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Journal of Experimental… | 1 |
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Hester, Yvette – 1996
Analysis of variance (ANOVA) was invented in the 1920s to partition variance of a single dependent variable into uncorrelated parts. Having uncorrelated parts makes the computations involved in ANOVA incredibly easier. This was important before computers were invented, when calculations were all done by hand, and also were done repeatedly to check…
Descriptors: Analysis of Variance, Computation, Correlation, Heuristics
Dolenz, Beverly – 1992
The correlation coefficient is an integral part of many other statistical techniques (analysis of variance, t-tests, etc.), since all analytic methods are actually correlational (G. V. Glass and K. D. Hopkins, 1984). The correlation coefficient is a statistical summary that represents the degree and direction of relationship between two variables.…
Descriptors: Analysis of Variance, Correlation, Heuristics, Relationship

Campbell, Kathleen T.; Taylor, Dianne L. – Journal of Experimental Education, 1996
A hypothesized data set is used to illustrate that canonical correlation analysis is a general linear model, subsuming other parametric procedures as special cases. Specific techniques included in analyses are t tests, Pearson correlation, multiple regression, analysis of variance, multivariate analysis of variance, and discriminant analysis. (SLD)
Descriptors: Analysis of Variance, Correlation, Heuristics, Multivariate Analysis
Leister, K. Dawn – 1996
Commonality analysis is a method of partitioning variance that has advantages over more traditional "OVA" methods. Commonality analysis indicates the amount of explanatory power that is "unique" to a given predictor variable and the amount of explanatory power that is "common" to or shared with at least one predictor…
Descriptors: Analysis of Variance, Correlation, Heuristics, Power (Statistics)
Campbell, Kathleen T.; Taylor, Dianne L. – 1993
Using a hypothetical data set of 24 cases concerning opinions on contemporary issues on which Democrats and Republicans might disagree, concrete examples are provided to illustrate that canonical correlation analysis is the most general linear model, subsuming other parametric procedures as special cases. Specific statistical techniques included…
Descriptors: Analysis of Variance, Correlation, Discriminant Analysis, Heuristics
Freidrich, Katherine R. – 1992
It is argued that, given the importance and the increased use of multivariate techniques such as factor analysis and canonical correlation, students need to be made aware of multivariate methods and the appropriate ways in which they can be applied. As a general linear model that subsumes all other parametric measures, canonical correlation…
Descriptors: Analysis of Covariance, Analysis of Variance, College Mathematics, Comparative Analysis