secondary data dissertation structure

Library Guides

Dissertations 4: methodology: methods.

• Introduction & Philosophy
• Methodology

Primary & Secondary Sources, Primary & Secondary Data

When describing your research methods, you can start by stating what kind of secondary and, if applicable, primary sources you used in your research. Explain why you chose such sources, how well they served your research, and identify possible issues encountered using these sources.  

Definitions  

There is some confusion on the use of the terms primary and secondary sources, and primary and secondary data. The confusion is also due to disciplinary differences (Lombard 2010). Whilst you are advised to consult the research methods literature in your field, we can generalise as follows:  

Secondary sources 

Secondary sources normally include the literature (books and articles) with the experts' findings, analysis and discussions on a certain topic (Cottrell, 2014, p123). Secondary sources often interpret primary sources.  

Primary sources 

Primary sources are "first-hand" information such as raw data, statistics, interviews, surveys, law statutes and law cases. Even literary texts, pictures and films can be primary sources if they are the object of research (rather than, for example, documentaries reporting on something else, in which case they would be secondary sources). The distinction between primary and secondary sources sometimes lies on the use you make of them (Cottrell, 2014, p123). 

Primary data 

Primary data are data (primary sources) you directly obtained through your empirical work (Saunders, Lewis and Thornhill 2015, p316). 

Secondary data 

Secondary data are data (primary sources) that were originally collected by someone else (Saunders, Lewis and Thornhill 2015, p316).   

Comparison between primary and secondary data   

Use  

Virtually all research will use secondary sources, at least as background information. 

Often, especially at the postgraduate level, it will also use primary sources - secondary and/or primary data. The engagement with primary sources is generally appreciated, as less reliant on others' interpretations, and closer to 'facts'. 

The use of primary data, as opposed to secondary data, demonstrates the researcher's effort to do empirical work and find evidence to answer her specific research question and fulfill her specific research objectives. Thus, primary data contribute to the originality of the research.    

Ultimately, you should state in this section of the methodology: 

What sources and data you are using and why (how are they going to help you answer the research question and/or test the hypothesis. 

If using primary data, why you employed certain strategies to collect them. 

What the advantages and disadvantages of your strategies to collect the data (also refer to the research in you field and research methods literature). 

Quantitative, Qualitative & Mixed Methods

The methodology chapter should reference your use of quantitative research, qualitative research and/or mixed methods. The following is a description of each along with their advantages and disadvantages. 

Quantitative research 

Quantitative research uses numerical data (quantities) deriving, for example, from experiments, closed questions in surveys, questionnaires, structured interviews or published data sets (Cottrell, 2014, p93). It normally processes and analyses this data using quantitative analysis techniques like tables, graphs and statistics to explore, present and examine relationships and trends within the data (Saunders, Lewis and Thornhill, 2015, p496). 

Qualitative research  

Qualitative research is generally undertaken to study human behaviour and psyche. It uses methods like in-depth case studies, open-ended survey questions, unstructured interviews, focus groups, or unstructured observations (Cottrell, 2014, p93). The nature of the data is subjective, and also the analysis of the researcher involves a degree of subjective interpretation. Subjectivity can be controlled for in the research design, or has to be acknowledged as a feature of the research. Subject-specific books on (qualitative) research methods offer guidance on such research designs.  

Mixed methods 

Mixed-method approaches combine both qualitative and quantitative methods, and therefore combine the strengths of both types of research. Mixed methods have gained popularity in recent years.  

When undertaking mixed-methods research you can collect the qualitative and quantitative data either concurrently or sequentially. If sequentially, you can for example, start with a few semi-structured interviews, providing qualitative insights, and then design a questionnaire to obtain quantitative evidence that your qualitative findings can also apply to a wider population (Specht, 2019, p138). 

Ultimately, your methodology chapter should state: 

Whether you used quantitative research, qualitative research or mixed methods. 

Why you chose such methods (and refer to research method sources). 

Why you rejected other methods. 

How well the method served your research. 

The problems or limitations you encountered. 

Doug Specht, Senior Lecturer at the Westminster School of Media and Communication, explains mixed methods research in the following video:

LinkedIn Learning Video on Academic Research Foundations: Quantitative

The video covers the characteristics of quantitative research, and explains how to approach different parts of the research process, such as creating a solid research question and developing a literature review. He goes over the elements of a study, explains how to collect and analyze data, and shows how to present your data in written and numeric form.

Link to quantitative research video

Some Types of Methods

There are several methods you can use to get primary data. To reiterate, the choice of the methods should depend on your research question/hypothesis. 

Whatever methods you will use, you will need to consider: 

why did you choose one technique over another? What were the advantages and disadvantages of the technique you chose? 

what was the size of your sample? Who made up your sample? How did you select your sample population? Why did you choose that particular sampling strategy?) 

ethical considerations (see also tab...)  

safety considerations  

validity  

feasibility  

recording  

procedure of the research (see box procedural method...).  

Check Stella Cottrell's book  Dissertations and Project Reports: A Step by Step Guide  for some succinct yet comprehensive information on most methods (the following account draws mostly on her work). Check a research methods book in your discipline for more specific guidance.  

Experiments 

Experiments are useful to investigate cause and effect, when the variables can be tightly controlled. They can test a theory or hypothesis in controlled conditions. Experiments do not prove or disprove an hypothesis, instead they support or not support an hypothesis. When using the empirical and inductive method it is not possible to achieve conclusive results. The results may only be valid until falsified by other experiments and observations. 

For more information on Scientific Method, click here . 

Observations 

Observational methods are useful for in-depth analyses of behaviours in people, animals, organisations, events or phenomena. They can test a theory or products in real life or simulated settings. They generally a qualitative research method.  

Questionnaires and surveys 

Questionnaires and surveys are useful to gain opinions, attitudes, preferences, understandings on certain matters. They can provide quantitative data that can be collated systematically; qualitative data, if they include opportunities for open-ended responses; or both qualitative and quantitative elements. 

Interviews  

Interviews are useful to gain rich, qualitative information about individuals' experiences, attitudes or perspectives. With interviews you can follow up immediately on responses for clarification or further details. There are three main types of interviews: structured (following a strict pattern of questions, which expect short answers), semi-structured (following a list of questions, with the opportunity to follow up the answers with improvised questions), and unstructured (following a short list of broad questions, where the respondent can lead more the conversation) (Specht, 2019, p142). 

This short video on qualitative interviews discusses best practices and covers qualitative interview design, preparation and data collection methods. 

Focus groups   

In this case, a group of people (normally, 4-12) is gathered for an interview where the interviewer asks questions to such group of participants. Group interactions and discussions can be highly productive, but the researcher has to beware of the group effect, whereby certain participants and views dominate the interview (Saunders, Lewis and Thornhill 2015, p419). The researcher can try to minimise this by encouraging involvement of all participants and promoting a multiplicity of views. 

This video focuses on strategies for conducting research using focus groups.  

Check out the guidance on online focus groups by Aliaksandr Herasimenka, which is attached at the bottom of this text box. 

Case study 

Case studies are often a convenient way to narrow the focus of your research by studying how a theory or literature fares with regard to a specific person, group, organisation, event or other type of entity or phenomenon you identify. Case studies can be researched using other methods, including those described in this section. Case studies give in-depth insights on the particular reality that has been examined, but may not be representative of what happens in general, they may not be generalisable, and may not be relevant to other contexts. These limitations have to be acknowledged by the researcher.     

Content analysis 

Content analysis consists in the study of words or images within a text. In its broad definition, texts include books, articles, essays, historical documents, speeches, conversations, advertising, interviews, social media posts, films, theatre, paintings or other visuals. Content analysis can be quantitative (e.g. word frequency) or qualitative (e.g. analysing intention and implications of the communication). It can detect propaganda, identify intentions of writers, and can see differences in types of communication (Specht, 2019, p146). Check this page on collecting, cleaning and visualising Twitter data.

Extra links and resources:  

Research Methods  

A clear and comprehensive overview of research methods by Emerald Publishing. It includes: crowdsourcing as a research tool; mixed methods research; case study; discourse analysis; ground theory; repertory grid; ethnographic method and participant observation; interviews; focus group; action research; analysis of qualitative data; survey design; questionnaires; statistics; experiments; empirical research; literature review; secondary data and archival materials; data collection. 

Doing your dissertation during the COVID-19 pandemic  

Resources providing guidance on doing dissertation research during the pandemic: Online research methods; Secondary data sources; Webinars, conferences and podcasts; 

• Virtual Focus Groups Guidance on managing virtual focus groups

5 Minute Methods Videos

The following are a series of useful videos that introduce research methods in five minutes. These resources have been produced by lecturers and students with the University of Westminster's School of Media and Communication. 

Case Study Research

Research Ethics

Quantitative Content Analysis 

Sequential Analysis 

Qualitative Content Analysis 

Thematic Analysis 

Social Media Research 

Mixed Method Research 

Procedural Method

In this part, provide an accurate, detailed account of the methods and procedures that were used in the study or the experiment (if applicable!). 

Include specifics about participants, sample, materials, design and methods. 

If the research involves human subjects, then include a detailed description of who and how many participated along with how the participants were selected.  

Describe all materials used for the study, including equipment, written materials and testing instruments. 

Identify the study's design and any variables or controls employed. 

Write out the steps in the order that they were completed. 

Indicate what participants were asked to do, how measurements were taken and any calculations made to raw data collected. 

Specify statistical techniques applied to the data to reach your conclusions. 

Provide evidence that you incorporated rigor into your research. This is the quality of being thorough and accurate and considers the logic behind your research design. 

Highlight any drawbacks that may have limited your ability to conduct your research thoroughly. 

You have to provide details to allow others to replicate the experiment and/or verify the data, to test the validity of the research. 

Bibliography

Cottrell, S. (2014). Dissertations and project reports: a step by step guide. Hampshire, England: Palgrave Macmillan.

Lombard, E. (2010). Primary and secondary sources.  The Journal of Academic Librarianship , 36(3), 250-253

Saunders, M.N.K., Lewis, P. and Thornhill, A. (2015).  Research Methods for Business Students.  New York: Pearson Education. 

Specht, D. (2019).  The Media And Communications Study Skills Student Guide . London: University of Westminster Press.  

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Write Your Dissertation Using Only Secondary Research

Writing a dissertation is already difficult to begin with but it can appear to be a daunting challenge when you only have other people’s research as a guide for proving a brand new hypothesis! You might not be familiar with the research or even confident in how to use it but if secondary research is what you’re working with then you’re in luck. It’s actually one of the easiest methods to write about!

Secondary research is research that has already been carried out and collected by someone else. It means you’re using data that’s already out there rather than conducting your own research – this is called primary research. Thankfully secondary will save you time in the long run! Primary research often means spending time finding people and then relying on them for results, something you could do without, especially if you’re in a rush. Read more about the advantages and disadvantages of primary research .

So, where do you find secondary data?

