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Baggaley, Andrew R. – Educational and Psychological Measurement, 1979
A computer program is described that tests for the slope, quadratic, and cubic coefficients of a growth function generated from repeated observations. (Author)
Descriptors: Analysis of Variance, Computer Programs, Hypothesis Testing, Program Descriptions
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Conard, Elizabeth H.; Lutz, J. Gary – Educational and Psychological Measurement, 1979
A program is described which selects the most powerful among four methods for conducting a priori comparisons in an analysis of variance: orthogonal contrasts, Scheffe's method, Dunn's method, and Dunnett's test. The program supplies the critical t ratio and the per-comparison Type I error risk for each of the relevant methods. (Author/JKS)
Descriptors: Analysis of Variance, Computer Programs, Hypothesis Testing, Power (Statistics)
Huberty, Carl J.; Smith, Jerry D. – 1981
A particular strategy for investigating effects resulting from a multivariate analysis of variance (MANOVA) is proposed. The strategy involves multiple two-group multivariate analyses. The two groups result from considering multivariate pairwise group contrasts or multivariate complex group contrasts. Assuming a given two-group analysis yields…
Descriptors: Analysis of Variance, Comparative Analysis, Discriminant Analysis, Hypothesis Testing
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James, Michael – Educational and Psychological Measurement, 1979
Details are given for the use of the mixed effects multivariate analysis of variance table provided by the BMD12V computer program to compute raw generalized variances and hence the U and F statistics for the mixed effects model. (Author/JKS)
Descriptors: Analysis of Variance, Computer Programs, Hypothesis Testing, Program Descriptions
Tritchler, D. L.; Pedrini, D. T. – 1983
The N=1 analysis differs from a typical analysis of variance in that there is no within-cell error term. Thus interaction terms are used as estimates of error variance. If the interaction term in question represents a significant interaction, the F tests will be conservative. Tukey's test for nonadditivity will detect a common form of interaction.…
Descriptors: Analysis of Variance, Computer Programs, Data Analysis, Error of Measurement
Tate, Richard L. – 1981
An approach to the analysis of an aptitude-treatment-interaction (ATI) design in which the treatment groups are based on an underlying factorial structure is described and illustrated. The approach emphasizes description with point and interval estimation. The example design considered consisted of two nominal treatment variables and one interval…
Descriptors: Analysis of Variance, Aptitude Treatment Interaction, Experimental Groups, Hypothesis Testing
Ekstrand, Lars Henric – Rassegna Italiana di Linguistica Applicata, 1980
Discusses the theoretical premises of two experiments in early second language teaching undertaken in Sweden, the first labelled "English without a book", the second, "English in the preparatory school". Analyzes results from the first experiment which led to the decision to begin English instruction as early as grade three.…
Descriptors: Analysis of Variance, Children, Developmental Psychology, Elementary Education
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Sciutto, Mark J. – Teaching of Psychology, 2000
Describes a demonstration that helps students experience the influence of various factors on the F ratio in order for students to understand how effect size, individual differences, and measurement error combine to influence the ability to detect a false null hypothesis (power) in a one-way ANOVA. (CMK)
Descriptors: Analysis of Variance, Demonstrations (Educational), Educational Strategies, Effect Size
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Katz, Richard S.; Eagles, Munroe – PS: Political Science and Politics, 1996
Constructs a model that explains a large fraction of the variance in political science departmental rankings. Divides the objective predictors into two sets: one reflecting faculty quality ratings of department members, the other the effects of circumstances beyond a department's control. This model works well with most social science disciplines.…
Descriptors: Achievement Rating, Analysis of Variance, Causal Models, Credentials
Peer reviewed Peer reviewed
Jackman, Robert W.; Siverson, Randolph M. – PS: Political Science and Politics, 1996
Analyzes the National Research Council's rating of political science departments and discovers the ratings reflect two general sets of characteristics, the size and productivity of the faculty. Reveals that the quality and impact of faculty research is more important than the overall output. Includes tables of statistical data. (MJP)
Descriptors: Achievement Rating, Analysis of Variance, Credentials, Departments