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dc.contributor.authorPreacher, Kristopher J.
dc.contributor.authorRucker, Derek D.
dc.contributor.authorHayes, Andrew F.
dc.date.accessioned2007-07-08T13:53:29Z
dc.date.available2007-07-08T13:53:29Z
dc.date.issued2007
dc.identifier.citationMultivariate Behavioral Research, 42, 182-227.
dc.identifier.urihttp://hdl.handle.net/1808/1658
dc.description.abstractThis 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.
dc.language.isoen_US
dc.publisherLawrence Erlbaum Associates
dc.subjectmediationen
dc.subjectmoderationen
dc.subjectmoderated mediationen
dc.titleAddressing moderated mediation hypotheses: Theory, methods, and prescriptions
dc.typeArticle
kusw.kuauthorPreacher, K. J.
dc.rights.accessrightsopenAccess


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