In this article, we attempt to clarify our statements regarding the effects of mean centering. In a multiple regression with predictors A, B, and A × B (where A × B serves as an interaction term), mean centering A and B prior to computing the product term can clarify the regression...

Some behavioral researchers occasionally wish to conduct a median split on a continuous variable and use the result in subsequent modeling to facilitate analytic ease and communication clarity. Traditionally, this practice of dichotomization has been criticized for the resulting loss of...

In this rebuttal, we discuss the comments of Rucker, McShane, & Preacher (2015) and McClelland, Lynch, Irwin, Spiller, & Fitzsimons (2015). Both commentaries raise interesting points, and although both teams clearly put a lot of work into their papers, the bottom line is this: our research sets...

This research examines four frequently used centrality indices — degree, closeness, betweenness, and eigenvectors — to understand the extent to which their clear theoretical distinctions are reflected in differences in empirical performance. Even for stylized networks in which one centrality...

This research uses random networks as benchmarks for inferential tests of network structures. Specifically, we develop formulas for expected values and confidence intervals for four frequently employed social network centrality indices. The first study begins with analyses of stylized networks,...

Among the many centrality indices used to detect structures of actors’ positions in networks is the use of the first eigenvector of an adjacency matrix that captures the connections among the actors. This research considers the seeming pervasive current practice of using only the first...

This paper applies receiver operating characteristics (ROC) analysis to M3 Competition, micro monthly time series for one-month-ahead forecasts. Using the partial area under the curve (PAUC) criterion as a forecast accuracy measure and paired-comparison testing via bootstrapping, we find that...