dissertation data analysis services

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Whether you’ve just started collecting your data, are in the thick of analysing it, or you’ve already written a draft chapter – we’re here to help. 

Make sense of the data

If you’ve collected your data, but are feeling confused about what to do and how to make sense of it all, we can help. One of our friendly coaches will hold your hand through each step and help you interpret your dataset .

Alternatively, if you’re still planning your data collection and analysis strategy, we can help you craft a rock-solid methodology  that sets you up for success.

Get your thinking onto paper

If you’ve analysed your data, but are struggling to get your thoughts onto paper, one of our friendly Grad Coaches can help you structure your results and/or discussion chapter to kickstart your writing.

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If you’ve already written up your results but need a second set of eyes, our popular Content Review service can help you identify and address key issues within your writing, before you submit it for grading .

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Have a question ?

Below we address some of the most popular questions we receive regarding our data analysis support, but feel free to get in touch if you have any other questions.

Dissertation Coaching

I have no idea where to start. can you help.

Absolutely. We regularly work with students who are completely new to data analysis (both qualitative and quantitative) and need step-by-step guidance to understand and interpret their data.

Can you analyse my data for me?

The short answer – no. 

The longer answer:

If you’re undertaking qualitative research , we can fast-track your project with our Qualitative Coding Service. With this service, we take care of the initial coding of your dataset (e.g., interview transcripts), providing a firm foundation on which you can build your qualitative analysis (e.g., thematic analysis, content analysis, etc.).

If you’re undertaking quantitative research , we can fast-track your project with our Statistical Testing Service . With this service, we run the relevant statistical tests using SPSS or R, and provide you with the raw outputs. You can then use these outputs/reports to interpret your results and develop your analysis.

Importantly, in both cases, we are not analysing the data for you or providing an interpretation or write-up for you. If you’d like coaching-based support with that aspect of the project, we can certainly assist you with this (i.e., provide guidance and feedback, review your writing, etc.). But it’s important to understand that you, as the researcher, need to engage with the data and write up your own findings. 

Can you help me choose the right data analysis methods?

Yes, we can assist you in selecting appropriate data analysis methods, based on your research aims and research questions, as well as the characteristics of your data.

Which data analysis methods can you assist with?

We can assist with most qualitative and quantitative analysis methods that are commonplace within the social sciences.

Qualitative methods:

• Qualitative content analysis
• Thematic analysis
• Discourse analysis
• Narrative analysis
• Grounded theory

Quantitative methods:

• Descriptive statistics
• Inferential statistics

Can you provide data sets for me to analyse?

If you are undertaking secondary research , we can potentially assist you in finding suitable data sets for your analysis.

If you are undertaking primary research , we can help you plan and develop data collection instruments (e.g., surveys, questionnaires, etc.), but we cannot source the data on your behalf. 

Can you write the analysis/results/discussion chapter/section for me?

No. We can provide you with hands-on guidance through each step of the analysis process, but the writing needs to be your own. Writing anything for you would constitute academic misconduct .

Can you help me organise and structure my results/discussion chapter/section?

Yes, we can assist in structuring your chapter to ensure that you have a clear, logical structure and flow that delivers a clear and convincing narrative.

Can you review my writing and give me feedback?

Absolutely. Our Content Review service is designed exactly for this purpose and is one of the most popular services here at Grad Coach. In a Content Review, we carefully read through your research methodology chapter (or any other chapter) and provide detailed comments regarding the key issues/problem areas, why they’re problematic and what you can do to resolve the issues. You can learn more about Content Review here .

Do you provide software support (e.g., SPSS, R, etc.)?

It depends on the software package you’re planning to use, as well as the analysis techniques/tests you plan to undertake. We can typically provide support for the more popular analysis packages, but it’s best to discuss this in an initial consultation.

Can you help me with other aspects of my research project?

Yes. Data analysis support is only one aspect of our offering at Grad Coach, and we typically assist students throughout their entire dissertation/thesis/research project. You can learn more about our full service offering here .

Can I get a coach that specialises in my topic area?

It’s important to clarify that our expertise lies in the research process itself , rather than specific research areas/topics (e.g., psychology, management, etc.).

In other words, the support we provide is topic-agnostic, which allows us to support students across a very broad range of research topics. That said, if there is a coach on our team who has experience in your area of research, as well as your chosen methodology, we can allocate them to your project (dependent on their availability, of course).

If you’re unsure about whether we’re the right fit, feel free to drop us an email or book a free initial consultation.

What qualifications do your coaches have?

All of our coaches hold a doctoral-level degree (for example, a PhD, DBA, etc.). Moreover, they all have experience working within academia, in many cases as dissertation/thesis supervisors. In other words, they understand what markers are looking for when reviewing a student’s work.

Is my data/topic/study kept confidential?

Yes, we prioritise confidentiality and data security. Your written work and personal information are treated as strictly confidential. We can also sign a non-disclosure agreement, should you wish.

I still have questions…

No problem. Feel free to email us or book an initial consultation to discuss.

