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Tendeiro, Jorge N.; Meijer, Rob R. – Applied Psychological Measurement, 2013
To classify an item score pattern as not fitting a nonparametric item response theory (NIRT) model, the probability of exceedance (PE) of an observed response vector x can be determined as the sum of the probabilities of all response vectors that are, at most, as likely as x, conditional on the test's total score. Vector x is to be considered…
Descriptors: Probability, Nonparametric Statistics, Goodness of Fit, Test Length
Kruyen, Peter M.; Emons, Wilco H. M.; Sijtsma, Klaas – International Journal of Testing, 2012
Personnel selection shows an enduring need for short stand-alone tests consisting of, say, 5 to 15 items. Despite their efficiency, short tests are more vulnerable to measurement error than longer test versions. Consequently, the question arises to what extent reducing test length deteriorates decision quality due to increased impact of…
Descriptors: Measurement, Personnel Selection, Decision Making, Error of Measurement
Glas, Cees A. W.; Pimentel, Jonald L. – Educational and Psychological Measurement, 2008
In tests with time limits, items at the end are often not reached. Usually, the pattern of missing responses depends on the ability level of the respondents; therefore, missing data are not ignorable in statistical inference. This study models data using a combination of two item response theory (IRT) models: one for the observed response data and…
Descriptors: Intelligence Tests, Statistical Inference, Item Response Theory, Modeling (Psychology)
Hills, John R. – 1979
Six experimental approaches to the problems of setting cutoff scores and choosing proper test length are briefly mentioned. Most of these methods share the premise that a test is a random sample of items, from a domain associated with a carefully specified objective. Each item is independent and is scored zero or one, with no provision for…
Descriptors: Academic Standards, Aptitude Treatment Interaction, Criterion Referenced Tests, Cutting Scores

Misanchuk, Earl R. – 1978
Multiple matrix sampling of three subscales of the California Psychological Inventory was used to investigate the effects of four variables on error estimates of the mean (EEM) and variance (EEV). The four variables were examinee population size (600, 450, 300, 150, 100, and 75); number of subtests, (2, 3, 4, 5, 6, and 7), hence the number of…
Descriptors: Adults, Analysis of Variance, Error of Measurement, Item Sampling