tag:blogger.com,1999:blog-4594832939334410220.post6235860926449664538..comments2024-04-18T04:30:35.033-05:00Comments on Deeply Trivial: Statistics Sunday: What is Bootstrapping?Unknownnoreply@blogger.comBlogger1125tag:blogger.com,1999:blog-4594832939334410220.post-25944071603076835012017-11-15T08:31:24.527-06:002017-11-15T08:31:24.527-06:00Hi Sara, interesting blog post!
One thing I am wo...Hi Sara, interesting blog post!<br /><br />One thing I am wondering: I am pretty sure that the strength of indirect effects across different specifications is not informative with respects to causality and can not help us decide between different models. Felix Thoemmes wrote a paper about that: Reversing Arros in Mediation Models Does Not Distinguish Plausible Models (http://www.tandfonline.com/doi/full/10.1080/01973533.2015.1049351?scroll=top&needAccess=true). <br /><br />Thus, I think it's not valid to say that you find stronger support for hypothesis A over B. You can only say that if we assume that hypothesis A is true, the estimated indirect effect would be larger. But the estimated indirect effect is only meaningful if we assume that the underlying mediation model (including the flow of causality) was valid to begin with! So no way to "bootstrap" causality from data alone. <br /><br />Leaving that aside, there are of course plausible alternative models (Facebook usage <- well-being -> rumination) which you also cannot possibly distinguish based on cross-sectional data alone. The only thing you can do is assume that one model is true based on e.g. theoretical considerations.Julia Rohrernoreply@blogger.com