The concept of the general linear model (GLM) is illustrated and how canonical correlation analysis is the GLM is explained, using a heuristic data set to demonstrate how canonical correlation analysis subsumes various multivariate and univariate methods. The paper shows how each of these analyses produces a synthetic variable, like the Yhat…

This paper illustrates how canonical correlation analysis can be employed to implement all the parametric tests that canonical methods subsume as special cases. The point is heuristic: all analyses are correlational, all apply weights to measured variables to create synthetic variables, and all yield effect sizes analogous to "r"…

Canonical analysis is frequently used in studies of relationships between sets of variables which are difficult to measure accurately, partly because of the true nature of the data and partly because of errors associated with the measurement instruments. Meredith's solution to the fallible data problem is examined. (Author/BJG)

The multivariate technique OVERALS is introduced as a non-linear generalization of canonical correlation analysis (CCA). First, two sets CCA is introduced. Two sets CCA is a technique that computes linear combinations of sets of variables that correlate in an optimal way. Two sets CCA is then expanded to generalized (or k sets) CCA. The…

Many purposes can be served by using multiple dependent variables (MDVs) in program evaluations, and the application of path analysis to multiple measures can increase both conceptual and statistical power. Alternative types of MDV designs are described and evaluated. (Author/TJH)

The shape of the response surface associated with a discriminant analysis provides insight into the value of the derived optimal discriminant variates. A procedure for the determination of "indifference regions," presented in this article, allows the assessment of the degree of flatness of the response surface for any analysis.…

Constructing scales on the basis of components analysis by assigning weights 1 to variables with high positive loadings on the components and -1 to variables with high negative loadings was compared with other strategies of scale construction, which assign weights 1 or -1 to variables with high weights for the components. (Author/BW)

Description of a computer program performing rotated canonical variate analysis was presented. The program is compatible with the IBM Scientific Subroutines Package. Examples of rotated and unrotated solutions of a sample problem were given, together with their interpretations illustrating the advantages of the rotated solution. (Author)

Theoretical hypotheses generated from data analysis of a single sample should not be advanced until the replicability issue is treated. At least one of three questions usually arises when evaluating the invariance of results obtained from a canonical correlation analysis (CCA): (1) "Will an effect occur in subsequent studies?"; (2)…

Canonical correlation analysis is the most general linear model subsuming all other univariate and multivariate cases (N. Kerlinger & E. Pedhazur, 1973; B. Thompson, 1985, 1991). Because "reality" is a complex place, a multivariate analysis such as canonical correlation analysis is demanded to match the research design. The purpose…

Monte Carlo methods were used to investigate the effects of removing extreme data points identified by five indices of influence. Multivariate normal data were simulated and observations were removed from samples if they exceeded the criteria suggested in the literature for each influence statistic. Factors included in the design of the Monte…

Many multivariate statistical methods call upon the assumption of multivariate normality. However, many applied researchers fail to test this assumption. This omission could be due to ignorance of the existence of tests of multivariate normality or confusion about which test to use. Although at least 50 tests of multivariate normality exist,…

Multivariate techniques have been implemented with greater and greater frequency. In order to use multivariate techniques researchers must understand the fundamental assumptions. The purpose of this paper is to evaluate one of the assumptions of multivariate analysis, normality. Overall, normal distributions are unimodal and symmetrical, and they…

The use of generalized discriminant analysis as a descriptive technique which can be employed outside of the traditional analysis of variance studies is discussed. Examples based on real data are provided. (Author/JKS)

Three potential applications of stepwise procedures in canonical analysis and several alternative stepping decision rules are described. Results of an empirical investigation of the procedures indicated that more parsimonious approaches to maintaining variables held up better under cross-validation. (Author/JKS)