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

Abstract

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.

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Correspondence to Kristopher J. Preacher.

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This work was funded in part by National Institute on Drug Abuse Grant DA16883, awarded to K.J.P. while at the University of North Carolina at Chapel Hill.

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Preacher, K.J., Hayes, A.F. Asymptotic and resampling strategies for assessing and comparing indirect effects in multiple mediator models. Behavior Research Methods 40, 879–891 (2008). https://doi.org/10.3758/BRM.40.3.879

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Keywords

  • Indirect Effect
  • Structural Equation Modeling
  • Residual Covariance
  • Total Indirect Effect
  • Multiple Mediator Model