how to write a dissertation data analysis

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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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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How To Write The Results/Findings Chapter

For quantitative studies (dissertations & theses).

By: Derek Jansen (MBA) | Expert Reviewed By: Kerryn Warren (PhD) | July 2021

So, you’ve completed your quantitative data analysis and it’s time to report on your findings. But where do you start? In this post, we’ll walk you through the results chapter (also called the findings or analysis chapter), step by step, so that you can craft this section of your dissertation or thesis with confidence. If you’re looking for information regarding the results chapter for qualitative studies, you can find that here .

Overview: Quantitative Results Chapter

• What exactly the results chapter is
• What you need to include in your chapter
• How to structure the chapter
• Tips and tricks for writing a top-notch chapter
• Free results chapter template

What exactly is the results chapter?

The results chapter (also referred to as the findings or analysis chapter) is one of the most important chapters of your dissertation or thesis because it shows the reader what you’ve found in terms of the quantitative data you’ve collected. It presents the data using a clear text narrative, supported by tables, graphs and charts. In doing so, it also highlights any potential issues (such as outliers or unusual findings) you’ve come across.

But how’s that different from the discussion chapter?

Well, in the results chapter, you only present your statistical findings. Only the numbers, so to speak – no more, no less. Contrasted to this, in the discussion chapter , you interpret your findings and link them to prior research (i.e. your literature review), as well as your research objectives and research questions . In other words, the results chapter presents and describes the data, while the discussion chapter interprets the data.

Let’s look at an example.

In your results chapter, you may have a plot that shows how respondents to a survey  responded: the numbers of respondents per category, for instance. You may also state whether this supports a hypothesis by using a p-value from a statistical test. But it is only in the discussion chapter where you will say why this is relevant or how it compares with the literature or the broader picture. So, in your results chapter, make sure that you don’t present anything other than the hard facts – this is not the place for subjectivity.

It’s worth mentioning that some universities prefer you to combine the results and discussion chapters. Even so, it is good practice to separate the results and discussion elements within the chapter, as this ensures your findings are fully described. Typically, though, the results and discussion chapters are split up in quantitative studies. If you’re unsure, chat with your research supervisor or chair to find out what their preference is.

What should you include in the results chapter?

Following your analysis, it’s likely you’ll have far more data than are necessary to include in your chapter. In all likelihood, you’ll have a mountain of SPSS or R output data, and it’s your job to decide what’s most relevant. You’ll need to cut through the noise and focus on the data that matters.

This doesn’t mean that those analyses were a waste of time – on the contrary, those analyses ensure that you have a good understanding of your dataset and how to interpret it. However, that doesn’t mean your reader or examiner needs to see the 165 histograms you created! Relevance is key.

How do I decide what’s relevant?

At this point, it can be difficult to strike a balance between what is and isn’t important. But the most important thing is to ensure your results reflect and align with the purpose of your study .  So, you need to revisit your research aims, objectives and research questions and use these as a litmus test for relevance. Make sure that you refer back to these constantly when writing up your chapter so that you stay on track.

As a general guide, your results chapter will typically include the following:

• Some demographic data about your sample
• Reliability tests (if you used measurement scales)
• Descriptive statistics
• Inferential statistics (if your research objectives and questions require these)
• Hypothesis tests (again, if your research objectives and questions require these)

We’ll discuss each of these points in more detail in the next section.

Importantly, your results chapter needs to lay the foundation for your discussion chapter . This means that, in your results chapter, you need to include all the data that you will use as the basis for your interpretation in the discussion chapter.

For example, if you plan to highlight the strong relationship between Variable X and Variable Y in your discussion chapter, you need to present the respective analysis in your results chapter – perhaps a correlation or regression analysis.

Need a helping hand?

How do I write the results chapter?

There are multiple steps involved in writing up the results chapter for your quantitative research. The exact number of steps applicable to you will vary from study to study and will depend on the nature of the research aims, objectives and research questions . However, we’ll outline the generic steps below.

Step 1 – Revisit your research questions

The first step in writing your results chapter is to revisit your research objectives and research questions . These will be (or at least, should be!) the driving force behind your results and discussion chapters, so you need to review them and then ask yourself which statistical analyses and tests (from your mountain of data) would specifically help you address these . For each research objective and research question, list the specific piece (or pieces) of analysis that address it.

At this stage, it’s also useful to think about the key points that you want to raise in your discussion chapter and note these down so that you have a clear reminder of which data points and analyses you want to highlight in the results chapter. Again, list your points and then list the specific piece of analysis that addresses each point. 

Next, you should draw up a rough outline of how you plan to structure your chapter . Which analyses and statistical tests will you present and in what order? We’ll discuss the “standard structure” in more detail later, but it’s worth mentioning now that it’s always useful to draw up a rough outline before you start writing (this advice applies to any chapter).

Step 2 – Craft an overview introduction

As with all chapters in your dissertation or thesis, you should start your quantitative results chapter by providing a brief overview of what you’ll do in the chapter and why . For example, you’d explain that you will start by presenting demographic data to understand the representativeness of the sample, before moving onto X, Y and Z.

This section shouldn’t be lengthy – a paragraph or two maximum. Also, it’s a good idea to weave the research questions into this section so that there’s a golden thread that runs through the document.

Step 3 – Present the sample demographic data

The first set of data that you’ll present is an overview of the sample demographics – in other words, the demographics of your respondents.

For example:

• What age range are they?
• How is gender distributed?
• How is ethnicity distributed?
• What areas do the participants live in?

The purpose of this is to assess how representative the sample is of the broader population. This is important for the sake of the generalisability of the results. If your sample is not representative of the population, you will not be able to generalise your findings. This is not necessarily the end of the world, but it is a limitation you’ll need to acknowledge.

Of course, to make this representativeness assessment, you’ll need to have a clear view of the demographics of the population. So, make sure that you design your survey to capture the correct demographic information that you will compare your sample to.

But what if I’m not interested in generalisability?

Well, even if your purpose is not necessarily to extrapolate your findings to the broader population, understanding your sample will allow you to interpret your findings appropriately, considering who responded. In other words, it will help you contextualise your findings . For example, if 80% of your sample was aged over 65, this may be a significant contextual factor to consider when interpreting the data. Therefore, it’s important to understand and present the demographic data.

 Step 4 – Review composite measures and the data “shape”.

Before you undertake any statistical analysis, you’ll need to do some checks to ensure that your data are suitable for the analysis methods and techniques you plan to use. If you try to analyse data that doesn’t meet the assumptions of a specific statistical technique, your results will be largely meaningless. Therefore, you may need to show that the methods and techniques you’ll use are “allowed”.

Most commonly, there are two areas you need to pay attention to:

#1: Composite measures

The first is when you have multiple scale-based measures that combine to capture one construct – this is called a composite measure .  For example, you may have four Likert scale-based measures that (should) all measure the same thing, but in different ways. In other words, in a survey, these four scales should all receive similar ratings. This is called “ internal consistency ”.

Internal consistency is not guaranteed though (especially if you developed the measures yourself), so you need to assess the reliability of each composite measure using a test. Typically, Cronbach’s Alpha is a common test used to assess internal consistency – i.e., to show that the items you’re combining are more or less saying the same thing. A high alpha score means that your measure is internally consistent. A low alpha score means you may need to consider scrapping one or more of the measures.

#2: Data shape

The second matter that you should address early on in your results chapter is data shape. In other words, you need to assess whether the data in your set are symmetrical (i.e. normally distributed) or not, as this will directly impact what type of analyses you can use. For many common inferential tests such as T-tests or ANOVAs (we’ll discuss these a bit later), your data needs to be normally distributed. If it’s not, you’ll need to adjust your strategy and use alternative tests.

To assess the shape of the data, you’ll usually assess a variety of descriptive statistics (such as the mean, median and skewness), which is what we’ll look at next.

Step 5 – Present the descriptive statistics

Now that you’ve laid the foundation by discussing the representativeness of your sample, as well as the reliability of your measures and the shape of your data, you can get started with the actual statistical analysis. The first step is to present the descriptive statistics for your variables.

For scaled data, this usually includes statistics such as:

• The mean – this is simply the mathematical average of a range of numbers.
• The median – this is the midpoint in a range of numbers when the numbers are arranged in order.
• The mode – this is the most commonly repeated number in the data set.
• Standard deviation – this metric indicates how dispersed a range of numbers is. In other words, how close all the numbers are to the mean (the average).
• Skewness – this 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 (this is called a normal or parametric distribution), or do they lean to the left or right (this is called a non-normal or non-parametric distribution).
• Kurtosis – this metric indicates whether the data are heavily or lightly-tailed, relative to the normal distribution. In other words, how peaked or flat the distribution is.

A large table that indicates all the above for multiple variables can be a very effective way to present your data economically. You can also use colour coding to help make the data more easily digestible.

For categorical data, where you show the percentage of people who chose or fit into a category, for instance, you can either just plain describe the percentages or numbers of people who responded to something or use graphs and charts (such as bar graphs and pie charts) to present your data in this section of the chapter.

When using figures, make sure that you label them simply and clearly , so that your reader can easily understand them. There’s nothing more frustrating than a graph that’s missing axis labels! Keep in mind that although you’ll be presenting charts and graphs, your text content needs to present a clear narrative that can stand on its own. In other words, don’t rely purely on your figures and tables to convey your key points: highlight the crucial trends and values in the text. Figures and tables should complement the writing, not carry it .

Depending on your research aims, objectives and research questions, you may stop your analysis at this point (i.e. descriptive statistics). However, if your study requires inferential statistics, then it’s time to deep dive into those .

Step 6 – Present the inferential statistics

Inferential statistics are used to make generalisations about a population , whereas descriptive statistics focus purely on the sample . Inferential statistical techniques, broadly speaking, can be broken down into two groups .

First, there are those that compare measurements between groups , such as t-tests (which measure differences between two groups) and ANOVAs (which measure differences between multiple groups). Second, there are techniques that assess the relationships between variables , such as correlation analysis and regression analysis. Within each of these, some tests can be used for normally distributed (parametric) data and some tests are designed specifically for use on non-parametric data.

There are a seemingly endless number of tests that you can use to crunch your data, so it’s easy to run down a rabbit hole and end up with piles of test data. Ultimately, the most important thing is to make sure that you adopt the tests and techniques that allow you to achieve your research objectives and answer your research questions .

In this section of the results chapter, you should try to make use of figures and visual components as effectively as possible. For example, if you present a correlation table, use colour coding to highlight the significance of the correlation values, or scatterplots to visually demonstrate what the trend is. The easier you make it for your reader to digest your findings, the more effectively you’ll be able to make your arguments in the next chapter.

