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

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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