3/19 Advantages and disadvantages of quantitative data analysis
Advantages of quantitative data analysis:
- allow for a broader study, involving a greater number of subjects, and enhancing the generalisation of the results
- can allow for greater objectivity and accuracy of results. Generally, quantitative methods are designed to provide summaries of data that support generalisations about the phenomenon under study. In order to accomplish this, quantitative research usually involves few variables and many cases, and employs prescribed procedures to ensure validity and reliability
- using standards means that the research can be replicated, and then analysed and compared with similar studies. Kruger (2003) confirms that 'quantitative methods allow us to summarize vast sources of information and facilitate comparisons across categories and over time'
- personal bias can be avoided by researchers keeping a 'distance' from participating subjects and employing subjects unknown to them
Disadvantages of quantitative data analysis:
- collect a much narrower and sometimes superficial dataset
- results are limited as they provide numerical descriptions rather than detailed narrative and generally provide less elaborate accounts of human perception
- the research is often carried out in an unnatural, artificial environment so that a level of control can be applied to the exercise. This level of control might not normally be in place in the real world yielding laboratory results as opposed to real world results
- in addition preset answers will not necessarily reflect how people really feel about a subject and in some cases might just be the closest match.
- the development of standard questions by researchers can lead to 'structural' bias and false representation, where the data actually reflects the view of them instead of the participating subject.
Quantitative methods are ideally suited for finding out who, what, when and where.
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