Step 7 – Test your hypotheses

If your study requires it, the next stage is hypothesis testing. A hypothesis is a statement , often indicating a difference between groups or relationship between variables, that can be supported or rejected by a statistical test. However, not all studies will involve hypotheses (again, it depends on the research objectives), so don’t feel like you “must” present and test hypotheses just because you’re undertaking quantitative research.

The basic process for hypothesis testing is as follows:

• Specify your null hypothesis (for example, “The chemical psilocybin has no effect on time perception).
• Specify your alternative hypothesis (e.g., “The chemical psilocybin has an effect on time perception)
• Set your significance level (this is usually 0.05)
• Calculate your statistics and find your p-value (e.g., p=0.01)
• Draw your conclusions (e.g., “The chemical psilocybin does have an effect on time perception”)

Finally, if the aim of your study is to develop and test a conceptual framework , this is the time to present it, following the testing of your hypotheses. While you don’t need to develop or discuss these findings further in the results chapter, indicating whether the tests (and their p-values) support or reject the hypotheses is crucial.

Step 8 – Provide a chapter summary

To wrap up your results chapter and transition to the discussion chapter, you should provide a brief summary of the key findings . “Brief” is the keyword here – much like the chapter introduction, this shouldn’t be lengthy – a paragraph or two maximum. Highlight the findings most relevant to your research objectives and research questions, and wrap it up.

Some final thoughts, tips and tricks

Now that you’ve got the essentials down, here are a few tips and tricks to make your quantitative results chapter shine:

• When writing your results chapter, report your findings in the past tense . You’re talking about what you’ve found in your data, not what you are currently looking for or trying to find.
• Structure your results chapter systematically and sequentially . If you had two experiments where findings from the one generated inputs into the other, report on them in order.
• Make your own tables and graphs rather than copying and pasting them from statistical analysis programmes like SPSS. Check out the DataIsBeautiful reddit for some inspiration.
• Once you’re done writing, review your work to make sure that you have provided enough information to answer your research questions , but also that you didn’t include superfluous information.

If you’ve got any questions about writing up the quantitative results chapter, please leave a comment below. If you’d like 1-on-1 assistance with your quantitative analysis and discussion, check out our hands-on coaching service , or book a free consultation with a friendly coach.

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Thank you. I will try my best to write my results.

Awesome content 👏🏾

this was great explaination

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How do I write a dissertation data analysis plan?

How do I do dissertation data analysis?

Data Analysis Plan Overview

Dissertation methodologies require a data analysis plan . Your dissertation data analysis plan should clearly state the statistical tests and assumptions of these tests to examine each of the research questions, how scores are cleaned and created, and the desired sample size for that test. The selection of statistical tests depend on two factors: (1) how the research questions and hypotheses are phrased and (2) the level of measurement of the variables. For example, if the question examines the impact of variable x on variable y, we are talking about regressions, if the question seeks associations or relationships, we are into correlation and chi-square tests, if differences are examined, then t-tests and ANOVA’s are likely the correct test.

Discover How We Assist to Edit Your Dissertation Chapters

Aligning theoretical framework, gathering articles, synthesizing gaps, articulating a clear methodology and data plan, and writing about the theoretical and practical implications of your research are part of our comprehensive dissertation editing services.

• Bring dissertation editing expertise to chapters 1-5 in timely manner.
• Track all changes, then work with you to bring about scholarly writing.
• Ongoing support to address committee feedback, reducing revisions.

Level of Measurement

The level of measurement is the second factor used in selecting the correct statistical test. If the research question will examine the impact of X on Y variable, and that outcome variable Y is scale, a linear regression is the correct test. For example, what is the impact of Income on Savings (as a scale variable), the linear regression is the test.  If that outcome variable Y is ordinal, then an ordinal regression is the correct test (e.g., what is the impact of Income on Savings (with Savings as an ordinal $0-$100, $101-$1000, $1001-$10,000, variable), then an ordinal regression is the correct test. If the research question examines relationships, and the X and Y variable are categorical, then chi-square is the appropriate test. The main point is that both the phasing of the research question and the level of measurement of the variables dictate the selection of the test. This video on decision trees may be useful.

Statistical Assumptions in Data Analysis Plan

Part of the data analysis plan is to document the assumptions of a particular statistical test. Most assumptions fall into the normality, homogeneity of variance, and outlier bucket of assumptions. Other tests have additional assumptions. For example, in a linear regression with several predictors, the variance inflation factor needs to be assessed to determine that the predictors are not too highly correlated. This data analysis plan video may be helpful.

Composite Scores and Data Cleaning

Data analysis plans should discuss any reverse coding of the variables and the creation of composite or subscale scores. Before creating composite scores, alpha reliability should be planned to be examined. Data cleaning procedure should be documented.  For example, the removal of outliers, transforming variables to meet normality assumption, etc. 

Sample Size and Power Analysis

After selecting the appropriate statistical tests, data analysis plans should follow-up with a power analysis. The power analysis determines the sample size for a statistical test, given an alpha of .05, a given effect size (small, medium, or large) at a power of .80 (that is, an 80% chance of detecting differences or relationships if in fact difference are present in the data. This power analysis video may be helpful.

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Mastering Dissertation Data Analysis: A Comprehensive Guide

By Laura Brown on 29th December 2023

To craft an effective dissertation data analysis chapter, you need to follow some simple steps:

• Start by planning the structure and objectives of the chapter.
• Clearly set the stage by providing a concise overview of your research design and methodology.
• Proceed to thorough data preparation, ensuring accuracy and organisation.
• Justify your methods and present the results using visual aids for clarity.
• Discuss the findings within the context of your research questions.
• Finally, review and edit your chapter to ensure coherence.

This approach will ensure a well-crafted and impactful analysis section.

Before delving into details on how you can come up with an engaging data analysis show in your dissertation, we first need to understand what it is and why it is required.

What Is Data Analysis In A Dissertation?

The data analysis chapter is a crucial section of a research dissertation that involves the examination, interpretation, and synthesis of collected data. In this chapter, researchers employ statistical techniques, qualitative methods, or a combination of both to make sense of the data gathered during the research process.

Why Is The Data Analysis Chapter So Important?

The primary objectives of the data analysis chapter are to identify patterns, trends, relationships, and insights within the data set. Researchers use various tools and software to conduct a thorough analysis, ensuring that the results are both accurate and relevant to the research questions or hypotheses. Ultimately, the findings derived from this chapter contribute to the overall conclusions of the dissertation, providing a basis for drawing meaningful and well-supported insights.

Steps Required To Craft Data Analysis Chapter To Perfection

Now that we have an idea of what a dissertation analysis chapter is and why it is necessary to put it in the dissertation, let’s move towards how we can create one that has a significant impact. Our guide will move around the bulleted points that have been discussed initially in the beginning. So, it’s time to begin.

Step 1: Planning Your Data Analysis Chapter

Planning your data analysis chapter is a critical precursor to its successful execution.

• Begin by outlining the chapter structure to provide a roadmap for your analysis.
• Start with an introduction that succinctly introduces the purpose and significance of the data analysis in the context of your research.
• Following this, delineate the chapter into sections such as Data Preparation, where you detail the steps taken to organise and clean your data.
• Plan on to clearly define the Data Analysis Techniques employed, justifying their relevance to your research objectives.
• As you progress, plan for the Results Presentation, incorporating visual aids for clarity. Lastly, earmark a section for the Discussion of Findings, where you will interpret results within the broader context of your research questions.

This structured approach ensures a comprehensive and cohesive data analysis chapter, setting the stage for a compelling narrative that contributes significantly to your dissertation. You can always seek our dissertation data analysis help to plan your chapter.

Step 2: Setting The Stage – Introduction to Data Analysis

Your primary objective is to establish a solid foundation for the analytical journey. You need to skillfully link your data analysis to your research questions, elucidating the direct relevance and purpose of the upcoming analysis.

Simultaneously, define key concepts to provide clarity and ensure a shared understanding of the terms integral to your study. Following this, offer a concise overview of your data set characteristics, outlining its source, nature, and any noteworthy features.

This meticulous groundwork alongside our help with dissertation data analysis lays the base for a coherent and purposeful chapter, guiding readers seamlessly into the subsequent stages of your dissertation.

Step 3: Data Preparation

Now this is another pivotal phase in the data analysis process, ensuring the integrity and reliability of your findings. You should start with an insightful overview of the data cleaning and preprocessing procedures, highlighting the steps taken to refine and organise your dataset. Then, discuss any challenges encountered during the process and the strategies employed to address them.

Moving forward, delve into the specifics of data transformation procedures, elucidating any alterations made to the raw data for analysis. Clearly describe the methods employed for normalisation, scaling, or any other transformations deemed necessary. It will not only enhance the quality of your analysis but also foster transparency in your research methodology, reinforcing the robustness of your data-driven insights.

Step 4: Data Analysis Techniques

The data analysis section of a dissertation is akin to choosing the right tools for an artistic masterpiece. Carefully weigh the quantitative and qualitative approaches, ensuring a tailored fit for the nature of your data.

Quantitative Analysis

• Descriptive Statistics: Paint a vivid picture of your data through measures like mean, median, and mode. It’s like capturing the essence of your data’s personality.
• Inferential Statistics:Take a leap into the unknown, making educated guesses and inferences about your larger population based on a sample. It’s statistical magic in action.

Qualitative Analysis

• Thematic Analysis: Imagine your data as a novel, and thematic analysis as the tool to uncover its hidden chapters. Dissect the narrative, revealing recurring themes and patterns.
• Content Analysis: Scrutinise your data’s content like detectives, identifying key elements and meanings. It’s a deep dive into the substance of your qualitative data.

Providing Rationale for Chosen Methods

You should also articulate the why behind the chosen methods. It’s not just about numbers or themes; it’s about the story you want your data to tell. Through transparent rationale, you should ensure that your chosen techniques align seamlessly with your research goals, adding depth and credibility to the analysis.

Step 5: Presentation Of Your Results

You can simply break this process into two parts.

a.    Creating Clear and Concise Visualisations

Effectively communicate your findings through meticulously crafted visualisations. Use tables that offer a structured presentation, summarising key data points for quick comprehension. Graphs, on the other hand, visually depict trends and patterns, enhancing overall clarity. Thoughtfully design these visual aids to align with the nature of your data, ensuring they serve as impactful tools for conveying information.

b.    Interpreting and Explaining Results

Go beyond mere presentation by providing insightful interpretation by taking data analysis services for dissertation. Show the significance of your findings within the broader research context. Moreover, articulates the implications of observed patterns or relationships. By weaving a narrative around your results, you guide readers through the relevance and impact of your data analysis, enriching the overall understanding of your dissertation’s key contributions.

Step 6: Discussion of Findings

While discussing your findings and dissertation discussion chapter , it’s like putting together puzzle pieces to understand what your data is saying. You can always take dissertation data analysis help to explain what it all means, connecting back to why you started in the first place.

