secondary data dissertation structure

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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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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How to do your dissertation secondary research in 4 steps

(Last updated: 12 May 2021)

Since 2006, Oxbridge Essays has been the UK’s leading paid essay-writing and dissertation service

We have helped 10,000s of undergraduate, Masters and PhD students to maximise their grades in essays, dissertations, model-exam answers, applications and other materials. If you would like a free chat about your project with one of our UK staff, then please just reach out on one of the methods below.

If you are reading this guide, it's very likely you may be doing secondary research for your dissertation, rather than primary. If this is indeed you, then here's the good news: secondary research is the easiest type of research! Congratulations!

In a nutshell, secondary research is far more simple. So simple, in fact, that we have been able to explain how to do it completely in just 4 steps (see below). If nothing else, secondary research avoids the all-so-tiring efforts usually involved with primary research. Like recruiting your participants, choosing and preparing your measures, and spending days (or months) collecting your data.

That said, you do still need to know how to do secondary research. Which is what you're here for. So, go make a decent-sized mug of your favourite hot beverage (consider a glass of water , too) then come back and get comfy.

Here's what we'll cover in this guide:

The basics: What's secondary research all about?

Understanding secondary research, advantages of secondary research, disadvantages of secondary research, methods and purposes of secondary research, types of secondary data, sources of secondary data, secondary research process in 4 steps, step 1: develop your research question(s), step 2: identify a secondary data set, step 3: evaluate a secondary data set, step 4: prepare and analyse secondary data.

To answer this question, let’s first recall what we mean by primary research . As you probably already know, primary research is when the researcher collects the data himself or herself. The researcher uses so-called “real-time” data, which means that the data is collected during the course of a specific research project and is under the researcher’s direct control.

In contrast, secondary research involves data that has been collected by somebody else previously. This type of data is called “past data” and is usually accessible via past researchers, government records, and various online and offline resources.

So to recap, secondary research involves re-analysing, interpreting, or reviewing past data. The role of the researcher is always to specify how this past data informs his or her current research.

In contrast to primary research, secondary research is easier, particularly because the researcher is less involved with the actual process of collecting the data. Furthermore, secondary research requires less time and less money (i.e., you don’t need to provide your participants with compensation for participating or pay for any other costs of the research).

One of the most obvious advantages is that, compared to primary research, secondary research is inexpensive . Primary research usually requires spending a lot of money. For instance, members of the research team should be paid salaries. There are often travel and transportation costs. You may need to pay for office space and equipment, and compensate your participants for taking part. There may be other overhead costs too.

These costs do not exist when doing secondary research. Although researchers may need to purchase secondary data sets, this is always less costly than if the research were to be conducted from scratch.

As an undergraduate or graduate student, your dissertation project won't need to be an expensive endeavour. Thus, it is useful to know that you can further reduce costs, by using freely available secondary data sets.

But this is far from the only consideration.

Most students value another important advantage of secondary research, which is that secondary research saves you time . Primary research usually requires months spent recruiting participants, providing them with questionnaires, interviews, or other measures, cleaning the data set, and analysing the results. With secondary research, you can skip most of these daunting tasks; instead, you merely need to select, prepare, and analyse an existing data set.

Moreover, you probably won’t need a lot of time to obtain your secondary data set, because secondary data is usually easily accessible . In the past, students needed to go to libraries and spend hours trying to find a suitable data set. New technologies make this process much less time-consuming. In most cases, you can find your secondary data through online search engines or by contacting previous researchers via email.

A third important advantage of secondary research is that you can base your project on a large scope of data . If you wanted to obtain a large data set yourself, you would need to dedicate an immense amount of effort. What's more, if you were doing primary research, you would never be able to use longitudinal data in your graduate or undergraduate project, since it would take you years to complete. This is because longitudinal data involves assessing and re-assessing a group of participants over long periods of time.

When using secondary data, however, you have an opportunity to work with immensely large data sets that somebody else has already collected. Thus, you can also deal with longitudinal data, which may allow you to explore trends and changes of phenomena over time.

With secondary research, you are relying not only on a large scope of data, but also on professionally collected data . This is yet another advantage of secondary research. For instance, data that you will use for your secondary research project has been collected by researchers who are likely to have had years of experience in recruiting representative participant samples, designing studies, and using specific measurement tools.

If you had collected this data yourself, your own data set would probably have more flaws, simply because of your lower level of expertise when compared to these professional researchers.

The first such disadvantage is that your secondary data may be, to a greater or lesser extent, inappropriate for your own research purposes. This is simply because you have not collected the data yourself.

When you collect your data personally, you do so with a specific research question in mind. This makes it easy to obtain the relevant information. However, secondary data was always collected for the purposes of fulfilling other researchers’ goals and objectives.

Thus, although secondary data may provide you with a large scope of professionally collected data, this data is unlikely to be fully appropriate to your own research question. There are several reasons for this. For instance, you may be interested in the data of a particular population, in a specific geographic region, and collected during a specific time frame. However, your secondary data may have focused on a slightly different population, may have been collected in a different geographical region, or may have been collected a long time ago.

Apart from being potentially inappropriate for your own research purposes, secondary data could have a different format than you require. For instance, you might have preferred participants’ age to be in the form of a continuous variable (i.e., you want your participants to have indicated their specific age). But the secondary data set may contain a categorical age variable; for example, participants might have indicated an age group they belong to (e.g., 20-29, 30-39, 40-49, etc.). Or another example: A secondary data set may contain too few ethnic categories (e.g., “White” and “Other”), while you would ideally want a wider range of racial categories (e.g., “White”, “Black or African American”, “American Indian”, and “Asian”). Differences such as these mean that secondary data may not be perfectly appropriate for your research.

The above two disadvantages may lead to yet another one: the existing data set may not answer your own research question(s) in an ideal way. As noted above, secondary data was collected with a different research question in mind, and this may limit its application to your own research purpose.

Unfortunately, the list of disadvantages does not end here. An additional weakness of secondary data is that you have a lack of control over the quality of data. All researchers need to establish that their data is reliable and valid. But if the original researchers did not establish the reliability and validity of their data, this may limit its reliability and validity for your research as well. To establish reliability and validity, you are usually advised to critically evaluate how the data was gathered, analysed, and presented.

But here lies the final disadvantage of doing secondary research: original researchers may fail to provide sufficient information on how their research was conducted. You might be faced with a lack of information on recruitment procedures, sample representativeness, data collection methods, employed measurement tools and statistical analyses, and the like. This may require you to take extra steps to obtain such information, if that is possible at all.

At this point, we should ask: “What are the methods of secondary research?” and “When do we use each of these methods?” Here, we can differentiate between three methods of secondary research: using a secondary data set in isolation , combining two secondary data sets, and combining secondary and primary data sets. Let’s outline each of these separately, and also explain when to use each of these methods.

Initially, you can use a secondary data set in isolation – that is, without combining it with other data sets. You dig and find a data set that is useful for your research purposes and then base your entire research on that set of data. You do this when you want to re-assess a data set with a different research question in mind.

Let’s illustrate this with a simple example. Suppose that, in your research, you want to investigate whether pregnant women of different nationalities experience different levels of anxiety during different pregnancy stages. Based on the literature, you have formed an idea that nationality may matter in this relationship between pregnancy and anxiety.

If you wanted to test this relationship by collecting the data yourself, you would need to recruit many pregnant women of different nationalities and assess their anxiety levels throughout their pregnancy. It would take you at least a year to complete this research project.

Instead of undertaking this long endeavour, you thus decide to find a secondary data set – one that investigated (for instance) a range of difficulties experienced by pregnant women in a nationwide sample. The original research question that guided this research could have been: “to what extent do pregnant women experience a range of mental health difficulties, including stress, anxiety, mood disorders, and paranoid thoughts?” The original researchers might have outlined women’s nationality, but weren’t particularly interested in investigating the link between women’s nationality and anxiety at different pregnancy stages. You are, therefore, re-assessing their data set with your own research question in mind.

Your research may, however, require you to combine two secondary data sets . You will use this kind of methodology when you want to investigate the relationship between certain variables in two data sets or when you want to compare findings from two past studies.

To take an example: One of your secondary data sets may focus on a target population’s tendency to smoke cigarettes, while the other data set focuses on the same population’s tendency to drink alcohol. In your own research, you may thus be looking at whether there is a correlation between smoking and drinking among this population.

Here is a second example: Your two secondary data sets may focus on the same outcome variable, such as the degree to which people go to Greece for a summer vacation. However, one data set could have been collected in Britain and the other in Germany. By comparing these two data sets, you can investigate which nation tends to visit Greece more.

Finally, your research project may involve combining primary and secondary data . You may decide to do this when you want to obtain existing information that would inform your primary research.

Let’s use another simple example and say that your research project focuses on American versus British people’s attitudes towards racial discrimination. Let’s say that you were able to find a recent study that investigated Americans’ attitudes of these kind, which were assessed with a certain set of measures. However, your search finds no recent studies on Britons’ attitudes. Let’s also say that you live in London and that it would be difficult for you to assess Americans’ attitudes on the topic, but clearly much more straightforward to conduct primary research on British attitudes.

In this case, you can simply reuse the data from the American study and adopt exactly the same measures with your British participants. Your secondary data is being combined with your primary data. Alternatively, you may combine these types of data when the role of your secondary data is to outline descriptive information that supports your research. For instance, if your project is focusing on attitudes towards McDonald’s food, you may want to support your primary research with secondary data that outlines how many people eat McDonald’s in your country of choice.

TABLE 3 summarises particular methods and purposes of secondary research:

We have already provided above several examples of using quantitative secondary data. This type of data is used when the original study has investigated a population’s tendency to smoke or drink alcohol, the degree to which people from different nationalities go to Greece for their summer vacation, or the degree to which pregnant women experience anxiety.

In all these examples, outcome variables were assessed by questionnaires, and thus the obtained data was numerical.

Quantitative secondary research is much more common than qualitative secondary research. However, this is not to say that you cannot use qualitative secondary data in your research project. This type of secondary data is used when you want the previously-collected information to inform your current research. More specifically, it is used when you want to test the information obtained through qualitative research by implementing a quantitative methodology.

For instance, a past qualitative study might have focused on the reasons why people choose to live on boats. This study might have interviewed some 30 participants and noted the four most important reasons people live on boats: (1) they can lead a transient lifestyle, (2) they have an increased sense of freedom, (3) they feel that they are “world citizens”, and (4) they can more easily visit their family members who live in different locations. In your own research, you can therefore reuse this qualitative data to form a questionnaire, which you then give to a larger population of people who live on boats. This will help you to generalise the previously-obtained qualitative results to a broader population.

Importantly, you can also re-assess a qualitative data set in your research, rather than using it as a basis for your quantitative research. Let’s say that your research focuses on the kind of language that people who live on boats use when describing their transient lifestyles. The original research did not focus on this research question per se – however, you can reuse the information from interviews to “extract” the types of descriptions of a transient lifestyle that were given by participants.

TABLE 4 highlights the two main types of secondary data and their associated purposes:

Internal sources of data are those that are internal to the organisation in question. For instance, if you are doing a research project for an organisation (or research institution) where you are an intern, and you want to reuse some of their past data, you would be using internal data sources.

