Methods of Discourse and Representation Analysis

Discourse and representation analysis methods generally require three major steps: data collection (Subsection 1), coding (Subsection 2), and analysis (Subsection 3).

1. Collecting Discourse or Representations

There are two main types of methods for collecting representations or dis­course: structured (or a priori) and non-structured methods.

1.1. Structured or a priori methods

The objective of structured methods is to directly generate a subject’s represen­tation concerning the problem or theme that the researcher is interested in. These representations are established graphically in the form of cognitive maps (for example, Figures 16.2 and 16.3 later in this chapter). Structured methods are not based on natural discourse data. They can not, therefore, be used for content analysis (method based on coding ‘natural’ textual or interview data).

With structured methods, the researcher establishes the subject’s represen­tation using a predefined framework (whence the term ‘a priori for this type of method). Whatever type of representation the researcher wishes to generate, structured methods require two steps:

1. The researcher chooses a set of categories (to establish category schemes) or concepts – also known as constructs or variables – (to establish cognitive maps). A category is a class of objects that are supposed to share similar attributes. If we are concerned, for example, with an executive’s representa­tion of the competitive environment in the textile sector, the following cate­gories might be selected: ‘textile firms’, ‘firms selling trendy fabric’, ‘classics’, ‘firms with top-quality merchandise’, ‘lower-quality’, ‘successful firms’, ‘less successful’, etc. A concept, a construct or a variable is an idea that is likely to describe a particular problem or domain, and that can acquire dif­ferent values or represent the level, presence or absence of a phenomenon or an object (Axelrod, 1976), for example, ‘corporate cost-effectiveness’, ‘the manager has a strategic mind’, or ‘the employees are highly adaptable’.
2. Once a certain number of elements have been selected (generally around ten), the researcher submits them to the respondent and asks what kind of link he or she sees between them: hierarchical (is Category A included in Category B?); similarity or difference (used to establish category schemes); or influence or causal links (does A influence B? If so, how: positively or negatively), which are used to establish, for example, cognitive maps.

The advantage of structured methods is that they generate reliable data: researchers will obtain the same type of data if they use the same methods on other subjects or the same subjects on different occasions (stability), and if other researchers use these methods, they will also get the same results (replicability) Laukkanen, 1992: 22). These methods do not require data coding, they spare the researcher from a tremendous amount of pre-collection work and from the reli­ability problems related to this phase. They are therefore usable on a large scale. But the main advantage of these methods is that they generate representations emanating from the same set of initial concepts or categories. Representations established in this manner can thus be immediately compared to each other and are easily aggregated.

The major drawback in this type of method is that the elements of the rep­resentation do not originate from the subject. So we run the risk of dispossess­ing the subject of part of its representation or even of introducing elements that do not belong to it (Cossette and Audet, 1992).

1.2. Non-structured methods

The purpose of these methods is to generate data that is as natural as possible.

These methods dissociate the data collection phase from the coding and analysis phases.

Interview methods If the researcher wishes to establish the representation of a subject concerning a particular domain, or if there is no existing data about the theme in question, the researcher will collect discourse data from a free or semi­structured interview. These interviews are generally recorded and then retrans­cribed in their entirety in order to then be coded (for more details about this step, see below).

The main advantage of these methods is the validity of the data produced. The data, having usually been generated spontaneously by the respondent or in response to open questions, is more likely to reflect what they really think (Cossette and Audet, 1992). In addition, these methods generate much richer data than do structured methods.

The logical counterpart to these advantages is that these methods reduce the reliability of the data produced. And, insofar as they demand a lot of work on the researcher’s part before the data can be coded, they are not practicable on a large scale. In fact, they are mostly used for in-depth studies of discourse or representations of a small number of subjects (see Cossette and Audet, 1992).

Documentary methods When the researcher has transcriptions of discourse or meetings, or else documents (for example, strategic plans, letters to shareholders, activity reports) at their disposal, they will use the documentary methods.

The main advantage of these methods is that they avoid data reliability problems, as the researcher does not intervene in the data-production process. In addition, these methods do not require any transcription work.

These methods are commonly used to establish the representation or to analyze the organization or group’s discourse.

2. Coding

The coding process consists of breaking down the contents of a discourse or text into units of analysis (words, phrases, themes, etc.) and integrating them into categories which are determined by the purpose of the research.

2.1. Defining the unit of analysis

The unit of analysis is the basic unit for breaking down the discourse or text.

Depending on the chosen method of analysis (content analysis or cognitive mapping) and the purpose of the research, the researcher usually opts for one of the six units of analysis below (Weber, 1990):

• a word – for example, proper or common nouns, verbs or pronouns
• the meaning of a word or group of words – certain computer programs can now identify different meanings for the same word or expression
• whole sentences
• parts of sentences of the subject/verb/object type. For example, the sen­tence, ‘The price reduction attracts new customers and stymies the compe­tition’, will be divided into two units of analysis: first, ‘The price reduction attracts new customers’, and then, ‘The price reduction stymies the compe­tition’. Identifying this type of unit of analysis, which does not correspond to a precise unit of text (for example, word, sentence) can be relatively tricky
• one or more paragraphs, or even an entire text. Weber (1990) points out the disadvantages in choosing this type of unit of analysis, in terms of coding reliability. It is much more difficult to come to an agreement on the classifi­cation of a set of phrases than of a word.

2.2. Classifying units of analysis

Once the units of analysis have been pinpointed in the discourse or text, the next step is to place them in categories. A category is a set of units of text. All units of analysis belonging to the same category should have either similar meanings (synonyms like ‘old’ and ‘elderly’, or equivalent connotations like ‘power’ and ‘wealth’) or shared formal characteristics (for example, one cate­gory could be ‘interrogative sentences’, another ‘affirmative sentences’, a third ‘silence’, a fourth ‘active verbs’, another ‘passive verbs’).

The more clear and precise the definitions of the units of analysis and categories are, the more reliable the coding will be. For this reason, it is advisable to establish a protocol specifying the rules and definitions of these elements.

2.3. Coding reliability

The combination of the ambiguity of discourse and the lack of precision in the definitions of categories and coded units or other coding rules makes it neces­sary to check coding reliability.

Reliability can be declined into three more specific subcriteria (Weber, 1990):

• Stability: this is the extent to which the coding results are the same when the same data is coded by the same coder more than once.
• Accuracy: this dimension measures the proximity of a text’s classifications to a standard or norm. It is possible to establish this when the standard coding for a text has been elaborated. This type of reliability is rarely evaluated. Nevertheless, it can be useful to establish it when a coding protocol created by another researcher is being used.
• Replicability (or inter-coder reliability): this criterion refers to the extent to which the coding produces the same results when the same data is coded by different coders. This is the most common method for evaluating coding reliability.

3. Analyzing Data

Analyzing data is equivalent to making inferences based on the characteristics of the message which appeared in the data-coding results. The researcher can decide to analyze more specifically the structure of the representations or their contents, using quantitative or qualitative methods, in order to compare, describe, explain or predict objectives which all require different methods of analysis.

3.1. Analyzing content or structure

Content analysis consists of inferring the signification of the discourse through detailed analysis of the words used, their frequency and their associations. The different modalities of analysis will be described in greater detail later on in this chapter, in relation to the methodology used (content analysis or cognitive mapping).

When analyzing the structure of a text, discourse or representation, the goal is to discover the rules of organization of the words, sentences and themes employed. Analysis of the structure of discourse or representations, although it does not enable us to perceive all of the thought or decision-making processes, does reveal certain cognitive characteristics, such as the subject’s cognitive com­plexity. Structure analysis can be used in particular for explaining or predicting behavior.

3.2. Quantitative or qualitative analysis

After coding, interpreting the text or discourse data can be done with either quantitative or qualitative techniques. Quantitative analyses depend essentially on counting the units of analysis, or on more elaborate statistical analyses. These can be performed with the help of specialized software. Qualitative analyses allow us to interpret the arrangement of these units by placing them in a more global context. These analyses can be based on procedures which are not speci­fic to discourse or text data analysis, such as, for example, seeking the opinions of experts. These judges, who could be the researcher himself or herself, members of the organization under study, the subject interrogated or outside experts, will evaluate the similarities or differences in the coded data in a more global manner.

These quantitative and qualitative analyses are complementary and should be used conjointly for a richer interpretation of the data.

3.3. Describing, comparing, explaining or predicting

Analyzing discourse or representation can be done solely for the purpose of description. In that case, the point is to describe, using the data collected, the contents or structure of a text or representation. The researcher can also attempt to describe the structure of a discourse’s argumentation, or the state of beliefs of an active member of the organization (see Cossette and Audet, 1992).

If the researcher’s goal is to compare the discourse or representations of several different individuals, groups of individuals, or organizations, or to eval­uate their evolution over time, then they will have to reveal the similarities and differences in their contents or structure (see Laukkanen, 1994). The researcher can undertake quantitative¹ or qualitative comparative analyses. These methods will be presented further on in this chapter.

The researcher can also attempt to explain and, by extrapolation, predict, certain phenomena or behavior through discourse and representation analysis. For example, exposing important or antagonistic concepts within a representa­tion can testify to their importance for an individual or organization, and there­fore explain some of their behavior or decisions in situations where these concepts are activated (see Komokar, 1994).

After this overview of the general steps in representation and discourse analysis methods, we will present two of these methods more precisely here: content analysis and cognitive mapping.

Source: Thietart Raymond-Alain et al. (2001), Doing Management Research: A Comprehensive Guide, SAGE Publications Ltd; 1 edition.