Our second major category of data types relates to the extent to which data is number-based or otherwise in this respect it is conventional to distinguish between quantitative and qualitative data.
Ghosh and Chopra (2003), in their ‘Dictionary of Research Methods’ define these two types of data as follows:
Qualitative data is data in the form of descriptive accounts of observations or data which is classified by type.
Quantitative data is data which can be expressed numerically or classified by some numerical value.
Quantitative data is often thought of as being more objective and scientific than its qualitative counterpart and is therefore associated with the more tra-ditional scientific approaches to research as used in the physical sciences and discussed in Chapter 3. Because quantitative data is in the form of numbers, it can often be analysed using standard statistical techniques to, for example, test validity. Quantitative data of course implies that what is being measured or researched can be quantified in the first place. It is therefore only applicable to phenomena that can be quantified and measured.
Qualitative data on the other hand, relates to data that cannot be subjected to quantitative or numerical analysis. It is therefore associated with phenomena that cannot be, or is difficult to quantify.
In the past, qualitative and quantitative data have been seen not only as different types of data but essentially from different perspectives or approaches with regard to data and research methodology. Oakley (1999), for example, is not saying anything unusual when he suggests that qualitative and quantitative research and data are essentially different paradigms. He compares and contrasts them as shown in Table 6.1.
The dichotomy between quantitative and qualitative research stems essen-tially from the notions of what constitutes scientific as opposed to non-scientific research methodologies. The natural sciences that are primarily concerned with phenomena that are quantitative in nature have, not surprisingly, advanced knowledge in their subjects through the application of quantitative research tech-niques to the extent that in some quarters only quantitative data is felt to have any real value and validity. In the social sciences, however, much of what the researcher is concerned to measure and evaluate is qualitative in nature and, therefore, qualitative research techniques are more appropriate. Increasingly, it is recognized that there is much overlap between qualitative and quantitative data and research techniques and that at the very least each type of data can make valuable contributions towards the development of knowledge or in the solving of specific problems (Hakim 2000). The overlap and similarities between qualitative and quantitative research are illustrated well by Blaxter et al. (2001) and illustrated in Table 6.2.
We can see that there are as many similarities as there are differences between qualitative and quantitative research and therefore, what at first sight appears to be dichotomy between them is less clear-cut than at first sight it appears.
Table 6.1 Qualitative versus quantitative research
Qualitative paradigms Quantitative paradigms
● Concerned with understanding ● Seek the facts/causes of behaviour from actors’ own social phenomena frames of reference
● Naturalistic and uncontrolled ● Obtrusive and controlled
observation measurement
● Subjective ● Objective
● Close to the data: ● Removed from the data:
the ‘insider’ perspective the ‘outside’ perspective
● Grounded, discovery-oriented, ● Ungrounded, verification oriented, exploratory, expansionist, reductionist, hypothetico-deductive descriptive, inductive
● Process-oriented ● Outcome-oriented
● Valid: real, rich, deep data ● Reliable: hard and replicable data
● Ungeneralizable: ● Generalizable: multiple
single case studies case studies
● Holistic ● Particularistic
● Assume a dynamic reality ● Assume a stable reality Source: Oakley, A. (1999).
Activity 6.3. To what extent do you feel the fact that qualitative research is concerned with phenomena or events that cannot readily be measured, and does this mean that such research is not scientific?
6.3 Methods of data collection
If quantitative versus qualitative and primary versus secondary represent the first broad categories of data types and data collection, the researcher can now distinguish between various research approaches, again, with a view to select-ing those which are most appropriate. Determinselect-ing the research approach to data collection is often referred to as the research methodology. Although there are several facets to the design and categorization of research methodologies a major distinguishing feature between different research methodologies is indeed the different approaches to data collection. Unlike our categories of primary versus secondary and quantitative versus qualitative data types, however, when it comes to types of research approaches for data collection there exist several models or taxonomies of classifying the various research methodologies.
Examples of taxonomies of methods of data collection include those of Becker (1998), Gill and Johnson (2002) and Seale (2004). Although these taxonomies of data-collection methods have differences, the following represent the major alternative research methods with regard to data collection.
● secondary data collection,
● case studies,
● experimentation,
● observation/ethnographics,
● interviews and surveys,
● action research.
These, then, represent the major approaches or methodologies of data collection that the researcher may use. They encompass the full continuum from what was Table 6.2 Similarities between qualitative and quantitative research
● While quantitative research may be mostly used for testing theory, it can also be used for exploring an area and generating hypotheses and theory.
● Similarly, qualitative research can be used for testing hypotheses and theories, even though it is mostly used for theory generation.
● Qualitative data often include quantification (e.g. statements such as more than, less than, most, as well as specific numbers).
● Quantitative approaches (e.g. large-scale surveys) can collect qualitative (non-numeric) data through open-ended questions.
● The underlying philosophical positions are not necessarily as distinct as the stereotypes suggest.
Source: Blaxter et al. (2001) p. 65.
referred to in Chapter 3, at one extreme, as nomothetic methods of which, for example, experimentation is probably the best example, through, at the other extreme, to what we referred to as ideographic methods like action research.
Because these are, in our view, the major alternative techniques of data collec-tion, we shall be considering each of them in more detail in the chapters that follow in order to explore in-depth the meaning of, approaches to, uses of and advantages and limitations of each of them. We shall see that within each major category of research methodology there are also a variety of research techniques and instruments that again the researcher can choose from. For example, we shall see that when it comes to surveys, the researcher can choose between a number of specific research techniques and instruments for collecting survey data such as, postal questionnaires, or more open-ended face to face interviewing techniques. We cannot, encompass every single possible research instrument and technique but we shall be considering some of the most important and useful ones in the context of consultancy research.
As a prelude to examining the various research methods of data collection, however, we need to consider some of the issues related to data and data collection in general. Related to this, we also need to consider some of the key criteria in evaluating and selecting between our alternative methods of data collection.
6.4 Issues in data collection
Irrespective of the method(s) of data collection there are a number of issues with regard to data and data collection with which the consultant/researcher needs to be familiar. The most important of these issues are now discussed.