The benefit of using these sources is that they are easily accessible and there is no associated financial cost of obtaining them.

External sources of data, on the other hand, are those that are external to an organisation or a research institution. This type of data has been collected by “somebody else”, in the literal sense of the term. The benefit of external sources of data is that they provide comprehensive data – however, you may sometimes need more effort (or money) to obtain it.

Let’s now focus on different types of internal and external secondary data sources.

There are several types of internal sources. For instance, if your research focuses on an organisation’s profitability, you might use their sales data . Each organisation keeps a track of its sales records, and thus your data may provide information on sales by geographical area, types of customer, product prices, types of product packaging, time of the year, and the like.

Alternatively, you may use an organisation’s financial data . The purpose of using this data could be to conduct a cost-benefit analysis and understand the economic opportunities or outcomes of hiring more people, buying more vehicles, investing in new products, and so on.

Another type of internal data is transport data . Here, you may focus on outlining the safest and most effective transportation routes or vehicles used by an organisation.

Alternatively, you may rely on marketing data , where your goal would be to assess the benefits and outcomes of different marketing operations and strategies.

Some other ideas would be to use customer data to ascertain the ideal type of customer, or to use safety data to explore the degree to which employees comply with an organisation’s safety regulations.

The list of the types of internal sources of secondary data can be extensive; the most important thing to remember is that this data comes from a particular organisation itself, in which you do your research in an internal manner.

The list of external secondary data sources can be just as extensive. One example is the data obtained through government sources . These can include social surveys, health data, agricultural statistics, energy expenditure statistics, population censuses, import/export data, production statistics, and the like. Government agencies tend to conduct a lot of research, therefore covering almost any kind of topic you can think of.

Another external source of secondary data are national and international institutions , including banks, trade unions, universities, health organisations, etc. As with government, such institutions dedicate a lot of effort to conducting up-to-date research, so you simply need to find an organisation that has collected the data on your own topic of interest.

Alternatively, you may obtain your secondary data from trade, business, and professional associations . These usually have data sets on business-related topics and are likely to be willing to provide you with secondary data if they understand the importance of your research. If your research is built on past academic studies, you may also rely on scientific journals as an external data source.

Once you have specified what kind of secondary data you need, you can contact the authors of the original study.

As a final example of a secondary data source, you can rely on data from commercial research organisations. These usually focus their research on media statistics and consumer information, which may be relevant if, for example, your research is within media studies or you are investigating consumer behaviour.

At this point, you should have a clearer understanding of secondary research in general terms.

Now it may be useful to focus on the actual process of doing secondary research. This next section is organised to introduce you to each step of this process, so that you can rely on this guide while planning your study. At the end of this blog post, in Table 6 , you will find a summary of all the steps of doing secondary research.

For an undergraduate thesis, you are often provided with a specific research question by your supervisor. But for most other types of research, and especially if you are doing your graduate thesis, you need to arrive at a research question yourself.

The first step here is to specify the general research area in which your research will fall. For example, you may be interested in the topic of anxiety during pregnancy, or tourism in Greece, or transient lifestyles. Since we have used these examples previously, it may be useful to rely on them again to illustrate our discussion.

Once you have identified your general topic, your next step consists of reading through existing papers to see whether there is a gap in the literature that your research can fill. At this point, you may discover that previous research has not investigated national differences in the experiences of anxiety during pregnancy, or national differences in a tendency to go to Greece for a summer vacation, or that there is no literature generalising the findings on people’s choice to live on boats.

Having found your topic of interest and identified a gap in the literature, you need to specify your research question. In our three examples, research questions would be specified in the following manner: (1) “Do women of different nationalities experience different levels of anxiety during different stages of pregnancy?”, (2) “Are there any differences in an interest in Greek tourism between Germans and Britons?”, and (3) “Why do people choose to live on boats?”.

It is at this point, after reviewing the literature and specifying your research questions, that you may decide to rely on secondary data. You will do this if you discover that there is past data that would be perfectly reusable in your own research, therefore helping you to answer your research question more thoroughly (and easily).

But how do you discover if there is past data that could be useful for your research? You do this through reviewing the literature on your topic of interest. During this process, you will identify other researchers, organisations, agencies, or research centres that have explored your research topic.

Somewhere there, you may discover a useful secondary data set. You then need to contact the original authors and ask for a permission to use their data. (Note, however, that this happens only if you are relying on external sources of secondary data. If you are doing your research internally (i.e., within a particular organisation), you don’t need to search through the literature for a secondary data set – you can just reuse some past data that was collected within the organisation itself.)

In any case, you need to ensure that a secondary data set is a good fit for your own research question. Once you have established that it is, you need to specify the reasons why you have decided to rely on secondary data.

For instance, your choice to rely on secondary data in the above examples might be as follows: (1) A recent study has focused on a range of mental difficulties experienced by women in a multinational sample and this data can be reused; (2) There is existing data on Germans’ and Britons’ interest in Greek tourism and these data sets can be compared; and (3) There is existing qualitative research on the reasons for choosing to live on boats, and this data can be relied upon to conduct a further quantitative investigation.

Because such disadvantages of secondary data can limit the effectiveness of your research, it is crucial that you evaluate a secondary data set. To ease this process, we outline here a reflective approach that will allow you to evaluate secondary data in a stepwise fashion.

Step 3(a): What was the aim of the original study?

During this step, you also need to pay close attention to any differences in research purposes and research questions between the original study and your own investigation. As we have discussed previously, you will often discover that the original study had a different research question in mind, and it is important for you to specify this difference.

Let’s put this step of identifying the aim of the original study in practice, by referring to our three research examples. The aim of the first research example was to investigate mental difficulties (e.g., stress, anxiety, mood disorders, and paranoid thoughts) in a multinational sample of pregnant women.

How does this aim differ from your research aim? Well, you are seeking to reuse this data set to investigate national differences in anxiety experienced by women during different pregnancy stages. When it comes to the second research example, you are basing your research on two secondary data sets – one that aimed to investigate Germans’ interest in Greek tourism and the other that aimed to investigate Britons’ interest in Greek tourism.

While these two studies focused on particular national populations, the aim of your research is to compare Germans’ and Britons’ tendency to visit Greece for summer vacation. Finally, in our third example, the original research was a qualitative investigation into the reasons for living on boats. Your research question is different, because, although you are seeking to do the same investigation, you wish to do so by using a quantitative methodology.

Importantly, in all three examples, you conclude that secondary data may in fact answer your research question. If you conclude otherwise, it may be wise to find a different secondary data set or to opt for primary research.

Step 3(b): Who has collected the data?

Let’s say that, in our example of research on pregnancy, data was collected by the UK government; that in our example of research on Greek tourism, the data was collected by a travel agency; and that in our example of research on the reasons for choosing to live on boats, the data was collected by researchers from a UK university.

Let’s also say that you have checked the background of these organisations and researchers, and that you have concluded that they all have a sufficiently professional background, except for the travel agency. Given that this agency’s research did not lead to a publication (for instance), and given that not much can be found about the authors of the research, you conclude that the professionalism of this data source remains unclear.

Step 3(c): Which measures were employed?

Original authors should have documented all their sample characteristics, measures, procedures, and protocols. This information can be obtained either in their final research report or through contacting the authors directly.

It is important for you to know what type of data was collected, which measures were used, and whether such measures were reliable and valid (if they were quantitative measures). You also need to make a clear outline of the type of data collected – and especially the data relevant for your research.

Let’s say that, in our first example, researchers have (among other assessed variables) used a demographic measure to note women’s nationalities and have used the State Anxiety Inventory to assess women’s anxiety levels during different pregnancy stages, both of which you conclude are valid and reliable tools. In our second example, the authors might have crafted their own measure to assess interest in Greek tourism, but there may be no established validity and reliability for this measure. And in our third example, the authors have employed semi-structured interviews, which cover the most important reasons for wanting to live on boats.

Step 3(d): When was the data collected?

Ideally, you want your secondary data to have been collected within the last five years. For the sake of our examples, let’s say that all three original studies were conducted within this time-range.

Step 3(e): What methodology was used to collect the data?

We have already noted that you need to evaluate the reliability and validity of employed measures. In addition to this, you need to evaluate how the sample was obtained, whether the sample was large enough, if the sample was representative of the population, if there were any missing responses on employed measures, whether confounders were controlled for, and whether the employed statistical analyses were appropriate. Any drawbacks in the original methodology may limit your own research as well.

For the sake of our examples, let’s say that the study on mental difficulties in pregnant women recruited a representative sample of pregnant women (i.e., they had different nationalities, different economic backgrounds, different education levels, etc.) in maternity wards of seven hospitals; that the sample was large enough (N = 945); that the number of missing values was low; that many confounders were controlled for (e.g., education level, age, presence of partnership, etc.); and that statistical analyses were appropriate (e.g., regression analyses were used).

Let’s further say that our second research example had slightly less sufficient methodology. Although the number of participants in the two samples was high enough (N1 = 453; N2 = 488), the number of missing values was low, and statistical analyses were appropriate (descriptive statistics), the authors failed to report how they recruited their participants and whether they controlled for any confounders.

Let’s say that these authors also failed to provide you with more information via email. Finally, let’s assume that our third research example also had sufficient methodology, with a sufficiently large sample size for a qualitative investigation (N = 30), high sample representativeness (participants with different backgrounds, coming from different boat communities), and sufficient analyses (thematic analysis).

Note that, since this was a qualitative investigation, there is no need to evaluate the number of missing values and the use of confounders.

Step 3(f): Making a final evaluation

We would conclude that the secondary data from our first research example has a high quality. Data was recently collected by professionals, the employed measures were both reliable and valid, and the methodology was more than sufficient. We can be confident that our new research question can be sufficiently answered with the existing data. Thus, the data set for our first example is ideal.

The two secondary data sets from our second research example seem, however, less than ideal. Although we can answer our research questions on the basis of these recent data sets, the data was collected by an unprofessional source, the reliability and validity of the employed measure is uncertain, and the employed methodology has a few notable drawbacks.

Finally, the data from our third example seems sufficient both for answering our research question and in terms of the specific evaluations (data was collected recently by a professional source, semi-structured interviews were well made, and the employed methodology was sufficient).

The final question to ask is: “what can be done if our evaluation reveals the lack of appropriateness of secondary data?”. The answer, unfortunately, is “nothing”. In this instance, you can only note the drawbacks of the original data set, present its limitations, and conclude that your own research may not be sufficiently well grounded.

Your first sub-step here (if you are doing quantitative research) is to outline all variables of interest that you will use in your study. In our first example, you could have at least five variables of interest: (1) women’s nationality, (2) anxiety levels at the beginning of pregnancy, (3) anxiety levels at three months of pregnancy, (4) anxiety levels at six months of pregnancy, and (5) anxiety levels at nine months of pregnancy. In our second example, you will have two variables of interest: (1) participants’ nationality, and (2) the degree of interest in going to Greece for a summer vacation. Once your variables of interest are identified, you need to transfer this data into a new SPSS or Excel file. Remember simply to copy this data into the new file – it is vital that you do not alter it!

