Specifying Variables and Concepts of the Research

Specifying a model’s different concepts and variables is, above all, dependent on the researcher’s chosen approach, which can be inductive or deductive, qualita­tive or quantitative. There are two levels of specification. One is conceptual, and enables researchers to determine the nature of concepts. The other is opera­tional, and enables researchers to move from concepts to the variables that result from field observation. An inductive approach specifies conceptual and opera­tional levels simultaneously, whereas a deductive approach moves from the con­ceptual level to the operational level. But even with a deductive approach, the researcher may need to return to both of these two levels at a later stage.

1. Qualitative Method

A particular characteristic of qualitative methods is that they do not necessitate numerical evaluation of the model’s variables. Specification therefore involves describing the model’s concepts without quantifying them. We can, however, evaluate concepts according to a number of dimensions, representing the vari­ous ‘forms’ they can take. There is no reason why researchers should not use quantitative data to specify certain of their model’s concepts.

1.1. Qualitative inductive method

When researchers decide to employ a qualitative/inductive method, field data is used to draw out the concepts that represent the phenomenon being studied. Glaser and Strauss (1967) propose an inductive method of coding, which they call ‘open coding’. This method enables ‘the process of breaking down, examin­ing, comparing, conceptualizing, and categorizing data’ (Strauss and Corbin, 1990: 61) to occur. It has four interactive phases:

Phase 1: Labeling phenomena This phase involves taking an observation – spoken or written – and giving a name to each incident, idea, or event it contains. To facilitate this first ‘conceptualization’ (passage from data to concept) researc­hers can ask themselves the following questions: what is it? and what does that represent?

Phase 2: Discovering categories This phase involves grouping together the concepts resulting from the first phase, to reduce their number. For this catego­rization, researchers can group together those concepts that are most closely related to each other. Alternatively, they can group together their observations, while keeping the concepts in mind.

Phase 3: Naming a category Researchers can invent this name themselves or borrow it from the literature. It can also be based on words or phrases used by interviewees.

Phase 4: Developing categories in terms of their properties and dimensions Now the researcher defines the ‘properties’ and ‘dimensions’ of each of the categories created during the previous phases. Properties are a category’s characteristics or attributes, while dimensions represent the localization of each property on a continuum. Dimensions translate the different forms the property can take (a phenomenon’s intensity, for example). They enable researchers to construct dif­ferent profiles of a phenomenon, and to represent its specific properties under different conditions.

Miles and Huberman (1984a) propose a process which, while remaining inductive, is based on a conceptual framework and gives researchers a focus for collecting data in the field. They suggest a number of tactics for drawing out these concepts, which we will explain briefly. The aim of the first tactic is to cal­culate, or isolate items that recur during interviews or observations. This tactic aims at isolating the model’s concepts (which the authors also call themes). The second tactic involves grouping the elements in one or several dimensions together to create categories. This can be done by association (grouping together similar elements) or dissociation (separating dissimilar elements). The third tactic is to subdivide the categories created earlier; to explore whether a designated category might actually correspond to two or more categories. Researchers need to be cautious about wanting to subdivide each category, and so guard against excessive atomization. The fourth tactic is to relate the particular to the general – to ask: of what is this element an example? and does it belong to a larger class?

The fifth and final tactic involves factorizing. The term ‘factor’ stems from factor analysis, a statistical tool used to reduce a large number of observed vari­ables to a small number of concepts that are not directly observed. Factorization occurs in several stages. First, researchers take an inventory of items arising during their interviews or observations. They then group the items according to a logical rule they have defined in advance. The rule could be that items arising concomitantly during the interviews should be grouped together. Alternatively, one could group together items translating a particular event. At the end of this phase, researchers have several lists of items at their disposal. They then describe the various items so as to produce a smaller list of code names. They group these code names together under a common factor, which they then describe.

The two methods explained above enable researchers to draw out the model’s variables, and then its concepts, from observations in the field: to spe­cify the model’s components. These methods are inductive, but can still be used in a theoretical framework. In both cases, researchers are advised to loop back continually between fieldwork data and relevant literature during the coding process. In this way, they should be able to specify and formalize the variables (or concepts) they have defined.

1.2. The qualitative deductive method

With this method researchers draw up a list of the concepts that make up the phenomenon being studied, using information gleaned from the results of earlier research. They then operationalize these concepts using data from the empirical study so as to obtain variables. However, researchers adopting this method are advised to enrich and remodel the concepts obtained from the lit­erature using data gathered in the field (Miles and Huberman, 1984a). To do this, they can turn to techniques for specifying variables that are appropriate for use in inductive research.

Example: Specifying a model’s variables using a qualitative deductive method

The principle of this method is to start with a list of codes or concepts stemming from a conceptual framework, research questions or initial hypotheses (or research propositions). These concepts are then operationalized into directly observed vari­ables. In their study on teaching reforms, Miles and Huberman (1984a) initially conceptualized the process of innovation ‘as a reciprocal transformation of the innovation itself, of those making use of it, and the host classroom or school’ (Miles and Huberman, 1984a: 98). They drew up a list of seven general codes (or concepts): property of innovation (PI), external context (EC), internal context (IC), adoption process (AP), site dynamic and transformation (SDT), and new configurations and final results (NCR). This list breaks down into subcodes (or variables), which can be directly observed in the field (operationalization of concepts). For example the code internal context breaks down into: characteristics of the internal context (CI-CAR), norms and authority (CI-NORM) and history of the innovation (CI-HIST), etc.

2. The Quantitative Method

Quantitative causal modeling techniques place the identifying variables and concepts center-stage. They have systematized the theoretical distinction between variables and concepts.

Usually, causal models contain variables that are not directly observable (known as latent variables, concepts or constructs) and directly observable vari­ables (known as manifest or observed variables, indicators, or variables of measurement). The notion of a latent variable is central in human and social sciences. Concepts such as intelligence, attitude or personality are latent vari­ables. Manifest variables are approximate measurements of latent variables. A score in an IQ test can be considered as a manifest variable that is an approxima­tion of the latent variable ‘intelligence’. In causal modeling, it is recommended that each latent variable should be measured by several manifest variables. The latent variable is defined by what happens within the community of diverse manifest variables that are supposed to measure it (Hoyle, 1995). From this point of view, latent variables correspond to the common factors we recognize in factor analysis. They can, as a result, be considered as devoid of measurement errors.

2.1. The quantitative deductive method

In specifying concepts, there are several possibilities. The model’s concepts may already be precisely defined. In strategy, for example, the concept of a strategic group univocally indicates a group of firms in a given sector that have the same strategy. Researchers using such a concept in their model will not be spending time redefining it. Johansson and Yip (1994) provide other examples of identifying the concepts of industry structure, global strategy, organization structure, etc. Already defined methods of operationalizing concepts may even be available. This is true in the case of the aforementioned strategic groups. Strategic groups can be operationalized through a cluster analysis of companies, characterized by variables that measure strategic positioning choices and resource allocation. If a method of operationalizing concepts is already avail­able, the researcher’s main preoccupation will be to verify its validity.

That said, even when the concepts are defined and the operationalization method has been determined, researchers are still advised systematically to try and enrich and remodel the variables/concepts stemming from earlier works by means of observation or theory.

Researchers need to clearly define their concepts and clearly formulate their method of operationalization. They can opt for either an inductive, qualitative or quantitative method to specify the concepts.

2.2. Quantitative inductive method

While quantitative methods are more readily associated with deductive research, they can very well be called into use for inductive research. It is altogether possible, when specifying a model’s variables and concepts, to use statistical methods to draw out these concepts from the available data. This practice is very common in what is known as ‘French’ data analysis, which was popular­ized by Jean-Paul Benzecri’s team (see Benzecri, 1980; Lebart et al., 1984). In general, it involves using a table of empirical data to extract structures, classes and regularities.

Data analysis methods such as correspondence analysis, factor analysis or cluster analysis (classification analysis) are fruitful and very simple methods for drawing out concepts from empirical data. For example, several strategic management researchers have been able to use factor analysis to identify ‘generic strategies’ that can be classed as factors stemming from data. The work of Dess and Davis (1984) illustrates this process. Equally, many other researchers have turned to cluster analysis to identify ‘strategic groups’ – classes stemming from classification analyses. Thomas and Venkatraman (1988) present many such research works.

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