Dissertation data collection section


Step 1: Explain your methodological approach

Discuss anomalies as well consistencies, assessing the significance and impact of each. If you are using interviews, make sure to include representative quotes to in your discussion. What are the essential points that emerge after the analysis of your data? These findings should be clearly stated, their assertions supported with tightly argued reasoning and empirical backing. Towards the end of your data analysis, it is advisable to begin comparing your data with that published by other academics, considering points of agreement and difference. Are your findings consistent with expectations, or do they make up a controversial or marginal position?

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Discuss reasons as well as implications. At this stage it is important to remember what, exactly, you said in your literature review. What were the key themes you identified? What were the gaps? How does this relate to your own findings? It is very important that you show this link clearly and explicitly. Top 10 tips for writing a dissertation data analysis. Relevance Do not blindly follow the data you have collected; make sure your original research objectives inform which data does and does not make it into your analysis.

Analysis It is important that you use methods appropriate both to the type of data collected and the aims of your research. Quantitative work Quantitative data, which is typical of scientific and technical research, and to some extent sociological and other disciplines, requires rigorous statistical analysis. Presentational devices It can be difficult to represent large volumes of data in intelligible ways.

Appendix You may find your data analysis chapter becoming cluttered, yet feel yourself unwilling to cut down too heavily the data which you have spent such a long time collecting. Discussion In discussing your data, you will need to demonstrate a capacity to identify trends, patterns and themes within the data. Findings What are the essential points that emerge after the analysis of your data?


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Relation with literature Towards the end of your data analysis, it is advisable to begin comparing your data with that published by other academics, considering points of agreement and difference. Need help with dissertation writing? Our dissertation writing service, offered by our network of over 3, world-class academic writers, can provide you with a model dissertation you can use as a customised map to the results you need. Find out more. You may also like Top 10 tips for writing a dissertation methodology. Advice for successfully writing a dissertation.

Previous post. Next post. In contrast, secondary research involves data that has been collected by somebody else previously. 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.

WRITING CHAPTER 3: THE METHODOLOGY

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. 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.


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  8. 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.

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    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.

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    Data Gathering Procedure for Research Papers

    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.

    [Q&A] How do I collect dissertation data with an organization?

    But the secondary data set may contain a categorical age variable; for example, participants might have indicated an age group they belong to e. Or another example: 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: 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.

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