Once this is done, you should address missing data (identify and label them) and recode variables if necessary (e.g., giving a value of 1 to German participants and a value of 2 to British participants). You may also need to reverse-score some items, so that higher scores on all items indicate a higher degree of what is being assessed.

Most of the time, you will also need to create new variables – that is, to compute final scores. For instance, in our example of research on anxiety during pregnancy, your data will consist of scores on each item of the State Anxiety Inventory, completed at various times during pregnancy. You will need to calculate final anxiety scores for each time the measure was completed.

Your final step consists of analysing the data. You will always need to decide on the most suitable analysis technique for your secondary data set. In our first research example, you would rely on MANOVA (to see if women of different nationalities experience different stress levels at the beginning, at three months, at six months, and at nine months of pregnancy); and in our second example, you would use an independent samples t-test (to see if interest in Greek tourism differs between Germans and Britons).

The process of preparing and analysing a secondary data set is slightly different if your secondary data is qualitative. In our example on the reasons for living on boats, you would first need to outline all reasons for living on boats, as recognised by the original qualitative research. Then you would need to craft a questionnaire that assesses these reasons in a broader population.

Finally, you would need to analyse the data by employing statistical analyses.

Note that this example combines qualitative and quantitative data. But what if you are reusing qualitative data, as in our previous example of re-coding the interviews from our study to discover the language used when describing transient lifestyles? Here, you would simply need to recode the interviews and conduct a thematic analysis.

In summary…

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Dissertation Structure & Layout 101: How to structure your dissertation, thesis or research project.

By: Derek Jansen (MBA) Reviewed By: David Phair (PhD) | July 2019

So, you’ve got a decent understanding of what a dissertation is , you’ve chosen your topic and hopefully you’ve received approval for your research proposal . Awesome! Now its time to start the actual dissertation or thesis writing journey.

To craft a high-quality document, the very first thing you need to understand is dissertation structure . In this post, we’ll walk you through the generic dissertation structure and layout, step by step. We’ll start with the big picture, and then zoom into each chapter to briefly discuss the core contents. If you’re just starting out on your research journey, you should start with this post, which covers the big-picture process of how to write a dissertation or thesis .

*The Caveat *

In this post, we’ll be discussing a traditional dissertation/thesis structure and layout, which is generally used for social science research across universities, whether in the US, UK, Europe or Australia. However, some universities may have small variations on this structure (extra chapters, merged chapters, slightly different ordering, etc).

So, always check with your university if they have a prescribed structure or layout that they expect you to work with. If not, it’s safe to assume the structure we’ll discuss here is suitable. And even if they do have a prescribed structure, you’ll still get value from this post as we’ll explain the core contents of each section.  

Overview: S tructuring a dissertation or thesis

• Acknowledgements page
• Abstract (or executive summary)
• Table of contents , list of figures and tables
• Chapter 1: Introduction
• Chapter 2: Literature review
• Chapter 3: Methodology
• Chapter 4: Results
• Chapter 5: Discussion
• Chapter 6: Conclusion
• Reference list

As I mentioned, some universities will have slight variations on this structure. For example, they want an additional “personal reflection chapter”, or they might prefer the results and discussion chapter to be merged into one. Regardless, the overarching flow will always be the same, as this flow reflects the research process , which we discussed here – i.e.:

• The introduction chapter presents the core research question and aims .
• The literature review chapter assesses what the current research says about this question.
• The methodology, results and discussion chapters go about undertaking new research about this question.
• The conclusion chapter (attempts to) answer the core research question .

In other words, the dissertation structure and layout reflect the research process of asking a well-defined question(s), investigating, and then answering the question – see below.

To restate that – the structure and layout of a dissertation reflect the flow of the overall research process . This is essential to understand, as each chapter will make a lot more sense if you “get” this concept. If you’re not familiar with the research process, read this post before going further.

Right. Now that we’ve covered the big picture, let’s dive a little deeper into the details of each section and chapter. Oh and by the way, you can also grab our free dissertation/thesis template here to help speed things up.

The title page of your dissertation is the very first impression the marker will get of your work, so it pays to invest some time thinking about your title. But what makes for a good title? A strong title needs to be 3 things:

• Succinct (not overly lengthy or verbose)
• Specific (not vague or ambiguous)
• Representative of the research you’re undertaking (clearly linked to your research questions)

Typically, a good title includes mention of the following:

• The broader area of the research (i.e. the overarching topic)
• The specific focus of your research (i.e. your specific context)
• Indication of research design (e.g. quantitative , qualitative , or  mixed methods ).

For example:

A quantitative investigation [research design] into the antecedents of organisational trust [broader area] in the UK retail forex trading market [specific context/area of focus].

Again, some universities may have specific requirements regarding the format and structure of the title, so it’s worth double-checking expectations with your institution (if there’s no mention in the brief or study material).

Acknowledgements

This page provides you with an opportunity to say thank you to those who helped you along your research journey. Generally, it’s optional (and won’t count towards your marks), but it is academic best practice to include this.

So, who do you say thanks to? Well, there’s no prescribed requirements, but it’s common to mention the following people:

• Your dissertation supervisor or committee.
• Any professors, lecturers or academics that helped you understand the topic or methodologies.
• Any tutors, mentors or advisors.
• Your family and friends, especially spouse (for adult learners studying part-time).

There’s no need for lengthy rambling. Just state who you’re thankful to and for what (e.g. thank you to my supervisor, John Doe, for his endless patience and attentiveness) – be sincere. In terms of length, you should keep this to a page or less.

Abstract or executive summary

The dissertation abstract (or executive summary for some degrees) serves to provide the first-time reader (and marker or moderator) with a big-picture view of your research project. It should give them an understanding of the key insights and findings from the research, without them needing to read the rest of the report – in other words, it should be able to stand alone .

For it to stand alone, your abstract should cover the following key points (at a minimum):

• Your research questions and aims – what key question(s) did your research aim to answer?
• Your methodology – how did you go about investigating the topic and finding answers to your research question(s)?
• Your findings – following your own research, what did do you discover?
• Your conclusions – based on your findings, what conclusions did you draw? What answers did you find to your research question(s)?

So, in much the same way the dissertation structure mimics the research process, your abstract or executive summary should reflect the research process, from the initial stage of asking the original question to the final stage of answering that question.

In practical terms, it’s a good idea to write this section up last , once all your core chapters are complete. Otherwise, you’ll end up writing and rewriting this section multiple times (just wasting time). For a step by step guide on how to write a strong executive summary, check out this post .

Need a helping hand?

Table of contents

This section is straightforward. You’ll typically present your table of contents (TOC) first, followed by the two lists – figures and tables. I recommend that you use Microsoft Word’s automatic table of contents generator to generate your TOC. If you’re not familiar with this functionality, the video below explains it simply:

If you find that your table of contents is overly lengthy, consider removing one level of depth. Oftentimes, this can be done without detracting from the usefulness of the TOC.

Right, now that the “admin” sections are out of the way, its time to move on to your core chapters. These chapters are the heart of your dissertation and are where you’ll earn the marks. The first chapter is the introduction chapter – as you would expect, this is the time to introduce your research…

It’s important to understand that even though you’ve provided an overview of your research in your abstract, your introduction needs to be written as if the reader has not read that (remember, the abstract is essentially a standalone document). So, your introduction chapter needs to start from the very beginning, and should address the following questions:

• What will you be investigating (in plain-language, big picture-level)?
• Why is that worth investigating? How is it important to academia or business? How is it sufficiently original?
• What are your research aims and research question(s)? Note that the research questions can sometimes be presented at the end of the literature review (next chapter).
• What is the scope of your study? In other words, what will and won’t you cover ?
• How will you approach your research? In other words, what methodology will you adopt?
• How will you structure your dissertation? What are the core chapters and what will you do in each of them?

These are just the bare basic requirements for your intro chapter. Some universities will want additional bells and whistles in the intro chapter, so be sure to carefully read your brief or consult your research supervisor.

If done right, your introduction chapter will set a clear direction for the rest of your dissertation. Specifically, it will make it clear to the reader (and marker) exactly what you’ll be investigating, why that’s important, and how you’ll be going about the investigation. Conversely, if your introduction chapter leaves a first-time reader wondering what exactly you’ll be researching, you’ve still got some work to do.

Now that you’ve set a clear direction with your introduction chapter, the next step is the literature review . In this section, you will analyse the existing research (typically academic journal articles and high-quality industry publications), with a view to understanding the following questions:

• What does the literature currently say about the topic you’re investigating?
• Is the literature lacking or well established? Is it divided or in disagreement?
• How does your research fit into the bigger picture?
• How does your research contribute something original?
• How does the methodology of previous studies help you develop your own?

Depending on the nature of your study, you may also present a conceptual framework towards the end of your literature review, which you will then test in your actual research.

Again, some universities will want you to focus on some of these areas more than others, some will have additional or fewer requirements, and so on. Therefore, as always, its important to review your brief and/or discuss with your supervisor, so that you know exactly what’s expected of your literature review chapter.

Now that you’ve investigated the current state of knowledge in your literature review chapter and are familiar with the existing key theories, models and frameworks, its time to design your own research. Enter the methodology chapter – the most “science-ey” of the chapters…

In this chapter, you need to address two critical questions:

• Exactly HOW will you carry out your research (i.e. what is your intended research design)?
• Exactly WHY have you chosen to do things this way (i.e. how do you justify your design)?

Remember, the dissertation part of your degree is first and foremost about developing and demonstrating research skills . Therefore, the markers want to see that you know which methods to use, can clearly articulate why you’ve chosen then, and know how to deploy them effectively.

Importantly, this chapter requires detail – don’t hold back on the specifics. State exactly what you’ll be doing, with who, when, for how long, etc. Moreover, for every design choice you make, make sure you justify it.

In practice, you will likely end up coming back to this chapter once you’ve undertaken all your data collection and analysis, and revise it based on changes you made during the analysis phase. This is perfectly fine. Its natural for you to add an additional analysis technique, scrap an old one, etc based on where your data lead you. Of course, I’m talking about small changes here – not a fundamental switch from qualitative to quantitative, which will likely send your supervisor in a spin!

You’ve now collected your data and undertaken your analysis, whether qualitative, quantitative or mixed methods. In this chapter, you’ll present the raw results of your analysis . For example, in the case of a quant study, you’ll present the demographic data, descriptive statistics, inferential statistics , etc.

Typically, Chapter 4 is simply a presentation and description of the data, not a discussion of the meaning of the data. In other words, it’s descriptive, rather than analytical – the meaning is discussed in Chapter 5. However, some universities will want you to combine chapters 4 and 5, so that you both present and interpret the meaning of the data at the same time. Check with your institution what their preference is.

Now that you’ve presented the data analysis results, its time to interpret and analyse them. In other words, its time to discuss what they mean, especially in relation to your research question(s).

What you discuss here will depend largely on your chosen methodology. For example, if you’ve gone the quantitative route, you might discuss the relationships between variables . If you’ve gone the qualitative route, you might discuss key themes and the meanings thereof. It all depends on what your research design choices were.

