Answering the "Call for a New Psychometrics"

In the 1991 Review of Research in Education yearbook, Wolf, Bixby, Glenn and Gardner perceive a need for a "new psychometrics" designed for student assessment. They claim this "new psychometrics" would have to satisfy four criteria: 1) be capable of measuring alternative pathways to mastery; 2) supersede the need for inter-judge agreement as a criterion for valid measurement; 3) measure and report many aspects of student performance; and 4) handle different units of analysis beyond the individual or the total. We are delighted to proclaim that these criteria have already been well-met by the psychometric theory, design and practice of many-faceted Rasch measurement (MFRM).

Examine the criteria:
1) MFRM is based on the reality that performance assessment involves judging complex behaviors. Many facets contribute to the measures earned by examinees. Facets frequently delineated are examinee ability, judge severity and task difficulty. But this does not mean that the analyst is constrained to think in some arbitrary way. Different observations of student performance can be conceptualized, for one analysis, as distinct elements of the same facet, but, for another analysis, as separate facets.

Consider an example in which students are required to perform two tasks: write an expository essay, and critique an essay. The tasks can be modeled together or separately, graded with the same or different grading schemes, and given identical or different weights. The contribution of the tasks to the final measure of the student depends upon the definition of the assessment. The assignment of weights and the combination of different tasks in separate models can be used to define alternative pathways to mastery. The student can demonstrate proficiency in areas that meet the requirements of one or more of several alternative models while failing to demonstrate proficiency in the areas represented by the remaining models and still receive a measure that justifies recognition of mastery. The ability to award partial credit also provides for differentiation in degrees of mastery. In this way, different pathways to mastery can be recognized and incorporated into the final student measure. Even further, a variety of different measurement instruments can be incorporated into a single analysis (Stahl & Lunz 1991) and substantively-determined weights assigned.

2) MFRM accepts and controls for differences in judge severities. Judges are not expected or required to grade identically just because they have expertise in the same field. Some judges may have higher standards and therefore grade more severely than others. Because the severity of the judge is an additive factor in the model and is taken into consideration before the student measures are calculated, the unique perception of any judge in the measurement process is acknowledged and accounted for.

3) In MFRM, interactions between facets can be specified and used as a diagnostic tool for identifying specific strengths and weaknesses of individual students or judges. Interactions between students and items or students and tasks, or judges and items, or judges and tasks can be explicitly specified and measured. Unusual performances can be flagged and students or judges informed about their unexpected behavior. Discussion may reveal a logical explanation for the performance or a need that can be remedied with additional instruction.

4) Groups with unique characteristics, whether the group consists of items, students or judges, can be analyzed as entities when MFRM strategies are employed. Group membership can be modelled as a separate facet and linear measures of the performance of different groups can be calculated. Groups of students or judges or selected groups of items may also be of interest in diagnosing student ability.

In summary, MFRM, splendidly implemented in the FACETS computer program, is an extremely flexible mode of analysis that yields objective, conjointly additive measures for all pertinent facets of a particular examination situation. The analysis models offer many options for studying the data so that the results may be diagnostic or evaluative for students, judges or items. MFRM provides the opportunity to control all aspects of an examination with more than two facets in a way that meets all the basic requirements in the "call for a new psychometrics". Rejoice oh Wolf, Bixby, Glenn and Gardner! Your "new psychometrics" is here.

Answering the "Call for a New Psychometrics", J Stahl & M Lunz … Rasch Measurement Transactions, 1991, 5:1 p. 127-128

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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