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...

There seems to be confusion among researchers regarding whether it is good practice to center variables at their means prior to calculating a product term to estimate an interaction in a multiple regression model. Many researchers use mean centered variables because they believe it’s the thing...

This research examines price variations that are perceived to be unfair which then induce consumers to retaliate online. These phenomena are growing in online retailing given the data-based means for companies to set differential pricing, and the social media venues for consumer complaints. In...

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,...

We now turn our attention to stochastic models for social network data. The methodology described here continues the development of statistical methods for network data begun in Chapter 13. We begin in Chapter 15 by considering a (very special) class of statistical distributions for random...

This chapter presents the terminology and concepts of graph theory, and describes basic matrix operations that are used in social network analysis. Both graph theory and matrix operations have served as the foundations of many concepts in the analysis of social networks (Hage and Harary 1983;...

In this chapter we explore the classic factor analytical model. Factor analysis is extremely important in its scientific historical role in explicitly recognizing measurement error in behavioral research,. and in providing a rigorous means of conceptualizing unobservable constructs and the...

This chapter illustrates the analysis of variance (ANOVA) and related techniques including the analysis of covariance (ANCOVA), the multivariate analysis of variance (MANOVA), and the multivariate analysis of covariance (MANCOVA). The analysis of variance will be introduced as a simple extension...

This paper presents a literature review in the area of services marketing and management from 1986 to the present. An electronic library search under the key words "service marketing" supplemented with a manual search yielded 865 articles. These articles are organized into a framework based on...