analysis of data thesis example

11 Tips For Writing a Dissertation Data Analysis

Since the evolution of the fourth industrial revolution – the Digital World; lots of data have surrounded us. There are terabytes of data around us or in data centers that need to be processed and used. The data needs to be appropriately analyzed to process it, and Dissertation data analysis forms its basis. If data analysis is valid and free from errors, the research outcomes will be reliable and lead to a successful dissertation. 

Considering the complexity of many data analysis projects, it becomes challenging to get precise results if analysts are not familiar with data analysis tools and tests properly. The analysis is a time-taking process that starts with collecting valid and relevant data and ends with the demonstration of error-free results.

So, in today’s topic, we will cover the need to analyze data, dissertation data analysis, and mainly the tips for writing an outstanding data analysis dissertation. If you are a doctoral student and plan to perform dissertation data analysis on your data, make sure that you give this article a thorough read for the best tips!

What is Data Analysis in Dissertation?

Dissertation Data Analysis  is the process of understanding, gathering, compiling, and processing a large amount of data. Then identifying common patterns in responses and critically examining facts and figures to find the rationale behind those outcomes.

Data Analysis Tools

There are plenty of indicative tests used to analyze data and infer relevant results for the discussion part. Following are some tests  used to perform analysis of data leading to a scientific conclusion:

11 Most Useful Tips for Dissertation Data Analysis

Doctoral students need to perform dissertation data analysis and then dissertation to receive their degree. Many Ph.D. students find it hard to do dissertation data analysis because they are not trained in it.

1. Dissertation Data Analysis Services

The first tip applies to those students who can afford to look for help with their dissertation data analysis work. It’s a viable option, and it can help with time management and with building the other elements of the dissertation with much detail.

Dissertation Analysis services are professional services that help doctoral students with all the basics of their dissertation work, from planning, research and clarification, methodology, dissertation data analysis and review, literature review, and final powerpoint presentation.

One great reference for dissertation data analysis professional services is Statistics Solutions , they’ve been around for over 22 years helping students succeed in their dissertation work. You can find the link to their website here .

For a proper dissertation data analysis, the student should have a clear understanding and statistical knowledge. Through this knowledge and experience, a student can perform dissertation analysis on their own. 

Following are some helpful tips for writing a splendid dissertation data analysis:

2. Relevance of Collected Data

3. data analysis.

For analysis, it is crucial to use such methods that fit best with the types of data collected and the research objectives. Elaborate on these methods and the ones that justify your data collection methods thoroughly. Make sure to make the reader believe that you did not choose your method randomly. Instead, you arrived at it after critical analysis and prolonged research.

The overall objective of data analysis is to detect patterns and inclinations in data and then present the outcomes implicitly.  It helps in providing a solid foundation for critical conclusions and assisting the researcher to complete the dissertation proposal. 

4. Qualitative Data Analysis

Qualitative data refers to data that does not involve numbers. You are required to carry out an analysis of the data collected through experiments, focus groups, and interviews. This can be a time-taking process because it requires iterative examination and sometimes demanding the application of hermeneutics. Note that using qualitative technique doesn’t only mean generating good outcomes but to unveil more profound knowledge that can be transferrable.

Presenting qualitative data analysis in a dissertation  can also be a challenging task. It contains longer and more detailed responses. Placing such comprehensive data coherently in one chapter of the dissertation can be difficult due to two reasons. Firstly, we cannot figure out clearly which data to include and which one to exclude. Secondly, unlike quantitative data, it becomes problematic to present data in figures and tables. Making information condensed into a visual representation is not possible. As a writer, it is of essence to address both of these challenges.

This method involves analyzing qualitative data based on an argument that a researcher already defines. It’s a comparatively easy approach to analyze data. It is suitable for the researcher with a fair idea about the responses they are likely to receive from the questionnaires.

5. Quantitative Data Analysis

Quantitative data contains facts and figures obtained from scientific research and requires extensive statistical analysis. After collection and analysis, you will be able to conclude. Generic outcomes can be accepted beyond the sample by assuming that it is representative – one of the preliminary checkpoints to carry out in your analysis to a larger group. This method is also referred to as the “scientific method”, gaining its roots from natural sciences.

The Presentation of quantitative data  depends on the domain to which it is being presented. It is beneficial to consider your audience while writing your findings. Quantitative data for  hard sciences  might require numeric inputs and statistics. As for  natural sciences , such comprehensive analysis is not required.

6. Data Presentation Tools

Since large volumes of data need to be represented, it becomes a difficult task to present such an amount of data in coherent ways. To resolve this issue, consider all the available choices you have, such as tables, charts, diagrams, and graphs. 

Tables help in presenting both qualitative and quantitative data concisely. While presenting data, always keep your reader in mind. Anything clear to you may not be apparent to your reader. So, constantly rethink whether your data presentation method is understandable to someone less conversant with your research and findings. If the answer is “No”, you may need to rethink your Presentation. 

7. Include Appendix or Addendum

After presenting a large amount of data, your dissertation analysis part might get messy and look disorganized. Also, you would not be cutting down or excluding the data you spent days and months collecting. To avoid this, you should include an appendix part. 

The data you find hard to arrange within the text, include that in the  appendix part of a dissertation . And place questionnaires, copies of focus groups and interviews, and data sheets in the appendix. On the other hand, one must put the statistical analysis and sayings quoted by interviewees within the dissertation. 

8. Thoroughness of Data

Thoroughly demonstrate the ideas and critically analyze each perspective taking care of the points where errors can occur. Always make sure to discuss the anomalies and strengths of your data to add credibility to your research.

9. Discussing Data

Discussion of data involves elaborating the dimensions to classify patterns, themes, and trends in presented data. In addition, to balancing, also take theoretical interpretations into account. Discuss the reliability of your data by assessing their effect and significance. Do not hide the anomalies. While using interviews to discuss the data, make sure you use relevant quotes to develop a strong rationale. 

10. Findings and Results

Findings refer to the facts derived after the analysis of collected data. These outcomes should be stated; clearly, their statements should tightly support your objective and provide logical reasoning and scientific backing to your point. This part comprises of majority part of the dissertation. 

11. Connection with Literature Review

The role of data analytics at the senior management level, 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

Wrapping up.

Writing data analysis in the dissertation involves dedication, and its implementations demand sound knowledge and proper planning. Choosing your topic, gathering relevant data, analyzing it, presenting your data and findings correctly, discussing the results, connecting with the literature and conclusions are milestones in it. Among these checkpoints, the Data analysis stage is most important and requires a lot of keenness.

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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• GETTING STARTED
• Introduction
• FUNDAMENTALS

Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

In STAGE NINE: Data analysis , we discuss the data you will have collected during STAGE EIGHT: Data collection . However, before you collect your data, having followed the research strategy you set out in this STAGE SIX , it is useful to think about the data analysis techniques you may apply to your data when it is collected.

The statistical tests that are appropriate for your dissertation will depend on (a) the research questions/hypotheses you have set, (b) the research design you are using, and (c) the nature of your data. You should already been clear about your research questions/hypotheses from STAGE THREE: Setting research questions and/or hypotheses , as well as knowing the goal of your research design from STEP TWO: Research design in this STAGE SIX: Setting your research strategy . These two pieces of information - your research questions/hypotheses and research design - will let you know, in principle , the statistical tests that may be appropriate to run on your data in order to answer your research questions.

We highlight the words in principle and may because the most appropriate statistical test to run on your data not only depend on your research questions/hypotheses and research design, but also the nature of your data . As you should have identified in STEP THREE: Research methods , and in the article, Types of variables , in the Fundamentals part of Lærd Dissertation, (a) not all data is the same, and (b) not all variables are measured in the same way (i.e., variables can be dichotomous, ordinal or continuous). In addition, not all data is normal , nor is the data when comparing groups necessarily equal , terms we explain in the Data Analysis section in the Fundamentals part of Lærd Dissertation. As a result, you might think that running a particular statistical test is correct at this point of setting your research strategy (e.g., a statistical test called a dependent t-test ), based on the research questions/hypotheses you have set, but when you collect your data (i.e., during STAGE EIGHT: Data collection ), the data may fail certain assumptions that are important to such a statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Wilcoxon signed-rank test instead of a dependent t-test ).

At this stage in the dissertation process, it is important, or at the very least, useful to think about the data analysis techniques you may apply to your data when it is collected. We suggest that you do this for two reasons:

REASON A Supervisors sometimes expect you to know what statistical analysis you will perform at this stage of the dissertation process

This is not always the case, but if you have had to write a Dissertation Proposal or Ethics Proposal , there is sometimes an expectation that you explain the type of data analysis that you plan to carry out. An understanding of the data analysis that you will carry out on your data can also be an expected component of the Research Strategy chapter of your dissertation write-up (i.e., usually Chapter Three: Research Strategy ). Therefore, it is a good time to think about the data analysis process if you plan to start writing up this chapter at this stage.

REASON B It takes time to get your head around data analysis

When you come to analyse your data in STAGE NINE: Data analysis , you will need to think about (a) selecting the correct statistical tests to perform on your data, (b) running these tests on your data using a statistics package such as SPSS, and (c) learning how to interpret the output from such statistical tests so that you can answer your research questions or hypotheses. Whilst we show you how to do this for a wide range of scenarios in the in the Data Analysis section in the Fundamentals part of Lærd Dissertation, it can be a time consuming process. Unless you took an advanced statistics module/option as part of your degree (i.e., not just an introductory course to statistics, which are often taught in undergraduate and master?s degrees), it can take time to get your head around data analysis. Starting this process at this stage (i.e., STAGE SIX: Research strategy ), rather than waiting until you finish collecting your data (i.e., STAGE EIGHT: Data collection ) is a sensible approach.

Final thoughts...

Setting the research strategy for your dissertation required you to describe, explain and justify the research paradigm, quantitative research design, research method(s), sampling strategy, and approach towards research ethics and data analysis that you plan to follow, as well as determine how you will ensure the research quality of your findings so that you can effectively answer your research questions/hypotheses. However, from a practical perspective, just remember that the main goal of STAGE SIX: Research strategy is to have a clear research strategy that you can implement (i.e., operationalize ). After all, if you are unable to clearly follow your plan and carry out your research in the field, you will struggle to answer your research questions/hypotheses. Once you are sure that you have a clear plan, it is a good idea to take a step back, speak with your supervisor, and assess where you are before moving on to collect data. Therefore, when you are ready, proceed to STAGE SEVEN: Assessment point .

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How to Write a Dissertation Conclusion? | Tips & Examples

What is PhD Thesis Writing? | Beginner’s Guide

A data analysis dissertation is a complex and challenging project requiring significant time, effort, and expertise. Fortunately, it is possible to successfully complete a data analysis dissertation with careful planning and execution.

As a student, you must know how important it is to have a strong and well-written dissertation, especially regarding data analysis. Proper data analysis is crucial to the success of your research and can often make or break your dissertation.

To get a better understanding, you may review the data analysis dissertation examples listed below;

• Impact of Leadership Style on the Job Satisfaction of Nurses
• Effect of Brand Love on Consumer Buying Behaviour in Dietary Supplement Sector
• An Insight Into Alternative Dispute Resolution
• An Investigation of Cyberbullying and its Impact on Adolescent Mental Health in UK

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Types of data analysis for dissertation.

The various types of data Analysis in a Dissertation are as follows;

1.   Qualitative Data Analysis

Qualitative data analysis is a type of data analysis that involves analyzing data that cannot be measured numerically. This data type includes interviews, focus groups, and open-ended surveys. Qualitative data analysis can be used to identify patterns and themes in the data.

2.   Quantitative Data Analysis

Quantitative data analysis is a type of data analysis that involves analyzing data that can be measured numerically. This data type includes test scores, income levels, and crime rates. Quantitative data analysis can be used to test hypotheses and to look for relationships between variables.

3.   Descriptive Data Analysis

Descriptive data analysis is a type of data analysis that involves describing the characteristics of a dataset. This type of data analysis summarizes the main features of a dataset.

4.   Inferential Data Analysis

Inferential data analysis is a type of data analysis that involves making predictions based on a dataset. This type of data analysis can be used to test hypotheses and make predictions about future events.

5.   Exploratory Data Analysis

Exploratory data analysis is a type of data analysis that involves exploring a data set to understand it better. This type of data analysis can identify patterns and relationships in the data.

Time Period to Plan and Complete a Data Analysis Dissertation?

When planning dissertation data analysis, it is important to consider the dissertation methodology structure and time series analysis as they will give you an understanding of how long each stage will take. For example, using a qualitative research method, your data analysis will involve coding and categorizing your data.

This can be time-consuming, so allowing enough time in your schedule is important. Once you have coded and categorized your data, you will need to write up your findings. Again, this can take some time, so factor this into your schedule.

Finally, you will need to proofread and edit your dissertation before submitting it. All told, a data analysis dissertation can take anywhere from several weeks to several months to complete, depending on the project’s complexity. Therefore, starting planning early and allowing enough time in your schedule to complete the task is important.

Essential Strategies for Data Analysis Dissertation

A.   Planning

The first step in any dissertation is planning. You must decide what you want to write about and how you want to structure your argument. This planning will involve deciding what data you want to analyze and what methods you will use for a data analysis dissertation.

B.   Prototyping

Once you have a plan for your dissertation, it’s time to start writing. However, creating a prototype is important before diving head-first into writing your dissertation. A prototype is a rough draft of your argument that allows you to get feedback from your advisor and committee members. This feedback will help you fine-tune your argument before you start writing the final version of your dissertation.

C.   Executing

After you have created a plan and prototype for your data analysis dissertation, it’s time to start writing the final version. This process will involve collecting and analyzing data and writing up your results. You will also need to create a conclusion section that ties everything together.

D.   Presenting

The final step in acing your data analysis dissertation is presenting it to your committee. This presentation should be well-organized and professionally presented. During the presentation, you’ll also need to be ready to respond to questions concerning your dissertation.

Data Analysis Tools

Numerous suggestive tools are employed to assess the data and deduce pertinent findings for the discussion section. The tools used to analyze data and get a scientific conclusion are as follows:

a.     Excel

Excel is a spreadsheet program part of the Microsoft Office productivity software suite. Excel is a powerful tool that can be used for various data analysis tasks, such as creating charts and graphs, performing mathematical calculations, and sorting and filtering data.

b.     Google Sheets

Google Sheets is a free online spreadsheet application that is part of the Google Drive suite of productivity software. Google Sheets is similar to Excel in terms of functionality, but it also has some unique features, such as the ability to collaborate with other users in real-time.

c.     SPSS

SPSS is a statistical analysis software program commonly used in the social sciences. SPSS can be used for various data analysis tasks, such as hypothesis testing, factor analysis, and regression analysis.

d.     STATA

STATA is a statistical analysis software program commonly used in the sciences and economics. STATA can be used for data management, statistical modelling, descriptive statistics analysis, and data visualization tasks.

SAS is a commercial statistical analysis software program used by businesses and organizations worldwide. SAS can be used for predictive modelling, market research, and fraud detection.

R is a free, open-source statistical programming language popular among statisticians and data scientists. R can be used for tasks such as data wrangling, machine learning, and creating complex visualizations.

g.     Python

A variety of applications may be used using the distinctive programming language Python, including web development, scientific computing, and artificial intelligence. Python also has a number of modules and libraries that can be used for data analysis tasks, such as numerical computing, statistical modelling, and data visualization.

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Tips to Compose a Successful Data Analysis Dissertation

a.   Choose a Topic You’re Passionate About

The first step to writing a successful data analysis dissertation is to choose a topic you’re passionate about. Not only will this make the research and writing process more enjoyable, but it will also ensure that you produce a high-quality paper.

Choose a topic that is particular enough to be covered in your paper’s scope but not so specific that it will be challenging to obtain enough evidence to substantiate your arguments.

b.   Do Your Research

data analysis in research is an important part of academic writing. Once you’ve selected a topic, it’s time to begin your research. Be sure to consult with your advisor or supervisor frequently during this stage to ensure that you are on the right track. In addition to secondary sources such as books, journal articles, and reports, you should also consider conducting primary research through surveys or interviews. This will give you first-hand insights into your topic that can be invaluable when writing your paper.

c.   Develop a Strong Thesis Statement

After you’ve done your research, it’s time to start developing your thesis statement. It is arguably the most crucial part of your entire paper, so take care to craft a clear and concise statement that encapsulates the main argument of your paper.

Remember that your thesis statement should be arguable—that is, it should be capable of being disputed by someone who disagrees with your point of view. If your thesis statement is not arguable, it will be difficult to write a convincing paper.

d.   Write a Detailed Outline

Once you have developed a strong thesis statement, the next step is to write a detailed outline of your paper. This will offer you a direction to write in and guarantee that your paper makes sense from beginning to end.

Your outline should include an introduction, in which you state your thesis statement; several body paragraphs, each devoted to a different aspect of your argument; and a conclusion, in which you restate your thesis and summarize the main points of your paper.

e.   Write Your First Draft

With your outline in hand, it’s finally time to start writing your first draft. At this stage, don’t worry about perfecting your grammar or making sure every sentence is exactly right—focus on getting all of your ideas down on paper (or onto the screen). Once you have completed your first draft, you can revise it for style and clarity.

And there you have it! Following these simple tips can increase your chances of success when writing your data analysis dissertation. Just remember to start early, give yourself plenty of time to research and revise, and consult with your supervisor frequently throughout the process.

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Studying the above examples gives you valuable insight into the structure and content that should be included in your own data analysis dissertation. You can also learn how to effectively analyze and present your data and make a lasting impact on your readers.

In addition to being a useful resource for completing your dissertation, these examples can also serve as a valuable reference for future academic writing projects. By following these examples and understanding their principles, you can improve your data analysis skills and increase your chances of success in your academic career.

You may also contact Premier Dissertations to develop your data analysis dissertation.

For further assistance, some other resources in the dissertation writing section are shared below;

How Do You Select the Right Data Analysis

How to Write Data Analysis For A Dissertation?

