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Peer reviewedFowler, Robert L. – Educational and Psychological Measurement, 1984
This study compared two approximations for normalizing noncentral F distributions: one based on the square root of the chi-square distribution (SRA), the other derived from a cube root of the chi-square distribution (CRA). The CRA was superior, and generally provided an excellent approximation for noncentral F. (Author/BW)
Descriptors: Estimation (Mathematics), Hypothesis Testing, Mathematical Formulas, Probability
Kennedy, Charlotte A. – 2002
The use of and emphasis on statistical significance testing has pervaded educational and behavioral research for many decades in spite of criticism by prominent researchers in this field. Much of the controversy is caused by lack of understanding or misinterpretations. This paper reviews criticisms of statistical significance testing and discusses…
Descriptors: Educational Research, Hypothesis Testing, Research Methodology, Sampling
Peer reviewedGames, Paul A. – American Educational Research Journal, 1971
See EJ 041 277 for original article. (DG)
Descriptors: Analysis of Variance, Hypothesis Testing, Research Methodology, Statistical Analysis
Peer reviewedElster, Richard S.; Dunnette, Marvin D. – Educational and Psychological Measurement, 1971
Descriptors: Hypothesis Testing, Measurement Techniques, Probability, Sampling
Peer reviewedGames, Paul A. – American Educational Research Journal, 1971
Twelve multiple comparison tests, both simultaneous and sequential, are described, analyzed, and evaluated. Overall recommendations are presented in a flow-diagram. (DG)
Descriptors: Analysis of Variance, Hypothesis Testing, Research Methodology, Statistical Analysis
Peer reviewedBerenson, Mark L. – Psychometrika, 1982
The statistical power of nine k-sample tests against ordered location alternatives under completely randomized designs are investigated. The results are intended to aid researchers in selecting appropriate statistical procedures. (Author/JKS)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Research Design
Peer reviewedHuberty, Carl J.; Holmes, Susan E. – Educational and Psychological Measurement, 1983
An alternative analysis of the two-group single response variable design is proposed. It involves the classification of experimental units to populations represented by the two groups. Three real data sets are provided to illustrate the utility of the classification analysis. A table of sample sizes required for the analysis is presented.…
Descriptors: Classification, Data Analysis, Hypothesis Testing, Research Design
Peer reviewedRenner, Barbara Rochen; Ball, Donald W. – Educational and Psychological Measurement, 1983
To determine the effect of violating the assumption of homogeneity of covariance for the Tukey Wholly Significant Difference (WSD) test, Monte Carlo simulations varied the number of treatment groups, sample size, and degree of covariance heterogeneity. As covariance heterogeneity was increased, the empirical significance levels increased beyond…
Descriptors: Data Analysis, Hypothesis Testing, Monte Carlo Methods, Research Methodology
Peer reviewedHaase, Richard F. – Educational and Psychological Measurement, 1983
This paper reviews the distinctions between classical and partial eta square and derives a formula for use in those complex analysis of variance designs in which the investigator desires a measure of classical eta square and has access only to the F-tests and relevant degrees of freedom. (Author/BW)
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Formulas, Research Design
Peer reviewedRoss, Donald C. – Educational and Psychological Measurement, 1983
Theta is a statistic which measures the degree to which a designated pattern successfully partitions a matrix of pre- and post-treatment ratings into regions typical of each of two treatments. In this paper, theta is extended to multivariate and multigroup cases. (Author/BW)
Descriptors: Hypothesis Testing, Matrices, Multivariate Analysis, Research Methodology
Peer reviewedKeselman, H. J. – Multivariate Behavioral Research, 1982
The need for multiple comparison procedures for repeated measures means employing a pooled estimate of error variance to conform to the sphericity assumptions of the design in order to provide a valid test is discussed. An alternative approach which does not require this assumption is presented. (Author/JKS)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Research Design
Peer reviewedRae, Gordon – Educational and Psychological Measurement, 1982
Analyses of artificial data involving repeated, related binary measures to different samples suggest that Tideman's generalized chi-square statistic and conventional repeated-measures analysis of variance do not produce conflicting outcomes. Provided the appropriate assumptions are met, analysis of variance may provide a more versatile approach.…
Descriptors: Analysis of Variance, Hypothesis Testing, Research Design, Statistical Analysis
Peer reviewedRonis, David L. – Educational and Psychological Measurement, 1981
Many researchers draw the conclusion that one independent variable has more impact than another without testing the null hypothesis that their impact is equal. This paper presents and recommends a technique for testing the relative magnitude of effects, rather than basing conclusions solely on descriptive statistics. (Author/BW)
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Models, Research Design
Peer reviewedRamsey, Philip H. – Journal of Educational Statistics, 1980
Disagreements have arisen about the robustness of the t test in normal populations with unequal variances. Employing liberal but objective standards for assessing robustness, it is shown that the t test is not always robust to the assumption of equal population variances even when sample sizes are equal. (Author/JKS)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Mathematical Models
Peer reviewedDyer, Frank J. – Educational and Psychological Measurement, 1980
Power analysis is in essence a technique for estimating the probability of obtaining a specific minimum observed effect size. Power analysis techniques are applied to research planning problems in test reliability studies. A table for use in research planning and hypothesis testing is presented. (Author)
Descriptors: Hypothesis Testing, Mathematical Formulas, Power (Statistics), Probability


