Rasch Measurement as a Kuhnian Revolution

The advance of scientific knowledge is generally thought to be cumulative. T.S. Kuhn (1970), however, argues that progress should be measured in revolutions, as one theory or "paradigm" supersedes another.

A mature science initiates each new research project with a paradigmatic base of useful theory that enables researchers to concentrate upon the problems that are a result of the paradigm rather than continually having to justify their beliefs regarding the separation of critical from irrelevant data. Thus acceptance of a paradigm is necessary to focus thought and force useful research. In developing their theory of mass extinction, Sepkowski and Raup were "...manipulating the data less and less as [they] understood it better and better". As they continued to work with their data, its meaning became more clearly defined because evolving theory focused their expectations.

In the poor quality of research methods, in how haphazardly these methods are used, and in their slow progress, the social sciences are still, in Kuhn's conception, a pre-paradigmatic field which lacks well-defined theory.

Paradoxically, the rise of the computer has abetted this slow progress in developing social science theory. "Maybe it has been a kind of chronocentrism, a conviction that the new machines of your own age must rank as the most stupendous or the scariest ever, but whatever the source, computers have acquired great mystique. Almost every commentator has assured the public that the computer is bringing on a revolution" (Kidder, 1981, pg. 241). Unfortunately for progress, as a tool capable of analyzing immense amounts of data at incredible speeds, the computer encourages less forethought than when analyses were labor-intensive. This has been an incentive to develop data rather than theory. Social science research will continue to founder until the massive amount of data available for analysis can be evaluated in terms of a simplifying objective framework which enables data crucial to particular research objectives to be separated from chaff.

The prevailing belief that the quality of measurement in the social sciences can never match that of the physical sciences is one of the consequences of the dominance of data over theory. Rasch analysis is an evolving paradigm (Andrich 1988) which challenges a data-dominated approach to how science should be done and which research problems are useful. It demands precise thought and understanding, and confidence that the results of such research are more valuable than those obtained from currently accepted data-dominated research methods. The theoretical community is slow to accept the superiority of the Rasch paradigm as the basis for measurement, but, as practitioners experience the utility of the method, pressure from their practice is bringing about a revolution.

Rasch Measurement as a Kuhnian Revolution, M Singleton … Rasch Measurement Transactions, 1991, 4:4 p. 119

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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
in Spanish:	Análisis de Rasch para todos, Agustín Tristán		Mediciones, Posicionamientos y Diagnósticos Competitivos, Juan Ramón Oreja Rodríguez

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