Secondary research is available in many different places and it’s important to explore all areas so you can be sure you’re looking at research you can trust. If you’re just starting your dissertation you might be feeling a little overwhelmed with where to begin but once you’ve got your subject clarified, it’s time to get researching! Some good places to search include:

• Libraries (your own university or others – books and journals are the most popular resources!)
• Government records
• Online databases
• Credible Surveys (this means they need to be from a reputable source)
• Search engines (google scholar for example).

The internet has everything you’ll need but you’ve got to make sure it’s legitimate and published information. It’s also important to check out your student library because it’s likely you’ll have access to a great range of materials right at your fingertips. There’s a strong chance someone before you has looked for the same topic so it’s a great place to start.

What are the two different types of secondary data?

It’s important to know before you start looking that they are actually two different types of secondary research in terms of data, Qualitative and quantitative. You might be looking for one more specifically than the other, or you could use a mix of both. Whichever it is, it’s important to know the difference between them.

• Qualitative data – This is usually descriptive data and can often be received from interviews, questionnaires or observations. This kind of data is usually used to capture the meaning behind something.
• Quantitative data – This relates to quantities meaning numbers. It consists of information that can be measured in numerical data sets.

The type of data you want to be captured in your dissertation will depend on your overarching question – so keep it in mind throughout your search!

Getting started

When you’re getting ready to write your dissertation it’s a good idea to plan out exactly what you’re looking to answer. We recommend splitting this into chapters with subheadings and ensuring that each point you want to discuss has a reliable source to back it up. This is always a good way to find out if you’ve collected enough secondary data to suit your workload. If there’s a part of your plan that’s looking a bit empty, it might be a good idea to do some more research and fill the gap. It’s never a bad thing to have too much research, just as long as you know what to do with it and you’re willing to disregard the less important parts. Just make sure you prioritise the research that backs up your overall point so each section has clarity.

Then it’s time to write your introduction. In your intro, you will want to emphasise what your dissertation aims to cover within your writing and outline your research objectives. You can then follow up with the context around this question and identify why your research is meaningful to a wider audience.

The body of your dissertation

Before you get started on the main chapters of your dissertation, you need to find out what theories relate to your chosen subject and the research that has already been carried out around it.

Literature Reviews

Your literature review will be a summary of any previous research carried out on the topic and should have an intro and conclusion like any other body of the academic text. When writing about this research you want to make sure you are describing, summarising, evaluating and analysing each piece. You shouldn’t just rephrase what the researcher has found but make your own interpretations. This is one crucial way to score some marks. You also want to identify any themes between each piece of research to emphasise their relevancy. This will show that you understand your topic in the context of others, a great way to prove you’ve really done your reading!

Theoretical Frameworks

The theoretical framework in your dissertation will be explaining what you’ve found. It will form your main chapters after your lit review. The most important part is that you use it wisely. Of course, depending on your topic there might be a lot of different theories and you can’t include them all so make sure to select the ones most relevant to your dissertation. When starting on the framework it’s important to detail the key parts to your hypothesis and explain them. This creates a good foundation for what you’re going to discuss and helps readers understand the topic.

To finish off the theoretical framework you want to start suggesting where your research will fit in with those texts in your literature review. You might want to challenge a theory by critiquing it with another or explain how two theories can be combined to make a new outcome. Either way, you must make a clear link between their theories and your own interpretations – remember, this is not opinion based so don’t make a conclusion unless you can link it back to the facts!

Concluding your dissertation

Your conclusion will highlight the outcome of the research you’ve undertaken. You want to make this clear and concise without repeating information you’ve already mentioned in your main body paragraphs. A great way to avoid repetition is to highlight any overarching themes your conclusions have shown

When writing your conclusion it’s important to include the following elements:

• Summary – A summary of what you’ve found overall from your research and the conclusions you have come to as a result.
• Recommendations – Recommendations on what you think the next steps should be. Is there something you would change about this research to improve it or further develop it?
• Show your contribution – It’s important to show how you’ve contributed to the current knowledge on the topic and not just repeated what other researchers have found.

Hopefully, this helps you with your secondary data research for your dissertation! It’s definitely not as hard as it seems, the hardest part will be gathering all of the information in the first place. It may take a while but once you’ve found your flow – it’ll get easier, promise! You may also want to read about the advantages and disadvantages of secondary research .

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A Guide To Secondary Data Analysis

What is secondary data analysis? How do you carry it out? Find out in this post.  

Historically, the only way data analysts could obtain data was to collect it themselves. This type of data is often referred to as primary data and is still a vital resource for data analysts.   

However, technological advances over the last few decades mean that much past data is now readily available online for data analysts and researchers to access and utilize. This type of data—known as secondary data—is driving a revolution in data analytics and data science.

Primary and secondary data share many characteristics. However, there are some fundamental differences in how you prepare and analyze secondary data. This post explores the unique aspects of secondary data analysis. We’ll briefly review what secondary data is before outlining how to source, collect and validate them. We’ll cover:

• What is secondary data analysis?
• How to carry out secondary data analysis (5 steps)
• Summary and further reading

Ready for a crash course in secondary data analysis? Let’s go!

1. What is secondary data analysis?

Secondary data analysis uses data collected by somebody else. This contrasts with primary data analysis, which involves a researcher collecting predefined data to answer a specific question. Secondary data analysis has numerous benefits, not least that it is a time and cost-effective way of obtaining data without doing the research yourself.

It’s worth noting here that secondary data may be primary data for the original researcher. It only becomes secondary data when it’s repurposed for a new task. As a result, a dataset can simultaneously be a primary data source for one researcher and a secondary data source for another. So don’t panic if you get confused! We explain exactly what secondary data is in this guide . 

In reality, the statistical techniques used to carry out secondary data analysis are no different from those used to analyze other kinds of data. The main differences lie in collection and preparation. Once the data have been reviewed and prepared, the analytics process continues more or less as it usually does. For a recap on what the data analysis process involves, read this post . 

In the following sections, we’ll focus specifically on the preparation of secondary data for analysis. Where appropriate, we’ll refer to primary data analysis for comparison. 

2. How to carry out secondary data analysis

Step 1: define a research topic.

The first step in any data analytics project is defining your goal. This is true regardless of the data you’re working with, or the type of analysis you want to carry out. In data analytics lingo, this typically involves defining:

• A statement of purpose
• Research design

Defining a statement of purpose and a research approach are both fundamental building blocks for any project. However, for secondary data analysis, the process of defining these differs slightly. Let’s find out how.

Step 2: Establish your statement of purpose

Before beginning any data analytics project, you should always have a clearly defined intent. This is called a ‘statement of purpose.’ A healthcare analyst’s statement of purpose, for example, might be: ‘Reduce admissions for mental health issues relating to Covid-19′. The more specific the statement of purpose, the easier it is to determine which data to collect, analyze, and draw insights from.

A statement of purpose is helpful for both primary and secondary data analysis. It’s especially relevant for secondary data analysis, though. This is because there are vast amounts of secondary data available. Having a clear direction will keep you focused on the task at hand, saving you from becoming overwhelmed. Being selective with your data sources is key.

Step 3: Design your research process

After defining your statement of purpose, the next step is to design the research process. For primary data, this involves determining the types of data you want to collect (e.g. quantitative, qualitative, or both ) and a methodology for gathering them.

For secondary data analysis, however, your research process will more likely be a step-by-step guide outlining the types of data you require and a list of potential sources for gathering them. It may also include (realistic) expectations of the output of the final analysis. This should be based on a preliminary review of the data sources and their quality.

Once you have both your statement of purpose and research design, you’re in a far better position to narrow down potential sources of secondary data. You can then start with the next step of the process: data collection.

Step 4: Locate and collect your secondary data

Collecting primary data involves devising and executing a complex strategy that can be very time-consuming to manage. The data you collect, though, will be highly relevant to your research problem.

Secondary data collection, meanwhile, avoids the complexity of defining a research methodology. However, it comes with additional challenges. One of these is identifying where to find the data. This is no small task because there are a great many repositories of secondary data available. Your job, then, is to narrow down potential sources. As already mentioned, it’s necessary to be selective, or else you risk becoming overloaded.  

Some popular sources of secondary data include:  

• Government statistics , e.g. demographic data, censuses, or surveys, collected by government agencies/departments (like the US Bureau of Labor Statistics).
• Technical reports summarizing completed or ongoing research from educational or public institutions (colleges or government).
• Scientific journals that outline research methodologies and data analysis by experts in fields like the sciences, medicine, etc.
• Literature reviews of research articles, books, and reports, for a given area of study (once again, carried out by experts in the field).
• Trade/industry publications , e.g. articles and data shared in trade publications, covering topics relating to specific industry sectors, such as tech or manufacturing.
• Online resources: Repositories, databases, and other reference libraries with public or paid access to secondary data sources.

Once you’ve identified appropriate sources, you can go about collecting the necessary data. This may involve contacting other researchers, paying a fee to an organization in exchange for a dataset, or simply downloading a dataset for free online .

Step 5: Evaluate your secondary data

Secondary data is usually well-structured, so you might assume that once you have your hands on a dataset, you’re ready to dive in with a detailed analysis. Unfortunately, that’s not the case! 

First, you must carry out a careful review of the data. Why? To ensure that they’re appropriate for your needs. This involves two main tasks:

Evaluating the secondary dataset’s relevance

• Assessing its broader credibility

Both these tasks require critical thinking skills. However, they aren’t heavily technical. This means anybody can learn to carry them out.

Let’s now take a look at each in a bit more detail.  

The main point of evaluating a secondary dataset is to see if it is suitable for your needs. This involves asking some probing questions about the data, including:

What was the data’s original purpose?

Understanding why the data were originally collected will tell you a lot about their suitability for your current project. For instance, was the project carried out by a government agency or a private company for marketing purposes? The answer may provide useful information about the population sample, the data demographics, and even the wording of specific survey questions. All this can help you determine if the data are right for you, or if they are biased in any way.

When and where were the data collected?

Over time, populations and demographics change. Identifying when the data were first collected can provide invaluable insights. For instance, a dataset that initially seems suited to your needs may be out of date.

On the flip side, you might want past data so you can draw a comparison with a present dataset. In this case, you’ll need to ensure the data were collected during the appropriate time frame. It’s worth mentioning that secondary data are the sole source of past data. You cannot collect historical data using primary data collection techniques.

Similarly, you should ask where the data were collected. Do they represent the geographical region you require? Does geography even have an impact on the problem you are trying to solve?

What data were collected and how?

A final report for past data analytics is great for summarizing key characteristics or findings. However, if you’re planning to use those data for a new project, you’ll need the original documentation. At the very least, this should include access to the raw data and an outline of the methodology used to gather them. This can be helpful for many reasons. For instance, you may find raw data that wasn’t relevant to the original analysis, but which might benefit your current task.

What questions were participants asked?