What our clients say

We've worked 1:1 with 3000+ students . Here's what some of them have to say:

David's depth of knowledge in research methodology was truly impressive. He demonstrated a profound understanding of the nuances and complexities of my research area, offering insights that I hadn't even considered. His ability to synthesize information, identify key research gaps, and suggest research topics was truly inspiring. I felt like I had a true expert by my side, guiding me through the complexities of the proposal.

Cyntia Sacani (US)

I had been struggling with the first 3 chapters of my dissertation for over a year. I finally decided to give GradCoach a try and it made a huge difference. Alexandra provided helpful suggestions along with edits that transformed my paper. My advisor was very impressed.

Tracy Shelton (US)

Working with Kerryn has been brilliant. She has guided me through that pesky academic language that makes us all scratch our heads. I can't recommend Grad Coach highly enough; they are very professional, humble, and fun to work with. If like me, you know your subject matter but you're getting lost in the academic language, look no further, give them a go.

Tony Fogarty (UK)

So helpful! Amy assisted me with an outline for my literature review and with organizing the results for my MBA applied research project. Having a road map helped enormously and saved a lot of time. Definitely worth it.

Jennifer Hagedorn (Canada)

Everything about my experience was great, from Dr. Shaeffer’s expertise, to her patience and flexibility. I reached out to GradCoach after receiving a 78 on a midterm paper. Not only did I get a 100 on my final paper in the same class, but I haven’t received a mark less than A+ since. I recommend GradCoach for everyone who needs help with academic research.

Antonia Singleton (Qatar)

I started using Grad Coach for my dissertation and I can honestly say that if it wasn’t for them, I would have really struggled. I would strongly recommend them – worth every penny!

Richard Egenreider (South Africa)

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Statistics Power

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Dissertation & Thesis Data Analysis Services

Hire a Statistician for Your Dissertation or Thesis

Statistics Power provides thesis and dissertation data analysis services. We work with students at all stages of the dissertation and thesis process, from those in the earliest stages (deciding what topic to study) to those in the latter stages (only needing their data analyzed and their chapter 4 completed).  Contact us for a free consultation . No pressure and we’ll take a look at your sample data if you wish for us to propose a plan specific for your needs.

Our Dissertation & Thesis Data Services

We specialize in all disciplines in  the humanities, sciences, and social sciences :

• Anthropology
• Business and Technology
• Criminal Justice
• Educational Leadership and Management
• Information Systems and Technology
• Health Sciences
• Social Work
• Human Services
• Political Science
• Public Administration
• Public Health

How the Process Works

• First, we try to truly understand the issue you are studying. We will do this by reading everything you have produced so far (your earlier chapters, your proposal, etc), as well as any articles of your suggestion. At this point, we will be ready to suggest a proper way of moving forward.
• We then work on your statistical methodology. If you’ve not yet collected your data, we begin by assisting you in the development of the optimal sampling strategy as well as the best methods of statistician dissertationfor the data to be collected. At this point, we also determine the necessary sample size for your study by performing the appropriate power analysis, and we examine the reliability and validity of your constructs. Experimental design (planning the collection of data) is quite possibly the most important step of a project. Time (and money) lost by poor sampling often cannot be recovered through a data analysis. Let us help you design a data collection plan that will avoid any pitfalls.
• At this point, we assist you with the provision of a draft of your methods section (chapter 3) in APA format, to be submitted to your committee for feedback. We will make any/all changes required by your committee to get it approved, no matter how long (or how many revisions) it takes.
• If you need assistance with data entry, we can help you to set up a template, or we can provide data entry for you for a reasonable extra charge.

If you already have a completed/approved methods section, we will begin at this step:

• When your methods section is approved and your data is collected, we will conduct the statistician dissertationof the data. Unlike many other consultants, we are proficient with virtually every statistical method and test, and various statistical software packages.
• We will then send you a professional draft of your results (chapter 4). This will include the outputs of our analysis (figures, tables, etc) all in APA format, along with a detailed summary of the findings.
• We can also assist you with the development of your discussion section (chapter 5). Call or e-mail to discuss this option with us.
• Ongoing support – We can demonstrate how to interpret the results, provide ample instruction on the methods used (and why) and what the results mean, suggest reading materials for you to greater understand the particular statistical methods used, give you a PowerPoint of the main points of the results, if requested, and allow unlimited email and phone support to ensure that you completely understand the results of the analysis and can discuss them freely. This includes preparation for the defense and peer review process.

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We work with students remotely with phone, email and screen sharing. We are therefore able to work with dissertation and thesis students throughout the United States:

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How to Write a Dissertation Conclusion? | Tips & Examples

What is PhD Thesis Writing? | Beginner’s Guide

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

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

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

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

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Types of data analysis for dissertation.

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

1.   Qualitative Data Analysis

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

2.   Quantitative Data Analysis

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

3.   Descriptive Data Analysis

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

4.   Inferential Data Analysis

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

5.   Exploratory Data Analysis

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

Time Period to Plan and Complete a Data Analysis Dissertation?