Be honest about any limitations or possible biases in your study; it’s like showing your cards to make your research more trustworthy. Comparing your results to what other smart people have found before you adds to the conversation, showing where your work fits in.

Looking ahead, you suggest ideas for what future researchers could explore, keeping the conversation going. So, it’s not just about what you found, but also about what comes next and how it all fits into the big picture of what we know.

Step 7: Writing Style and Tone

In order to perfectly come up with this chapter, follow the below points in your writing and adjust the tone accordingly,

• Use clear and concise language to ensure your audience easily understands complex concepts.
• Avoid unnecessary jargon in data analysis for thesis, and if specialised terms are necessary, provide brief explanations.
• Keep your writing style formal and objective, maintaining an academic tone throughout.
• Avoid overly casual language or slang, as the data analysis chapter is a serious academic document.
• Clearly define terms and concepts, providing specific details about your data preparation and analysis procedures.
• Use precise language to convey your ideas, minimising ambiguity.
• Follow a consistent formatting style for headings, subheadings, and citations to enhance readability.
• Ensure that tables, graphs, and visual aids are labelled and formatted uniformly for a polished presentation.
• Connect your analysis to the broader context of your research by explaining the relevance of your chosen methods and the importance of your findings.
• Offer a balance between detail and context, helping readers understand the significance of your data analysis within the larger study.
• Present enough detail to support your findings but avoid overwhelming readers with excessive information.
• Use a balance of text and visual aids to convey information efficiently.
• Maintain reader engagement by incorporating transitions between sections and effectively linking concepts.
• Use a mix of sentence structures to add variety and keep the writing engaging.
• Eliminate grammatical errors, typos, and inconsistencies through thorough proofreading.
• Consider seeking feedback from peers or mentors to ensure the clarity and coherence of your writing.

You can seek a data analysis dissertation example or sample from CrowdWriter to better understand how we write it while following the above-mentioned points.

Step 8: Reviewing and Editing

Reviewing and editing your data analysis chapter is crucial for ensuring its effectiveness and impact. By revising your work, you refine the clarity and coherence of your analysis, enhancing its overall quality.

Seeking feedback from peers, advisors or dissertation data analysis services provides valuable perspectives, helping identify blind spots and areas for improvement. Addressing common writing pitfalls, such as grammatical errors or unclear expressions, ensures your chapter is polished and professional.

Taking the time to review and edit not only strengthens the academic integrity of your work but also contributes to a final product that is clear, compelling, and ready for scholarly scrutiny.

Concluding On This Data Analysis Help

Be it master thesis data analysis, an undergraduate one or for PhD scholars, the steps remain almost the same as we have discussed in this guide. The primary focus is to be connected with your research questions and objectives while writing your data analysis chapter.

Do not lose your focus and choose the right analysis methods and design. Make sure to present your data through various visuals to better explain your data and engage the reader as well. At last, give it a detailed read and seek assistance from experts and your supervisor for further improvement.

Laura Brown, a senior content writer who writes actionable blogs at Crowd Writer.

Raw Data to Excellence: Master Dissertation Analysis

Discover the secrets of successful dissertation data analysis. Get practical advice and useful insights from experienced experts now!

Have you ever found yourself knee-deep in a dissertation, desperately seeking answers from the data you’ve collected? Or have you ever felt clueless with all the data that you’ve collected but don’t know where to start? Fear not, in this article we are going to discuss a method that helps you come out of this situation and that is Dissertation Data Analysis.

Dissertation data analysis is like uncovering hidden treasures within your research findings. It’s where you roll up your sleeves and explore the data you’ve collected, searching for patterns, connections, and those “a-ha!” moments. Whether you’re crunching numbers, dissecting narratives, or diving into qualitative interviews, data analysis is the key that unlocks the potential of your research.

Dissertation Data Analysis

Dissertation data analysis plays a crucial role in conducting rigorous research and drawing meaningful conclusions. It involves the systematic examination, interpretation, and organization of data collected during the research process. The aim is to identify patterns, trends, and relationships that can provide valuable insights into the research topic.

The first step in dissertation data analysis is to carefully prepare and clean the collected data. This may involve removing any irrelevant or incomplete information, addressing missing data, and ensuring data integrity. Once the data is ready, various statistical and analytical techniques can be applied to extract meaningful information.

Descriptive statistics are commonly used to summarize and describe the main characteristics of the data, such as measures of central tendency (e.g., mean, median) and measures of dispersion (e.g., standard deviation, range). These statistics help researchers gain an initial understanding of the data and identify any outliers or anomalies.

Furthermore, qualitative data analysis techniques can be employed when dealing with non-numerical data, such as textual data or interviews. This involves systematically organizing, coding, and categorizing qualitative data to identify themes and patterns.

Types of Research

When considering research types in the context of dissertation data analysis, several approaches can be employed:

1. Quantitative Research

This type of research involves the collection and analysis of numerical data. It focuses on generating statistical information and making objective interpretations. Quantitative research often utilizes surveys, experiments, or structured observations to gather data that can be quantified and analyzed using statistical techniques.

2. Qualitative Research

In contrast to quantitative research, qualitative research focuses on exploring and understanding complex phenomena in depth. It involves collecting non-numerical data such as interviews, observations, or textual materials. Qualitative data analysis involves identifying themes, patterns, and interpretations, often using techniques like content analysis or thematic analysis.

3. Mixed-Methods Research

This approach combines both quantitative and qualitative research methods. Researchers employing mixed-methods research collect and analyze both numerical and non-numerical data to gain a comprehensive understanding of the research topic. The integration of quantitative and qualitative data can provide a more nuanced and comprehensive analysis, allowing for triangulation and validation of findings.

Primary vs. Secondary Research

Primary research.

Primary research involves the collection of original data specifically for the purpose of the dissertation. This data is directly obtained from the source, often through surveys, interviews, experiments, or observations. Researchers design and implement their data collection methods to gather information that is relevant to their research questions and objectives. Data analysis in primary research typically involves processing and analyzing the raw data collected.

Secondary Research

Secondary research involves the analysis of existing data that has been previously collected by other researchers or organizations. This data can be obtained from various sources such as academic journals, books, reports, government databases, or online repositories. Secondary data can be either quantitative or qualitative, depending on the nature of the source material. Data analysis in secondary research involves reviewing, organizing, and synthesizing the available data.

If you wanna deepen into Methodology in Research, also read: What is Methodology in Research and How Can We Write it?

Types of Analysis 

Various types of analysis techniques can be employed to examine and interpret the collected data. Of all those types, the ones that are most important and used are:

• Descriptive Analysis: Descriptive analysis focuses on summarizing and describing the main characteristics of the data. It involves calculating measures of central tendency (e.g., mean, median) and measures of dispersion (e.g., standard deviation, range). Descriptive analysis provides an overview of the data, allowing researchers to understand its distribution, variability, and general patterns.
• Inferential Analysis: Inferential analysis aims to draw conclusions or make inferences about a larger population based on the collected sample data. This type of analysis involves applying statistical techniques, such as hypothesis testing, confidence intervals, and regression analysis, to analyze the data and assess the significance of the findings. Inferential analysis helps researchers make generalizations and draw meaningful conclusions beyond the specific sample under investigation.
• Qualitative Analysis: Qualitative analysis is used to interpret non-numerical data, such as interviews, focus groups, or textual materials. It involves coding, categorizing, and analyzing the data to identify themes, patterns, and relationships. Techniques like content analysis, thematic analysis, or discourse analysis are commonly employed to derive meaningful insights from qualitative data.
• Correlation Analysis: Correlation analysis is used to examine the relationship between two or more variables. It determines the strength and direction of the association between variables. Common correlation techniques include Pearson’s correlation coefficient, Spearman’s rank correlation, or point-biserial correlation, depending on the nature of the variables being analyzed.

Basic Statistical Analysis

When conducting dissertation data analysis, researchers often utilize basic statistical analysis techniques to gain insights and draw conclusions from their data. These techniques involve the application of statistical measures to summarize and examine the data. Here are some common types of basic statistical analysis used in dissertation research:

• Descriptive Statistics
• Frequency Analysis
• Cross-tabulation
• Chi-Square Test
• Correlation Analysis

Advanced Statistical Analysis

In dissertation data analysis, researchers may employ advanced statistical analysis techniques to gain deeper insights and address complex research questions. These techniques go beyond basic statistical measures and involve more sophisticated methods. Here are some examples of advanced statistical analysis commonly used in dissertation research:

Regression Analysis

• Analysis of Variance (ANOVA)
• Factor Analysis
• Cluster Analysis
• Structural Equation Modeling (SEM)
• Time Series Analysis

Examples of Methods of Analysis

Regression analysis is a powerful tool for examining relationships between variables and making predictions. It allows researchers to assess the impact of one or more independent variables on a dependent variable. Different types of regression analysis, such as linear regression, logistic regression, or multiple regression, can be used based on the nature of the variables and research objectives.

Event Study

An event study is a statistical technique that aims to assess the impact of a specific event or intervention on a particular variable of interest. This method is commonly employed in finance, economics, or management to analyze the effects of events such as policy changes, corporate announcements, or market shocks.

Vector Autoregression

Vector Autoregression is a statistical modeling technique used to analyze the dynamic relationships and interactions among multiple time series variables. It is commonly employed in fields such as economics, finance, and social sciences to understand the interdependencies between variables over time.

Preparing Data for Analysis

1. become acquainted with the data.

It is crucial to become acquainted with the data to gain a comprehensive understanding of its characteristics, limitations, and potential insights. This step involves thoroughly exploring and familiarizing oneself with the dataset before conducting any formal analysis by reviewing the dataset to understand its structure and content. Identify the variables included, their definitions, and the overall organization of the data. Gain an understanding of the data collection methods, sampling techniques, and any potential biases or limitations associated with the dataset.

2. Review Research Objectives

This step involves assessing the alignment between the research objectives and the data at hand to ensure that the analysis can effectively address the research questions. Evaluate how well the research objectives and questions align with the variables and data collected. Determine if the available data provides the necessary information to answer the research questions adequately. Identify any gaps or limitations in the data that may hinder the achievement of the research objectives.

3. Creating a Data Structure

This step involves organizing the data into a well-defined structure that aligns with the research objectives and analysis techniques. Organize the data in a tabular format where each row represents an individual case or observation, and each column represents a variable. Ensure that each case has complete and accurate data for all relevant variables. Use consistent units of measurement across variables to facilitate meaningful comparisons.

4. Discover Patterns and Connections

In preparing data for dissertation data analysis, one of the key objectives is to discover patterns and connections within the data. This step involves exploring the dataset to identify relationships, trends, and associations that can provide valuable insights. Visual representations can often reveal patterns that are not immediately apparent in tabular data. 

