Bibliometricians strive for linearity!

In library science, the goal is to discover a new, subtle but powerful linearity underlying the data. This is the world encountered by David Andrich and Michael Linacre when they presented Rasch-based papers at the Fourth International Conference on Bibliometrics, Informetrics and Scientometrics, held in Berlin, Germany in September 1993.

Dr. Hildrun Kretschmer, the force behind the Conference, discovered Rasch analysis during her visits to library scientists Abe Bookstein and Don Swanson at the University of Chicago. Our common concern for linear measures made us "birds of a feather who flock together" - a maxim that has become one of Dr. Kretschmer's guiding tenets!

Michael Linacre's paper recounted the importance of linearity in measurement and the principles underlying the Rasch model. Though the model itself was new to the audience, he soon found he was "preaching to the choir." For, ever since the work of Derek De Solla Price, library scientists have realized that it is only when trends and patterns in the data have been expressed in linear form, that the data can be understood or become the basis for prediction. Price did this with trends in the history of science. His findings (1961, 1963) have become scientific lore, e.g., "90% of all scientists who ever lived are alive today", together with concepts such as "invisible colleges".

David Andrich struck a resonant note with his warning that arbitrary combining or splitting of rating scale categories damages the measurement properties, and so the meaning, of the variable underlying the data. This accords with a dictum of the most influential library scientist, Eugene Garfield, originator of the Social Sciences Citation Index bibliographic database, "May we respectfully caution against the serious implication that quantitative data [counts and ratings] can be used without considered qualitative judgments" (Essays of an Information Scientist, Vol. 1, 1977, p.120).

Library scientists examine their data to discover underlying linear patterns in behavior such as book borrowing frequency, paper co-authorship recurrence, and journal citations. Their dedication in this endeavor is an object lesson yet to be learned by social scientists for whom the maxim "if it seems numerical, then it must be linear" suffices.

John Michael Linacre

Bibliometricians strive for linearity. Linacre JM. Rasch Measurement Transactions 1993 7:3 p.313


Bibliometricians strive for linearity. Linacre JM. … Rasch Measurement Transactions, 1993, 7:3 p.313



Rasch-Related Resources: Rasch Measurement YouTube Channel
Rasch Measurement Transactions & Rasch Measurement research papers - free An Introduction to the Rasch Model with Examples in R (eRm, etc.), Debelak, Strobl, Zeigenfuse Rasch Measurement Theory Analysis in R, Wind, Hua Applying the Rasch Model in Social Sciences Using R, Lamprianou El modelo métrico de Rasch: Fundamentación, implementación e interpretación de la medida en ciencias sociales (Spanish Edition), Manuel González-Montesinos M.
Rasch Models: Foundations, Recent Developments, and Applications, Fischer & Molenaar Probabilistic Models for Some Intelligence and Attainment Tests, Georg Rasch Rasch Models for Measurement, David Andrich Constructing Measures, Mark Wilson Best Test Design - free, Wright & Stone
Rating Scale Analysis - free, Wright & Masters
Virtual Standard Setting: Setting Cut Scores, Charalambos Kollias Diseño de Mejores Pruebas - free, Spanish Best Test Design A Course in Rasch Measurement Theory, Andrich, Marais Rasch Models in Health, Christensen, Kreiner, Mesba Multivariate and Mixture Distribution Rasch Models, von Davier, Carstensen
Rasch Books and Publications: Winsteps and Facets
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 Appliquer le modèle de Rasch: Défis et pistes de solution (Winsteps) E. Dionne, S. Béland
Introduction to Many-Facet Rasch Measurement (Facets), Thomas Eckes Rasch Models for Solving Measurement Problems (Facets), George Engelhard, Jr. & Jue Wang Statistical Analyses for Language Testers (Facets), Rita Green Invariant Measurement with Raters and Rating Scales: Rasch Models for Rater-Mediated Assessments (Facets), George Engelhard, Jr. & Stefanie Wind Aplicação do Modelo de Rasch (Português), de Bond, Trevor G., Fox, Christine M
Exploring Rating Scale Functioning for Survey Research (R, Facets), Stefanie Wind Rasch Measurement: Applications, Khine Winsteps Tutorials - free
Facets Tutorials - free
Many-Facet Rasch Measurement (Facets) - free, J.M. Linacre Fairness, Justice and Language Assessment (Winsteps, Facets), McNamara, Knoch, Fan

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