We’ve already touched on this, but the wording of survey questions—especially for qualitative datasets—is significant. Questions may deliberately be phrased to preclude certain answers. A question’s context may also impact the findings in a way that’s not immediately obvious. Understanding these issues will shape how you perceive the data.  

What is the form/shape/structure of the data?

Finally, to practical issues. Is the structure of the data suitable for your needs? Is it compatible with other sources or with your preferred analytics approach? This is purely a structural issue. For instance, if a dataset of people’s ages is saved as numerical rather than continuous variables, this could potentially impact your analysis. In general, reviewing a dataset’s structure helps better understand how they are categorized, allowing you to account for any discrepancies. You may also need to tidy the data to ensure they are consistent with any other sources you’re using.  

This is just a sample of the types of questions you need to consider when reviewing a secondary data source. The answers will have a clear impact on whether the dataset—no matter how well presented or structured it seems—is suitable for your needs.

Assessing secondary data’s credibility

After identifying a potentially suitable dataset, you must double-check the credibility of the data. Namely, are the data accurate and unbiased? To figure this out, here are some key questions you might want to include:

What are the credentials of those who carried out the original research?

Do you have access to the details of the original researchers? What are their credentials? Where did they study? Are they an expert in the field or a newcomer? Data collection by an undergraduate student, for example, may not be as rigorous as that of a seasoned professor.  

And did the original researcher work for a reputable organization? What other affiliations do they have? For instance, if a researcher who works for a tobacco company gathers data on the effects of vaping, this represents an obvious conflict of interest! Questions like this help determine how thorough or qualified the researchers are and if they have any potential biases.

Do you have access to the full methodology?

Does the dataset include a clear methodology, explaining in detail how the data were collected? This should be more than a simple overview; it must be a clear breakdown of the process, including justifications for the approach taken. This allows you to determine if the methodology was sound. If you find flaws (or no methodology at all) it throws the quality of the data into question.  

How consistent are the data with other sources?

Do the secondary data match with any similar findings? If not, that doesn’t necessarily mean the data are wrong, but it does warrant closer inspection. Perhaps the collection methodology differed between sources, or maybe the data were analyzed using different statistical techniques. Or perhaps unaccounted-for outliers are skewing the analysis. Identifying all these potential problems is essential. A flawed or biased dataset can still be useful but only if you know where its shortcomings lie.

Have the data been published in any credible research journals?

Finally, have the data been used in well-known studies or published in any journals? If so, how reputable are the journals? In general, you can judge a dataset’s quality based on where it has been published. If in doubt, check out the publication in question on the Directory of Open Access Journals . The directory has a rigorous vetting process, only permitting journals of the highest quality. Meanwhile, if you found the data via a blurry image on social media without cited sources, then you can justifiably question its quality!  

Again, these are just a few of the questions you might ask when determining the quality of a secondary dataset. Consider them as scaffolding for cultivating a critical thinking mindset; a necessary trait for any data analyst!

Presuming your secondary data holds up to scrutiny, you should be ready to carry out your detailed statistical analysis. As we explained at the beginning of this post, the analytical techniques used for secondary data analysis are no different than those for any other kind of data. Rather than go into detail here, check out the different types of data analysis in this post.

3. Secondary data analysis: Key takeaways

In this post, we’ve looked at the nuances of secondary data analysis, including how to source, collect and review secondary data. As discussed, much of the process is the same as it is for primary data analysis. The main difference lies in how secondary data are prepared.

Carrying out a meaningful secondary data analysis involves spending time and effort exploring, collecting, and reviewing the original data. This will help you determine whether the data are suitable for your needs and if they are of good quality.

Why not get to know more about what data analytics involves with this free, five-day introductory data analytics short course ? And, for more data insights, check out these posts:

• Discrete vs continuous data variables: What’s the difference?
• What are the four levels of measurement? Nominal, ordinal, interval, and ratio data explained
• What are the best tools for data mining?

• How it works

How to Structure a Dissertation – A Step by Step Guide

Published by Owen Ingram at August 11th, 2021 , Revised On September 20, 2023

A dissertation – sometimes called a thesis –  is a long piece of information backed up by extensive research. This one, huge piece of research is what matters the most when students – undergraduates and postgraduates – are in their final year of study.

On the other hand, some institutions, especially in the case of undergraduate students, may or may not require students to write a dissertation. Courses are offered instead. This generally depends on the requirements of that particular institution.

If you are unsure about how to structure your dissertation or thesis, this article will offer you some guidelines to work out what the most important segments of a dissertation paper are and how you should organise them. Why is structure so important in research, anyway?

One way to answer that, as Abbie Hoffman aptly put it, is because: “Structure is more important than content in the transmission of information.”

Also Read:   How to write a dissertation – step by step guide .

How to Structure a Dissertation or Thesis

It should be noted that the exact structure of your dissertation will depend on several factors, such as:

• Your research approach (qualitative/quantitative)
• The nature of your research design (exploratory/descriptive etc.)
• The requirements set for forth by your academic institution.
• The discipline or field your study belongs to. For instance, if you are a humanities student, you will need to develop your dissertation on the same pattern as any long essay .

This will include developing an overall argument to support the thesis statement and organizing chapters around theories or questions. The dissertation will be structured such that it starts with an introduction , develops on the main idea in its main body paragraphs and is then summarised in conclusion .

However, if you are basing your dissertation on primary or empirical research, you will be required to include each of the below components. In most cases of dissertation writing, each of these elements will have to be written as a separate chapter.

But depending on the word count you are provided with and academic subject, you may choose to combine some of these elements.

For example, sciences and engineering students often present results and discussions together in one chapter rather than two different chapters.

If you have any doubts about structuring your dissertation or thesis, it would be a good idea to consult with your academic supervisor and check your department’s requirements.

Parts of  a Dissertation or Thesis

Your dissertation will  start with a t itle page that will contain details of the author/researcher, research topic, degree program (the paper is to be submitted for), and research supervisor. In other words, a title page is the opening page containing all the names and title related to your research.

The name of your university, logo, student ID and submission date can also be presented on the title page. Many academic programs have stringent rules for formatting the dissertation title page.

Acknowledgements

The acknowledgments section allows you to thank those who helped you with your dissertation project. You might want to mention the names of your academic supervisor, family members, friends, God, and participants of your study whose contribution and support enabled you to complete your work.

However, the acknowledgments section is usually optional.

Tip: Many students wrongly assume that they need to thank everyone…even those who had little to no contributions towards the dissertation. This is not the case. You only need to thank those who were directly involved in the research process, such as your participants/volunteers, supervisor(s) etc.

Perhaps the smallest yet important part of a thesis, an abstract contains 5 parts:

• A brief introduction of your research topic.
• The significance of your research.
•  A line or two about the methodology that was used.
• The results and what they mean (briefly); their interpretation(s).
• And lastly, a conclusive comment regarding the results’ interpretation(s) as conclusion .

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Tip: Make sure to highlight key points to help readers figure out the scope and findings of your research study without having to read the entire dissertation. The abstract is your first chance to impress your readers. So, make sure to get it right. Here are detailed guidelines on how to write abstract for dissertation .

Table of Contents

Table of contents is the section of a dissertation that guides each section of the dissertation paper’s contents. Depending on the level of detail in a table of contents, the most useful headings are listed to provide the reader the page number on which said information may be found at.

Table of contents can be inserted automatically as well as manually using the Microsoft Word Table of Contents feature.

List of Figures and Tables

If your dissertation paper uses several illustrations, tables and figures, you might want to present them in a numbered list in a separate section . Again, this list of tables and figures can be auto-created and auto inserted using the Microsoft Word built-in feature.

List of Abbreviations

Dissertations that include several abbreviations can also have an independent and separate alphabetised  list of abbreviations so readers can easily figure out their meanings.

If you think you have used terms and phrases in your dissertation that readers might not be familiar with, you can create a  glossary  that lists important phrases and terms with their meanings explained.

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Introduction

Introduction chapter  briefly introduces the purpose and relevance of your research topic.

Here, you will be expected to list the aim and key objectives of your research so your readers can easily understand what the following chapters of the dissertation will cover. A good dissertation introduction section incorporates the following information:

• It provides background information to give context to your research.
• It clearly specifies the research problem you wish to address with your research. When creating research questions , it is important to make sure your research’s focus and scope are neither too broad nor too narrow.
• it demonstrates how your research is relevant and how it would contribute to the existing knowledge.
• It provides an overview of the structure of your dissertation. The last section of an introduction contains an outline of the following chapters. It could start off with something like: “In the following chapter, past literature has been reviewed and critiqued. The proceeding section lays down major research findings…”
• Theoretical framework – under a separate sub-heading – is also provided within the introductory chapter. Theoretical framework deals with the basic, underlying theory or theories that the research revolves around.

All the information presented under this section should be relevant, clear, and engaging. The readers should be able to figure out the what, why, when, and how of your study once they have read the introduction. Here are comprehensive guidelines on how to structure the introduction to the dissertation .

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Literature Review 

The  literature review chapter  presents previous research performed on the topic and improves your understanding of the existing literature on your chosen topic. This is usually organised to complement your  primary research  work completed at a later stage.

Make sure that your chosen academic sources are authentic and up-to-date. The literature review chapter must be comprehensive and address the aims and objectives as defined in the introduction chapter. Here is what your literature research chapter should aim to achieve:

• Data collection from authentic and relevant academic sources such as books, journal articles and research papers.
• Analytical assessment of the information collected from those sources; this would involve a critiquing the reviewed researches that is, what their strengths/weaknesses are, why the research method they employed is better than others, importance of their findings, etc.
• Identifying key research gaps, conflicts, patterns, and theories to get your point across to the reader effectively.

While your literature review should summarise previous literature, it is equally important to make sure that you develop a comprehensible argument or structure to justify your research topic. It would help if you considered keeping the following questions in mind when writing the literature review:

• How does your research work fill a certain gap in exiting literature?
• Did you adopt/adapt a new research approach to investigate the topic?
• Does your research solve an unresolved problem?
• Is your research dealing with some groundbreaking topic or theory that others might have overlooked?
• Is your research taking forward an existing theoretical discussion?
• Does your research strengthen and build on current knowledge within your area of study? This is otherwise known as ‘adding to the existing body of knowledge’ in academic circles.

Tip: You might want to establish relationships between variables/concepts to provide descriptive answers to some or all of your research questions. For instance, in case of quantitative research, you might hypothesise that variable A is positively co-related to variable B that is, one increases and so does the other one.

Research Methodology

The methods and techniques ( secondary and/or primar y) employed to collect research data are discussed in detail in the  Methodology chapter. The most commonly used primary data collection methods are:

• questionnaires
• focus groups
• observations

Essentially, the methodology chapter allows the researcher to explain how he/she achieved the findings, why they are reliable and how they helped him/her test the research hypotheses or address the research problem.

You might want to consider the following when writing methodology for the dissertation:

• Type of research and approach your work is based on. Some of the most widely used types of research include experimental, quantitative and qualitative methodologies.
• Data collection techniques that were employed such as questionnaires, surveys, focus groups, observations etc.
• Details of how, when, where, and what of the research that was conducted.
• Data analysis strategies employed (for instance, regression analysis).
• Software and tools used for data analysis (Excel, STATA, SPSS, lab equipment, etc.).
• Research limitations to highlight any hurdles you had to overcome when carrying our research. Limitations might or might not be mentioned within research methodology. Some institutions’ guidelines dictate they be mentioned under a separate section alongside recommendations.
• Justification of your selection of research approach and research methodology.

Here is a comprehensive article on  how to structure a dissertation methodology .

Research Findings

In this section, you present your research findings. The dissertation findings chapter  is built around the research questions, as outlined in the introduction chapter. Report findings that are directly relevant to your research questions.

Any information that is not directly relevant to research questions or hypotheses but could be useful for the readers can be placed under the  Appendices .

As indicated above, you can either develop a  standalone chapter  to present your findings or combine them with the discussion chapter. This choice depends on  the type of research involved and the academic subject, as well as what your institution’s academic guidelines dictate.

For example, it is common to have both findings and discussion grouped under the same section, particularly if the dissertation is based on qualitative research data.

On the other hand, dissertations that use quantitative or experimental data should present findings and analysis/discussion in two separate chapters. Here are some sample dissertations to help you figure out the best structure for your own project.

Sample Dissertation

Tip: Try to present as many charts, graphs, illustrations and tables in the findings chapter to improve your data presentation. Provide their qualitative interpretations alongside, too. Refrain from explaining the information that is already evident from figures and tables.

The findings are followed by the  Discussion chapter , which is considered the heart of any dissertation paper. The discussion section is an opportunity for you to tie the knots together to address the research questions and present arguments, models and key themes.

This chapter can make or break your research.

The discussion chapter does not require any new data or information because it is more about the interpretation(s) of the data you have already collected and presented. Here are some questions for you to think over when writing the discussion chapter:

• Did your work answer all the research questions or tested the hypothesis?
• Did you come up with some unexpected results for which you have to provide an additional explanation or justification?
• Are there any limitations that could have influenced your research findings?

Here is an article on how to  structure a dissertation discussion .

Conclusions corresponding to each research objective are provided in the  Conclusion section . This is usually done by revisiting the research questions to finally close the dissertation. Some institutions may specifically ask for recommendations to evaluate your critical thinking.

By the end, the readers should have a clear apprehension of your fundamental case with a focus on  what methods of research were employed  and what you achieved from this research.

Quick Question: Does the conclusion chapter reflect on the contributions your research work will make to existing knowledge?

Answer: Yes, the conclusion chapter of the research paper typically includes a reflection on the research’s contributions to existing knowledge.  In the “conclusion chapter”, you have to summarise the key findings and discuss how they add value to the existing literature on the current topic.

Reference list

All academic sources that you collected information from should be cited in-text and also presented in a  reference list (or a bibliography in case you include references that you read for the research but didn’t end up citing in the text), so the readers can easily locate the source of information when/if needed.

At most UK universities, Harvard referencing is the recommended style of referencing. It has strict and specific requirements on how to format a reference resource. Other common styles of referencing include MLA, APA, Footnotes, etc.

Each chapter of the dissertation should have relevant information. Any information that is not directly relevant to your research topic but your readers might be interested in (interview transcripts etc.) should be moved under the Appendices section .

Things like questionnaires, survey items or readings that were used in the study’s experiment are mostly included under appendices.

An Outline of Dissertation/Thesis Structure

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FAQs About Structure a Dissertation

What does the title page of a dissertation contain.

The title page will contain details of the author/researcher, research topic , degree program (the paper is to be submitted for) and research supervisor’s name(s). The name of your university, logo, student number and submission date can also be presented on the title page.

What is the purpose of adding acknowledgement?

The acknowledgements section allows you to thank those who helped you with your dissertation project. You might want to mention the names of your academic supervisor, family members, friends, God and participants of your study whose contribution and support enabled you to complete your work.

Can I omit the glossary from the dissertation?

Yes, but only if you think that your paper does not contain any terms or phrases that the reader might not understand. If you think you have used them in the paper,  you must create a glossary that lists important phrases and terms with their meanings explained.

What is the purpose of appendices in a dissertation?

Any information that is not directly relevant to research questions or hypotheses but could be useful for the readers can be placed under the Appendices, such as questionnaire that was used in the study.

Which referencing style should I use in my dissertation?

You can use any of the referencing styles such as APA, MLA, and Harvard, according to the recommendation of your university; however, almost all UK institutions prefer Harvard referencing style .

What is the difference between references and bibliography?

References contain all the works that you read up and used and therefore, cited within the text of your thesis. However, in case you read on some works and resources that you didn’t end up citing in-text, they will be referenced in what is called a bibliography.

Additional readings might also be present alongside each bibliography entry for readers.

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Not sure how to start your dissertation and get it right the first time? Here are some tips and guidelines for you to kick start your dissertation project.

A literature review is a survey of theses, articles, books and other academic sources. Here are guidelines on how to write dissertation literature review.

Dissertation conclusion is perhaps the most underrated part of a dissertation or thesis paper. Learn how to write a dissertation conclusion.

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How To Write The Methodology Chapter

The what, why & how explained simply (with examples).

By: Jenna Crossley (PhD) | Reviewed By: Dr. Eunice Rautenbach | September 2021 (Updated April 2023)

So, you’ve pinned down your research topic and undertaken a review of the literature – now it’s time to write up the methodology section of your dissertation, thesis or research paper . But what exactly is the methodology chapter all about – and how do you go about writing one? In this post, we’ll unpack the topic, step by step .

Overview: The Methodology Chapter

• The purpose  of the methodology chapter
• Why you need to craft this chapter (really) well
• How to write and structure the chapter
• Methodology chapter example
• Essential takeaways

What (exactly) is the methodology chapter?

The methodology chapter is where you outline the philosophical underpinnings of your research and outline the specific methodological choices you’ve made. The point of the methodology chapter is to tell the reader exactly how you designed your study and, just as importantly, why you did it this way.

Importantly, this chapter should comprehensively describe and justify all the methodological choices you made in your study. For example, the approach you took to your research (i.e., qualitative, quantitative or mixed), who  you collected data from (i.e., your sampling strategy), how you collected your data and, of course, how you analysed it. If that sounds a little intimidating, don’t worry – we’ll explain all these methodological choices in this post .

Why is the methodology chapter important?

The methodology chapter plays two important roles in your dissertation or thesis:

Firstly, it demonstrates your understanding of research theory, which is what earns you marks. A flawed research design or methodology would mean flawed results. So, this chapter is vital as it allows you to show the marker that you know what you’re doing and that your results are credible .

Secondly, the methodology chapter is what helps to make your study replicable. In other words, it allows other researchers to undertake your study using the same methodological approach, and compare their findings to yours. This is very important within academic research, as each study builds on previous studies.

The methodology chapter is also important in that it allows you to identify and discuss any methodological issues or problems you encountered (i.e., research limitations ), and to explain how you mitigated the impacts of these. Every research project has its limitations , so it’s important to acknowledge these openly and highlight your study’s value despite its limitations . Doing so demonstrates your understanding of research design, which will earn you marks. We’ll discuss limitations in a bit more detail later in this post, so stay tuned!

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How to write up the methodology chapter

First off, it’s worth noting that the exact structure and contents of the methodology chapter will vary depending on the field of research (e.g., humanities, chemistry or engineering) as well as the university . So, be sure to always check the guidelines provided by your institution for clarity and, if possible, review past dissertations from your university. Here we’re going to discuss a generic structure for a methodology chapter typically found in the sciences.

Before you start writing, it’s always a good idea to draw up a rough outline to guide your writing. Don’t just start writing without knowing what you’ll discuss where. If you do, you’ll likely end up with a disjointed, ill-flowing narrative . You’ll then waste a lot of time rewriting in an attempt to try to stitch all the pieces together. Do yourself a favour and start with the end in mind .

Section 1 – Introduction

As with all chapters in your dissertation or thesis, the methodology chapter should have a brief introduction. In this section, you should remind your readers what the focus of your study is, especially the research aims . As we’ve discussed many times on the blog, your methodology needs to align with your research aims, objectives and research questions. Therefore, it’s useful to frontload this component to remind the reader (and yourself!) what you’re trying to achieve.

In this section, you can also briefly mention how you’ll structure the chapter. This will help orient the reader and provide a bit of a roadmap so that they know what to expect. You don’t need a lot of detail here – just a brief outline will do.

Section 2 – The Methodology

The next section of your chapter is where you’ll present the actual methodology. In this section, you need to detail and justify the key methodological choices you’ve made in a logical, intuitive fashion. Importantly, this is the heart of your methodology chapter, so you need to get specific – don’t hold back on the details here. This is not one of those “less is more” situations.

Let’s take a look at the most common components you’ll likely need to cover. 

Methodological Choice #1 – Research Philosophy

Research philosophy refers to the underlying beliefs (i.e., the worldview) regarding how data about a phenomenon should be gathered , analysed and used . The research philosophy will serve as the core of your study and underpin all of the other research design choices, so it’s critically important that you understand which philosophy you’ll adopt and why you made that choice. If you’re not clear on this, take the time to get clarity before you make any further methodological choices.

While several research philosophies exist, two commonly adopted ones are positivism and interpretivism . These two sit roughly on opposite sides of the research philosophy spectrum.

Positivism states that the researcher can observe reality objectively and that there is only one reality, which exists independently of the observer. As a consequence, it is quite commonly the underlying research philosophy in quantitative studies and is oftentimes the assumed philosophy in the physical sciences.

Contrasted with this, interpretivism , which is often the underlying research philosophy in qualitative studies, assumes that the researcher performs a role in observing the world around them and that reality is unique to each observer . In other words, reality is observed subjectively .

These are just two philosophies (there are many more), but they demonstrate significantly different approaches to research and have a significant impact on all the methodological choices. Therefore, it’s vital that you clearly outline and justify your research philosophy at the beginning of your methodology chapter, as it sets the scene for everything that follows.

Methodological Choice #2 – Research Type

The next thing you would typically discuss in your methodology section is the research type. The starting point for this is to indicate whether the research you conducted is inductive or deductive .

Inductive research takes a bottom-up approach , where the researcher begins with specific observations or data and then draws general conclusions or theories from those observations. Therefore these studies tend to be exploratory in terms of approach.

Conversely , d eductive research takes a top-down approach , where the researcher starts with a theory or hypothesis and then tests it using specific observations or data. Therefore these studies tend to be confirmatory in approach.

Related to this, you’ll need to indicate whether your study adopts a qualitative, quantitative or mixed  approach. As we’ve mentioned, there’s a strong link between this choice and your research philosophy, so make sure that your choices are tightly aligned . When you write this section up, remember to clearly justify your choices, as they form the foundation of your study.

Methodological Choice #3 – Research Strategy

Next, you’ll need to discuss your research strategy (also referred to as a research design ). This methodological choice refers to the broader strategy in terms of how you’ll conduct your research, based on the aims of your study.

Several research strategies exist, including experimental , case studies , ethnography , grounded theory, action research , and phenomenology . Let’s take a look at two of these, experimental and ethnographic, to see how they contrast.

Experimental research makes use of the scientific method , where one group is the control group (in which no variables are manipulated ) and another is the experimental group (in which a specific variable is manipulated). This type of research is undertaken under strict conditions in a controlled, artificial environment (e.g., a laboratory). By having firm control over the environment, experimental research typically allows the researcher to establish causation between variables. Therefore, it can be a good choice if you have research aims that involve identifying causal relationships.

Ethnographic research , on the other hand, involves observing and capturing the experiences and perceptions of participants in their natural environment (for example, at home or in the office). In other words, in an uncontrolled environment.  Naturally, this means that this research strategy would be far less suitable if your research aims involve identifying causation, but it would be very valuable if you’re looking to explore and examine a group culture, for example.

As you can see, the right research strategy will depend largely on your research aims and research questions – in other words, what you’re trying to figure out. Therefore, as with every other methodological choice, it’s essential to justify why you chose the research strategy you did.

Methodological Choice #4 – Time Horizon

The next thing you’ll need to detail in your methodology chapter is the time horizon. There are two options here: cross-sectional and longitudinal . In other words, whether the data for your study were all collected at one point in time (cross-sectional) or at multiple points in time (longitudinal).

The choice you make here depends again on your research aims, objectives and research questions. If, for example, you aim to assess how a specific group of people’s perspectives regarding a topic change over time , you’d likely adopt a longitudinal time horizon.

Another important factor to consider is simply whether you have the time necessary to adopt a longitudinal approach (which could involve collecting data over multiple months or even years). Oftentimes, the time pressures of your degree program will force your hand into adopting a cross-sectional time horizon, so keep this in mind.

Methodological Choice #5 – Sampling Strategy

Next, you’ll need to discuss your sampling strategy . There are two main categories of sampling, probability and non-probability sampling.

Probability sampling involves a random (and therefore representative) selection of participants from a population, whereas non-probability sampling entails selecting participants in a non-random  (and therefore non-representative) manner. For example, selecting participants based on ease of access (this is called a convenience sample).

The right sampling approach depends largely on what you’re trying to achieve in your study. Specifically, whether you trying to develop findings that are generalisable to a population or not. Practicalities and resource constraints also play a large role here, as it can oftentimes be challenging to gain access to a truly random sample. In the video below, we explore some of the most common sampling strategies.

Methodological Choice #6 – Data Collection Method

Next up, you’ll need to explain how you’ll go about collecting the necessary data for your study. Your data collection method (or methods) will depend on the type of data that you plan to collect – in other words, qualitative or quantitative data.

Typically, quantitative research relies on surveys , data generated by lab equipment, analytics software or existing datasets. Qualitative research, on the other hand, often makes use of collection methods such as interviews , focus groups , participant observations, and ethnography.

So, as you can see, there is a tight link between this section and the design choices you outlined in earlier sections. Strong alignment between these sections, as well as your research aims and questions is therefore very important.

Methodological Choice #7 – Data Analysis Methods/Techniques

The final major methodological choice that you need to address is that of analysis techniques . In other words, how you’ll go about analysing your date once you’ve collected it. Here it’s important to be very specific about your analysis methods and/or techniques – don’t leave any room for interpretation. Also, as with all choices in this chapter, you need to justify each choice you make.

What exactly you discuss here will depend largely on the type of study you’re conducting (i.e., qualitative, quantitative, or mixed methods). For qualitative studies, common analysis methods include content analysis , thematic analysis and discourse analysis . In the video below, we explain each of these in plain language.

For quantitative studies, you’ll almost always make use of descriptive statistics , and in many cases, you’ll also use inferential statistical techniques (e.g., correlation and regression analysis). In the video below, we unpack some of the core concepts involved in descriptive and inferential statistics.

In this section of your methodology chapter, it’s also important to discuss how you prepared your data for analysis, and what software you used (if any). For example, quantitative data will often require some initial preparation such as removing duplicates or incomplete responses . Similarly, qualitative data will often require transcription and perhaps even translation. As always, remember to state both what you did and why you did it.

Section 3 – The Methodological Limitations

With the key methodological choices outlined and justified, the next step is to discuss the limitations of your design. No research methodology is perfect – there will always be trade-offs between the “ideal” methodology and what’s practical and viable, given your constraints. Therefore, this section of your methodology chapter is where you’ll discuss the trade-offs you had to make, and why these were justified given the context.

Methodological limitations can vary greatly from study to study, ranging from common issues such as time and budget constraints to issues of sample or selection bias . For example, you may find that you didn’t manage to draw in enough respondents to achieve the desired sample size (and therefore, statistically significant results), or your sample may be skewed heavily towards a certain demographic, thereby negatively impacting representativeness .

In this section, it’s important to be critical of the shortcomings of your study. There’s no use trying to hide them (your marker will be aware of them regardless). By being critical, you’ll demonstrate to your marker that you have a strong understanding of research theory, so don’t be shy here. At the same time, don’t beat your study to death . State the limitations, why these were justified, how you mitigated their impacts to the best degree possible, and how your study still provides value despite these limitations .

Section 4 – Concluding Summary

Finally, it’s time to wrap up the methodology chapter with a brief concluding summary. In this section, you’ll want to concisely summarise what you’ve presented in the chapter. Here, it can be a good idea to use a figure to summarise the key decisions, especially if your university recommends using a specific model (for example, Saunders’ Research Onion ).

Importantly, this section needs to be brief – a paragraph or two maximum (it’s a summary, after all). Also, make sure that when you write up your concluding summary, you include only what you’ve already discussed in your chapter; don’t add any new information.

Methodology Chapter Example

In the video below, we walk you through an example of a high-quality research methodology chapter from a dissertation. We also unpack our free methodology chapter template so that you can see how best to structure your chapter.

Wrapping Up

And there you have it – the methodology chapter in a nutshell. As we’ve mentioned, the exact contents and structure of this chapter can vary between universities , so be sure to check in with your institution before you start writing. If possible, try to find dissertations or theses from former students of your specific degree program – this will give you a strong indication of the expectations and norms when it comes to the methodology chapter (and all the other chapters!).

Also, remember the golden rule of the methodology chapter – justify every choice ! Make sure that you clearly explain the “why” for every “what”, and reference credible methodology textbooks or academic sources to back up your justifications.

If you need a helping hand with your research methodology (or any other component of your research), be sure to check out our private coaching service , where we hold your hand through every step of the research journey. Until next time, good luck!

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Home » Secondary Data – Types, Methods and Examples

Secondary Data – Types, Methods and Examples

Table of Contents

Secondary Data

Definition:

Secondary data refers to information that has been collected, processed, and published by someone else, rather than the researcher gathering the data firsthand. This can include data from sources such as government publications, academic journals, market research reports, and other existing datasets.

Secondary Data Types

Types of secondary data are as follows:

• Published data: Published data refers to data that has been published in books, magazines, newspapers, and other print media. Examples include statistical reports, market research reports, and scholarly articles.
• Government data: Government data refers to data collected by government agencies and departments. This can include data on demographics, economic trends, crime rates, and health statistics.
• Commercial data: Commercial data is data collected by businesses for their own purposes. This can include sales data, customer feedback, and market research data.
• Academic data: Academic data refers to data collected by researchers for academic purposes. This can include data from experiments, surveys, and observational studies.
• Online data: Online data refers to data that is available on the internet. This can include social media posts, website analytics, and online customer reviews.
• Organizational data: Organizational data is data collected by businesses or organizations for their own purposes. This can include data on employee performance, financial records, and customer satisfaction.
• Historical data : Historical data refers to data that was collected in the past and is still available for research purposes. This can include census data, historical documents, and archival records.
• International data: International data refers to data collected from other countries for research purposes. This can include data on international trade, health statistics, and demographic trends.
• Public data : Public data refers to data that is available to the general public. This can include data from government agencies, non-profit organizations, and other sources.
• Private data: Private data refers to data that is not available to the general public. This can include confidential business data, personal medical records, and financial data.
• Big data: Big data refers to large, complex datasets that are difficult to manage and analyze using traditional data processing methods. This can include social media data, sensor data, and other types of data generated by digital devices.

Secondary Data Collection Methods

Secondary Data Collection Methods are as follows:

• Published sources: Researchers can gather secondary data from published sources such as books, journals, reports, and newspapers. These sources often provide comprehensive information on a variety of topics.
• Online sources: With the growth of the internet, researchers can now access a vast amount of secondary data online. This includes websites, databases, and online archives.
• Government sources : Government agencies often collect and publish a wide range of secondary data on topics such as demographics, crime rates, and health statistics. Researchers can obtain this data through government websites, publications, or data portals.
• Commercial sources: Businesses often collect and analyze data for marketing research or customer profiling. Researchers can obtain this data through commercial data providers or by purchasing market research reports.
• Academic sources: Researchers can also obtain secondary data from academic sources such as published research studies, academic journals, and dissertations.
• Personal contacts: Researchers can also obtain secondary data from personal contacts, such as experts in a particular field or individuals with specialized knowledge.

Secondary Data Formats

Secondary data can come in various formats depending on the source from which it is obtained. Here are some common formats of secondary data:

• Numeric Data: Numeric data is often in the form of statistics and numerical figures that have been compiled and reported by organizations such as government agencies, research institutions, and commercial enterprises. This can include data such as population figures, GDP, sales figures, and market share.
• Textual Data: Textual data is often in the form of written documents, such as reports, articles, and books. This can include qualitative data such as descriptions, opinions, and narratives.
• Audiovisual Data : Audiovisual data is often in the form of recordings, videos, and photographs. This can include data such as interviews, focus group discussions, and other types of qualitative data.
• Geospatial Data: Geospatial data is often in the form of maps, satellite images, and geographic information systems (GIS) data. This can include data such as demographic information, land use patterns, and transportation networks.
• Transactional Data : Transactional data is often in the form of digital records of financial and business transactions. This can include data such as purchase histories, customer behavior, and financial transactions.
• Social Media Data: Social media data is often in the form of user-generated content from social media platforms such as Facebook, Twitter, and Instagram. This can include data such as user demographics, content trends, and sentiment analysis.

