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Rogosa, 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)
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Vegelius, 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
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Keselman, 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
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Hollingsworth, 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
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Boik, Robert J. – Psychometrika, 1981
The validity conditions for univariate repeated measures designs are described. Attention is focused on the sphericity (equality of variance) requirement. It is recommended that separate rather than pooled error term procedures be routinely used to test a priori hypotheses. (Author/JKS)
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Models, Research Design
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Huynh, Huynh – Psychometrika, 1981
Procedures for the analysis of profiles of means in repeated measures designs under order restriction for patterns of mean change are described. Tables of critical values are provided for the case of simple-order alternatives. (Author/JKS)
Descriptors: Analysis of Variance, Hypothesis Testing, Mathematical Models, Profiles
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Horn, 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
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Lee, Howard B.; Comrey, Andrew L. – Multivariate Behavioral Research, 1979
The popular factor analytic procedure of putting unities in the main diagonal and retaining all factors with an eigenvalue greater than one is criticized with respect to the number of factors retained and resultant estimates of common variance. Ways of avoiding these difficulties are discussed. (JKS)
Descriptors: Factor Analysis, Hypothesis Testing, Oblique Rotation, Orthogonal Rotation
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Blair, R. Clifford; Higgins, James J. – Journal of Educational Statistics, 1980
Monte Carlo techniques were used to compare the power of Wilcoxon's rank-sum test to the power of the two independent means t test for situations in which samples were drawn from (1) uniform, (2) Laplace, (3) half-normal, (4) exponential, (5) mixed-normal, and (6) mixed-uniform distributions. (Author/JKS)
Descriptors: Data Analysis, Hypothesis Testing, Mathematical Formulas, Nonparametric Statistics
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Katz, 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)
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Budescu, David V.; Appelbaum, Mark I. – Journal of Educational Statistics, 1981
The effect of the variance stabilizing transformations on the significance level and power of the F test, applied to Binomial and Poisson variables, was examined by a Monte Carlo study. The results indicate that the significance level is not affected by the transformations. (Author/JKS)
Descriptors: Analysis of Variance, Data Analysis, Evaluation, Hypothesis Testing
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Feldt, 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
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Acito, Franklin; Anderson, Ronald D. – Multivariate Behavioral Research, 1980
Orthogonal target analysis, a technique employed in confirmatory factor analysis, is investigated via a simulation study. The results indicate that the technique will recover the correct underlying population pattern except under very unfavorable data conditions and that a close fit to a binary target is not necessarily forced. (Author/JKS)
Descriptors: Data Analysis, Factor Analysis, Hypothesis Testing, Oblique Rotation
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Langeheine, Rolf – Studies in Educational Evaluation, 1980
Detailed reanalyses of data reported in Studies in Educational Evaluation: Monograph No. 1 by Y. Kashti and Monograph No. 5 by U. Kattmann, 1979, were performed using an explicitly structurally oriented approach via target analysis (PINDIS). Results contradict those reached by Kashti and Kattmann. (RL)
Descriptors: Comparative Analysis, Foreign Countries, Hypothesis Testing, Mathematical Models
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Cohen, S. Alan; Hyman, Joan S. – Educational Researcher, 1979
The authors contend that most research in education lacks statistical power. They feel that the poor use of statistics as a tool for enhancing internal validity must be remediated. The adoption of a new convention is proposed in order to put statistical certainty into reasonable perspective. (RLV)
Descriptors: Educational Research, Hypothesis Testing, Predictive Validity, Statistical Analysis
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