- How should you discriminate between the different sources of secondary research?
- Why should you discriminate between the different sources of secondary research?
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Solution Preview - How should you discriminate between the different sources of secondary research?
- Why should you discriminate between the different sources of secondary research?
YOU SHOULD DISCRIMINATE BETWEEN THE DIFFERENT SOURCES OF SECONDARY DATA BASED ON THE FOLLOWING CRITERIA
The researcher has to be careful, when making use of secondary data, of the definitions used by those responsible for its preparation. Suppose, for example, researchers are interested in rural communities and their average family size. If published statistics are consulted then a check must be done on how terms such as “family size” have been defined. They may refer only to the nucleus family or include the extended family. Even apparently simple terms such as ‘farm size’ need careful handling. Such figures may refer to any one of the following: the land an individual owns, the land an individual owns plus any additional land he/she rents, the land an individual owns minus any land he/she rents out, all of his land or only that part of it which he actually cultivates. It should be noted that definitions may change over time and where this is not recognised erroneous conclusions may be drawn. Geographical areas may have their boundaries redefined, units of measurement and grades may change and imported goods can be reclassified from time to time for purposes of levying customs and excise duties.
Measurement error When a researcher conducts fieldwork she/he is possibly able to estimate inaccuracies in measurement through the standard deviation and standard error, but these are sometimes not published in secondary sources. The only solution is to try to speak to the individuals involved in the collection of the data to obtain some guidance on the level of accuracy of the data. The problem is sometimes not so much ‘error’ but differences in levels of accuracy required by decision makers. When the research has to do with large investments in, say, food manufacturing, management will want to set very tight margins of error in making market demand estimates. In other cases, having a high level of accuracy is not so critical. For instance, if a food manufacturer is merely assessing the prospects for one more flavour for a snack food already produced by the company then there is no need for highly accurate estimates in order to make the investment decision.
Source bias Researchers have to be aware of vested interests when they consult secondary sources. Those responsible for their compilation may have reasons for wishing to present a more optimistic or pessimistic set of results for their organisation. It is not unknown, for example, for officials responsible for estimating food shortages to exaggerate figures before sending aid requests to potential donors. Similarly, and with equal frequency, …
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