Multicollinearity is irrelevant to the search for moderator variables, contrary to the implications of Iacobucci, Schneider, Popovich, and Bakamitsos (Behavior Research Methods, 2016, this issue). Multicollinearity is like the red herring in a mystery novel that distracts the statistical detective from the pursuit of a true moderator relationship. We show multicollinearity is completely irrelevant for tests of moderator variables. Furthermore, readers of Iacobucci et al. might be confused by a number of their errors. We note those errors, but more positively, we describe a variety of methods researchers might use to test and interpret their moderated multiple regression models, including two-stage testing, mean-centering, spotlighting, orthogonalizing, and floodlighting without regard to putative issues of multicollinearity. We cite a number of recent studies in the psychological literature in which the researchers used these methods appropriately to test, to interpret, and to report their moderated multiple regression models. We conclude with a set of recommendations for the analysis and reporting of moderated multiple regression that should help researchers better understand their models and facilitate generalizations across studies.Keywords Moderated multiple regression . Interactions . Multicollinearity . Regression analysis . Tutorial Saunders (1955Saunders ( , 1956) introduced moderated multiple regression (MMR), a simple but very general statistical method for determining whether the relationship between two variables, say Y and X, depends on or is moderated by a third variable Z. The analysis determines whether adding the product XZ to an additive regression model (ADD) containing the components X and Z significantly increases the explained variation or, equivalently, whether the coefficient for the product XZ is statistically significant. Specifically, for these two estimated models . The model is agnostic as to which variable is moderating the other so researchers often simply refer to a significant contribution from the product XZ as an Binteraction.^Interactions form the basis for many psychological theories and data analyses. For example, Riglin et al. (2016), investigated whether the relationship between stress (X) and depressive symptoms (Y) was buffered or moderated by cognitive ability (Z, a continuous variable) and by gender (G, a dichotomous variable).Iacobucci, Schneider, Popovich, and Bakamitsos (2016, this issue, hereinafter ISPB) attempt to sort out various multicollinearity issues in the testing of moderated regression models. Unfortunately their attempt is substantially flawed and is likely to leave many readers confused about how best to test moderated regression models. The reasoning of ISPB

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Traditionally, the value of a product has been assessed according to the direct revenues the product creates. However, products do not exist in isolation but rather influence one another's sales. Such influence is especially evident in e-commerce environments, in which products are often presented as a collection of web pages linked by recommendation hyperlinks, creating a large-scale product network. The authors present a systematic approach to estimate products’ true value to a firm in such a product network. Their approach, which is in the spirit of the PageRank algorithm, uses available data from large-scale e-commerce sites and separates a product's value into its own intrinsic value, the value it receives from the network, and the value it contributes to the network. The authors demonstrate their approach using data collected from the product network of books on Amazon.com . Specifically, they show that the value of low sellers may be underestimated, whereas the value of best sellers may be overestimated. The authors explore the sources of this discrepancy and discuss the implications for managing products in the growing environment of product networks.

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