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2. WHAT'S A METHODOLOGY?

Throughout the literature, confusion reigns about several terms that occur frequently in the context under consideration. Words like method, technique, instrument, and methodology are often used indiscriminately. To clarify my point of view, a definition is given of some of these terms in a way that seems to hang together. These definitions, however, are idiosyncratic. They are not universally known, let alone adopted. As this is not the place to discuss these issues, it will be assumed that they are useful in making distinctions that matter.

In its original meaning, a methodology refers to knowledge about methods. This immediately implies that though methodology and methods are in the same domain, they are not the same. The former clearly is some kind of meta-knowledge if we assume that methods also contain knowledge. The next question to answer is about the nature of this (meta)knowledge. It seems to be different from the kind of knowledge we encounter in most scientific theories, which are mostly about causal relations ("laws of nature") that hold in a particular domain. Probably nobody will argue that methodological knowledge is in the same class. What is commonly understood to be methodological knowledge is "know what," "know-how," and "know when." These "knows" are normative or prescriptive: they don't describe a state of the world, but prescribe how a sensible agent should act to achieve a certain goal. Therefore, the quality of prescriptive knowledge is not whether it enables you to "predict" or "explain" something, but whether you have achieved the goal. Additional criteria may come in here, like speed and cost. The bottom line for any prescription is that it should improve goal achievement over "unaided" behavior.

Prescriptive statements may differ greatly in preciseness. For example, the prescription "work hard" is rather imprecise if the goal is to become rich. In the same vein, shouting "faster" to a runner trying to beat a world record will generally not be of great help (though the motivating power of such exhortations should not be underestimated). At the other end of the spectrum, we come across very detailed specification about what, how, and when. Anybody who has submitted a funding proposal to major funding agencies has experienced the myriad prescriptions one has to follow about how and when to submit. Not following them usually misses the goal: the proposal will be rejected before it has been judged. The most restrictive prescriptions we know are computer algorithms, which leave no room for interpretation by the agent (the computer). Methodological knowledge of the type we are discussing here, will clearly fall between these two extremes. If it's too general, it will most of the time not be of great help; if it's too specific, it will suffer from very limited applicability and a high vulnerability to slightly different contexts.

To pull these notions together, the methodological pyramid2 introduced by Wielinga et al. (1994) is a convenient way to characterize what is involved in a methodology (see Figure 1).


2The metaphor of a "pyramid" is not chosen accidentally. Most methodologies rely on a limited number of key principles, which in turn spawn more elaborate theories that can be operationalized in a set of methods/techniques, which can be implemented in different tools and used by a large number of users.

The world view top layer of the pyramid refers to the principles and assumptions that underlie the methodology. They often include the goal(s) that is being served. The theory layer elaborates these principles and assumptions and forms the core of the knowledge in the domain of the methodology. Methods and techniques operationalize the content of the theories, the main "how" part of it. Tools are computerized instances of methods and techniques in the previous layer. Being computerized often requires additional use knowledge attached to them. The use layer represents the touchstone of a methodology. It will reveal shortcomings in the prescriptions provided by the layers above, which will lead to revisions in the different components of the methodology. From the arrows in Figure 1 it can be gauged that the higher in the pyramid an arrow ends, the more serious the repercussions for the methodology are. If the methodology hangs together, a change in its assumptions and principles will propagate through the pyramid, leading to major modifications.


FIGURE 1 The methodological pyramid.

The pyramid depicted in Figure 1 can be illustrated by means of a well-established and fairly complete example. In the social sciences there has been for many decades a concern about how to conduct "proper" research. The dominant definition for "proper" refers to the goal of obtaining time, location, and observer-independent knowledge: that is, knowledge that can be generalized, not unlike the "laws of nature." Without going into the philosophical merits of these goals, it can be said that for more than three-quarters of a century, a systematic methodology in the sense of Figure 1 has been developed that can assist and guide the researcher in achieving these goals. To the world view principles mentioned above, a few more are added that are mainly borrowed from statistics (e.g., uncertainty). These principles have spawned an extensive set of theories (prescriptive statements) about conducting research. Some are based on statistics, for example sampling theory and inferential statistics; others are based on more general theories about human behavior (e.g., response biases). Almost all theories became embodied in methods and techniques that operationalize the theories (e.g., how to draw a random sample, how to phrase questions to avoid response bias) and, in particular, the ones based on statistics are now widely available in computerized tools (e.g., SPSS™, but also tools for designing experiments). This body of knowledge has grown over the years based on feedback from use. The discovery that not all variables that are interesting in social science have well-defined statistical distributions has led to the incorporation of nonparametric statistics in the theory and subsequently in the methods and tools. An example of a major addition to the world view is the notion that not always all hypotheses are equally likely, which is the cornerstone of classical statistics. This led to including Bayesian statistics in the methodology, thus increasing the scope and applicability of the methodology.


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