Secondary Data Analysis Methods

Secondary data analysis involves the use of pre-existing data for research purposes. Here are some common methods of secondary data analysis:

• Descriptive Analysis: This method involves describing the characteristics of a dataset, such as the mean, standard deviation, and range of the data. Descriptive analysis can be used to summarize data and provide an overview of trends.
• Inferential Analysis: This method involves making inferences and drawing conclusions about a population based on a sample of data. Inferential analysis can be used to test hypotheses and determine the statistical significance of relationships between variables.
• Content Analysis: This method involves analyzing textual or visual data to identify patterns and themes. Content analysis can be used to study the content of documents, media coverage, and social media posts.
• Time-Series Analysis : This method involves analyzing data over time to identify trends and patterns. Time-series analysis can be used to study economic trends, climate change, and other phenomena that change over time.
• Spatial Analysis : This method involves analyzing data in relation to geographic location. Spatial analysis can be used to study patterns of disease spread, land use patterns, and the effects of environmental factors on health outcomes.
• Meta-Analysis: This method involves combining data from multiple studies to draw conclusions about a particular phenomenon. Meta-analysis can be used to synthesize the results of previous research and provide a more comprehensive understanding of a particular topic.

Secondary Data Gathering Guide

Here are some steps to follow when gathering secondary data:

• Define your research question: Start by defining your research question and identifying the specific information you need to answer it. This will help you identify the type of secondary data you need and where to find it.
• Identify relevant sources: Identify potential sources of secondary data, including published sources, online databases, government sources, and commercial data providers. Consider the reliability and validity of each source.
• Evaluate the quality of the data: Evaluate the quality and reliability of the data you plan to use. Consider the data collection methods, sample size, and potential biases. Make sure the data is relevant to your research question and is suitable for the type of analysis you plan to conduct.
• Collect the data: Collect the relevant data from the identified sources. Use a consistent method to record and organize the data to make analysis easier.
• Validate the data: Validate the data to ensure that it is accurate and reliable. Check for inconsistencies, missing data, and errors. Address any issues before analyzing the data.
• Analyze the data: Analyze the data using appropriate statistical and analytical methods. Use descriptive and inferential statistics to summarize and draw conclusions from the data.
• Interpret the results: Interpret the results of your analysis and draw conclusions based on the data. Make sure your conclusions are supported by the data and are relevant to your research question.
• Communicate the findings : Communicate your findings clearly and concisely. Use appropriate visual aids such as graphs and charts to help explain your results.

Examples of Secondary Data

Here are some examples of secondary data from different fields:

• Healthcare : Hospital records, medical journals, clinical trial data, and disease registries are examples of secondary data sources in healthcare. These sources can provide researchers with information on patient demographics, disease prevalence, and treatment outcomes.
• Marketing : Market research reports, customer surveys, and sales data are examples of secondary data sources in marketing. These sources can provide marketers with information on consumer preferences, market trends, and competitor activity.
• Education : Student test scores, graduation rates, and enrollment statistics are examples of secondary data sources in education. These sources can provide researchers with information on student achievement, teacher effectiveness, and educational disparities.
• Finance : Stock market data, financial statements, and credit reports are examples of secondary data sources in finance. These sources can provide investors with information on market trends, company performance, and creditworthiness.
• Social Science : Government statistics, census data, and survey data are examples of secondary data sources in social science. These sources can provide researchers with information on population demographics, social trends, and political attitudes.
• Environmental Science : Climate data, remote sensing data, and ecological monitoring data are examples of secondary data sources in environmental science. These sources can provide researchers with information on weather patterns, land use, and biodiversity.

Purpose of Secondary Data

The purpose of secondary data is to provide researchers with information that has already been collected by others for other purposes. Secondary data can be used to support research questions, test hypotheses, and answer research objectives. Some of the key purposes of secondary data are:

• To gain a better understanding of the research topic : Secondary data can be used to provide context and background information on a research topic. This can help researchers understand the historical and social context of their research and gain insights into relevant variables and relationships.
• To save time and resources: Collecting new primary data can be time-consuming and expensive. Using existing secondary data sources can save researchers time and resources by providing access to pre-existing data that has already been collected and organized.
• To provide comparative data : Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• To support triangulation: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• To supplement primary data : Secondary data can be used to supplement primary data by providing additional information or insights that were not captured by the primary research. This can help researchers gain a more complete understanding of the research topic and draw more robust conclusions.

When to use Secondary Data

Secondary data can be useful in a variety of research contexts, and there are several situations in which it may be appropriate to use secondary data. Some common situations in which secondary data may be used include:

• When primary data collection is not feasible : Collecting primary data can be time-consuming and expensive, and in some cases, it may not be feasible to collect primary data. In these situations, secondary data can provide valuable insights and information.
• When exploring a new research area : Secondary data can be a useful starting point for researchers who are exploring a new research area. Secondary data can provide context and background information on a research topic, and can help researchers identify key variables and relationships to explore further.
• When comparing and contrasting research findings: Secondary data can be used to compare and contrast findings across different studies or datasets. This can help researchers identify trends, patterns, and relationships that may not have been apparent from individual studies.
• When triangulating research findings: Triangulation is the process of using multiple sources of data to confirm or refute research findings. Secondary data can be used to support triangulation by providing additional sources of data to support or refute primary research findings.
• When validating research findings : Secondary data can be used to validate primary research findings by providing additional sources of data that support or refute the primary findings.

Characteristics of Secondary Data

Secondary data have several characteristics that distinguish them from primary data. Here are some of the key characteristics of secondary data:

• Non-reactive: Secondary data are non-reactive, meaning that they are not collected for the specific purpose of the research study. This means that the researcher has no control over the data collection process, and cannot influence how the data were collected.
• Time-saving: Secondary data are pre-existing, meaning that they have already been collected and organized by someone else. This can save the researcher time and resources, as they do not need to collect the data themselves.
• Wide-ranging : Secondary data sources can provide a wide range of information on a variety of topics. This can be useful for researchers who are exploring a new research area or seeking to compare and contrast research findings.
• Less expensive: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Potential for bias : Secondary data may be subject to biases that were present in the original data collection process. For example, data may have been collected using a biased sampling method or the data may be incomplete or inaccurate.
• Lack of control: The researcher has no control over the data collection process and cannot ensure that the data were collected using appropriate methods or measures.
• Requires careful evaluation : Secondary data sources must be evaluated carefully to ensure that they are appropriate for the research question and analysis. This includes assessing the quality, reliability, and validity of the data sources.

Advantages of Secondary Data

There are several advantages to using secondary data in research, including:

• Time-saving : Collecting primary data can be time-consuming and expensive. Secondary data can be accessed quickly and easily, which can save researchers time and resources.
• Cost-effective: Secondary data are generally less expensive than primary data, as they do not require the researcher to incur the costs associated with data collection.
• Large sample size : Secondary data sources often have larger sample sizes than primary data sources, which can increase the statistical power of the research.
• Access to historical data : Secondary data sources can provide access to historical data, which can be useful for researchers who are studying trends over time.
• No ethical concerns: Secondary data are already in existence, so there are no ethical concerns related to collecting data from human subjects.
• May be more objective : Secondary data may be more objective than primary data, as the data were not collected for the specific purpose of the research study.

Limitations of Secondary Data

While there are many advantages to using secondary data in research, there are also some limitations that should be considered. Some of the main limitations of secondary data include:

• Lack of control over data quality : Researchers do not have control over the data collection process, which means they cannot ensure the accuracy or completeness of the data.
• Limited availability: Secondary data may not be available for the specific research question or study design.
• Lack of information on sampling and data collection methods: Researchers may not have access to information on the sampling and data collection methods used to gather the secondary data. This can make it difficult to evaluate the quality of the data.
• Data may not be up-to-date: Secondary data may not be up-to-date or relevant to the current research question.
• Data may be incomplete or inaccurate : Secondary data may be incomplete or inaccurate due to missing or incorrect data points, data entry errors, or other factors.
• Biases in data collection: The data may have been collected using biased sampling or data collection methods, which can limit the validity of the data.
• Lack of control over variables: Researchers have limited control over the variables that were measured in the original data collection process, which can limit the ability to draw conclusions about causality.

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Muhammad Hassan

Researcher, Academic Writer, Web developer

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• Dissertation

How to Write a Dissertation | A Guide to Structure & Content

A dissertation or thesis is a long piece of academic writing based on original research, submitted as part of an undergraduate or postgraduate degree.

The structure of a dissertation depends on your field, but it is usually divided into at least four or five chapters (including an introduction and conclusion chapter).

The most common dissertation structure in the sciences and social sciences includes:

• An introduction to your topic
• A literature review that surveys relevant sources
• An explanation of your methodology
• An overview of the results of your research
• A discussion of the results and their implications
• A conclusion that shows what your research has contributed

Dissertations in the humanities are often structured more like a long essay , building an argument by analysing primary and secondary sources . Instead of the standard structure outlined here, you might organise your chapters around different themes or case studies.

Other important elements of the dissertation include the title page , abstract , and reference list . If in doubt about how your dissertation should be structured, always check your department’s guidelines and consult with your supervisor.

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Table of contents

Acknowledgements, table of contents, list of figures and tables, list of abbreviations, introduction, literature review / theoretical framework, methodology, reference list.

The very first page of your document contains your dissertation’s title, your name, department, institution, degree program, and submission date. Sometimes it also includes your student number, your supervisor’s name, and the university’s logo. Many programs have strict requirements for formatting the dissertation title page .

The title page is often used as cover when printing and binding your dissertation .

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The acknowledgements section is usually optional, and gives space for you to thank everyone who helped you in writing your dissertation. This might include your supervisors, participants in your research, and friends or family who supported you.

The abstract is a short summary of your dissertation, usually about 150-300 words long. You should write it at the very end, when you’ve completed the rest of the dissertation. In the abstract, make sure to:

• State the main topic and aims of your research
• Describe the methods you used
• Summarise the main results
• State your conclusions

Although the abstract is very short, it’s the first part (and sometimes the only part) of your dissertation that people will read, so it’s important that you get it right. If you’re struggling to write a strong abstract, read our guide on how to write an abstract .

In the table of contents, list all of your chapters and subheadings and their page numbers. The dissertation contents page gives the reader an overview of your structure and helps easily navigate the document.

All parts of your dissertation should be included in the table of contents, including the appendices. You can generate a table of contents automatically in Word.

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If you have used a lot of tables and figures in your dissertation, you should itemise them in a numbered list . You can automatically generate this list using the Insert Caption feature in Word.

If you have used a lot of abbreviations in your dissertation, you can include them in an alphabetised list of abbreviations so that the reader can easily look up their meanings.

If you have used a lot of highly specialised terms that will not be familiar to your reader, it might be a good idea to include a glossary . List the terms alphabetically and explain each term with a brief description or definition.

In the introduction, you set up your dissertation’s topic, purpose, and relevance, and tell the reader what to expect in the rest of the dissertation. The introduction should:

• Establish your research topic , giving necessary background information to contextualise your work
• Narrow down the focus and define the scope of the research
• Discuss the state of existing research on the topic, showing your work’s relevance to a broader problem or debate
• Clearly state your objectives and research questions , and indicate how you will answer them
• Give an overview of your dissertation’s structure

Everything in the introduction should be clear, engaging, and relevant to your research. By the end, the reader should understand the what , why and how of your research. Not sure how? Read our guide on how to write a dissertation introduction .