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

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

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

Essential Strategies for Data Analysis Dissertation

A.   Planning

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

B.   Prototyping

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

C.   Executing

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

D.   Presenting

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

Data Analysis Tools

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

a.     Excel

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

b.     Google Sheets

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

c.     SPSS

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

d.     STATA

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

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

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

g.     Python

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

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Tips to Compose a Successful Data Analysis Dissertation

a.   Choose a Topic You’re Passionate About

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

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

b.   Do Your Research

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

c.   Develop a Strong Thesis Statement

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

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

d.   Write a Detailed Outline

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

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

e.   Write Your First Draft

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

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

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

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

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

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

How Do You Select the Right Data Analysis

How to Write Data Analysis For A Dissertation?

How to Develop a Conceptual Framework in Dissertation?

What is a Hypothesis in a Dissertation?

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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.

Even f you have the data collected and compiled in the form of facts and figures, it is not enough for proving your research outcomes. There is still a need to apply dissertation data analysis on your data; to use it in the dissertation. It provides scientific support to the thesis and conclusion of the research.

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

If the data is irrelevant and not appropriate, you might get distracted from the point of focus. To show the reader that you can critically solve the problem, make sure that you write a theoretical proposition regarding the selection  and analysis of 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.

On the other hand,  quantitative analysis  refers to the analysis and interpretation of facts and figures – to build reasoning behind the advent of primary findings. An assessment of the main results and the literature review plays a pivotal role in qualitative and quantitative analysis.

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.

          Qualitative Data Analysis Methods

Following are the methods used to perform quantitative data analysis. 

•   Deductive Method

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.

•  Inductive Method

In this method, the researcher analyzes the data not based on any predefined rules. It is a time-taking process used by students who have very little knowledge of the research phenomenon.

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.

                Quantitative Analysis Methods

Following are some of the methods used to perform quantitative data analysis. 

• Trend analysis:  This corresponds to a statistical analysis approach to look at the trend of quantitative data collected over a considerable period.
• Cross-tabulation:  This method uses a tabula way to draw readings among data sets in research.  
• Conjoint analysis :   Quantitative data analysis method that can collect and analyze advanced measures. These measures provide a thorough vision about purchasing decisions and the most importantly, marked parameters.
• TURF analysis:  This approach assesses the total market reach of a service or product or a mix of both. 
• Gap analysis:  It utilizes the  side-by-side matrix  to portray quantitative data, which captures the difference between the actual and expected performance. 
• Text analysis:  In this method, innovative tools enumerate  open-ended data  into easily understandable data. 

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

It is a common misconception that the data presented is self-explanatory. Most of the students provide the data and quotes and think that it is enough and explaining everything. It is not sufficient. Rather than just quoting everything, you should analyze and identify which data you will use to approve or disapprove your standpoints. 

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. 

It also involves answering what you are trying to do with the data and how you have structured your findings. Once you have presented the results, the reader will be looking for interpretation. Hence, it is essential to deliver the understanding as soon as you have submitted your data.

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. 

In the finding part, you should tell the reader what they are looking for. There should be no suspense for the reader as it would divert their attention. State your findings clearly and concisely so that they can get the idea of what is more to come in your dissertation.

11. Connection with Literature Review

At the ending of your data analysis in the dissertation, make sure to compare your data with other published research. In this way, you can identify the points of differences and agreements. Check the consistency of your findings if they meet your expectations—lookup for bottleneck position. Analyze and discuss the reasons behind it. Identify the key themes, gaps, and the relation of your findings with the literature review. In short, you should link your data with your research question, and the questions should form a basis for literature.

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

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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.

In this article, we thoroughly looked at the tips that prove valuable for writing a data analysis in a dissertation. Make sure to give this article a thorough read before you write data analysis in the dissertation leading to the successful future of your research.

Oxbridge Essays. Top 10 Tips for Writing a Dissertation Data Analysis.

Emidio Amadebai

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

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Dissertation & Thesis Statistics Coaching & Consulting

The Dissertation Coach statistical team is committed to excellence. We recognize that high caliber statistical consulting requires a firm knowledge of statistics, solid people skills, and an awareness of how to handle the challenges that arise as part of quantitative research. Our staff of highly trained and experienced experts will work closely with you to provide a positive service experience and ensure that your quantitative analysis needs are met.

All our statisticians have doctoral degrees and significant experience with quantitative research. They will provide you with step-by-step guidance to ensure you fully understand all aspects of the data analysis process and results. Our statisticians are here to demystify statistics and partner with you so that you will successfully finish your dissertation, thesis, or research project.

We will work with you on an hourly basis to give you more control over the cost of the services we provide. Once we have a chance to speak with you, we can provide a personalized estimate for the cost of assisting you based on your specific needs. We will be pleased to offer you a free consultation.

The Dissertation Coach statistical team is committed to excellence. We recognize that high caliber statistical consulting requires an advanced knowledge of statistics, solid people skills, and an awareness of how to handle the challenges that arise as part of quantitative research. Our staff of highly experienced experts will work closely with you to provide a personalized experience and ensure that your quantitative analysis needs are met.

Over the past 23 years we have successfully worked with thousands of clients seeking statistical assistance. We provide our assistance in an ethical, straightforward manner.

All our statisticians have doctoral degrees and world-class experience with quantitative research.They have served as professors, dissertation committee members, thesis advisors, outside experts, and industry analysts. We will provide you with step-by-step guidance to ensure you fully understand all aspects of the data analysis process and results. Our statisticians are here to demystify statistics and be a part of your team so that you will successfully finish your dissertation, thesis, or research project.

