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Peer reviewedSchneider, Pamela J.; Penfield, Douglas A. – Journal of Experimental Education, 1997
A Monte Carlo simulation was conducted to study the Type I error rate and power of the 1994 approximation developed by R. A. Alexander and D. M. Govern as an alternative to the analysis of variance "F" test. Conditions under which this test is the best approach are discussed. (SLD)
Descriptors: Analysis of Variance, Monte Carlo Methods, Power (Statistics), Simulation
Download full textBarnette, J. Jackson; McLean, James E. – 1997
J. Barnette and J. McLean (1996) proposed a method of controlling Type I error in pairwise multiple comparisons after a significant omnibus F test. This procedure, called Alpha-Max, is based on a sequential cumulative probability accounting procedure in line with Bonferroni inequality. A missing element in the discussion of Alpha-Max was the…
Descriptors: Analysis of Variance, Comparative Analysis, Monte Carlo Methods, Probability
Peer reviewedRomanoski, Joseph; Douglas, Graham – Journal of Applied Measurement, 2002
Used Monte Carlo simulation to determine the psychometric conditions under which differences between raw scores and Rasch transformations of those raw scores are detectable through two-way analysis of variance. Findings demonstrate the inherent inadequacy of untransformed raw scores for two-way analysis of variance. (SLD)
Descriptors: Analysis of Variance, Item Response Theory, Monte Carlo Methods, Psychometrics
Johnson, Colleen Cook; Rakow, Ernest A. – 1994
This research explored the degree to which group sizes can differ before the robustness of analysis of variance (ANOVA) and analysis of covariance (ANCOVA) are jeopardized. Monte Carlo methodology was used, allowing for the experimental investigation of potential threats to robustness under conditions common to researchers in education. The…
Descriptors: Analysis of Covariance, Analysis of Variance, Educational Research, Monte Carlo Methods
