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Journal of Educational… | 9 |
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Reports - Research | 5 |
Reports - Evaluative | 4 |
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Reddon, John R. – Journal of Educational Statistics, 1987
Computer sampling from a multivariate normal spherical population was used to evaluate Type I error rates for a test of P = I based on Fisher's tanh(sup minus 1) variance stabilizing transformation of the correlation coefficient. (Author/TJH)
Descriptors: Computer Simulation, Correlation, Monte Carlo Methods, Multivariate Analysis

Alexander, Ralph A.; Govern, Diane M. – Journal of Educational Statistics, 1994
A new approximation is proposed for testing the equality of "k" independent means in the face of heterogeneity of variance. Monte Carlo simulations show that the new procedure has nearly nominal Type I error rates and Type II error rates that are close to those produced by James's second-order approximation. (SLD)
Descriptors: Analysis of Variance, Computer Simulation, Equations (Mathematics), Monte Carlo Methods

Reddon, John R.; And Others – Journal of Educational Statistics, 1985
Computer sampling from a multivariate normal spherical population was used to evaluate the type one error rates for a test of sphericity based on the distribution of the determinant of the sample correlation matrix. (Author/LMO)
Descriptors: Computer Simulation, Correlation, Error of Measurement, Matrices

Huck, Schuyler W.; And Others – Journal of Educational Statistics, 1985
Classroom demonstrations can help students gain insights into statistical concepts and phenomena. After discussing four kinds of demonstrations, the authors present three possible approaches for determining how much data are needed for the demonstration to have a reasonable probability for success. (Author/LMO)
Descriptors: Computer Simulation, Demonstrations (Educational), Higher Education, Monte Carlo Methods

Chen, Ru San; Dunlap, William P. – Journal of Educational Statistics, 1994
The present simulation study confirms that the corrected epsilon approximate test of B. Lecoutre yields a less biased estimation of population epsilon and reduces Type I error rates when compared to the epsilon approximate test of H. Huynh and L. S. Feldt. (SLD)
Descriptors: Computer Simulation, Estimation (Mathematics), Evaluation Methods, Monte Carlo Methods

Harwell, Michael R. – Journal of Educational Statistics, 1992
A methodological framework is provided for quantitatively integrating Type I error rates and power values for Monte Carlo studies. An example is given using Monte Carlo studies of a test of equality of variances, and the importance of relating metanalytic results to exact statistical theory is emphasized. (SLD)
Descriptors: Computer Simulation, Data Interpretation, Mathematical Models, Meta Analysis

Wilcox, Rand R.; Charlin, Ventura L. – Journal of Educational Statistics, 1986
This paper investigates three methods for comparing medians rather than means in studying two independent treatment groups. The method that gave the best results is based on a normal approximation of the distribution of the sample median where the variance is estimated using results reported by Maritz and Jarrett. (Author/JAZ)
Descriptors: Comparative Analysis, Computer Simulation, Computer Software, Equations (Mathematics)

Nandakumar, Ratna; Stout, William – Journal of Educational Statistics, 1993
A detailed investigation is provided of Stout's statistical procedure (the computer program DIMTEST) for testing the hypothesis that an essentially unidimensional latent trait model fits observed binary item response data from a psychological test. Three refinements achieve greater power. The revised approach is validated using real data sets.…
Descriptors: Computer Simulation, Equations (Mathematics), Hypothesis Testing, Item Response Theory

Donoghue, John R.; Allen, Nancy L. – Journal of Educational Statistics, 1993
Forming the matching variable for the Mantel-Haenszel differential item functioning (DIF) procedure through use of the total score as the matching variable (thin) and forming the matching variable by pooling total score levels (thick) were compared in a Monte Carlo study. Reasons thick matching is superior are discussed. (SLD)
Descriptors: Comparative Analysis, Computer Simulation, Equations (Mathematics), Graphs