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We will work with you on an hourly basis to give you control over the cost of the services we provide. Once we have a chance to speak with you, we can provide a personalized estimate for the cost of assisting you based on your specific needs. We will be pleased to offer you a free, low-pressure consultation so you can get to know us better and learn about how we can help you.

Our Statisticians Offer the Following:

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• Step-by-step guidance on how to conduct analyses in statistical software packages. We can run your data analysis for you, complete it with you over a shared screen, or teach you how to independently conduct your analysis. We can also review any statistics you have already run to make sure your analysis was completed correctly. 
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• Provide developmental editing for your methods, results, discussion, and conclusion chapters.
• Provide effective coaching and consulting to help you prepare for your oral defense meeting and preparation to answer methods and statistical questions from your committee.

*Please note that The Dissertation Coach is not a dissertation or thesis writing service. We will not write a student’s dissertation or thesis on their behalf under any circumstances. We act as consultants, advisors, coaches and mentors to students but never as the authors of their doctoral dissertations or master’s theses.

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Our team of statisticians are ready to conduct a variety of basic and advanced analytic techniques to meet your needs, regardless of whether you are doing univariate, bivariate, or multivariate analyses. Our staff is knowledgeable and skilled in using SPSS, Stata, SAS, R, and Minitab. Our statisticians are experts in the following statistical techniques:

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• Correlations (Pearson, Spearman, partial)
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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.

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Whether you are a student or a business professional, our team of phd-trained statisticians can help you with your statistical analysis with a service we guarantee will meet the academic standard required for a phd and peer-review publications. our professional statisticians have expertise and experience to help you with:, quantitative, qualitative, and mixed methods techniques., designing your research proposal (by making sure your methods and analysis plan are aligned with your project goals prior to data collection)., designing your research questions and hypotheses to test., determining which data collection methods you require., selecting which statistical analysis techniques you should use., reporting and interpreting your results (including development of related tables and figures)., help with discussing the meaning of your statistical analyses., the methods, results and discussion sections of your dissertation by performing a critical review of your work that provides constructive feedback on how to improve it., our statisticians have experience and expertise with:, spss, r, nvivo, maxqda, amos, and more..

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Are you worried about doing data analysis for research papers? Our services help scholars like you complete their research projects and papers by analyzing data and interpreting their findings. You can get help with dissertation statistics by simply sending us your raw data, we’ll refine it and run the appropriate analyses and tests with your methodology for your research papers .

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Let’s Define Data Analysis

• Data analysis refers to the process of evaluating a huge volume of data through analytical and logical reasoning patterns for examining each component of the same. The process may include complete analysis, research, experiment, and validation of the same. Under this, each set of data is gathered, reviewed, and later analyzed to meaningful outcomes that are helpful for an organization.
• There are several other specific data analysis methods used for the complete process such as - data mining, text analytics, business intelligence statistical data analysis, and data visualizations.

Data Analysis: Uses

• Data analysis examines a large volume of data to identify the relative patterns, correlations, and insights that are useful for business operations.
• It helps in speedy and efficient analysis of data regularly.
• It offers an accurate analysis of data that fosters immediate decision-making in the most significant sectors of business, healthcare, and even research.
• It has automatically reduced the manual work and started delivering reliable results within the shortest time.
• It has enforced an amazing ability to work faster, stay agile, and reinforce a better competitive edge within the organizations, unlike earlier times.
• Big data analysis technologies like- Hadoop and cloud-based analytics have brought down the significant costs involved with working on large amounts of data.
• It fosters faster and better decision-making capability
• In the education sector - PhD researcher , dissertation, research, and scientist (it helps the research scholar to write the thesis, and dissertation accurately and gives the scientist and researcher specific and accurate data)

Significant Highlights of Data Analysis

• Integration of data analysis could save up to $300 billion a year in the healthcare sector
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• Performing data analysis practices helps boost a company’s revenue by approximately 66%
• The average Fortune 1000 company can easily increase their data usability standards by 10% by being a part of data analysis services which would in turn increase their revenue by over 2 billion dollars.
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Dissertation committees usually vigorously attack the way a study’s results are analyzed..

Dissertation committees usually vigorously attack the way a study’s results are analyzed; as such, data analysis can be extremely difficult and intimidating for students.

Dissertation data analysis is one of Dissertation Genius’s core competencies; we have PhD-level statistical consultants on our team as well as over 22 years of experience in all types of data analysis. For more details on our qualitative analysis services, visit our Qualitative Analysis  page.

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Qualitative Data Analysis Services For Dissertation In The UK

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What Is The Data Analysis Process?

Before getting to the data analysis process, let’s first discuss “What is data analysis?”. Data analysis in research refers to the process of examining and interpreting data collected from various sources, such as surveys, experiments, observations, or existing databases, to draw meaningful conclusions and insights. It involves using statistical techniques to identify patterns, trends, and relationships in the data and to test hypotheses or answer research questions.

The specific methods and techniques used in data analysis will depend on the type of data collected, the research question, and the research design. Commonly used data analysis methods in research include descriptive statistics, inferential statistics, regression analysis, factor analysis, and content analysis.

