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Showing 181 to 195 of 209 results Save | Export
Powers, James E. – 1973
Recently the application of Bayesian statistical procedures within the field of guidance and counseling has been receiving attention. This paper justifies and illustrates a simple, direct application of Bayes' Formula to expectancy tables. To make the application a counselor must specify prior probabilities that a student will attain success…
Descriptors: Bayesian Statistics, College Bound Students, Expectancy Tables, Guidance
Peer reviewed Peer reviewed
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
Peer reviewed Peer reviewed
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
Steinheiser, Frederick H., Jr.; Hirshfeld, Stephen L. – 1978
The scientific implications and practical applications of the Stein estimator approach for estimating true scores from observed scores are of potentially great importance. The conceptual complexity is not much greater than that required for more conventional regression models. The empirical Bayesian aspect allows the examiner to incorporate…
Descriptors: Bayesian Statistics, Goodness of Fit, Mathematical Models, Measurement
Carroll, Stephen J.; Relles, Daniel A. – 1976
Examined are methodologies for modeling students' choices among higher education institutions. A statistical technique called "conditional logit analysis" is applicable to the problem studied. These applications are reviewed and certain weaknesses inherent in the approach are pointed out. Alternative approaches are offered, based on the…
Descriptors: Bayesian Statistics, Comparative Analysis, Data Analysis, Databases
Peer reviewed Peer reviewed
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Hu, Xiangen, Ed.; Barnes, Tiffany, Ed.; Hershkovitz, Arnon, Ed.; Paquette, Luc, Ed. – International Educational Data Mining Society, 2017
The 10th International Conference on Educational Data Mining (EDM 2017) is held under the auspices of the International Educational Data Mining Society at the Optics Velley Kingdom Plaza Hotel, Wuhan, Hubei Province, in China. This years conference features two invited talks by: Dr. Jie Tang, Associate Professor with the Department of Computer…
Descriptors: Data Analysis, Data Collection, Graphs, Data Use
Peer reviewed Peer reviewed
Lissitz, Robert W.; Schoenfeldt, Lyle F. – American Educational Research Journal, 1974
The purpose of this study was to compare five predictor models, including two least-square procedures, two probability weighting (semi-Bayesian) methods, and a Bayesian model developed by Lindley. (See also TM 501 088, TM 501 089, and TM 501 090) (Author/NE)
Descriptors: Bayesian Statistics, College Freshmen, Models, Multiple Regression Analysis
Jones, Paul K.; Novick, Melvin R. – 1972
A summary of the technical problems encountered in performing Bayesian m group regression is given. Grade-point averages for students entering a vocational-technical program are predicted using ability assessments from the Career Planning Profile (CPP), a development of The American College Testing Program (ACT). The theory derived by Lindley (see…
Descriptors: Academic Ability, Bayesian Statistics, Grade Point Average, Mathematical Models
Lind, Douglas A. – 1979
The use of subjective probability as a theoretical model for enrollment forecasting is proposed, and the results of an application of subjective probability to enrollment forecasting at the University of Toledo are reported. Subjective probability can be used as an enrollment forecasting technique for both headcount and full-time equivalent using…
Descriptors: Bayesian Statistics, Conference Reports, Enrollment Projections, Higher Education
Peer reviewed Peer reviewed
Shigemasu, 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 reviewed Peer reviewed
Novick, Melvin R.; Jackson, Paul H. – American Educational Research Journal, 1974
(See also TM 501 087, TM 501 089 and TM 501 090.)
Descriptors: Bayesian Statistics, Models, Multiple Regression Analysis, Prediction
Peer reviewed Peer reviewed
Weiss, Robert – Evaluation Review, 1994
Coping style and effects of counseling intervention on pain tolerance was studied for 61 elementary school students through immersion of hands in cold water. Bayesian predictive inference tools are able to distinguish between subject characteristics and manipulable treatments. Sensitivity analysis strengthens the certainty of conclusions about…
Descriptors: Attention, Bayesian Statistics, Children, Coping
Peer reviewed Peer reviewed
Massaro, Dominic W.; Friedman, Daniel – Psychological Review, 1990
Several models of information integration are developed and analyzed in the context of a prototypical pattern-recognition task. Evaluation, integration, and decision-making processes are specified for each. Simulations and predictions are carried out to provide a measure of identifiability or extent to which they can be distinguished from one…
Descriptors: Bayesian Statistics, Cognitive Processes, Criteria, Decision Making
Peer reviewed Peer reviewed
Thornton, Gayle D.; And Others – Planning and Changing, 1975
Focuses on three management tools--the Delphi technique, Bayesian statistics, and Monte Carlo simulation--in order to simulate a problem-solving/decision-making situation with which an educational administrator may be faced. (Author)
Descriptors: Bayesian Statistics, Decision Making, Educational Administration, Elementary Secondary Education
Peer reviewed Peer reviewed
Castellan, N. John, Jr. – Psychometrika, 1973
This paper discusses the Lens Model' approach to the analysis of subject performance in multiple-cue judgment tasks embedded in probabilistic environments. (Author/RK)
Descriptors: Analysis of Covariance, Bayesian Statistics, Data Analysis, Mathematical Models
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