title: Equating of multi-facet tests across administrations.
abstract: The purpose of this study is to explore criteria for common element test equating for performance examinations. Using the multi-facet Rasch model, each element of each facet is calibrated or placed in a relative position on a Benchmark or reference scale. Common elements from each facet, included on the examinations being equated, are used to anchor the facet elements to the Benchmark Scale. This places all examinations on the same scale so that the same criterion standard can be used. Performance examinations typically have three to four facets including examinees, raters, items and tasks. Raters rate examinees on tasks related to the items included in the test. The initial anchoring of a current test administration to the Benchmark Scale is evaluated for invariance and fit. If there is too much variance or lack of fit for particular facet elements, it may be necessary to unanchor those elements, which means they are not used in the equating. The equating process was applied to an exam with four facets and another with five facets. Results found few common facet elements that could not be used in the test equating process and that differences in the difficulty of the equated exams were identified so that the criterion standard on the Benchmark Scale could be used. It was necessary to use careful quality control for anchoring the common elements in each facet. The common elements should be unaltered from their original use. Strict criteria for displacement and fit must be established and used consistently. Unanchoring inconsistent and/or misfitting facet elements improves the quality of the test equating.
authors.affiliation: Measurement Incorporated. mlunz@measurementresearch.com
authors.forename: Mary
authors.initials: M
authors.lastname: Lunz
authors.forename: Surintorn
authors.initials: S
authors.lastname: Suanthong
mesh_terms: D019985:Benchmarking / Q000706:statistics & numerical data*; D004521:Educational Measurement / Q000379:methods* / Q000706:statistics & numerical data; D015233:Models, Statistical