The dissertation topic you have selected, to a considerable degree, informs the way you are going to collect and analyse data. Some topics imply the collection of primary data, while others can be explored using secondary data. Selecting an appropriate data type is vital not only for your ability to achieve the main aim and objectives of your dissertation but also an important part of the dissertation writing process since it is what your whole project will rest on.
Selecting the most appropriate data type for your dissertation may not be as straightforward as it may seem. As you keep diving into your research, you will be discovering more and more details and nuances associated with this or that type of data. At some point, you might find yourself in a position where you have absolutely no clue what data you should select, collect, and analyse during the course of your study to make sure your aim is fully achieved and the dissertation topic is properly explored. Here, we have a look at some advanced elements of the data selection process you might find useful while writing your dissertation.
Selecting Quantitative or Qualitative Data…or Both
Once you have formulated your research aim and objectives and reviewed the most relevant literature in your field, you should decide whether you need qualitative or quantitative data.
- If you are willing to test the relationship between variables or examine hypotheses and theories in practice, you should probably focus on collecting quantitative data. Methodologies based on this data provide cut-and-dry results and are highly effective when you need to obtain a large amount of data in a cost-effective manner.
- Alternatively, qualitative research will help you better understand meanings, experience, beliefs, values and other intangibles like these.
- While it is totally okay to use either a qualitative or quantitative methodology, using them together will allow you to back up one type of data with another type of data and research your topic in more depth. However, note that using qualitative and quantitative methodologies in combination can take much more time and effort than you originally planned.
Different Types of Dissertation Data
In addition to qualitative and quantitative data, it is also important to distinguish between primary and secondary data. What is exciting about these types of data is that they can be used with each other in different combinations. Each combination is employed for different purposes, which you should have a clear understanding of to make the right choice. If you are unsure what type of data is best suited for your dissertation research design, read on!
- Primary data is obtained from first-hand sources. In most cases, primary data is collected from the source, i.e. where it originates from and is regarded as the best of its kind. Researchers usually select and tailor the sources of primary data to the needs of their particular study, which allows for adopting a more focused approach to the exploration of the research phenomenon. Hence, issues such as the research aim and objectives as well as the target population and sampling need to be considered.
- This is the data collected from human participants through interviews or surveys.
- Interviews provide you with the opportunity to collect detailed insights from industry participants about their company, customers, or competitors.
- Questionnaire surveys allow for obtaining a large amount of data from a sizeable population in a cost-efficient way.
- This is usually cross-sectional data (i.e. the data collected at one point of time from different respondents). Time-series are found very rarely or almost never in primary data. Nonetheless, depending on the research aims and objectives, certain designs of data collection instruments allow researchers to conduct a longitudinal study.
1.1. Primary Quantitative Data
- Primary quantitative data is basically any quantitative information, which can be used for statistical analysis and mathematical calculations. It is used to answer research questions such as ‘How often?’, ‘How much?’, and ‘How many?’.
- Studies that use this type of data also ask ‘What’ questions (e.g. What are the determinants of customer loyalty? To what extent does marketing affect sales? etc.).
- This is the data that can be converted to numbers. For example, the use of the Likert scale methodology allows researchers not only to properly assess respondents’ perceptions of and attitudes towards certain phenomena but also to assign a code to each individual response and make it suitable for graphical and statistical analysis. It is also possible to convert yes/no questions to dummy variables to present them in the form of numbers.
- Primary quantitative data can be verified and conveniently evaluated by researchers.
- This data is usually collected through surveys using the method of structured questionnaires with closed-ended questions.
- Can be analysed with mathematical and statistical analysis software such as Excel and SPSS.
- Can be limited in terms of breadth and depth as compared to qualitative data. This is because more often than not respondents have to select from a short list of options instead of giving expanded, detailed answers.
1.2. Primary Qualitative Data
- Unlike quantitative research, collecting and analysing primary qualitative data is more open-ended in eliciting the anecdotes, stories, and lengthy descriptions and evaluations people make of products, services, lifestyle attributes, or any other phenomenon.
- This is non-numerical primary data represented mostly by text or quotes from interviewees.
- This is best used in social studies including management and marketing when there are few respondents and if they are asked open-ended.
- Studies that use this type of data usually ask ‘Why’ and ‘How’ questions (e.g. Why does social media marketing is more effective than traditional marketing? How do consumers make their purchase decisions?).
- The analysis of primary qualitative data usually provides deep insights into the phenomenon or issue being under study because respondents are not limited in their ability to give detailed answers.
- Can be analysed with nVivo.
- This type of data does not allow for establishing cause-and-effect links between variables.
- The analysis of primary qualitative data often involves interpretations, which can limit the extent to which it yields reliable outcomes.