Before you start on your research, you should have conducted a literature review to gain a thorough understanding of the academic work that already exists on your topic. This means:

• Collecting sources (e.g. books and journal articles) and selecting the most relevant ones
• Critically evaluating and analysing each source
• Drawing connections between them (e.g. themes, patterns, conflicts, gaps) to make an overall point

In the dissertation literature review chapter or section, you shouldn’t just summarise existing studies, but develop a coherent structure and argument that leads to a clear basis or justification for your own research. For example, it might aim to show how your research:

• Addresses a gap in the literature
• Takes a new theoretical or methodological approach to the topic
• Proposes a solution to an unresolved problem
• Advances a theoretical debate
• Builds on and strengthens existing knowledge with new data

The literature review often becomes the basis for a theoretical framework , in which you define and analyse the key theories, concepts and models that frame your research. In this section you can answer descriptive research questions about the relationship between concepts or variables.

The methodology chapter or section describes how you conducted your research, allowing your reader to assess its validity. You should generally include:

• The overall approach and type of research (e.g. qualitative, quantitative, experimental, ethnographic)
• Your methods of collecting data (e.g. interviews, surveys, archives)
• Details of where, when, and with whom the research took place
• Your methods of analysing data (e.g. statistical analysis, discourse analysis)
• Tools and materials you used (e.g. computer programs, lab equipment)
• A discussion of any obstacles you faced in conducting the research and how you overcame them
• An evaluation or justification of your methods

Your aim in the methodology is to accurately report what you did, as well as convincing the reader that this was the best approach to answering your research questions or objectives.

Next, you report the results of your research . You can structure this section around sub-questions, hypotheses, or topics. Only report results that are relevant to your objectives and research questions. In some disciplines, the results section is strictly separated from the discussion, while in others the two are combined.

For example, for qualitative methods like in-depth interviews, the presentation of the data will often be woven together with discussion and analysis, while in quantitative and experimental research, the results should be presented separately before you discuss their meaning. If you’re unsure, consult with your supervisor and look at sample dissertations to find out the best structure for your research.

In the results section it can often be helpful to include tables, graphs and charts. Think carefully about how best to present your data, and don’t include tables or figures that just repeat what you have written  –  they should provide extra information or usefully visualise the results in a way that adds value to your text.

Full versions of your data (such as interview transcripts) can be included as an appendix .

The discussion  is where you explore the meaning and implications of your results in relation to your research questions. Here you should interpret the results in detail, discussing whether they met your expectations and how well they fit with the framework that you built in earlier chapters. If any of the results were unexpected, offer explanations for why this might be. It’s a good idea to consider alternative interpretations of your data and discuss any limitations that might have influenced the results.

The discussion should reference other scholarly work to show how your results fit with existing knowledge. You can also make recommendations for future research or practical action.

The dissertation conclusion should concisely answer the main research question, leaving the reader with a clear understanding of your central argument. Wrap up your dissertation with a final reflection on what you did and how you did it. The conclusion often also includes recommendations for research or practice.

In this section, it’s important to show how your findings contribute to knowledge in the field and why your research matters. What have you added to what was already known?

You must include full details of all sources that you have cited in a reference list (sometimes also called a works cited list or bibliography). It’s important to follow a consistent reference style . Each style has strict and specific requirements for how to format your sources in the reference list.

The most common styles used in UK universities are Harvard referencing and Vancouver referencing . Your department will often specify which referencing style you should use – for example, psychology students tend to use APA style , humanities students often use MHRA , and law students always use OSCOLA . M ake sure to check the requirements, and ask your supervisor if you’re unsure.

To save time creating the reference list and make sure your citations are correctly and consistently formatted, you can use our free APA Citation Generator .

Your dissertation itself should contain only essential information that directly contributes to answering your research question. Documents you have used that do not fit into the main body of your dissertation (such as interview transcripts, survey questions or tables with full figures) can be added as appendices .

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Dissertation methodology.

What Is The Methodology?

This is the section of your dissertation that explains how you carried out your research, where your data comes from, what sort of data gathering techniques you used, and so forth. Generally, someone reading your methodology should have enough information to be able to create methods very similar to the ones you used to obtain your data, but you do not have to include any questionnaires, reviews, interviews, etc that you used to conduct your research here. This section is primarily for explaining why you chose to use those particular techniques to gather your data. Read more about postgraduate research projects here .

Scientific Approach

The information included in the dissertation methodology is similar to the process of creating a science project: you need to present the subject that you aim to examine, and explain the way you chose to go about approaching your research. There are several different types of research , and research analysis, including primary and secondary research, and qualitative  and quantitative analysis, and in your dissertation methodology, you will explain what types you have employed in assembling and analysing your data.

Explain Your methods

This aspect of the methodology section is important, not just for detailing how your research was conducted, but also how the methods you used served your purposes, and were more appropriate to your area of study than other methods. For example, if you create and use a series of ‘yes’ or ‘no’ survey questions, which you then processed into percentages per response, then the quantitative method of data analysis to determine the results of data gathered using a primary research method. You would then want to explain why this combination was more appropriate to your topic than say, a review of a book that included interviews with participants asking open-ended questions: a combination of secondary research and qualitative data analysis.

Writing A Dissertation Methodology

It's important to keep in mind that your dissertation methodology is about description: you need to include details that will help others understand exactly what you aimed to do, how you went about doing it, and why you chose to do it that way. Don’t get too bogged down in listing methods and sources, and forget to include why and how they were suitable for your particular research. Be sure you speak to your course advisor about what specific requirements there may be for your particular course. It is possible that you may need to include more or less information depending on your subject. The type of research you conducted will also determine how much detail you will need to include in the description of your methods. If you have created a series of primary research sources, such as interviews, surveys, and other first hand accounts taken by either yourself or another person active during the time period you are examining, then you will need to include more detail in specifically breaking down the steps you took to both create your sources and use them in conducting your research. If you are using secondary sources when writing your dissertation methodology, or books containing data collected by other researchers, then you won’t necessarily need to include quite as much detail in your description of your methods, although you may want to be more thorough in your description of your analysis.

Research Techniques

You may also want to do some research into research techniques – it sounds redundant, but it will help you identify what type of research you are doing, and what types will be best to achieve the most cohesive results from your project. It will also help you write your dissertation methodology section, as you won’t have to guess when it comes to whether documents written in one time period, re-printed in another, and serialised in book form in a third are primary, secondary, or tertiary sources. Read more on dissertation research here .

Whether or not you have conducted your research using primary sources, you will still want to be sure that you include relevant references to existing studies on your topic. It is important to show that you have carefully researched what data already exists, and are seeking to build on the knowledge that has already been collected. As with all of your dissertation, be sure that you’ve fully supported your research with a strong academic basis. Use research that has already been conducted to illustrate that you know your subject well.

Draft As You Go

Because your dissertation methodology is basically an explanation of your research, you may want to consider writing it – or at least drafting it – as you gather your data. If you are on a PhD course, or a longer masters course, then you may be able to finish researching before you begin writing but it doesn’t hurt to start working on it early that way you can keep on  top of what you need to do. Analysing your own methods of research may help you spot any errors in data collection, interpretation or sources.

Dissertation Methodology Structure Example

There are several ways that you can structure your dissertation methodology, and the following headings are designed to further give you a better idea of what you may want to include, as well as how you might want to present your findings. By referring to this example you should be able to effectively structure your dissertation methodology.

Research Overview: where you reiterate the topic of your research.  

Research Design: How you’ve set up your project, and what each piece of it aims to accomplish. Data Collection: What you used to collect the data (surveys, questionnaires, interviews, trials, etc.). Don’t forget to includes sample size and any attempts to defeat bias.

Data Analysis: Finally, what does your data mean in the context of your research? Were your results conclusive or not? Remember to include what type of data you were working with (qualitative or quantitative? Primary or secondary sources?) and how any variables, spurious or otherwise factor into your results.

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What is Secondary Research?

Secondary research, also known as a literature review , preliminary research , historical research , background research , desk research , or library research , is research that analyzes or describes prior research. Rather than generating and analyzing new data, secondary research analyzes existing research results to establish the boundaries of knowledge on a topic, to identify trends or new practices, to test mathematical models or train machine learning systems, or to verify facts and figures. Secondary research is also used to justify the need for primary research as well as to justify and support other activities. For example, secondary research may be used to support a proposal to modernize a manufacturing plant, to justify the use of newly a developed treatment for cancer, to strengthen a business proposal, or to validate points made in a speech.

Why Is Secondary Research Important?

Because secondary research is used for so many purposes in so many settings, all professionals will be required to perform it at some point in their careers. For managers and entrepreneurs, regardless of the industry or profession, secondary research is a regular part of worklife, although parts of the research, such as finding the supporting documents, are often delegated to juniors in the organization. For all these reasons, it is essential to learn how to conduct secondary research, even if you are unlikely to ever conduct primary research.

Secondary research is also essential if your main goal is primary research. Research funding is obtained only by using secondary research to show the need for the primary research you want to conduct. In fact, primary research depends on secondary research to prove that it is indeed new and original research and not just a rehash or replication of somebody else’s work.

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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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Dissertation Secondary Research In 4 Steps Explained – Uniresearchers

Are you looking for a comprehensive guide on secondary research ? Well, yeah!! You have come to the right place to shed away all your worries. The topic of secondary and primary research appears to be challenging for the students that makes them anxious, nervous and worried at the same time. As a result, they end up getting poor scores and lower grades in academics. Please don’t be ashamed of it, because this is a very common problem faced by the students amidst their tiring long days jam-packed with classes, lectures, seminars, part-time jobs, etc. 

But let me tell you, secondary research is very simple than you have ever thought of. So here we have come to simplify the overall process of secondary research by completing it in just 4 steps. Want to know how? Here we go. 

Before getting into details, let us understand what exactly “ secondary research ” is. 

To be precise, secondary research refers to the collection of data from the existing research that has been conducted by others (Authors). In other words, secondary research indicates the “past data” that are usually collected from online or offline resources, government records, books, and journal articles pre-existing in the inventory. Secondary research goes exactly opposite to primary research where the main agenda is to conduct your research to collect raw and real-time data. The best part is, that secondary research saves a lot of time, effort and money in the process. To differentiate between the two, primary research is complicated enough which will consume a lot of time in finding the right participants who would provide the data findings to proceed with the research. 

Now, we shall go ahead with the process of secondary research in 4 simple steps. 

Step 1: You need to frame out your research questions 

Yes, correct!! Secondary research will begin with the framing of research questions right after you have settled on the topic of investigation. Now your job is to find the research gap in the literature that will create a strong base for framing the research questions. Once you are done with the research questions, you have almost created the correct roadmap for your research study. 

Step 2: Get the secondary data sets 

Majority of the research proceeds with identifying the secondary data sets in the literature, which are perfectly reusable and aid in addressing the research question more thoroughly. It is your duty to identify useful secondary data which will perfectly fit your research questions. 

