Three Essential Analytical Techniques for the Behavioral Marketing Researcher: Median Splits, Mean-Centering, and Mediation Analysis

Published on Dec 1, 2015
Dawn Iacobucci51
Estimated H-index: 51
(Vandy: Vanderbilt University),
Deidre Popovich6
Estimated H-index: 6
(TTU: Texas Tech University)
+ 2 AuthorsFrank R. Kardes50
Estimated H-index: 50
(UC: University of Cincinnati)
Sources
Abstract
For the behavioral marketing scholar, experimentation and the analysis of variance are among the most important and frequently relied upon tools of the trade, and many useful texts exist to guide researchers on these topics. This monograph is intended to be a supplemental resource and a helpful guide for conducting three essential analytical techniques that are also frequently useful to the behavioral researcher: (1) we discuss the practice of conducting a median split on a continuous variable to facilitate communication clarity. (2) We demonstrate the practice of centering variables about their means prior to creating product terms to reflect interaction effects in a moderated multiple regression model. (3) We discuss the practice of a mediation analysis to test for the relative impact of direct and indirect effects of predictors on dependent variables.
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References50
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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 to do or because reviewers ask them to, without quite understanding why. Adding to the confusion is the fact that there is also a perspective in the literature that mean centering does not reduce multicoll...
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Abstract 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 information and reduction in power. More recently, this practice has been criticized for sometimes producing Type I errors for effects regarding other terms in a model, resulting in a recommendation of t...
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In this rebuttal, we discuss the comments of Rucker, McShane, and Preacher (2015) and McClelland, Lynch, Irwin, Spiller, and 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 the record straight that median splits are perfectly acceptable to use when independent variables are uncorrelated. The commentaries do a good job of furthering the discussion to help readers better d...
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We comment on Iacobucci, Posavac, Kardes, Schneider, and Popovich (2015) by evaluating the practice of discretizing continuous variables. We show that dichotomizing a continuous variable via the median split procedure or otherwise and analyzing the resulting data via ANOVA involves a large number of costs that can be avoided by preserving the continuous nature of the variable and analyzing the data via linear regression. As a consequence, we recommend that regression remain the normative procedu...
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Considerable prior statistical work has criticized replacing a continuously measured variable in a general linear model with a dichotomy based on a median split of that variable. Iacobucci, Posovac, Kardes, Schneider, and Popovich (2015-in this issue) defend the practice of “median splits” using both conceptual arguments and simulations. We dispute their conceptual arguments, and we have identified technical errors in their simulations that dramatically change the conclusions that follow from th...
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