![]() ![]() Relatively untouched is that there are cross-cutting concerns related to the fact that what is considered appropriate for a mediation claim depends not only on statistical and theoretical criteria, but also on the experience, assumptions, needs, and general point of view of a researcher. As still further evidence of the difficulty of making mediation claims, parameter bias, and sensitivity have emerged as common concerns (e.g., Sobel, 2008 Imai et al., 2010 VanderWeele, 2010 Fritz et al., 2016), as has statistical power for testing both indirect (e.g., Shrout and Bolger, 2002 Fritz and MacKinnon, 2007 Preacher and Hayes, 2008) and total effects ( Kenny and Judd, 2014 Loeys et al., 2015 O'Rourke and MacKinnon, 2015). There are multiple schools of thought and discussions regarding mediation that provide detailed arguments and criteria regarding mediation claims for specific models or sets of assumptions (e.g., Baron and Kenny, 1986 Kraemer et al., 2002 Jo, 2008 Pearl, 2009 Imai et al., 2010). While the conceptual model of mediation is straight-forward, applying it is much less so ( Bullock et al., 2010). In other words, the relationship between X and Y is decomposed into a direct link and an indirect link. The total effect of X on Y is referred to as the total effect ( TE), and that effect is then partitioned into a combination of a direct effect (DE) of X on Y, and an indirect effect ( IE) of X on Y that is transmitted through M. Without respect to a given statistical model, mediation processes are framed in terms of intermediate variables between an independent variable and a dependent variable, with a minimum of three variables required in total: X, M, and Y, where X is the independent variable (IV), Y is the dependent variable (DV), and M is the (hypothesized) mediator variable that is supposed to transmit the causal effect of X to Y. Some philosophical and linguistic considerations are briefly discussed, as well as some other perspectives we do not develop here. Discussion of the perspectives is facilitated by a small simulation study. null hypothesis testing, and hypothesized vs. indirectness of causation, effect size vs. ![]() without a mediation hypothesis, specific effects vs. We discuss five such pairs of perspectives on mediation analysis, their associated advantages and disadvantages, and their implications: with vs. Explicitly discussing these perspectives and their motivations, advantages, and disadvantages can help to provide clarity to conversations and research regarding the use and refinement of mediation models. Mediation analysis has become a very popular approach in psychology, and it is one that is associated with multiple perspectives that are often at odds, often implicitly. 3Department of Psychology, KU Leuven, Leuven, Belgium.2Division of Epidemiology, College of Public Health, Ohio State University, Columbus, OH, United States.1Department of Psychology, Ohio State University, Columbus, OH, United States.
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |