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| Journal of Educational… | 4 |
Author
| Alexander, Ralph A. | 1 |
| Chen, Ru San | 1 |
| Dunlap, William P. | 1 |
| Govern, Diane M. | 1 |
| Harrison, David A. | 1 |
| Jarjoura, David | 1 |
| Kolen, Michael J. | 1 |
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| Journal Articles | 4 |
| Reports - Research | 3 |
| Reports - Evaluative | 1 |
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Peer reviewedAlexander, 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
Peer reviewedChen, 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
Peer reviewedJarjoura, David; Kolen, Michael J. – Journal of Educational Statistics, 1985
An equating design in which two groups of examinees from slightly different populations are administered a different test form with a subset of common items is widely used. This paper presents standard errors and a simulation that verifies the equation for large samples for an equipercentile equating procedure for this design. (Author/BS)
Descriptors: Computer Simulation, Equated Scores, Error of Measurement, Estimation (Mathematics)
Peer reviewedHarrison, David A. – Journal of Educational Statistics, 1986
Multidimensional item response data were created. The strength of a general factor, the number of common factors, the distribution of items loadingon common factors, and the number of items in simulated tests were manipulated. LOGIST effectively recovered both item and trait parameters in nearly all of the experimental conditions. (Author/JAZ)
Descriptors: Adaptive Testing, Computer Assisted Testing, Computer Simulation, Correlation