Let’s look at the main steps of how data analysis is performed.

• Define the research question or problem to be solved.
• Gather the relevant data from various sources.
• Clean and preprocess the data to ensure it’s usable for analysis.
• Explore the data through visualisations and summary statistics to gain insights.
• Develop a hypothesis or set of hypotheses based on the findings.
• Test the hypotheses through statistical analysis or other methods.
• Conclude and make recommendations based on the results.
• Communicate the findings to stakeholders clearly and concisely.
• Document the process and methods used for future reference.
• Iterate and refine the analysis as needed based on feedback or additional data.

Why Is Data Analysis Important In Research?

Data analysis is a vital part of any research project as it allows researchers to make sense of the collected data and draw meaningful conclusions. The importance of data analysis in research can be summarised as follows:

Identify Patterns And Relationships – helps researchers to identify patterns and relationships in their data that may not be immediately apparent. Researchers can uncover important insights that would otherwise be missed by using various statistical and analytical techniques.

Test hypotheses allow researchers to test their hypotheses and determine whether their findings are statistically significant. This is important because it helps validate the study’s results and ensures that conclusions are based on evidence rather than intuition or guesswork.

Make Informed Decisions – gives researchers the information they need to make informed decisions about their research project. By analysing the data, researchers can determine the effectiveness of their research methods and adjust their approach accordingly.

Evaluate Outcomes – important for evaluating the outcomes of a research project. By analysing the data, researchers can determine whether their project achieved its objectives and identify areas for improvement in future projects.

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FAQs About Dissertation Statistical Analysis Service

What is the qualitative data analysis service.

Our data analysis experts are equally capable of qualitative statistics for dissertations. Over the last several years, we have helped thousands of Master’s and Ph.D. students from across the globe by performing complete qualitative data analysis for their dissertation projects. Although this particular type of data analysis brings its own unique set of challenges, ResearchProspect data analysis consultants have the expertise and experience to help you with every aspect of qualitative data analysis.

What does the Quantitative Dissertation Analysis Service include?

• Developing a detailed and comprehensive plan to work with all aspects of statistical analysis, database development, and data analysis software.
• Reviewing your data in detail
• Developing an analysis technique based on your research aim, objectives, and limitations
• Identifying the best statistical analysis method and the best data analysis software keeping your school’s requirements in consideration
• Filtering the data to ensure optimized results and analysis
• Developing a database in an appropriate tool and entering the data
• Writing, proofreading, and finalizing the statistical analysis section of your dissertation
• Amending the data analysis section as many times as needed until you are fully satisfied with the quality of the works
• Continuous support throughout the process so you can be confident that every bit of your requirements will be satisfied.

How can I order for your qualitative data analysis service?

To place your order for your qualitative data analysis service, simply choose data analysis service under the “Dissertation Services” menu at the first step of the order form.

Why Take Help From Data Analysis Services For Dissertation?

Data analysis for dissertations is a crucial step in many research projects. However, it can be a complex and time-consuming task, especially for those who do not have prior experience with statistical analysis. This is where data analysis services can help. Data analysis services for dissertations have a team of experienced data analysts skilled in using various statistical software and techniques to analyse data. Good services in the United Kingdom, such as ResearchProspect, can work on data analysis software according to your requirements. They even provide data analysis services for small businesses . A few examples of tools include:

• Excel – A spreadsheet software commonly used for data manipulation and basic statistical analysis.
• SPSS – A software package used for statistical analysis in social sciences.
• SAS – A powerful statistical software used in various industries for data analysis and modelling.
• R – An open-source programming language for statistical computing and graphics.
• Stata – A statistical software package used for data analysis, management, and graphics.
• Tableau – A business intelligence and data visualisation tool to create interactive visualisations and dashboards.
• MATLAB – A programming language and numerical computing environment used for scientific computing and data analysis.
• Python – A popular programming language used for data analysis, machine learning, and scientific computing.
• Power BI – A business analytics service by Microsoft used for data analysis and visualisation.
• QlikView – A business intelligence tool used for data discovery and data visualisation.

Data analysis services for dissertations can help you save time, produce accurate results, and improve the quality of your dissertation. They can also work on different data analysis methods tailored to your requirements. Data analysis services can also clearly interpret your results and help you understand how they relate to your research questions. Getting help from data analysis services can help you produce a high-quality dissertation with reliable data analysis, improving your chances of publishing your research in academic journals and achieving better grades.

Data Analysis For Dissertation

As one of the most significant parts of a dissertation, data analysis requires you to showcase extensive research abilities, depending on the type of research involved, the data analysis either requires calculations or not.

What Are The Different Types Of Research?

It can be difficult to choose the type of research you would have to conduct in your dissertation because as you get deeper into the research, you find more details associated with your data. Here are the main types of research.

Quantitative research – deals with numerical data and has an objective. It focuses on the collection, quantifying and analysing of data. This data type is usually analysed using data analysis software such as Excel, SPSS, Stata, etc.

Qualitative research – deals with the data collected mostly through the opinions of relevant people. It focuses on answering a research’s ‘Why’and ‘How’ parts. This research also showcases results using images and other visual representations that can further be used in SWOT analysis and PEST analysis etc. In qualitative research, tools such as Tableau and nVivo are commonly used.

