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Murrah, William M. – Educational and Psychological Measurement, 2020
Multiple regression is often used to compare the importance of two or more predictors. When the predictors being compared are measured with error, the estimated coefficients can be biased and Type I error rates can be inflated. This study explores the impact of measurement error on comparing predictors when one is measured with error, followed by…
Descriptors: Error of Measurement, Statistical Bias, Multiple Regression Analysis, Predictor Variables

Lindell, Michael K. – Educational and Psychological Measurement, 1978
An artifact encountered in regression models of human judgment is explored. The direction and magnitude of the artifactual effect is shown to depend upon the nature of the experimental task and task conditions. Use of an alternative index is recommended. (Author/JKS)
Descriptors: Cognitive Processes, Comparative Analysis, Correlation, Mathematical Models

Carter, David S. – Educational and Psychological Measurement, 1979
There are a variety of formulas for reducing the positive bias which occurs in estimating R squared in multiple regression or correlation equations. Five different formulas are evaluated in a Monte Carlo study, and recommendations are made. (JKS)
Descriptors: Comparative Analysis, Correlation, Mathematical Formulas, Multiple Regression Analysis
Li, Andrew; Bagger, Jessica – Educational and Psychological Measurement, 2007
The Balanced Inventory of Desirable Responding (BIDR) is one of the most widely used social desirability scales. The authors conducted a reliability generalization study to examine the typical reliability coefficients of BIDR scores and explored factors that explained the variability of reliability estimates across studies. The results indicated…
Descriptors: Reliability, Generalization, Social Desirability, Scores

Alumbaugh, Richard V.; And Others – Educational and Psychological Measurement, 1978
Three approaches to the prediction of juvenile recidivism--factor analysis, stepwise multiple regression, and stepwise discriminant analysis--are contrasted. Stepwise discriminant analysis provided the most consistent selection of variables in the data set used. Problems and advantages of the three approaches are discussed. (Author/JKS)
Descriptors: Adolescents, Comparative Analysis, Delinquency, Discriminant Analysis

Chissom, Brad S.; And Others – Educational and Psychological Measurement, 1972
Factors seem to indicate that the OQS could be effectively substituted for the NTE as a screening measure for graduate students in education. The D-48 was not added to the prediction equation, since it made a negligible contribution to the total prediction. (Authors/CB)
Descriptors: Comparative Analysis, Education Majors, Graduate Students, Measurement Instruments

Claudy, John G. – Educational and Psychological Measurement, 1972
First purpose of this study was to investigate empirically the accuracy of prediction using regression weights selected through the use of variance-reduction procedures. (Author)
Descriptors: Comparative Analysis, Error Patterns, Factor Structure, Item Sampling

Smith, Richard L.; And Others – Educational and Psychological Measurement, 1992
Different approaches to defining suppression in multiple regression/correlation are compared, and their differences are illustrated. A test for determining the significance of a suppressor effect, which is based on the definition of suppression of W. F. Velicer, is extended to the general multiple predictor case and analysis of variance. (SLD)
Descriptors: Analysis of Variance, Comparative Analysis, Correlation, Definitions

Houston, Lawrence N. – Educational and Psychological Measurement, 1983
The Ammons Quick Test (AQT) was investigated to determine the degree to which it could predict first-semester freshman year college grade point average for a sample of 63 specially-admitted Black female undergraduates. A multiple regression analysis indicated that the AQT did not appear to be a valid predictor. (Author/PN)
Descriptors: Black Students, Class Rank, College Entrance Examinations, Comparative Analysis