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. This type of data has been collected by “somebody else”, in the literal sense of the term. 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.
Let’s now focus on different types of internal and external secondary data sources.
There are several types of internal sources. For instance, if your research focuses on an organisation’s profitability, you might use their sales data. 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.
Alternatively, you may use an organisation’s financial data. 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.
Some other ideas would be to use customer data to ascertain the ideal type of customer, or to use safety data to explore the degree to which employees comply with an organisation’s safety regulations.
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. These can include social surveys, health data, agricultural statistics, energy expenditure statistics, population censuses, import/export data, production statistics, and the like. 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:
Secondary data is one type of quantitative data that has already been collected by someone else for a different purpose to yours. For example, this could mean using:
- data collected by a hotel on its customers through its guest history system.
- data supplied by a marketing organization.
- annual school testing reports.
- government health statistics.
Secondary data can be used in different ways:
- You can simply report the data in its original format. If so, then it is most likely that the place for this data will be in your main introduction or literature review as support or evidence for your argument.
- You can do something with the data. If you use it (analyze it or re-interpret it) for a different purpose to the original then the most likely place would be in the ‘Analysis of findings’ section of your dissertation.
Example: A good example of this usage was the work on suicide carried out by Durkheim. He took the official suicide statistics of different countries (recorded by coroners or their equivalent) and analyzed them to see if he could identify variables that would mean that some people are more likely to commit suicide than others. He found, for example, that Catholics were less likely to commit suicide than Protestants. In this way, he took data that had been collected for quite a different purpose and used it in his own study – but he had to do a lot of comparisons and statistical correlations himself in order to analyze the data. (See Haralambos, 1995, for details of Durkheim’s work).
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. It is always a good idea to use data collected by someone else if it exists – it may be on a much larger scale than you could hope to collect and could contribute to your findings considerably.
As secondary data has been collected for a different purpose to yours, you should treat it with care. The basic questions you should ask are:
- Where has the data come from?
- Does it cover the correct geographical location?
- Is it current (not too out of date)?
- If you are going to combine with other data are the data the same (for example, units, time, etc.)?
- If you are going to compare with other data are you comparing like with like?
Thus you should make a detailed examination of the following:
- Title (for example, the time period that the data refers to and the geographical coverage).
- Units of the data.
- Source (some secondary data is already secondary data).
- Column and row headings, if presented in tabular form.
- Definitions and abbreviations, for example, what does SIC stand for? For example, how is ‘small’ defined in the phrase ‘small hotel’? Is ‘small’ based on the number of rooms, value of sales, number of employees, profit, turnover, square meters of space, etc., and do different sources use the word ‘small’ in different ways? Even if the same unit of measurement is used, there still could be problems. For example, in Norway, firms with 200-499 employees are defined as ‘medium’, whereas in the USA firms with less than 500 employees are defined as ‘small’.
There are many sources of data and most people tend to underestimate the number of sources and the amount of data within each of these sources.
Sources can be classified as:
- paper-based sources– books, journals, periodicals, abstracts, indexes, directories, research reports, conference papers, market reports, annual reports, internal records of organizations, newspapers and magazines
- electronic sources– CD-ROMs, on-line databases, Internet, videos and broadcasts.
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