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Hancock, Gregory R.; McNeish, Daniel M. – Journal of Experimental Education, 2017
For the two-way factorial design in analysis of variance, the current article explicates and compares three methods for controlling the Type I error rate for all possible simple interaction contrasts following a statistically significant interaction, including a proposed modification to the Bonferroni procedure that increases the power of…
Descriptors: Statistical Analysis, Hypothesis Testing, Comparative Analysis, Statistical Significance
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Kang, Yoonjeong; Hancock, Gregory R. – Journal of Experimental Education, 2017
Structured means analysis is a very useful approach for testing hypotheses about population means on latent constructs. In such models, a z test is most commonly used for testing the statistical significance of the relevant parameter estimates or of the differences between parameter estimates, where a z value is computed based on the asymptotic…
Descriptors: Models, Statistical Analysis, Hypothesis Testing, Statistical Significance
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Cromley, Jennifer G.; Perez, Tony C.; Fitzhugh, Shannon L.; Newcombe, Nora S.; Wills, Theodore W.; Tanaka, Jacqueline C. – Journal of Experimental Education, 2013
The authors tested whether students can be taught to better understand conventional representations in diagrams, photographs, and other visual representations in science textbooks. The authors developed a teacher-delivered, workbook-and-discussion-based classroom instructional method called Conventions of Diagrams (COD). The authors trained 1…
Descriptors: Visual Aids, Textbooks, Biology, Grade 10
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Ramseyer, Gary C. – Journal of Experimental Education, 1979
A procedure is discussed for testing the significance of the difference in two correlated correlation coefficients, using Fisher's Z-Transformation. The procedure is applicable to a wide range of problems involving tests between dependent correlations and has documented mathematical support when its power curves are examined. (MH)
Descriptors: Correlation, Hypothesis Testing, Statistical Analysis, Statistical Significance
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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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Williams, John D. – Journal of Experimental Education, 1979
Hollingsworth recently showed a posttest contrast for analysis of variance situations that, for equal sample sizes, had several favorable qualities. However, for unequal sample sizes, the contrast fails to achieve status as a maximized contrast; thus, separate testing of the contrast is required. (Author/GSK)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing, Statistical Analysis
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Zimmerman, Donald W. – Journal of Experimental Education, 1986
A computer program randomly sampled ordered pairs of scores from known populations that departed from bivariate normal form and calculated correlation coefficients from sample values. Hypotheses were tested (1) that population correlations are zero using the t statistic; and (2) that population correlations have non-zero values using the r to z…
Descriptors: Correlation, Hypothesis Testing, Sampling, Statistical Distributions
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Levy, Kenneth J. – Journal of Experimental Education, 1978
Monte Carlo techniques were employed to compare the familiar F-test with Welch's V-test procedure for testing hypotheses concerning a priori contrasts among K treatments. The two procedures were compared under homogeneous and heterogeneous variance conditions. (Author)
Descriptors: Analysis of Variance, Comparative Analysis, Hypothesis Testing, Monte Carlo Methods
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Olejnik, Stephen F. – Journal of Experimental Education, 1984
This paper discusses the sample size problem and four factors affecting its solution: significance level, statistical power, analysis procedure, and effect size. The interrelationship between these factors is discussed and demonstrated by calculating minimal sample size requirements for a variety of research conditions. (Author)
Descriptors: Effect Size, Error of Measurement, Hypothesis Testing, Research Design
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Levy, Kenneth J. – Journal of Experimental Education, 1979
Hawkin's procedure for testing a sequence of observations for a shift in location could have applicability for assessing change within a single subject. Monte Carlo results suggest that Hawkins' procedure is robust with respect to moderate violations of its underlying assumptions of homogeneity of variance and normality. (Author/GDC)
Descriptors: Case Studies, Hypothesis Testing, Individual Development, Mathematical Models
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Katz, Barry M.; McSweeney, Maryellen – Journal of Experimental Education, 1979
Errors of misclassification and their effects on categorical data analysis are discussed. The chi-square test for equality of two proportions is examined in the context of errorful categorical data. The effects of such errors are illustrated. A correction procedure is developed and discussed. (Author/MH)
Descriptors: Classification, Data Analysis, Data Collection, Error Patterns
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Levin, Joel R.; And Others – Journal of Experimental Education, 1993
Journal editors respond to criticisms of reliance on statistical significance in research reporting. Joel R. Levin ("Journal of Educational Psychology") defends its use, whereas William D. Schafer ("Measurement and Evaluation in Counseling and Development") emphasizes the distinction between statistically significant and important. William Asher…
Descriptors: Editing, Editors, Educational Assessment, Educational Research