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McNeil, Keith; Lewis, Ernest L. – Measurement and Evaluation in Guidance, 1972
This article illustrates the role multiple linear regression can play in developing prediction equations by providing examples of regression models that could be used in answering questions relative to the importance of a single predictor variable, interactions between predictor variables, and the cross-validation and generalizability of…
Descriptors: Measurement Techniques, Multiple Regression Analysis, Prediction, Predictor Variables

Brooks, Gordon P.; Barcikowski, Robert S. – Mid-Western Educational Researcher, 1996
Analyzes advantages and disadvantages of methods of selecting sample sizes in multiple regression. Discusses importance of cross-validity to prediction studies. Describes categories of sample size selection methods: cross-validation approaches, rules of thumb, and statistical power methods. Uses multiple examples to present the precision power…
Descriptors: Multiple Regression Analysis, Power (Statistics), Prediction, Predictive Validity
Halinski, Ronald S.; Feldt, Leonard S. – J Educ Meas, 1970
Four commonly employed procedures were repeatedly applied to computer-simulated samples to provide comparative data pertaining to two questions: (a) which procedure can be expected to produce and equation that yields the most accurate predictions for the population, and (b) which procedure is most likely to identify the optimal set of independent…
Descriptors: Correlation, Multiple Regression Analysis, Prediction, Predictive Measurement
Hynes, Kevin – 1976
One aspect of multiple regression--the shrinkage of the multiple correlation coefficient on cross-validation is reviewed. The paper consists of four sections. In section one, the distinction between a fixed and a random multiple regression model is made explicit. In section two, the cross-validation paradigm and an explanation for the occurrence…
Descriptors: Correlation, Error Patterns, Literature Reviews, Mathematical Models

Jones, Molly M.; Jackson, Kirby L. – Journal of Early Intervention, 1992
This paper encourages the use of multiple logistic analysis in early intervention research, to assess the degree of association of multiple factors (such as subject or situational characteristics) with a dichotomous outcome (such as benefitting or not benefitting from an intervention) and to estimate the probability of each outcome. (JDD)
Descriptors: Disabilities, Early Intervention, Multiple Regression Analysis, Prediction
Gustafson, Richard A. – 1971
Twenty-nine community characteristics were studied to determine which were statistically most useful as predictors of per-pupil Federal aid to the 169 school districts of Connecticut. Three regression models were developed using community traits as predictors of Federal aid allocations. Cross-validation of regression models to predict future…
Descriptors: Community Characteristics, Federal Aid, Models, Multiple Regression Analysis