Learning outcomes
By the end of this chapter you will:
● understand the role and importance of analysis in turning data into information,
● understand the elements of effective data analysis,
● understand the differences and interrelationships between the analysis of qualitative and quantitative data,
● understand the meaning and nature of content and grounded data analysis techniques,
● Understand the meaning and potential application of semiotics.
Introduction
Earlier we discussed the distinction between data and information. Most researchers and virtually all managers are ultimately interested in information rather than data. Information derives from the process of subjecting data to analysis. It is this process of turning data into information through analysis that is considered in this chapter.
In assessing the process of analysing data in this chapter, we shall be less concerned with the detailing of individual techniques of analysis and more with the overall approach to this process, and in particular some of the major dif-ferences between analysing qualitative and quantitative data. Without effective analysis both researcher and manager potentially face being overwhelmed with a mass of data which does not really mean much, and certainly cannot be used to address organizational and managerial problems and issues.
We shall be primarily concerned with examining the importance of analysis and the types of analysis related to different types of data in this chapter. Detailed
ways of summarizing and presenting data and techniques of analysing data including some of the more frequently used statistical and mathematical tech-niques are not covered in this book as this dimension is not within its remit, but there are many excellent texts on these aspects (Wisniewski 1997; Burns 2000; Greenfield, 2002; Graziano and Raulin 2004).
11.1 Analysis: nature and roles
As already mentioned, analysing data is the process of turning data into infor-mation. Information, remember, is data which is in a form which can be used for explanation, or more specifically in the context of this book, for decision making. Four key roles for analysis in this respect involve the processes of distillation, classification, identification and communication.
Distillation
Most research/consultancy exercises often result in huge amounts of data.
Neither the researcher nor the client wants to be faced with a mass of data with the ensuing need to sift through it and try and establish what it all means. A key purpose of analysis, therefore, is to distil potentially large amounts of data into forms that are more readily managed and absorbed, and also discard data that is not appropriate in the context of the research project. At its simplest, this distillation will take the form of summarizing data using, for example, tables, diagrams, or may alternatively, and in addition, summarize and distil data numerically through measures such as average dispersion, standard deviation, and so on. Failure to distil data effectively is one of the most frequent reasons for failures to understand and implement research findings.
Classification
Related to the above, data analysis should also help to classify data. This involves the grouping of data into categories that allow the researcher and manager to quickly see what factors are involved and potentially what the data means.
Classifying data helps to encourage the development of order from chaos.
Identification
Much data analysis is concerned with establishing causes and/or relationships between factors. Data analysis enables these relationships, and particularly causal relationships to be identified (Krzanowski 1988)
Communication
The final purpose for analysis involves the important aspect of communicating research findings. It is very difficult to communicate raw data, either to managers
in an organization, or to other researchers in a field of enquiry. The processes of distillation, classification and identification referred to above ultimately allow the researcher to communicate research findings and their meanings to other people.
Blaxter et al. (2001) identifies four related terms that he puts at the heart of the purpose and process of analysis. These terms are: concepts, theories, expla-nations and understanding. The meaning of Blaxter’s terms is explained below:
● Concepts: Analysis is often aimed at developing concepts regarding how we think about particular subjects or issues.
● Theories: Analysis may also seek to explain something. In particular, it seeks to explain the nature of cause and effect.
● Explanations: This form of analysis seeks to make things intelligible explain-ing why thexplain-ings are the way they are.
● Understanding: A development of explanations, this aspect of analysis seeks to develop and underpin knowledge about the meaning of a subject area, issues, or the research problem under consideration.
We can see that the process of analysis performs several functions, and where effective, produces several possible outcomes. Although the precise purpose of the analysis may differ, all analysis is ultimately about explaining and under-standing which in turn may stem from the development of concepts and the-ories (Kuhn 1970; Babbie 2001).
In the context of management consultancy research, to some extent concepts and theories play a secondary role in the process of analysis. In this type of research, understanding and explanations are much more important in as much as these lead directly to possible solutions to management issues and problems, together with proposals for their implementation. Again, I would stress within the context of consultancy research, that we are much more concerned with turning data into information, and moreover, the information should enable us to plan courses of action to resolve organizational and management problems, which in turn may lead to more effective organizational performance. In some ways, both manager and researcher are less concerned with the subtleties and technicalities of data analysis and how this is performed, and more with the output of this analysis with particular regard to what it means for management and organizational practice and performance. Having said this, how data is analysed and interpreted, and in particular how effectively these processes are performed is crucial to developing recommendations and action programmes.
(Seale and Kelly 1998; de Vaus 2002; Thomas 2004) Put simply, ineffective data analysis can lead to a number of potentially disastrous outcomes with regard to tackling organizational and management problems. Just some of the possible results of ineffective or inappropriate data analysis methods and techniques include the following:
● Key cause and effect relationships may be missed entirely,
● Management may not be provided with a sufficient understanding of the nature of the management problem/issue being researched,
● Key data, which may have been expensive to collect may not be sufficiently explored and assessed,
● Sophisticated/complex data analysis techniques may begin to take precedence over understanding
Ultimately, ineffective or inappropriate data analysis may lead to key informa-tion being missed or misunderstood, and as a result, can sometimes lead to inappropriate courses of action. In the Hawthorne Studies example cited in ear-lier chapters, initially, a misunderstanding of what the observed results or data was telling the researchers led them to miss the observation that a major issue/problem affecting productivity in the organization where the research was being conducted was group processes and effects. As a result, the man-agement of the company initially took the wrong steps to improving produc-tivity by concentrating on the physical environment rather than the more important element of effective workgroup design and management.
