data. Where appropriate, you also want to make connections to prior research studies: do your data points support or diverge from prior studies? How? Why might this be?
  • Detailed Evidence . Don’t hesitate to remind your reader of the data collected or even to elaborate more on it. Remember, more details and discussion of data will help convince your reader about the significance of your claim.
  • Limitations . Some researchers put this in the Discussion section while others make an entirely new section. Either way, be upfront with all the limitations, shortcomings, etc. of your research. Be thorough in your thinking here: did you run out of time, have a small number of responses, or recognize a methodological flaw along the way? Being transparent and honest with your reader is most important.
  • Potential Future Research . Generally, either in the Discussion section or in a final, short Conclusion section, primary research projects make note of future potential projects based on the current one. If your results were unclear, then further research might be justified. If your results were clear, then perhaps that indicates that a narrower sample group should be investigated or a new or slightly different variable should be examined. There are many possible routes to take here, but you want to base it on what you did (and/or did not) find in your study and help future researchers dig further into your research topic

This section usually reads more like a “traditional” essay you are used to writing than some of the other sections of an empirical project. From clear topic sentences to supporting evidence, the skills you have been learning throughout your writing career are easily applicable here. The major difference is that instead of solely citing other sources, you are the one providing the evidence. You are producing new knowledge and questions. Be proud!

  • Incorporating your Data. Authored by : Sarah Wilson & Trey Bagwell . Provided by : University of Mississippi. Project : WRIT 250 Committee OER Project. License : CC BY-SA: Attribution-ShareAlike

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

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General Education

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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)

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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.

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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.

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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 !

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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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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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Methodology

  • How to Do Thematic Analysis | Step-by-Step Guide & Examples

How to Do Thematic Analysis | Step-by-Step Guide & Examples

Published on September 6, 2019 by Jack Caulfield . Revised on June 22, 2023.

Thematic analysis is a method of analyzing qualitative data . It is usually applied to a set of texts, such as an interview or transcripts . The researcher closely examines the data to identify common themes – topics, ideas and patterns of meaning that come up repeatedly.

There are various approaches to conducting thematic analysis, but the most common form follows a six-step process: familiarization, coding, generating themes, reviewing themes, defining and naming themes, and writing up. Following this process can also help you avoid confirmation bias when formulating your analysis.

This process was originally developed for psychology research by Virginia Braun and Victoria Clarke . However, thematic analysis is a flexible method that can be adapted to many different kinds of research.

Table of contents

When to use thematic analysis, different approaches to thematic analysis, step 1: familiarization, step 2: coding, step 3: generating themes, step 4: reviewing themes, step 5: defining and naming themes, step 6: writing up, other interesting articles.

Thematic analysis is a good approach to research where you’re trying to find out something about people’s views, opinions, knowledge, experiences or values from a set of qualitative data – for example, interview transcripts , social media profiles, or survey responses .

Some types of research questions you might use thematic analysis to answer:

  • How do patients perceive doctors in a hospital setting?
  • What are young women’s experiences on dating sites?
  • What are non-experts’ ideas and opinions about climate change?
  • How is gender constructed in high school history teaching?

To answer any of these questions, you would collect data from a group of relevant participants and then analyze it. Thematic analysis allows you a lot of flexibility in interpreting the data, and allows you to approach large data sets more easily by sorting them into broad themes.

However, it also involves the risk of missing nuances in the data. Thematic analysis is often quite subjective and relies on the researcher’s judgement, so you have to reflect carefully on your own choices and interpretations.

Pay close attention to the data to ensure that you’re not picking up on things that are not there – or obscuring things that are.

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Once you’ve decided to use thematic analysis, there are different approaches to consider.

There’s the distinction between inductive and deductive approaches:

  • An inductive approach involves allowing the data to determine your themes.
  • A deductive approach involves coming to the data with some preconceived themes you expect to find reflected there, based on theory or existing knowledge.

Ask yourself: Does my theoretical framework give me a strong idea of what kind of themes I expect to find in the data (deductive), or am I planning to develop my own framework based on what I find (inductive)?

There’s also the distinction between a semantic and a latent approach:

  • A semantic approach involves analyzing the explicit content of the data.
  • A latent approach involves reading into the subtext and assumptions underlying the data.

Ask yourself: Am I interested in people’s stated opinions (semantic) or in what their statements reveal about their assumptions and social context (latent)?

After you’ve decided thematic analysis is the right method for analyzing your data, and you’ve thought about the approach you’re going to take, you can follow the six steps developed by Braun and Clarke .

The first step is to get to know our data. It’s important to get a thorough overview of all the data we collected before we start analyzing individual items.

This might involve transcribing audio , reading through the text and taking initial notes, and generally looking through the data to get familiar with it.

Next up, we need to code the data. Coding means highlighting sections of our text – usually phrases or sentences – and coming up with shorthand labels or “codes” to describe their content.

