Hypotheses involving mediation are common in the behavioral sciences. Mediation exists when a predictor affects a dependent variable indirectly through at least one intervening variable, or mediator. Methods to assess mediation involving multiple simultaneous mediators have received little attention in the methodological literature despite a clear need. We provide an overview of simple and multiple mediation and explore three approaches that can be used to investigate indirect processes, as well as methods for contrasting two or more mediators within a single model. We present an illustrative example, assessing and contrasting potential mediators of the relationship between the helpfulness of socialization agents and job satisfaction. We also provide SAS and SPSS macros, as well as Mplus and LISREL syntax, to facilitate the use of these methods in applications.

https://link.springer.com/content/pdf/10.3758/BRM.40.3.879.pdf

Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models

Understanding communication processes is the goal of most communication researchers. Rarely are we satisfied merely ascertaining whether messages have an effect on some outcome of focus in a specif...

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Beyond Baron and Kenny: Statistical Mediation Analysis in the New Millennium

The goals of this article are to (a) describe differences between moderator and mediator effects; (b) provide nontechnical descriptions of how to examine each type of effect, including study design, analysis, and interpretation of results; (c) demonstrate how to analyze each type of effect; and (d) provide suggestions for further reading. The authors focus on the use of multiple regression because it is an accessible data-analytic technique contained in major statistical packages. When appropriate, they also note limitations of using regression to detect moderator and mediator effects and describe alternative procedures, particularly structural equation modeling. Finally, to illustrate areas of confusion in counseling psychology research, they review research testing moderation and mediation that was published in the Journal of Counseling Psychology during 2001.

Testing Moderator and Mediator Effects in Counseling Psychology Research.

Studies that combine moderation and mediation are prevalent in basic and applied psychology research. Typically, these studies are framed in terms of moderated mediation or mediated moderation, both of which involve similar analytical approaches. Unfortunately, these approaches have important shortcomings that conceal the nature of the moderated and the mediated effects under investigation. This article presents a general analytical framework for combining moderation and mediation that integrates moderated regression analysis and path analysis. This framework clarifies how moderator variables influence the paths that constitute the direct, indirect, and total effects of mediated models. The authors empirically illustrate this framework and give step-by-step instructions for estimation and interpretation. They summarize the advantages of their framework over current approaches, explain how it subsumes moderated mediation and mediated moderation, and describe how it can accommodate additional moderator and mediator variables, curvilinear relationships, and structural equation models with latent variables.

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Methods for integrating moderation and mediation: a general analytical framework using moderated path analysis.

https://journals.plos.org/plosmedicine/article/file?id=10.1371/journal.pmed.0040297&type=printable

Strengthening the Reporting of Observational Studies in Epidemiology (STROBE): Explanation and elaboration

This article provides researchers with a guide to properly construe and conduct analyses of conditional indirect effects, commonly known as moderated mediation effects. We disentangle conflicting definitions of moderated mediation and describe approaches for estimating and testing a variety of hypotheses involving conditional indirect effects. We introduce standard errors for hypothesis testing and construction of confidence intervals in large samples but advocate that researchers use bootstrapping whenever possible. We also describe methods for probing significant conditional indirect effects by employing direct extensions of the simple slopes method and Johnson-Neyman technique for probing significant interactions. Finally, we provide an SPSS macro to facilitate the implementation of the recommended asymptotic and bootstrapping methods. We illustrate the application of these methods with an example drawn from the Michigan Study of Adolescent Life Transitions, showing that the indirect effect of intrinsic student interest on mathematics performance through teacher perceptions of talent is moderated by student math self-concept.

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Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions.

The authors examine the practice of dichotomization of quantitative measures, wherein relationships among variables are examined after 1 or more variables have been converted to dichotomous variables by splitting the sample at some point on the scale(s) of measurement. A common form of dichotomization is the median split, where the independent variable is split at the median to form high and low groups, which are then compared with respect to their means on the dependent variable. The consequences of dichotomization for measurement and statistical analyses are illustrated and discussed. The use of dichotomization in practice is described, and justifications that are offered for such usage are examined. The authors present the case that dichotomization is rarely defensible and often will yield misleading results. We consider here some simple statistical procedures for studying relationships of one or more independent variables to one dependent variable, where all variables are quantitative in nature and are measured on meaningful numerical scales. Such measures are often referred to as individual-differences measures, meaning that observed values of such measures are interpretable as reflecting individual differences on the attribute of interest. It is of course straightforward to analyze such data using correlational methods. In the case of a single independent variable, one can use simple linear regression and/or obtain a simple correlation coefficient. In the case of multiple independent variables, one can use multiple regression, possibly including interaction terms. Such methods are routinely used in practice. However, another approach to analysis of such data is also rather widely used. Considering the case of one independent variable, many investigators begin by converting that variable into a dichotomous variable by splitting the scale at some point and designating individuals above and below that point as defining

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On the Practice of Dichotomization of Quantitative Variables

A key aim of social psychology is to understand the psychological processes through which independent variables affect dependent variables in the social domain. This objective has given rise to statistical methods for mediation analysis. In mediation analysis, the significance of the relationship between the independent and dependent variables has been integral in theory testing, being used as a basis to determine (1) whether to proceed with analyses of mediation and (2) whether one or several proposed mediator(s) fully or partially accounts for an effect. Synthesizing past research and offering new arguments, we suggest that the collective evidence raises considerable concern that the focus on the significance between the independent and dependent variables, both before and after mediation tests, is unjustified and can impair theory development and testing. To expand theory involving social psychological processes, we argue that attention in mediation analysis should be shifted towards assessing the magnitude and significance of indirect effects.

http://quantpsy.org/pubs/rucker_preacher_tormala_petty_2011.pdf

Mediation Analysis in Social Psychology: Current Practices and New Recommendations

Three experiments examine how power affects consumers' spending propensities. By integrating literatures suggesting that ( a ) powerlessness is aversive, ( b ) status is one basis of power, and ( c ) products can signal status, the authors argue that low power fosters a desire to acquire products associated with status to compensate for lacking power. Supporting this compensatory hypothesis, results show that low power increased consumers' willingness to pay for auction items and consumers' reservation prices in negotiations but only when products were status related. The link between powerlessness and compensatory consumption has broad implications both for consumers' health and well-being and for understanding the psychological state of power.

https://www.jstor.org/stable/10.1086/588569

Desire to Acquire: Powerlessness and Compensatory Consumption

Analysis of continuous variables sometimes proceeds by selecting individuals on the basis of extreme scores of a sample distribution and submitting only those extreme scores to further analysis. This sampling method is known as the extreme groups approach (EGA). EGA is often used to achieve greater statistical power in subsequent hypothesis tests. However, there are several largely unrecognized costs associated with EGA that must be considered. The authors illustrate the effects EGA can have on power, standardized effect size, reliability, model specification, and the interpretability of results. Finally, the authors discuss alternative procedures, as well as possible legitimate uses of EGA. The authors urge researchers, editors, reviewers, and consumers to carefully assess the extent to which EGA is an appropriate tool in their own research and in that of others.

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Derek D. Rucker

Papers

Addressing Moderated Mediation Hypotheses: Theory, Methods, and Prescriptions.

On the Practice of Dichotomization of Quantitative Variables

Mediation Analysis in Social Psychology: Current Practices and New Recommendations

Desire to Acquire: Powerlessness and Compensatory Consumption

Use of the extreme groups approach: a critical reexamination and new recommendations.