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Stamm, Carol Lee – Journal of Educational Statistics, 1978
A study was conducted using generated data sets that contained specified amounts of error to determine empirically which of two large sample approximations for the coefficient of concordance or weighted average tau was more appropriate for various numbers of judges and numbers of objects. (CTM)
Descriptors: Correlation, Nonparametric Statistics, Sampling, Statistical Significance
Peer reviewed Peer reviewed
Knapp, Thomas R. – Journal of Educational Statistics, 1979
This paper presents the generalized symmetric means approach to the estimation of population covariances, complete with derivations and examples. Particular attention is paid to the problem of missing data, which is handled very naturally in the incidence sampling framework. (CTM)
Descriptors: Analysis of Covariance, Matrices, Sampling, Statistical Analysis
Peer reviewed Peer reviewed
Hodges, J. L., Jr.; And Others – Journal of Educational Statistics, 1990
An Edgeworth approximation for accurate significance probabilities for the Wilcoxon two-sample test is substantially simplified. A method is developed that allows quick calculations of very accurate probabilities. Exact formulas are given for most of the remaining cases, and tables are presented comparing the new simplification to likely…
Descriptors: Equations (Mathematics), Mathematical Models, Probability, Sampling
Peer reviewed Peer reviewed
Hedges, Larry V. – Journal of Educational Statistics, 1984
If the quantitative result of a study is observed only when the mean difference is statistically significant, the observed mean difference, variance, and effect size are biased estimators of corresponding population parameters. The exact distribution of sample effect size and the maximum likelihood estimator of effect size are derived. (Author/BW)
Descriptors: Effect Size, Estimation (Mathematics), Maximum Likelihood Statistics, Meta Analysis
Peer reviewed Peer reviewed
Harris, Richard J.; Quade, Dana – Journal of Educational Statistics, 1992
A method is proposed for calculating the sample size needed to achieve acceptable statistical power with a given test. The minimally important difference significant (MIDS) criterion for sample size is explained and supported with recommendations for determining sample size. The MIDS criterion is computationally simple and easy to explain. (SLD)
Descriptors: Equations (Mathematics), Estimation (Mathematics), Experimental Groups, Mathematical Models
Peer reviewed Peer reviewed
Ross, Kenneth N. – Journal of Educational Statistics, 1979
It is shown that using formulae for the estimation of sampling errors based on simple random sampling, when a design actually involves cluster sampling, can lead to serious underestimation of error. Jackknife and balanced repeated replication are recommended as techniques for dealing with this problem. (Author/CTM)
Descriptors: Foreign Countries, Hypothesis Testing, Research Design, Research Problems