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Milligan, Glenn W. – Educational and Psychological Measurement, 1987
The use of the arc-sine transformation in analysis of variance can lead to difficult inference situations and pose problems in interpretation. It can also produce tests of noticeably lower power when the null hypothesis is false, and is not recommended as a standard tool. Simulated illustrations are provided. (Author/GDC)
Descriptors: Analysis of Variance, Computer Simulation, Monte Carlo Methods, Statistical Bias
Chou, Tungshan; Wang, Lih-Shing – 1992
P. O. Johnson and J. Neyman (1936) proposed a general linear hypothesis testing procedure for testing the null hypothesis of no treatment difference in the presence of some covariates. This is generally known as the Johnson-Neyman (JN) technique. The need for the hypothesis testing step (often omitted) as originally presented and the…
Descriptors: Computer Simulation, Equations (Mathematics), Foreign Countries, Hypothesis Testing
Levy, Roy; Mislevy, Robert J. – US Department of Education, 2004
The challenges of modeling students' performance in simulation-based assessments include accounting for multiple aspects of knowledge and skill that arise in different situations and the conditional dependencies among multiple aspects of performance in a complex assessment. This paper describes a Bayesian approach to modeling and estimating…
Descriptors: Probability, Markov Processes, Monte Carlo Methods, Bayesian Statistics
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Caulkins, Jonathan P. – Journal of Policy Analysis and Management, 2002
In this article, the author discusses the use in policy analysis of models that incorporate uncertainty. He believes that all models should consider incorporating uncertainty, but that at the same time it is important to understand that sampling variability is not usually the dominant driver of uncertainty in policy analyses. He also argues that…
Descriptors: Statistical Inference, Models, Policy Analysis, Sampling