Virtual Equating of Items

When two test forms have no respondents in common ("common-person equating") and no items in common ("common-item equating"), then the data for each test form comprises a separate analysis. If the test forms are designed to be parallel, then it may be reasonable to assert that the mean item difficulties of the two test forms are the same. If the distributions of the respondents administered each test forms are considered to be randomly equivalent, then it may be reasonable to assert that the mean respondent abilities are the same. But what if these assertions are unreasonable or need to be verified? Then conduct a qualitatively-based virtual equating (Luppescu, 1996).

Step 1. Identify pairs of items of similar content and difficulty in the two tests. Be generous about interpreting "similar" at this stage.

Steps 2-4 by print-out: The two item difficulty hierarchies are printed with the difficulties spaced according to their Rasch measures. Equivalent items are identified. The sheets of paper are moved relative to each other until the overall joint hierarchy makes the most sense. The value on Test A corresponding to the zero on Test B is the equating constant to use for Test B. If the item spacing on one test appears expanded or compressed relative to the other test, then rescale the measures on one test form to compensate.

Or: Step 2 by graphing: From the separate analyses, crossplot the difficulties of the pairs of items, with Test B on the y-axis and Test A on the x-axis. The slope of the best-fit line i.e., the line though the point at the means of the common items and through the (mean ±1 S.D.) point should have slope near 1.0. If it does, then the intercept of the line with the x-axis is the equating constant. To place Test B in the Test A frame of reference: add the x-axis intercept to all Test B measures.

Step 3. Examine the scatterplot. Points far away from the best fit line indicate items that are not good pairs. You may wish to consider these to be no longer paired. Drop the items from the plot and redraw the best fit line.

Step 4. The slope of the best fit is: slope = (S.D. of Test B common items) / (S.D. of Test A common items) . So multiply Test B measures by the value of 1/slope, and add the value of the x-intercept. Then reanalyze Test B. Test B is now in the Test A frame of reference, so a cross plot of the equating items should approximate the identity lined. If so, the person and item measures from Test A and Test B can be reported together.

Luppescu, Stuart (1996). Virtual equating: An approach to reading test equating by concept matching of items. Doctoral dissertation, University of Chicago.

Virtual equating of examinee groups is discussed in "Equating Without an Anchor for Nonequivalent Groups of Examinees", Nicholas T. Longford, Journal of Educational and Behavioral Statistics, June 2015, vol. 40, no. 3, 227-253

Virtual Equating. Luppescu S. … Rasch Measurement Transactions, 2005, 19:3 p. 1025

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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