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Berry, Kenneth J.; Mielke, Paul W., Jr. – Educational and Psychological Measurement, 1986
An algorithm and associated FORTRAN-77 computer subroutine are described for computing Goodman and Kruskal's tau-b statistic along with the associated nonasymptotic probability value under the null hypothesis tau=O. (Author)
Descriptors: Algorithms, Computer Software, Programing Languages, Sampling
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Gradstein, Mark – Journal of Educational Statistics, 1986
The purpose of this paper is to calculate the upper limit of the correlation between normal and dichotomous variables. An empirically obtained correlation should be evaluated in view of this limit, instead of the usual limit of Pearson correlation. (Author)
Descriptors: Correlation, Equations (Mathematics), Predictor Variables, Probability
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Stallings, William M. – Educational Researcher, 1985
In the educational research literature, alpha and p are often conflated. Paradoxically, alpha retains a prominent place in textbook discussions, but it is often supplanted by p in the results sections of journal articles. Because alpha and p have unique uses, researchers should continue to employ both conventions in summarizing the outcomes of…
Descriptors: Educational Research, Research Methodology, Statistical Analysis, Statistical Significance
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Rae, Gordon – Educational and Psychological Measurement, 1984
Various indices for measuring agreement among several raters on the presence or absence of a trait can be interpreted as intraclass correlation coefficients. Such a reformulation clarifies the relationships among the measures, simplifies the computations involved, and permits simple significance tests to be carried out. An illustrative example is…
Descriptors: Correlation, Mathematical Models, Observation, Research Methodology
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Tate, Richard L. – Journal of Educational Statistics, 1983
Statistical issues concerning the analysis of multilevel data common in educational studies are discussed. Computer simulation results are presented to argue for a modification of two currently popular approaches to multilevel or contextual analytical procedures. (JKS)
Descriptors: Data Analysis, Regression (Statistics), Statistical Significance, Statistical Studies
Thompson, Bruce – 1997
Given some consensus that statistical significance tests are broken, misused, or at least have somewhat limited utility, the focus of discussion within the field ought to move beyond additional bashing of statistical significance tests, and toward more constructive suggestions for improved practice. Five suggestions for improved practice are…
Descriptors: Effect Size, Research Methodology, Statistical Significance, Test Use
Sullivan, Jeremy R. – 2000
This paper summarizes the literature regarding statistical significance testing with an emphasis on: (1) the post-1994 literature in various disciplines; (2) alternatives to statistical significance testing; and (3) literature exploring why researchers have demonstrably failed to be influenced by the 1994 American Psychological Association…
Descriptors: Effect Size, Literature Reviews, Statistical Significance, Test Use
Kieffer, Kevin M.; Thompson, Bruce – 1999
As the 1994 publication manual of the American Psychological Association emphasized, "p" values are affected by sample size. As a result, it can be helpful to interpret the results of statistical significant tests in a sample size context by conducting so-called "what if" analyses. However, these methods can be inaccurate…
Descriptors: Educational Research, Sample Size, Statistical Significance, Test Interpretation
Newman, Isadore; Fraas, John W.; Herbert, Alan – 2001
Statistical significance and practical significance can be considered jointly through the use of non-nil null hypotheses that are based on values deemed to be practically significant. When examining differences between the means of two groups, researchers can use a randomization test or an independent t test. The issue addressed in this paper is…
Descriptors: Groups, Hypothesis Testing, Monte Carlo Methods, Statistical Significance
Lane, Ginny G. – 1999
For years, researchers have debated the misinterpretation of the null hypothesis significance test (NHST). Many researchers overemphasize the results of the NHST and underemphasize or even omit effect size measures. This paper addresses the common mistaken perceptions regarding the NHST. Several common effect size estimates are discussed. A small…
Descriptors: Effect Size, Hypothesis Testing, Research Methodology, Statistical Significance
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Smith, Robert A.; And Others – Journal of Educational and Psychological Measurement, 1974
Descriptors: Computer Programs, Hypothesis Testing, Statistical Analysis, Statistical Significance
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Bedford, Crayton W. – Mathematics Teacher, 1972
The Wilcoxon two-sample test used to examine judge bias. (MM)
Descriptors: Bias, Mathematics, Probability, Statistical Analysis
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Sachdeva, Darshan – Journal of Experimental Education, 1971
This paper provides the computational formulas necessary for testing the significance of the difference between mean values of two bivariate normal populations. (Author)
Descriptors: Hypothesis Testing, Measurement Techniques, Statistical Analysis, Statistical Significance
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Parker, Randall M. – Educational and Psychological Measurement, 1971
Descriptors: Analysis of Variance, Computer Programs, Probability, Statistical Significance
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Cross, Edward M.; Chaffin, Wilkie W. – Educational and Psychological Measurement, 1982
It is suggested that the binomial theorem be used to compute the probability that a given number of Type I errors would occur when a group of null hypotheses are true and that this result be used as the level of significance for the test of an overall hypothesis. (Author/BW)
Descriptors: Hypothesis Testing, Research Problems, Statistical Analysis, Statistical Significance
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