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American Educational Research… | 6 |
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Granzin, Kent L.; Painter, John J. – American Educational Research Journal, 1973
Authors discovered significant correlations between couse ratings and variables representing commitment and course-end attitudes toward the course; conclusions suggested steps an instructor might take to improve his ratings.'' (Authors)
Descriptors: Course Evaluation, Multiple Regression Analysis, Predictive Validity, Predictor Variables

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

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
Moderator Subgroups for the Estimation of Educational Performance: A Comparison of Prediction Models

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

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

Morris, John D. – American Educational Research Journal, 1979
Computer-based Monte Carlo methods compared the predictive accuracy upon replication of regression of five complete and four incomplete factor score estimation methods. Prediction on incomplete factor scores showed better double cross-validated prediction accuracy than on complete scores. The unique unit-weighted factor score was superior among…
Descriptors: Correlation, Factor Analysis, Monte Carlo Methods, Multiple Regression Analysis