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Peer reviewedHuberty, Carl J.; Mourad, Salah A. – Educational and Psychological Measurement, 1980
Real data are used to illustrate the comparison of two estimators for the square of a population correlation coefficient and the true validity of a sample prediction equation. Interpretive approaches to, and problems in, multiple correlation/prediction estimation are discussed. (Author/BW)
Descriptors: Correlation, Multiple Regression Analysis, Predictive Measurement, Validity
Peer reviewedLutz, J. Gary – Educational and Psychological Measurement, 1983
A method is presented for the construction of an artificial data set which will illustrate the behavior of the traditional, the negative, and the reciprocal suppressor variable in multiple regression analysis. It extends the method of Dayton (1972) and includes the previously reciprocal suppression defined by Conger (1974). (Author)
Descriptors: Multiple Regression Analysis, Predictive Measurement, Research Methodology, Suppressor Variables
Fusilier, Marcelline; Durlabhji, Subhash; Cucchi, Alain – Journal of Educational Computing Research, 2008
National background of users may influence the process of technology acceptance. The present study explored this issue with the new, integrated technology use model proposed by Sun and Zhang (2006). Data were collected from samples of college students in India, Mauritius, Reunion Island, and United States. Questionnaire methodology and…
Descriptors: Foreign Countries, Data Analysis, Internet, Technology Integration
Henard, David H. – 1998
The important and sometimes difficult-to-grasp concept of regression suppressor variable effects is explored. An inquiry into the phenomenon of suppressor effects is accomplished via a synthesis of the existing literature and the use of a small heuristic data set to improve the accessibility of the concept. Implications for researchers are also…
Descriptors: Heuristics, Multiple Regression Analysis, Predictive Measurement, Predictor Variables
Peer reviewedO'Grady, Kevin E.; Medoff, Deborah R. – Multivariate Behavioral Research, 1988
Limitations of dummy coding and nonsense coding as methods of coding categorical variables for use as predictors in multiple regression analysis are discussed. The combination of these approaches often yields estimates and tests of significance that are not intended by researchers for inclusion in their models. (SLD)
Descriptors: Multiple Regression Analysis, Predictive Measurement, Regression (Statistics), Research Problems
Peer reviewedKnapp, Martin R. J. – Journal of Gerontology, 1976
Taking multidimensional life satisfaction as the basic premise of this study, a four-equation multiple regression model was constructed for its prediction. Results indicated that the pattern of regressor influence varied greatly between equations, providing fairly specific evidence on a number of previously espoused hypotheses. (Author)
Descriptors: Gerontology, Multiple Regression Analysis, Older Adults, Predictive Measurement
Peer reviewedMuhich, Dolores – Educational and Psychological Measurement, 1972
Major objective in this study was the structuring of a predictive model that would assess combinations of variables that most effectively and parsimoniously measure and forecast college success. (Author)
Descriptors: Criteria, Mathematical Models, Multiple Regression Analysis, Predictive Measurement
Peer reviewedRock, Donald A.; And Others – Educational and Psychological Measurement, 1970
Descriptors: Monte Carlo Methods, Multiple Regression Analysis, Predictive Measurement, Predictor Variables
Harris, Richard J. – 1992
Interpretation of emergent variables on the basis of structure coefficients (zero order correlations between original and emergent variables) is potentially very misleading and should be avoided in favor of interpretation on the basis of scoring coefficients. This is most apparent in multiple regression analysis and its special case, two-group…
Descriptors: Correlation, Discriminant Analysis, Mathematical Models, Multiple Regression Analysis
Peer reviewedSchau, Candace Garrett; Heyward, Vivian H. – American Educational Research Journal, 1987
Stepwise multiple regression was used to develop salary prediction equations, one from each of two faculty samples used most frequently used in this type of research. An analysis of the model found that on average women were paid significantly less than men. (RB)
Descriptors: College Faculty, Models, Multiple Regression Analysis, Predictive Measurement
Peer reviewedEyman, Richard K.; And Others – Educational and Psychological Measurement, 1973
Descriptors: Multiple Regression Analysis, Predictive Measurement, Predictive Validity, Test Reliability
Korfhage, Mary Margaretha – 1979
The uses and restrictions of commonality analysis are described. Commonality analysis has been increasingly used as a method to examine the relative importance of independent variables, through the partitioning of variance among the variables of the regression equation into unique and common components. The effects of all other independent…
Descriptors: Guides, Mathematical Models, Multiple Regression Analysis, Predictive Measurement
Basler, Marilyn L.; And Others – 1974
This study predicts gymnastic performance, arousal, and anxiety measures from past performances. Pulse rate and the Palmar Sweat Index were utilized as indicants of arousal. Anxiety was assessed by means of the State-Trait Anxiety Inventory. Eighteen members of the Ithaca College women's varsity gymnastic team were tested throughout the 1973-74…
Descriptors: Anxiety, Arousal Patterns, Gymnastics, Higher Education
Peer reviewedAyers, Jerry B.; And Others – Educational and Psychological Measurement, 1973
Descriptors: Academic Achievement, College Language Programs, Language Tests, Multiple Regression Analysis
Peer reviewedReed, Cheryl L.; And Others – Journal of Educational Measurement, 1972
Purpose of this investigation was to determine whether the inclusion of quadratic and/or interaction terms in a regression model would improve the prediction of student nurses' grade point averages. (Authors)
Descriptors: Grade Point Average, Mathematical Models, Multiple Regression Analysis, Predictive Measurement

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