Self-completion questionnaires are good for collecting data on relatively simple topics, and for gaining a general overview of an issue. Questionnaires need to have clear questions, an easy to follow design, and not be too long.
Similar to a self-completion questionnaire, except that the questions that are asked by an interviewer to the interviewee. The same questions are read out in the same way to all respondents. There will typically be a fixed choice of answers for the respondents. Watching people and recording systematically their behaviour. Prior to the observation, an observation schedule will be produced which details what exactly the researcher should look for and how those observations should be recorded.
If you are conducting a qualitative analysis you are likely to wish to use at least some original material. This may be collected through in-depth interviews, participant observation recordings and fieldnotes, non-participant observation, or some combination of these. Below are some data collection methods that you might want to use for your dissertation:. A way of asking questions which allows the interviewee to have more control of the interview.
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. The moderator tries to provide a relatively free rein to the discussion. This involves studying people in naturally occurring settings. The researcher participates directly in the setting and collects data in a systematic manner. The researcher will observe behaviour, listen to conversations, and ask questions.
Spend some time looking at general books about research - they will give you an overview of the data collection methods available and help you to make the best choice for your project. Bryman would be a useful starting point. For any piece of research you conduct, be it empirically based quantitative or qualitative or library based, its methods must be justified. You need to show in the final dissertation how you have given consideration to different methods, and why you have chosen and eliminated these.
Often in early supervision meetings they ask students to justify their reasons for choosing a library-based or an empirical study. Todd, Smith and Bannister , p This was particularly useful for one of our respondents:. With other essays you can rush them if you have to Todd, Bannister and Clegg, , p …. My reasons for data collection is literature based as my research question involved sensitive subjects which would have been unsuitable for primary data collection.
Level 6 students at Sheffield Hallam University I chose primary data because it would enable me to build skills that would be useful for postgraduate study. Level 6 students at Sheffield Hallam University 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 'poverty', as poverty is the experience. Theories and policies are not. However, to do justice to the subject, theories and policies will be included so Iam able to demonstrate where failures in the system may exist. Level 6 students at Sheffield Hallam University. Research must be conducted in a sensible and ethical manner; data must be analysed and presented in a rational manner.
It is important that students do not expose themselves or others to dangers or risks when conducting research. Students need the approval of their dissertation supervisor before embarking on any type of fieldwork see the section on Research Ethics for more information. In general, deductive research is theory-testing and inductive research is theory-generating. Often people link deductive research with quantitative experiments or surveys, and inductive research with qualitative interviews or ethnographic work.
These links are not hard and fast — for instance, experimental research, designed to test a particular theory through developing a hypothesis and creating an experimental design, may use quantitative or qualitative data or a combination. If your research starts with a theory and is driven by hypotheses that you are testing e. However much research combines deductive and inductive elements. Research design is vital to conducting a good piece of work. At the start of your research you need to set down clearly:.
You and your supervisor will discuss your design and decide whether the research is 'do-able'. Your university may require you to produce a report e. Other people may have to look at the design to ascertain whether there are ethical issues that affect your research.
Qualitative, Quantitative, and Mixed Methods Approaches. Researching society and culture. London, Sage Here are some references for specific methods: Interviewing for social scientists: Questionnaire Design, Interviewing and Attitude Measurement. Identifying a research topic: A template for structured observation: 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? 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. 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.
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.
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. 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. TABLE 5 summarises the two sources of secondary data and associated examples: Internal to the organisation or research institution where you conduct your research Definition: External to the organisation or research institution where you conduct your research Examples: 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.
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: 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.
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: 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.
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Most research requires the collection of primary data (data that you collect at first hand), and this is what students concentrate on. Unfortunately, many research reports do not include secondary data in their findings section although it is perfectly acceptable to do so, providing you have analyzed it. 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.
Secondary market research is when you use previously completed studies and apply the results to your own situation. These studies are easy enough to find via an internet search or by researching marketing journals—and, on the upside, are usually free or low cost. Secondary research or desk research is a research method that involves using already existing data. Existing data is summarized and collated to increase the overall effectiveness of research. Existing data is summarized and collated to increase the overall effectiveness of research.