Missing data are a pervasive problem in many psychological applications in the real world. In this article we study the impact of dropout on the operational characteristics of several approaches that can be easily implemented with commercially available software. These approaches include the covariance pattern model based on an unstructured…

When two or more univariate population means are compared, the proportion of variation in the dependent variable accounted for by population group membership is eta-squared. This effect size can be generalized by using multivariate measures of association, based on the multivariate analysis of variance (MANOVA) statistics, to establish whether…

Simulating multivariate nonnormal data with specified correlation matrices is difficult. One especially popular method is Vale and Maurelli's (1983) extension of Fleishman's (1978) polynomial transformation technique to multivariate applications. This requires the specification of distributional moments and the calculation of an intermediate…

Clusterwise linear regression is a multivariate statistical procedure that attempts to cluster objects with the objective of minimizing the sum of the error sums of squares for the within-cluster regression models. In this article, we show that the minimization of this criterion makes no effort to distinguish the error explained by the…

This article evaluates the Smallest Canonical Correlation Method (SCAN) and the Extended Sample Autocorrelation Function (ESACF), automated methods for the Autoregressive Integrated Moving-Average (ARIMA) model selection commonly available in current versions of SAS for Windows, as identification tools for integrated processes. SCAN and ESACF can…

A variance-to-range ratio variable weighting procedure is proposed. We show how this weighting method is theoretically grounded in the inherent variability found in data exhibiting cluster structure. In addition, a variable selection procedure is proposed to operate in conjunction with the variable weighting technique. The performances of these…

In this article we extend and implement the forward search algorithm for identifying atypical subjects/observations in factor analysis models. The forward search has been mainly developed for detecting aberrant observations in regression models (Atkinson, 1994) and in multivariate methods such as cluster and discriminant analysis (Atkinson, Riani,…

Model evaluation in covariance structure analysis is critical before the results can be trusted. Due to finite sample sizes and unknown distributions of real data, existing conclusions regarding a particular statistic may not be applicable in practice. The bootstrap procedure automatically takes care of the unknown distribution and, for a given…

Presents a latent class extension of signal detection theory and illustrates applications of this approach. Introduces an extension of the signal detection model to more than two latent classes, with a simple restriction on the detection parameters and provides some sample programs to fit the models. (SLD)

Extends the Triadic Relations Model of C. Bond, E. Horn, and D. Kenny (1997) to analyze the covariances between triadic variables. Specifies a bivariate version of the model and presents estimation methods that can be used to decompose the covariance between 2 triadic variables into 33 covariance components. (Author/SLD)

K-means cluster analysis is known for its tendency to produce spherical and equally sized clusters. To assess the magnitude of these effects, a simulation study was conducted, in which populations were created with varying departures from sphericity and group sizes. An analysis of the recovery of clusters in the samples taken from these…

Miller and Farr's algorithm for the index of redundancy is shown to be incorrect by means of a counterexample. The consequences of this error for other conclusions drawn by the authors are discussed. (Author/JKS)

Proposes a Bayesian analysis of the multivariate linear model with polytomous variables. Shows how a Gibbs sampler algorithm is implemented to produce the Bayesian estimates. Illustrates the proposed methodology through examples using multivariate linear regression and multivariate two-way analysis of variance with real data. (SLD)

Studied a multiple group model with ordinal categorical observed variables that are manifestations of underlying normal variables. Proposed to apply across-group stochastic constraints on thresholds to identify the model and used a Bayesian approach to analyze the model. Simulation findings and the analysis of a real data set show the usefulness…

Through simulation, showed that a multivariate test of interactions for aligned ranks in a split-plot design controlled Type I error rates for nonnormal data with nonspherical covariance structures. This method also performed well in the presence of a strong repeated measures main effect and demonstrated more statistical power than parametric…