How to Develop a Conceptual Framework in Dissertation?

What is a Hypothesis in a Dissertation?

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Quantitative Data Analysis 101

The lingo, methods and techniques, explained simply.

By: Derek Jansen (MBA)  and Kerryn Warren (PhD) | December 2020

Quantitative data analysis is one of those things that often strikes fear in students. It’s totally understandable – quantitative analysis is a complex topic, full of daunting lingo , like medians, modes, correlation and regression. Suddenly we’re all wishing we’d paid a little more attention in math class…

The good news is that while quantitative data analysis is a mammoth topic, gaining a working understanding of the basics isn’t that hard , even for those of us who avoid numbers and math . In this post, we’ll break quantitative analysis down into simple , bite-sized chunks so you can approach your research with confidence.

Overview: Quantitative Data Analysis 101

• What (exactly) is quantitative data analysis?
• When to use quantitative analysis
• How quantitative analysis works

The two “branches” of quantitative analysis

• Descriptive statistics 101
• Inferential statistics 101
• How to choose the right quantitative methods
• Recap & summary

What is quantitative data analysis?

Despite being a mouthful, quantitative data analysis simply means analysing data that is numbers-based – or data that can be easily “converted” into numbers without losing any meaning.

For example, category-based variables like gender, ethnicity, or native language could all be “converted” into numbers without losing meaning – for example, English could equal 1, French 2, etc.

This contrasts against qualitative data analysis, where the focus is on words, phrases and expressions that can’t be reduced to numbers. If you’re interested in learning about qualitative analysis, check out our post and video here .

What is quantitative analysis used for?

Quantitative analysis is generally used for three purposes.

• Firstly, it’s used to measure differences between groups . For example, the popularity of different clothing colours or brands.
• Secondly, it’s used to assess relationships between variables . For example, the relationship between weather temperature and voter turnout.
• And third, it’s used to test hypotheses in a scientifically rigorous way. For example, a hypothesis about the impact of a certain vaccine.

Again, this contrasts with qualitative analysis , which can be used to analyse people’s perceptions and feelings about an event or situation. In other words, things that can’t be reduced to numbers.

How does quantitative analysis work?

Well, since quantitative data analysis is all about analysing numbers , it’s no surprise that it involves statistics . Statistical analysis methods form the engine that powers quantitative analysis, and these methods can vary from pretty basic calculations (for example, averages and medians) to more sophisticated analyses (for example, correlations and regressions).

Sounds like gibberish? Don’t worry. We’ll explain all of that in this post. Importantly, you don’t need to be a statistician or math wiz to pull off a good quantitative analysis. We’ll break down all the technical mumbo jumbo in this post.

Need a helping hand?

As I mentioned, quantitative analysis is powered by statistical analysis methods . There are two main “branches” of statistical methods that are used – descriptive statistics and inferential statistics . In your research, you might only use descriptive statistics, or you might use a mix of both , depending on what you’re trying to figure out. In other words, depending on your research questions, aims and objectives . I’ll explain how to choose your methods later.

So, what are descriptive and inferential statistics?

Well, before I can explain that, we need to take a quick detour to explain some lingo. To understand the difference between these two branches of statistics, you need to understand two important words. These words are population and sample .

First up, population . In statistics, the population is the entire group of people (or animals or organisations or whatever) that you’re interested in researching. For example, if you were interested in researching Tesla owners in the US, then the population would be all Tesla owners in the US.

However, it’s extremely unlikely that you’re going to be able to interview or survey every single Tesla owner in the US. Realistically, you’ll likely only get access to a few hundred, or maybe a few thousand owners using an online survey. This smaller group of accessible people whose data you actually collect is called your sample .

So, to recap – the population is the entire group of people you’re interested in, and the sample is the subset of the population that you can actually get access to. In other words, the population is the full chocolate cake , whereas the sample is a slice of that cake.

So, why is this sample-population thing important?

Well, descriptive statistics focus on describing the sample , while inferential statistics aim to make predictions about the population, based on the findings within the sample. In other words, we use one group of statistical methods – descriptive statistics – to investigate the slice of cake, and another group of methods – inferential statistics – to draw conclusions about the entire cake. There I go with the cake analogy again…

With that out the way, let’s take a closer look at each of these branches in more detail.

Branch 1: Descriptive Statistics

Descriptive statistics serve a simple but critically important role in your research – to describe your data set – hence the name. In other words, they help you understand the details of your sample . Unlike inferential statistics (which we’ll get to soon), descriptive statistics don’t aim to make inferences or predictions about the entire population – they’re purely interested in the details of your specific sample .

When you’re writing up your analysis, descriptive statistics are the first set of stats you’ll cover, before moving on to inferential statistics. But, that said, depending on your research objectives and research questions , they may be the only type of statistics you use. We’ll explore that a little later.

So, what kind of statistics are usually covered in this section?

Some common statistical tests used in this branch include the following:

• Mean – this is simply the mathematical average of a range of numbers.
• Median – this is the midpoint in a range of numbers when the numbers are arranged in numerical order. If the data set makes up an odd number, then the median is the number right in the middle of the set. If the data set makes up an even number, then the median is the midpoint between the two middle numbers.
• Mode – this is simply the most commonly occurring number in the data set.
• In cases where most of the numbers are quite close to the average, the standard deviation will be relatively low.
• Conversely, in cases where the numbers are scattered all over the place, the standard deviation will be relatively high.
• Skewness . As the name suggests, skewness indicates how symmetrical a range of numbers is. In other words, do they tend to cluster into a smooth bell curve shape in the middle of the graph, or do they skew to the left or right?

Feeling a bit confused? Let’s look at a practical example using a small data set.

On the left-hand side is the data set. This details the bodyweight of a sample of 10 people. On the right-hand side, we have the descriptive statistics. Let’s take a look at each of them.

First, we can see that the mean weight is 72.4 kilograms. In other words, the average weight across the sample is 72.4 kilograms. Straightforward.

Next, we can see that the median is very similar to the mean (the average). This suggests that this data set has a reasonably symmetrical distribution (in other words, a relatively smooth, centred distribution of weights, clustered towards the centre).

In terms of the mode , there is no mode in this data set. This is because each number is present only once and so there cannot be a “most common number”. If there were two people who were both 65 kilograms, for example, then the mode would be 65.

Next up is the standard deviation . 10.6 indicates that there’s quite a wide spread of numbers. We can see this quite easily by looking at the numbers themselves, which range from 55 to 90, which is quite a stretch from the mean of 72.4.

And lastly, the skewness of -0.2 tells us that the data is very slightly negatively skewed. This makes sense since the mean and the median are slightly different.

As you can see, these descriptive statistics give us some useful insight into the data set. Of course, this is a very small data set (only 10 records), so we can’t read into these statistics too much. Also, keep in mind that this is not a list of all possible descriptive statistics – just the most common ones.

But why do all of these numbers matter?

While these descriptive statistics are all fairly basic, they’re important for a few reasons:

• Firstly, they help you get both a macro and micro-level view of your data. In other words, they help you understand both the big picture and the finer details.
• Secondly, they help you spot potential errors in the data – for example, if an average is way higher than you’d expect, or responses to a question are highly varied, this can act as a warning sign that you need to double-check the data.
• And lastly, these descriptive statistics help inform which inferential statistical techniques you can use, as those techniques depend on the skewness (in other words, the symmetry and normality) of the data.

Simply put, descriptive statistics are really important , even though the statistical techniques used are fairly basic. All too often at Grad Coach, we see students skimming over the descriptives in their eagerness to get to the more exciting inferential methods, and then landing up with some very flawed results.

Don’t be a sucker – give your descriptive statistics the love and attention they deserve!

Branch 2: Inferential Statistics

As I mentioned, while descriptive statistics are all about the details of your specific data set – your sample – inferential statistics aim to make inferences about the population . In other words, you’ll use inferential statistics to make predictions about what you’d expect to find in the full population.

What kind of predictions, you ask? Well, there are two common types of predictions that researchers try to make using inferential stats:

• Firstly, predictions about differences between groups – for example, height differences between children grouped by their favourite meal or gender.
• And secondly, relationships between variables – for example, the relationship between body weight and the number of hours a week a person does yoga.

In other words, inferential statistics (when done correctly), allow you to connect the dots and make predictions about what you expect to see in the real world population, based on what you observe in your sample data. For this reason, inferential statistics are used for hypothesis testing – in other words, to test hypotheses that predict changes or differences.

Of course, when you’re working with inferential statistics, the composition of your sample is really important. In other words, if your sample doesn’t accurately represent the population you’re researching, then your findings won’t necessarily be very useful.

For example, if your population of interest is a mix of 50% male and 50% female , but your sample is 80% male , you can’t make inferences about the population based on your sample, since it’s not representative. This area of statistics is called sampling, but we won’t go down that rabbit hole here (it’s a deep one!) – we’ll save that for another post .

What statistics are usually used in this branch?

There are many, many different statistical analysis methods within the inferential branch and it’d be impossible for us to discuss them all here. So we’ll just take a look at some of the most common inferential statistical methods so that you have a solid starting point.

First up are T-Tests . T-tests compare the means (the averages) of two groups of data to assess whether they’re statistically significantly different. In other words, do they have significantly different means, standard deviations and skewness.

This type of testing is very useful for understanding just how similar or different two groups of data are. For example, you might want to compare the mean blood pressure between two groups of people – one that has taken a new medication and one that hasn’t – to assess whether they are significantly different.

Kicking things up a level, we have ANOVA, which stands for “analysis of variance”. This test is similar to a T-test in that it compares the means of various groups, but ANOVA allows you to analyse multiple groups , not just two groups So it’s basically a t-test on steroids…

Next, we have correlation analysis . This type of analysis assesses the relationship between two variables. In other words, if one variable increases, does the other variable also increase, decrease or stay the same. For example, if the average temperature goes up, do average ice creams sales increase too? We’d expect some sort of relationship between these two variables intuitively , but correlation analysis allows us to measure that relationship scientifically .

Lastly, we have regression analysis – this is quite similar to correlation in that it assesses the relationship between variables, but it goes a step further to understand cause and effect between variables, not just whether they move together. In other words, does the one variable actually cause the other one to move, or do they just happen to move together naturally thanks to another force? Just because two variables correlate doesn’t necessarily mean that one causes the other.

Stats overload…

I hear you. To make this all a little more tangible, let’s take a look at an example of a correlation in action.

Here’s a scatter plot demonstrating the correlation (relationship) between weight and height. Intuitively, we’d expect there to be some relationship between these two variables, which is what we see in this scatter plot. In other words, the results tend to cluster together in a diagonal line from bottom left to top right.

As I mentioned, these are are just a handful of inferential techniques – there are many, many more. Importantly, each statistical method has its own assumptions and limitations .

For example, some methods only work with normally distributed (parametric) data, while other methods are designed specifically for non-parametric data. And that’s exactly why descriptive statistics are so important – they’re the first step to knowing which inferential techniques you can and can’t use.

How to choose the right analysis method

To choose the right statistical methods, you need to think about two important factors :

• The type of quantitative data you have (specifically, level of measurement and the shape of the data). And,
• Your research questions and hypotheses

Let’s take a closer look at each of these.

Factor 1 – Data type

The first thing you need to consider is the type of data you’ve collected (or the type of data you will collect). By data types, I’m referring to the four levels of measurement – namely, nominal, ordinal, interval and ratio. If you’re not familiar with this lingo, check out the video below.

Why does this matter?

Well, because different statistical methods and techniques require different types of data. This is one of the “assumptions” I mentioned earlier – every method has its assumptions regarding the type of data.

For example, some techniques work with categorical data (for example, yes/no type questions, or gender or ethnicity), while others work with continuous numerical data (for example, age, weight or income) – and, of course, some work with multiple data types.

If you try to use a statistical method that doesn’t support the data type you have, your results will be largely meaningless . So, make sure that you have a clear understanding of what types of data you’ve collected (or will collect). Once you have this, you can then check which statistical methods would support your data types here .

If you haven’t collected your data yet, you can work in reverse and look at which statistical method would give you the most useful insights, and then design your data collection strategy to collect the correct data types.

Another important factor to consider is the shape of your data . Specifically, does it have a normal distribution (in other words, is it a bell-shaped curve, centred in the middle) or is it very skewed to the left or the right? Again, different statistical techniques work for different shapes of data – some are designed for symmetrical data while others are designed for skewed data.

This is another reminder of why descriptive statistics are so important – they tell you all about the shape of your data.

Factor 2: Your research questions

The next thing you need to consider is your specific research questions, as well as your hypotheses (if you have some). The nature of your research questions and research hypotheses will heavily influence which statistical methods and techniques you should use.

If you’re just interested in understanding the attributes of your sample (as opposed to the entire population), then descriptive statistics are probably all you need. For example, if you just want to assess the means (averages) and medians (centre points) of variables in a group of people.

On the other hand, if you aim to understand differences between groups or relationships between variables and to infer or predict outcomes in the population, then you’ll likely need both descriptive statistics and inferential statistics.

So, it’s really important to get very clear about your research aims and research questions, as well your hypotheses – before you start looking at which statistical techniques to use.

Never shoehorn a specific statistical technique into your research just because you like it or have some experience with it. Your choice of methods must align with all the factors we’ve covered here.

Time to recap…

You’re still with me? That’s impressive. We’ve covered a lot of ground here, so let’s recap on the key points:

• Quantitative data analysis is all about  analysing number-based data  (which includes categorical and numerical data) using various statistical techniques.
• The two main  branches  of statistics are  descriptive statistics  and  inferential statistics . Descriptives describe your sample, whereas inferentials make predictions about what you’ll find in the population.
• Common  descriptive statistical methods include  mean  (average),  median , standard  deviation  and  skewness .
• Common  inferential statistical methods include  t-tests ,  ANOVA ,  correlation  and  regression  analysis.
• To choose the right statistical methods and techniques, you need to consider the  type of data you’re working with , as well as your  research questions  and hypotheses.

Psst... there’s more!

This post was based on one of our popular Research Bootcamps . If you're working on a research project, you'll definitely want to check this out ...

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77 Comments

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I am doing a quality improvement project to determine if the implementation of a protocol will change prescribing habits. Would this be a t-test?

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Do female employees report higher job satisfaction than male employees with similar job descriptions across the South African telecommunications sector? – I though that maybe Chi Square could be used here. – Is there a gender difference in talented employees’ actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – Is there a gender difference in the cost of actual turnover decisions across the South African telecommunications sector? T-tests or Correlation in this one. – What practical recommendations can be made to the management of South African telecommunications companies on leveraging gender to mitigate employee turnover decisions?

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Data analysis thesis

What analysis methods are there.

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Which data analysis method should I use?

Where to place the data analysis section in your thesis, example of thesis data analysis, want to have your thesis checked.

You collect a large amount of data for your thesis research. At some point, it is time for the next step: the data analysis for your thesis. You will translate the data found from your research into concrete results. This will allow you to answer the research question. But first, you need to understand the following: Which data analysis methods are there? What else should you pay attention to in the data analysis section of your thesis? 

There are various data analysis methods. Which one to use depends on the type of research you've done. You approach the analysis of quantitative data differently from that of qualitative research data. 

Numerical data analyses

In quantitative research, your data can be expressed in numbers, percentages, averages, etc. You often analyze this data using statistics. 

Based on your research question, you determine which statistical test you need. For example, for a comparison of two groups, you need a different type of test than when you compare three groups with each other. 

Some examples of analytical methods for quantitative research include: 

● means and standard deviations; 

● correlation coefficients indicating the relationship between two variables; 

● statistical tests to determine whether a hypothesis is true or false (in the case of a relationship between two variables or the difference between two groups);

● regression analysis to determine the relationship between more than two variables or the difference between more than two groups. 

Numerical data must first be entered in a program such as Excel or SPSS. This is necessary for you to be able to properly test the data. Make sure all information is in the correct fields, so you can conduct the data analysis correctly. 

Data analysis in qualitative research 

If you have done qualitative research, your results are not numerical. For example, you have interviews, observations, images or texts to analyze. How you analyze the data depends on what kind of data you have and how your research method works. 

data analysis in qualitative research may include, for example:

● coding interviews; 

● encrypting data; 

● analysis according to a more commonly used method or template (also discuss this in your theoretical framework); 

● a comparison of the collected data. 

In the method chapter in your thesis, you indicate how you have analyzed your data. In this chapter, you will discuss, among other things, the research design, the participants and the method for data collection. You also state how you proceeded with the data analysis. Also, you must indicate the software you have processed your data with. Did you use SPSS, Excel or another program for this?

Incidentally, for some study programs, you do not have to devote a separate heading to data analysis in your thesis. Instead, you may have to devote a separate heading to this in your results chapter. Check with your thesis supervisor about what applies in your case. 

The outcome of the data analysis is discussed in the results chapter. Later, in the conclusion, you will arrive at answers to your research question based on these results. 

We would like to show you what the data analysis in your thesis can look like. Below is an example of data analysis for quantitative and qualitative research. 

Example data analysis: quantitative research

For the data analysis, we first processed the answers from the survey in SPSS. The data was then analyzed using regression analysis. This made it possible to determine to what extent the outcomes of the three groups of participants in the experiment differed significantly from each other. 

Example data analysis: qualitative research 

For the data analysis, we first transcribed the recorded GP-patient conversations. Subsequently, according to the model, XX codes were assigned to the moments in these conversations when the GP showed his authority. The data was then analyzed in SPSS based on these codes. Regression analysis was used to answer the research question. 

More examples?

Are you curious about how other students write about their data analysis in their thesis? View several thesis examples. Look specifically for an example from your academic field. That will give you a good idea of ​​how other students write this part of their thesis.

Want to have your thesis checked? 

Writing a thesis is quite a big project and there is a lot that you have to pay attention to. A language error or poorly-written sentence might find its way into your thesis. Don't worry: our editors will help you filter out any errors. 

