Theory, Instrumentation and Data

Workers in the history, philosophy, and social studies of science are increasingly taking a pragmatist, or pragmatist-compatible, approach to the study of instrument functioning. Because these workers focus on defining instruments by what they do, they stress that "instruments often determine theory, because instruments determine what is possible, and what is possible determines to a large extent what can be thought" (Hankins & Silverman 1995).

This focus on instruments determining theory is emerging partly in response to the problems raised by the post-positivist claims that data are at least theory-laden (Kuhn 1961; Hanson 1958), and more often than not fully theoretical, which of course was the perspective that undermined the positivists' exclusive focus on data as the determinant of theory.

A more balanced alternative to this sequence or prioritization of determinations is offered (or at least suggested) by Ackermann (1985), Wise (1988), Daston & Galison (1992): theory, instruments and data each serve to mediate the relationship of the other two in a spiraling series of dialectical interactions shaped by the way each impacts the interpretations of the other two. The construct is the object of the ongoing conversation. It does not matter at all which of the three is the initial point of entry into the process as long as each pair of the three components is mediated as fully as possible by the other component.

Because the psychosocial sciences have such poorly designed instruments, theory and data have suffered accordingly. As Ackermann (1985: 168) says, "What the human sciences require for more dramatic progress is not simply more data (of the same kind), as many empiricists have stated, but new instrumentation for obtaining data, or reasonable theoretical restrictions of data domains so that more exhaustive explanatory possibilities can be tried."

Rasch models are making both new instrumentation and new theoretical restrictions of data domains possible, and these have resulted in more exhaustive explanatory possibilities. We have not realized the full potential of the explanatory possibilities within our grasp largely because of our failure to take full advantage of the possibilities for generalization presented by our methods and results (Fisher 1997a, 1997b).

we have taken only the first step in the creation of measurement systems, that in which objective reference-standard construct expressions (scale-free measures) have begun to emerge as stable entities across independent experiments. This step in itself is substantive new knowledge, but it won't result in full quantification until the second step is taken and different instruments measuring the same thing are made traceable to a common metric via metrology.

O'Connell (1993: 166) states the problem we face exactly: "The challenge to social scientists is to stop taking the universality of entities like the volt for granted, and to start treating the volt like the society which it truly is - a distributed collective connected by continually renewed structured relations of exchange and authority. Scientific entities are not universal until scientists or their technicians take the trouble to make them so."

William P. Fisher, Jr.

Ackermann, J. R. (1985). Data, instruments, and theory: A dialectical approach to understanding science. Princeton, New Jersey: Princeton University Press.

Hankins, T. L., & Silverman, R. J. (1999). Instruments and the imagination. Princeton, NJ: Princeton University Press.

Daston, L., & Galison, P. (1992, Fall). The image of objectivity. Representations, pp. 81-128.

Fisher, W. P., Jr. (1997a). Physical disability construct convergence across instruments: Towards a universal metric. Journal of Outcome Measurement, 1(2), 87-113.

Fisher, W. P., Jr. (1997b). What scale-free measurement means to health outcomes research. Physical Medicine & Rehabilitation State of the Art Reviews, pp. 357-373.

O'Connell, J. (1993). Metrology: The creation of universality by the circulation of particulars. Social Studies of Science, 23, 129-173.

Wise, M. N. (1988). Mediating machines. Science in Context, 2(1), 77-113.

Theory, Instrumentation and Data. Fisher, W.P. Jr. … Rasch Measurement Transactions, 2000, 14:3 p.760

Rasch Books and Publications
Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, 2nd Edn. George Engelhard, Jr. & Jue Wang	Applying the Rasch Model (Winsteps, Facets) 4th Ed., Bond, Yan, Heene	Advances in Rasch Analyses in the Human Sciences (Winsteps, Facets) 1st Ed., Boone, Staver	Advances in Applications of Rasch Measurement in Science Education, X. Liu & W. J. Boone	Rasch Analysis in the Human Sciences (Winsteps) Boone, Staver, Yale
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