data METODE teori — Datoen i dag er: 5.6.2020


Av Ronny Klæboe Dato: 31.8.2009

As one would expect there is an awful lot of work published on the various approaches to understanding or characterising science and scientific method. As a start, almost any introduction to the philosophy of science will contain a chapter outlining the main schools of thought. Search for texts by Wesley Salmon, Peter Suppes,

It’s common to find philosophers addressing scientific method by attempting to characterise explanation in science and doing this often means accounting for laws and causation. There are several good introductions to these issues. Try this one and note the references it contains:

Psillos, S. (2002). Causation and Explanation. Acumen.

This one’s a classic and well worth a read:
Hacking, I, (1983) Representing and Intervening, Cambridge: Cambridge University Press.

Do note times change in philosophy of science and recent work is not necessarily a natural progression of prior work. You mention Popper and Kuhn for instance, you will find philosophers today generally do not hold the same view of these authors and their debates about realism and empiricism they held once did. There are many nuances that are now taken into account that weren’t even several years ago. Recent work on the Vienna Circle is another example of this - positivism is not so straight forward a view as some texts describe.

As you might expect a relatively large literature has developed specialising in the philosophy of social science and economics as well as modelling in science.

Multidisciplinary attempts:
McKim, Vaughn R., and Stephen P. Turner. (1997) Causality in crisis?: statistical methods and the search for causal knowledge in the social sciences. Notre Dame, Ind.: University of Notre Dame Press.

Hegselmann, Rainer, Ulrich Müller and Klaus Troitzsch (eds.) (1996), Modelling and Simulation in the Social Sciences from the Philosophy of Science Point of View. Theory and Decision Library. Dordrecht: Kluwer.

More recent work:
Russo F. (2008) Causality and Causal Modelling in the Social Sciences. Measuring Variations, Methodos Series, Springer.

Magnani, Lorenzo, and Nancy Nersessian (eds.) (2002), Model-Based Reasoning: Science, Technology, Values. Dordrecht: Kluwer.

Winsberg, Eric (2001), “Simulations, Models and Theories: Complex Physical Systems and their Representations”, Philosophy of Science 68 (Proceedings): 442-454.

Something controversial to sort wheat from chaff:
Sklar, L. (2003). Dappled Theories in a Uniform World. Philosophy of Science 70 (2):424-441.

One area where latent variables were addressed by philosophers was the attempts to build a good account of probabilistic laws and causality. Two authors worth reading here are Nancy Cartwright and Judea Pearl. Though Pearl isn’t a philosopher (or, at least, I don’t think he takes himself to be one) he made several excellent contributions to philosophical debate in this area.

Of course, if you search the archives you will find many fine references and discussions on this issue by Paul, Denny and others.


I found the following books helpful (order is order of preference)

Proctor, R. W., & Capaldi, E. J. (2006). Why science matters: Understanding the methods of psychological research. Oxford: Blackwell Publishing.

Godfrey-Smith, P. (2003). Theory and reality. An introduction to the philosophy of science. Chicago, London: The University of Chicago Press.

Chalmers, A. F. (1999). What is this thing called science? Berkshire: Open University Press.

Fra Mulaik

Mulaik,S. A. (2004). Objectivity in science and structural equation modeling. In Kaplan, D.
  (Editor) _The Sage handbook of quantitative methodology for the social sciences. Thousand
  Oaks, CA: Sage Publications. pp. 425 - 446.

Mulaik, S. A. (2009). _Linear Causal Modeling with Structural Equations_. Boca Raton,FL, London,
  New York: Chapman and Hall/CRC Taylor and Francis Group.

In the last named book, see especially my chapter on causation.  It represents a historical review along with an expression of my “naturalistic, cognitive science” approach to the philosophy of science, objectivity, and causality.
After studying the history of science and the philosophy of science for about 20 years, I came to a point where I thought it would profit by a new approach that has developed among some philosophers of science at the end of the 20th Century, known as the naturalistic or cognitive approach to science.  I have worked within that framework.
As we know more and more about how the brain works, about how we function with certain cognitive schemas, and realize how incoherent the introspectionist accounts of knowing are, we find a science of science itself makes more sense.  Yes, it is to some extent circular, but not entirely. We can see familiar patterns of thought and action in different domains of behavior. We can observe the brain functioning as someone thinks, using MRI’s, CAT scans, and EKG’s of the brain, and fill in gaps in what is ordinarily unconscious for us. And cognitive experiments also yield objective knowledge of how we know the world.  Science has replaced much of the subjective, introspectionistic, understanding of how we know the world with experimental and observational knowledge of brain functioning and behavior.

  Seeing objectivity as central to science, which I argue comes from a metaphoric schema, “SCIENCE IS KNOWLEDGE OF OBJECTS” mapped from perceptual schemas for object knowledge to the domain of conceptual knowledge involving the integration of experiences over longer and wider time and space intervals, science develops concepts (not
percepts) of abstract objects.  Objects, according to J. J.
Gibson, the realist expert on the psychology of objective perception, are invariants in the perceptual field that are independent of the actions and motions in the world as we as embodied observers move around, act upon and observe the world, or as the objects themselves move around. That leads to why we seek to test hypotheses in SEM, for these are about invariant, causal relations between variables, which represent the attributes of objects and their relations of determination. The other main influence on my thought are the works of George Lakoff, the cognitive linguist at the University of California, Berkeley. He has laid bear how conceptual thought is structured by metaphors and blends of cognitive schemas taken from embodied perceptual and motor experience. In a series of books, that together are a tour-de-force, he has revealed the metaphors of philosophy, the metaphoric toolbox of mathematics, the metaphors of political thought, the metaphors of literature, and the metaphors of everyday thought—all conceptual thought is at bottom metaphoric in its structuring.  That we like to think causally with path diagrams, where paths or arrows are like conduits conveying proportional effects of variation of causal variables to constitute effect variables, is just a special case of this approach.  Visual metaphors are rampant, because vision synthesizes in space information from numerous sources and kinds, and relations are distances or connections between objects in space. We seek to render the perspicuous point of view, that sees all in a single view. Mirror neurons are behind all this ability to think visually without seeing.

Stan Mulaik

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