You can have your thesis checked for language, structure and/or common thread. Then, you can be sure that you hand in your thesis with clear sentences, a clear structure and correct spelling. 

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Writing the Data Analysis Chapter(s): Results and Evidence

Posted by Rene Tetzner | Oct 19, 2021 | PhD Success | 0 |

4.4 Writing the Data Analysis Chapter(s): Results and Evidence

Unlike the introduction, literature review and methodology chapter(s), your results chapter(s) will need to be written for the first time as you draft your thesis even if you submitted a proposal, though this part of your thesis will certainly build upon the preceding chapters. You should have carefully recorded and collected the data (test results, participant responses, computer print outs, observations, transcriptions, notes of various kinds etc.) from your research as you conducted it, so now is the time to review, organise and analyse the data. If your study is quantitative in nature, make sure that you know what all the numbers mean and that you consider them in direct relation to the topic, problem or phenomenon you are investigating, and especially in relation to your research questions and hypotheses. You may find that you require the services of a statistician to help make sense of the data, in which case, obtaining that help sooner rather than later is advisable, because you need to understand your results thoroughly before you can write about them. If, on the other hand, your study is qualitative, you will need to read through the data you have collected several times to become familiar with them both as a whole and in detail so that you can establish important themes, patterns and categories. Remember that ‘qualitative analysis is a creative process and requires thoughtful judgments about what is significant and meaningful in the data’ (Roberts, 2010, p.174; see also Miles & Huberman, 1994) – judgements that often need to be made before the findings can be effectively analysed and presented. If you are combining methodologies in your research, you will also need to consider relationships between the results obtained from the different methods, integrating all the data you have obtained and discovering how the results of one approach support or correlate with the results of another. Ideally, you will have taken careful notes recording your initial thoughts and analyses about the sources you consulted and the results and evidence provided by particular methods and instruments as you put them into practice (as suggested in Sections 2.1.2 and 2.1.4), as these will prove helpful while you consider how best to present your results in your thesis.

Although the ways in which to present and organise the results of doctoral research differ markedly depending on the nature of the study and its findings, as on author and committee preferences and university and department guidelines, there are several basic principles that apply to virtually all theses. First and foremost is the need to present the results of your research both clearly and concisely, and in as objective and factual a manner as possible. There will be time and space to elaborate and interpret your results and speculate on their significance and implications in the final discussion chapter(s) of your thesis, but, generally speaking, such reflection on the meaning of the results should be entirely separate from the factual report of your research findings. There are exceptions, of course, and some candidates, supervisors and departments may prefer the factual presentation and interpretive discussion of results to be blended, just as some thesis topics may demand such treatment, but this is rare and best avoided unless there are persuasive reasons to avoid separating the facts from your thoughts about them. If you do find that you need to blend facts and interpretation in reporting your results, make sure that your language leaves no doubt about the line between the two: words such as ‘seems,’ ‘appears,’ ‘may,’ ‘might,’ probably’ and the like will effectively distinguish analytical speculation from more factual reporting (see also Section 4.5).

You need not dedicate much space in this part of the thesis to the methods you used to arrive at your results because these have already been described in your methodology chapter(s), but they can certainly be revisited briefly to clarify or lend structure to your report. Results are most often presented in a straightforward narrative form which is often supplemented by tables and perhaps by figures such as graphs, charts and maps. An effective approach is to decide immediately which information would be best included in tables and figures, and then to prepare those tables and figures before you begin writing the text for the chapter (see Section 4.4.1 on designing effective tables and figures). Arranging your data into the visually immediate formats provided by tables and figures can, for one, produce interesting surprises by enabling you to see trends and details that you may not have noticed previously, and writing the report of your results will prove easier when you have the tables and figures to work with just as your readers ultimately will. In addition, while the text of the results chapter(s) should certainly highlight the most notable data included in tables and figures, it is essential not to repeat information unnecessarily, so writing with the tables and figures already constructed will help you keep repetition to a minimum. Finally, writing about the tables and figures you create will help you test their clarity and effectiveness for your readers, and you can make any necessary adjustments to the tables and figures as you work. Be sure to refer to each table and figure by number in your text and to make it absolutely clear what you want your readers to see or understand in the table or figure (e.g., ‘see Table 1 for the scores’ and ‘Figure 2 shows this relationship’).

Beyond combining textual narration with the data presented in tables and figures, you will need to organise your report of the results in a manner best suited to the material. You may choose to arrange the presentation of your results chronologically or in a hierarchical order that represents their importance; you might subdivide your results into sections (or separate chapters if there is a great deal of information to accommodate) focussing on the findings of different kinds of methodology (quantitative versus qualitative, for instance) or of different tests, trials, surveys, reviews, case studies and so on; or you may want to create sections (or chapters) focussing on specific themes, patterns or categories or on your research questions and/or hypotheses. The last approach allows you to cluster results that relate to a particular question or hypothesis into a single section and can be particularly useful because it provides cohesion for the thesis as a whole and forces you to focus closely on the issues central to the topic, problem or phenomenon you are investigating. You will, for instance, be able to refer back to the questions and hypotheses presented in your introduction (see Section 3.1), to answer the questions and confirm or dismiss the hypotheses and to anticipate in relation to those questions and hypotheses the discussion and interpretation of your findings that will appear in the next part of the thesis (see Section 4.5). Less effective is an approach that organises the presentation of results according to the items of a survey or questionnaire, because these lend the structure of the instrument used to the results instead of connecting those results directly to the aims, themes and argument of your thesis, but such an organisation can certainly be an important early step in your analysis of the findings and might even be valid for the final thesis if, for instance, your work focuses on developing the instrument involved.

The results generated by doctoral research are unique, and this book cannot hope to outline all the possible approaches for presenting the data and analyses that constitute research results, but it is essential that you devote considerable thought and special care to the way in which you structure the report of your results (Section 6.1 on headings may prove helpful). Whatever structure you choose should accurately reflect the nature of your results and highlight their most important and interesting trends, and it should also effectively allow you (in the next part of the thesis) to discuss and speculate upon your findings in ways that will test the premises of your study, work well in the overall argument of your thesis and lead to significant implications for your research. Regardless of how you organise the main body of your results chapter(s), however, you should include a final paragraph (or more than one paragraph if necessary) that briefly summarises and explains the key results and also guides the reader on to the discussion and interpretation of those results in the following chapter(s).

Why PhD Success?

To Graduate Successfully

This article is part of a book called "PhD Success" which focuses on the writing process of a phd thesis, with its aim being to provide sound practices and principles for reporting and formatting in text the methods, results and discussion of even the most innovative and unique research in ways that are clear, correct, professional and persuasive.

The assumption of the book is that the doctoral candidate reading it is both eager to write and more than capable of doing so, but nonetheless requires information and guidance on exactly what he or she should be writing and how best to approach the task. The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples.

The basic components of a doctoral thesis are outlined and described, as are the elements of complete and accurate scholarly references, and detailed descriptions of writing practices are clarified through the use of numerous examples. PhD Success provides guidance for students familiar with English and the procedures of English universities, but it also acknowledges that many theses in the English language are now written by candidates whose first language is not English, so it carefully explains the scholarly styles, conventions and standards expected of a successful doctoral thesis in the English language.

Individual chapters of this book address reflective and critical writing early in the thesis process; working successfully with thesis supervisors and benefiting from commentary and criticism; drafting and revising effective thesis chapters and developing an academic or scientific argument; writing and formatting a thesis in clear and correct scholarly English; citing, quoting and documenting sources thoroughly and accurately; and preparing for and excelling in thesis meetings and examinations. 

Completing a doctoral thesis successfully requires long and penetrating thought, intellectual rigour and creativity, original research and sound methods (whether established or innovative), precision in recording detail and a wide-ranging thoroughness, as much perseverance and mental toughness as insight and brilliance, and, no matter how many helpful writing guides are consulted, a great deal of hard work over a significant period of time. Writing a thesis can be an enjoyable as well as a challenging experience, however, and even if it is not always so, the personal and professional rewards of achieving such an enormous goal are considerable, as all doctoral candidates no doubt realise, and will last a great deal longer than any problems that may be encountered during the process.

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Rene Tetzner

Rene Tetzner's blog posts dedicated to academic writing. Although the focus is on How To Write a Doctoral Thesis, many other important aspects of research-based writing, editing and publishing are addressed in helpful detail.

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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.

How do I make a data analysis for my bachelor, master or PhD thesis?

A data analysis is an evaluation of formal data to gain knowledge for the bachelor’s, master’s or doctoral thesis. The aim is to identify patterns in the data, i.e. regularities, irregularities or at least anomalies.

Data can come in many forms, from numbers to the extensive descriptions of objects. As a rule, this data is always in numerical form such as time series or numerical sequences or statistics of all kinds. However, statistics are already processed data.

Data analysis requires some creativity because the solution is usually not obvious. After all, no one has conducted an analysis like this before, or at least you haven't found anything about it in the literature.

The results of a data analysis are answers to initial questions and detailed questions. The answers are numbers and graphics and the interpretation of these numbers and graphics.

What are the advantages of data analysis compared to other methods?

• Numbers are universal
• The data is tangible.
• There are algorithms for calculations and it is easier than a text evaluation.
• The addressees quickly understand the results.
• You can really do magic and impress the addressees.
• It’s easier to visualize the results.

What are the disadvantages of data analysis?

• Garbage in, garbage out. If the quality of the data is poor, it’s impossible to obtain reliable results.
• The dependency in data retrieval can be quite annoying. Here are some tips for attracting participants for a survey.
• You have to know or learn methods or find someone who can help you.
• Mistakes can be devastating.
• Missing substance can be detected quickly.
• Pictures say more than a thousand words. Therefore, if you can’t fill the pages with words, at least throw in graphics. However, usually only the words count.

Under what conditions can or should I conduct a data analysis?

• If I have to.
• You must be able to get the right data.
• If I can perform the calculations myself or at least understand, explain and repeat the calculated evaluations of others.
• You want a clear personal contribution right from the start.

How do I create the evaluation design for the data analysis?

The most important thing is to ask the right questions, enough questions and also clearly formulated questions. Here are some techniques for asking the right questions:

Good formulation: What is the relationship between Alpha and Beta?

Poor formulation: How are Alpha and Beta related?

Now it’s time for the methods for the calculation. There are dozens of statistical methods, but as always, most calculations can be done with only a handful of statistical methods.

• Which detailed questions can be formulated as the research question?
• What data is available? In what format? How is the data prepared?
• Which key figures allow statements?
• What methods are available to calculate such indicators? Do my details match? By type (scales), by size (number of records).
• Do I not need to have a lot of data for a data analysis?

It depends on the media, the questions and the methods I want to use.

A fixed rule is that I need at least 30 data sets for a statistical analysis in order to be able to make representative statements about the population. So statistically it doesn't matter if I have 30 or 30 million records. That's why statistics were invented...

What mistakes do I need to watch out for?

• Don't do the analysis at the last minute.
• Formulate questions and hypotheses for evaluation BEFORE data collection!
• Stay persistent, keep going.
• Leave the results for a while then revise them.
• You have to combine theory and the state of research with your results.
• You must have the time under control

Which tools can I use?

You can use programs of all kinds for calculations. But asking questions is your most powerful aide.

Who can legally help me with a data analysis?

The great intellectual challenge is to develop the research design, to obtain the data and to interpret the results in the end.

Am I allowed to let others perform the calculations?

That's a thing. In the end, every program is useful. If someone else is operating a program, then they can simply be seen as an extension of the program. But this is a comfortable view... Of course, it’s better if you do your own calculations.

A good compromise is to find some help, do a practical calculation then follow the calculation steps meticulously so next time you can do the math yourself. Basically, this functions as a permitted training. One can then justify each step of the calculation in the defense.

What's the best place to start?

Clearly with the detailed questions and hypotheses. These two guide the entire data analysis. So formulate as many detailed questions as possible to answer your main question or research question. You can find detailed instructions and examples for the formulation of these so-called detailed questions in the Thesis Guide.

How does the Aristolo Guide help with data evaluation for the bachelor’s or master’s thesis or dissertation?

The Thesis Guide or Dissertation Guide has instructions for data collection, data preparation, data analysis and interpretation. The guide can also teach you how to formulate questions and answer them with data to create your own experiment. We also have many templates for questionnaires and analyses of all kinds. Good luck writing your text! Silvio and the Aristolo Team PS: Check out the Thesis-ABC and the Thesis Guide for writing a bachelor or master thesis in 31 days.

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What is a thesis | A Complete Guide with Examples

Table of Contents

A thesis is a comprehensive academic paper based on your original research that presents new findings, arguments, and ideas of your study. It’s typically submitted at the end of your master’s degree or as a capstone of your bachelor’s degree.

However, writing a thesis can be laborious, especially for beginners. From the initial challenge of pinpointing a compelling research topic to organizing and presenting findings, the process is filled with potential pitfalls.

Therefore, to help you, this guide talks about what is a thesis. Additionally, it offers revelations and methodologies to transform it from an overwhelming task to a manageable and rewarding academic milestone.

What is a thesis?

A thesis is an in-depth research study that identifies a particular topic of inquiry and presents a clear argument or perspective about that topic using evidence and logic.

Writing a thesis showcases your ability of critical thinking, gathering evidence, and making a compelling argument. Integral to these competencies is thorough research, which not only fortifies your propositions but also confers credibility to your entire study.

Furthermore, there's another phenomenon you might often confuse with the thesis: the ' working thesis .' However, they aren't similar and shouldn't be used interchangeably.

A working thesis, often referred to as a preliminary or tentative thesis, is an initial version of your thesis statement. It serves as a draft or a starting point that guides your research in its early stages.

As you research more and gather more evidence, your initial thesis (aka working thesis) might change. It's like a starting point that can be adjusted as you learn more. It's normal for your main topic to change a few times before you finalize it.

While a thesis identifies and provides an overarching argument, the key to clearly communicating the central point of that argument lies in writing a strong thesis statement.

What is a thesis statement?

A strong thesis statement (aka thesis sentence) is a concise summary of the main argument or claim of the paper. It serves as a critical anchor in any academic work, succinctly encapsulating the primary argument or main idea of the entire paper.

Typically found within the introductory section, a strong thesis statement acts as a roadmap of your thesis, directing readers through your arguments and findings. By delineating the core focus of your investigation, it offers readers an immediate understanding of the context and the gravity of your study.

Furthermore, an effectively crafted thesis statement can set forth the boundaries of your research, helping readers anticipate the specific areas of inquiry you are addressing.

Different types of thesis statements

A good thesis statement is clear, specific, and arguable. Therefore, it is necessary for you to choose the right type of thesis statement for your academic papers.

Thesis statements can be classified based on their purpose and structure. Here are the primary types of thesis statements:

Argumentative (or Persuasive) thesis statement

Purpose : To convince the reader of a particular stance or point of view by presenting evidence and formulating a compelling argument.

Example : Reducing plastic use in daily life is essential for environmental health.

Analytical thesis statement

Purpose : To break down an idea or issue into its components and evaluate it.

Example : By examining the long-term effects, social implications, and economic impact of climate change, it becomes evident that immediate global action is necessary.

Expository (or Descriptive) thesis statement

Purpose : To explain a topic or subject to the reader.

Example : The Great Depression, spanning the 1930s, was a severe worldwide economic downturn triggered by a stock market crash, bank failures, and reduced consumer spending.

Cause and effect thesis statement

Purpose : To demonstrate a cause and its resulting effect.

Example : Overuse of smartphones can lead to impaired sleep patterns, reduced face-to-face social interactions, and increased levels of anxiety.

Compare and contrast thesis statement

Purpose : To highlight similarities and differences between two subjects.

Example : "While both novels '1984' and 'Brave New World' delve into dystopian futures, they differ in their portrayal of individual freedom, societal control, and the role of technology."

When you write a thesis statement , it's important to ensure clarity and precision, so the reader immediately understands the central focus of your work.

What is the difference between a thesis and a thesis statement?

While both terms are frequently used interchangeably, they have distinct meanings.

A thesis refers to the entire research document, encompassing all its chapters and sections. In contrast, a thesis statement is a brief assertion that encapsulates the central argument of the research.

Here’s an in-depth differentiation table of a thesis and a thesis statement.

Now, to craft a compelling thesis, it's crucial to adhere to a specific structure. Let’s break down these essential components that make up a thesis structure

15 components of a thesis structure

Navigating a thesis can be daunting. However, understanding its structure can make the process more manageable.

Here are the key components or different sections of a thesis structure:

Your thesis begins with the title page. It's not just a formality but the gateway to your research.

Here, you'll prominently display the necessary information about you (the author) and your institutional details.

• Title of your thesis
• Your full name
• Your department
• Your institution and degree program
• Your submission date
• Your Supervisor's name (in some cases)
• Your Department or faculty (in some cases)
• Your University's logo (in some cases)
• Your Student ID (in some cases)

In a concise manner, you'll have to summarize the critical aspects of your research in typically no more than 200-300 words.

This includes the problem statement, methodology, key findings, and conclusions. For many, the abstract will determine if they delve deeper into your work, so ensure it's clear and compelling.

Acknowledgments

Research is rarely a solitary endeavor. In the acknowledgments section, you have the chance to express gratitude to those who've supported your journey.

This might include advisors, peers, institutions, or even personal sources of inspiration and support. It's a personal touch, reflecting the humanity behind the academic rigor.

Table of contents

A roadmap for your readers, the table of contents lists the chapters, sections, and subsections of your thesis.

By providing page numbers, you allow readers to navigate your work easily, jumping to sections that pique their interest.

List of figures and tables

Research often involves data, and presenting this data visually can enhance understanding. This section provides an organized listing of all figures and tables in your thesis.

It's a visual index, ensuring that readers can quickly locate and reference your graphical data.

Introduction

Here's where you introduce your research topic, articulate the research question or objective, and outline the significance of your study.

