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Peer reviewedHart, Leslie A. – Teachers College Record, 1971
The author is extemely critical of long-held concepts concerning poor children. (CK)
Descriptors: Disadvantaged Youth, Grouping (Instructional Purposes), Hypothesis Testing
Peer reviewedSterk, Sam – Adolescence, 1971
Descriptors: Adolescents, Counseling Services, Hypothesis Testing, Personality Development
Mann, Lester; And Others – J Educ Meas, 1970
Descriptors: Anxiety, Grade 7, Hypothesis Testing, Test Results
Spence, Janet T. – J Exp Psychol, 1970
Descriptors: Concept Formation, Hypothesis Testing, Reinforcement, Verbal Learning
Carver, Ronald P. – J Educ Res, 1970
Descriptors: Hypothesis Testing, Learning Processes, Time Factors (Learning)
Peer reviewedSchmauk, Frank J. – Journal of Abnormal Psychology, 1970
Descriptors: Emotional Disturbances, Hypothesis Testing, Learning Processes, Testing
Bugelski, B. R. – J Exp Psychol, 1970
Descriptors: Hypothesis Testing, Learning Processes, Time Factors (Learning)
Mosberg, Ludwig – J Exp Psychol, 1970
Descriptors: Feedback, Hypothesis Testing, Paired Associate Learning, Responses
Merrill, M. David; And Others – J Educ Psychol, 1970
Descriptors: Hypothesis Testing, Learning Experience, Organization, Task Performance
Peer reviewedWilcox, Rand R. – Educational and Psychological Measurement, 1983
When comparing k normal populations an investigator might want to know the probability that the population with the largest population mean will have the largest sample mean. This paper describes and illustrates methods of approximating this probability when the variances are unknown and possibly unequal. (Author/BW)
Descriptors: Data Analysis, Hypothesis Testing, Mathematical Formulas, Probability
Peer reviewedEkbohm, Gunnar – Psychometrika, 1982
The problem of testing two correlated proportions with incomplete data is considered by means of Monte Carlo simulations studies. A test proposed in this paper, which can be regarded as a generalization of McNemar's test, is recommended in all cases with incomplete data and not too small samples. (Author)
Descriptors: Correlation, Hypothesis Testing, Nonparametric Statistics, Statistical Significance
Peer reviewedMendoza, Jorge L. – Psychometrika, 1980
The paper obtains a maximum likelihood criterion test for multisample sphericity. The test contains Mauchly's sphericity test as a special case. (Author)
Descriptors: Analysis of Variance, Data Analysis, Hypothesis Testing
Peer reviewedRoche, Alex F. – Child Development, 1981
Contrary to the deterministic nature of the adipocyte- number hypothesis, correlations between adiposity data recorded during infancy and data recorded during the school- age period or later are very low. There is no convincing evidence the obese infant has more than a slight tendency to become an obese adult. (Author/MP)
Descriptors: Hypothesis Testing, Obesity, Research Methodology, Research Problems
Peer reviewedToothaker, Larry E.; Chang, Horng-shing – Journal of Educational Statistics, 1980
Extensions of the Kruskal-Wallis procedure for a factorial design are examined under various degrees and kinds of nonnullity. It was found that the distributions of these test statistics are a function of effects other than those being tested, except under the completely null situation. Their use is discouraged. (Author/JKS)
Descriptors: Analysis of Variance, Hypothesis Testing, Nonparametric Statistics
Peer reviewedBudescu, David V. – Psychometrika, 1980
A recent paper by Wainer and Thissen has renewed the interest in Gini's mean difference, G, by pointing out its robust characteristics. This note presents distribution-free asymptotic confidence intervals for its population value in the one sample case and for difference in the two sample situations. (Author/JKS)
Descriptors: Analysis of Variance, Hypothesis Testing, Nonparametric Statistics