Most importantly, you need to discuss your results in relation to your research questions and aims, as well as the existing literature. What do the results tell you about your research questions? Are they aligned with the existing research or at odds? If so, why might this be? Dig deep into your findings and explain what the findings suggest, in plain English.

The final chapter – you’ve made it! Now that you’ve discussed your interpretation of the results, its time to bring it back to the beginning with the conclusion chapter . In other words, its time to (attempt to) answer your original research question s (from way back in chapter 1). Clearly state what your conclusions are in terms of your research questions. This might feel a bit repetitive, as you would have touched on this in the previous chapter, but its important to bring the discussion full circle and explicitly state your answer(s) to the research question(s).

Next, you’ll typically discuss the implications of your findings . In other words, you’ve answered your research questions – but what does this mean for the real world (or even for academia)? What should now be done differently, given the new insight you’ve generated?

Lastly, you should discuss the limitations of your research, as well as what this means for future research in the area. No study is perfect, especially not a Masters-level. Discuss the shortcomings of your research. Perhaps your methodology was limited, perhaps your sample size was small or not representative, etc, etc. Don’t be afraid to critique your work – the markers want to see that you can identify the limitations of your work. This is a strength, not a weakness. Be brutal!

This marks the end of your core chapters – woohoo! From here on out, it’s pretty smooth sailing.

The reference list is straightforward. It should contain a list of all resources cited in your dissertation, in the required format, e.g. APA , Harvard, etc.

It’s essential that you use reference management software for your dissertation. Do NOT try handle your referencing manually – its far too error prone. On a reference list of multiple pages, you’re going to make mistake. To this end, I suggest considering either Mendeley or Zotero. Both are free and provide a very straightforward interface to ensure that your referencing is 100% on point. I’ve included a simple how-to video for the Mendeley software (my personal favourite) below:

Some universities may ask you to include a bibliography, as opposed to a reference list. These two things are not the same . A bibliography is similar to a reference list, except that it also includes resources which informed your thinking but were not directly cited in your dissertation. So, double-check your brief and make sure you use the right one.

The very last piece of the puzzle is the appendix or set of appendices. This is where you’ll include any supporting data and evidence. Importantly, supporting is the keyword here.

Your appendices should provide additional “nice to know”, depth-adding information, which is not critical to the core analysis. Appendices should not be used as a way to cut down word count (see this post which covers how to reduce word count ). In other words, don’t place content that is critical to the core analysis here, just to save word count. You will not earn marks on any content in the appendices, so don’t try to play the system!

Time to recap…

And there you have it – the traditional dissertation structure and layout, from A-Z. To recap, the core structure for a dissertation or thesis is (typically) as follows:

• Acknowledgments page

Most importantly, the core chapters should reflect the research process (asking, investigating and answering your research question). Moreover, the research question(s) should form the golden thread throughout your dissertation structure. Everything should revolve around the research questions, and as you’ve seen, they should form both the start point (i.e. introduction chapter) and the endpoint (i.e. conclusion chapter).

I hope this post has provided you with clarity about the traditional dissertation/thesis structure and layout. If you have any questions or comments, please leave a comment below, or feel free to get in touch with us. Also, be sure to check out the rest of the  Grad Coach Blog .

Psst... there’s more!

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

36 Comments

many thanks i found it very useful

Glad to hear that, Arun. Good luck writing your dissertation.

Such clear practical logical advice. I very much needed to read this to keep me focused in stead of fretting.. Perfect now ready to start my research!

what about scientific fields like computer or engineering thesis what is the difference in the structure? thank you very much

Thanks so much this helped me a lot!

Very helpful and accessible. What I like most is how practical the advice is along with helpful tools/ links.

Thanks Ade!

Thank you so much sir.. It was really helpful..

You’re welcome!

Hi! How many words maximum should contain the abstract?

Thank you so much 😊 Find this at the right moment

You’re most welcome. Good luck with your dissertation.

best ever benefit i got on right time thank you

Many times Clarity and vision of destination of dissertation is what makes the difference between good ,average and great researchers the same way a great automobile driver is fast with clarity of address and Clear weather conditions .

I guess Great researcher = great ideas + knowledge + great and fast data collection and modeling + great writing + high clarity on all these

You have given immense clarity from start to end.

Morning. Where will I write the definitions of what I’m referring to in my report?

Thank you so much Derek, I was almost lost! Thanks a tonnnn! Have a great day!

Thanks ! so concise and valuable

This was very helpful. Clear and concise. I know exactly what to do now.

Thank you for allowing me to go through briefly. I hope to find time to continue.

Really useful to me. Thanks a thousand times

Very interesting! It will definitely set me and many more for success. highly recommended.

Thank you soo much sir, for the opportunity to express my skills

Usefull, thanks a lot. Really clear

Very nice and easy to understand. Thank you .

That was incredibly useful. Thanks Grad Coach Crew!

My stress level just dropped at least 15 points after watching this. Just starting my thesis for my grad program and I feel a lot more capable now! Thanks for such a clear and helpful video, Emma and the GradCoach team!

Do we need to mention the number of words the dissertation contains in the main document?

It depends on your university’s requirements, so it would be best to check with them 🙂

Such a helpful post to help me get started with structuring my masters dissertation, thank you!

Great video; I appreciate that helpful information

It is so necessary or avital course

This blog is very informative for my research. Thank you

Doctoral students are required to fill out the National Research Council’s Survey of Earned Doctorates

wow this is an amazing gain in my life

This is so good

How can i arrange my specific objectives in my dissertation?

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Methodology

• What is Secondary Research? | Definition, Types, & Examples

What is Secondary Research? | Definition, Types, & Examples

Published on January 20, 2023 by Tegan George . Revised on January 12, 2024.

Secondary research is a research method that uses data that was collected by someone else. In other words, whenever you conduct research using data that already exists, you are conducting secondary research. On the other hand, any type of research that you undertake yourself is called primary research .

Secondary research can be qualitative or quantitative in nature. It often uses data gathered from published peer-reviewed papers, meta-analyses, or government or private sector databases and datasets.

Table of contents

When to use secondary research, types of secondary research, examples of secondary research, advantages and disadvantages of secondary research, other interesting articles, frequently asked questions.

Secondary research is a very common research method, used in lieu of collecting your own primary data. It is often used in research designs or as a way to start your research process if you plan to conduct primary research later on.

Since it is often inexpensive or free to access, secondary research is a low-stakes way to determine if further primary research is needed, as gaps in secondary research are a strong indication that primary research is necessary. For this reason, while secondary research can theoretically be exploratory or explanatory in nature, it is usually explanatory: aiming to explain the causes and consequences of a well-defined problem.

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Secondary research can take many forms, but the most common types are:

Statistical analysis

Literature reviews, case studies, content analysis.

There is ample data available online from a variety of sources, often in the form of datasets. These datasets are often open-source or downloadable at a low cost, and are ideal for conducting statistical analyses such as hypothesis testing or regression analysis .

Credible sources for existing data include:

• The government
• Government agencies
• Non-governmental organizations
• Educational institutions
• Businesses or consultancies
• Libraries or archives
• Newspapers, academic journals, or magazines

A literature review is a survey of preexisting scholarly sources on your topic. It provides an overview of current knowledge, allowing you to identify relevant themes, debates, and gaps in the research you analyze. You can later apply these to your own work, or use them as a jumping-off point to conduct primary research of your own.

Structured much like a regular academic paper (with a clear introduction, body, and conclusion), a literature review is a great way to evaluate the current state of research and demonstrate your knowledge of the scholarly debates around your topic.

A case study is a detailed study of a specific subject. It is usually qualitative in nature and can focus on  a person, group, place, event, organization, or phenomenon. A case study is a great way to utilize existing research to gain concrete, contextual, and in-depth knowledge about your real-world subject.

You can choose to focus on just one complex case, exploring a single subject in great detail, or examine multiple cases if you’d prefer to compare different aspects of your topic. Preexisting interviews , observational studies , or other sources of primary data make for great case studies.

Content analysis is a research method that studies patterns in recorded communication by utilizing existing texts. It can be either quantitative or qualitative in nature, depending on whether you choose to analyze countable or measurable patterns, or more interpretive ones. Content analysis is popular in communication studies, but it is also widely used in historical analysis, anthropology, and psychology to make more semantic qualitative inferences.

Secondary research is a broad research approach that can be pursued any way you’d like. Here are a few examples of different ways you can use secondary research to explore your research topic .

Secondary research is a very common research approach, but has distinct advantages and disadvantages.

Advantages of secondary research

Advantages include:

• Secondary data is very easy to source and readily available .
• It is also often free or accessible through your educational institution’s library or network, making it much cheaper to conduct than primary research .
• As you are relying on research that already exists, conducting secondary research is much less time consuming than primary research. Since your timeline is so much shorter, your research can be ready to publish sooner.
• Using data from others allows you to show reproducibility and replicability , bolstering prior research and situating your own work within your field.

Disadvantages of secondary research

Disadvantages include:

• Ease of access does not signify credibility . It’s important to be aware that secondary research is not always reliable , and can often be out of date. It’s critical to analyze any data you’re thinking of using prior to getting started, using a method like the CRAAP test .
• Secondary research often relies on primary research already conducted. If this original research is biased in any way, those research biases could creep into the secondary results.

Many researchers using the same secondary research to form similar conclusions can also take away from the uniqueness and reliability of your research. Many datasets become “kitchen-sink” models, where too many variables are added in an attempt to draw increasingly niche conclusions from overused data . Data cleansing may be necessary to test the quality of the research.

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

• Normal distribution
• Degrees of freedom
• Null hypothesis
• Discourse analysis
• Control groups
• Mixed methods research
• Non-probability sampling
• Quantitative research
• Inclusion and exclusion criteria

Research bias

• Rosenthal effect
• Implicit bias
• Cognitive bias
• Selection bias
• Negativity bias
• Status quo bias

A systematic review is secondary research because it uses existing research. You don’t collect new data yourself.

The research methods you use depend on the type of data you need to answer your research question .

• If you want to measure something or test a hypothesis , use quantitative methods . If you want to explore ideas, thoughts and meanings, use qualitative methods .
• If you want to analyze a large amount of readily-available data, use secondary data. If you want data specific to your purposes with control over how it is generated, collect primary data.
• If you want to establish cause-and-effect relationships between variables , use experimental methods. If you want to understand the characteristics of a research subject, use descriptive methods.

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

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

Sources in this article

We strongly encourage students to use sources in their work. You can cite our article (APA Style) or take a deep dive into the articles below.

George, T. (2024, January 12). What is Secondary Research? | Definition, Types, & Examples. Scribbr. Retrieved August 7, 2024, from https://www.scribbr.com/methodology/secondary-research/

Largan, C., & Morris, T. M. (2019). Qualitative Secondary Research: A Step-By-Step Guide (1st ed.). SAGE Publications Ltd.

Peloquin, D., DiMaio, M., Bierer, B., & Barnes, M. (2020). Disruptive and avoidable: GDPR challenges to secondary research uses of data. European Journal of Human Genetics , 28 (6), 697–705. https://doi.org/10.1038/s41431-020-0596-x

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• 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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• How It Works

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 To Do Secondary Research or a Literature Review

What is secondary research, why is secondary research important.