• Present the research topic : Clearly articulate the central theme or subject of your research.
• Background information : Ground your research topic, providing any necessary context or background information your readers might need to understand the significance of your study.
• Define the scope : Clearly delineate the boundaries of your research, indicating what will and won't be covered.
• Literature review : Introduce any relevant existing research on your topic, situating your work within the broader academic conversation and highlighting where your research fits in.
• State the research Question(s) or objective(s) : Clearly articulate the primary questions or objectives your research aims to address.
• Outline the study's structure : Give a brief overview of how the subsequent sections of your work will unfold, guiding your readers through the journey ahead.

The introduction should captivate your readers, making them eager to delve deeper into your research journey.

Literature review section

Your study correlates with existing research. Therefore, in the literature review section, you'll engage in a dialogue with existing knowledge, highlighting relevant studies, theories, and findings.

It's here that you identify gaps in the current knowledge, positioning your research as a bridge to new insights.

To streamline this process, consider leveraging AI tools. For example, the SciSpace literature review tool enables you to efficiently explore and delve into research papers, simplifying your literature review journey.

Methodology

In the research methodology section, you’ll detail the tools, techniques, and processes you employed to gather and analyze data. This section will inform the readers about how you approached your research questions and ensures the reproducibility of your study.

Here's a breakdown of what it should encompass:

• Research Design : Describe the overall structure and approach of your research. Are you conducting a qualitative study with in-depth interviews? Or is it a quantitative study using statistical analysis? Perhaps it's a mixed-methods approach?
• Data Collection : Detail the methods you used to gather data. This could include surveys, experiments, observations, interviews, archival research, etc. Mention where you sourced your data, the duration of data collection, and any tools or instruments used.
• Sampling : If applicable, explain how you selected participants or data sources for your study. Discuss the size of your sample and the rationale behind choosing it.
• Data Analysis : Describe the techniques and tools you used to process and analyze the data. This could range from statistical tests in quantitative research to thematic analysis in qualitative research.
• Validity and Reliability : Address the steps you took to ensure the validity and reliability of your findings to ensure that your results are both accurate and consistent.
• Ethical Considerations : Highlight any ethical issues related to your research and the measures you took to address them, including — informed consent, confidentiality, and data storage and protection measures.

Moreover, different research questions necessitate different types of methodologies. For instance:

• Experimental methodology : Often used in sciences, this involves a controlled experiment to discern causality.
• Qualitative methodology : Employed when exploring patterns or phenomena without numerical data. Methods can include interviews, focus groups, or content analysis.
• Quantitative methodology : Concerned with measurable data and often involves statistical analysis. Surveys and structured observations are common tools here.
• Mixed methods : As the name implies, this combines both qualitative and quantitative methodologies.

The Methodology section isn’t just about detailing the methods but also justifying why they were chosen. The appropriateness of the methods in addressing your research question can significantly impact the credibility of your findings.

Results (or Findings)

This section presents the outcomes of your research. It's crucial to note that the nature of your results may vary; they could be quantitative, qualitative, or a mix of both.

Quantitative results often present statistical data, showcasing measurable outcomes, and they benefit from tables, graphs, and figures to depict these data points.

Qualitative results , on the other hand, might delve into patterns, themes, or narratives derived from non-numerical data, such as interviews or observations.

Regardless of the nature of your results, clarity is essential. This section is purely about presenting the data without offering interpretations — that comes later in the discussion.

In the discussion section, the raw data transforms into valuable insights.

Start by revisiting your research question and contrast it with the findings. How do your results expand, constrict, or challenge current academic conversations?

Dive into the intricacies of the data, guiding the reader through its implications. Detail potential limitations transparently, signaling your awareness of the research's boundaries. This is where your academic voice should be resonant and confident.

Practical implications (Recommendation) section

Based on the insights derived from your research, this section provides actionable suggestions or proposed solutions.

Whether aimed at industry professionals or the general public, recommendations translate your academic findings into potential real-world actions. They help readers understand the practical implications of your work and how it can be applied to effect change or improvement in a given field.

When crafting recommendations, it's essential to ensure they're feasible and rooted in the evidence provided by your research. They shouldn't merely be aspirational but should offer a clear path forward, grounded in your findings.

The conclusion provides closure to your research narrative.

It's not merely a recap but a synthesis of your main findings and their broader implications. Reconnect with the research questions or hypotheses posited at the beginning, offering clear answers based on your findings.

Reflect on the broader contributions of your study, considering its impact on the academic community and potential real-world applications.

Lastly, the conclusion should leave your readers with a clear understanding of the value and impact of your study.

References (or Bibliography)

Every theory you've expounded upon, every data point you've cited, and every methodological precedent you've followed finds its acknowledgment here.

In references, it's crucial to ensure meticulous consistency in formatting, mirroring the specific guidelines of the chosen citation style .

Proper referencing helps to avoid plagiarism , gives credit to original ideas, and allows readers to explore topics of interest. Moreover, it situates your work within the continuum of academic knowledge.

To properly cite the sources used in the study, you can rely on online citation generator tools  to generate accurate citations!

Here’s more on how you can cite your sources.

Often, the depth of research produces a wealth of material that, while crucial, can make the core content of the thesis cumbersome. The appendix is where you mention extra information that supports your research but isn't central to the main text.

Whether it's raw datasets, detailed procedural methodologies, extended case studies, or any other ancillary material, the appendices ensure that these elements are archived for reference without breaking the main narrative's flow.

For thorough researchers and readers keen on meticulous details, the appendices provide a treasure trove of insights.

Glossary (optional)

In academics, specialized terminologies, and jargon are inevitable. However, not every reader is versed in every term.

The glossary, while optional, is a critical tool for accessibility. It's a bridge ensuring that even readers from outside the discipline can access, understand, and appreciate your work.

By defining complex terms and providing context, you're inviting a wider audience to engage with your research, enhancing its reach and impact.

Remember, while these components provide a structured framework, the essence of your thesis lies in the originality of your ideas, the rigor of your research, and the clarity of your presentation.

As you craft each section, keep your readers in mind, ensuring that your passion and dedication shine through every page.

Thesis examples

To further elucidate the concept of a thesis, here are illustrative examples from various fields:

Example 1 (History): Abolition, Africans, and Abstraction: the Influence of the ‘Noble Savage’ on British and French Antislavery Thought, 1787-1807 by Suchait Kahlon.

Example 2 (Climate Dynamics): Influence of external forcings on abrupt millennial-scale climate changes: a statistical modelling study by Takahito Mitsui · Michel Crucifix

Checklist for your thesis evaluation

Evaluating your thesis ensures that your research meets the standards of academia. Here's an elaborate checklist to guide you through this critical process.

Content and structure

• Is the thesis statement clear, concise, and debatable?
• Does the introduction provide sufficient background and context?
• Is the literature review comprehensive, relevant, and well-organized?
• Does the methodology section clearly describe and justify the research methods?
• Are the results/findings presented clearly and logically?
• Does the discussion interpret the results in light of the research question and existing literature?
• Is the conclusion summarizing the research and suggesting future directions or implications?

Clarity and coherence

• Is the writing clear and free of jargon?
• Are ideas and sections logically connected and flowing?
• Is there a clear narrative or argument throughout the thesis?

Research quality

• Is the research question significant and relevant?
• Are the research methods appropriate for the question?
• Is the sample size (if applicable) adequate?
• Are the data analysis techniques appropriate and correctly applied?
• Are potential biases or limitations addressed?

Originality and significance

• Does the thesis contribute new knowledge or insights to the field?
• Is the research grounded in existing literature while offering fresh perspectives?

Formatting and presentation

• Is the thesis formatted according to institutional guidelines?
• Are figures, tables, and charts clear, labeled, and referenced in the text?
• Is the bibliography or reference list complete and consistently formatted?
• Are appendices relevant and appropriately referenced in the main text?

Grammar and language

• Is the thesis free of grammatical and spelling errors?
• Is the language professional, consistent, and appropriate for an academic audience?
• Are quotations and paraphrased material correctly cited?

Feedback and revision

• Have you sought feedback from peers, advisors, or experts in the field?
• Have you addressed the feedback and made the necessary revisions?

Overall assessment

• Does the thesis as a whole feel cohesive and comprehensive?
• Would the thesis be understandable and valuable to someone in your field?

Ensure to use this checklist to leave no ground for doubt or missed information in your thesis.

After writing your thesis, the next step is to discuss and defend your findings verbally in front of a knowledgeable panel. You’ve to be well prepared as your professors may grade your presentation abilities.

Preparing your thesis defense

A thesis defense, also known as "defending the thesis," is the culmination of a scholar's research journey. It's the final frontier, where you’ll present their findings and face scrutiny from a panel of experts.

Typically, the defense involves a public presentation where you’ll have to outline your study, followed by a question-and-answer session with a committee of experts. This committee assesses the validity, originality, and significance of the research.

The defense serves as a rite of passage for scholars. It's an opportunity to showcase expertise, address criticisms, and refine arguments. A successful defense not only validates the research but also establishes your authority as a researcher in your field.

Here’s how you can effectively prepare for your thesis defense .

Now, having touched upon the process of defending a thesis, it's worth noting that scholarly work can take various forms, depending on academic and regional practices.

One such form, often paralleled with the thesis, is the 'dissertation.' But what differentiates the two?

Dissertation vs. Thesis

Often used interchangeably in casual discourse, they refer to distinct research projects undertaken at different levels of higher education.

To the uninitiated, understanding their meaning might be elusive. So, let's demystify these terms and delve into their core differences.

Here's a table differentiating between the two.

Wrapping up

From understanding the foundational concept of a thesis to navigating its various components, differentiating it from a dissertation, and recognizing the importance of proper citation — this guide covers it all.

As scholars and readers, understanding these nuances not only aids in academic pursuits but also fosters a deeper appreciation for the relentless quest for knowledge that drives academia.

It’s important to remember that every thesis is a testament to curiosity, dedication, and the indomitable spirit of discovery.

Good luck with your thesis writing!

Frequently Asked Questions

A thesis typically ranges between 40-80 pages, but its length can vary based on the research topic, institution guidelines, and level of study.

A PhD thesis usually spans 200-300 pages, though this can vary based on the discipline, complexity of the research, and institutional requirements.

To identify a thesis topic, consider current trends in your field, gaps in existing literature, personal interests, and discussions with advisors or mentors. Additionally, reviewing related journals and conference proceedings can provide insights into potential areas of exploration.

The conceptual framework is often situated in the literature review or theoretical framework section of a thesis. It helps set the stage by providing the context, defining key concepts, and explaining the relationships between variables.

A thesis statement should be concise, clear, and specific. It should state the main argument or point of your research. Start by pinpointing the central question or issue your research addresses, then condense that into a single statement, ensuring it reflects the essence of your paper.

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• How It Works

Writing a Dissertation Data Analysis the Right Way

Do you want to be a college professor? Most teaching positions at four-year universities and colleges require the applicants to have at least a doctoral degree in the field they wish to teach in. If you are looking for information about the dissertation data analysis, it means you have already started working on yours. Congratulations!

Truth be told, learning how to write a data analysis the right way can be tricky. This is, after all, one of the most important chapters of your paper. It is also the most difficult to write, unfortunately. The good news is that we will help you with all the information you need to write a good data analysis chapter right now. And remember, if you need an original dissertation data analysis example, our PhD experts can write one for you in record time. You’ll be amazed how much you can learn from a well-written example.

OK, But What Is the Data Analysis Section?

Don’t know what the data analysis section is or what it is used for? No problem, we’ll explain it to you. Understanding the data analysis meaning is crucial to understanding the next sections of this blog post.

Basically, the data analysis section is the part where you analyze and discuss the data you’ve uncovered. In a typical dissertation, you will present your findings (the data) in the Results section. You will explain how you obtained the data in the Methodology chapter.

The data analysis section should be reserved just for discussing your findings. This means you should refrain from introducing any new data in there. This is extremely important because it can get your paper penalized quite harshly. Remember, the evaluation committee will look at your data analysis section very closely. It’s extremely important to get this chapter done right.

Learn What to Include in Data Analysis

Don’t know what to include in data analysis? Whether you need to do a quantitative data analysis or analyze qualitative data, you need to get it right. Learning how to analyze research data is extremely important, and so is learning what you need to include in your analysis. Here are the basic parts that should mandatorily be in your dissertation data analysis structure:

• The chapter should start with a brief overview of the problem. You will need to explain the importance of your research and its purpose. Also, you will need to provide a brief explanation of the various types of data and the methods you’ve used to collect said data. In case you’ve made any assumptions, you should list them as well.
• The next part will include detailed descriptions of each and every one of your hypotheses. Alternatively, you can describe the research questions. In any case, this part of the data analysis chapter will make it clear to your readers what you aim to demonstrate.
• Then, you will introduce and discuss each and every piece of important data. Your aim is to demonstrate that your data supports your thesis (or answers an important research question). Go in as much detail as possible when analyzing the data. Each question should be discussed in a single paragraph and the paragraph should contain a conclusion at the end.
• The very last part of the data analysis chapter that an undergraduate must write is the conclusion of the entire chapter. It is basically a short summary of the entire chapter. Make it clear that you know what you’ve been talking about and how your data helps answer the research questions you’ve been meaning to cover.

Dissertation Data Analysis Methods

If you are reading this, it means you need some data analysis help. Fortunately, our writers are experts when it comes to the discussion chapter of a dissertation, the most important part of your paper. To make sure you write it correctly, you need to first ensure you learn about the various data analysis methods that are available to you. Here is what you can – and should – do during the data analysis phase of the paper:

• Validate the data. This means you need to check for fraud (were all the respondents really interviewed?), screen the respondents to make sure they meet the research criteria, check that the data collection procedures were properly followed, and then verify that the data is complete (did each respondent receive all the questions or not?). Validating the data is no as difficult as you imagine. Just pick several respondents at random and call them or email them to find out if the data is valid.

For example, an outlier can be identified using a scatter plot or a box plot. Points (values) that are beyond an inner fence on either side are mild outliers, while points that are beyond an outer fence are called extreme outliers.

• If you have a large amount of data, you should code it. Group similar data into sets and code them. This will significantly simplify the process of analyzing the data later.

For example, the median is almost always used to separate the lower half from the upper half of a data set, while the percentage can be used to make a graph that emphasizes a small group of values in a large set o data.

ANOVA, for example, is perfect for testing how much two groups differ from one another in the experiment. You can safely use it to find a relationship between the number of smartphones in a family and the size of the family’s savings.

Analyzing qualitative data is a bit different from analyzing quantitative data. However, the process is not entirely different. Here are some methods to analyze qualitative data:

You should first get familiar with the data, carefully review each research question to see which one can be answered by the data you have collected, code or index the resulting data, and then identify all the patterns. The most popular methods of conducting a qualitative data analysis are the grounded theory, the narrative analysis, the content analysis, and the discourse analysis. Each has its strengths and weaknesses, so be very careful which one you choose.

Of course, it goes without saying that you need to become familiar with each of the different methods used to analyze various types of data. Going into detail for each method is not possible in a single blog post. After all, there are entire books written about these methods. However, if you are having any trouble with analyzing the data – or if you don’t know which dissertation data analysis methods suits your data best – you can always ask our dissertation experts. Our customer support department is online 24 hours a day, 7 days a week – even during holidays. We are always here for you!

Tips and Tricks to Write the Analysis Chapter

Did you know that the best way to learn how to write a data analysis chapter is to get a great example of data analysis in research paper? In case you don’t have access to such an example and don’t want to get assistance from our experts, we can still help you. Here are a few very useful tips that should make writing the analysis chapter a lot easier:

• Always start the chapter with a short introductory paragraph that explains the purpose of the chapter. Don’t just assume that your audience knows what a discussion chapter is. Provide them with a brief overview of what you are about to demonstrate.
• When you analyze and discuss the data, keep the literature review in mind. Make as many cross references as possible between your analysis and the literature review. This way, you will demonstrate to the evaluation committee that you know what you’re talking about.
• Never be afraid to provide your point of view on the data you are analyzing. This is why it’s called a data analysis and not a results chapter. Be as critical as possible and make sure you discuss every set of data in detail.
• If you notice any patterns or themes in the data, make sure you acknowledge them and explain them adequately. You should also take note of these patterns in the conclusion at the end of the chapter.
• Do not assume your readers are familiar with jargon. Always provide a clear definition of the terms you are using in your paper. Not doing so can get you penalized. Why risk it?
• Don’t be afraid to discuss both the advantage and the disadvantages you can get from the data. Being biased and trying to ignore the drawbacks of the results will not get you far.
• Always remember to discuss the significance of each set of data. Also, try to explain to your audience how the various elements connect to each other.
• Be as balanced as possible and make sure your judgments are reasonable. Only strong evidence should be used to support your claims and arguments. Weak evidence just shows that you did not do your best to uncover enough information to answer the research question.
• Get dissertation data analysis help whenever you feel like you need it. Don’t leave anything to chance because the outcome of your dissertation depends in large part on the data analysis chapter.

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Final Considerations

Finally, you need to be aware that the data analysis chapter should not be rushed in any way. We do agree that the Results chapter is extremely important, but we consider that the Discussion chapter is equally as important. Why? Because you will be explaining your findings and not just presenting some results. You will have the option to talk about your personal opinions. You are free to unleash your critical thinking and impress the evaluation committee. The data analysis section is where you can really shine.

Also, you need to make sure that this chapter is as interesting as it can be for the reader. Make sure you discuss all the interesting results of your research. Explain peculiar findings. Make correlations and reference other works by established authors in your field. Show your readers that you know that subject extremely well and that you are perfectly capable of conducting a proper analysis no matter how complex the data may be. This way, you can ensure that you get maximum points for the data analysis chapter. If you can’t do a great job, get help ASAP!

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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?

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.

Why is data analysis important?

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.

Types of data analysis (with examples)

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?

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. 

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

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. 

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. 

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: 

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: 

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. 

Data analysis process: How to get started

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?" 