Finding Data Participants and Getting Surveys Completed
Always remember that your sample size should be large enough to make confident management and marketing decisions based on your empirical findings. For example, if you are investigating a single company’s marketing strategy, interviewing 10 managers and employees could provide you with reliable outcomes.
However, exploring price tolerance for a new service or product will require a much larger sample size to be confident in the results (100 and more respondents). Remember that not everyone is willing to participate in any kind of survey; so, plan your sample size wisely and leave enough time for pilot tests.
- Evaluate Your Sample Feasibility
You can remove a lot of stressors when writing an elaborated proposal or simply outlining the scope of your research. Try to identify your questionnaire sample and its accessibility in advance. Should you distribute the forms among a specific population? How easy would it be to contact the participants from this group? Do you need to run a pilot survey? Some samples are inherently more difficult to survey than others. For example, company CEOs typically do not have time to complete questionnaire forms. It may be wise to suggest a more realistic sample asking to participate regular employees or line managers.
- Establish Contacts within Your Target Population
It’s quite difficult to recruit participants if you don’t know anybody from your target population. When writing a dissertation or research paper, try to personally contact one or two people belonging to your desired audience. Having a friend or a good acquaintance who can help with data collection and providing background information is a huge boon. You can boldly aim at analysing BP if you have a couple of personal contacts (better managers) inside this company. Just make sure you are not breaching any ethical regulations by completing an ethics form first.
- Use Digital Media to Your Advantage
Digital media are a quick way to recruit additional 20-30 people in a pinch. Try not to limit yourself to conventional options, which are Facebook and Twitter. Message boards (Reddit), streaming websites (Twitch) and forums (SomethingAwful) are active communities representing different demographics and population segments. Do not forget to mention these in your methodology chapter! However, a word of caution. You will never have a guarantee that your questionnaire is completed by the actual representatives of the target population. The visitors of your chosen website would also need to be informed of the purpose of the study and reassured that their information is protected. Anonymising all personal data and storing it safely is crucial.
- The main characteristic of secondary data is that it has previously been collected for some other purpose and can be accessed by researchers.
- Although often employed to supplement primary data (e.g. to increase the sample size of studies), many researchers rely on secondary data as the main source of evidence.
- This data is more relevant for economic and financial research but it can also be found in management and marketing research.
- This is the data collected from databases or websites; it does not involve human participants.
- This can be both cross-sectional data (e.g. an indicator for different countries/companies at one point of time) and time-series (e.g. an indicator for one company/country for several years). A combination of cross-sectional data and time-series data is panel data.
2.1. Secondary Quantitative Data
- The most popular data in economics and finance. This popularity is largely explained by the speed and efficiency that come with using already existing resources, such as databases, censuses, and online portals for statistics.
- One of the key advantages of using this type of data is that much of the preliminary work is already done. In most cases, you don’t have to arrange the data because it has already been sorted, published, and reviewed by someone else.
- Examples of secondary quantitative data are share prices; accounting information such as earnings, total asset, revenue, etc.; macroeconomic variables such as GDP, inflation, unemployment, interest rates, etc.; microeconomic variables such as market share, concentration ratio, etc.
- Examples of dissertation that will most likely use secondary quantitative data are FDI dissertations, Mergers and Acquisitions dissertations, Event Studies, Economic Growth dissertations, International Trade dissertations, Corporate Governance dissertations.
- Often carries more legitimacy as compared to primary data and can help the researcher verify primary data.
- Since this data has been collected for some other purpose, it may not fit the needs of your study.
- You may not have access to relevant secondary quantitative data.
2.2. Secondary Qualitative Data
- This is any textual or visual data (infographics) that have been gathered from reports, websites and other secondary sources that do not involve interactions between the research and human participants.
- Academic articles, journals, books, and conference papers are also examples of secondary qualitative data you can use in your study.
- It is impossible to assess the extent to which this data is relevant and yields reliable findings. While previous researchers often test the validity and reliability of their studies, there is no way you can test their statements and have to take them for granted.
- Often used in case studies.
- Cannot be analysed with statistical or econometric software such as Eviews, Stata, Matlab, SPSS.
- Just like secondary quantitative data, this type of data may not fit the needs of your study.
Selecting the most suitable data or a combination of several data types is a challenging process because it requires having a full understanding of their features and characteristics. While the above differences may seem a little bit over the top at first, they are quite easy to understand and comprehend. Once you fully understand them, you can easily select what type of data suits best the aim and objectives of your study. If after reading this article you still have doubts as to what data type to use in your dissertation, you can get professional help by contacting our dissertation writing service. Our professional UK writers will help you select the most suitable research design, whether it will be qualitative, quantitative, primary, or secondary.