Monte Carlo simulations (MCSs) provide important information about statistical phenomena that would be impossible to assess otherwise. This article introduces MCS methods and their applications to research and statistical pedagogy using a novel software package for the R Project for Statistical Computing constructed to lessen the often steep learning curve when organizing simulation code. A primary goal of this article is to demonstrate how well-suited MCS designs are to classroom demonstrations, and how they provide a hands-on method for students to become acquainted with complex statistical concepts. In this article, essential programming aspects for writing MCS code in R are overviewed, multiple applied examples with relevant code are provided, and the benefits of using a generate-analyze-summarize coding structure over the typical "for-loop" strategy are discussed.

Suppressing the expression of negative emotions tends to undermine individuals' and their partners' wellbeing. However, sometimes expressive suppression may be relatively innocuous given that individuals commonly withhold negative emotions in order to maintain close relationships, and this may be especially the case when expressive suppression is enacted by people who exhibit amplified expressions of negative emotions, such as those high in attachment anxiety. The current research examined when and for whom expressive suppression may be more or less costly by testing whether the curvilinear effect of individuals' expressive suppression on individuals' and partners' outcomes is moderated by individuals' attachment anxiety. Our results across 3 dyadic studies revealed a linear effect of expressive suppression when predicting individuals' outcomes: greater expressive suppression had costs for individuals (lower relationship satisfaction, reported responsiveness and discussion success, and greater discussion threat). Furthermore, in 4 of the 5 models, a moderated curvilinear effect of expressive suppression emerged when predicting partners' outcomes. For individuals low in attachment anxiety, low levels of expressive suppression did not incur costs for their partners' relationship satisfaction, perceptions of individuals' responsiveness, discussion success, and discussion threat. Once expressive suppression surpassed moderate levels, however, greater expressive suppression had a detrimental effect on partners' outcomes. In contrast, for individuals high in attachment anxiety, the negative effect of moderate-to-high levels of expressive suppression on partners' outcomes was attenuated. These novel results demonstrate how considering curvilinear methods can uncover when and for whom expressive suppression may be more or less costly in intimate relationships.

This article examines the origins of psychology's adoption of a standardized style and format for its publications and the controversies that this decision engendered. The present account draws on perspectives derived from the history of reading and the sociology of professions to explain the historical appeal of such standards. Archival documents are used to trace the events that led to the drafting of the first set of publication standards in 1929. Where previous historical accounts of the publication manual have stressed the influence of behaviorism, the discipline's leaders embraced these instructions because of the perception of information overload resulting from the rapid expansion and professionalization of psychology following World War I. Under the auspices of the National Research Council, a committee on publication practices surveyed scientists, editors, and publishers in the hopes of making more efficient the communication of psychological knowledge within an increasingly large and anonymous discipline. Archival documents also reveal an animated debate over whether the progress of psychology as a science required the adoption of universally followed rules trusted by all or the cultivation of the scientist's creativity and individuality of expression.

This paper is designed as a tutorial to highlight some recent developments for visualizing the relationships among response and predictor variables in multivariate linear models (MLMs), and implemented in convenient packages for R. These models include multivariate multiple regression analysis (MMRA), multivariate analysis of variance (MANOVA) and multivariate analysis of covariance (MANCOVA). The methods we describe go well beyond what can be understood and explained from simple univariate graphical methods for the separate response variables. We describe extensions of these methods for the case of more than just a few response variables, where the important relationships can be readily seen in the low-dimensional (2D) space that accounts for most of the relevant information. As befits the tutorial nature of this paper, we analyze some sample psychological research studies utilizing these multivariate designs, showing examples in R. In the process, we also take up several practical problems related to the assumptions of MLMs, and how these can be dealt with using graphical methods. Finally, we provide guidelines to aid researchers in conducting multivariate research, pertaining to the analysis, visualization, and reporting of such designs. The graphical and statistical methods described here are all freely available and implemented in the R packages candisc, car, heplots, and mvinfluence.

This paper explores a variety of topics related to the question of testing the equality of covariance matrices in multivariate linear models, particularly in the MANOVA setting. The main focus is on graphical methods that can be used to address the evaluation of this assumption. We introduce some extensions of data ellipsoids, hypothesis-error (HE) plots and canonical discriminant plots and demonstrate how they can be applied to the testing of equality of covariance matrices. Further, a simple plot of the components of Box's M test is proposed that shows how groups differ in covariance and also suggests other visualizations and alternative test statistics. These methods are implemented and freely available in the heplots and candisc packages for R. Examples from the paper are available in supplementary materials.