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. 

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 

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. 

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

Automate your data collection, frequently asked questions.

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: 

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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Chapter 4 – Data Analysis and Discussion (example)

Disclaimer: This is not a sample of our professional work. The paper has been produced by a student. You can view samples of our work here . Opinions, suggestions, recommendations and results in this piece are those of the author and should not be taken as our company views.

Type of Academic Paper – Dissertation Chapter

Academic Subject – Marketing

Word Count – 2964 words

Reliability Analysis

Before conducting any analysis on the data, all the data’s reliability was analyzed based on Cronbach’s Alpha value. The reliability analysis was performed on the complete data of the questionnaire. The reliability of the data was found to be (0.922), as shown in the results of the reliability analysis provided below in table 4.1. However, the complete results output of the reliability analysis is given in the appendix.

Reliability Analysis (N=200)

The Cronbach’s Alpha value between (0.7-1.0) is considered to have excellent reliability. The Cronbach’s Alpha value of the data was found to be (0.922); therefore, this indicated that the questionnaire data had excellent reliability. All of the 29 items of the questionnaire had excellent reliability, and if they are taken for further analysis, they can generate results with 92.2% reliability.

Frequency Distribution Analysis

First of all, the frequency distribution analysis was performed on the demographic variables using SPSS to identify the respondents’ demographic composition. Section 1 of the questionnaire had 5 demographic questions to identify; gender, age group, annual income, marital status, and education level of the research sample. The frequency distribution results shown in table 4.2 below indicated that there were 200 respondents in total, out of which 50% were male, and 50% were female. This shows that the research sample was free from gender-based biases as males and females had equal representation in the sample.

Moreover, the frequency distribution analysis suggested three age groups; ‘20-35’, ‘36-60’ and ‘Above 60’. 39% of the respondents belonged to the ‘20-35’ age group, while 56.5% of the respondents belonged to the ‘36-60’ age group and the remaining 4.5% belonged to the age group of ‘Above 60’.

Furthermore, the annual income level was divided into four categories. The income values were in GBP. It was found that 13% of the respondents had income ‘up to 30000’, 27% had income between ‘31000 to 50000’, 52.5% had income between ‘51000 to 100000’, and 7.5% had income ‘Above 100000’. This suggests that most of the respondents had an annual income between ‘31000 to 50000’ GBP.

The frequency distribution analysis indicated that 61% of respondents were single, while 39% were married, as indicated in table 4.2. This means that most of the respondents were single. Based on frequency distribution, it was also found that the education level of the respondents was analyzed using four categories of education level, namely; diploma, graduate, master, and doctorate. The results depicted that 37% of the respondents were diploma holders, 46% were graduates, 16% had master-level education, while only 2% had a doctorate. This suggests that most of the respondents were either graduate or diploma holders.

Frequency Distribution of the Demographic Characteristics of the respondents (N=200)

Multiple Regression Analysis

The hypotheses were tested using linear multiple regression analysis to determine which of the dependent variables had a significant positive effect on the customer loyalty of the five-star hotel brands. The results of the regression analysis are summarized in the following table 4.3. However, the complete SPSS output of the regression analysis is given in the appendix. Table 4.3

Multiple regression analysis showing the predictive values of dependent variables (Brand image, corporate identity, public relation, perceived quality, and trustworthiness) on customer loyalty (N=200)

Predictors: (Constant), Trustworthiness, Public Relation, Brand Image, Corporate Identity, Perceived Quality Dependent Variable: Customer Loyalty

The significance value (p-value) of ANOVA was found to be (0.000) as shown in the above

table, which was less than 0.05. This suggested that the model equation was significantly fitted

on the data. Moreover, the adjusted R-Square value was (0.897), which indicated that the model’s predictors explained 89.7% variation in customer loyalty.

Furthermore, the presence of the significant effect of the 5 predicting variables on customer loyalty was identified based on their sig. Values. The effect of a predicting variable is significant if its sig. Value is less than 0.05 or if its t-Statistics value is greater than 2. It was found that the variable ‘brand image’ had sig. Value (0.046), the variable ‘corporate identity had sig. Value (0.482), the variable ‘public relation’ had sig. Value (0.400), while the variable ‘perceived quality’ had sig. value (0.000), and the variable ‘trustworthiness’ had sig. value (0.652).

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Hypotheses Assessment

Based on the regression analysis, it was found that brand image and perceived quality have a significant positive effect on customer loyalty. In contrast, corporate identity, public relations, and trustworthiness have an insignificant effect on customer loyalty. Therefore the two hypotheses; H1 and H4 were accepted, however the three hypotheses; H2, H3, and H5 were rejected as indicated in table 4.4.

Hypothesis Assessment Summary Table (N=200)

The insignificant variables (corporate identity, public relation and trustworthiness) were excluded from equation 1. After excluding the insignificant variables from the model equation 1, the final equation becomes as follows;

Customer loyalty                 = α + 0.074 (Brand image) + 0.991 (Perceived quality) + €

The above equation suggests that a 1 unit increase in brand image is likely to result in 0.074 units increase customer loyalty. In comparison, 1 unit increase in perceived quality can result in 0.991 units increase in customer loyalty.

Cross Tabulation Analysis

To further explore the results, the demographic variables’ data were cross-tabulated against the respondents’ responses regarding customer loyalty using SPSS. In this regards the five demographic variables; gender, age group, annual income, marital status and education level were cross-tabulated against the five questions regarding customer loyalty to know the difference between the customer loyalty of five-star hotels of UK based on demographic differences. The results of the cross-tabulation analysis are given in the appendix. The results are graphically presented in bar charts too, which are also given in the appendix.

Cross Tabulation of Gender against Customer Loyalty

The gender was cross-tabulated against question 1 to 5 of the questionnaire to identify the gender differences between male and female respondents’ responses regarding customer loyalty of five-star hotels of the UK. The results indicated that out of 100 males, 57% were extremely agreed that they stay at one hotel, while out of 100 females, 80% were extremely agreed they stay at one hotel. This shows that in comparison with a male, females were more agreed that they stayed at one hotel and were found to be more loyal towards their respective hotel brands.

The cross-tabulation results further indicated that out of 100 males, 53% agreed that they always say positive things about their respective hotel brand to other people. In contrast, out of 100 females, 77% were extremely agreed. Based on the results, the females were found to be in more agreement than males that they always say positive things about their respective hotel brand to other people.

It was further found that out of 100 males, 53% were extremely agreed that they recommend their hotel brand to others, however, out of 100 females, 74% were extremely agreed to this statement. This result also suggested that females were more in agreement than males to recommend their hotel brand to others.

Moreover, it was found that out of 100 males, 54% were extremely agreed that they don’t seek alternative hotel brands, while out of 100 females, 79% were extremely agreed to this statement. This result also suggested that females were more agreed than males that they don’t seek alternative hotel brands, and so were found to be more loyal than males.

Furthermore, it was identified that out of 100 male respondents 56% were extremely agreed that they would continue to go to the same hotel irrespective of the prices, however out of 100 females 79% were extremely agreed. Based on this result, it was clear that females were more agreed than males that they would continue to go to the same hotel irrespective of the prices, so females were found to be more loyal than males.

After cross tabulating ‘gender’ against the response of the 5 questions regarding customer loyalty the females were found to be more loyal customers of the five-star hotel brands than males as they were found to be more in agreement than the man that they stay at one hotel, always say positive things about their hotel brand to other people, recommend their hotel brand to others, don’t seek alternative hotel brands and would continue to go to the same hotel irrespective of the prices.

Cross Tabulation of Age Group against Customer Loyalty

Afterward, the second demographic variable, ‘age groups’ was cross-tabulated against questions 1 to 5 of the questionnaire to identify the difference between the customer loyalty of customers of different age groups. The results indicated that out of 78 respondents between 20 to 35 years of age, 61.5% were extremely agreed that they stayed at one hotel. While out of 113 respondents who were between 36 to 60 years of age, 72.6% were extremely agreed that they always stay at one hotel. However, out of 9 respondents who were above 60 years of age, 77.8% agreed that they always stay at one hotel. This indicated that customers of 36-60 and above 60 age groups were more loyal to their hotel brands as they were keener to stay at a respective hotel brand.

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Cross Tabulation of Annual Income against Customer Loyalty

The third demographic variable, ‘annual income’ was cross-tabulated against questions 1 to 5 of the questionnaire to identify which of the customers were most loyal based on their respective annual income levels. The results indicated that out of 26 respondents who had annual income up to 30000 GBP, 84.6% were extremely agreed that they always stay at one hotel. However, out of 54 respondents who had annual income from 31000 to 50000 GBP, 98.1% agreed that they always stay at one hotel. Although out of 105 respondents had annual income from 50000 to 100000 GBP, 49.5% were extremely agreed that they always stay at one hotel. While out of 10 respondents who had annual income from 50000 to 1000000 GBP, 66.7% agreed that they always stay at one hotel. This indicated that customers of annual income levels from 31000 to 50000 GBP were more loyal to their hotel brands than the customers having other annual income levels.

Cross Tabulation of Marital Status against Customer Loyalty

Furthermore, the fourth demographic variable the ‘marital status’ was cross-tabulated against questions 1 to 5 of the questionnaire to understand the difference between married and unmarried respondents regarding customer loyalty of five-star hotels of the UK. The cross-tabulation analysis results indicated that out of 122 single respondents, 59.8% were extremely agreed that they stay at one hotel. However, out of 78 married respondents, around 82% of respondents agreed that they stay at one hotel. Thus, the married customers were more loyal to their hotel brands than unmarried customers because, in comparison, married customers prefer to stay at one hotel brand.

To proceed with the cross-tabulation results, out of 122 single respondents, 55.7% were extremely agreed upon always saying positive things about their hotel brands to other people. On the other hand, out of 78 married respondents, 79.5% were extremely agreed. Hence, upon evaluating the results, it can be said that married customers have more customer loyalty as they are in more agreement than singles. They always give positive feedback regarding their respective hotel brand to other people.

Cross Tabulation of Education Level against Customer Loyalty

Subsequently, the fifth demographic variable, ‘education level’ was cross-tabulated against questions 1 to 5 of the questionnaire to identify which of the customers were most loyal based on their respective education levels. The results indicated that out of 50 respondents who were diploma holders, 67.6% were extremely agreed that they always stay at one hotel. While out of 64 respondents who were graduates, 69.6% were extremely agreed that they always stay at one hotel. Although out of 22 respondents who were masters, 68.8% were extremely agreed that they always stay at one hotel. However, out of 2 respondents with doctorates, 50% were extremely agreed to always stay at one hotel. This indicated that customers who were graduates were more loyal than the customers with diplomas, masters, or doctorates.

Moreover, 66.2% of the diploma holders were extremely agreed that they always say positive things about their hotel brand to other people. In comparison, 64.1% of the respondents who were graduates were extremely agreed. However, 65.5% of the respondents who had masters were extremely agreed, and 50% of the respondents who had doctorates agreed with the statement. Based on this result customers having masters were the most loyal customers of their respective five-star hotel brands.

Need a Dissertation Chapter On a Similar Topic?

In this subsection, the findings of this study are compared and contrasted with the literature to identify which of the past research supports the present research findings. This present study based on regression analysis suggested that brand image can have a significant positive effect on the customer loyalty of five-star hotels in the UK. This finding was supported by the research of Heung et al. (1996), who also suggested that the hotel’s brand image can play a vital role in preserving a high ratio of customer loyalty.

Moreover, this present study also suggested that perceived quality was the second factor that was found to have a significant positive effect on customer loyalty. The perceived quality was evaluated based on; service quality, comfort, staff courtesy, customer satisfaction, and service quality expectations. In this regard, Tat and Raymond (2000) research supports the findings of this study. The staff service quality was found to affect customer loyalty and the level of satisfaction. Teas (1994) had also found service quality to affect customer loyalty. However, Teas also found that staff empathy (staff courtesy) towards customers can also affect customer loyalty. The research of Rowley and Dawes (1999) also supports the finding of this present study. The users’ expectations about the quality and nature of the services affect customer loyalty. A study by Oberoi and Hales (1990) was found to agree with the present study’s findings, as they had found the quality of staff service to affect customer loyalty.

Summary of the Findings

• The brand image was found to have a significant positive effect on customer loyalty. Therefore customer loyalty is likely to increase with the increase in brand image.
• The corporate identity was found to have an insignificant effect on customer loyalty. Therefore customer loyalty is not likely to increase with the increase in corporate identity.
• Public relations was found to have an insignificant effect on customer loyalty. Therefore customer loyalty is not likely to increase with the increase in public relations.
• Perceived quality was found to have a significant positive effect on customer loyalty. Therefore customer loyalty is likely to increase with the increase in perceived quality.
• Trustworthiness was found to have an insignificant effect on customer loyalty. Therefore customer loyalty is not likely to increase with the increase in trustworthiness.
• The female customers were found to be more loyal customers of the five-star hotel brands than male customers.
• The customers of age from 36 to 60 years were more loyal to their hotel brands than the customers of age from 20 to 35 and above 60.
• The customers who had annual income from 31000 to 50000 were more loyal customers of their respective hotel brands than those who had an annual income level of less than 31000 or more than 50000.
• The married respondents had more customer loyalty than unmarried customers, towards five-star hotel brands of the UK.

The customers who had bachelor degrees and the customers who had master degrees were more loyal to the customers who had a diploma or doctorate.

Bryman, A., Bell, E., 2015. Business Research Methods. Oxford University Press.

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Perspective and its Implication for Public Relations Consultancies. diplom.de.

Guetterman, T.C., 2015. Descriptions of Sampling Practices Within Five Approaches to Qualitative Research in Education and the Health Sciences. Forum Qualitative Sozialforschung /

Forum: Qualitative Social Research 16.

Haq, M., 2014. A Comparative Analysis of Qualitative and Quantitative Research Methods and a Justification for Adopting Mixed Methods in Social Research (PDF Download Available).

ResearchGate 1–22. doi:http://dx.doi.org/10.13140/RG.2.1.1945.8640

Kelley, ., Clark, B., Brown, V., Sitzia, J., 2003. Good practice in the conduct and reporting of survey research. Int J Qual Health Care 15, 261–266. doi:10.1093/intqhc/mzg031

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Health Promotion Practice 16, 473–475. doi:10.1177/1524839915580941

Saunders, M., 2003. Research Methods for Business Students. Pearson Education India.

Saunders, M.N.K., Tosey, P., 2015. Handbook of Research Methods on Human Resource

Development. Edward Elgar Publishing.

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A thesis is a comprehensive research paper that presents a central argument or claim supported by evidence. Typically written by students pursuing advanced degrees, a thesis demonstrates a deep understanding of a subject. It includes a clear research question, literature review, methodology, analysis, and conclusions. The process enhances critical thinking, research skills , and subject expertise, culminating in a significant academic contribution.

Thesis paper . Many students tend to fear this word and there is a good reason as to why they do.  You may already have tried making a thesis before and at some point, you would also realize the trial and error stage of making one. 

What Is a Thesis?

A thesis a research paper writing that is made for a purpose. Thesis papers consists of a research statement , a kind of statement , a theory, a purpose. The thesis is made in order to prove your theory and make it into a fact. There are a lot of kinds of thesis, but the most common thesis kinds are analytical thesis, an argumentative thesis and an explanatory thesis.

Types of Thesis

Analytical thesis.

An analytical thesis breaks down an issue or idea into its component parts, evaluates the topic, and presents this breakdown and evaluation to the audience. It is often used in literature, history, and social sciences.

Expository Thesis

An expository thesis explains a topic to the audience. It provides a comprehensive overview of a subject, presenting facts and analysis without personal opinion. This type is common in science and technical writing.

Argumentative Thesis

An argumentative thesis makes a claim about a topic and justifies this claim with specific evidence. The goal is to persuade the reader of a particular viewpoint. This type is prevalent in fields like philosophy, political science, and law.

Narrative Thesis

A narrative thesis tells a story or recounts an event. It includes personal experiences or detailed descriptions of events to support the main argument. This type is often used in creative writing and autobiographies.

Comparative Thesis

A comparative thesis compares and contrasts two or more subjects, evaluating their similarities and differences. It is commonly used in literature, history, and social sciences to draw meaningful conclusions.

Descriptive Thesis

A descriptive thesis provides a detailed description of a topic without arguing a specific point. It paints a vivid picture of the subject, often used in fields like anthropology and sociology to explore cultural phenomena.

Empirical Thesis

An empirical thesis is based on original research and data collection. It involves experiments, surveys, or observations to answer a specific research question. This type is typical in natural and social sciences.

Examples of Thesis

Thesis examples in literature, 1: analysis of a single work.

Title: “The Use of Symbolism in ‘The Great Gatsby’ by F. Scott Fitzgerald”

Thesis Statement: In F. Scott Fitzgerald’s ‘The Great Gatsby,’ the use of symbolism, particularly through the green light at the end of Daisy’s dock, the eyes of Doctor T. J. Eckleburg, and the Valley of Ashes, serves to illustrate the overarching themes of the American Dream, moral decay, and the quest for identity.

2: Comparative Analysis

Title: “The Role of Women in ‘Pride and Prejudice’ by Jane Austen and ‘Jane Eyre’ by Charlotte Brontë”

Thesis Statement: While both Jane Austen’s ‘Pride and Prejudice’ and Charlotte Brontë’s ‘Jane Eyre’ critique the limited roles and expectations of women in 19th-century British society, Austen’s Elizabeth Bennet and Brontë’s Jane Eyre embody different forms of rebellion against societal norms, highlighting the evolving perception of women’s independence and self-worth.

3: Thematic Analysis

Title: “Exploring the Theme of Isolation in ‘Frankenstein’ by Mary Shelley”

Thesis Statement: Mary Shelley’s ‘Frankenstein’ explores the theme of isolation through the experiences of Victor Frankenstein and his creation, the monster, demonstrating how isolation leads to destructive consequences for both individuals and society.