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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.

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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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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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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Dissertations & projects: Literature-based projects

• Research questions
• The process of reviewing
• Project management
• Literature-based projects

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“As a general rule, the introduction is usually around 5 to 10 per cent of the word limit; each chapter around 15 to 25 per cent; and the conclusion around 5 per cent.” Bryan Greetham, How to Write Your Undergraduate Dissertation

This page gives guidance on the structure of a literature-based project.   That is, a project where the data is found in existing literature rather than found through primary research. They may also include information from primary sources such as original documents or other sources.

How to structure a literature-based project

The structure of a literature-based dissertation is usually thematic, but make sure to check with your supervisor to make sure you are abiding by your department’s project specifications. A typical literature-based dissertation will be broken up into the following sections:

Abstract or summary

Acknowledgments, contents page, introduction, themed chapters.

• Bibliography/Reference list

Use this basic structure as your document plan . Remember that you do not need to write it in the order it will finally be written in. 

For more advice on managing the order of your project, see our section on Project Management.   

If you use the template provided on our Formatting page, you will see that it already has a title page included. You just need to fill in the appropriate boxes by typing or choosing from the drop-down-lists. The information you need to provide is: 

• Type of assignment (thesis, dissertation or independent project)
• Partial or full fulfilment information
• Subject area
• Your name (and previous qualifications if applicable)
• Month and year of submission

This may not always be required - check with your tutor.

• It is  independent  of the rest of the report - it is a mini-report, which needs to make sense completely on its own.
• References should  not  be included.
• Nothing should appear in the abstract that is not in the rest of the report.
• Usually between 200-300 words.
• Write as a  single  paragraph.

It is recommended that you write your abstract  after  your report.

If you choose not to use the template, then you will need to go through the document after it is written and create a list showing which heading is on which page of your document.

Purpose: To thank those who were directly involved in your work .

• Do not confuse the acknowledgements section with a dedication - this is not where you thank your friends and relatives unless they have helped you with your manuscript.
• Acknowledgments are about courtesy, where you thank those who were directly involved in your work, or were involved in supporting your work (technicians, tutors, other students, financial support etc).
• This section tends to be  very brief , a few lines at the most. Identify those who provided you with the most support, and thank them appropriately.
• At the very least, make sure you acknowledge your supervisor!!

Purpose: To state the research problem and give a brief introduction to the background literature, provide justification for your research questions and explain your methodology and main findings.

• Explain what the problem you will be addressing is, what your research questions are, and why they will help address the issue.
• Explain (and justify) your methodology - where you searched, what your keywords were, what your inclusion and exclusion criteria were,
• Define the scope of the dissertation, explaining any limitations.
• Lay out the structure of the dissertation, taking the reader through each section and providing any key definitions.
• Very briefly describe what your main findings are - but leave the detail for the sections below.

It is good practice to come back to the introduction after you have finished writing up the rest of the document to ensure it sets the appropriately scene for subsequent sections.

Should you have a separate literature review chapter?

Not usually , as your project is basically a big literature review, it isn't necessary to have a separate chapter. You would normally introduce background literature in your introduction instead.

However, if your supervisor suggests a separate chapter then it could go at this point, after the main introduction (which would then not include background literature). 

For more advice on writing a literature review see the Literature Review pages on this guide.

Purpose: To present the themes you have identified in your research and explain how they contribute to answering your research questions

You will typically have 3-5 themed chapters. Each one should contain:

• An introduction to the theme - what things it means and what it incorporates.
• How the theme was addressed within the literature - this should be analytical not just descriptive.
• A conclusion which shows how the theme relates to the research question(s).

Ensuring your themed chapters flow

Choosing the order of your theme chapters is an important part of the structure to your project. For example, if you study History and your project covers a topic that develops over a large time period, it may be best to order each chapter chronologically. Other subjects may have a natural narrative running through the themes. Think about how your reader will be able to follow along with your overall argument.

Although each chapter must be dedicated to a particular theme, it must link back to previous chapters and flow into the following chapter. You need to ensure they do not seem like they are unrelated to each other. There will be overlaps, mention these.

Some literature-based projects will focus on primary sources. If yours does, make sure primary sources are at the core of your paragraphs and chapters, and use secondary sources to expand and explore the theme further. 

Purpose: To present the conclusion that you have reached as a result of both the background literature review and the analysis in your thematic chapters

A conclusion summarises all the points you have previously made and it  should not  include any evidence or topics you have not included in your introduction or themed chapters. There should be no surprises.

It should be about 5-10% of your word limit so make sure you leave enough words to do it justice. There will be marks in the marking scheme specifically allocated to the strength of your conclusion which cannot be made up elsewhere.

Some conclusions will also include recommendations for practice or ideas for further research. Check with your supervisor to see if they are expecting either or both of these.

Reference list

It is good practice to develop a reference list whilst  writing the project, rather than leaving it until the end. This prevents a lot of searching around trying to remember where you accessed a particular source. If using primary sources, it also allows you to monitor the balance between primary and secondary sources included in the project. There is software available to help manage your references and the university officially supports RefWorks and EndNote. 

For more advice on reference management, see our Skills Guide: Referencing Software

• Transcriptions
• Correspondence
• Ethical approval forms

If you have information that you would like to include but are finding it disrupts the main body of text as its too cumbersome, or would distract from the main arguments of your dissertation, the information can be included in the appendix section. Each appendix should be focused on one item. 

Appendices  should not include any information that is key to your topic or overall argument. 

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How to... Use secondary data & archival material

Find out what secondary data is – as opposed to primary data – and how to go about collecting and using it.

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What is secondary data & archival material, using published data sets, using archival data, secondary data as part of the research design, gaining access to, and using, archives, primary & secondary data.

All research will involve the collection of data. Much of this data will be collected directly through some form of interaction between the researcher and the people or organisation concerned, using such methods as interviews, focus groups, surveys and participant observation. Such methods involve the collection of primary data, and herein lies the opportunity for the researcher to develop and demonstrate the greatest skill.

However sometimes the researcher will use data which has already been collected for other purposes – in other words, he or she is going to an existing source rather than directly interacting with people. The data may have been:

• Deliberately collected and analysed, for example for some official survey such as the  UK Labour Market Trends  (now published as  Economic & Labour Market Review (ELMR) ) or  General Household Survey .
• Created in a more informal sense as a record of people's activities, for example, letters or other personal items, household bills, company records, etc. At some point, they may have been deliberately collected and organised into an archive.

Either way, such material is termed secondary data.

Rather confusingly, the latter form of secondary data is also referred to as primary source material.

"Primary resources are sources that are usually created at the time of an event. Primary resources are the direct evidence or first hand accounts of historical events without secondary analysis or interpretation." (York University Libraries Archival Research Tutorial)

This distinguishes them from secondary sources which describe, analyse and refer to the primary sources.

The above definitions and distinctions can be described diagrammatically as follows:

Types of secondary data

Secondary data is found in print or electronic form, if the latter, on CD-ROM, as an online computer database, or on the Internet. Furthermore, it can be in the form of statistics collected by governments, trade associations, organisations that exist to collect and sell statistical data, or just as plain documents in archives or company records.

A crucial distinction is whether or not the data has been interpreted, or whether it exists in raw form.

• Raw data, also referred to as documentary or archival data, will exist in the form in which it was originally intended, for example meeting minutes, staff records, reports on new markets, accounts of sales of goods/services etc.
• Interpreted data, which may also be referred to as survey data, will have been collected for a particular purpose, for example, to analyse spending patterns.

Because interpreted data will have been collected deliberately, the plan behind its collection and interpretation will also have been deliberate – that is, it will have been subjected to a particular research design. 

By contrast, raw data will not have been processed, and will exist in its original form. (See " Using archival data " section in this guide.)

When and why to use secondary data

There are various reasons for using secondary data:

• A particularly good collection of data already exists.
• You are doing a historical study – that is, your study begins and ends at a particular point in time.
• You are covering an extended period, and analysing development over that period – a longitudinal study.
• The unit that you are studying may be difficult, or simply too large, to study directly.
• You are doing a case study of a particular organisation/industry/area, and it is important to look at the relevant documents.

You should pay particular attention to the place of secondary documents within your research design. How prominent a role you give to this method may depend on your subject: for example, if you are researching in the area of accounting, finance or business history, secondary documentary sources are likely to play an important part. Otherwise, use of secondary data is likely to play a complementary part in your research design. For example, if you are studying a particular organisation, you would probably want to supplement observation/interviews with a look at particular documents produced by that organisation.

In " Learning lessons? The registration of lobbyists at the Scottish parliament " ( Journal of Communication Management , Vol. 10 No. 1), the author uses archival research at the Scottish parliament as a supplementary research method (along with the media and focus groups), his main method being interviews and participant observation of meetings.

This point is further developed in the " Secondary data as part of the research design " section of this guide. Reasons for using the different types of secondary data are further developed in the individual sections.

NB  If you are doing a research project/dissertation/thesis, check your organisation's view of secondary data. Some organisations may require you to use primary data as your principle research method.

Advantages and disadvantages of secondary data collection

The advantages of using secondary data are:

• The fact that much information exists in documented form – whether deliberately processed or not – means that such information cannot be ignored by the researcher, and generally saves time and effort collecting data which would otherwise have to be collected directly. In particular:
• Many existing data sets are enormous, and far greater than the researcher would be able to collect him or herself, with a far larger sample.
• The data may be particularly good quality, which can apply both to archival data (e.g. a complete collection of records on a particular topic) and to published data sets, particularly those which come from a government source, or from one of the leading commercial providers of data and statistics.
• You can access information which you may otherwise have had to secure in a more obtrusive manner.
• Existence of a large amount of data can facilitate different types of analysis, such as:
• longitudinal or international analysis of information which would have otherwise been difficult to collect due to scale.
• manipulation of data within the particular data set, including the comparison of particular subsets.
• Unforseen discoveries can be made – for example, the link between smoking and lung cancer was made by analysing medical records.

The disadvantages of secondary data collection are:

• There may be a cost to acquiring the data set.
• You will need to familiarise yourself with the data, and if you are dealing with a large and complex data set, it will be hard to manage.
• The data may not match the research question: there may be too much data, or there may be gaps, or the data may have been collected for a completely different purpose.
• The measures, for example between countries/states/historical periods, may not be directly comparable. (See the " Secondary data as part of the research design " section of this guide for a further development of this topic.)
• The researcher has no control over the quality of the data, which may not be seen as rigorous and reliable as data which are specifically collected by the researcher, who has adopted a specific research design for the question.
• Collecting primary data builds up more research skills than collecting secondary data.
• Company data particularly may be seen as commercially sensitive, and it may be difficult to gain access to company archives, which may be stored in different departments or on the company intranet, to which access may be difficult.  

What are they?

