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Wellington, Roger – Psychometrika, 1976
Generalized symmetric means are redefined in a way which allows them to be calculated for any matrix sampling design. It is proved that these sample generalized symmetric means are unbiased estimates of the analogous population generalized symmetric means. Illustrative examples are given. (Author)
Descriptors: Item Sampling, Matrices, Research Design, Sampling
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Jarjoura, David – Psychometrika, 1983
The problem of predicting universe scores for samples of examinees based on their responses to samples of items is treated. The measurement model categorizes items according to the cells of a table of test specifications, and the linear function derived for minimizing error variance in prediction uses responses to these categories. (Author/JKS)
Descriptors: Error of Measurement, Generalizability Theory, Item Sampling, Prediction
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Gross, Alan L. – Psychometrika, 1973
Expressions for the expected value, density, and distribution function (DF) of GS (gain from selection) are derived and studied in terms of sample size, number of predictors, and the prior distribution assigned to the population multiple correlation. (Author/RK)
Descriptors: Academic Achievement, College Admission, Item Sampling, Predictive Measurement
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Lord, Frederic M. – Psychometrika, 1985
Given a loss function, an asymptotic method for optimal test design for a specified target population of examinees is presented. Also, of more practical use, given an existing unidimensional test and target population, a way is presented to find the loss function for which the test is optimal. (NSF)
Descriptors: Error of Measurement, Higher Education, Item Sampling, Latent Trait Theory
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Revuelta, Javier – Psychometrika, 2004
Two psychometric models are presented for evaluating the difficulty of the distractors in multiple-choice items. They are based on the criterion of rising distractor selection ratios, which facilitates interpretation of the subject and item parameters. Statistical inferential tools are developed in a Bayesian framework: modal a posteriori…
Descriptors: Multiple Choice Tests, Psychometrics, Models, Difficulty Level
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Tucker, Ledyard R.; Lewis, Charles – Psychometrika, 1973
Maximum likelihood factor analysis provides an effective method for estimation of factor matrices and a useful test statistic in the likelihood ratio for rejection of overly simple factor models. A reliability coefficient is proposed for analysis of factor solution. (Author/RK)
Descriptors: Analysis of Variance, Factor Analysis, Goodness of Fit, Item Sampling
Peer reviewed Peer reviewed
Cliff, Norman; Donoghue, John R. – Psychometrika, 1992
A test theory using only ordinal assumptions is presented, based on the idea that the test items are a sample from a universe of items. The sum across items of the ordinal relations for a pair of persons on the universe items is analogous to a true score. (SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Item Response Theory, Item Sampling