Step 3: Simply evaluate the secondary dataset 

The criteria for evaluating the secondary dataset stand on the following metrics – 

• Who collected the data 
• What were the purpose and goal 
• When and how the data was collected 
• Type of data and its consistency with other data sources. 

All of these factors are essential for evaluating the secondary dataset because not always do the secondary data you have found appropriately align with the current research purpose. Moreover, the secondary datasets may lack the validity and reliability to answer your research questions.  Hence, needless to say, the collection of wrong secondary datasets can limit the effectiveness of your study. So never forget to evaluate the secondary datasets that you have planned to present in your research. 

Step 4: Prepare to analyze the secondary data 

In dissertation writing services , we follow this part religiously as it becomes the key part of the secondary research . Firstly, we outline the variables of interest and transfer this data into the Excel file or new SPSS. The next part would be addressing the missing data and recoding variables when necessary. For analyzing the data, we have to select the most suitable technique of analysis that can be through the use of statistical methods, thematic analysis, descriptive, etc. Make sure to be perfect on your part to avoid inconsistencies in the data analysis. 

If you find the facts are varying from one source to another, you must plan your primary research in the same context to get the facts correct using real-time raw data. 

Get your own checklist 

Hold on!! That’s not all!! With tremendous accessibility to the internet nowadays, the reliability and validity of secondary data have stooped down remarkably. So before utilizing external sources for secondary data, make a checklist to ensure the validity and accuracy of your secondary data. Be mindful, that failing to find the correct and valid data will lead you to inaccurate and poor analysis. 

So all you need to do is, be attentive and focused throughout the research study. 

Do you want our dissertation writing services? 

While we have reached almost the end of this article, let us give you some brief ideas about our dissertation writing services . With best-in-class experts in our kitty, we can offer you immense support and guidance in your primary and secondary research . Backed by a team of highly qualified professionals, we take pride in completing numerous dissertations so far. Apart from a perfectly crafted dissertation, we offer you multiple revisions at no cost. 

Our dissertation writing services come up with various other benefits in series. If you need any urgent assistance or support, our 24/7 support teams are always at your service. You must be thinking about how to place your order now. Well, it’s simpler than ever. Visit our website, fill out the order form with all the vital details, and make sure to specify the deadline to get an accurate response. Once your order is approved, we will assign you to the consultant who would lead your order. Trust me, your order for secondary research will be ready in a blink. Yes, it’s so much easy with us!! 

Now shed off your hesitation, and take a step ahead to place the order. Well, do not forget to check our client reviews and testimonials on our website for better clarity on our services. We ensure all the comfort and safety of our clients by maintaining absolute confidentiality. So hurry up and place your order right now to build a bright future.  

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Dissertations and research projects

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• Planning your research

Developing a theoretical framework

Reflecting on your position, extended literature reviews, presenting qualitative data.

• Quantitative research
• Writing up your research project
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What is a theoretical framework?

Developing a theoretical framework for your dissertation is one of the key elements of a qualitative research project. Through writing your literature review, you are likely to have identified either a problem that need ‘fixing’ or a gap that your research may begin to fill.

The theoretical framework is your toolbox . In the toolbox are your handy tools: a set of theories, concepts, ideas and hypotheses that you will use to build a solution to the research problem or gap you have identified.

The methodology is the instruction manual: the procedure and steps you have taken, using your chosen tools, to tackle the research problem.

Why do I need a theoretical framework?

Developing a theoretical framework shows that you have thought critically about the different ways to approach your topic, and that you have made a well-reasoned and evidenced decision about which approach will work best. theoretical frameworks are also necessary for solving complex problems or issues from the literature, showing that you have the skills to think creatively and improvise to answer your research questions. they also allow researchers to establish new theories and approaches, that future research may go on to develop., how do i create a theoretical framework for my dissertation.

First, select your tools. You are likely to need a variety of tools in qualitative research – different theories, models or concepts – to help you tackle different parts of your research question.  

When deciding what tools would be best for the job of answering your research questions or problem, explore what existing research in your area has used. You may find that there is a ‘standard toolbox’ for qualitative research in your field that you can borrow from or apply to your own research.

You will need to justify why your chosen tools are best for the job of answering your research questions, at what stage they are most relevant, and how they relate to each other. Some theories or models will neatly fit together and appear in the toolboxes of other researchers. However, you may wish to incorporate a model or idea that is not typical for your research area – the ‘odd one out’ in your toolbox. If this is the case, make sure you justify and account for why it is useful to you, and look for ways that it can be used in partnership with the other tools you are using.

You should also be honest about limitations, or where you need to improvise (for example, if the ‘right’ tool or approach doesn’t exist in your area).

This video from the Skills Centre includes an overview and example of how you might create a theoretical framework for your dissertation:

How do I choose the 'right' approach?

When designing your framework and choosing what to include, it can often be difficult to know if you’ve chosen the ‘right’ approach for your research questions. One way to check this is to look for consistency between your objectives, the literature in your framework, and your overall ethos for the research. This means ensuring that the literature you have used not only contributes to answering your research objectives, but that you also use theories and models that are true to your beliefs as a researcher.

Reflecting on your values and your overall ambition for the project can be a helpful step in making these decisions, as it can help you to fully connect your methodology and methods to your research aims.

Should I reflect on my position as a researcher?

If you feel your position as a researcher has influenced your choice of methods or procedure in any way, the methodology is a good place to reflect on this.  Positionality  acknowledges that no researcher is entirely objective: we are all, to some extent, influenced by prior learning, experiences, knowledge, and personal biases. This is particularly true in qualitative research or practice-based research, where the student is acting as a researcher in their own workplace, where they are otherwise considered a practitioner/professional. It's also important to reflect on your positionality if you belong to the same community as your participants where this is the grounds for their involvement in the research (ie. you are a mature student interviewing other mature learners about their experences in higher education). 

The following questions can help you to reflect on your positionality and gauge whether this is an important section to include in your dissertation (for some people, this section isn’t necessary or relevant):

• How might my personal history influence how I approach the topic?
• How am I positioned in relation to this knowledge? Am I being influenced by prior learning or knowledge from outside of this course?
• How does my gender/social class/ ethnicity/ culture influence my positioning in relation to this topic?
• Do I share any attributes with my participants? Are we part of a s hared community? How might this have influenced our relationship and my role in interviews/observations?
• Am I invested in the outcomes on a personal level? Who is this research for and who will feel the benefits?

One option for qualitative projects is to write an extended literature review. This type of project does not require you to collect any new data. Instead, you should focus on synthesising a broad range of literature to offer a new perspective on a research problem or question.  

The main difference between an extended literature review and a dissertation where primary data is collected, is in the presentation of the methodology, results and discussion sections. This is because extended literature reviews do not actively involve participants or primary data collection, so there is no need to outline a procedure for data collection (the methodology) or to present and interpret ‘data’ (in the form of interview transcripts, numerical data, observations etc.) You will have much more freedom to decide which sections of the dissertation should be combined, and whether new chapters or sections should be added.

Here is an overview of a common structure for an extended literature review:

Introduction

• Provide background information and context to set the ‘backdrop’ for your project.
• Explain the value and relevance of your research in this context. Outline what do you hope to contribute with your dissertation.
• Clarify a specific area of focus.
• Introduce your research aims (or problem) and objectives.

Literature review

You will need to write a short, overview literature review to introduce the main theories, concepts and key research areas that you will explore in your dissertation. This set of texts – which may be theoretical, research-based, practice-based or policies – form your theoretical framework. In other words, by bringing these texts together in the literature review, you are creating a lens that you can then apply to more focused examples or scenarios in your discussion chapters.

Methodology

As you will not be collecting primary data, your methodology will be quite different from a typical dissertation. You will need to set out the process and procedure you used to find and narrow down your literature. This is also known as a search strategy.

Including your search strategy

A search strategy explains how you have narrowed down your literature to identify key studies and areas of focus. This often takes the form of a search strategy table, included as an appendix at the end of the dissertation. If included, this section takes the place of the traditional 'methodology' section.

If you choose to include a search strategy table, you should also give an overview of your reading process in the main body of the dissertation.  Think of this as a chronology of the practical steps you took and your justification for doing so at each stage, such as:

• Your key terms, alternatives and synonyms, and any terms that you chose to exclude.
• Your choice and combination of databases;
• Your inclusion/exclusion criteria, when they were applied and why. This includes filters such as language of publication, date, and country of origin;
• You should also explain which terms you combined to form search phrases and your use of Boolean searching (AND, OR, NOT);
• Your use of citation searching (selecting articles from the bibliography of a chosen journal article to further your search).
• Your use of any search models, such as PICO and SPIDER to help shape your approach.
• Search strategy template A simple template for recording your literature searching. This can be included as an appendix to show your search strategy.

The discussion section of an extended literature review is the most flexible in terms of structure. Think of this section as a series of short case studies or ‘windows’ on your research. In this section you will apply the theoretical framework you formed in the literature review – a combination of theories, models and ideas that explain your approach to the topic – to a series of different examples and scenarios. These are usually presented as separate discussion ‘chapters’ in the dissertation, in an order that you feel best fits your argument.

Think about an order for these discussion sections or chapters that helps to tell the story of your research. One common approach is to structure these sections by common themes or concepts that help to draw your sources together. You might also opt for a chronological structure if your dissertation aims to show change or development over time. Another option is to deliberately show where there is a lack of chronology or narrative across your case studies, by ordering them in a fragmentary order! You will be able to reflect upon the structure of these chapters elsewhere in the dissertation, explaining and defending your decision in the methodology and conclusion.

A summary of your key findings – what you have concluded from your research, and how far you have been able to successfully answer your research questions.

• Recommendations – for improvements to your own study, for future research in the area, and for your field more widely.
• Emphasise your contributions to knowledge and what you have achieved.

Alternative structure

Depending on your research aims, and whether you are working with a case-study type approach (where each section of the dissertation considers a different example or concept through the lens established in your literature review), you might opt for one of the following structures:

Splitting the literature review across different chapters:

This structure allows you to pull apart the traditional literature review, introducing it little by little with each of your themed chapters. This approach works well for dissertations that attempt to show change or difference over time, as the relevant literature for that section or period can be introduced gradually to the reader.

Whichever structure you opt for, remember to explain and justify your approach. A marker will be interested in why you decided on your chosen structure, what it allows you to achieve/brings to the project and what alternatives you considered and rejected in the planning process. Here are some example sentence starters:

In qualitative studies, your results are often presented alongside the discussion, as it is difficult to include this data in a meaningful way without explanation and interpretation. In the dsicussion section, aim to structure your work thematically, moving through the key concepts or ideas that have emerged from your qualitative data. Use extracts from your data collection - interviews, focus groups, observations - to illustrate where these themes are most prominent, and refer back to the sources from your literature review to help draw conclusions. 

Here's an example of how your data could be presented in paragraph format in this section:

Example from  'Reporting and discussing your findings ', Monash University .

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