4: Character Analysis

Title: “The Evolution of Hamlet’s Character in William Shakespeare’s ‘Hamlet'”

Thesis Statement: In William Shakespeare’s ‘Hamlet,’ the protagonist undergoes a significant transformation from a grief-stricken and indecisive prince to a determined and introspective avenger, reflecting the complexities of human nature and the impact of existential contemplation.

5: Genre Analysis

Title: “Gothic Elements in ‘Wuthering Heights’ by Emily Brontë”

Thesis Statement: Emily Brontë’s ‘Wuthering Heights’ employs key elements of Gothic literature, including a brooding atmosphere, supernatural occurrences, and the exploration of human psychology, to create a haunting and timeless tale of passion and revenge.

6: Symbolic Analysis

Title: “The Symbolism of the Green Light in ‘The Great Gatsby’ by F. Scott Fitzgerald”

Thesis Statement: The green light in F. Scott Fitzgerald’s ‘The Great Gatsby’ symbolizes Gatsby’s unattainable dreams and the elusive nature of the American Dream, reflecting the broader themes of hope, disillusionment, and the pursuit of an idealized future.

7: Historical Context

Title: “Historical Influences on George Orwell’s ‘1984’”

Thesis Statement: George Orwell’s ‘1984’ draws heavily on the political climate of the early 20th century, particularly the rise of totalitarian regimes and the impact of World War II, to present a dystopian vision of a future where government surveillance and propaganda control every aspect of life.

8: Feminist Critique

Title: “Feminist Perspectives in ‘The Handmaid’s Tale’ by Margaret Atwood”

Thesis Statement: Margaret Atwood’s ‘The Handmaid’s Tale’ critiques the patriarchal structures of contemporary society by depicting a dystopian world where women’s rights are stripped away, illustrating the extreme consequences of gender oppression and the resilience of female solidarity.

9: Psychoanalytic Criticism

Title: “Freudian Elements in ‘The Turn of the Screw’ by Henry James”

Thesis Statement: Henry James’s ‘The Turn of the Screw’ can be interpreted through a Freudian lens, where the governess’s experiences and the ambiguous nature of the ghosts reflect deep-seated psychological conflicts and repressed desires, highlighting the novella’s exploration of the human psyche.

10: Postcolonial Analysis

Title: “Postcolonial Themes in ‘Things Fall Apart’ by Chinua Achebe”

Thesis Statement: Chinua Achebe’s ‘Things Fall Apart’ addresses postcolonial themes by portraying the clash between traditional Igbo society and British colonial forces, illustrating the devastating effects of colonialism on indigenous cultures and the struggle for cultural identity and autonomy.

Thesis Examples for Essays

1: persuasive essay.

Topic: “The Importance of Renewable Energy”

Thesis Statement: Governments around the world should invest heavily in renewable energy sources like solar and wind power to reduce dependency on fossil fuels, combat climate change, and create sustainable job opportunities.

2: Analytical Essay

Topic: “The Symbolism in ‘The Great Gatsby’ by F. Scott Fitzgerald”

Thesis Statement: In ‘The Great Gatsby,’ F. Scott Fitzgerald uses the symbols of the green light, the eyes of Doctor T. J. Eckleburg, and the Valley of Ashes to illustrate the moral and social decay of America during the Roaring Twenties.

3: Expository Essay

Topic: “The Impact of Social Media on Teenagers”

Thesis Statement: Social media has significantly impacted teenagers’ mental health, social skills, and academic performance, both positively and negatively, necessitating a balanced approach to its usage.

4: Compare and Contrast Essay

Topic: “Public vs. Private School Education”

Thesis Statement: While public schools offer a more diverse social environment and extracurricular opportunities, private schools provide smaller class sizes and specialized curriculums, making the choice dependent on individual student needs and family priorities.

5: Cause and Effect Essay

Topic: “The Causes and Effects of the Rise in Obesity Rates”

Thesis Statement: The rise in obesity rates can be attributed to poor dietary habits, sedentary lifestyles, and genetic factors, leading to serious health issues such as diabetes, heart disease, and decreased life expectancy.

6: Narrative Essay

Topic: “A Life-Changing Experience”

Thesis Statement: My trip to volunteer at a rural school in Kenya was a life-changing experience that taught me the value of education, the importance of cultural exchange, and the power of empathy and compassion.

7: Argumentative Essay

Topic: “The Necessity of Free College Education”

Thesis Statement: Free college education is essential for ensuring equal opportunities for all, reducing student debt burdens, and fostering a more educated and productive workforce.

8: Descriptive Essay

Topic: “The Beauty of a Sunset”

Thesis Statement: A sunset, with its vibrant hues and serene ambiance, evokes a sense of peace and reflection, illustrating nature’s ability to inspire awe and tranquility in our daily lives.

9: Definition Essay

Topic: “What is Happiness?”

Thesis Statement: Happiness is a complex and multifaceted emotion characterized by feelings of contentment, fulfillment, and joy, influenced by both internal factors like mindset and external factors such as relationships and achievements.

10: Process Essay

Topic: “How to Bake the Perfect Chocolate Cake”

Thesis Statement: Baking the perfect chocolate cake involves selecting high-quality ingredients, precisely following the recipe, and understanding the nuances of baking techniques, from mixing to temperature control.

Thesis Examples for Argumentative Essay

1: gun control.

Topic: “Stricter Gun Control Laws”

Thesis Statement: Stricter gun control laws are necessary to reduce gun violence in the United States, as evidenced by lower rates of gun-related deaths in countries with stringent regulations.

2: Climate Change

Topic: “Addressing Climate Change”

Thesis Statement: To effectively combat climate change, governments worldwide must implement aggressive policies to reduce carbon emissions, invest in renewable energy, and promote sustainable practices.

3: Animal Testing

Topic: “Ban on Animal Testing”

Thesis Statement: Animal testing for cosmetics should be banned globally due to its ethical implications, the availability of alternative testing methods, and the questionable reliability of animal-based results for human safety.

4: Education Reform

Topic: “Standardized Testing in Schools”

Thesis Statement: Standardized testing should be eliminated in schools as it narrows the curriculum, causes undue stress to students, and fails to accurately measure a student’s potential and abilities.

5: Universal Basic Income

Topic: “Implementing Universal Basic Income”

Thesis Statement: Implementing a universal basic income would help alleviate poverty, reduce income inequality, and provide financial stability in an increasingly automated and unpredictable job market.

6: Health Care

Topic: “Universal Health Care”

Thesis Statement: Universal health care should be adopted in the United States to ensure that all citizens have access to essential medical services, reduce overall healthcare costs, and improve public health outcomes.

7: Immigration Policy

Topic: “Reforming Immigration Policies”

Thesis Statement: Comprehensive immigration reform is essential to address undocumented immigration, protect human rights, and contribute to economic growth by recognizing the contributions of immigrants to society.

8: Death Penalty

Topic: “Abolishing the Death Penalty”

Thesis Statement: The death penalty should be abolished as it is an inhumane practice, prone to judicial errors, and has not been proven to deter crime more effectively than life imprisonment.

9: Social Media Regulation

Topic: “Regulating Social Media Platforms”

Thesis Statement: Social media platforms should be regulated to prevent the spread of misinformation, protect user privacy, and reduce the negative impact on mental health, particularly among adolescents.

10: College Tuition

Topic: “Free College Tuition”

Thesis Statement: Providing free college tuition at public universities would increase access to higher education, reduce student debt, and help create a more educated and skilled workforce to meet future economic demands.

Thesis Examples for Research Papers

1: environmental science.

Topic: “Impact of Plastic Pollution on Marine Life”

Thesis Statement: Plastic pollution in the oceans is causing significant harm to marine life, leading to ingestion and entanglement of plastic debris, disruption of ecosystems, and bioaccumulation of toxic substances in the food chain.

2: Psychology

Topic: “Effects of Social Media on Adolescent Mental Health”

Thesis Statement: Excessive use of social media negatively impacts adolescent mental health by increasing the risk of anxiety, depression, and poor sleep quality, while also contributing to body image issues and cyberbullying.

3: Education

Topic: “Benefits of Bilingual Education Programs”

Thesis Statement: Bilingual education programs enhance cognitive abilities, improve academic performance, and promote cultural awareness, making them a valuable approach in the increasingly globalized and multicultural society.

4: Public Health

Topic: “Addressing the Obesity Epidemic”

Thesis Statement: Addressing the obesity epidemic requires a multifaceted approach that includes implementing public health campaigns, promoting healthy eating habits, increasing physical activity, and regulating food advertising targeted at children.

5: Economics

Topic: “Universal Basic Income and Economic Stability”

Thesis Statement: Implementing a universal basic income can provide economic stability by reducing poverty, ensuring a safety net during economic downturns, and stimulating consumer spending, thereby supporting overall economic growth.

6: Political Science

Topic: “Impact of Voter ID Laws on Voter Turnout”

Thesis Statement: Voter ID laws disproportionately reduce voter turnout among minority and low-income populations, undermining the democratic process and exacerbating existing inequalities in political participation.

7: Sociology

Topic: “Gender Stereotypes in Media Representation”

Thesis Statement: Media representation perpetuates gender stereotypes by consistently portraying men and women in traditional roles, which reinforces societal norms and limits the opportunities for gender equality.

8: Technology

Topic: “Artificial Intelligence in Healthcare”

Thesis Statement: The integration of artificial intelligence in healthcare can improve patient outcomes, enhance diagnostic accuracy, and streamline administrative processes, but it also raises ethical concerns regarding data privacy and the potential for job displacement.

Topic: “Causes and Consequences of the American Civil War”

Thesis Statement: The American Civil War was primarily caused by deep-seated economic, social, and political differences between the North and South, particularly over the issue of slavery, and it resulted in significant social and political changes, including the abolition of slavery and the reconstruction of the South.

10: Environmental Policy

Topic: “Renewable Energy Policies and Their Effectiveness”

Thesis Statement: Renewable energy policies, such as subsidies for solar and wind power and carbon pricing, are effective in reducing greenhouse gas emissions and promoting sustainable energy sources, but their success depends on comprehensive implementation and international cooperation.

Thesis Examples for Informative Essay

Topic: “The Water Cycle”

Thesis Statement: The water cycle, which includes processes such as evaporation, condensation, precipitation, and infiltration, is essential for distributing water across the Earth’s surface and maintaining ecological balance.

2: Health and Wellness

Topic: “The Benefits of Regular Exercise”

Thesis Statement: Regular exercise is crucial for maintaining physical health, improving mental well-being, and reducing the risk of chronic diseases such as obesity, diabetes, and cardiovascular conditions.

3: Technology

Topic: “The Development of Artificial Intelligence”

Thesis Statement: The development of artificial intelligence has progressed from simple machine learning algorithms to complex neural networks capable of performing tasks such as natural language processing, image recognition, and autonomous driving.

Topic: “The Causes and Effects of the American Civil Rights Movement”

Thesis Statement: The American Civil Rights Movement was driven by factors such as racial segregation, economic disparity, and political disenfranchisement, leading to significant legislative and social changes that improved the rights and freedoms of African Americans.

5: Education

Topic: “The Montessori Method of Education”

Thesis Statement: The Montessori method of education, developed by Dr. Maria Montessori, emphasizes self-directed learning, hands-on activities, and collaborative play, fostering independence and critical thinking skills in young children.

6: Sociology

Topic: “The Impact of Urbanization on Community Life”

Thesis Statement: Urbanization significantly impacts community life by altering social structures, increasing economic opportunities, and presenting challenges such as overcrowding, pollution, and loss of green spaces.

7: Environmental Policy

Topic: “The Role of Renewable Energy in Combating Climate Change”

Thesis Statement: Renewable energy sources, such as solar, wind, and hydroelectric power, play a critical role in combating climate change by reducing greenhouse gas emissions and providing sustainable alternatives to fossil fuels.

8: Business

Topic: “The Rise of Gig Economy”

Thesis Statement: The rise of the gig economy has transformed the labor market by offering flexible work opportunities, fostering entrepreneurship, and posing challenges such as job insecurity and lack of benefits for workers.

9: Psychology

Topic: “The Importance of Sleep for Cognitive Function”

Thesis Statement: Adequate sleep is essential for cognitive function, memory consolidation, and emotional regulation, with chronic sleep deprivation leading to impaired mental performance and increased risk of mental health disorders.

10: Cultural Studies

Topic: “The Influence of Japanese Anime on Global Pop Culture”

Thesis Statement: Japanese anime has significantly influenced global pop culture by shaping trends in fashion, art, and storytelling, and fostering a dedicated international fanbase that celebrates its unique aesthetic and thematic elements.

Thesis Examples for Synthesis Essay

1: climate change.

Topic: “Combating Climate Change through Policy and Innovation”

Thesis Statement: Combating climate change requires a multifaceted approach that includes stringent environmental policies, investment in renewable energy technologies, and community-based initiatives to reduce carbon footprints, integrating efforts from government, industry, and society.

2: Education

Topic: “Balancing Technology and Traditional Teaching Methods in Education”

Thesis Statement: A balanced approach to education that combines the benefits of technology, such as interactive learning tools and online resources, with traditional teaching methods, like face-to-face instruction and hands-on activities, can enhance student engagement and academic achievement.

Topic: “Addressing the Opioid Crisis through Comprehensive Strategies”

Thesis Statement: Addressing the opioid crisis requires comprehensive strategies that include better access to addiction treatment programs, stricter regulations on prescription opioids, and increased public awareness campaigns to educate communities about the risks of opioid misuse.

4: Technology

Topic: “The Impact of Social Media on Political Mobilization”

Thesis Statement: Social media has revolutionized political mobilization by providing platforms for grassroots campaigns, enabling real-time communication, and fostering civic engagement, but it also poses challenges such as the spread of misinformation and echo chambers.

5: Business

Topic: “Corporate Social Responsibility and Its Impact on Brand Loyalty”

Thesis Statement: Corporate social responsibility (CSR) initiatives, when genuinely implemented, can significantly enhance brand loyalty by aligning company values with consumer expectations, fostering trust, and contributing positively to societal well-being.

Topic: “The Role of Gender Stereotypes in Media Representation”

Thesis Statement: Media representation perpetuates gender stereotypes by consistently depicting men and women in traditional roles, which influences societal perceptions and expectations, but progressive portrayals are gradually challenging these norms and promoting gender equality.

Topic: “Sustainable Urban Development and Green Infrastructure”

Thesis Statement: Sustainable urban development that incorporates green infrastructure, such as green roofs, urban gardens, and eco-friendly public transportation, is essential for mitigating environmental impacts, improving public health, and enhancing the quality of urban life.

8: Psychology

Topic: “The Effects of Mindfulness Practices on Mental Health”

Thesis Statement: Mindfulness practices, including meditation, yoga, and mindful breathing, have been shown to significantly improve mental health by reducing stress, enhancing emotional regulation, and promoting overall well-being, supported by a growing body of scientific research.

9: Economics

Topic: “Universal Basic Income as a Solution to Economic Inequality”

Thesis Statement: Universal Basic Income (UBI) presents a viable solution to economic inequality by providing financial security, reducing poverty, and supporting economic stability, though it requires careful consideration of funding mechanisms and potential societal impacts.

10: Public Health

Topic: “The Importance of Vaccination Programs in Preventing Epidemics”

Thesis Statement: Vaccination programs are crucial for preventing epidemics, protecting public health, and achieving herd immunity, as evidenced by the successful eradication of diseases like smallpox and the control of outbreaks such as measles and influenza.

Thesis Examples for Persuasive Essays

Thesis Statement: Stricter gun control laws are essential to reduce gun violence in the United States, as they will help prevent firearms from falling into the wrong hands, decrease the number of mass shootings, and enhance public safety.

Topic: “Urgent Action on Climate Change”

Thesis Statement: Immediate and robust action is needed to combat climate change, including reducing carbon emissions, transitioning to renewable energy sources, and implementing sustainable practices to mitigate the devastating effects on our planet.

3: Animal Rights

Topic: “Ban on Animal Testing for Cosmetics”

Thesis Statement: Animal testing for cosmetics should be banned worldwide due to its ethical implications, the availability of alternative testing methods, and the questionable reliability of animal-based results for human safety.

Topic: “Abolishing Standardized Testing in Schools”

Thesis Statement: Standardized testing should be abolished in schools as it narrows the curriculum, places undue stress on students, and fails to accurately measure a student’s potential and abilities, thereby hindering educational growth.

5: Universal Health Care

Topic: “Adopting Universal Health Care in the United States”

Thesis Statement: The United States should adopt a universal health care system to ensure that all citizens have access to essential medical services, reduce overall healthcare costs, and improve public health outcomes.

6: Immigration Policy

Thesis Statement: Comprehensive immigration reform is essential to address undocumented immigration, protect human rights, and contribute to economic growth by recognizing the contributions of immigrants to society and ensuring a fair, efficient legal process.

7: Death Penalty

Thesis Statement: The death penalty should be abolished as it is an inhumane practice, prone to judicial errors, and has not been proven to deter crime more effectively than life imprisonment, while also being more costly to taxpayers.

8: Social Media Regulation

Thesis Statement: Social media platforms should be regulated to prevent the spread of misinformation, protect user privacy, and reduce the negative impact on mental health, particularly among adolescents, to create a safer online environment.

9: College Tuition

Topic: “Providing Free College Tuition”

10: Renewable Energy

Topic: “Investing in Renewable Energy Sources”

Thesis Statement: Governments should invest heavily in renewable energy sources like solar and wind power to reduce dependency on fossil fuels, combat climate change, and create sustainable job opportunities, ensuring a cleaner and healthier future.

Thesis Examples for Analysis Essays

1: literary analysis.

Topic: “Symbolism in ‘The Great Gatsby’ by F. Scott Fitzgerald”

Thesis Statement: In ‘The Great Gatsby,’ F. Scott Fitzgerald uses symbols such as the green light, the Valley of Ashes, and the eyes of Doctor T. J. Eckleburg to critique the American Dream and explore themes of ambition, disillusionment, and moral decay.

