22
views
0
recommends
+1 Recommend
0 collections
    0
    shares
      • Record: found
      • Abstract: found
      • Article: found
      Is Open Access

      A cautionary note on the power of the test for the indirect effect in mediation analysis

      research-article

      Read this article at

      Bookmark
          There is no author summary for this article yet. Authors can add summaries to their articles on ScienceOpen to make them more accessible to a non-specialist audience.

          Abstract

          Recent simulation studies have pointed to the higher power of the test for the mediated effect vs. the test for the total effect, even in the presence of a direct effect. This has motivated applied researchers to investigate mediation in settings where there is no evidence of a total effect. In this paper we provide analytical insight into the circumstances under which higher power of the test for the mediated effect vs. the test for the total effect can be expected in the absence of a direct effect. We argue that the acclaimed power gain is somewhat deceptive and comes with a big price. On the basis of the results, we recommend that when the primary interest lies in mediation only, a significant test for the total effect should not be used as a prerequisite for the test for the indirect effect. However, because the test for the indirect effect is vulnerable to bias when common causes of mediator and outcome are not measured or not accounted for, it should be evaluated in a sensitivity analysis.

          Related collections

          Most cited references16

          • Record: found
          • Abstract: found
          • Article: not found

          Equivalence of the mediation, confounding and suppression effect.

          This paper describes the statistical similarities among mediation, confounding, and suppression. Each is quantified by measuring the change in the relationship between an independent and a dependent variable after adding a third variable to the analysis. Mediation and confounding are identical statistically and can be distinguished only on conceptual grounds. Methods to determine the confidence intervals for confounding and suppression effects are proposed based on methods developed for mediated effects. Although the statistical estimation of effects and standard errors is the same, there are important conceptual differences among the three types of effects.
            Bookmark
            • Record: found
            • Abstract: not found
            • Article: not found

            Process Analysis: Estimating Mediation in Treatment Evaluations

              Bookmark
              • Record: found
              • Abstract: found
              • Article: not found

              Yes, but what's the mechanism? (don't expect an easy answer).

              Psychologists increasingly recommend experimental analysis of mediation. This is a step in the right direction because mediation analyses based on nonexperimental data are likely to be biased and because experiments, in principle, provide a sound basis for causal inference. But even experiments cannot overcome certain threats to inference that arise chiefly or exclusively in the context of mediation analysis-threats that have received little attention in psychology. The authors describe 3 of these threats and suggest ways to improve the exposition and design of mediation tests. Their conclusion is that inference about mediators is far more difficult than previous research suggests and is best tackled by an experimental research program that is specifically designed to address the challenges of mediation analysis.
                Bookmark

                Author and article information

                Contributors
                Journal
                Front Psychol
                Front Psychol
                Front. Psychol.
                Frontiers in Psychology
                Frontiers Media S.A.
                1664-1078
                12 January 2015
                2014
                : 5
                : 1549
                Affiliations
                [1] 1Department of Data Analysis, Ghent University Ghent, Belgium
                [2] 2Department of Applied Mathematics, Computer Science and Statistics, Ghent University Ghent, Belgium
                Author notes

                Edited by: Jeremy Miles, RAND Corporation, USA

                Reviewed by: Daniel Saverio John Costa, The University of Sydney, Australia; David P. MacKinnon, Arizona State University, USA

                *Correspondence: Tom Loeys, Department of Data Analysis, Faculty of Psychology and Educational Sciences, Ghent University, Henri Dunantlaan 1, 9000 Ghent, Belgium e-mail: tom.loeys@ 123456ugent.be

                This article was submitted to Quantitative Psychology and Measurement, a section of the journal Frontiers in Psychology.

                Article
                10.3389/fpsyg.2014.01549
                4290592
                25628585
                7cde8f0d-0fce-403c-8f63-72a451586436
                Copyright © 2015 Loeys, Moerkerke and Vansteelandt.

                This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

                History
                : 08 October 2014
                : 14 December 2014
                Page count
                Figures: 4, Tables: 1, Equations: 13, References: 33, Pages: 8, Words: 7359
                Categories
                Psychology
                Original Research Article

                Clinical Psychology & Psychiatry
                mediation analysis,power,indirect effect,type i error,confounding,sensitivity analysis

                Comments

                Comment on this article