Qualitative analysis involves the analysis of data that is not amenable to numeri-cal measurement. This is not to say that all qualitative data cannot be trans-lated into data that is amendable to quantitative techniques. For example, the measurement of attitudes, essentially a qualitative dimension, can be translated into numerical attitudinal scales that can then be analysed using quantitative techniques. Again, we can see that the distinction between quantitative and qualitative data is not always clear-cut. However, some qualitative data simply
cannot be subjected to numerical measurement and analysis, even if the researcher should wish to do so. In many social science research projects, it is qualitative data which is sought and attempts to turn the data into numbers so as to quantify it, even where this is possible, might detract from its potential richness. This potential conflict between the desire to quantify, and the inher-ent richness of unquantified qualitative data, has led to two broad alternative ways of analysing qualitative data. The first of these ways is referred to as content analysisand the second as grounded theory.
Content analysis
This has long been used as an approach to analysing qualitative data (Miles and Huberman 1984; Cassell and Symon, 1994). The precise application of this technique will vary according to the nature of the research project and the objec-tives of the researcher. Essentially, the researcher decides in advance what is being looked for and measured through the qualitative research, and then devel-ops frameworks of classifications for assessing the content of the data with regard to these measures. For example, if the researcher is interested in exam-ining the role of money as a motivator in the workforce, and has then conducted a series of personal interviews with a sample of respondents in the organiza-tion about this aspect, the content of the interviews can then be examined and, say, the number of times the word ‘money’ is mentioned by the interviewee noted. Another example might be, say, where observation has been used to gen-erate the data the researcher might note the number of times a particular event or action takes place. We can see, then, that content analysis is an attempt to quantify qualitative data by noting, for example, frequencies of events, words, actions and so on. A key aspect of content analysis, therefore, is deciding on what is to be measured or noted. It could be, for example, as already indicated, looking for specific words or particular events. Deciding on what is to be meas-ured is sometimes known as unitising the data. This is because the researcher needs to decide which units will be used for the analysis. Obviously, which units are appropriate depends again on the precise nature of the research and the research objectives. Decisions about the units to be analysed are part of the research design process and should be determined before the data is collected.
The researcher can use existing theories, client needs, or simply hunch and intu-ition to determine the units to be measured in the first place. However, the units and categories can be refined and amended during the research so that if the initial units and categories are not appropriate, further research can refine and improve them.
Once the data has been analysed and the units categorized and measured, the researcher can then seek to identify themes and relationships between the observed frequency, for example, of the units. From these, the researcher can then develop explanations and conclusions with regard to what the observed categories and frequencies might mean. Content analysis, then, is essentially an objective/deductive approach and more nearly accords to the conventional scientific method for testing hypotheses.
Grounded theory
This theory was first suggested by Glaser and Strauss (1967) who introduced the idea that the methods and approaches to analysing much qualitative data will vary according to the nature and purpose of each research project and the predilections of the individual researcher. Put another way, Glaser and Strauss were arguing against a standardization of analysis methods for qualitative data.
As they saw it, the large amounts of non-standard data produced by qualita-tive methods renders a predetermined external structure to analysing the data unsuitable. In grounded theory, therefore, the researcher takes the qualitative data and attempts to identify key themes, patterns and categories from the data itself. Obviously, the researcher will inevitably have preconceptions and cer-tainly personal values when it comes to looking for and explaining patterns and categories in data. Essentially, the concepts and categories derived from the empirical data itself, and to the extent that it is possible, the researcher who uses the principles of grounded theory should assess the data with an open mind. Grounded theory, therefore, does not set out to test, for example, an hypothesis, or even with a preconceived set of ideas that shape the research process and the methods of data collection. Any theories or explanations devel-oped by the researcher are instead derived from or grounded (hence the term for this approach) in empirical reality. Grounded theory, and the approach to the analysis of qualitative data which it gives rise to represents, therefore, essen-tially an inductive approach to research based on a much more holistic view.
There is no doubt that grounded theory is particularly appropriate to the analy-sis of much of the type of qualitative data that is often generated in organiza-tional research and partly for this reason we have seen a significant growth in this approach to data collection and analysis in recent years (Goulding 2002).
It does, however, require the researcher to accept that the data itself will deter-mine the outcomes and findings of the research irrespective, very often, of what the researcher would hope or wish to find. Adopting this attitude and approach to analysing data can sometimes be very difficult for the management con-sultant researcher to achieve. However, grounded theory, and the analysis approaches which it gives rise to, can be extremely valuable, particularly where the researcher is not certain about the nature of the research problem and the information required when assessing and resolving this criterion. The researcher must also accept that the individual nature of grounded theory and analysis techniques means that it is not an effective approach to producing or proving general theories, but rather offers explanations which are relevant to a partic-ular set of circumstances and situations. Given that the management consult-ant researcher is more concerned with producing results and recommendations for a particular organization or problem rather than the production of grand theories, in some ways grounded theory and analysis techniques are extremely useful and relevant to consultancy-type research. We can also see that grounded analysis would be particularly appropriate to some of the methodologies of data collection outlined in earlier chapters and in particular observational and action research. It is probably fair to say, however, that compared to
quantitative data analysis, the techniques of qualitative data analysis are still in their infancy.