Qualitative Data Analysis

Qualitative data analysis methods are employed to analyze and interpret non-numerical or textual data. These methods are particularly useful in fields such as social sciences, humanities, and qualitative research studies where the focus is on understanding meaning, context, and subjective experiences. Here are some common qualitative data analysis methods:

Thematic Analysis

The thematic analysis involves identifying and analyzing recurring themes, patterns, or concepts within the qualitative data. Researchers immerse themselves in the data, categorize information into meaningful themes, and explore the relationships between them. This method helps in capturing the underlying meanings and interpretations within the data.

Content Analysis

Content analysis involves systematically coding and categorizing qualitative data based on predefined categories or emerging themes. Researchers examine the content of the data, identify relevant codes, and analyze their frequency or distribution. This method allows for a quantitative summary of qualitative data and helps in identifying patterns or trends across different sources.

Grounded Theory

Grounded theory is an inductive approach to qualitative data analysis that aims to generate theories or concepts from the data itself. Researchers iteratively analyze the data, identify concepts, and develop theoretical explanations based on emerging patterns or relationships. This method focuses on building theory from the ground up and is particularly useful when exploring new or understudied phenomena.

Discourse Analysis

Discourse analysis examines how language and communication shape social interactions, power dynamics, and meaning construction. Researchers analyze the structure, content, and context of language in qualitative data to uncover underlying ideologies, social representations, or discursive practices. This method helps in understanding how individuals or groups make sense of the world through language.

Narrative Analysis

Narrative analysis focuses on the study of stories, personal narratives, or accounts shared by individuals. Researchers analyze the structure, content, and themes within the narratives to identify recurring patterns, plot arcs, or narrative devices. This method provides insights into individuals’ live experiences, identity construction, or sense-making processes.

Applying Data Analysis to Your Dissertation

Applying data analysis to your dissertation is a critical step in deriving meaningful insights and drawing valid conclusions from your research. It involves employing appropriate data analysis techniques to explore, interpret, and present your findings. Here are some key considerations when applying data analysis to your dissertation:

Selecting Analysis Techniques

Choose analysis techniques that align with your research questions, objectives, and the nature of your data. Whether quantitative or qualitative, identify the most suitable statistical tests, modeling approaches, or qualitative analysis methods that can effectively address your research goals. Consider factors such as data type, sample size, measurement scales, and the assumptions associated with the chosen techniques.

Data Preparation

Ensure that your data is properly prepared for analysis. Cleanse and validate your dataset, addressing any missing values, outliers, or data inconsistencies. Code variables, transform data if necessary, and format it appropriately to facilitate accurate and efficient analysis. Pay attention to ethical considerations, data privacy, and confidentiality throughout the data preparation process.

Execution of Analysis

Execute the selected analysis techniques systematically and accurately. Utilize statistical software, programming languages, or qualitative analysis tools to carry out the required computations, calculations, or interpretations. Adhere to established guidelines, protocols, or best practices specific to your chosen analysis techniques to ensure reliability and validity.

Interpretation of Results

Thoroughly interpret the results derived from your analysis. Examine statistical outputs, visual representations, or qualitative findings to understand the implications and significance of the results. Relate the outcomes back to your research questions, objectives, and existing literature. Identify key patterns, relationships, or trends that support or challenge your hypotheses.

Drawing Conclusions

Based on your analysis and interpretation, draw well-supported conclusions that directly address your research objectives. Present the key findings in a clear, concise, and logical manner, emphasizing their relevance and contributions to the research field. Discuss any limitations, potential biases, or alternative explanations that may impact the validity of your conclusions.

Validation and Reliability

Evaluate the validity and reliability of your data analysis by considering the rigor of your methods, the consistency of results, and the triangulation of multiple data sources or perspectives if applicable. Engage in critical self-reflection and seek feedback from peers, mentors, or experts to ensure the robustness of your data analysis and conclusions.

In conclusion, dissertation data analysis is an essential component of the research process, allowing researchers to extract meaningful insights and draw valid conclusions from their data. By employing a range of analysis techniques, researchers can explore relationships, identify patterns, and uncover valuable information to address their research objectives.

Turn Your Data Into Easy-To-Understand And Dynamic Stories

Decoding data is daunting and you might end up in confusion. Here’s where infographics come into the picture. With visuals, you can turn your data into easy-to-understand and dynamic stories that your audience can relate to. Mind the Graph is one such platform that helps scientists to explore a library of visuals and use them to amplify their research work. Sign up now to make your presentation simpler. 

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• Cookies & Privacy
• 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

CONSIDERATION ONE

The data analysis process.

The data analysis process involves three steps : (STEP ONE) select the correct statistical tests to run on your data; (STEP TWO) prepare and analyse the data you have collected using a relevant statistics package; and (STEP THREE) interpret the findings properly so that you can write up your results (i.e., usually in Chapter Four: Results ). The basic idea behind each of these steps is relatively straightforward, but the act of analysing your data (i.e., by selecting statistical tests, preparing your data and analysing it, and interpreting the findings from these tests) can be time consuming and challenging. We have tried to make this process as easy as possible by providing comprehensive, step-by-step guides in the Data Analysis part of Lærd Dissertation, but you should leave time at least one week to analyse your data.

STEP ONE Select the correct statistical tests to run on your data

It is common that dissertation students collect good data, but then report the wrong findings because of selecting the incorrect statistical tests to run in the first place. Selecting the correct statistical tests to perform on the data that you have collected will depend on (a) the research questions/hypotheses you have set, together with the research design you have adopted, and (b) the type and nature of your data:

The research questions/hypotheses you have set, together with the research design you have adopted

Your research questions/hypotheses and research design explain what variables you are measuring and how you plan to measure these variables. These highlight whether you want to (a) predict a score or a membership of a group, (b) find out differences between groups or treatments, or (c) explore associations/relationships between variables. These different aims determine the statistical tests that may be appropriate to run on your data. We highlight the word may because the most appropriate test that is identified based on your research questions/hypotheses and research design can change depending on the type and nature of the data you collect; something we discuss next.

The type and nature of the data you collected

Data is not all the same. As you will have identified by now, not all variables are measured in the same way; variables can be dichotomous, ordinal, or continuous. In addition, not all data is normal , as term we explain the Data Analysis section, nor is the data you have collected when comparing groups necessarily equal for each group. As a result, you might think that running a particular statistical test is correct (e.g., a dependent t-test), based on the research questions/hypotheses you have set, but the data you have collected fails certain assumptions that are important to this statistical test (i.e., normality and homogeneity of variance ). As a result, you have to run another statistical test (e.g., a Mann-Whitney U instead of a dependent t-test).

To select the correct statistical tests to run on the data in your dissertation, we have created a Statistical Test Selector to help guide you through the various options.

STEP TWO Prepare and analyse your data using a relevant statistics package

The preparation and analysis of your data is actually a much more practical step than many students realise. Most of the time required to get the results that you will present in your write up (i.e., usually in Chapter Four: Results ) comes from knowing (a) how to enter data into a statistics package (e.g., SPSS) so that it can be analysed correctly, and (b) what buttons to press in the statistics package to correctly run the statistical tests you need:

Entering data is not just about knowing what buttons to press, but: (a) how to code your data correctly to recognise the types of variables that you have, as well as issues such as reverse coding ; (b) how to filter your dataset to take into account missing data and outliers ; (c) how to split files (i.e., in SPSS) when analysing the data for separate subgroups (e.g., males and females) using the same statistical tests; (d) how to weight and unweight data you have collected; and (e) other things you need to consider when entering data. What you have to do when it comes to entering data (i.e., in terms of coding, filtering, splitting files, and weighting/unweighting data) will depend on the statistical tests you plan to run. Therefore, entering data starts with using the Statistical Test Selector to help guide you through the various options. In the Data Analysis section, we help you to understand what you need to know about entering data in the context of your dissertation.

Running statistical tests

Statistics packages do the hard work of statistically analysing your data, but they rely on you making a number of choices. This is not simply about selecting the correct statistical test, but knowing, when you have selected a given test to run on your data, what buttons to press to: (a) test for the assumptions underlying the statistical test; (b) test whether corrections can be made when assumptions are violated ; (c) take into account outliers and missing data ; (d) choose between the different numerical and graphical ways to approach your analysis; and (e) other standard and more advanced tips. In the Data Analysis section, we explain what these considerations are (i.e., assumptions, corrections, outliers and missing data, numerical and graphical analysis) so that you can apply them to your own dissertation. We also provide comprehensive , step-by-step instructions with screenshots that show you how to enter data and run a wide range of statistical tests using the statistics package, SPSS. We do this on the basis that you probably have little or no knowledge of SPSS.

STEP THREE Interpret the findings properly

SPSS produces many tables of output for the typical tests you will run. In addition, SPSS has many new methods of presenting data using its Model viewer. You need to know which of these tables is important for your analysis and what the different figures/numbers mean. Interpreting these findings properly and communicating your results is one of the most important aspects of your dissertation. In the Data Analysis section, we show you how to understand these tables of output, what part of this output you need to look at, and how to write up the results in an appropriate format (i.e., so that you can answer you research hypotheses).

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• Knowledge Base
• Dissertation
• What Is a Research Methodology? | Steps & Tips

What Is a Research Methodology? | Steps & Tips

Published on August 25, 2022 by Shona McCombes and Tegan George. Revised on November 20, 2023.

Your research methodology discusses and explains the data collection and analysis methods you used in your research. A key part of your thesis, dissertation , or research paper , the methodology chapter explains what you did and how you did it, allowing readers to evaluate the reliability and validity of your research and your dissertation topic .

It should include:

• The type of research you conducted
• How you collected and analyzed your data
• Any tools or materials you used in the research
• How you mitigated or avoided research biases
• Why you chose these methods
• Your methodology section should generally be written in the past tense .
• Academic style guides in your field may provide detailed guidelines on what to include for different types of studies.
• Your citation style might provide guidelines for your methodology section (e.g., an APA Style methods section ).

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Table of contents

How to write a research methodology, why is a methods section important, step 1: explain your methodological approach, step 2: describe your data collection methods, step 3: describe your analysis method, step 4: evaluate and justify the methodological choices you made, tips for writing a strong methodology chapter, other interesting articles, frequently asked questions about methodology.

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Your methods section is your opportunity to share how you conducted your research and why you chose the methods you chose. It’s also the place to show that your research was rigorously conducted and can be replicated .

It gives your research legitimacy and situates it within your field, and also gives your readers a place to refer to if they have any questions or critiques in other sections.

You can start by introducing your overall approach to your research. You have two options here.

Option 1: Start with your “what”

What research problem or question did you investigate?

• Aim to describe the characteristics of something?
• Explore an under-researched topic?
• Establish a causal relationship?

