Data collected from surveys can result in hundreds of variables and thousands of respondents. This implies that time and energy must be devoted to (a) carefully entering the data into a database, (b) running preliminary analyses to identify any problems (e.g., missing data, potential outliers), (c) checking the reliability and validity of the…

Reviews of statistical procedures (e.g., Bangert & Baumberger, 2005; Kieffer, Reese, & Thompson, 2001; Warne, Lazo, Ramos, & Ritter, 2012) show that one of the most common multivariate statistical methods in psychological research is multivariate analysis of variance (MANOVA). However, MANOVA and its associated procedures are often not…

Both predictive discriminant analysis (PDA) and descriptive discriminant analysis (DDA) require a decision to pool group covariance matrices, or alternatively, to retain separate group covariance matrices when the group covariance matrices are too dissimilar to pool. Pooling the group covariance matrices involves the so-called "linear"…

This paper discusses how a seldom-used statistical procedure, recursive regression (RR), can numerically and graphically illustrate data-driven nonlinear relationships and interaction of variables. This routine falls into the family of exploratory techniques, yet a few interesting features make it a valuable compliment to factor analysis and…

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…

This paper contains information concerning the following: 1. An overview of multivariate analysis of variance, and discriminant (DA) and canonical (CA) analyses. 2. An introduction to specification and measurement errors, and collinearity. 3. The sparsity of information concerning specification and measurement errors and collinearity as they…

We describe a principled way of imposing a metric representing dissimilarities on any discrete set of stimuli (symbols, handwritings, consumer products, X-ray films, etc.), given the probabilities with which they are discriminated from each other by a perceiving system, such as an organism, person, group of experts, neuronal structure, technical…

Item bias is a major threat to measurement validity. Methods for detecting differential item functioning (DIF) are now commonly used to identify potentially biased items. DIF detection methods for dichotomous items are well developed, but those for ordinal items are less well developed. In this article, the authors compare four methods for…

This paper discusses the principles underlying discriminant analysis and constructs a simulated data set to illustrate its methods. Discriminant analysis is a multivariate technique for identifying the best combination of variables to maximally discriminate between groups. Discriminant functions are established on existing groups and used to…

This paper comments on the impact of computers on statistical analysis and presents a concise, nontechnical overview of five statistical methods now being applied in educational research. Appropriate uses of these techniques are pointed out, along with dangers concerning misapplications. (PP)