Deciding which random effects to retain is a central decision in mixed effect models. Recent recommendations advise a maximal structure whereby all theoretically relevant random effects are retained. Nonetheless, including many random effects often leads to nonpositive definiteness. A typical remedy is to simplify the random effect structure by…

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…

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…

In recent years a number of multivariate approaches have been introduced to map neural systems in health and disease. In this review, we focus on spatial covariance methods applied to functional imaging data to identify patterns of regional activity associated with behavior. In the rest state, this form of network analysis can be used to detect…

This short contribution is a comment on M. Moerbeek's exploration of consequences of ignoring a level of clustering in a multilevel model, which was published in the first issue of the 2004 volume of Multivariate Behavioral Research. After having recapitulated the framework and extended the results of Moerbeek's study, we formulate two critical…

The use of multivariate statistics in behavioral research is investigated, with emphasis on the reasons why multivariate methods can be so important. The concepts of testwise and experimentwise error are explained, and it is noted that multivariate methods can be used to control the inflation of experimentwise Type I error. It is also noted that…

Explains factor analysis, discussing its relation to other multivariate techniques and describing characteristics of the data to consider in determining the appropriateness of factor analysis. Reviews considerations in making decisions about communality estimates, factor extraction, the number of factors to rotate, methods of factor rotation,…

Classical methods for detecting outliers deal with continuous variables. These methods are not readily applicable to categorical data, such as incorrect/correct scores (0/1) and ordered rating scale scores (e.g., 0,..., 4) typical of multi-item tests and questionnaires. This study proposes two definitions of outlier scores suited for categorical…

Presents and illustrates structural equation modeling (multivariate analysis with latent variables, also called causal modeling or covariance structure analysis), discussing issues and problems related to the use of this methodology, possible applications of structural equation modeling to counseling psychology research, and resources for further…

The authors propose a reinforcement-learning mechanism as a model for recurrent choice and extend it to account for skill learning. The model was inspired by recent research in neurophysiological studies of the basal ganglia and provides an integrated explanation of recurrent choice behavior and skill learning. The behavior includes effects of…

Describes the method of discriminant analysis, including the concept of discriminant function, discriminant score, group centroid, and discriminant weights and loadings. Discusses methods for testing the statistical significance of a function, methods of using the function in classification, and the concept of rotating functions. Illustrates the…

Provides an example to illustrate the clustering approach. Discusses the variety of approaches in clustering; choice of cluster analytic techniques; the steps in cluster analysis; the data features such as level, shape, and scatter, that affect cluster results; alternate clustering methods and their relative effectiveness; and applications of…

Describes the loglinear model, and applies it to categorical data cross-tabulated on four dimensions. Defines contrasts similar to those of the analysis of variance and describes post hoc and planned comparison strategies. Illustrates hypothesis testing and model building for categorical data, providing guidelines for performing an analysis on…

The contribution of facet theory to applied psychological research is shown to be its ability to define problems and the solutions to them in terms relevant to those wishing to make practical use of research findings. Three examples illustrate the use of facet theory in applied research. (Author/CM)

Fuzzy trace theory posits that during development the use of verbatim information for solving transitive relationships shifts to the use of gist information. In cognitive developmental research that uses a cross-sectional design, the binomial mixture model is often used to identify such shifts. Because the binomial mixture model assumes equal task…