And what type of data did you need to achieve this aim?

• Quantitative data , qualitative data , or a mix of both?
• Primary data collected yourself, or secondary data collected by someone else?
• Experimental data gathered by controlling and manipulating variables, or descriptive data gathered via observations?

Option 2: Start with your “why”

Depending on your discipline, you can also start with a discussion of the rationale and assumptions underpinning your methodology. In other words, why did you choose these methods for your study?

• Why is this the best way to answer your research question?
• Is this a standard methodology in your field, or does it require justification?
• Were there any ethical considerations involved in your choices?
• What are the criteria for validity and reliability in this type of research ? How did you prevent bias from affecting your data?

Once you have introduced your reader to your methodological approach, you should share full details about your data collection methods .

Quantitative methods

In order to be considered generalizable, you should describe quantitative research methods in enough detail for another researcher to replicate your study.

Here, explain how you operationalized your concepts and measured your variables. Discuss your sampling method or inclusion and exclusion criteria , as well as any tools, procedures, and materials you used to gather your data.

Surveys Describe where, when, and how the survey was conducted.

• How did you design the questionnaire?
• What form did your questions take (e.g., multiple choice, Likert scale )?
• Were your surveys conducted in-person or virtually?
• What sampling method did you use to select participants?
• What was your sample size and response rate?

Experiments Share full details of the tools, techniques, and procedures you used to conduct your experiment.

• How did you design the experiment ?
• How did you recruit participants?
• How did you manipulate and measure the variables ?
• What tools did you use?

Existing data Explain how you gathered and selected the material (such as datasets or archival data) that you used in your analysis.

• Where did you source the material?
• How was the data originally produced?
• What criteria did you use to select material (e.g., date range)?

The survey consisted of 5 multiple-choice questions and 10 questions measured on a 7-point Likert scale.

The goal was to collect survey responses from 350 customers visiting the fitness apparel company’s brick-and-mortar location in Boston on July 4–8, 2022, between 11:00 and 15:00.

Here, a customer was defined as a person who had purchased a product from the company on the day they took the survey. Participants were given 5 minutes to fill in the survey anonymously. In total, 408 customers responded, but not all surveys were fully completed. Due to this, 371 survey results were included in the analysis.

• Information bias
• Omitted variable bias
• Regression to the mean
• Survivorship bias
• Undercoverage bias
• Sampling bias

Qualitative methods

In qualitative research , methods are often more flexible and subjective. For this reason, it’s crucial to robustly explain the methodology choices you made.

Be sure to discuss the criteria you used to select your data, the context in which your research was conducted, and the role you played in collecting your data (e.g., were you an active participant, or a passive observer?)

Interviews or focus groups Describe where, when, and how the interviews were conducted.

• How did you find and select participants?
• How many participants took part?
• What form did the interviews take ( structured , semi-structured , or unstructured )?
• How long were the interviews?
• How were they recorded?

Participant observation Describe where, when, and how you conducted the observation or ethnography .

• What group or community did you observe? How long did you spend there?
• How did you gain access to this group? What role did you play in the community?
• How long did you spend conducting the research? Where was it located?
• How did you record your data (e.g., audiovisual recordings, note-taking)?

Existing data Explain how you selected case study materials for your analysis.

• What type of materials did you analyze?
• How did you select them?

In order to gain better insight into possibilities for future improvement of the fitness store’s product range, semi-structured interviews were conducted with 8 returning customers.

Here, a returning customer was defined as someone who usually bought products at least twice a week from the store.

Surveys were used to select participants. Interviews were conducted in a small office next to the cash register and lasted approximately 20 minutes each. Answers were recorded by note-taking, and seven interviews were also filmed with consent. One interviewee preferred not to be filmed.

• The Hawthorne effect
• Observer bias
• The placebo effect
• Response bias and Nonresponse bias
• The Pygmalion effect
• Recall bias
• Social desirability bias
• Self-selection bias

Mixed methods

Mixed methods research combines quantitative and qualitative approaches. If a standalone quantitative or qualitative study is insufficient to answer your research question, mixed methods may be a good fit for you.

Mixed methods are less common than standalone analyses, largely because they require a great deal of effort to pull off successfully. If you choose to pursue mixed methods, it’s especially important to robustly justify your methods.

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Next, you should indicate how you processed and analyzed your data. Avoid going into too much detail: you should not start introducing or discussing any of your results at this stage.

In quantitative research , your analysis will be based on numbers. In your methods section, you can include:

• How you prepared the data before analyzing it (e.g., checking for missing data , removing outliers , transforming variables)
• Which software you used (e.g., SPSS, Stata or R)
• Which statistical tests you used (e.g., two-tailed t test , simple linear regression )

In qualitative research, your analysis will be based on language, images, and observations (often involving some form of textual analysis ).

Specific methods might include:

• Content analysis : Categorizing and discussing the meaning of words, phrases and sentences
• Thematic analysis : Coding and closely examining the data to identify broad themes and patterns
• Discourse analysis : Studying communication and meaning in relation to their social context

Mixed methods combine the above two research methods, integrating both qualitative and quantitative approaches into one coherent analytical process.

Above all, your methodology section should clearly make the case for why you chose the methods you did. This is especially true if you did not take the most standard approach to your topic. In this case, discuss why other methods were not suitable for your objectives, and show how this approach contributes new knowledge or understanding.

In any case, it should be overwhelmingly clear to your reader that you set yourself up for success in terms of your methodology’s design. Show how your methods should lead to results that are valid and reliable, while leaving the analysis of the meaning, importance, and relevance of your results for your discussion section .

• Quantitative: Lab-based experiments cannot always accurately simulate real-life situations and behaviors, but they are effective for testing causal relationships between variables .
• Qualitative: Unstructured interviews usually produce results that cannot be generalized beyond the sample group , but they provide a more in-depth understanding of participants’ perceptions, motivations, and emotions.
• Mixed methods: Despite issues systematically comparing differing types of data, a solely quantitative study would not sufficiently incorporate the lived experience of each participant, while a solely qualitative study would be insufficiently generalizable.

Remember that your aim is not just to describe your methods, but to show how and why you applied them. Again, it’s critical to demonstrate that your research was rigorously conducted and can be replicated.

1. Focus on your objectives and research questions

The methodology section should clearly show why your methods suit your objectives and convince the reader that you chose the best possible approach to answering your problem statement and research questions .

2. Cite relevant sources

Your methodology can be strengthened by referencing existing research in your field. This can help you to:

• Show that you followed established practice for your type of research
• Discuss how you decided on your approach by evaluating existing research
• Present a novel methodological approach to address a gap in the literature

3. Write for your audience

Consider how much information you need to give, and avoid getting too lengthy. If you are using methods that are standard for your discipline, you probably don’t need to give a lot of background or justification.

Regardless, your methodology should be a clear, well-structured text that makes an argument for your approach, not just a list of technical details and procedures.

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

• Normal distribution
• Measures of central tendency
• Chi square tests
• Confidence interval
• Quartiles & Quantiles

Methodology

• Cluster sampling
• Stratified sampling
• Thematic analysis
• Cohort study
• Peer review
• Ethnography

Research bias

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

Methodology refers to the overarching strategy and rationale of your research project . It involves studying the methods used in your field and the theories or principles behind them, in order to develop an approach that matches your objectives.

Methods are the specific tools and procedures you use to collect and analyze data (for example, experiments, surveys , and statistical tests ).

In shorter scientific papers, where the aim is to report the findings of a specific study, you might simply describe what you did in a methods section .

In a longer or more complex research project, such as a thesis or dissertation , you will probably include a methodology section , where you explain your approach to answering the research questions and cite relevant sources to support your choice of methods.

In a scientific paper, the methodology always comes after the introduction and before the results , discussion and conclusion . The same basic structure also applies to a thesis, dissertation , or research proposal .

Depending on the length and type of document, you might also include a literature review or theoretical framework before the methodology.

Quantitative research deals with numbers and statistics, while qualitative research deals with words and meanings.

Quantitative methods allow you to systematically measure variables and test hypotheses . Qualitative methods allow you to explore concepts and experiences in more detail.

Reliability and validity are both about how well a method measures something:

• Reliability refers to the  consistency of a measure (whether the results can be reproduced under the same conditions).
• Validity   refers to the  accuracy of a measure (whether the results really do represent what they are supposed to measure).

If you are doing experimental research, you also have to consider the internal and external validity of your experiment.

A sample is a subset of individuals from a larger population . Sampling means selecting the group that you will actually collect data from in your research. For example, if you are researching the opinions of students in your university, you could survey a sample of 100 students.

In statistics, sampling allows you to test a hypothesis about the characteristics of a population.

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Dissertation Methodology – Structure, Example and Writing Guide

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Dissertation Methodology

In any research, the methodology chapter is one of the key components of your dissertation. It provides a detailed description of the methods you used to conduct your research and helps readers understand how you obtained your data and how you plan to analyze it. This section is crucial for replicating the study and validating its results.

Here are the basic elements that are typically included in a dissertation methodology:

• Introduction : This section should explain the importance and goals of your research .
• Research Design : Outline your research approach and why it’s appropriate for your study. You might be conducting an experimental research, a qualitative research, a quantitative research, or a mixed-methods research.
• Data Collection : This section should detail the methods you used to collect your data. Did you use surveys, interviews, observations, etc.? Why did you choose these methods? You should also include who your participants were, how you recruited them, and any ethical considerations.
• Data Analysis : Explain how you intend to analyze the data you collected. This could include statistical analysis, thematic analysis, content analysis, etc., depending on the nature of your study.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your study. For instance, you could discuss measures taken to reduce bias, how you ensured that your measures accurately capture what they were intended to, or how you will handle any limitations in your study.
• Ethical Considerations : This is where you state how you have considered ethical issues related to your research, how you have protected the participants’ rights, and how you have complied with the relevant ethical guidelines.
• Limitations : Acknowledge any limitations of your methodology, including any biases and constraints that might have affected your study.
• Summary : Recap the key points of your methodology chapter, highlighting the overall approach and rationalization of your research.

Types of Dissertation Methodology

The type of methodology you choose for your dissertation will depend on the nature of your research question and the field you’re working in. Here are some of the most common types of methodologies used in dissertations:

Experimental Research

This involves creating an experiment that will test your hypothesis. You’ll need to design an experiment, manipulate variables, collect data, and analyze that data to draw conclusions. This is commonly used in fields like psychology, biology, and physics.

Survey Research

This type of research involves gathering data from a large number of participants using tools like questionnaires or surveys. It can be used to collect a large amount of data and is often used in fields like sociology, marketing, and public health.

Qualitative Research

This type of research is used to explore complex phenomena that can’t be easily quantified. Methods include interviews, focus groups, and observations. This methodology is common in fields like anthropology, sociology, and education.

