Multivariate analysis of variance (MANOVA) is a statistical method used to examine group differences on multiple outcomes. This article reports results of a review of MANOVA in gifted education journals between 2011 and 2017 (N = 56). Findings suggest a number of conceptual and procedural misunderstandings about the nature of MANOVA and its…

Objective: This study examined the relationship between three heuristic cues (consistency, liking and social proof) and condom use in casual sex relationships utilising the theory of planned behaviour. Participants: Totally, 388 US college students were surveyed. Method: Three vignettes for each cue primed students to project their willingness to…

Numerous organizations collect and distribute non-aggregate personal data for a variety of different purposes, including demographic and public health research. In these situations, the data distributor is responsible with the protection of the anonymity and personal information of individuals. Microaggregation is one of the most commonly used…

The selection of a subset of variables from a pool of candidates is an important problem in several areas of multivariate statistics. Within the context of principal component analysis (PCA), a number of authors have argued that subset selection is crucial for identifying those variables that are required for correct interpretation of the…

The purpose of this experiment was to test the influence of mixed reviews appearing as computer-mediated word-of-mouth communication (WOM) on student perceptions of instructors (attractiveness and credibility) and attitudes toward learning course content (affective learning and state motivation). Using the heuristic-systematic processing model, it…

Although the "K"-means algorithm for minimizing the within-cluster sums of squared deviations from cluster centroids is perhaps the most common method for applied cluster analyses, a variety of other criteria are available. The "p"-median model is an especially well-studied clustering problem that requires the selection of "p" objects to serve as…

Statistical procedures based on the general linear model (GLM) share much in common with one another, both conceptually and practically. The use of structural equation modeling path diagrams as tools for teaching the GLM as a body of connected statistical procedures is presented. A heuristic data set is used to demonstrate a variety of univariate…

In General Linear Model (GLM) analyses, it is important to interpret structure coefficients, along with standardized weights, when evaluating variable contribution to observed effects. Although often used in canonical correlation analysis, structure coefficients are less frequently used in multiple regression and several other multivariate…

Minimization of the within-cluster sums of squares (WCSS) is one of the most important optimization criteria in cluster analysis. Although cluster analysis modules in commercial software packages typically use heuristic methods for this criterion, optimal approaches can be computationally feasible for problems of modest size. This paper presents a…

According to some researchers canonical correlation results should be interpreted in part by consulting redundancy coefficients (Rd). This paper, however, makes the case that Rd coefficients generally should not be interpreted. Rd coefficients are not multivariate. Furthermore, it makes little sense to interpret coefficients not optimized as part…

This paper suggests that multivariate analysis techniques are very important in educational research, and that one multivariate technique--canonical correlation analysis--may be particularly useful. The logic of canonical analysis is explained. It is suggested that a backward variable elimination strategy can make the method even more powerful, by…

All parametric methods are special cases of canonical correlation analysis and can, in fact, be performed using the canonical correlation procedures found in commonly available computer statistics packages such as the Statistical Analysis System (SAS) software. It is suggested that canonical analysis has advantages of versatility since it can…

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"…

The use of repeated measures research designs is explored. Repeated measures designs are often advantageous and can be implemented in a variety of research settings. One of the main advantages in repeated measures designs is the control of subject variability. Other advantages are the reduction of error variance and economy in subject recruitment.…