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Isaac, Paul D.; Poor, David D. S. – Psychometrika, 1974
Descriptors: Error Patterns, Factor Analysis, Goodness of Fit, Mathematical Models
Wilcox, Rand – 1977
False-positive and false-negative dicisions are the fundamental errors committed with a mastery test; yet the estimation of the likelihood of committing these errors has not been investigated. Accordingly, two methods of estimating the likelihood of committing these errors are described and then investigated using Monte Carlo techniques.…
Descriptors: Bayesian Statistics, Computer Programs, Error Patterns, Item Analysis
Chou, Tungshan; Huberty, Carl J. – 1992
The empirical performance of the technique proposed by P. O. Johnson and J. Neyman (1936) (the JN technique) and the modification of R. F. Potthoff (1964) was studied in simulated data settings. The robustness of the two JN techniques was investigated with respect to their ability to control Type I and Type III errors. Factors manipulated in the…
Descriptors: Analysis of Variance, Computer Simulation, Equations (Mathematics), Error Patterns
Wilcox, Rand R. – 1977
Three statistical problems related to criterion-referenced testing are investigated: estimation of the likelihood of a false-positive or false-negative decision with a mastery test, estimation of true scores in the Compound Binomial Error Model, and comparison of the examinees to a control. Two methods for estimating the likelihood of…
Descriptors: Criterion Referenced Tests, Cutting Scores, Error Patterns, Item Sampling

Hand, Michael L. – Evaluation Review, 1990
Use of the bootstrap resampling technique (BRT) is assessed in its application to resampling analysis associated with measurement of payment allocation errors by federally funded Family Assistance Programs. The BRT is applied to a food stamp quality control database in Oregon. This analysis highlights the outlier-sensitivity of the…
Descriptors: Equations (Mathematics), Error Patterns, Family Income, Federal Aid
Tatsuoka, Kikumi K.; Tatsuoka, Maurice M. – 1985
The study examines the rule space model, a probabilistic model capable of measuring cognitive skill acquisition and of diagnosing erroneous rules of operation in a procedural domain. The model involves two important components: (1) determination of a set of bug distributions (bug density functions representing clusters around the rules); and (2)…
Descriptors: Artificial Intelligence, Cognitive Processes, Computer Assisted Testing, Computer Software