Ineffective or inappropriate data analysis methods can lead to a waste of part of expensive data collection. Even worse, it can lead to a misunderstand-ing of the issues and problems bemisunderstand-ing researched, leadmisunderstand-ing in turn to inappro-priate courses of action. Effective data analysis, supported by the selection of appropriate data analysis methods is essential. However, it can be questioned what constitutes effective data analysis and what methods are available for this purpose? In analysing this question we can usefully distinguish between the purpose(s) of data analysis in any given research exercise, and between the two major categories of data types, namely quantitative and qualitative.
Together, the purpose(s) of data analysis and the two categories of data enable researchers to identify the range of applicable techniques of data analysis that might be utilized.
11.2 The purpose of analysis
Although we have seen that overall, analysis is the process of turning data into information that in turn can serve to develop concepts, theories, explanations or understanding, we can develop this notion of the purpose of data further, which in turn we can use to identify and ultimately select the most applicable techniques of data analysis. The assumption here, of course, is that the purpose of analysis is a major determinant of the technique(s) of data analysis selected.
Sharp and Howard (1996) make this assumption in relating the purpose of the analysis to examples of applicable techniques. In so doing, they provide an extremely useful taxonomy for analysis purposes, linking the different cat-egories of purpose to techniques. This is shown in Table 11.1.
You should note that although this framework provides a useful link between the purpose of research and the aims and techniques of analysis that relate to the purpose, it does not purport to show every category of purpose nor every available technique. However, the notion that purpose and techniques are linked, the latter deriving essentially from the former, is correct. The researcher
must, therefore, decide what the purpose of the analysis is defining, specific aims or outcomes. These aims or outcomes should link to the objectives of the research or consultancy project decided at the initial planning stages of the research/consultancy brief. As was noted in Chapter 5, in planning the con-sultancy and research process, methods of data analysis interpretation and diag-nosis should be decided at the planning stage of the project, and in turn should stem from the agreed upon research/consultancy objectives. In some ways, therefore, the methods of data analysis, like the data collection methods, are predetermined and certainly constrained by the research plan. Put another way, the researcher should not collect the data first and then decide how to best analyse it. Rather, the researcher should determine from the research objectives and other considerations such as time, resources, and so on, the type of data required, the methods of data collection and the methods of data analysis. These should all be part of a consistent and totally planned process. In fact, the meth-ods of data analysis may, to some extent, shape the earlier stages of data col-lection, and even the determination of research objectives, rather than always being the other way around. For example, lack of access to sophisticated tech-niques and tools of analysis that may require, extensive computer analysis may suggest a particular research design and method of data collection. Having said this, today’s professional researcher/consultant should be skilled in the full range of data analysis techniques and therefore this should be less of a con-straint or influencing factor on the research plan.
Table 11.1 Common tasks of analysis and techniques applicable to them
Purpose Aim of analysis Applicable techniques
Description Concept Content Analysis
formulation
Factor analysis Classification Cluster analysis Construction of Multiattribute scale Unidimensional scaling
measurement scales construction Multidimensional
scaling
Generation of Pattern recon Correlation methods
empirical
relationships Deprivation of Graphical techniques
empirical laws
Explanation and Policy analysis Loglinear analysis
prediction Experimental design
Theory Generation model
Regression model Path Analysis Source: Sharp J.A. and K Howard (1996) p. 108.
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11.3 Quantitative versus qualitative data analysis
The second major influence on the nature of the data analysis step, and the selection of the most relevant and effective techniques of analysis, relates to what has been described as being the two major categories or types of data col-lected as part of a research exercise, namely, quantitative versus qualitative data.
You will recall that we have discussed the distinction between these two major categories of data types at several points in earlier chapters. In Chapter 5 in particular, it was suggested that both methods of data collection and methods of data analysis would in part be determined by whether or not the researcher was interested in qualitative or quantitative data. This distinction between the two major categories of data is particularly important when it comes to the data collection and analysis steps of the research. It is certainly true that some tech-niques of data analysis are specifically designed and are only, therefore, applicable to quantitative data, whereas other techniques have been specifically designed for and are only applicable to data that is qualitative in nature. We shall examine these two categories of data and some of the techniques appli-cable to each shortly. However, we need to remember that often the distinction between qualitative and quantitative data is blurred. Some qualitative data can often be translated into, and analysed using some of the quantitative techniques, whereas some quantitative data often needs to be analysed further using quali-tative techniques. The point that is being made here is that as Cronbach (1975) points out, quantitative as opposed to qualitative is not a dichotomy, and the researcher may often combine both quantitative and qualitative analysis of the same data so as to develop a richer understanding of a phenomenon or issue through the data collected, while at the same time being able to use a combi-nation of techniques to check data for aspects such as representativeness, reli-ability and validity. Bearing this in mind, outlined below are some of the tools and techniques of data analysis for each category of quantitative and qualita-tive data.