Quantitative methodologists almost universally adopt a "fit-models-to-data" approach, because they have been trained in statistical methods that ignore the requirements of inferential parameter separation. That small minority of methodologists who assert the value of a "fit-data-to-models" approach are mistakenly regarded as zealously advocating the adoption of a single approach to quantification. In the minds of the majority of quantitative methodologists, this perceived zealotry is sufficient reason in itself to dismiss the alternative paradigm without further examination. And even when some further consideration is given, it is almost always in the terms of their dominant paradigm, which says that parameter separation is expendable when data do not fit a measurement model.
Many applications of Rasch's models in the medical, psychological, and psychiatric literature are opportunistic. They report data that happen to fit a Rasch model, even though the instrument was not designed to produce data that would do so. Further, the designers have no intention of following through on the interpretation and redesign implications of Rasch measures and fit statistics in their implementation of a measurement system.
Designing instruments to fit a model requires development of a theory of what is to be measured. This can be demanding. It is much easier to make up some questions without worrying about how they relate to any particular variable. But when opportunistic success occurs, it shows that Rasch "works" even under adverse circumstances. This may encourage researchers to reconsider their methodology and to capitalize on the benefits of Rasch measurement.
Following through on successful use of the Rasch model would be a matter of actually deploying the measurement units in self-scoring diagnostic work-sheets, in research using the instrument, in developing parallel forms and item banks, and in management reports to administrators, payors, clinicians, referring parties, patients, etc. All these involve the problems of explaining what measurement units really are and communicating their substantive meaning. In these respects, Richard Woodcock's KeyMath Profile and Geofferey Masters' KidMap are still leaders, ahead of their time.
Despite pervasive resistance, Rasch's work has an historical inevitability. This can be seen in the relationship between the supply of advantages Rasch brings to scientific methods (both qualitative and quantitative), and the demand for them erupting throughout society as business, government, education, and health care find themselves increasingly accountable and in need of ways to assess and improve quality, effectiveness, and efficiency.
In health care there are widespread calls for "standards" of measurement that will make it possible for consumers, accreditors, HMOs to compare service quality across providers. Virtually all references to such standards mistakenly assume or assert that they can be derived only by having everyone use the same items and rating scale. Resistance to proposals for standards is strong, because everyone also assumes that they will be required to give up the rating scales they already have in place, or they will have to duplicate their efforts on a second instrument.
What is happening, however, is that the structural commonalities of construct-related Rasch-calibrated instruments are becoming evident, helping empirically-derived rather than legislated-by-committee standards become the de facto standards. As more and more instruments in the same substantive area are fit to Rasch's models for measurement, there will be increasing demand for theoretical explanations of their similarities and differences, and increasing recognition of when and where their measurement units are linearly transformable. Obvious structural similarities between the functional independence in motor skills scales of the Patient Evaluation Conference System (PECS-TM) and the Functional Independence Measure (FIM-SM) led to their recent equating (RMT 9:1 p. 420). More commonalities with other instruments are becoming apparent. As the scale-free metric structures of the instruments are further studied and better understood in relation to one another, the added efficiency of having all of the instruments in the same area measure in a single common metric unit will be too little trouble not to implement.
Every time another instrument is calibrated by fitting its data to a probabilistic conjoint model, one more molecule of water condenses on the dusty old problem of meaningful measurement. When enough water has condensed on enough dust particles, it will surely rain. Then the desert of the psychosocial sciences will bloom like a rose!
Opportunism: a first step to inevitability? Fisher WP Jr. Rasch Measurement Transactions, 1995, 9:2 p.426
|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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