The term “data” is the plural of “datum”. A datum is a piece of information, so data are pieces of information. As such, data is a term which encapsulates a very broad range of meanings. After all, almost anything might be considered a piece of information, depending upon the way one looks at it. This is what data collection methods are all about: they give us specific and systematic ways of looking at information. They allow us to organise and process information in a structured and consistent manner.
Numerous different kinds of data collection methods are available, but two very broad overarching categories may nonetheless be identified. These relate to primary and secondary data. Primary data concerns information gathered first-hand by, you, the student. This can include a wide array of different methods, such conducting surveys, interviews, field observations, laboratory experiments, and many more. Secondary data are pieces of information not gathered by you, but which come from other sources. Here we may list censuses, government statistics, organisational records, and other academic content. Obviously these different kinds of data are best fitted to different purposes. Choosing the right kind of data is therefore important in deciding upon one's data collection methods.
One of the first items to consider is whether existing (secondary) data may be used or new (primary) data must be generated for analysis. Using data that already exists saves much time and effort in a research project. Many kinds of data are readily available to the researcher. These include records, historical data and existing data sets collected for research purposes. Such secondary data sets are becoming increasingly common and are thus more likely to be used in your work. So, collecting data will be required for students who are carrying out a primary research project. Students who are carrying out a secondary research project, such as an extended Literature Review, will produce two or three literature review chapters, with the purpose of presenting what other researchers have found. Other information may come from original research found in secondary sources, such as scholarly journal articles. The authors may have collected their own data via scientific experiments, observations and surveys; they may also use data collected by other researchers, analysing them in a new form.
You need to be forward thinking in deciding upon data collection. You should start out by making a data collection plan. This is a broad roadmap of the various major stages to be taken. This begins by identifying your data needs. This relates to research question: you need to figure out which kinds of information would best answer or help to explain your research problem. Next, you must select the type of data collection approach and measurement that will be employed. You will then need to select, adapt or develop data collection instruments. Developing such an instrument can be time consuming and highly complex. Hence this approach is probably best avoided at undergraduate level – unless one has a demonstrably workable and necessary instrument. It is advisable to use established data collection instruments. Consider how you will record any information collected and set up a system for analysing and storing your data. Make sure to factor in to this plan enough time to negotiate access to the research site. Finally one needs to develop data collection forms and procedures, to test them (in the manner in which they are intended to be employed). To recap, the data collection in primary research can be time-consuming and needs to be well thought out to avoid collecting more data than you can use. It is essential that you ask for your supervisor's guidance when it comes to data collection. They will be able to point you in the right direction, ensuring you do not waste valuable time. Remember, the more effort you put into the data collection stage, the better quality the data are going to be. Good planning is the essence of good research.
Methodologically speaking, academic convention identifies two main kinds of data, quantitative and qualitative. Qualitative data come in numerical form – they are to do with quantities. Quantitative data may be subdivided into continuous/variable data and discrete data. Continuous data would relate to numerical values along a scale whose range could be any of an infinite number of decimals. Discrete data would be something in which a finite number of choices were at play. Qualitative data describe qualities; they relate to non-numerical information. On the qualitative side, then, you have open questions (for example, responses on a questionnaire); data that do not collate neatly into unambiguous units of measurement (as with numbers). In sum, qualitative data relate information that is not otherwise easily parsed.
Deciding upon whether you will need quantitative or qualitative data is a crucial consideration in determining what kinds of data collection methods you use within your study. Obviously the process of gathering statistical information will be quite different from that of collecting soil samples. There are as many collection methods as there are sources of data to collect. You need to determine which kind of data is most appropriate to conceptualising your research problem. This means you need to consider what type of data will best offer explanations for a certain phenomenon. For example, if you were trying to determine how second-generation immigrants feel about national identity in their adoptive country, soil samples from beneath their homes would not shed much light. Here, an interview would surely better serve. Contrarily, if you were investigating the iron-content of soil in a park, say, giving questionnaires to people sat upon the grass would probably not yield appropriate data. You need to tailor the method the objective, and the objective will be determined by the Research Question.
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