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Schoenfeldt, Lyle F.; Lissitz, Robert W. – American Educational Research Journal, 1974
(See also TM 501 087, TM 501 088, TM 501 090.)
Descriptors: Bayesian Statistics, Models, Multiple Regression Analysis, Prediction
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Novick, Melvin R. – American Educational Research Journal, 1974
(See also TM 501 087, TM 501 088, and TM 501 089.)
Descriptors: Bayesian Statistics, Models, Multiple Regression Analysis, Prediction
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Daniel, Wayne W.; And Others – Educational and Psychological Measurement, 1982
To test the use of Bayes's theorem to adjust for nonresponse bias, 600 hospitals were used in a simulated sample survey. On the basis of known information on five variables, Bayes's formula correctly predicted the status of 92 of the 100 "nonrespondents" relative to a sixth variable. (Author/BW)
Descriptors: Bayesian Statistics, Data Analysis, Data Collection, Hospitals
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Saar, Shalom Saada – Educational Evaluation and Policy Analysis, 1980
The Multiattribute Utility Model combines subjective goal definition with objective data analysis. Goals are defined, ranked, and weighted. Subjective opinions about their attainment are assigned against decision alternatives considered by the school. Bayesian analysis of data enables revision of prior opinions about the realization of goals…
Descriptors: Bayesian Statistics, Decision Making, Educational Objectives, Evaluation Methods
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Thum, Yeow Meng – Journal of Educational and Behavioral Statistics, 1997
A class of two-stage models is developed to accommodate three common characteristics of behavioral data: (1) its multivariate nature; (2) the typical small sample size; and (3) the possibility of missing observations. The model, as illustrated, permits estimation of the full spectrum of plausible measurement error structures. (SLD)
Descriptors: Bayesian Statistics, Behavior Patterns, Estimation (Mathematics), Maximum Likelihood Statistics
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Maris, Gunter; Maris, Eric – Psychometrika, 2002
Introduces a new technique for estimating the parameters of models with continuous latent data. To streamline presentation of this Markov Chain Monte Carlo (MCMC) method, the Rasch model is used. Also introduces a new sampling-based Bayesian technique, the DA-T-Gibbs sampler. (SLD)
Descriptors: Bayesian Statistics, Equations (Mathematics), Estimation (Mathematics), Markov Processes
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Wang, Xiaohui; Bradlow, Eric T.; Wainer, Howard – Applied Psychological Measurement, 2002
Proposes a modified version of commonly employed item response models in a fully Bayesian framework and obtains inferences under the model using Markov chain Monte Carlo techniques. Demonstrates use of the model in a series of simulations and with operational data from the North Carolina Test of Computer Skills and the Test of Spoken English…
Descriptors: Bayesian Statistics, Item Response Theory, Markov Processes, Mathematical Models
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Baker, Frank B. – Applied Psychological Measurement, 1990
The equating of results from the PC-BILOG computer program to an underlying metric was studied through simulation when a two-parameter item response theory model was used. Results are discussed in terms of the identification problem and implications for test equating. (SLD)
Descriptors: Bayesian Statistics, Computer Simulation, Equated Scores, Item Response Theory
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Fligner, Michael A.; Verducci, Joseph S. – Psychometrika, 1990
The concept of consensus ordering is defined, and formulas for exact and approximate posterior probabilities for consensus ordering are developed under the assumption of a generalized Mallows' model with a diffuse conjugate prior. These methods are applied to a data set concerning 98 college students. (SLD)
Descriptors: Bayesian Statistics, College Students, Equations (Mathematics), Estimation (Mathematics)
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Jannarone, Robert J.; And Others – Psychometrika, 1990
A Bayes estimation procedure for Rasch-type model estimation that has statistical and computational advantages over existing methods is described. It involves constructing posterior distributions based on sample data and artificial data reflecting prior information. Its use for some Rasch-type cases, and how it can improve parameter estimation are…
Descriptors: Bayesian Statistics, Equations (Mathematics), Estimation (Mathematics), Item Response Theory
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Meredith, William; Millsap, Roger E. – Psychometrika, 1992
A unified treatment is presented for conditions that should allow detection of measurement bias using statistical procedures involving only observed or manifest variables. Computational results demonstrate that methods for studying bias that rely exclusively on manifest variables are not generally diagnostic of the presence or absence of…
Descriptors: Bayesian Statistics, Equations (Mathematics), Identification, Item Bias
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van der Linden, Wim J. – Psychometrika, 1998
This paper suggests several item selection criteria for adaptive testing that are all based on the use of the true posterior. Some of the ability estimators produced by these criteria are discussed and empirically criticized. (SLD)
Descriptors: Ability, Adaptive Testing, Bayesian Statistics, Computer Assisted Testing
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Patz, Richard J.; Junker, Brian W. – Journal of Educational and Behavioral Statistics, 1999
Extends the basic Markov chain Monte Carlo (MCMC) strategy of R. Patz and B. Junker (1999) for Bayesian inference in complex Item Response Theory settings to address issues such as nonresponse, designed missingness, multiple raters, guessing behaviors, and partial credit (polytomous) test items. Applies the MCMC method to data from the National…
Descriptors: Bayesian Statistics, Item Response Theory, Markov Processes, Monte Carlo Methods
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Fox, Jean-Paul; Glas, Cees A. W. – Psychometrika, 2001
Imposed a two-level regression model on the ability parameters in an item response theory (IRT) model. Uses a simulation study and an empirical data set to show that the parameters of the two-parameter normal ogive model and the multilevel model can be estimated in a Bayesian framework using Gibbs sampling. (SLD)
Descriptors: Ability, Bayesian Statistics, Equations (Mathematics), Estimation (Mathematics)
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Alcala-Quintana, Rocio; Garcia-Perez, Miguel A. – Psychological Methods, 2004
Variants of adaptive Bayesian procedures for estimating the 5% point on a psychometric function were studied by simulation. Bias and standard error were the criteria to evaluate performance. The results indicated a superiority of (a) uniform priors, (b) model likelihood functions that are odd symmetric about threshold and that have parameter…
Descriptors: Psychometrics, Computation, Error of Measurement, Bayesian Statistics
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