Canonical correlation analysis is the most general linear model subsuming all other univariate and multivariate cases (Kerlinger & Pedhazur, 1973; Thompson, 1985, 1991). Because “reality ” is a complex place, a multivariate analysis such as canonical correlation analysis is demanded to match the research design. It is the purpose of this paper to increase the awareness and use of canonical correlation analysis and, specifically to demonstrate the value of the related procedure of commonality analysis. Commonality analysis provides the researcher with information regarding the variance explained by each of the measured variables and the common contribution from one or more of the other variables in a canonical analysis (Beaton, 1973; Fre...
When approximately the same amount of variance can be reproduced with a larger variable set and a sm...
The concept of the general linear model (GLM) is illustrated and how canonical correlation analysis ...
This study illustrates that Structural Equation Modeling (SEM) provides a more accurate representati...
Canonical correlation analysis is the most general linear model subsuming all other univariate and m...
Commonality analysis may be used as an adjunct to general linear methods as a means of determining t...
Advantages of the use of multivariate commonality analysis are discussed and a small data set is use...
This paper illustrates the value of applying the law of parsimony to canonical correlation analysis ...
Canonical Correlation Analysis (CCA) can be conceptualized as a multivariate regression involving mu...
A multivariate technique named Canonical Concordance Correlation Analysis (CCCA) is introduced. In c...
Multiple regression is a widely used technique to study complex interrelationships among people, in...
Commonality analysis is a method of decomposing the R squared in a multiple regression analysis into...
Hypothetical data sets are used to demonstrate how canonical correlation methods subsume other commo...
Canonical correlation (CC) analysis is discussed with a view toward providing an intuitive understan...
This paper describes and demonstrates Canonical Correlation Analysis (CCA) with orthogonal rotation ...
Canonical correlation analysis (CCA) is a natural generalization of PCA when the data contain two se...
When approximately the same amount of variance can be reproduced with a larger variable set and a sm...
The concept of the general linear model (GLM) is illustrated and how canonical correlation analysis ...
This study illustrates that Structural Equation Modeling (SEM) provides a more accurate representati...
Canonical correlation analysis is the most general linear model subsuming all other univariate and m...
Commonality analysis may be used as an adjunct to general linear methods as a means of determining t...
Advantages of the use of multivariate commonality analysis are discussed and a small data set is use...
This paper illustrates the value of applying the law of parsimony to canonical correlation analysis ...
Canonical Correlation Analysis (CCA) can be conceptualized as a multivariate regression involving mu...
A multivariate technique named Canonical Concordance Correlation Analysis (CCCA) is introduced. In c...
Multiple regression is a widely used technique to study complex interrelationships among people, in...
Commonality analysis is a method of decomposing the R squared in a multiple regression analysis into...
Hypothetical data sets are used to demonstrate how canonical correlation methods subsume other commo...
Canonical correlation (CC) analysis is discussed with a view toward providing an intuitive understan...
This paper describes and demonstrates Canonical Correlation Analysis (CCA) with orthogonal rotation ...
Canonical correlation analysis (CCA) is a natural generalization of PCA when the data contain two se...
When approximately the same amount of variance can be reproduced with a larger variable set and a sm...
The concept of the general linear model (GLM) is illustrated and how canonical correlation analysis ...
This study illustrates that Structural Equation Modeling (SEM) provides a more accurate representati...