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Lewis, Charles; And Others – Psychometrika, 1975
A Bayesian Model II approach to the estimation of proportions in m groups is extended to obtain posterior marginal distributions for the proportions. The approach is extended to allow greater use of prior information than previously and the specification of this prior information is discussed. (Author/RC)
Descriptors: Bayesian Statistics, Data Analysis, Individualized Instruction, Models
Peer reviewed Peer reviewed
Wilcox, Rand R. – Psychometrika, 1978
Several Bayesian approaches to the simultaneous estimation of the means of k binomial populations are discussed. This has particular applicability to criterion-referenced or mastery testing. (Author/JKS)
Descriptors: Bayesian Statistics, Criterion Referenced Tests, Mastery Tests, Probability
Peer reviewed Peer reviewed
Novick, Melvin R.; And Others – Psychometrika, 1971
Descriptors: Analysis of Variance, Bayesian Statistics, Error of Measurement, Mathematical Models
Peer reviewed Peer reviewed
Lin, Miao-Hsiang; Hsiung, Chao A. – Psychometrika, 1992
Four bootstrap methods are identified for constructing confidence intervals for the binomial-error model. The extent to which similar results are obtained and the theoretical foundation of each method and its relevance and ranges of modeling the true score uncertainty are discussed. (SLD)
Descriptors: Bayesian Statistics, Computer Simulation, Equations (Mathematics), Estimation (Mathematics)
Peer reviewed Peer reviewed
Segall, Daniel O. – Psychometrika, 1996
Maximum likelihood and Bayesian procedures are presented for item selection and scoring of multidimensional adaptive tests. A demonstration with simulated response data illustrates that multidimensional adaptive testing can provide equal or higher reliabilities with fewer items than are required in one-dimensional adaptive testing. (SLD)
Descriptors: Adaptive Testing, Bayesian Statistics, Computer Assisted Testing, Equations (Mathematics)
Peer reviewed Peer reviewed
Kim, Jwa K.; Nicewander, W. Alan – Psychometrika, 1993
Bias, standard error, and reliability of five ability estimators were evaluated using Monte Carlo estimates of the unknown conditional means and variances of the estimators. Results indicate that estimates based on Bayesian modal, expected a posteriori, and weighted likelihood estimators were reasonably unbiased with relatively small standard…
Descriptors: Ability, Bayesian Statistics, Equations (Mathematics), Error of Measurement