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Peer reviewedDacey, John S.; Madaus, George F. – Journal of Educational Research, 1971
Descriptors: Comparative Analysis, Creativity, Hypothesis Testing, Intelligence
Peer reviewedOkey, James R. – Educational Leadership, 1971
Descriptors: Curriculum Development, Educational Objectives, Hypothesis Testing
Nahinsky, Irwin D.; and others – J Exp Psychol, 1970
Descriptors: Concept Formation, Cues, Hypothesis Testing, Stimuli
Hogge, James H.; Picklesimer, Judith H. – Educ Psychol Meas, 1970
Descriptors: Computer Programs, Hypothesis Testing, Statistical Analysis
Lester, Gene – Percept Mot Skills, 1969
Tests the hypothesis that the Mueller-Lyer illusion will be less strong when an observer is distant from figures than when he is close, and presents results that do not confirm the hypothesis. (RW)
Descriptors: Hypothesis Testing, Predictive Validity, Visual Perception
Peer reviewedLibby, David L.; Novick, Melvin R. – Journal of Educational Statistics, 1982
Two multivariate probability distributions, a generalized beta distribution and a generalized F distribution, are derived. Formulas for the moments of these distributions are given and an example of the bivariate generalized beta is presented. (Author/JKS)
Descriptors: Hypothesis Testing, Multivariate Analysis, Statistical Distributions
Peer reviewedHawkins, Vincent J. – Education, 1982
Research on Piaget's four stages of cognitive development has shown that although nearly everyone passes through sensorimotor, preoperational, and concrete operational stages, most do not reach the stage of formal operations. Those people who do attain formal operations seem to have a curiosity factor not operative in those who don't. (Author/BRR)
Descriptors: Cognitive Development, Curiosity, Definitions, Hypothesis Testing
Peer reviewedBudescu, David V. – Educational and Psychological Measurement, 1982
An empirical study of the power of the F test in normal populations with variances proportional to the cell means is reported. The results indicate that the power of the test can be approximated by the noncentral F distribution with a modified parameter of noncentrality. (Author/CM)
Descriptors: Hypothesis Testing, Research Problems, Statistical Analysis
Peer reviewedMartin, Charles G.; Games, Paul A. – Journal of Experimental Education, 1981
Power and stability of Type I error rates are investigated for the Box-Scheffe test of homogeneity of variance with varying subsample sizes under conditions of normality and nonnormality. The test is shown to be robust to violation of the normality assumption when sampling is from a leptokurtic population. (Author/GK)
Descriptors: Hypothesis Testing, Mathematical Formulas, Statistical Analysis
Peer reviewedWillson, Victor L. – Educational and Psychological Measurement, 1980
Guilford's average interrater correlation coefficient is shown to be related to the Friedman Rank Sum statistic. Under the null hypothesis of zero correlation, the resultant distribution is known and the hypothesis can be tested. Large sample and tied score cases are also considered. An example from Guilford (1954) is presented. (Author)
Descriptors: Correlation, Hypothesis Testing, Mathematical Formulas, Reliability
Peer reviewedMorrow, James R.; Hopkins, Kenneth D. – Journal of Experimental Education, 1979
The F-distribution approximation suggested by Dixon was investigated at various combinations of alpha and degrees of freedom. Tabled values were compared with values computed utilizing the suggested formula. (Author/GSK)
Descriptors: Hypothesis Testing, Mathematical Formulas, Statistical Analysis
Peer reviewedKraemer, Helena Chmura – Journal of Educational Statistics, 1980
The robustness of hypothesis tests for the correlation coefficient under varying conditions is discussed. The effects of violations of the assumptions of linearity, homoscedasticity, and kurtosis are examined. (JKS)
Descriptors: Correlation, Hypothesis Testing, Reliability, Statistical Analysis
Peer reviewedArmenakis, Achilles A.; And Others – Educational and Psychological Measurement, 1977
The coefficient of congruence is a quantitative measure of the similarity of factor structures for different samples of subjects. This paper is intended to inform interested readers of the availability of a computer program capable of computing coefficients of congruence of factor structures. (Author)
Descriptors: Computer Programs, Factor Analysis, Hypothesis Testing
Peer reviewedSchafer, William D. – Educational and Psychological Measurement, 1977
This program generates maximum likelihood estimates of the parameters of a mixture of two normal distributions and tests the significance of the mixture hypothesis against that of a single normal distribution. (Author)
Descriptors: Computer Programs, Hypothesis Testing, Statistical Analysis
Peer reviewedCoombs, William T.; And Others – Review of Educational Research, 1996
Methods to compare population means in the univariate case and population mean vectors in the multivariate case are presented in terms of hypotheses tested by various procedures. Tests relevant to each hypothesis are described and compared in terms of maximizing power while controlling Type I error rates over the widest variety of conditions. (SLD)
Descriptors: Comparative Analysis, Hypothesis Testing, Multivariate Analysis


