Kuhn and Measurement

Thomas Kuhn (1961, 1970) contrasts the presentation of measurement in science textbooks with the way it looks in scientific journals. As is now well known, he points out that textbooks smooth out the social, economic, political and methodological wrinkles of research to display a seamless conformity with the rules of experimental design. "Pedagogy misdirects the historical imagination about the relation between creative science and measurement" (1961, p. 169). The purpose of textbooks is to familiarize students with theories and show them what measures constitute reasonable agreement with them; they learn how to read the literature, not how to create or evaluate it.

The vast majority of measures actually made in the natural sciences are ones that Kuhn (1961, p. 168) terms "mopping up operations"; these fill the vacuum created by the latest theoretical breakthrough, consolidating its position with confirmed observations. Another very large category of measures are those made simply to gather factual information. Their numerical details are irrelevant to the theory being tested or to the application devised.

Kuhn's argument is that, because of the way we are socialized into science and because of the dull, unexciting nature of most measurement work, we have the nearly indomitable misperception that data exist in the structure of our measures "out there" in nature. Measures are construed from the textbook point of view as the "stubborn and irreducible facts" to which scientists must make their theories conform, but when we actually look at how measurement is practiced by researchers, we see something different.

Kuhn (1961, p. 171) points out that in scientific practice, as seen through the journal literature, the scientist often seems rather to be struggling with "facts", trying to force them into conformity with a theory the scientist does not doubt. Quantitative facts cease to seem simply "the given". They must be fought for and with, and in this fight the theory with which they are to be compared proves the most potent weapon. Often scientists cannot get numbers that compare well with theory until they know what numbers they should be making nature yield.

William P. Fisher, Jr.
Louisiana State University Medical Center

Moral for Rasch: We must wrestle with the data to force them to produce measures meaningful to theory - both measurement theory and construct theory. We need both "more and less" and "more of something" to advance science, commerce and common sense.

Kuhn T. S. 1970. The Structure of Scientific Revolutions. 2d ed. Chicago: University of Chicago Press.

Kuhn T. S. 1961. The function of measurement in modern physical science. Isis 52(168), 161-193.

Kuhn and Measurement. Fisher W. P. Jr. … Rasch Measurement Transactions, 1997, 11:3 p. 577

Please help with Standard Dataset 4: Andrich Rating Scale Model

Rasch Publications
Rasch Measurement Transactions (free, online) Rasch Measurement research papers (free, online) Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Applying the Rasch Model 3rd. Ed., Bond & Fox Best Test Design, Wright & Stone
Rating Scale Analysis, Wright & Masters Introduction to Rasch Measurement, E. Smith & R. Smith Introduction to Many-Facet Rasch Measurement, Thomas Eckes Invariant Measurement: Using Rasch Models in the Social, Behavioral, and Health Sciences, George Engelhard, Jr. Statistical Analyses for Language Testers, Rita Green
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Journal of Applied Measurement Rasch models for measurement, David Andrich Constructing Measures, Mark Wilson Rasch Analysis in the Human Sciences, Boone, Stave, Yale
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