Let’s take a short example text. Say we’re researching perceptions of climate change among conservative voters aged 50 and up, and we have collected data through a series of interviews. An extract from one interview looks like this:

In this extract, we’ve highlighted various phrases in different colors corresponding to different codes. Each code describes the idea or feeling expressed in that part of the text.

At this stage, we want to be thorough: we go through the transcript of every interview and highlight everything that jumps out as relevant or potentially interesting. As well as highlighting all the phrases and sentences that match these codes, we can keep adding new codes as we go through the text.

After we’ve been through the text, we collate together all the data into groups identified by code. These codes allow us to gain a a condensed overview of the main points and common meanings that recur throughout the data.

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Next, we look over the codes we’ve created, identify patterns among them, and start coming up with themes.

Themes are generally broader than codes. Most of the time, you’ll combine several codes into a single theme. In our example, we might start combining codes into themes like this:

At this stage, we might decide that some of our codes are too vague or not relevant enough (for example, because they don’t appear very often in the data), so they can be discarded.

Other codes might become themes in their own right. In our example, we decided that the code “uncertainty” made sense as a theme, with some other codes incorporated into it.

Again, what we decide will vary according to what we’re trying to find out. We want to create potential themes that tell us something helpful about the data for our purposes.

Now we have to make sure that our themes are useful and accurate representations of the data. Here, we return to the data set and compare our themes against it. Are we missing anything? Are these themes really present in the data? What can we change to make our themes work better?

If we encounter problems with our themes, we might split them up, combine them, discard them or create new ones: whatever makes them more useful and accurate.

For example, we might decide upon looking through the data that “changing terminology” fits better under the “uncertainty” theme than under “distrust of experts,” since the data labelled with this code involves confusion, not necessarily distrust.

Now that you have a final list of themes, it’s time to name and define each of them.

Defining themes involves formulating exactly what we mean by each theme and figuring out how it helps us understand the data.

Naming themes involves coming up with a succinct and easily understandable name for each theme.

For example, we might look at “distrust of experts” and determine exactly who we mean by “experts” in this theme. We might decide that a better name for the theme is “distrust of authority” or “conspiracy thinking”.

Finally, we’ll write up our analysis of the data. Like all academic texts, writing up a thematic analysis requires an introduction to establish our research question, aims and approach.

We should also include a methodology section, describing how we collected the data (e.g. through semi-structured interviews or open-ended survey questions ) and explaining how we conducted the thematic analysis itself.

The results or findings section usually addresses each theme in turn. We describe how often the themes come up and what they mean, including examples from the data as evidence. Finally, our conclusion explains the main takeaways and shows how the analysis has answered our research question.

In our example, we might argue that conspiracy thinking about climate change is widespread among older conservative voters, point out the uncertainty with which many voters view the issue, and discuss the role of misinformation in respondents’ perceptions.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

  • Normal distribution
  • Measures of central tendency
  • Chi square tests
  • Confidence interval
  • Quartiles & Quantiles
  • Cluster sampling
  • Stratified sampling
  • Discourse analysis
  • Cohort study
  • Peer review
  • Ethnography

Research bias

  • Implicit bias
  • Cognitive bias
  • Conformity bias
  • Hawthorne effect
  • Availability heuristic
  • Attrition bias
  • Social desirability bias

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Home — Essay Samples — Information Science and Technology — Data Analysis — Research of Data Analysis and Different Types of Analysis

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Research of Data Analysis and Different Types of Analysis

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Published: Sep 20, 2018

Words: 1171 | Pages: 2 | 6 min read

Table of contents

Introduction, types of anaysis.

  • To think in terms of significant tables that the data permit.
  • To examine carefully the statement of problem and earlier analysis and to study the original records of data.
  • To get away from the data to think about the problem in layman’s terms or to actually discuss the problems with others.
  • To attack the data by making various statistical calculations. Any of these approaches can be used to start analysis of data. The data analysis strategy is influenced by factors like the type of data, the research design researcher’s qualifications and assumptions underlying a statistical technique.

Data requirements

Data processing, data cleaning.

Should follow an “upside down” triangle format, meaning, the writer should start off broad and introduce the text and author or topic being discussed, and then get more specific to the thesis statement.

Provides a foundational overview, outlining the historical context and introducing key information that will be further explored in the essay, setting the stage for the argument to follow.

The topic sentence serves as the main point or focus of a paragraph in an essay, summarizing the key idea that will be discussed in that paragraph.

The body of each paragraph builds an argument in support of the topic sentence, citing information from sources as evidence.

After each piece of evidence is provided, the author should explain HOW and WHY the evidence supports the claim.

Should follow a right side up triangle format, meaning, specifics should be mentioned first such as restating the thesis, and then get more broad about the topic at hand. Lastly, leave the reader with something to think about and ponder once they are done reading.

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