RMT 19:3 Quotes

Communicating Conclusions

"We have the duty of formulating, of summarizing, and of communicating our conclusions, in intelligible form, in recognition of the right of other free minds to utilize them in making their own decisions."

Ronald A. Fisher, 1955, Statistical methods and scientific induction. Journal of the Royal Statistical Society, B, 17, 69-78.

The Truth about Statistical Models

"All models are wrong. Some are useful."

George E. P. Box, University of Wisconsin

"The hallmark of good science is that it uses models and "theories" but never believes them"

Martin Bradbury Wilk, in John Tukey, The future of data analysis, Ann. Math. Statist. 33, 1-67, p.7.

"Models must be used but must never be believed."

Attributed to Martin Bradbury Wilk

The Truth about Significance Tests

"Significance tests are things to do while one is trying to think of something sensible to do."

Attributed to Martin Bradbury Wilk

The Truth about Item Response Theory

"Building statistical models is just like this. You take a real situation with real data, messy as this is, and build a model that works to explain the behavior of real data."

Martha Stocking's summary of Item Response Theory, the statistical methodology of Frederic M. Lord (1912-2000), New York Times, 2-10-2000

[Later note: Rasch works the other way round. We try our best to construct ideal data that manifests the intended variable. We strive to clean up any mess. When the data conform to our model of linear measurement along the latent variable we want, then we are explaining the behavior of real people in a useful way.]

The Truth about Factor Analysis

"Factor analysis is useful, especially in those domains where basic and essential concepts are essentially lacking and where crucial experiments are difficult to conceive ... In a domain where fundamental and fruitful concepts are already well formulated and tested, it would be absurd to use the factorial methods except for didactic purposes to illustrate factorial logic."

L.L. Thurstone (1947) Multiple Factor Analysis, Chicago: University of Chicago Press. p. 56.

The Truth is in the Eye of the Beholder

A reporter showed a photograph of the Earth taken from Space to the late Samuel Shenton, then President of the International Flat Earth (Research) Society. Shenton studied it for a moment and said, "It's easy to see how such a picture could fool the untrained eye."

Attributed

Numerical Nonsense

"In developing procedures, mathematical statisticians have assumed that techniques involving numerical scores [etc.] ... are to be applied where these numbers ... are appropriate and meaningful within the experimenter's problem. If the statistical method involves the procedures of arithmetic used on numerical scores, then the numerical answer is formally correct. Even if the numbers are the purest nonsense, having no relation to real magnitudes or the properties of real things, the answers are still right as numbers. The difficulty comes with the interpretation of those numbers back into statements about the real world. If nonsense is put into the mathematical system, nonsense is sure to come out."

Hays, W. L. (1973). Statistics for the social sciences (2d ed.). New York: Holt, Rinehart and Winston. p. 88, as quoted on p. 18 of Shavelson, R. J. (1996). Statistical reasoning for the behavioral sciences (3d Ed.). Boston: Allyn and Bacon. Courtesy of William P. Fisher, Jr.

Everything Relates to Everything Else

"Our current scientific understanding has moved considerably from the view that the universe, both on the cosmic scale and in the inner workings of matter, is understandable in terms of a sticks-and-balls mechanism, the behavior of which can be elucidated and predicted with greater and greater precision. Instead, we see a large, interactive process with a great deal of unpredictability built into the very nature of things. What is even more fascinating is that the observer, you and me and the scientist behind the measuring instruments, - become a part of the process. The observer, in a curious way, becomes part of what the outcome of the observation is. Mind, in effect, can be seen as an additional reality of the universe, inseparable from its time-space dimensions. Rather than consisting of a lot of separate objects, the universe is comprehensible as a whole of complex events in which everything relates to everything else."

J. John Keggi, "Stillness and the Storm", Augusta, Maine, 22 June 1997


RMT 19:3 Quotes … Rasch Measurement Transactions, 2005, 19:3 p. 1027

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

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