2: Film Analysis

Topic: “Themes of Redemption in ‘The Shawshank Redemption'”

Thesis Statement: ‘The Shawshank Redemption’ explores themes of hope, friendship, and the human spirit’s resilience, using the character arcs of Andy Dufresne and Red to highlight the transformative power of hope and redemption within the confines of a corrupt prison system.

3: Rhetorical Analysis

Topic: “Martin Luther King Jr.’s ‘I Have a Dream’ Speech”

Thesis Statement: In his ‘I Have a Dream’ speech, Martin Luther King Jr. employs rhetorical strategies such as repetition, parallelism, and powerful imagery to effectively convey his vision of racial equality and galvanize the civil rights movement.

4: Historical Analysis

Topic: “Causes of the Fall of the Roman Empire”

Thesis Statement: The fall of the Roman Empire was the result of a complex interplay of factors, including political corruption, economic instability, military defeats, and the gradual erosion of civic virtue, which collectively undermined the empire’s ability to sustain itself.

5: Character Analysis

Topic: “The Complexity of Hamlet in William Shakespeare’s ‘Hamlet'”

Thesis Statement: In William Shakespeare’s ‘Hamlet,’ the titular character’s complexity is revealed through his introspective nature, moral ambiguity, and fluctuating resolve, which collectively illustrate the play’s exploration of existential themes and the human condition.

6: Social Analysis

Topic: “The Impact of Social Media on Modern Communication”

Thesis Statement: Social media has significantly altered modern communication by enabling instantaneous sharing of information and fostering global connectivity, while also contributing to issues such as reduced face-to-face interactions, cyberbullying, and the spread of misinformation.

7: Cultural Analysis

Topic: “Cultural Significance of Traditional Festivals”

Thesis Statement: Traditional festivals play a crucial role in preserving cultural heritage, fostering community identity, and promoting social cohesion, as they provide a platform for the transmission of customs, values, and shared history across generations.

8: Economic Analysis

Topic: “The Effects of Globalization on Local Economies”

Thesis Statement: Globalization has profoundly impacted local economies by enhancing market access, fostering economic growth, and encouraging cultural exchange, but it has also led to job displacement, wage suppression, and the erosion of local industries in some regions.

9: Psychological Analysis

Topic: “Freudian Themes in ‘The Turn of the Screw’ by Henry James”

Thesis Statement: Henry James’s ‘The Turn of the Screw’ can be analyzed through a Freudian lens, where the governess’s experiences and the ambiguous nature of the ghosts reflect deep-seated psychological conflicts, repressed desires, and the complexities of the human psyche.

10: Political Analysis

Topic: “The Effectiveness of the New Deal Programs”

Thesis Statement: The New Deal programs implemented by President Franklin D. Roosevelt were effective in providing immediate relief during the Great Depression, spurring economic recovery, and implementing long-term reforms that reshaped the American social and economic landscape.

Thesis Examples for Compare and Contrast Essay

1: literature.

Topic: “Comparing ‘1984’ by George Orwell and ‘Brave New World’ by Aldous Huxley”

Thesis Statement: While George Orwell’s ‘1984’ presents a dystopian future of totalitarian control through fear and oppression, Aldous Huxley’s ‘Brave New World’ explores a similar theme through a society controlled by pleasure and conditioning, highlighting different methods of societal control and their implications.

Topic: “Public School vs. Private School Education”

Thesis Statement: Public schools offer a diverse social environment and a broad curriculum, whereas private schools provide smaller class sizes and specialized programs, making the choice between the two dependent on individual educational goals and personal preferences.

Topic: “E-books vs. Printed Books”

Thesis Statement: While e-books offer convenience, portability, and interactive features, printed books provide a tactile experience, lack of screen strain, and a sense of nostalgia, demonstrating how each format caters to different reader preferences and needs.

Topic: “Traditional Medicine vs. Modern Medicine”

Thesis Statement: Traditional medicine emphasizes holistic and natural treatments based on centuries-old practices, while modern medicine focuses on scientific research and technological advancements, highlighting the strengths and limitations of each approach in addressing health issues.

5: Social Media

Topic: “Facebook vs. Instagram”

Thesis Statement: Facebook facilitates in-depth social interaction and a wide range of features for communication and information sharing, whereas Instagram focuses on visual content and a streamlined user experience, catering to different user preferences and social engagement styles.

Topic: “Traveling by Plane vs. Traveling by Train”

Thesis Statement: Traveling by plane offers speed and efficiency for long distances, while traveling by train provides scenic views and a more relaxed experience, highlighting the trade-offs between convenience and leisure in different modes of transportation.

7: Economics

Topic: “Capitalism vs. Socialism”

Thesis Statement: Capitalism promotes economic growth and individual entrepreneurship through market competition, whereas socialism emphasizes social welfare and equitable distribution of resources, reflecting contrasting ideologies on economic management and social equity.

8: Literature

Topic: “Shakespeare’s ‘Hamlet’ vs. Sophocles’ ‘Oedipus Rex'”

Thesis Statement: While Shakespeare’s ‘Hamlet’ delves into themes of indecision, revenge, and existential angst, Sophocles’ ‘Oedipus Rex’ explores fate, self-discovery, and the inevitability of destiny, illustrating different approaches to tragedy in Western literature.

9: Lifestyle

Topic: “Urban Living vs. Rural Living”

Thesis Statement: Urban living offers convenience, diverse cultural experiences, and numerous job opportunities, while rural living provides a peaceful environment, close-knit communities, and a connection to nature, demonstrating the contrasting lifestyles and priorities of each setting.

10: History

Topic: “The American Revolution vs. The French Revolution”

Thesis Statement: The American Revolution focused on independence from colonial rule and the establishment of a democratic republic, whereas the French Revolution aimed to overthrow the monarchy and address social inequalities, highlighting different motivations, outcomes, and impacts on world history.

More Thesis Samples & Examples:

1. thesis statements.

2. University Thesis Research

3. Working Thesis

4. Master Thesis

5. Basics About Thesis Statements

6. Thesis Sample

7. Thesis Format

8. Thesis PDF

9. Graduate Students Thesis

10. Thesis Example

Tips for Writing Your Thesis

Start Early

• Begin your thesis process early to allow ample time for research , writing , and revisions.

Choose a Relevant Topic

• Select a topic that interests you and has sufficient research material available. Ensure it is specific enough to be manageable but broad enough to find sources.

Develop a Strong Thesis Statement

• Craft a clear, concise thesis statement that outlines the main argument or focus of your paper. This will guide your research and writing.

Create an Outline

• Plan your thesis structure with a detailed outline. Include sections for the introduction, literature review, methodology, results, discussion, and conclusion.

Conduct Thorough Research

• Use a variety of sources, such as books, journal articles, and credible websites. Take detailed notes and organize your research to support your thesis statement.

Write in Stages

• Break down the writing process into manageable stages. Start with the introduction, move to the literature review, then the methodology, and so on.

Maintain Consistent Formatting

• Follow the required formatting style (e.g., APA, MLA, Chicago) consistently throughout your thesis. Pay attention to citation rules and references.

Seek Feedback

• Regularly consult with your advisor and seek feedback from peers. Incorporate their suggestions to improve your work.

Edit and Revise

• Set aside time for multiple rounds of editing and revising. Check for clarity, coherence, grammar, and spelling errors.

Stay Organized

• Keep all your research materials, notes, and drafts well-organized. Use tools like folders, labels, and reference management software.

Stay Motivated

• Set small, achievable goals and reward yourself for meeting them. Stay positive and remember that writing a thesis is a marathon, not a sprint.

Proofread Thoroughly

• Conduct a final proofread to catch any remaining errors. Consider using grammar checking tools or hiring a professional proofreader.

What to include in a Thesis

Writing a thesis involves several critical sections that contribute to the overall structure and argumentation of the research. Here’s a guide on what to include in a thesis:

1. Title Page

• Title: Clear, concise, and descriptive.
• Author’s Name
• Institutional Affiliation
• Date of Submission
• Advisor’s Name

2. Abstract

• Summary: Brief overview of the research.
• Key Points: Main objectives, methods, results, and conclusions.
• Word Limit: Typically 150-300 words.

3. Table of Contents

• Sections and Subsections: With corresponding page numbers.

4. List of Figures and Tables

• Figures/Tables: Numbered and titled with page numbers.

5. Introduction

• Background: Context of the study.
• Problem Statement: The issue being addressed.
• Objectives: What the research aims to achieve.
• Research Questions/Hypotheses: Specific questions or hypotheses the study will test.
• Significance: Importance of the study.

6. Literature Review

• Overview of Existing Research: Summarize previous studies.
• Theoretical Framework: The theories guiding the research.
• Gaps in Literature: Identify what has not been addressed.

7. Methodology

• Research Design: Type of study (e.g., qualitative, quantitative).
• Participants: Who was involved in the study.
• Data Collection: How data was gathered (e.g., surveys, experiments).
• Data Analysis: Methods used to analyze the data.
• Ethical Considerations: How ethical issues were handled.
• Findings: Present data and key results.
• Visuals: Use tables, graphs, and charts for clarity.
• Statistical Analysis: Include relevant statistical tests.

9. Discussion

• Interpretation of Results: What the findings mean.
• Comparison with Existing Literature: How results align or contrast with previous research.
• Implications: Practical or theoretical implications.
• Limitations: Discuss limitations of the study.
• Future Research: Suggestions for future studies.

10. Conclusion

• Summary of Findings: Recap main findings.
• Restate Importance: Reiterate the study’s significance.
• Final Thoughts: Concluding remarks.

11. References

• Citations: Complete list of all sources cited in the thesis.
• Formatting: Follow a specific citation style (e.g., APA, MLA).

12. Appendices

• Supplementary Material: Additional data, questionnaires, or detailed descriptions.

Thesis vs. Dissertation

How do I know if my Thesis is strong?

Clear and specific thesis statement.

• Precision : Your thesis statement should be clear, specific, and concise. It should articulate the main argument or focus of your thesis.
• Focus : Ensure it directly addresses the research question without being too broad or vague.

Well-Defined Research Question

• Relevance : The research question should be significant to your field of study.
• Feasibility : Make sure it is practical and manageable within the scope of your resources and time frame.

Comprehensive Literature Review

• Depth : Your literature review should cover relevant research and show an understanding of key theories and findings.
• Gaps Identification : Highlight gaps in the existing literature that your thesis aims to fill.

Solid Methodology

• Appropriateness : The chosen methodology should be suitable for answering your research question.
• Detail : Clearly describe your research design, data collection methods, and data analysis procedures.
• Justification : Explain why these methods are the best fit for your study.

Strong Evidence and Analysis

• Support : Provide ample evidence to support your thesis statement and arguments.
• Critical Analysis : Critically analyze the data, showing how it supports or contradicts your hypothesis.
• Consistency : Ensure that all evidence is consistently interpreted and integrated into your argument.

Coherent Structure

• Organization : The thesis should be well-organized with a logical flow of ideas.
• Clarity : Each section should clearly contribute to the overall argument.
• Transitions : Use smooth transitions between sections to maintain coherence.

Original Contribution

• Innovation : Your thesis should offer new insights or findings in your field.
• Significance : Highlight the importance and impact of your research.

Proper Formatting and Style

• Formatting : Follow the required formatting guidelines (APA, MLA, Chicago, etc.) consistently.
• Grammar and Spelling : Proofread your work to ensure it is free from grammatical and spelling errors.
• Citations : Properly cite all sources and provide a comprehensive reference list.

Feedback and Revision

• Advisor Feedback : Regularly seek feedback from your advisor and incorporate their suggestions.
• Peer Review : Get input from peers to identify areas for improvement.
• Multiple Revisions : Be prepared to revise your thesis multiple times to enhance its quality.

Self-Assessment

• Alignment : Ensure that all parts of the thesis align with the thesis statement.
• Completeness : Check that all required sections are included and thoroughly addressed.
• Confidence : Be confident in your arguments and the quality of your research.

How to Make a Thesis

Where do you often begin when you want to make a thesis? Many may say to begin by drafting, to begin by making an outline or to start at the introduction. A lot of these answers may even confuse you and may make you think that making a thesis is difficult or confusing. Stop right there, there are answers to every question, and to show you the  thesis statement writing tips .

Step 1: Make an Outline for the Thesis

Start out by making a  thesis outline . The outline will help you as it acts as the backbone of your entire thesis. Making outlines also help you by giving you a good view of what comes first, what should be added here and what should not be added. Outlining your thesis is often the best way to begin.

Step 2: Start with a Thesis Proposal for Your Thesis Paper

Once you have a blank outline for your thesis, which you will be filling out in order to know what goes first, the next thing to do is to pick a topic or pick a thesis proposal . This is an important part of making your thesis paper. Start with thinking about what kind of thesis proposal you want to talk about.

Step 3: Write Down the Introduction of Your Thesis

Thesis introduction has an important role to play. Its role in your thesis is to give a short summary of what can be expected in your thesis. The introduction of your thesis is all about the topic or the proposal of your thesis. When you write your thesis, make sure that the introduction should be clear and concise. After the introduction, the heart of your thesis will follow.

Step 4: Finalize Your Thesis Paper

Finalizing your thesis paper may take a lot of time and effort. But not to worry. It is always necessary and understandable that finalizing your thesis paper is important. As long as you are making sure that everything that is necessary, the introduction, the proposal, the thesis problem, solution and conclusion are present.

How do I choose a thesis topic?

Choose a topic that interests you, has ample research material, is specific enough to be manageable, and aligns with your academic goals.

How long should my thesis be?

Thesis length varies by discipline and degree level; Master’s theses are usually 50-100 pages, while PhD dissertations can be 100-300+ pages.

What is a thesis statement?

A thesis statement is a concise summary of the main point or claim of your thesis, guiding your research and writing.

How do I structure my thesis?

A typical thesis structure includes a title page, abstract, table of contents, introduction, literature review, methodology, results, discussion, conclusion, and references.

How important is the literature review?

The literature review is crucial as it contextualizes your research, highlights gaps, and demonstrates your understanding of existing scholarship.

What is the difference between a thesis and a dissertation?

A thesis is usually for a Master’s degree and demonstrates mastery of a topic, while a dissertation for a PhD contributes new knowledge to the field.

How do I manage my time effectively while writing my thesis?

Create a detailed timeline, break the process into manageable tasks, set deadlines, and regularly consult with your advisor.

How do I ensure my thesis is original?

Conduct thorough research, properly cite sources, use plagiarism detection tools, and contribute unique insights or findings to your field.

What should I do if I encounter writer’s block?

Take breaks, set small writing goals, change your environment, seek feedback, and stay connected with your advisor for guidance and support.

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• Dissertation & Thesis Outline | Example & Free Templates

Dissertation & Thesis Outline | Example & Free Templates

Published on June 7, 2022 by Tegan George . Revised on November 21, 2023.

A thesis or dissertation outline is one of the most critical early steps in your writing process . It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding the specifics of your dissertation topic and showcasing its relevance to your field.

Generally, an outline contains information on the different sections included in your thesis or dissertation , such as:

• Your anticipated title
• Your abstract
• Your chapters (sometimes subdivided into further topics like literature review, research methods, avenues for future research, etc.)

In the final product, you can also provide a chapter outline for your readers. This is a short paragraph at the end of your introduction to inform readers about the organizational structure of your thesis or dissertation. This chapter outline is also known as a reading guide or summary outline.

Table of contents

How to outline your thesis or dissertation, dissertation and thesis outline templates, chapter outline example, sample sentences for your chapter outline, sample verbs for variation in your chapter outline, other interesting articles, frequently asked questions about thesis and dissertation outlines.

While there are some inter-institutional differences, many outlines proceed in a fairly similar fashion.

• Working Title
• “Elevator pitch” of your work (often written last).
• Introduce your area of study, sharing details about your research question, problem statement , and hypotheses . Situate your research within an existing paradigm or conceptual or theoretical framework .
• Subdivide as you see fit into main topics and sub-topics.
• Describe your research methods (e.g., your scope , population , and data collection ).
• Present your research findings and share about your data analysis methods.
• Answer the research question in a concise way.
• Interpret your findings, discuss potential limitations of your own research and speculate about future implications or related opportunities.

For a more detailed overview of chapters and other elements, be sure to check out our article on the structure of a dissertation or download our template .

To help you get started, we’ve created a full thesis or dissertation template in Word or Google Docs format. It’s easy adapt it to your own requirements.

 Download Word template    Download Google Docs template

It can be easy to fall into a pattern of overusing the same words or sentence constructions, which can make your work monotonous and repetitive for your readers. Consider utilizing some of the alternative constructions presented below.

Example 1: Passive construction

The passive voice is a common choice for outlines and overviews because the context makes it clear who is carrying out the action (e.g., you are conducting the research ). However, overuse of the passive voice can make your text vague and imprecise.

Example 2: IS-AV construction

You can also present your information using the “IS-AV” (inanimate subject with an active verb ) construction.

A chapter is an inanimate object, so it is not capable of taking an action itself (e.g., presenting or discussing). However, the meaning of the sentence is still easily understandable, so the IS-AV construction can be a good way to add variety to your text.

Example 3: The “I” construction

Another option is to use the “I” construction, which is often recommended by style manuals (e.g., APA Style and Chicago style ). However, depending on your field of study, this construction is not always considered professional or academic. Ask your supervisor if you’re not sure.

Example 4: Mix-and-match

To truly make the most of these options, consider mixing and matching the passive voice , IS-AV construction , and “I” construction .This can help the flow of your argument and improve the readability of your text.

As you draft the chapter outline, you may also find yourself frequently repeating the same words, such as “discuss,” “present,” “prove,” or “show.” Consider branching out to add richness and nuance to your writing. Here are some examples of synonyms you can use.