Quantitative Research

Quantitative research uses numerical data to answer research questions. This can include statistical, mathematical, or computational techniques. It’s common in fields like economics, psychology, and health sciences.

Case Study Research

This type of research involves in-depth investigation of a particular case, such as an individual, group, or event. This methodology is often used in psychology, social sciences, and business.

Mixed Methods Research

This combines qualitative and quantitative research methods in a single study. It’s used to answer more complex research questions and is becoming more popular in fields like social sciences, health sciences, and education.

Action Research

This type of research involves taking action and then reflecting upon the results. This cycle of action-reflection-action continues throughout the study. It’s often used in fields like education and organizational development.

Longitudinal Research

This type of research involves studying the same group of individuals over an extended period of time. This could involve surveys, observations, or experiments. It’s common in fields like psychology, sociology, and medicine.

Ethnographic Research

This type of research involves the in-depth study of people and cultures. Researchers immerse themselves in the culture they’re studying to collect data. This is often used in fields like anthropology and social sciences.

Structure of Dissertation Methodology

The structure of a dissertation methodology can vary depending on your field of study, the nature of your research, and the guidelines of your institution. However, a standard structure typically includes the following elements:

• Introduction : Briefly introduce your overall approach to the research. Explain what you plan to explore and why it’s important.
• Research Design/Approach : Describe your overall research design. This can be qualitative, quantitative, or mixed methods. Explain the rationale behind your chosen design and why it is suitable for your research questions or hypotheses.
• Data Collection Methods : Detail the methods you used to collect your data. You should include what type of data you collected, how you collected it, and why you chose this method. If relevant, you can also include information about your sample population, such as how many people participated, how they were chosen, and any relevant demographic information.
• Data Analysis Methods : Explain how you plan to analyze your collected data. This will depend on the nature of your data. For example, if you collected quantitative data, you might discuss statistical analysis techniques. If you collected qualitative data, you might discuss coding strategies, thematic analysis, or narrative analysis.
• Reliability and Validity : Discuss how you’ve ensured the reliability and validity of your research. This might include steps you took to reduce bias or increase the accuracy of your measurements.
• Ethical Considerations : If relevant, discuss any ethical issues associated with your research. This might include how you obtained informed consent from participants, how you ensured participants’ privacy and confidentiality, or any potential conflicts of interest.
• Limitations : Acknowledge any limitations in your research methodology. This could include potential sources of bias, difficulties with data collection, or limitations in your analysis methods.
• Summary/Conclusion : Briefly summarize the key points of your methodology, emphasizing how it helps answer your research questions or hypotheses.

How to Write Dissertation Methodology

Writing a dissertation methodology requires you to be clear and precise about the way you’ve carried out your research. It’s an opportunity to convince your readers of the appropriateness and reliability of your approach to your research question. Here is a basic guideline on how to write your methodology section:

1. Introduction

Start your methodology section by restating your research question(s) or objective(s). This ensures your methodology directly ties into the aim of your research.

2. Approach

Identify your overall approach: qualitative, quantitative, or mixed methods. Explain why you have chosen this approach.

• Qualitative methods are typically used for exploratory research and involve collecting non-numerical data. This might involve interviews, observations, or analysis of texts.
• Quantitative methods are used for research that relies on numerical data. This might involve surveys, experiments, or statistical analysis.
• Mixed methods use a combination of both qualitative and quantitative research methods.

3. Research Design

Describe the overall design of your research. This could involve explaining the type of study (e.g., case study, ethnography, experimental research, etc.), how you’ve defined and measured your variables, and any control measures you’ve implemented.

4. Data Collection

Explain in detail how you collected your data.

• If you’ve used qualitative methods, you might detail how you selected participants for interviews or focus groups, how you conducted observations, or how you analyzed existing texts.
• If you’ve used quantitative methods, you might detail how you designed your survey or experiment, how you collected responses, and how you ensured your data is reliable and valid.

5. Data Analysis

Describe how you analyzed your data.

• If you’re doing qualitative research, this might involve thematic analysis, discourse analysis, or grounded theory.
• If you’re doing quantitative research, you might be conducting statistical tests, regression analysis, or factor analysis.

Discuss any ethical issues related to your research. This might involve explaining how you obtained informed consent, how you’re protecting participants’ privacy, or how you’re managing any potential harms to participants.

7. Reliability and Validity

Discuss the steps you’ve taken to ensure the reliability and validity of your data.

• Reliability refers to the consistency of your measurements, and you might discuss how you’ve piloted your instruments or used standardized measures.
• Validity refers to the accuracy of your measurements, and you might discuss how you’ve ensured your measures reflect the concepts they’re supposed to measure.

8. Limitations

Every study has its limitations. Discuss the potential weaknesses of your chosen methods and explain any obstacles you faced in your research.

9. Conclusion

Summarize the key points of your methodology, emphasizing how it helps to address your research question or objective.

Example of Dissertation Methodology

An Example of Dissertation Methodology is as follows:

Chapter 3: Methodology

• Introduction

This chapter details the methodology adopted in this research. The study aimed to explore the relationship between stress and productivity in the workplace. A mixed-methods research design was used to collect and analyze data.

Research Design

This study adopted a mixed-methods approach, combining quantitative surveys with qualitative interviews to provide a comprehensive understanding of the research problem. The rationale for this approach is that while quantitative data can provide a broad overview of the relationships between variables, qualitative data can provide deeper insights into the nuances of these relationships.

Data Collection Methods

Quantitative Data Collection : An online self-report questionnaire was used to collect data from participants. The questionnaire consisted of two standardized scales: the Perceived Stress Scale (PSS) to measure stress levels and the Individual Work Productivity Questionnaire (IWPQ) to measure productivity. The sample consisted of 200 office workers randomly selected from various companies in the city.

Qualitative Data Collection : Semi-structured interviews were conducted with 20 participants chosen from the initial sample. The interview guide included questions about participants’ experiences with stress and how they perceived its impact on their productivity.

Data Analysis Methods

Quantitative Data Analysis : Descriptive and inferential statistics were used to analyze the survey data. Pearson’s correlation was used to examine the relationship between stress and productivity.

Qualitative Data Analysis : Interviews were transcribed and subjected to thematic analysis using NVivo software. This process allowed for identifying and analyzing patterns and themes regarding the impact of stress on productivity.

Reliability and Validity

To ensure reliability and validity, standardized measures with good psychometric properties were used. In qualitative data analysis, triangulation was employed by having two researchers independently analyze the data and then compare findings.

Ethical Considerations

All participants provided informed consent prior to their involvement in the study. They were informed about the purpose of the study, their rights as participants, and the confidentiality of their responses.

Limitations

The main limitation of this study is its reliance on self-report measures, which can be subject to biases such as social desirability bias. Moreover, the sample was drawn from a single city, which may limit the generalizability of the findings.

Where to Write Dissertation Methodology

In a dissertation or thesis, the Methodology section usually follows the Literature Review. This placement allows the Methodology to build upon the theoretical framework and existing research outlined in the Literature Review, and precedes the Results or Findings section. Here’s a basic outline of how most dissertations are structured:

• Acknowledgements
• Literature Review (or it may be interspersed throughout the dissertation)
• Methodology
• Results/Findings
• References/Bibliography

In the Methodology chapter, you will discuss the research design, data collection methods, data analysis methods, and any ethical considerations pertaining to your study. This allows your readers to understand how your research was conducted and how you arrived at your results.

Advantages of Dissertation Methodology

The dissertation methodology section plays an important role in a dissertation for several reasons. Here are some of the advantages of having a well-crafted methodology section in your dissertation:

• Clarifies Your Research Approach : The methodology section explains how you plan to tackle your research question, providing a clear plan for data collection and analysis.
• Enables Replication : A detailed methodology allows other researchers to replicate your study. Replication is an important aspect of scientific research because it provides validation of the study’s results.
• Demonstrates Rigor : A well-written methodology shows that you’ve thought critically about your research methods and have chosen the most appropriate ones for your research question. This adds credibility to your study.
• Enhances Transparency : Detailing your methods allows readers to understand the steps you took in your research. This increases the transparency of your study and allows readers to evaluate potential biases or limitations.
• Helps in Addressing Research Limitations : In your methodology section, you can acknowledge and explain the limitations of your research. This is important as it shows you understand that no research method is perfect and there are always potential weaknesses.
• Facilitates Peer Review : A detailed methodology helps peer reviewers assess the soundness of your research design. This is an important part of the publication process if you aim to publish your dissertation in a peer-reviewed journal.
• Establishes the Validity and Reliability : Your methodology section should also include a discussion of the steps you took to ensure the validity and reliability of your measurements, which is crucial for establishing the overall quality of your research.

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A Complete Guide to Dissertation Data Analysis

The analysis chapter is one of the most important parts of a dissertation where you demonstrate the unique research abilities. That is why it often accounts for up to 40% of the total mark. Given the significance of this chapter, it is essential to build your skills in dissertation data analysis .

Typically, the analysis section provides an output of calculations, interpretation of attained results and discussion of these results in light of theories and previous empirical evidence. Oftentimes, the chapter provides qualitative data analysis that do not require any calculations. Since there are different types of research design, let’s look at each type individually.

1. Types of Research

The dissertation topic you have selected, to a considerable degree, informs the way you are going to collect and analyse data. Some topics imply the collection of primary data, while others can be explored using secondary data. Selecting an appropriate data type is vital not only for your ability to achieve the main aim and objectives of your dissertation but also an important part of the dissertation writing process since it is what your whole project will rest on.

Selecting the most appropriate data type for your dissertation may not be as straightforward as it may seem. As you keep diving into your research, you will be discovering more and more details and nuances associated with this or that type of data. At some point, it is important to decide whether you will pursue the qualitative research design or the quantitative research design.

1.1. Qualitative vs Quantitative Research

1.1.1. quantitative research.

Quantitative data is any numerical data which can be used for statistical analysis and mathematical manipulations. This type of data can be used to answer research questions such as ‘How often?’, ‘How much?’, and ‘How many?’. Studies that use this type of data also ask the ‘What’ questions (e.g. What are the determinants of economic growth? To what extent does marketing affect sales? etc.).

An advantage of quantitative data is that it can be verified and conveniently evaluated by researchers. This allows for replicating the research outcomes. In addition, even qualitative data can be quantified and converted to numbers. For example, the use of the Likert scale allows researchers not only to properly assess respondents’ perceptions of and attitudes towards certain phenomena but also to assign a code to each individual response and make it suitable for graphical and statistical analysis. It is also possible to convert the yes/no responses to dummy variables to present them in the form of numbers. Quantitative data is typically analysed using dissertation data analysis software such as Eviews, Matlab, Stata, R, and SPSS.