As discussed in the previous section, these are sources of data which have already been collected and worked on by someone else, according to a particular research design. Other points to note are:

• Mostly they will have been collect by means of a survey, which may be:
• a census, which is an "official count", normally carried out by the government, with obligatory participation, for example the UK population censuses carried out every ten years
• a repeated survey, which involves collecting information at regular intervals, for example government surveys about household expenditure
• an ad hoc survey, done just once for a particular purpose, such as for example a market research survey.
• Interpreted data as referring to a particular social unit is termed a data set.
• A database is a structured data set, produced as a matrix with each social unit having a row, and each variable a column.
• Sometimes, different data sets are combined to produce multiple source secondary data: for example, the publication  Business Statistics of the United States: Patterns of Economic Change  contains data on virtually all aspects of the US economy from 1929 onwards. Such multiple source data sets may have been compiled on:
• a time series basis, that is they are based on repeated surveys (see above) or on comparable variables from different surveys to provide longitudinal data
• a geographical basis, providing information on different areas.

Key considerations

There are a number of points to consider when using data sets, some practical and others associated with the research design (yours and theirs).

Practical considerations relate to cost and use:

• Whilst much data is freely available, there may be a charge. For example,  Business Statistics of the United States: Patterns of Economic Change  is priced US$147. So, when deciding what data to use it's a good idea to check what's already in your library.
• Is the data available in computerised form, or will you have to enter it manually? If it is available in computerised form, is it in a form suitable to your research design (see below) or will you have to tabulate the data in a different form?

Research considerations include:

• Is the data set so important to your research that you cannot ignore it? For example, if you were doing a project which involved top corporations, you could not afford to ignore the publications which provided data and statistics, such as  Europe's 15,000 Largest Companies 2006 .
• Does the data generally cover the research question?
• Is the coverage relevant, or does it leave out areas (e.g. only Asia as opposed to Australasia) or time periods (e.g. only starting in 1942 when you wanted data from 1928)?
• Are the variables relevant, for example if you are interested in household expenditure does it break down the households in ways relevant to your project?
• Are the measures used the same, for example, is growth in sales expressed as an amount or a percentage?
• In the case of data from different countries, has the data been collected in the same way? For example, workers affected by strikes may include those directly affected in one country, and those indirectly affected in another.
• Is the data reliable, and current? Note that data from government, and reputable commercial sources, is likely to be trustworthy but you should be wary of information on the Internet unless you know its source. Data from trustworthy sources is likely to have been collected by a team of experts, with good quality research design and instruments.
• The advantage of survey data in particular is that you have access to a far larger sample than you would otherwise have been able to collect yourself.
• There is an obvious advantage to using a large data source, however you need to allow for the time needed to extract what you want, and to re-tabulate the data in a form suitable for your research.
• How has the data been collected, for example it it longitudinal or geographical? This will affect the type of research question it can help with, for example, if you were comparing France and Germany, you would obviously want geographical data.
• How intrinsic to your research design will the use of secondary data be? Beware of relying on it entirely, but it may be a useful way of triangulating other research, for example if you have done a survey of shopping habits, you can assess how generalisable your findings are by looking at a census.
• While use of secondary data sets may not be seen as rigorous as collecting data yourself, the big advantage is that they are in a permanently available form and can be checked by others, which is an important point for validity.

And finally...

• Will the benefits you gain from using secondary data sets as a research methods outweigh the costs of acquiring the data, and the time spent sorting out what is relevant?

Producers of published secondary data include:

• Governments and intergovermental organisations, who produce a wide variety of data. For example, from the US Government come such titles as  Budget of the United States Government ,  Business Statistics of the United States: Patterns of Economic Change ,  County and City Extra  (source of data for every state), and  Handbook of U.S. Labor Statistics .
• Trade associations and organisations representing particular interests, such as for example the American Marketing Association. These may have data and information relevant to their particular interest group.
• company information: for example AMADEUS provides pan European information on companies that includes balance sheets, profit and loss, ratios, descriptive etc., while FAME does a similar job for companies in the UK and Ireland.
• market research: for example, Mintel specialises in consumer, media and market research and published reports into particular market sectors, whilst Key Note "boasts one of the most comprehensive databases available to corporations in the UK", having published almost 1,000 reports spanning 30 industry sectors.

Where to find such information? The key is to have a very clear idea of what it is you are trying to find: what particular aspects of the research question are you attempting to answer?

You may well find sources listed in your literature review, or your tutor may point you in certain directions, but at some point you will need to consult the tertiary literature, which will point you in the direction of archives, indexes, catalogues and gateways. Your library will probably have Subject Guides covering your areas of interest. The following is a very basic list:

• UK Economic and Social Data Services (ESDS) . Contains links to: UK Data Archive (University of Essex); Institute for Social and Economic Research (University of Essex); Manchester Information and Associated Services (University of Manchester); and Cathie Marsh Centre for Census and Survey Research (University of Manchester). These contain access to a wide range of national and international data sets.
• http://epp.eurostat.ec.europa.eu . Statistics of the European Union.
• University of Michigan . Gateway to statistical resources on the Web.
• D&B Hoovers . Company information on US and international companies.  

Archival, or documentary secondary data, are documentary records left by people as a by product of their eveyday activity. They may be formally deposited in an archive or they may just exist as company records.

Historians make considerable use of archival material as a key research technique, using a wide range of personal documents such as letters, diaries, household bills, which are often stored in some sort of formal "archive".

Business researchers talk about "archival research" because they use many of the same techniques for recording and analysing information. Companies, by their very nature, tend to create records, both officially in the form of annual reports, declarations of share value etc., and unofficially in the e-mails, letters, meeting minutes and agendas, sales data, employee records etc. which are the by-product of their daily activities.

If you are studying a business and management related subject, you may make use of archival material for a number of reasons:

• Your research takes a historical perspective, and you want to gain insight into management decisions outside the memories of those whom you interview.
• Archival research is an important tool in your particular discipline – for example, finance and accounting.
• You wish to undertake archival research as part of qualitative research in order to triangulate with interviews, focus groups etc., or perhaps as exploratory research prior to the main research.
• You may be undertaking a case study, or basing your research project on your own organisation; in either case, you should look at company documents as part of this research.

In " Financial reporting and local government reform – a (mis)match? " ( Qualitative Research in Accounting & Management , Vol. 2 No. 2), Robyn Pilcher uses archival research – "Data was obtained from annual reports provided electronically to the DLG and checked against hard copies of these reports and supporting notes" – and interviews as exploratory research to investigate use of flawed financial figures by political parties, before carrying out a detailed examination of a few councils.

" Coalport Bridge Tollhouse, 1793-1995 " ( Structural Survey , Vol. 14 No. 4) is a historical study of this building drawing on such documents as maps, plans, photos, account books, meeting minutes, legal opinions and census records.

As distinct from published data sets, you will have to record and process the data yourself, in order to create your own data set.

Sometimes this archival material will be stored in "official" archives, such as the UK Public Record Office. Mostly however, it will be company specific, stored in official company archives or perhaps in smaller collections in individual departments or business units. Records can exist in physical or electronic form – the latter commonly on the company intranet.

Whatever the company's archiving policy, there is no doubt that businesses provide a rich source of data. Here is a (non exhaustive) list of the forms that data can take:

• Organisational records – for example HR, accounts, pay roll data etc.
• Data referring to the sales of goods or services
• Project files
• Organisation charts                
• Meeting minutes and agendas
• Sales literature: catalogues, copies of adverts, brochures etc.
• Annual reports
• Reports to shareholders
• Transcripts of speeches
• Non textual material: maps and plans, videos, tapes, photographs.

Management Information Systems can hold a considerable amount of data. For example, the following HR records may be held:

• data on recruitment, e.g. details of vacancies, dates, job details and criteria
• staff employment details, for example job analysis and evaluation, salary grades, terms and conditions of employment, job objectives, job competencies, performance appraisals
• data relevant to succession and career planning, e.g. the effects of not filling jobs
• management training and development, e.g. training records showing types of training.

Source:  Peter Kingsbury (1997),  IT Answers to HR Questions , CIPD.

The media (newspapers, magazines, advertisements, television and radio programmes, books, the Internet) can also throw valuable light on events, and media sources should not be ignored.

There are a number of points to consider when using archival material:

• You will need to gain access to the company, and this may prove difficult (see the " Gaining access to, and using, archives " section in this guide). On the other hand, if you are doing a report/project on your own organisation, access may be a lot easier, although even here you should gain agreement to access and use of material.
• Even if you are successful in gaining access to the company, it may be difficult and time-consuming to locate all the information you need, especially if the company does not have a clear archiving policy, and you may need to go through a vast range of documents.
• The data may be incomplete, and may not answer your research question – for example, there may be a gap in records, correspondence may be one-sided and not include responses.
• The data may be biased, in other words it will be written by people who have a particular view. For example, meeting minutes are the "official" version and often things go on in meetings which are not recorded; profitability in annual reports may be reported in such a way as to show a positive rather than a true picture.
• Informal and verbal interactions cannot be captured.
• Archival research is time-consuming, both in locating and in recording documents, so for that reason may not be feasible for smaller projects.
• You will also need to decide how to record data: historians are used to laboriously copying out documents considered too frail to photocopy, and business researchers may need to resort to this if (as is likely) company documents are considered confidential, although in such cases, note-taking may also be out. You will also need to find a suitable way of coding and referring to particular documents.
• Finally, you will need to construct your own data set, for which you will need to have a particular research method.

In " Participatory group observation – a tool to analyse strategic decision-making " ( Qualitative Market Research , Vol. 5 No. 1), Christine Vallaster and Oliver Koll highlight the benefit of multiple methods for studying complex issues, it being thus possible to supplement the weaknesses of one method with the strengths of another and study a phenomenon from a diversity of views, and achieve a high degree of validity. In the case in question, archival research was used to analyse documents (organisation charts, company reports, memos, meeting minutes), and whilst the limitations in terms of incompleteness, selectivity, and not being authored by interviewees were acknowledged, so was their supporting value to interviews, and the same textual analysis method was used for both methods.  

We have already mentioned, as part of our discussion of the two main types of secondary data, some considerations in respect to how they are used as part of the research. In this section, we shall look more generally at how secondary data can fit in to the overall research design.

Theoretical framework

Researchers take different views of the facts they are researching. For some, facts exist as independent reality; others admit the possibility of interpretation by the actors concerned. The two views, and their implication for the documents and data concerned, can be summed up as follows:

• Positivists  see facts as existing independently of interpretation, so documents are an objective reflection of reality.
• Interpretivists , and even more so realists, see reality as influenced by the social environment, open to manipulation by those who are part of it. A document must be seen in its social context, and an attempt to make sense of that context.

Some examples would be:

• minutes of a sales meeting the purpose of which was to monitor sales, with sales being affected by external influences
• brochure or flyer which was created for a particular item, and designed to appeal to current fashions
• training records of people doing National Vocational Qualifications (used in the UK to acknowledge the value of existing skills).

Reliability and validity

Reliability and validity is important to any research design, and an important consideration with secondary data is the extent to which it relates to the research question, in other words how reliably it can answer it. You need to consider the fit very carefully before deciding to proceed. Some questions which may help here are:

How reliable is the data?

In the case of published data, you will be able to make a judgement by looking at its provenance: does it come from the government, or from a reputable commercial source? The same applies to the Internet – what is the source? Look for publisher information and copyright statements. How up to date is the material?

