As developed by García-Pérez (1987), finite-state
scores are nonlinear transformations of the proportions
of conventional multiple-choice responses that are correct,
incorrect, and omitted. They estimate the proportions
of item alternatives which the examinees had the
knowledge needed to classify (as correct or incorrect)
before seeing them together in the items. The present
study used simulation techniques to generate conventional
test responses and to track the proportions of alternatives
the examinees could classify independently
before taking the test and the proportions they could
classify after taking the test. Then the finite-state
scores were computed and compared with these actual
values and with number-correct and formula scores
based on the conventional responses. Highly favorable
results were obtained leading to recommendations for
the use of finite-state scores. These results were almost
the same when the simulation proceeded according
to the model and when it was based on a naturalistic
process completely independent of the model.
Hence the scoring procedures on which finite-state
scores are based are both accurate and robust. Index
terms: applied measurement models, examinee behavior,
finite-state scores, guessing, multiple-choice tests,
García-Pérez, Miguel A & Frary, Robert B. (1989). Psychometric properties of finite-state scores versus number-correct and formula scores: A simulation study. Applied Psychological Measurement, 13, 403-417. doi:10.1177/014662168901300406
García-Pérez, Miguel A.; Frary, Robert B..
Psychometric properties of finite-state scores versus number-correct and formula scores: A simulation study.
Retrieved from the University of Minnesota Digital Conservancy,
Content distributed via the University of Minnesota's Digital Conservancy may be subject to additional license and use restrictions applied by the depositor.