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Guide to undergraduate dissertations in the social sciences

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

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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.

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. 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. But the secondary data set may contain a categorical age variable; for example, participants might have indicated an age group they belong to e. A secondary data set may contain too few ethnic categories e. 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: 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: 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. TABLE 2 provides a full summary of advantages and disadvantages of secondary research: Conducting secondary research is much cheaper than doing primary research Inappropriateness: Secondary data may not be fully appropriate for your research purposes Saves time: Secondary research takes much less time than primary research Wrong format: Secondary data may have a different format than you require Accessibility: Secondary data is usually easily accessible from online sources.

May not answer your research question: Secondary data was collected with a different research question in mind Large scope of data: You can rely on immensely large data sets that somebody else has collected Lack of control over the quality of data: Secondary data may lack reliability and validity, which is beyond your control Professionally collected data: Secondary data has been collected by researchers with years of experience Lack of sufficient information: Original authors may not have provided sufficient information on various research aspects.

At this point, we should ask: 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. 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: 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: 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. 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.

We have already provided above several examples of using quantitative secondary data. 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: 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. Both can be used when you want to a inform your current research with past data, and b re-assess a past data set. 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. 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.

There are several types of internal sources. 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. 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.

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Guide to undergraduate dissertations in the social sciences. Content About this site What is a Dissertation? How to start your dissertation Help with finding literature and research Formulating the research question Methodologies.

Introduction What approach should I take - qualitative or quantitative? Can my dissertation be entirely literature-based? What is case study research? What's an empirical study? What is secondary analysis? Where do I find existing research data?

Collecting you own data - primary research Will my research be inductive or deductive? What about research design? Resources Further reading Research papers. Methodologies 1 Introduction The way you approach your question will have a profound effect upon the way you construct your dissertation, so this section discusses the types of research you might undertake for your dissertation. This video clip contains comments from the following academics: What if I want to find out about social trends, or the measurable effects of particular policies?

What if I want to record people's views on an issue, and give them a 'voice'? Whether you choose qualitative or quantitative analysis will depend on several things: Your preferred philosophical approach realist, phenomenologist or constructionist. Your skills and abilities with methods of data collection if needed and analysis.

The topic or issue you are interested in. How you frame your research question. Can I combine qualitative and quantitative methods? You may be interested in doing an analysis that is primarily quantitative, looking at social trends, or policy implications. However you also want to introduce a 'human touch' by conducting one or several interviews asking what these trends mean to people or how particular individuals experience events.

After doing your quantitative analysis, you should include a chapter or section on the qualitative data you have collected. In your discussion of findings you can use the qualitative data to help you understand the patterns in the quantitative analysis. You may be interested in doing an evaluative case study of a process or policy. You will have a particular focus — a 'case' that you are looking at.

You will triangulate methods — i. You will analyse each type of data and describe this, and then write a discussion that shows how each piece of analysis contributes to the overall picture of what is going on. Download Case Study 6 Media research If you are interested, for example, in doing historical research, you may need to visit archives.

This has the following advantages: They allow you to discuss trends and social changes. The data are often collected through a random sample, which allows you to generalise to the population under consideration. They may also allow you to make comparisons over time, as some datasets are products of longitudinal studies. Smaller, more targeted datasets may also be available. Secondary analysis has disadvantages also: You have to find out something about that purpose, as well as the methods of collection, in order to justify your use of a secondary dataset.

Collecting you own data - primary research Quantitative data may also result from non-participant observations or other measurements e. Your research methods tutor can give you further information on these types of data, but here are some common quantitative data collection methods and their definitions: Self-completion questionnaires A series of questions that the respondent answers on their own.

Structured interviews Similar to a self-completion questionnaire, except that the questions that are asked by an interviewer to the interviewee. Structured observation Watching people and recording systematically their behaviour. Below are some data collection methods that you might want to use for your dissertation: In-depth interviews A way of asking questions which allows the interviewee to have more control of the interview.

Focus groups A form of interviewing where there are several participants; there is an emphasis in the questioning on a tightly defined topic; the accent is on interaction within the group and the joint construction of meaning.

Participant observation This involves studying people in naturally occurring settings. This was particularly useful for one of our respondents: Level 6 students at Sheffield Hallam University Note: Will my research be inductive or deductive? What's all this about research design? At the start of your research you need to set down clearly: Your research focus and research question.

How you propose to examine the topic: How you will access these sources of information be they people, existing datasets, biographical accounts, media articles or websites, official records. The proposed outcome of this research in your case, a dissertation and the form it will take.

A time-frame for all this. Summary Quantitative or qualitative? A quantitative approach will mean you will need substantial datasets, as well as the inclusion of tables and statistics in your final submission. This information could come from a variety of sources - remember to acknowledge them! A qualitative approach will probably mean conducting interviews or focus groups or observing behaviour.

Ask yourself if you are prepared to do this, and think about the best way of getting the answers you want from people. Will you stop people in the street? Will you conduct telephone interviews?

Will you send out survey forms and hope that people return them? Will you be a participant or non participant observer? Deductive research is theory-testing, which is often linked to datasets, surveys or quantitative analysis. Inductive research is theory-generating, and is often linked to qualitative interviews. An empirical study could involve close analysis of statistics or some form of qualitative research.

However, a theoretical study brings its own challenges, and you may be called upon to compare theories in terms of their applicability. Once you have decided upon your approach, you can write out a research design, i. Now look a little at the research methods that you have studied. How would you best be able to collect that data?


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It will involve primary data, secondary data, quantitative and qualitative research methods, lit reviews, theory and policy studies and an exploration of alternatives. My dissertation is to be based around the experience of . During such times you might ask yourself ‘should I use Primary or Secondary Research in my Dissertation’, as these are the two most common research methods. Thus, to make sure that you select the most suitable research strategy for your research, learn about the two most common research strategies to make the right choice.

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Primary and secondary sources. For some research projects, it is important (or you may be required) to use primary sources, instead of or in addition to secondary sources. This is mandatory if the dissertation consists of primary quantitative or qualitative research, but may not be needed in dissertations in theory subjects or focused on secondary or tertiary research. The importance and size of this section varies with discipline and with the method chosen.