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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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van den Wollenberg, Arnold L. – Psychometrika, 1982
Presently available test statistics for the Rasch model are shown to be insensitive to violations of the assumption of test unidimensionality. Two new statistics are presented. One is similar to available statistics, but with some improvements; the other addresses the problem of insensitivity to unidimensionality. (Author/JKS)
Descriptors: Item Analysis, Latent Trait Theory, Statistics, Test Reliability
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Streiner, David L.; Miller, Harold R. – Journal of Consulting and Clinical Psychology, 1979
A table is provided and described for prorating Minnesota Multiphasic Personality Inventory scales when the entire Form R has not been completed. Good concordance of profile types was found for 300 and 350 completed questions. Interpretations based on 200 items may be suspect. (Author)
Descriptors: Item Analysis, Patients, Personality Assessment, Personality Measures
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Foley, Daniel Patrick – Teaching of Psychology, 1981
Discusses the potential of a test to instruct students and thus facilitate their learning. The author shows various ways of giving a carefully prepared test on a second occasion. (RM)
Descriptors: Educational Testing, Higher Education, Teacher Developed Materials, Teaching Methods
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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
Eaves, Ronald C. – Diagnostique, 1981
Global achievement tests are best used for broad purposes (program evaluation) or purposes such as screening which will be followed up with more precise instruments. (SB)
Descriptors: Achievement Tests, Elementary Secondary Education, Test Interpretation, Test Selection
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Cantrell, D. Dean – English Education, 1981
Argues in favor of using assessment and achievement tests in the English curriculum. Points out the positive effects of testing. (RL)
Descriptors: Achievement Tests, Educational Testing, English Curriculum, Higher Education
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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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