Data Analysis Tips For Dissertation

Here are a few tips to follow for excellent data analysis for a dissertation.

• Ensure that your data is relevant to the aims and objectives of your research.
• Analyse your data thoroughly.
• Use visual representations for a better view of your data.
• Make sure to discuss the findings from your data.
• Take assistance from professional data analysis services for dissertations where necessary.
• Include an appendix
• Make sure that your data is connected to the conducted literature review.

Types Of Data Analysis

There are four most common types of data analysis. ResearchProspect works with the method of your choice as part of their data analysis services for small businesses.

Descriptive Data Analysis – using historical and current data to evaluate relationships. It is mainly used to measure a business’s Key Performance Indicators (KPIs). Descriptive data analysis refers to summarising and describing a dataset’s main characteristics or features using statistical and graphical methods. The goal of descriptive analysis is to provide a clear and concise summary of the data, allowing researchers to understand better the nature and distribution of the variables under study.

Diagnostic Data Analysis is mainly used to answer why something happened. Diagnostic data analysis aims to identify the root causes of problems or issues in a system or process. It involves analysing data from various sources to diagnose problems, assess their severity and impact, and recommend solutions.

Diagnostic data analysis is often used in fields such as healthcare, manufacturing, and finance to identify areas for improvement and optimise processes. The analysis may involve statistical methods, machine learning techniques, or other data-driven approaches. The ultimate goal of diagnostic data analysis is to enable decision-makers to take action based on the insights gained from the analysis.

Prescriptive Data Analysis – involves using data to provide recommendations on what actions to take to achieve a desired outcome. It goes beyond descriptive and diagnostic analysis by not only identifying patterns and causes of events but also by suggesting specific actions based on the insights gained from the data.

Prescriptive analytics uses techniques such as optimisation, simulation, and decision analysis to provide a range of possible outcomes for different courses of action. By using prescriptive data analysis, organisations can make data-driven decisions leading to more efficient and effective outcomes.

Predictive Data Analysis – using historical and current data to forecast future outcomes or behaviours. It uses computational modelling and machine learning techniques to identify patterns and relationships in the data and uses that information to make predictions about the future.

Predictive analytics can be used for various purposes, such as forecasting sales, predicting customer behaviour, identifying potential risks, and optimising operations. Using predictive data analysis, organisations can make proactive decisions and take preemptive actions to mitigate risks or capitalise on opportunities, leading to improved business performance and outcomes.

Take Professional Data Analysis Services From ResearchProspect

ResearchProspect is a leading data analysis service in the United Kingdom. We have helped thousands of students in achieving their academic success. We have had clients from the top British universities, and they have all loved working with us. The reason our clients are satisfied is because of our top-quality delivery. We have specialists who perform data analysis for your dissertation. Each data analysis writer is hired after a strict recruitment process to ensure perfection. ResearchProspect follows a strict confidentiality rule. Your information is safe with us. Moreover, we do not share your work with anyone else or use it in future work. So what are you waiting for? Get professional assistance from the best data analysis services in the UK now and ace your dissertations.

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• GETTING STARTED
• Introduction
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Getting to the main article

Choosing your route

Setting research questions/ hypotheses

Assessment point

Building the theoretical case

Setting your research strategy

Data collection

Data analysis

Data analysis techniques

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

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

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

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

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

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

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

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

Final thoughts...

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

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We Are Expert In:

• Schools & departments

Data and Decision Analytics (Online Learning) MSc

Awards: MSc

Study modes: Part-time

Online learning

Funding opportunities

Postgraduate Online Learning Open Days

Join us on 22 and 23 May to learn about studying an online degree at Edinburgh.

Find out more and register for the Online Learning Open Days

Programme description

The MSc Data and Decision Analytics programme prepares you to not only be able to analyse and digest data, but also to translate this into effective decision-making in this Big Data Age.

You will learn from world-class faculty how to apply cutting-edge business analytics and computing tools to make data-driven decisions in a plethora of business areas, such as:

• human resources
• technology and equipment

The courses provide you with methodological foundations and techniques, as well as applications in business, management, and economics.

To enhance your learning experience, develop your practical skills in analytics, and prepare you for the job market, the programme uses a combination of:

• problem-based learning
• case study-based learning
• hands-on experience using prominent analytics software

Through a balance of academic theory, crucial soft skills and the very latest industry practice, the programme provides opportunities for you to gain experience in planning, designing, executing, and reporting findings to a critical audience of specialists and non-specialists.

You will also gain experience through research processes, such as:

• primary data collection from individuals
• securing their cooperation and consent
• analysing and evaluating data
• framing recommendations
• other methods of field study and data collection

You will learn how to communicate complex ideas and information in a coherent and structured manner throughout the programme courses. The programme also provides opportunities for you to engage with each other through group projects, discussion forums and peer assessment.

The distinguishing feature of the MSc in Data and Decision Analytics (online learning) is that it will expose you to unique and balanced courses in the two most important analytics areas, namely, predictive and prescriptive. This enables you to not only make use of state-of-art machine learning methods to predict and understand data behaviours, but also to effectively apply decision optimisation to make better informed decisions as current business managers. The programme combines a plethora of state-of-the-art elective courses that enable you to make use of the most innovative and efficient methods to solve data and decision analytics problems.

