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| Journal of Educational… | 16 |
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| Raudenbush, Stephen W. | 2 |
| Viana, Marlos A. G. | 2 |
| Albert, James H. | 1 |
| Braun, Henry I. | 1 |
| Bryk, Anthony S. | 1 |
| Chen, James J. | 1 |
| Chuang, David T. | 1 |
| Guttman, Irwin | 1 |
| Jansen, Margo G. H. | 1 |
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| Journal Articles | 15 |
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Peer reviewedSmith, Philip J.; And Others – Journal of Educational Statistics, 1985
When the experimental units are measured twice, and the response variable is dichotomous, the equality of the two proportions is usally assessed by Mc Nemar's (1947) test. In this paper, Bayesian methods are presented for testing hypotheses regarding the two success probabilities in light of complete and incomplete data. (Author/BW)
Descriptors: Bayesian Statistics, Hypothesis Testing, Mathematical Models, Pretests Posttests
Peer reviewedLaird, Nan M.; Louis, Thomas A. – Journal of Educational Statistics, 1989
Based on the Gaussian model, methods for using measurements that depend on the true attribute to compute rankings are proposed and compared. Measurements based on an empirical Bayes model produce estimates that differ from ranking observed data. Ranking methods are illustrated with school achievement data. (TJH)
Descriptors: Bayesian Statistics, Class Rank, Mathematical Formulas, Mathematical Models
Peer reviewedVijn, Pieter; Molenaar, Ivo W. – Journal of Educational Statistics, 1981
In the case of dichotomous decisions, the total set of all assumptions/specifications for which the decision would have been the same is the robustness region. Inspection of this (data-dependent) region is a form of sensitivity analysis which may lead to improved decision making. (Author/BW)
Descriptors: Aptitude Treatment Interaction, Bayesian Statistics, Mastery Tests, Mathematical Models
Peer reviewedRaudenbush, Stephen W. – Journal of Educational Statistics, 1988
Estimation theory in educational statistics and the application of hierarchical linear models are reviewed. Observations within each group vary as a function of microparameters. Microparameters vary across the population of groups as a function of macroparameters. Bayes and empirical Bayes viewpoints review examples with two levels of hierarchy.…
Descriptors: Bayesian Statistics, Educational Research, Equations (Mathematics), Estimation (Mathematics)
Peer reviewedVos, Hans J. – Journal of Educational Statistics, 1990
An approach is presented to simultaneously optimize decision rules for combinations of elementary decisions through a framework derived from Bayesian decision theory. The developed linear utility model for selection-mastery decisions was applied to a sample of 43 first year medical students to illustrate the procedure. (SLD)
Descriptors: Bayesian Statistics, Decision Making, Equations (Mathematics), Higher Education
Peer reviewedChuang, David T.; And Others – Journal of Educational Statistics, 1981
Approaches to the determination of cut-scores have used threshold, normal ogive, linear and discrete utility functions. These approaches are examined by investigating conditions on the posterior, likelihood and utility functions required for setting cut-scores in a Bayesian approach. (Author/JKS)
Descriptors: Bayesian Statistics, Criterion Referenced Tests, Cutting Scores, Decision Making
Peer reviewedViana, Marlos A. G. – Journal of Educational Statistics, 1993
Use of linear combinations of Fisher's "z" transformations as a combined test for the common correlation parameter based on "k" independent sample correlations has been previously studied. This article considers additional "z" additive properties and methods of combining independent studies when planning the number of…
Descriptors: Bayesian Statistics, Correlation, Equations (Mathematics), Evaluation Criteria
Peer reviewedViana, Marlos A. G. – Journal of Educational Statistics, 1991
A Bayesian solution is suggested to the problem of jointly estimating "k is greater than 1" binomial parameters in conjunction with the problem of testing, in a Bayesian sense, the hypothesis "H" of parametric homogeneity. Applications of the estimates are illustrated with several types of data, including ophthalmological…
Descriptors: Bayesian Statistics, Elementary Secondary Education, Equations (Mathematics), Higher Education
Peer reviewedGuttman, Irwin; Olkin, Ingram – Journal of Educational Statistics, 1989
A model for student retention and attrition is presented. Focus is on alternative models for the "dampening" in attrition rates as educational programs progress. Maximum likelihood estimates for the underlying parameters in each model and a Bayesian analysis are provided. (TJH)
Descriptors: Bayesian Statistics, Grade Repetition, Mathematical Formulas, Mathematical Models
Peer reviewedBraun, Henry I.; Zwick, Rebecca – Journal of Educational Statistics, 1993
An approach to empirical Bayes analysis of aggregated survival data from different groups of subjects is presented based on a contingency table representation of data using transformations to permit the use of normal priors. Analysis of families of survival curves leads to improvements over classical estimates. (SLD)
Descriptors: Bayesian Statistics, Degrees (Academic), Educational Attainment, Equations (Mathematics)
Peer reviewedChen, James J.; Novick, Melvin, R. – Journal of Educational Statistics, 1984
The Libby-Novick class of three-parameter generalized beta distributions is shown to provide a rich class of prior distributions for the binomial model that removes some restrictions of the standard beta class. A numerical example indicates the desirability of using these wider classes of densities for binomial models. (Author/BW)
Descriptors: Bayesian Statistics, Computer Oriented Programs, Generalization, Goodness of Fit
Peer reviewedShigemasu, Kazuo – Journal of Educational Statistics, 1976
Context for the application and specialization of a Bayesian linear model is m-group regression and the application to the prediction of grade point average. Specialization involves the assumption of homogeneity of regression coefficients (but not intercepts) across groups. Model's predictive efficiency is compared with that of the full m-group…
Descriptors: Bayesian Statistics, Comparative Analysis, Grade Point Average, Least Squares Statistics
Peer reviewedSeltzer, Michael H. – Journal of Educational Statistics, 1993
A Bayesian approach to sensitivity of inferences to possible outliers involves recalculating marginal posterior distributions of parameters of interest under assumptions of heavy tails. This strategy is implemented in the hierarchical model setting through Gibbs sampling, a Monte Carlo technique, and illustrated through a reanalysis of data on…
Descriptors: Bayesian Statistics, Elementary Education, Equations (Mathematics), Mathematical Models
Peer reviewedRaudenbush, Stephen W.; Bryk, Anthony S. – Journal of Educational Statistics, 1985
To facilitate meta-analysis of diverse study findings, a mixed linear model with fixed random effects is presented and illustrated with data from teacher expectancy experiments. The standardized effect size is viewed as random and the variation among effect sizes is modeled as a function of study characteristics. (Author/BS).
Descriptors: Bayesian Statistics, Educational Research, Effect Size, Hypothesis Testing
Peer reviewedAlbert, James H. – Journal of Educational Statistics, 1992
Estimating item parameters from a two-parameter normal ogive model is considered using Gibbs sampling to simulate draws from the joint posterior distribution of ability and item parameters. The method gives marginal posterior density estimates for any parameter of interest, as illustrated using data from a 33-item mathematics placement…
Descriptors: Algorithms, Bayesian Statistics, Equations (Mathematics), Estimation (Mathematics)
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