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Andrich, David; Styles, Irene; Mercer, Annette; Puddey, Ian B. – Advances in Health Sciences Education, 2017
The possibility that the validity of assessment is compromised by repeated sittings of highly competitive and high profile selection tests has been documented and is of concern to stake-holders. An illustrative example is the Undergraduate Medicine and Health Sciences Admission Test (UMAT) used by some medical and dental courses in Australia and…
Descriptors: High Stakes Tests, College Entrance Examinations, Undergraduate Study, Selection Criteria
Humphry, Stephen; Heldsinger, Sandra; Andrich, David – Applied Measurement in Education, 2014
One of the best-known methods for setting a benchmark standard on a test is that of Angoff and its modifications. When scored dichotomously, judges estimate the probability that a benchmark student has of answering each item correctly. As in most methods of standard setting, it is assumed implicitly that the unit of the latent scale of the…
Descriptors: Foreign Countries, Standard Setting (Scoring), Judges, Item Response Theory

Andrich, David; Luo, Guanzhong – Applied Psychological Measurement, 1993
A unidimensional model for responses to statements that have an unfolding structure was constructed from the cumulative Rasch model for ordered response categories. A joint maximum likelihood estimation procedure was investigated. Analyses of data from a small simulation and a real data set show that the model is readily applicable. (SLD)
Descriptors: Attitude Measures, Data Collection, Equations (Mathematics), Item Response Theory

Andrich, David – Applied Psychological Measurement, 1989
A probabilistic item response theory (IRT) model is developed for pair-comparison design in which the unfolding principle governing the choice process uses a discriminant process analogous to Thurstone's Law of Comparative Judgment. A simulation study demonstrates the feasibility of estimation, and two examples illustrate the implications for…
Descriptors: Algorithms, Computer Simulation, Discrimination Learning, Equations (Mathematics)