On the other hand, a significant limitation of purely quantitative methods is that social phenomena explored in economic and behavioural sciences are often complex, so the use of quantitative data does not allow for thoroughly analysing these phenomena. That is, quantitative data can be limited in terms of breadth and depth as compared to qualitative data, which may allow for richer elaboration on the context of the study.

1.1.2. Qualitative Data

Studies that use this type of data usually ask the ‘Why’ and ‘How’ questions (e.g. Why does social media marketing is more effective than traditional marketing? How do consumers make their purchase decisions?). This is non-numerical primary data represented mostly by opinions of relevant persons.

Qualitative data also includes any textual or visual data (infographics) that have been gathered from reports, websites and other secondary sources that do not involve interactions between the researcher and human participants. Examples of the use of secondary qualitative data are texts, images and diagrams you can use in SWOT analysis, PEST analysis, 4Ps analysis, Porter’s Five Forces analysis, most types of Strategic Analysis, etc. Academic articles, journals, books, and conference papers are also examples of secondary qualitative data you can use in your study.

The analysis of qualitative data usually provides deep insights into the phenomenon or issue being under study because respondents are not limited in their ability to give detailed answers. Unlike quantitative research, collecting and analysing qualitative data is more open-ended in eliciting the anecdotes, stories, and lengthy descriptions and evaluations people make of products, services, lifestyle attributes, or any other phenomenon. This is best used in social studies including management and marketing.

It is not always possible to summarise qualitative data as opinions expressed by individuals are multi-faceted. This to some extent limits the dissertation data analysis  as it is not always possible to establish cause-and-effect links between factors represented in a qualitative manner. This is why the results of qualitative analysis can hardly be generalised, and case studies that explore very narrow contexts are often conducted.

For qualitative data analysis, you can use tools such as nVivo and Tableau.  

1.2. Primary vs Secondary Research

1.2.1. primary data.

Primary data is data that had not existed prior to your research and you collect it by means of a survey or interviews for the dissertation data analysis chapter. Interviews provide you with the opportunity to collect detailed insights from industry participants about their company, customers, or competitors. Questionnaire surveys allow for obtaining a large amount of data from a sizeable population in a cost-efficient way. Primary data is usually cross-sectional data (i.e., the data collected at one point of time from different respondents). Time-series are found very rarely or almost never in primary data. Nonetheless, depending on the research aims and objectives, certain designs of data collection instruments allow researchers to conduct a longitudinal study.

1.2.2. Secondary data

This data already exist before the research as they have already been generated, refined, summarized and published in official sources for purposes other than those of your study study. Secondary data often carries more legitimacy as compared to primary data and can help the researcher verify primary data. This is the data collected from databases or websites; it does not involve human participants. This can be both cross-sectional data (e.g. an indicator for different countries/companies at one point of time) and time-series (e.g. an indicator for one company/country for several years). A combination of cross-sectional data and time-series data is panel data. Therefore, all a researcher needs to do is to find the data that would be most appropriate for attaining the research objectives.

Examples of secondary quantitative data are share prices; accounting information such as earnings, total asset, revenue, etc.; macroeconomic variables such as GDP, inflation, unemployment, interest rates, etc.; microeconomic variables such as market share, concentration ratio, etc. Accordingly, dissertation topics that will most likely use secondary quantitative data are FDI dissertations, Mergers and Acquisitions dissertations, Event Studies, Economic Growth dissertations, International Trade dissertations, Corporate Governance dissertations.

Two main limitations of secondary data are the following. First, the freely available secondary data may not perfectly suit the purposes of your study so that you will have to additionally collect primary data or change the research objectives. Second, not all high-quality secondary data is freely available. Good sources of financial data such as WRDS, Thomson Bank Banker, Compustat and Bloomberg all stipulate pre-paid access which may not be affordable for a single researcher.

1.3. Quantitative or Qualitative Research… or Both?

Once you have formulated your research aim and objectives and reviewed the most relevant literature in your field, you should decide whether you need qualitative or quantitative data.

If you are willing to test the relationship between variables or examine hypotheses and theories in practice, you should rather focus on collecting quantitative data. Methodologies based on this data provide cut-and-dry results and are highly effective when you need to obtain a large amount of data in a cost-effective manner. Alternatively, qualitative research will help you better understand meanings, experience, beliefs, values and other non-numerical relationships.

While it is totally okay to use either a qualitative or quantitative methodology, using them together will allow you to back up one type of data with another type of data and research your topic in more depth. However, note that using qualitative and quantitative methodologies in combination can take much more time and effort than you originally planned.

2. Types of Analysis

2.1. basic statistical analysis.

The type of statistical analysis that you choose for the results and findings chapter depends on the extent to which you wish to analyse the data and summarise your findings. If you do not major in quantitative subjects but write a dissertation in social sciences, basic statistical analysis will be sufficient. Such an analysis would be based on descriptive statistics such as the mean, the median, standard deviation, and variance. Then, you can enhance the statistical analysis with visual information by showing the distribution of variables in the form of graphs and charts. However, if you major in a quantitative subject such as accounting, economics or finance, you may need to use more advanced statistical analysis.

2.2. Advanced Statistical Analysis

In order to run an advanced analysis, you will most likely need access to statistical software such as Matlab, R or Stata. Whichever program you choose to proceed with, make sure that it is properly documented in your research. Further, using an advanced statistical technique ensures that you are analysing all possible aspects of your data. For example, a difference between basic regression analysis and analysis at an advanced level is that you will need to consider additional tests and deeper explorations of statistical problems with your model. Also, you need to keep the focus on your research question and objectives as getting deeper into statistical details may distract you from the main aim. Ultimately, the aim of your dissertation is to find answers to the research questions that you defined.

Another important aspect to consider here is that the results and findings section is not all about numbers. Apart from tables and graphs, it is also important to ensure that the interpretation of your statistical findings is accurate as well as engaging for the users. Such a combination of advanced statistical software along with a convincing textual discussion goes a long way in ensuring that your dissertation is well received. Although the use of such advanced statistical software may provide you with a variety of outputs, you need to make sure to present the analysis output properly so that the readers understand your conclusions.

3. Examples of Methods of Analysis

3.1. event study.

If you are studying the effects of particular events on prices of financial assets, for example, it is worth to consider the Event Study Methodology. Events such as mergers and acquisitions, new product launches, expansion into new markets, earnings announcements and public offerings can have a major impact on stock prices and valuation of a firm. Event studies are methods used to measure the impact of a particular event or a series of events on the market value. The concept behind this is to try to understand whether sudden and abnormal stock returns can be attributed to market information pertaining to an event.

Event studies are based on the efficient market hypothesis. According to the theory, in an efficient capital market, all the new and relevant information is immediately reflected in the respective asset prices. Although this theory is not universally applicable, there are many instances in which it holds true. An event study implies a step-by-step analysis of the impact that a particular announcement has on a company’s valuation. In normal conditions, without the influence of the analysed event, it is assumed that expected returns on a stock would be determined by the risk-free rate, systematic risk of the stock and risk premium required by investors. These conditions are measured by the capital asset pricing model (CAPM).

There can primarily be three types of announcements which can constitute event studies. These include corporate announcements, macroeconomic announcements, as well as regulatory events. As the name suggests, corporate announcements could include bankruptcies, asset sales, M&As, credit rating downgrades, earnings announcements and announcements of dividends. These events usually have a major impact on stock prices simply because they are directly interlinked with the company. Macroeconomic announcements can include central bank announcements of changes in interest rates, an announcement of inflation projections and economic growth projections. Finally, regulatory announcements such as policy changes and new laws announcement can also impact the stock prices of companies, and therefore can be measured using the method of event studies.

A critical issue in event studies is choosing the right event window during which the analysed announcements are assumed to produce the strongest effect on share prices. According to the efficient market hypothesis, no statistically significant abnormal returns connected with any events would be expected. However, in reality, there could be rumours before official announcements and some investors may act on such rumours. Moreover, investors may react at different times due to differences in speed of information processing and reaction. In order to account for all these factors, event windows usually capture a short period before the announcement to account for rumours and an asymmetrical period after the announcement.

In order to make event studies stronger and statistically meaningful, a large number of similar or related cases are analysed. Then, abnormal returns are cumulated, and their statistical significance is assessed. The t-statistic is often used to evaluate whether the average abnormal returns are different from zero. So, researchers who use event studies are concerned not only with the positive or negative effects of specific events but also with the generalisation of the results and measuring the statistical significance of abnormal returns.

3.2. Regression Analysis

Regression analysis is a mathematical method applied to determine how explored variables are interconnected. In particular, the following questions can be answered. Which factors are the most influential ones? Which of them can be ignored? How do the factors interact with one another? And the main question, how significant are the findings?

The type most often applied in the dissertation studies is the ordinary least squares (OLS) regression analysis that assesses parameters of linear relationships between explored variables. Typically, three forms of OLS analysis are used.

Longitudinal analysis is applied when a single object with several characteristics is explored over a long period of time. In this case, observations represent the changes of the same characteristics over time. Examples of longitudinal samples are macroeconomic parameters in a particular country, preferences and changes in health characteristics of particular persons during their lives etc. Cross-sectional studies on the contrary, explore characteristics of many similar objects such as respondents, companies, countries, students over cities in a certain moment of time. The main similarity between longitudinal and cross-sectional studies is that the data over one dimension, namely across periods of time (days, weeks, years) or across objects, respectively.

However, it is often the case that we need to explore data that change over two dimensions, both across objects and periods of time. In this case, we need to use a panel regression analysis. Its main distinction from the two mentioned above is that specifics of each object (person, company, country) are accounted for.

The common steps of the regression analysis are the following:

• Start with descriptive statistics of the data. This is done to indicate the scope of the data observations included in the sample and identify potential outliers. A common practice is to get rid of the outliers to avoid the distortion of the analysis results.
• Estimate potential multicollinearity. This phenomenon is connected with strong correlation between explanatory variables. Multicollinearity is an undesirable feature of the sample as regression results, in particular the significance of certain variables, may be distorted. Once multicollinearity is detected, the easiest way to eliminate it is to omit one of the correlated variables.
• Run Regressions. First, the overall significance of the model is estimated using the F-statistic. After that, the significance of particular variable coefficient is assessed using t-statistics.
• Don’t forget about diagnostic tests. They are conducted to detect potential imperfections of the sample that could affect the regression outcomes.

Some nuances should be mentioned. When a time series OLS regression analysis is conducted, it is feasible to conduct a full battery of diagnostic tests including the test of linearity (the relationship between the independent and dependent variables should be linear); homoscedasticity (regression residuals should have the same variance); independence of observations; normality of variables; serial correlation (there should no patterns in a particular time series). These tests for longitudinal regression models are available in most software tools such as Eviews and Stata.

