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Peer reviewedTimm, Neil H.; Carlson, James E. – Psychometrika, 1976
Extending the definitions of part and bipartial correlation to sets of variates, the notion of part and bipartial canonical correlation analysis are developed and illustrated. (Author)
Descriptors: Correlation, Hypothesis Testing, Matrices, Multivariate Analysis
McClain, Andrew L. – 1995
The present paper discusses criticisms of statistical significance testing from both historical and contemporary perspectives. Statistical significance testing is greatly influenced by sample size and often results in meaningless information being over-reported. Variance-accounted-for-effect sizes are presented as an alternative to statistical…
Descriptors: Correlation, Effect Size, Research Methodology, Sample Size
Mahadevan, Lakshmi – 2000
Over the years, methodologists have been recommending that researchers use magnitude of effect estimates in result interpretation to highlight the distinction between statistical and practical significance (cf. R. Kirk, 1996). A magnitude of effect statistic (i.e., effect size) tells to what degree the dependent variable can be controlled,…
Descriptors: Data Analysis, Effect Size, Measurement Techniques, Meta Analysis
Deegear, James – 2001
This paper summarizes the literature regarding statistical significant testing with an emphasis on recent literature in various discipline and literature exploring why researchers have demonstrably failed to be influenced by the American Psychological Association publication manual's encouragement to report effect sizes. Also considered are…
Descriptors: Effect Size, Literature Reviews, Research Methodology, Statistical Significance
Onwuegbuzie, Anthony J. – 2001
D. Robinson and J. Levin (1997) proposed what they called a two-step procedure for analyzing statistical data in which researchers first evaluate the probability of an observed effect statistically (i.e., statistical significance), and, if and only if, it can be concluded that the underlying finding is too improbable to be due to chance, then they…
Descriptors: Effect Size, Error of Measurement, Hypothesis Testing, Probability
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


