Remember, in this book we have been primarily concerned with applied type business research, including consultancy research, that is, research that centres on helping to solve client problems. The client here, of course, will be for exam-ple an organization or rather a manager or management team within an organi-zation. It can also be an academic supervisor, but this person is equally looking for an objectively presented set of findings. It is important, therefore, that the methods of data analysis are presented and explained to a client in a way that is user-friendly. In particular, it is important not to try to impress the client by using overly complex and technical language. Sometimes the techniques of data analy-sis will be complex and technical and may be difficult for a non-specialist to understand. The consultant researcher, therefore, should ensure that data analy-sis techniques are explained as clearly as possible. In addition, the results of the data analysis should be made available in a clear, concise and again, user-friendly
format. Sometimes the client may not be interested at all in how the data was analysed, but only in what it means in terms of actions and recommendations.
We shall consider this aspect or outcome of data analysis in Chapter 12.
11.7 Concluding comments
In this chapter we have examined the nature and purpose of data analysis in the research process. In doing so, we have concentrated on outlining the impor-tance of effective data analysis and its role in turning data into information. We have seen that the techniques of data analysis differ according to the nature of the data itself, with a major distinction between the categories of qualitative and quantitative data. We have also seen that the purpose of the analysis is a key factor in the technique(s) of analysis selected. Quantitative techniques of data analysis range from the relatively simple and straightforward production of descriptive statistics, through inferential statistics and simple interrelationships to the most complex levels and techniques involving multivariant analysis. With regard to qualitative techniques, we have distinguished between content and grounded techniques of analysis; examined the key steps in analysing qualita-tive data; and introduced the intriguing techniques of semiotics for analysing interactions and processes in organizations. Finally we have highlighted the fact that data analysis and its presentation need to be user-friendly in meeting the needs and requirements of the client.
11.8 References
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Cronbach, L. J. (1975) ‘Beyond the two disciplines of scientific psychology’, American Psychologist, 30, pp. 116–26.
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11.9 Glossary
content analysis An approach to analysing qualitative data based on trying to quan-tify qualitative data by, for example, counting ‘frequency’ and so on.
grounded theory An approach to analysing qualitative data based on identifying and interpreting themes and patterns contained in the data.
triangulation A term borrowed from navigation and surveying where multiple reference points are taken to check an object’s location. In the con-text of data collection and analysis the term indicates that a num-ber of different and independent methods of data collection and analysis have been combined.
unitising Determining the objects of measurement in content analysis.