Scheduling your studies

You will need to set aside approximately 15 hours per week for reading, viewing lectures, tutorials, and assignments. This is an average across the duration of the entire two years, and it will vary depending on the course and also when assignments are due. Therefore, at peak times, you may need to put aside more time for your studies.

Live sessions

These sessions will be scheduled to take place between 09:00 and 18:00 GMT.

We are currently planning for live sessions to take place on Mondays and Tuesdays only. There will be a maximum of 4 hours required during these two days of the week for participating in the live online sessions.

Depending on your location this may mean attending before or after your usual day of work. We appreciate though for some it may mean requesting flexible working from your employers to attend these. Equally, if you are not able to attend some of these sessions, there will be recordings made available so you can catch up afterwards.

The courses will consist of live sessions that will be made up of a mixture of:

• computer lab sessions.

Lectures will introduce the theoretical foundation of the given subjects, while tutorials and computer labs will give you hands-on experience and allow you to practice the concepts covered by the lectures.

The programme's materials (access to videos, slides, briefings, and so on) and overall resources will be available on the online platform called Learn. Recordings of all online lectures, tutorials and computer lab sessions will be made available giving you a certain level of flexibility to study around other commitments as we appreciate that you may not be able to attend all the live sessions. Each individual course has its own Learn page. Assessment will consist of coursework. Students will submit their projects, essays, and so on through Learn and Turnitin. Feedback will also be provided through the same system.

• About Learn

Students will also interact and engage with each other through discussion forums, group projects, and other online tools.

Programme structure

The online Data and Decision Analytics MSc is delivered part-time with a start date in September each year. The programme takes 24 months to complete and combines academic study with practical application.

Please note that live sessions will be scheduled to take place on Mondays and Tuesdays during term time (c. 20 weeks each year). These live sessions will be a maximum of 8 hours per week and scheduled 9am-6pm GMT.

The programme encompasses a number of core courses and your studies will culminate with a dissertation.

Compulsory courses

• Applied Decision Optimisation
• Applied Machine Learning
• Data Analysis and Statistics for Business
• Python Programming
• Storytelling in Data and Decision Analytics

Option courses

• Time Series
• Data Management
• Analytics of Decision Making under Multiple Criteria
• Heuristic Optimisation
• Introduction to Stochastic Optimisation
• Advanced Stochastic Optimisation

Option courses are subject to change and demand. We cannot guarantee that all option courses will run each year and occasionally there will be last-minute amendments after this date due to unforeseen circumstances such as staff illness.

The content of individual courses and the programme for any given degree are under constant academic review in light of current circumstances and may change from time to time, with some programmes and courses being modified, discontinued, or replaced.

Dissertation

• Dissertation in Data and Decision Analytics

The dissertation is an in-depth study of a topic in which you are particularly interested in within the field of Data and Decision Analytics. Undertaking the dissertation requires you to develop a deep level of analysis and understanding of the theory and processes of organisations and the business environment through the completion of a piece of individual research.

Find out more about compulsory and optional courses

We link to the latest information available. Please note that this may be for a previous academic year and should be considered indicative.

Learning outcomes

By the end of the programme, you will be able to:

• understand and critically apply the concepts and methods of business analytics
• identify, model, and solve decision problems in different settings
• interpret results/solutions and identify appropriate courses of action for a given managerial situation, whether a problem or an opportunity
• create viable solutions to decision-making problems

MSc Data and Decision Analytics learning outcomes

Career opportunities

Organisations hold more information about their business environments than ever before. Increasingly, these organisations are recognising the role of data in gaining insights and out-thinking competitors. The worldwide big data analytics market was valued at USD 37.34 billion in 2018 and is anticipated to grow at a CAGR of 12.3% to arrive at USD 105.08 billion by 2027. This will inevitably lead to further growth of the market for employees in the area of analytics.

The MSc in Data and Decision Analytics will offer you, from a range of degree backgrounds, the opportunity to equip yourself with an artillery of concepts, methods and applications of data analytics along with hands-on and practical experience in applying them. It is not just about being able to analyse and digest the data available but to then translate this into effective decision-making.

We expect the programme to open a range of career pathways in analytics for our graduates or to allow them to progress further within their existing career. Roles we anticipate to be amongst these pathways include:

• business consultants
• business analysts
• data analysts
• business intelligence & analytics consultants
• metrics & analytics specialists
• analytics associates
• solution architects
• business process analysts
• management consulting associates
• operational research consultants

MSc Data and Decision Analytics career development

Entry requirements

These entry requirements are for the 2024/25 academic year and requirements for future academic years may differ. Entry requirements for the 2025/26 academic year will be published on 11 July 2024.

Entrance to our MSc programmes is strongly competitive. You can increase your chances of a successful application by exceeding the minimum programme requirements.

• Important points to note when applying for this programme

A UK 2:1 honours degree or its international equivalent in an area related to management science, operational research, statistics, econometrics, mathematics, physics, computer science, engineering, or business and management with a distinct quantitative content.

