Testing Moderating Effects in PLS Path
Models: An Illustration of Available Procedures
J¨ rg Henseler and Georg Fassott
Abstract Along with the development of scientiﬁc disciplines, namely social sciences, hypothesized relationships become increasingly more complex. Besides the examination of direct effects, researchers are more and more interested in moderating effects. Moderating effects are evoked by variables whose variation inﬂuences the strength or the direction of a relationship between an exogenous and an endogenous variable. Investigators using partial least squares path modeling need appropriate means to test their models for such moderating effects. We illustrate the identiﬁcation and quantiﬁcation of moderating effects in complex causal structures by means of Partial Least Squares Path Modeling. We also show that group comparisons, i.e. comparisons of model estimates for different groups of observations, represent a special case of moderating effects by having the grouping variable as a categorical moderator variable. We provide profound answers to typical questions related to testing moderating effects within PLS path models: 1. How can a moderating effect be drawn in a PLS path model, taking into account that the available software only permits direct effects?
2. How does the type of measurement model of the independent and the moderator variables inﬂuence the detection of moderating effects?
3. Before the model estimation, should the data be prepared in a particular manner? Should the indicators be centered (by having a mean of zero), standardized (by having a mean of zero and a standard deviation of one), or manipulated in any other way?
Nijmegen School of Management, Radboud University Nijmegen, P.O. Box 9108, 6500 HK Nijmegen, The Netherlands
Department of Marketing, University of Kaiserslautern, Postfach 30 49, 67653 Kaiserslautern, Germany
V. Esposito Vinzi et al. (eds.), Handbook of Partial Least Squares, Springer Handbooks of Computational Statistics, DOI 10.1007/978-3-540-32827-8 31, c Springer-Verlag Berlin Heidelberg 2010
J. Henseler and G. Fassott
4. How can the coefﬁcients of moderating effects be estimated and interpreted? And, ﬁnally:
5. How can the signiﬁcance of moderating effects be determined? Borrowing from the body of knowledge on modeling interaction effect within multiple regression, we develop a guideline on how to test moderating effects in PLS path models. In particular, we create a graphical representation of the necessary steps to take and decisions to make in the form of a ﬂow chart. Starting with the analysis of the type of data available, via the measurement model speciﬁcation, the ﬂow chart leads the researcher through the decisions on how to prepare the data and how to model the moderating effect. The ﬂow chart ends with the bootstrapping, as the preferred means to test signiﬁcance, and the ﬁnal interpretation of the model outcomes.
30.1 Moderating Effects – An Overview
Along with the development of scientiﬁc disciplines, namely social sciences, the complexity of hypothesized relationships has steadily increased (Cortina 1993). As Jaccard and Turrisi (2003) established, there are basically six types of relationships that can occur within causal models: (1) direct effects when an independent variable, X , causes a dependent variable, Y ; (2) indirect effects (also called mediating effects) when an independent variable, X , has an impact on a third variable, Z , which then inﬂuences the dependent variable, Y ; (3) spurious effects when a correlation between two variables stems from a common cause, Z ; (4) bidirectional effects when two variables, X and Y , inﬂuence each other; (5) unanalyzed effects; and (6) moderating effects (also called interaction effects) when a moderator variable inﬂuences the strength of the direct effect between...
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