3.3. Vector Autoregression

A vector autoregression model (VAR) is a model often used in statistical analysis, which explores interrelationships between several variables that are all treated as endogenous. So, a specific trait of this model is that it includes lagged values of the employed variables as regressors. This allows for estimating not only the instantaneous effects but also dynamic effects in the relationships up to n lags.

In fact, a VAR model consists of k OLS regression equations where k is the number of employed variables. Each equation has its own dependent variable while the explanatory variables are the lagged values of this variable and other variables.

• Selection of the optimal lag length

Information criteria (IC) are employed to determine the optimal lag length. The most commonly used ones are the Akaike, Hannah-Quinn and Schwarz criteria.

• Test for stationarity

A widely used method for estimating stationarity is the Augmented Dickey-Fuller test and the Phillips-Perron test.  If a variable is non-stationary, the first difference should be taken and tested for stationarity in the same way.

• Cointegration test

The variables may be non-stationary but integrated of the same order. In this case, they can be analysed with a Vector Error Correction Model (VECM) instead of VAR. The Johansen cointegration test is conducted to check whether the variables integrated of the same order share a common integrating vector(s). If the variables are cointegrated, VECM is applied in the following analysis instead of a VAR model. VECM is applied to non-transformed non-stationary series whereas VAR is run with transformed or stationary inputs.

• Model Estimation

A VAR model is run with the chosen number of lags and coefficients with standard errors and respective t-statistics are calculated to assess the statistical significance.

• Diagnostic tests

Next, the model is tested for serial correlation using the Breusch-Godfrey test, for heteroscedasticity using the Breusch-Pagan test and for stability.

• Impulse Response Functions (IRFs)

The IRFs are used to graphically represent the results of a VAR model and project the effects of variables on one another.

• Granger causality test

The variables may be related but there may exist no causal relationships between them, or the effect may be bilateral. The Granger test indicates the causal associations between the variables and shows the direction of causality based on interaction of current and past values of a pair of variables in the VAR system.

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How to Analyse Secondary Data for a Dissertation

Secondary data refers to data that has already been collected by another researcher. For researchers (and students!) with limited time and resources, secondary data, whether qualitative or quantitative can be a highly viable source of data.  In addition, with the advances in technology and access to peer reviewed journals and studies provided by the internet, it is increasingly popular as a form of data collection.  The question that frequently arises amongst students however, is: how is secondary data best analysed?

The process of data analysis in secondary research

Secondary analysis (i.e., the use of existing data) is a systematic methodological approach that has some clear steps that need to be followed for the process to be effective.  In simple terms there are three steps:

• Step One: Development of Research Questions
• Step Two: Identification of dataset
• Step Three: Evaluation of the dataset.

Let’s look at each of these in more detail:

Step One: Development of research questions

Using secondary data means you need to apply theoretical knowledge and conceptual skills to be able to use the dataset to answer research questions.  Clearly therefore, the first step is thus to clearly define and develop your research questions so that you know the areas of interest that you need to explore for location of the most appropriate secondary data.

Step Two: Identification of Dataset

This stage should start with identification, through investigation, of what is currently known in the subject area and where there are gaps, and thus what data is available to address these gaps.  Sources can be academic from prior studies that have used quantitative or qualitative data, and which can then be gathered together and collated to produce a new secondary dataset.  In addition, other more informal or “grey” literature can also be incorporated, including consumer report, commercial studies or similar.  One of the values of using secondary research is that original survey works often do not use all the data collected which means this unused information can be applied to different settings or perspectives.

Key point: Effective use of secondary data means identifying how the data can be used to deliver meaningful and relevant answers to the research questions.  In other words that the data used is a good fit for the study and research questions.

Step Three: Evaluation of the dataset for effectiveness/fit

A good tip is to use a reflective approach for data evaluation.  In other words, for each piece of secondary data to be utilised, it is sensible to identify the purpose of the work, the credentials of the authors (i.e., credibility, what data is provided in the original work and how long ago it was collected).  In addition, the methods used and the level of consistency that exists compared to other works. This is important because understanding the primary method of data collection will impact on the overall evaluation and analysis when it is used as secondary source. In essence, if there is no understanding of the coding used in qualitative data analysis to identify key themes then there will be a mismatch with interpretations when the data is used for secondary purposes.  Furthermore, having multiple sources which draw similar conclusions ensures a higher level of validity than relying on only one or two secondary sources.

A useful framework provides a flow chart of decision making, as shown in the figure below.

Following this process ensures that only those that are most appropriate for your research questions are included in the final dataset, but also demonstrates to your readers that you have been thorough in identifying the right works to use.

Writing up the Analysis

Once you have your dataset, writing up the analysis will depend on the process used.  If the data is qualitative in nature, then you should follow the following process.

Pre-Planning

• Read and re-read all sources, identifying initial observations, correlations, and relationships between themes and how they apply to your research questions.
• Once initial themes are identified, it is sensible to explore further and identify sub-themes which lead on from the core themes and correlations in the dataset, which encourages identification of new insights and contributes to the originality of your own work.

Structure of the Analysis Presentation

Introduction.

The introduction should commence with an overview of all your sources. It is good practice to present these in a table, listed chronologically so that your work has an orderly and consistent flow. The introduction should also incorporate a brief (2-3 sentences) overview of the key outcomes and results identified.

The body text for secondary data, irrespective of whether quantitative or qualitative data is used, should be broken up into sub-sections for each argument or theme presented. In the case of qualitative data, depending on whether content, narrative or discourse analysis is used, this means presenting the key papers in the area, their conclusions and how these answer, or not, your research questions. Each source should be clearly cited and referenced at the end of the work. In the case of qualitative data, any figures or tables should be reproduced with the correct citations to their original source. In both cases, it is good practice to give a main heading of a key theme, with sub-headings for each of the sub themes identified in the analysis.

Do not use direct quotes from secondary data unless they are:

• properly referenced, and
• are key to underlining a point or conclusion that you have drawn from the data.

All results sections, regardless of whether primary or secondary data has been used should refer back to the research questions and prior works. This is because, regardless of whether the results back up or contradict previous research, including previous works shows a wider level of reading and understanding of the topic being researched and gives a greater depth to your own work.

Summary of results

The summary of the results section of a secondary data dissertation should deliver a summing up of key findings, and if appropriate a conceptual framework that clearly illustrates the findings of the work. This shows that you have understood your secondary data, how it has answered your research questions, and furthermore that your interpretation has led to some firm outcomes.

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5 Tips for Handling your Thesis Data Analysis

3-minute read

• 23rd June 2015

When writing your thesis, the process of analyzing data and working with statistics can be pretty hard at first. This is true whether you’re using specialized data analysis software, like SPSS, or a more descriptive approach. But there are a few guidelines you can follow to make things simpler.

1. Choose the Best Analytical Method for Your Project

The sheer variety of techniques available for data analysis can be confusing! If you are writing a thesis  on internet marketing, for instance, your approach to analysis will be very different to someone writing about biochemistry. As such it is important to adopt an approach appropriate to your research.

2. Double Check Your Methodology

If you are working with quantitative data, it is important to make sure that your analytical techniques are compatible with the methods used to gather your data. Having a clear understanding of what you have done so far will ensure that you achieve accurate results.

For instance, when performing statistical analysis, you may have to choose between parametric and non-parametric testing. If your data is sampled from a population with a broadly Gaussian (i.e., normal) distribution, you will almost always want to use some form of non-parametric testing.

But if you can’t remember or aren’t sure how you selected your sample, you won’t necessarily know the best test to use!

3. Familiarize Yourself with Statistical Analysis and Analytical Software

Thanks to various clever computer programs, you no longer have to be a math genius to conduct top-grade statistical analysis. Nevertheless, learning the basics will help you make informed choices when designing your research and prevent you from making basic mistakes.

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Likewise, trying out different software packages will allow you to pick the one best suited to your needs on your current project.

4. Present Your Data Clearly and Consistently

This is possibly one of the most important parts of writing up your results. Even if your data and statistics are perfect, failure to present your analysis clearly will make it difficult for your reader to follow.

Ask yourself how your analysis would look to someone unfamiliar with your project. If they would be able to understand your analysis, you’re on the right track!

5. Make It Relevant!

Finally, remember that data analysis is about more than just presenting your data. You should also relate your analysis back to your research objectives, discussing its relevance and justifying your interpretations.

This will ensure that your work is easy to follow and demonstrate your understanding of the methods used. So no matter what you are writing about, the analysis is a great time to show off how clever you are!

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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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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.

Finally, don’t be afraid to make effective use of any quantitative data analysis software you can get your hands on. We know that many of these tools can be quite expensive, but we can assure you that the investment is a good idea. Many of these tools are of real help when it comes to analyzing huge amounts of data.

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!

Need Some Assistance With Data Analysis?

If you are a university student or a graduate, you may need some cheap help with writing the analysis chapter of your dissertation. Remember, time saving is extremely important because finishing the dissertation on time is mandatory. You should consider our amazing services the moment you notice you are not on track with your dissertation. Also, you should get help from our dissertation writing service in case you can’t do a terrific job writing the data analysis chapter. This is one of the most important chapters of your paper and the supervisor will look closely at it.

Why risk getting penalized when you can get high quality academic writing services from our team of experts? All our writers are PhD degree holders, so they know exactly how to write any chapter of a dissertation the right way. This also means that our professionals work fast. They can get the analysis chapter done for you in no time and bring you back on track. It’s also worth noting that we have access to the best software tools for data analysis. We will bring our knowledge and technical know-how to your project and ensure you get a top grade on your paper. Get in touch with us and let’s discuss the specifics of your project right now!

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• Welcome To Dissertation Center

How Do You Write Data Analysis Chapter For Dissertation

• March 4, 2020
• Dissertation Center

Your dissertation data analysis section should consist of the following:

• An in-depth description of the hypothesis and the research questions. 
• A brief overview that includes; the study purpose, steps in conducting the research, description of the type of data, data collection instruments that had been used, including assumptions made during the study.
• A conclusion of every question distinctly and the intuition drew by the researcher from the analysis.
• Detailed data collected, and the numerous mathematical, statistical, and qualitative analyses performed.
• A summary paragraph with a brief review of the chapter

Some of the best practices to follow while writing the analysis section

• Once any new theme surfaces from the analysis, then the researcher should acknowledge that linking such to an appropriate conclusion drawn from the study.
• Provide a judgment and critical view for the results provided by the analysis.
• Ensure to reference the analysis with the literature review, i.e., through cross-referencing.
• Avoiding  jargon and defining technical terms used in the analysis
• Ensure the introductory article explains the chapter.
• Follow a theme based structure that is the same as that of the literature review.
• What Should Be Included In The Data Analysis Section
• How do you analyze data for a dissertation?

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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