You also need to make intrinsic judgements, however: what is the methodology behind the survey, and how robust is it? How large was the sample and what was the response rate?

There are fewer obvious external measures you can use to check unpublished, archival material: that from businesses can be notoriously inconsistent and inaccurate. Records can be incomplete with some documents missing; sometimes, whole archives can disappear when companies are taken over. In addition, some documents such as letters, reports, e-mails, meeting minutes etc. have a subjective element, reflecting the view of the author, or the perceived wishes of the recipient. For example, meeting minutes may not reflect a controversial discussion that took place but only the agreed action points; a report on sales may be intended to put a positive spin on a situation and disguise its real seriousness. It helps when assessing reliability to consider who the intended audience is.

If you are using media reports, be aware that these may only include what they consider to be the most pertinent points.

Measurement validity

One of the biggest problems with secondary data is to do with the measurements involved. These may just not be the same as the ones you want (e.g. sales given in revenue rather than quantity), they may deliberately be distorted (e.g. non recording of minor accidents, sick leave etc.), or they may be different for different countries. If the measures are inexact, you need to take a view as to how serious the problem is and how you can address it.

Does the data cover the time frame, geographical area, and variable in which you are interested? For example, if you are studying a particular period in a company, do you have meeting minutes to cover that period, or do they stop/start at a time within the boundaries of that period? Do you have the sales figures for all the countries your are interested in, and all the product types?

You can greatly increase the validity and reliability of your use of secondary data if you triangulate with another research method. For example if you are seeking insights into a period of change within a company, you can use documentary records to compare with interviews with key informants.

" Leading beyond tragedy: the balance of personal identity and adaptability " ( Leadership & Organization Development Journal , Vol. 26 No. 6) is a case study of the Norwegian company Wilhelmson's Lines loss of key employees in a plane crash, and uses archival research along with on-site interviews and participant observation as the tools of case study analysis.

" The human resource management practice of retail branding: an ethnography within Oxfam Trading Division " ( International Journal of Retail & Distribution Management , Vol. 33 No. 7) uses an ethnographic approach and includes scanning the company intranet along with participant observation and interviews.

Quantitative or qualitative?

Documentary data can be used as part of a qualitative or quantitative research design.

Much data, whether from company archives or from published data sets, is statistical, and can therefore be used as part of a quantitative design, for example how many sales were made of a particular item, what were reasons for absenteeism, company profitability etc.

One way of using secondary data in quantitative research is to compare it with data you have collected yourself, probably by a survey. For example, you can compare your own survey data with that from a census or other published survey, which will inevitably have a much larger sample, thereby helping you generalise, and/or triangulate, your findings.

Textual data can also be used qualitatively, for example marketing literature can be used to as backup information on marketing campaigns, and e-mails, letters, meeting minutes etc. can throw additional light on management decisions.

Content analysis is often quoted as a method of analysis: this involves analysing occurrence of key concepts and ideas and either draw statistical inferences or carry out a qualitative assessment, looking at the main themes that emerge.

Archives may be found in national collections, such as the UK's Public Record Office, or as smaller collections associated with national, local or federal government organisations, academic libraries, professional or trade associations, or charities; they may also be found in companies. The latter are generally closely controlled; the former are most likely to be publically available. This page gives a brief overview of how to gain access to archival collections, and what you can expect when you get there.

Preparation

An archival collection, even an open one, is not like a library where you can just turn up. You need to establish opening hours, and then make arrangements to visit.

It is best to write ahead explaining:

• Your project
• Precisely what it is you are looking for.

In order to be clear about point 2, you will need to know not only the precise scope of your research but also how this particular collection can help you. You will therefore need to spend time researching (perhaps more than one) collection, so make sure that this is allowed for in your research plan.

You also need to understand the key difference between libraries and archives:

• Archives  are collections of unpublished material, housed in closed stacks, organised according to the principles of the original collector. You can only access the material in situ, and you will need to handle the collection with special care.
• Libraries  contain published material, in open stacks, classified according to a particular system, and you may be able to take the material out on loan.

Locating sources

Bibliographic databases are good sources for finding archival collections: you can search by subject, keyword, personal or geographical name. Whilst not containing records of each item, catalogue records of archival collections are generally lengthier than for published materials and may include a summary of materials contained in the collection.

More detailed information about the collection, usually at the level of the box or folder, is found in  Finding Aids .

You can find suitable databases through your library's Subject Guides.

Gaining access to commercial collections

As indicated above, commercial archival or document collections are more tightly controlled than public ones, access to which will depend upon a clearly stated request and proof of identity.

Commercial sources, by contrast, may require more negotiation, and more convincing, because of the perceived sensitivity of their material and the fact that they exist for their customers and shareholders, and not as an archival collection. Companies understandably count the opportunity cost of time spent "helping a researcher with their enquiries", not to mentioning opening up possibly sensitive documents to the prying eyes of an outsider.

This can cause problems to the researcher because if the research project is based on one or a few companies, if access is denied then the overall validity of the research will be prejudiced. Given the likelihood that other research methods, such as interview, survey etc. are also being used, it is best to approach access in the widest sense, and stress the benefits to the organisation, the credibility of the researcher, and assurance of confidentiality.

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Dissertation Methodology Writing Guide

Introduction.

The methodology section will be the chapter that you write following on from your literature review . After you have researched and discovered the gap in the available literature, it is possible for you to create ideas for your proposed research.

In your research proposal , you will have had a suggested methodology where you would have given ideas about how to approach the research: this would have been either through a primary data approach or through collecting secondary data .

Primary data

Primary data is any form of evidence that you collect yourself through your own research in the form of surveys, interviews, questionnaires, focus groups, observations, experiments. Primary data collection methods does not involve the collection of data from other researchers’ work and their studies.

Secondary data

Collecting secondary data is the collection of evidence from previous researchers’ work. An example could be focusing on another researchers’ experiment and using their findings as a basis for your dissertation. An example could be collecting the findings from two different experiments and comparing the findings of these studies in relation to the question posed.

Once you have decided what type of data you will be collecting, you will then need to determine whether the data being collected is qualitative or quantitative as this will have an impact on the analysis of your research.

Quantitative

Quantitative research only produces results on the specific issue that is being investigated and uses statistical, mathematical and computational programmes.

A closed-ended questionnaire would be analysed using quantitative research if the researcher merely computed the results and produced a series of comments as to the percentages of respondents who gave specific answers. A common programme by which to analyse quantitative research is SPSS.

Qualitative

Qualitative research tends to be used more in the social sciences and arts and is when a research seeks to ask ‘why’ and ‘how’ something has happened and explains the reasons with recourse to empirical mathematical models.

Within primary research that uses qualitative research, small focus groups can often be employed.

An open-ended questionnaire that collates and assesses a range of verbal responses would be analysed using qualitative techniques as the answers given do not lend themselves to being processed in the manner described above relating to closed questionnaires.

Mixed Methodology

Another option is through a mixed methods approach, which would be the collection of both primary and secondary data.

In a dissertation where one is assessing, for instance, the effects of flooding in the Wirral peninsula, it is likely that all the research techniques mentioned above would be used.

Secondary data would be used through a literature review. Closed-ended questionnaires could be analysed using a statistical panel and interviews with experts would be commented upon with reference to existing literature.

Accordingly, both primary and secondary research techniques would be utilised as well as qualitative and quantitative mechanisms.

Writing Your Methodology

You should begin your methodology with a brief introduction to the chapter, this should also include relaying the aims of the study. Following on from this, it is best to start by defining and choosing the research paradigm for the dissertation.

Research paradigms – there are 4 main approaches to research. These are positivism, interpretivism (also known as constructivism), post-positivism and critical theory.

• Positivism: philosophical viewpoint that the validity of research comes from objective experimental testing
• Interpretivism (Constructivism): usually associated with qualitative research, interpretivism research is subjective. This means that results from research are down to interpretation by the researcher i.e answers to questions in an interview
• Post-positivism: as opposed to positivism, post positivism accepts subjectivity in research and tests qualitative data alongside quantitative data

Once you have defined your research philosophy, the next step would be to identify your research approach and instrument.

Research approach – This can be separated by two types:

• Deductive research
• Inductive research

Deductive research is the approach you would take if you had hypotheses that were being tested, then you would be using a deductive research approach.

Inductive research is when there is a set of observations and a theory is developed to explain those observations or any patterns that are amongst those observations.

Following on from this, you would then be expected to discuss your chosen data collection method along with stating if the research is either quantitative or qualitative. When writing about key terms i.e. primary data; it is always best to define, explain and justify why.

In so doing, you should also note (briefly) what is inappropriate about the other approaches as well as the ways in which you have overcome any negatives that are associated with your approach.

If your chosen methodology is the collection of primary data, the next step would be the describe and explain the sampling and participant selection.

Here you would need to describe and explain the chosen sampling method along with the number of participants selected. It is always good to include how you contacted the participants and recruited them for the study.

If you are using primary data, it is always crucial to include a sub-chapter of the work that discusses any ethical concerns and considerations that arose due to your chosen methodology.

For both primary and secondary data, it is necessary to include a sub-section on the data analysis that will be used to collate and analyse the data gathered in the research.

Here you will discuss how you intend to analyse the data and why you have chosen this analytical technique.

Justification

Whichever approach you use it is important that you justify your decision and that you do so via reference to existing academic works – and writing only in the third person.

As with the background section of your dissertation, your methodology section needs to be grounded in existing academic opinion.

The following books provide not only an overview of methodological approaches (and the strengths and weaknesses associated with each) but are also the sorts of books that your lecturers may expect to see referenced within your methodology section, depending on the type of course you are doing.

Bell, J. (1993). Doing your research project . Maidenhead: Open University Press.

Bryman, A. (2012). Social research methods (4th edn). Oxford: Oxford University Press.

Denscombe, M. (2007). The good research guide (3rd edn). Maidenhead: Open University Press.

Flick, U. (2011). Introducing research methodology . London: SAGE.

Grinyer, A. (2002). ‘The anonymity of research participants: Assumptions, ethics and practicalities’. Social Research Update , Vol. 36, University of Surrey.

Morgan, G. and Smircich, L. (1980). ‘The case for qualitative research’, The Academy of Management Review . Vol. 5 (4), pp. 491-500.

Ritchie, J. and Lewis, L. (2003). Qualitative research practice: A guide for social science students and researchers . London: SAGE.

Robson, C. (2002). Real world research (2nd edn). Oxford: Blackwell Publishing.

Silverman, D. (2010). Doing qualitative research: A practical handbook (3rd edn). London: SAGE.

You do not need to read them all, but you should show (using appropriate and limited direct quotation for extra marks) at least some knowledge of the arguments contained within these books. For an undergraduate dissertation it would be good practice to include at least five of these books (or their equivalent – depending upon what is available within your library) in your bibliography.

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Secondary research: definition, methods, & examples.

19 min read This ultimate guide to secondary research helps you understand changes in market trends, customers buying patterns and your competition using existing data sources.

In situations where you’re not involved in the data gathering process ( primary research ), you have to rely on existing information and data to arrive at specific research conclusions or outcomes. This approach is known as secondary research.

In this article, we’re going to explain what secondary research is, how it works, and share some examples of it in practice.

