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Raykov, Tenko; Lee, Chun-Lung; Marcoulides, George A.; Chang, Chi – Educational and Psychological Measurement, 2013
The relationship between saturated path-analysis models and their fit to data is revisited. It is demonstrated that a saturated model need not fit perfectly or even well a given data set when fit to the raw data is examined, a criterion currently frequently overlooked by researchers utilizing path analysis modeling techniques. The potential of…
Descriptors: Structural Equation Models, Goodness of Fit, Path Analysis, Correlation
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Shear, Benjamin R.; Zumbo, Bruno D. – Educational and Psychological Measurement, 2013
Type I error rates in multiple regression, and hence the chance for false positive research findings, can be drastically inflated when multiple regression models are used to analyze data that contain random measurement error. This article shows the potential for inflated Type I error rates in commonly encountered scenarios and provides new…
Descriptors: Error of Measurement, Multiple Regression Analysis, Data Analysis, Computer Simulation
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Dutoit, Eugene F.; Penfield, Douglas A. – Educational and Psychological Measurement, 1979
Assuming a multiple linear regression model with q independent variables, a procedure is developed for determining the minimum statistically significant increase in the multiple correlation coefficient when an additional independent variable is considered for regression. The procedure is presented analytically and in table form. Examples are…
Descriptors: Correlation, Multiple Regression Analysis, Predictor Variables, Tables (Data)
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Gross, Alan L. – Educational and Psychological Measurement, 1982
It is generally believed that the correction formula will yield exact correlational values only when the regression of z on x is both linear and homoscedastic. The formula is shown to hold for nonlinear heteroscedastic relationships. A simple sufficient condition for formula validity and estimation predictions is demonstrated in a numerical…
Descriptors: Correlation, Data Analysis, Mathematical Formulas, Predictor Variables
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Chadwick, Carole S. – Educational and Psychological Measurement, 1972
Descriptors: Evaluation Criteria, Measurement Instruments, Music Teachers, Predictive Measurement
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Raymond, Mark R.; Roberts, Dennis M. – Educational and Psychological Measurement, 1987
Data were simulated to conform to covariance patterns taken from personnel selection literature. Incomplete data matrices were treated by four methods. Treated matrices were subjected to multiple regression analyses. Resulting regression equations were compared to equations from original, complete data. Results supported using covariate…
Descriptors: Data Analysis, Matrices, Multiple Regression Analysis, Personnel Selection
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Bean, Andrew G.; Covert, Robert W. – Educational and Psychological Measurement, 1973
Purpose of this study was to discriminate among college persisters, withdrawals, and academic dismissals through using measures of scholastic aptitude and personality. (Authors)
Descriptors: Academic Aptitude, College Students, Discriminant Analysis, Dropouts
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Holly, Keith A.; And Others – Educational and Psychological Measurement, 1973
Descriptors: Cognitive Ability, Data Analysis, Grade 7, Mathematics
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Conger, Anthony J.; Jackson, Douglas N. – Educational and Psychological Measurement, 1972
Paper discusses the nature of the suppressor variable, constraints upon the degree of gain in predictability by the addition of a suppressor, and the distinct aims of prediction and of construct measurement. (Authors/MB)
Descriptors: Correlation, Definitions, Prediction, Predictive Validity
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Schmidt, Frank L. – Educational and Psychological Measurement, 1972
Descriptors: Mathematical Models, Multiple Regression Analysis, Predictor Variables, Psychological Testing
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Reilly, Richard R. – Educational and Psychological Measurement, 1972
Study to investigate whether any increase in prediction of law school performance could be effected by a more thorough consideration of the undergraduate record. (Author)
Descriptors: Academic Achievement, Academic Records, Criteria, Grade Point Average
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Woodfin, Mary Jo – Educational and Psychological Measurement, 1972
Initial standing in language, reading, intelligence, socioeconomic status, and sex differences did not predict writing ability of third grade children any more accurately for structured than for nonstructured teaching methods, although children who did not spell or read as well as others were able to produce more volume in their writing when…
Descriptors: Intelligence Differences, Intermode Differences, Predictor Variables, Sex Differences
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Tucker, Ledyard R. – Educational and Psychological Measurement, 1973
The inter-battery factor analysis model for predictive systems appears to offer a useful formalization of these systems and provides some guides for the development of the system. (Author)
Descriptors: Analysis of Covariance, Data Collection, Factor Analysis, Mathematical Models
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Weiner, Max; Kay, Patricia M. – Educational and Psychological Measurement, 1972
Study presents findings to the effect that relatively inexpensive standardized tests which measure achievement in reading and mathematics below the college level may be used to predict scores on the SAT and CLEP tests. (Authors)
Descriptors: Academic Records, Achievement Tests, Aptitude Tests, College Entrance Examinations
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Miller, Doris Crane; Michael, William B. – Educational and Psychological Measurement, 1972
Descriptors: Academic Achievement, Community Colleges, Factor Structure, Measurement Instruments
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