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Barnette, J. Jackson; McLean, James E. – 2000
The level of standardized effect sizes obtained by chance and the use of significance tests to guard against spuriously high standardized effect sizes were studied. The concept of the "protected effect size" is also introduced. Monte Carlo methods were used to generate data for the study using random normal deviates as the basis for sample means…
Descriptors: Effect Size, Monte Carlo Methods, Simulation, Statistical Significance
Peer reviewedLienart, G. A. – Educational and Psychological Measurement, 1972
The G Index is the difference between the frequencies of the homonymly assigned cells and heteronymly assigned cells in a four-fold contingency table. (Author/MB)
Descriptors: Comparative Analysis, Hypothesis Testing, Mathematical Applications, Statistical Analysis
Backhouse, J. K. – Mathematical Gazette, 1971
Descriptors: Data Analysis, Mathematical Concepts, Mathematics, Statistical Analysis
Peer reviewedJung, Steven M. – Educational and Psychological Measurement, 1971
Descriptors: Computer Programs, Hypothesis Testing, Nonparametric Statistics, Statistical Analysis
Hawk, John – Meas Evaluation Guidance, 1970
An examination was made of a large number of GATB validation studies to determine the frequency of nonlinear relationships. The number of significantly nonlinear relationships fell very close to the chance level; about 5 per cent were significant at the .05 level and 1 per cent were significant at the .01 level. (Author)
Descriptors: Aptitude Tests, Data Analysis, Research, Statistical Analysis
Peer reviewedCohen, Jacob – Educational and Psychological Measurement, 1970
Descriptors: Hypothesis Testing, Predictive Measurement, Probability, Sampling
Peer reviewedTerrell, Colin D. – Educational and Psychological Measurement, 1982
Tables are presented giving the critical values of the biserial and the point biserial correlation coefficients (when the null hypothesis assumes a value of zero for the coefficient) at the 0.05 and the 0.01 levels of significance. (Author)
Descriptors: Correlation, Mathematical Formulas, Probability, Research Tools
Peer reviewedRogosa, David – Educational and Psychological Measurement, 1981
The form of the Johnson-Neyman region of significance is shown to be determined by the statistic for testing the null hypothesis that the population within-group regressions are parallel. Results are obtained for both simultaneous and nonsimultaneous regions of significance. (Author)
Descriptors: Hypothesis Testing, Mathematical Models, Predictor Variables, Regression (Statistics)
Peer reviewedVegelius, Jan – Educational and Psychological Measurement, 1981
The G index is a measure of the similarity between individuals over dichotomous items. Some tests for the G-index are described. For each case an example is included. (Author/GK)
Descriptors: Hypothesis Testing, Mathematical Formulas, Mathematical Models, Nonparametric Statistics
Peer reviewedKeselman, H. J.; And Others – Educational and Psychological Measurement, 1981
This paper demonstrates that multiple comparison tests using a pooled error term are dependent on the circularity assumption and shows how to compute tests which are insensitive (robust) to this assumption. (Author/GK)
Descriptors: Hypothesis Testing, Mathematical Models, Research Design, Statistical Significance
Peer reviewedHollingsworth, Holly H. – Educational and Psychological Measurement, 1981
If the null hypothesis of a one-sample test of multivariate means is rejected, the dimension of the line joining the population centroid and the hypothesized centroid can be interpreted with a linear function, using a discriminant function and the correlation of each dependent variable with a discriminant score. (Author/BW)
Descriptors: Discriminant Analysis, Hypothesis Testing, Mathematical Models, Statistical Analysis
Peer reviewedHazleton, Vincent; Riley, Patricia – Communication Quarterly, 1981
Communication researchers have recently expressed concern with the lack of statistical power in their literature. Authors propose a method for increasing statistical power: the partitioning of the decision region in three parts. This procedure results in an unambiguous interpretation of nonsignificant results and leads to increased power. (PD)
Descriptors: Communication Research, Research Methodology, Research Problems, Statistical Analysis
Peer reviewedHorn, John L.; Engstrom, Robert – Multivariate Behavioral Research, 1979
Cattell's scree test and Bartlett's chi-square test for the number of factors to be retained from a factor analysis are shown to be based on the same rationale, with the former reflecting subject sampling variability, and the latter reflecting variable sampling variability. (Author/JKS)
Descriptors: Comparative Analysis, Factor Analysis, Hypothesis Testing, Statistical Analysis
Peer reviewedKatz, Barry M.; McSweeney, Maryellen – Educational and Psychological Measurement, 1980
Errors of misclassification associated with two concept acquisition criteria and their effects on the actual significance level and power of a statistical test for sequential development of these concepts are presented. Explicit illustrations of actual significance levels and power values are provided for different misclassification models.…
Descriptors: Concept Formation, Hypothesis Testing, Mathematical Models, Power (Statistics)
Peer reviewedFeldt, Leonard S. – Psychometrika, 1980
Procedures are developed for testing the hypothesis that Cronbach's alpha reliability coefficient is equal for two tests given to the same subjects. (Author/JKS)
Descriptors: Error of Measurement, Hypothesis Testing, Measurement, Statistical Significance