Your background should ideally include courses and/or experience gained in topics such as linear algebra, calculus, probability, statistics, and computer programming.

If you have a UK 2:1 honours degree or its international equivalent in an unrelated subject we may consider your application if you have relevant work experience.

Work experience is desirable but not mandatory.

All students are recommended to have their own laptop for this programme.

Credit transfer from the MicroMasters in Predictive Analytics

We welcome applications from students who have successfully completed the University of Edinburgh's MicroMasters in Predictive Analytics. Learners who successfully completed the MicroMasters programme will be awarded 30 postgraduate credits towards our Data and Decision Analytics MSc (online).

• MicroMasters in Predictive Analytics

Learners who meet all the entry requirements and are successfully admitted onto the MSc Data and Decision Analytics can expect that their MicroMasters coursework will count towards their degree, comprising 20 credits of the total 180 credits of the masters programme. 10 credits would be recognised for the course in Python Programming where there is a strong equivalence between the course content and the other 10 credits would act as a discount towards the elective courses on the programme.

The MicroMasters must have been completed within two years of starting on the Data and Decision Analytics MSc, with September 2024 the last entry date when the credits will be accepted. Completing the MicroMasters will not guarantee acceptance and the standard University admissions process and criteria will apply. Decisions on admission to the programme lie solely with the University.

If you would like the 20 credits to count towards recognised prior learning, when applying you should upload your MicroMasters certificate to your application.

Students from China

This degree is Band B.

• Postgraduate entry requirements for students from China

International qualifications

Check whether your international qualifications meet our general entry requirements:

• Entry requirements by country
• English language requirements

Regardless of your nationality or country of residence, you must demonstrate a level of English language competency at a level that will enable you to succeed in your studies.

English language tests

We accept the following English language qualifications at the grades specified:

• IELTS Academic: total 7.0 with at least 6.0 in each component. We do not accept IELTS One Skill Retake to meet our English language requirements.
• TOEFL-iBT (including Home Edition): total 100 with at least 20 in each component. We do not accept TOEFL MyBest Score to meet our English language requirements.
• C1 Advanced ( CAE ) / C2 Proficiency ( CPE ): total 185 with at least 169 in each component.
• Trinity ISE : ISE III with passes in all four components.
• PTE Academic: total 70 with at least 59 in each component.

Your English language qualification must be no more than three and a half years old from the start date of the programme you are applying to study, unless you are using IELTS , TOEFL, Trinity ISE or PTE , in which case it must be no more than two years old.

Degrees taught and assessed in English

We also accept an undergraduate or postgraduate degree that has been taught and assessed in English in a majority English speaking country, as defined by UK Visas and Immigration:

• UKVI list of majority English speaking countries

We also accept a degree that has been taught and assessed in English from a university on our list of approved universities in non-majority English speaking countries (non-MESC).

• Approved universities in non-MESC

If you are not a national of a majority English speaking country, then your degree must be no more than five years old* at the beginning of your programme of study. (*Revised 05 March 2024 to extend degree validity to five years.)

Find out more about our language requirements:

Fees and costs

If you receive an offer of admission, either unconditional or conditional, you will be asked to pay a tuition fee deposit within 28 days to secure your place on the programme:

• £1,500 (this contributes towards your tuition fees)

The fee does not include the cost of textbooks for core and option courses so you should budget an additional amount for this required expenditure.

As this is an online programme, you will also require, and need to budget for, relevant IT equipment and broadband internet in order to pursue your studies.

See the programme website for more information on fees and deposits.

Tuition Fees

Scholarships and funding, uk government postgraduate loans.

If you live in the UK, you may be able to apply for a postgraduate loan from one of the UK’s government loan schemes.

The type and amount of financial support you are eligible for will depend on:

• your programme
• the duration of your studies
• your tuition fee status

Programmes studied on a part-time intermittent basis are not eligible.

• UK government and other external funding

Other funding opportunities

We offer a 10% discount on postgraduate tuition fees for alumni who have graduated with an undergraduate degree from the University of Edinburgh.

We also offer a 10% discount on postgraduate tuition fees for students who have previously matriculated on a "Visiting Programme" as an undergraduate student and completed a minimum of one semester of study at the University of Edinburgh.

The Scholarship and Student Funding site provides a list of programmes not covered by the discount scheme.

Search for scholarships and funding opportunities:

• Search for funding

Further information

• Enquiry Management Team
• Phone: +44 (0)131 650 9663
• Contact: [email protected]
• Programme Director, Douglas Alem
• Phone: +44 (0)131 651 1036
• Contact: [email protected]
• University of Edinburgh Business School
• 29 Buccleuch Place
• School: Business School
• College: Arts, Humanities & Social Sciences

Select your programme and preferred start date to begin your application.

MSc Data and Decision Analytics (Online Learning) - 24 months (Part-time)

Application deadlines.

If the programme is not full by the final application deadline, we may be able to consider applications submitted after that date. If we are still accepting applications following the final deadline, we will clearly indicate that on the programme's application page.

• How to apply

You must submit one reference with your application.

You will be required to supply a number of documents as part of your application. This includes:

• an official transcript
• degree certificate
• one reference
• personal statement
• English language qualification

Find out more about the general application process for postgraduate programmes: