Archive for the ‘Concepts’ Category

Ritualistic Science

April 18, 2010

Much of “science” today is not so much scientific as scientistic. That means that the forms and behaviors of science are used, but the underlying logic is missing. Scientism is very much like the phenomenon of cargo cults, in which Pacific islanders built airports and control towers out of wood in order to bring back the planes that came with World War II. They didn’t realize that air traffic control panels actually did something, so they simply built facsimiles out of wood.

We can see scientism in every aspect of published research today. We see it in papers that think that theory is a set of interconnected hypotheses, rather than as the reason why X leads to Y. We see it in work in which each hypothesis is justified by a collection of independent, even mutually contradictory, reasons why the hypothesis must be true. Instead of the hypotheses being tests of an underlying theory, the hypotheses are the principal claims of the theory, and the justifications for these hypothesis can be a smorgasbord of ideas that embody entirely different theories.

Another aspect of scientism is the belief that there are universal best practices in research that are independent of the research question and the research setting. As a result, there is a great deal of argumentation from authority. E.g., we use this measure because so and so did. We even see it used to justify hypothesis: we expect X to lead to Y because so-and-so said it would.

A tiny example of this ritualistic belief in best practices can be found in network analysis where people argue that certain variables *must* be collected. For example, it has been claimed that in organizational network research, every study must measure the “workflow” network, which indicates who is required to interact with whom because of the nature of their jobs. The idea is that this set of ties can determine many other ties, such as interaction, and so must be taken account of, regardless of the study objectives.

To borrow a page from the scientistic consider the following counter-argument from authority. Ron Burt is a major luminary in the field who has published more than 100 papers on dozens of research projects. How many of these included the workflow network? None. Mark Granovetter is also a major luminary. Does his work take account of the workflow network? No. What about Jim Coleman? Brian Uzzi? Jim Moody? Woody Powell? Anybody?

Graduate students need to be inoculated to generate antibodies against any prescription of this kind. The moment someone says “you can’t do meaningful research unless you include < insert favorite variable >” your scientific spider sense should start tingling and you should become wary. Such rules serve to replace and obviate the need for thought.

Groups versus Networks

January 24, 2009

What’s the difference between a group and a network? There is considerable confusion about this, and the question itself is complicit in the confusion.

Groups and networks are not alternatives to each other. We can point to a big leafy thing in our backyard and ask is that a tree or a bush? The dividing lines between trees and bushes maybe quite blurred, but the question is reasonable.

In contrast, asking whether something is a group or a network is anot a sensible question. A group defines a set of people, and the set of ties among those people is a network. Every group has a network as one of its aspects. So does any collection of people, such as the set of people attending a certain class. 

Furthermore, networks need not be connected. For example, at the beginning of a semester, the people attending a certain class may not have any connections, direct or indirect, with certain other members of the class. Later, these connections may develop. But it is always a network. 

A difference between groups and networks is that a group defines a set of actors, and a set of actors defines a network. 

Evolution of networks. It troubles people that networks can have no ties, or can have disconnected components. But this is important because it allows us to observe network evolution as it really is. Moody has some data showing that different components of a network form before it becomes connected, rather than starting from a core and diffusing outward.