If you want to know more about AI for academic writing, AI tools, or research bias, make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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When you mention different chapters within your text, it’s considered best to use Roman numerals for most citation styles. However, the most important thing here is to remain consistent whenever using numbers in your dissertation .

The title page of your thesis or dissertation goes first, before all other content or lists that you may choose to include.

A thesis or dissertation outline is one of the most critical first steps in your writing process. It helps you to lay out and organize your ideas and can provide you with a roadmap for deciding what kind of research you’d like to undertake.

• Your chapters (sometimes subdivided into further topics like literature review , research methods , avenues for future research, etc.)

Cite this Scribbr article

If you want to cite this source, you can copy and paste the citation or click the “Cite this Scribbr article” button to automatically add the citation to our free Citation Generator.

George, T. (2023, November 21). Dissertation & Thesis Outline | Example & Free Templates. Scribbr. Retrieved June 18, 2024, from https://www.scribbr.com/dissertation/dissertation-thesis-outline/

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Building Resilience through Effective Climate Risk Data: Six Key Considerations

19th June 2024

Matt Rooney

Associate Director

Utilising Existing Resources

Building in-house capacity.

• Alignment with Best Practice
• Visual Outputs
• Configurability for Unique Contexts

Being Audit-Ready

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Climate risk data management is crucial for organisations aiming to build resilience against both physical and transitional climate risks. By effectively collating and analysing climate risk data, organisations can not only safeguard their operations and assets but also seize opportunities that arise from the transition to a low-carbon economy. Climate risk data management is now more important than ever to support organisations to comply with increasing regulatory requirements, including those from the International Sustainability Standards Board (ISSB) and U.S. Securities and Exchange Commission (SEC).

This article delves into the key considerations for optimising climate risk data management for organisations aiming to become more resilient and responsive to climate risks and opportunities.

The foundation of effective climate risk data management begins with leveraging existing resources. Organisations often possess valuable data that can be repurposed for climate risk analysis, such as financial data, asset management records, and operational performance metrics. Integrating these data sets with climate-related information can provide a comprehensive view of an organisation’s vulnerabilities and strengths.

Tapping into publicly available resources such as climate databases and scientific research can provide additional insights without significant additional costs. For instance, resources like the Intergovernmental Panel on Climate Change (IPCC) reports offer robust data and projections that can be instrumental in scenario analysis and risk assessment.

A reputable climate risk data management solution provider should be able to utilise this data as part of a wider solution and understand where and how you should use it in conjunction with your own data.

Developing in-house capacity is essential for long-term climate risk resilience. By investing in expertise in climate science, data analytics, and risk management, organisations can maintain control over their data processes and swiftly adapt to emerging risks and opportunities.

As organisations will need to pull from a wide expertise base, it is likely to be more cost effective to outsource the specific expertise required to external advisors to interpret complex data, conduct scenario analyses and develop strategic responses, while building general in-house capacity and understanding.

Building general in-house capacity fosters a culture of sustainability and resilience within the organisation. Teams equipped with a deep understanding of climate risks are better positioned to advocate for and implement sustainable practices across all levels of the organisation, driving continuous improvement and innovation. Training for existing staff can significantly enhance the ability to implement plans within the organisation.

Alignment with Best Practice Climate Change Models for Scenario Analysis

Aligning with best practice climate change models is essential for conducting reliable scenario analysis. Models such as those developed by the IPCC or the Network for Greening the Financial System (NGFS) provide scientifically grounded frameworks for assessing climate risks and opportunities under different scenarios. These models help organisations understand potential future climate conditions and their impacts on operations, assets, and markets.

Scenario analysis allows organisations to explore a range of potential futures, identify critical vulnerabilities, and develop robust adaptation and mitigation strategies. Utilising external models derisks the scenario analysis and builds credibility and confidence in the outputs. As a result, organisations can gain insights into how different climate scenarios could affect business objectives and performance, supporting strategic planning and helping organisations prepare for diverse climate futures.

Visual Outputs and Engagement Maps

Effective communication of climate risk data is key to engaging stakeholders and driving action. Visual outputs and engagement maps are powerful tools for making complex data more accessible and understandable. Interactive maps, dashboards, and visualisations can illustrate climate risks and impacts in a clear and compelling way, helping stakeholders grasp the urgency and scale of the issues.

Visual tools also facilitate scenario planning and decision-making by allowing users to explore different risk scenarios and their implications visually. This enhances stakeholder engagement, fosters collaboration, and supports informed decision-making across the organisation, ensuring that climate risk management becomes an integral part of the organisational strategy.

Configurability for Unique Client Contexts

Off-the-shelf climate risk management software often lacks the flexibility to account for individual organisations’ unique contexts and parameters. If opting for a digital solution, configurability is essential to ensure that climate risk data management systems can be tailored to meet specific needs and circumstances. This includes the ability to incorporate unique asset profiles, existing mitigations and adaptation measures, and specific regulatory and market conditions.

A configurable system allows organisations to customise their data inputs, analytical models, and reporting outputs to align with their unique risk profiles and strategic objectives. Coupled with subject matter experts to analyse and interrogate the climate risk data and supplement with contextual information and more granular data on risk vulnerability where necessary, this ensures that the insights derived are relevant, actionable, and aligned with the organisation’s overall strategy, making the data management process more efficient and effective.

This approach of supplementing data models with an understanding of the specific context of the organisation will become more important over time as organisations are expected to show progress when reporting to internal and external stakeholders.

As regulatory requirements and stakeholder expectations around climate risk disclosure increase, being audit-ready is more important than ever. Organisations need to ensure that their climate risk data is accurate, comprehensive, and verifiable. Implementing robust data management practices, such as maintaining clear documentation, conducting regular data audits, connecting in stakeholders, such as finance teams, and using standardised metrics, can help organisations meet audit requirements and increase efficiency in the process.

Being audit-ready enhances transparency and credibility with investors, regulators, and other stakeholders. It demonstrates a commitment to accountability and continuous improvement, which can bolster an organisation’s reputation and trustworthiness.

Effectively managing climate risk data is a multifaceted challenge that requires careful consideration of various factors. By utilising existing resources, building in-house capability, being audit-ready, aligning with best practice climate change models, leveraging visual outputs, and ensuring configurability, organisations can enhance their resilience to climate risks and capitalise on opportunities presented by the transition to a sustainable economy. As well as mitigating risks, these considerations also enable organisations to proactively adapt to changing conditions and drive long-term value creation.

Climate risk analysis is often a starting point for a realisation that much has to change in terms of strategy, governance, and action. Our approach is to engage closely with clients throughout the process, to better understand the nuances and improve the analysis outputs, but also to advise on strategy and next steps, and be a trusted advisor.

We recommend an expert-led approach to Climate Risk, utilising robust modelling based on respected datasets, while recognising that the real value comes from the interpretation of that outputs (the “so what?”), the ability to adjust and fine-tune models to account for more specific context and data, and the consideration of actions and strategic responses that fit the client context.

For organisations seeking comprehensive climate risk management solutions, Anthesis is equipped to support. Our global team of 1,300 sustainability experts, climate risk consultants and data analysts can provide:

• Fully configurable digital solutions to automate data collection, support compliance with reporting requirements and provide visual outputs and engagement maps to make complex data more understandable and support decision making.
• Learning and training solutions to upskill and build your capacity in house, supplemented by expertise from our climate risk consultants to maximise the value to your organisation.
• Unrivalled specialist and sector-based expertise to provide scenario analysis, climate risk strategy and mitigation and adaptation actions required to reduce climate risk exposure and maximise the opportunities presented in a net zero transition.
• Experience developed from working with global organisations across sectors to understand best practice and benchmark against similar organisations.
• Audit readiness experience, navigating auditor requests and managing the risk of putting financial information in the public domain, which may other providers are less familiar with.

Get in touch

We are the world’s leading purpose driven, digitally enabled, science-based activator. And always welcome inquiries and partnerships to drive positive change together.

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Reference Examples

More than 100 reference examples and their corresponding in-text citations are presented in the seventh edition Publication Manual . Examples of the most common works that writers cite are provided on this page; additional examples are available in the Publication Manual .

To find the reference example you need, first select a category (e.g., periodicals) and then choose the appropriate type of work (e.g., journal article ) and follow the relevant example.

When selecting a category, use the webpages and websites category only when a work does not fit better within another category. For example, a report from a government website would use the reports category, whereas a page on a government website that is not a report or other work would use the webpages and websites category.

Also note that print and electronic references are largely the same. For example, to cite both print books and ebooks, use the books and reference works category and then choose the appropriate type of work (i.e., book ) and follow the relevant example (e.g., whole authored book ).

Examples on these pages illustrate the details of reference formats. We make every attempt to show examples that are in keeping with APA Style’s guiding principles of inclusivity and bias-free language. These examples are presented out of context only to demonstrate formatting issues (e.g., which elements to italicize, where punctuation is needed, placement of parentheses). References, including these examples, are not inherently endorsements for the ideas or content of the works themselves. An author may cite a work to support a statement or an idea, to critique that work, or for many other reasons. For more examples, see our sample papers .

Reference examples are covered in the seventh edition APA Style manuals in the Publication Manual Chapter 10 and the Concise Guide Chapter 10

Related handouts

• Common Reference Examples Guide (PDF, 147KB)
• Reference Quick Guide (PDF, 225KB)

Textual Works

Textual works are covered in Sections 10.1–10.8 of the Publication Manual . The most common categories and examples are presented here. For the reviews of other works category, see Section 10.7.

• Journal Article References
• Magazine Article References
• Newspaper Article References
• Blog Post and Blog Comment References
• UpToDate Article References
• Book/Ebook References
• Diagnostic Manual References
• Children’s Book or Other Illustrated Book References
• Classroom Course Pack Material References
• Religious Work References
• Chapter in an Edited Book/Ebook References
• Dictionary Entry References
• Wikipedia Entry References
• Report by a Government Agency References
• Report with Individual Authors References
• Brochure References
• Ethics Code References
• Fact Sheet References
• ISO Standard References
• Press Release References
• White Paper References
• Conference Presentation References
• Conference Proceeding References
• Published Dissertation or Thesis References
• Unpublished Dissertation or Thesis References
• ERIC Database References
• Preprint Article References

Data and Assessments

Data sets are covered in Section 10.9 of the Publication Manual . For the software and tests categories, see Sections 10.10 and 10.11.

• Data Set References
• Toolbox References

Audiovisual Media

Audiovisual media are covered in Sections 10.12–10.14 of the Publication Manual . The most common examples are presented together here. In the manual, these examples and more are separated into categories for audiovisual, audio, and visual media.

• Artwork References
• Clip Art or Stock Image References
• Film and Television References
• Musical Score References
• Online Course or MOOC References
• Podcast References
• PowerPoint Slide or Lecture Note References
• Radio Broadcast References
• TED Talk References
• Transcript of an Audiovisual Work References
• YouTube Video References

Online Media

Online media are covered in Sections 10.15 and 10.16 of the Publication Manual . Please note that blog posts are part of the periodicals category.

• Facebook References
• Instagram References
• LinkedIn References
• Online Forum (e.g., Reddit) References
• TikTok References
• X References
• Webpage on a Website References
• Clinical Practice References
• Open Educational Resource References
• Whole Website References

Defining the Role of Authors and Contributors

Page Contents

• Why Authorship Matters
• Who Is an Author?
• Non-Author Contributors
• Artificial Intelligence (AI)-Assisted Technology

1. Why Authorship Matters

Authorship confers credit and has important academic, social, and financial implications. Authorship also implies responsibility and accountability for published work. The following recommendations are intended to ensure that contributors who have made substantive intellectual contributions to a paper are given credit as authors, but also that contributors credited as authors understand their role in taking responsibility and being accountable for what is published.

Editors should be aware of the practice of excluding local researchers from low-income and middle-income countries (LMICs) from authorship when data are from LMICs. Inclusion of local authors adds to fairness, context, and implications of the research. Lack of inclusion of local investigators as authors should prompt questioning and may lead to rejection.

Because authorship does not communicate what contributions qualified an individual to be an author, some journals now request and publish information about the contributions of each person named as having participated in a submitted study, at least for original research. Editors are strongly encouraged to develop and implement a contributorship policy. Such policies remove much of the ambiguity surrounding contributions, but leave unresolved the question of the quantity and quality of contribution that qualify an individual for authorship. The ICMJE has thus developed criteria for authorship that can be used by all journals, including those that distinguish authors from other contributors.

2. Who Is an Author?

The ICMJE recommends that authorship be based on the following 4 criteria:

• Substantial contributions to the conception or design of the work; or the acquisition, analysis, or interpretation of data for the work; AND
• Drafting the work or reviewing it critically for important intellectual content; AND
• Final approval of the version to be published; AND
• Agreement to be accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

In addition to being accountable for the parts of the work done, an author should be able to identify which co-authors are responsible for specific other parts of the work. In addition, authors should have confidence in the integrity of the contributions of their co-authors.

All those designated as authors should meet all four criteria for authorship, and all who meet the four criteria should be identified as authors. Those who do not meet all four criteria should be acknowledged—see Section II.A.3 below. These authorship criteria are intended to reserve the status of authorship for those who deserve credit and can take responsibility for the work. The criteria are not intended for use as a means to disqualify colleagues from authorship who otherwise meet authorship criteria by denying them the opportunity to meet criterion #s 2 or 3. Therefore, all individuals who meet the first criterion should have the opportunity to participate in the review, drafting, and final approval of the manuscript.

The individuals who conduct the work are responsible for identifying who meets these criteria and ideally should do so when planning the work, making modifications as appropriate as the work progresses. We encourage collaboration and co-authorship with colleagues in the locations where the research is conducted. It is the collective responsibility of the authors, not the journal to which the work is submitted, to determine that all people named as authors meet all four criteria; it is not the role of journal editors to determine who qualifies or does not qualify for authorship or to arbitrate authorship conflicts. If agreement cannot be reached about who qualifies for authorship, the institution(s) where the work was performed, not the journal editor, should be asked to investigate. The criteria used to determine the order in which authors are listed on the byline may vary, and are to be decided collectively by the author group and not by editors. If authors request removal or addition of an author after manuscript submission or publication, journal editors should seek an explanation and signed statement of agreement for the requested change from all listed authors and from the author to be removed or added.

The corresponding author is the one individual who takes primary responsibility for communication with the journal during the manuscript submission, peer-review, and publication process. The corresponding author typically ensures that all the journal’s administrative requirements, such as providing details of authorship, ethics committee approval, clinical trial registration documentation, and disclosures of relationships and activities are properly completed and reported, although these duties may be delegated to one or more co-authors. The corresponding author should be available throughout the submission and peer-review process to respond to editorial queries in a timely way, and should be available after publication to respond to critiques of the work and cooperate with any requests from the journal for data or additional information should questions about the paper arise after publication. Although the corresponding author has primary responsibility for correspondence with the journal, the ICMJE recommends that editors send copies of all correspondence to all listed authors.

When a large multi-author group has conducted the work, the group ideally should decide who will be an author before the work is started and confirm who is an author before submitting the manuscript for publication. All members of the group named as authors should meet all four criteria for authorship, including approval of the final manuscript, and they should be able to take public responsibility for the work and should have full confidence in the accuracy and integrity of the work of other group authors. They will also be expected as individuals to complete disclosure forms.

Some large multi-author groups designate authorship by a group name, with or without the names of individuals. When submitting a manuscript authored by a group, the corresponding author should specify the group name if one exists, and clearly identify the group members who can take credit and responsibility for the work as authors. The byline of the article identifies who is directly responsible for the manuscript, and MEDLINE lists as authors whichever names appear on the byline. If the byline includes a group name, MEDLINE will list the names of individual group members who are authors or who are collaborators, sometimes called non-author contributors, if there is a note associated with the byline clearly stating that the individual names are elsewhere in the paper and whether those names are authors or collaborators.

3. Non-Author Contributors

Contributors who meet fewer than all 4 of the above criteria for authorship should not be listed as authors, but they should be acknowledged. Examples of activities that alone (without other contributions) do not qualify a contributor for authorship are acquisition of funding; general supervision of a research group or general administrative support; and writing assistance, technical editing, language editing, and proofreading. Those whose contributions do not justify authorship may be acknowledged individually or together as a group under a single heading (e.g. "Clinical Investigators" or "Participating Investigators"), and their contributions should be specified (e.g., "served as scientific advisors," "critically reviewed the study proposal," "collected data," "provided and cared for study patients," "participated in writing or technical editing of the manuscript").

Because acknowledgment may imply endorsement by acknowledged individuals of a study’s data and conclusions, editors are advised to require that the corresponding author obtain written permission to be acknowledged from all acknowledged individuals.

Use of AI for writing assistance should be reported in the acknowledgment section.

4. Artificial Intelligence (AI)-Assisted Technology

At submission, the journal should require authors to disclose whether they used artificial intelligence (AI)-assisted technologies (such as Large Language Models [LLMs], chatbots, or image creators) in the production of submitted work. Authors who use such technology should describe, in both the cover letter and the submitted work in the appropriate section if applicable, how they used it. For example, if AI was used for writing assistance, describe this in the acknowledgment section (see Section II.A.3). If AI was used for data collection, analysis, or figure generation, authors should describe this use in the methods (see Section IV.A.3.d). Chatbots (such as ChatGPT) should not be listed as authors because they cannot be responsible for the accuracy, integrity, and originality of the work, and these responsibilities are required for authorship (see Section II.A.1). Therefore, humans are responsible for any submitted material that included the use of AI-assisted technologies. Authors should carefully review and edit the result because AI can generate authoritative-sounding output that can be incorrect, incomplete, or biased. Authors should not list AI and AI-assisted technologies as an author or co-author, nor cite AI as an author. Authors should be able to assert that there is no plagiarism in their paper, including in text and images produced by the AI. Humans must ensure there is appropriate attribution of all quoted material, including full citations.

Next: Disclosure of Financial and Non-Financial Relationships and Activities, and Conflicts of Interest

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