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What is secondary research?

Secondary research, also known as desk research, is a research method that involves compiling existing data sourced from a variety of channels . This includes internal sources (e.g.in-house research) or, more commonly, external sources (such as government statistics, organizational bodies, and the internet).

Secondary research comes in several formats, such as published datasets, reports, and survey responses , and can also be sourced from websites, libraries, and museums.

The information is usually free — or available at a limited access cost — and gathered using surveys , telephone interviews, observation, face-to-face interviews, and more.

When using secondary research, researchers collect, verify, analyze and incorporate it to help them confirm research goals for the research period.

As well as the above, it can be used to review previous research into an area of interest. Researchers can look for patterns across data spanning several years and identify trends — or use it to verify early hypothesis statements and establish whether it’s worth continuing research into a prospective area.

How to conduct secondary research

There are five key steps to conducting secondary research effectively and efficiently:

1.    Identify and define the research topic

First, understand what you will be researching and define the topic by thinking about the research questions you want to be answered.

Ask yourself: What is the point of conducting this research? Then, ask: What do we want to achieve?

This may indicate an exploratory reason (why something happened) or confirm a hypothesis. The answers may indicate ideas that need primary or secondary research (or a combination) to investigate them.

2.    Find research and existing data sources

If secondary research is needed, think about where you might find the information. This helps you narrow down your secondary sources to those that help you answer your questions. What keywords do you need to use?

Which organizations are closely working on this topic already? Are there any competitors that you need to be aware of?

Create a list of the data sources, information, and people that could help you with your work.

3.    Begin searching and collecting the existing data

Now that you have the list of data sources, start accessing the data and collect the information into an organized system. This may mean you start setting up research journal accounts or making telephone calls to book meetings with third-party research teams to verify the details around data results.

As you search and access information, remember to check the data’s date, the credibility of the source, the relevance of the material to your research topic, and the methodology used by the third-party researchers. Start small and as you gain results, investigate further in the areas that help your research’s aims.

4.    Combine the data and compare the results

When you have your data in one place, you need to understand, filter, order, and combine it intelligently. Data may come in different formats where some data could be unusable, while other information may need to be deleted.

After this, you can start to look at different data sets to see what they tell you. You may find that you need to compare the same datasets over different periods for changes over time or compare different datasets to notice overlaps or trends. Ask yourself: What does this data mean to my research? Does it help or hinder my research?

5.    Analyze your data and explore further

In this last stage of the process, look at the information you have and ask yourself if this answers your original questions for your research. Are there any gaps? Do you understand the information you’ve found? If you feel there is more to cover, repeat the steps and delve deeper into the topic so that you can get all the information you need.

If secondary research can’t provide these answers, consider supplementing your results with data gained from primary research. As you explore further, add to your knowledge and update your findings. This will help you present clear, credible information.

Primary vs secondary research

Unlike secondary research, primary research involves creating data first-hand by directly working with interviewees, target users, or a target market. Primary research focuses on the method for carrying out research, asking questions, and collecting data using approaches such as:

• Interviews (panel, face-to-face or over the phone)
• Questionnaires or surveys
• Focus groups

Using these methods, researchers can get in-depth, targeted responses to questions, making results more accurate and specific to their research goals. However, it does take time to do and administer.

Unlike primary research, secondary research uses existing data, which also includes published results from primary research. Researchers summarize the existing research and use the results to support their research goals.

Both primary and secondary research have their places. Primary research can support the findings found through secondary research (and fill knowledge gaps), while secondary research can be a starting point for further primary research. Because of this, these research methods are often combined for optimal research results that are accurate at both the micro and macro level.

Sources of Secondary Research

There are two types of secondary research sources: internal and external. Internal data refers to in-house data that can be gathered from the researcher’s organization. External data refers to data published outside of and not owned by the researcher’s organization.

Internal data

Internal data is a good first port of call for insights and knowledge, as you may already have relevant information stored in your systems. Because you own this information — and it won’t be available to other researchers — it can give you a competitive edge . Examples of internal data include:

• Database information on sales history and business goal conversions
• Information from website applications and mobile site data
• Customer-generated data on product and service efficiency and use
• Previous research results or supplemental research areas
• Previous campaign results

External data

External data is useful when you: 1) need information on a new topic, 2) want to fill in gaps in your knowledge, or 3) want data that breaks down a population or market for trend and pattern analysis. Examples of external data include:

• Government, non-government agencies, and trade body statistics
• Company reports and research
• Competitor research
• Public library collections
• Textbooks and research journals
• Media stories in newspapers
• Online journals and research sites

Three examples of secondary research methods in action

How and why might you conduct secondary research? Let’s look at a few examples:

1.    Collecting factual information from the internet on a specific topic or market

There are plenty of sites that hold data for people to view and use in their research. For example, Google Scholar, ResearchGate, or Wiley Online Library all provide previous research on a particular topic. Researchers can create free accounts and use the search facilities to look into a topic by keyword, before following the instructions to download or export results for further analysis.

This can be useful for exploring a new market that your organization wants to consider entering. For instance, by viewing the U.S Census Bureau demographic data for that area, you can see what the demographics of your target audience are , and create compelling marketing campaigns accordingly.

2.    Finding out the views of your target audience on a particular topic

If you’re interested in seeing the historical views on a particular topic, for example, attitudes to women’s rights in the US, you can turn to secondary sources.

Textbooks, news articles, reviews, and journal entries can all provide qualitative reports and interviews covering how people discussed women’s rights. There may be multimedia elements like video or documented posters of propaganda showing biased language usage.

By gathering this information, synthesizing it, and evaluating the language, who created it and when it was shared, you can create a timeline of how a topic was discussed over time.

3.    When you want to know the latest thinking on a topic

Educational institutions, such as schools and colleges, create a lot of research-based reports on younger audiences or their academic specialisms. Dissertations from students also can be submitted to research journals, making these places useful places to see the latest insights from a new generation of academics.

Information can be requested — and sometimes academic institutions may want to collaborate and conduct research on your behalf. This can provide key primary data in areas that you want to research, as well as secondary data sources for your research.

Advantages of secondary research

There are several benefits of using secondary research, which we’ve outlined below:

• Easily and readily available data – There is an abundance of readily accessible data sources that have been pre-collected for use, in person at local libraries and online using the internet. This data is usually sorted by filters or can be exported into spreadsheet format, meaning that little technical expertise is needed to access and use the data.
• Faster research speeds – Since the data is already published and in the public arena, you don’t need to collect this information through primary research. This can make the research easier to do and faster, as you can get started with the data quickly.
• Low financial and time costs – Most secondary data sources can be accessed for free or at a small cost to the researcher, so the overall research costs are kept low. In addition, by saving on preliminary research, the time costs for the researcher are kept down as well.
• Secondary data can drive additional research actions – The insights gained can support future research activities (like conducting a follow-up survey or specifying future detailed research topics) or help add value to these activities.
• Secondary data can be useful pre-research insights – Secondary source data can provide pre-research insights and information on effects that can help resolve whether research should be conducted. It can also help highlight knowledge gaps, so subsequent research can consider this.
• Ability to scale up results – Secondary sources can include large datasets (like Census data results across several states) so research results can be scaled up quickly using large secondary data sources.

Disadvantages of secondary research

The disadvantages of secondary research are worth considering in advance of conducting research :

• Secondary research data can be out of date – Secondary sources can be updated regularly, but if you’re exploring the data between two updates, the data can be out of date. Researchers will need to consider whether the data available provides the right research coverage dates, so that insights are accurate and timely, or if the data needs to be updated. Also, fast-moving markets may find secondary data expires very quickly.
• Secondary research needs to be verified and interpreted – Where there’s a lot of data from one source, a researcher needs to review and analyze it. The data may need to be verified against other data sets or your hypotheses for accuracy and to ensure you’re using the right data for your research.
• The researcher has had no control over the secondary research – As the researcher has not been involved in the secondary research, invalid data can affect the results. It’s therefore vital that the methodology and controls are closely reviewed so that the data is collected in a systematic and error-free way.
• Secondary research data is not exclusive – As data sets are commonly available, there is no exclusivity and many researchers can use the same data. This can be problematic where researchers want to have exclusive rights over the research results and risk duplication of research in the future.

When do we conduct secondary research?

Now that you know the basics of secondary research, when do researchers normally conduct secondary research?

It’s often used at the beginning of research, when the researcher is trying to understand the current landscape . In addition, if the research area is new to the researcher, it can form crucial background context to help them understand what information exists already. This can plug knowledge gaps, supplement the researcher’s own learning or add to the research.

Secondary research can also be used in conjunction with primary research. Secondary research can become the formative research that helps pinpoint where further primary research is needed to find out specific information. It can also support or verify the findings from primary research.

You can use secondary research where high levels of control aren’t needed by the researcher, but a lot of knowledge on a topic is required from different angles.

Secondary research should not be used in place of primary research as both are very different and are used for various circumstances.

Questions to ask before conducting secondary research

Before you start your secondary research, ask yourself these questions:

• Is there similar internal data that we have created for a similar area in the past?

If your organization has past research, it’s best to review this work before starting a new project. The older work may provide you with the answers, and give you a starting dataset and context of how your organization approached the research before. However, be mindful that the work is probably out of date and view it with that note in mind. Read through and look for where this helps your research goals or where more work is needed.

• What am I trying to achieve with this research?

When you have clear goals, and understand what you need to achieve, you can look for the perfect type of secondary or primary research to support the aims. Different secondary research data will provide you with different information – for example, looking at news stories to tell you a breakdown of your market’s buying patterns won’t be as useful as internal or external data e-commerce and sales data sources.

• How credible will my research be?

If you are looking for credibility, you want to consider how accurate the research results will need to be, and if you can sacrifice credibility for speed by using secondary sources to get you started. Bear in mind which sources you choose — low-credibility data sites, like political party websites that are highly biased to favor their own party, would skew your results.

• What is the date of the secondary research?

When you’re looking to conduct research, you want the results to be as useful as possible , so using data that is 10 years old won’t be as accurate as using data that was created a year ago. Since a lot can change in a few years, note the date of your research and look for earlier data sets that can tell you a more recent picture of results. One caveat to this is using data collected over a long-term period for comparisons with earlier periods, which can tell you about the rate and direction of change.

• Can the data sources be verified? Does the information you have check out?

If you can’t verify the data by looking at the research methodology, speaking to the original team or cross-checking the facts with other research, it could be hard to be sure that the data is accurate. Think about whether you can use another source, or if it’s worth doing some supplementary primary research to replicate and verify results to help with this issue.

We created a front-to-back guide on conducting market research, The ultimate guide to conducting market research , so you can understand the research journey with confidence.

In it, you’ll learn more about:

• What effective market research looks like
• The use cases for market research
• The most important steps to conducting market research
• And how to take action on your research findings

Download the free guide for a clearer view on secondary research and other key research types for your business.

Related resources

Market intelligence 10 min read, marketing insights 11 min read, ethnographic research 11 min read, qualitative vs quantitative research 13 min read, qualitative research questions 11 min read, qualitative research design 12 min read, primary vs secondary research 14 min read, request demo.

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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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