Many people cite a white paper I wrote that you will find circulating online. Observations, recommendations, and implementation. I rarely respond to questions about interpretation of output. The templates for version 3 are not the same as the templates for version 2. If there is no difference, then the source of the discrepancy is something else you have done differently compared to what PROCESS is doing. Because many people refer to PROCESS models by their number, I didn't want to change any of the model numbers when I released version 3 and the second edition of the book. I've been told that it is wrong to control for a mediator when estimating the effect of X on Y. As I discuss in my book on mediation analysis, in my opinion, mediated moderation is rarely very interesting or substantively interpretable. That is, model 2 would be appropriate if you want the amount by which the effect of X on Y changes as W changes to be the same across values of Z.
The version 3 templates are available only in the 2nd edition of Introduction to Mediation, Moderation, and Conditional Process Analysis. For instructions on activating the syntax-driven macro, see the documentation. I articulate my position on this in Hayes, Montoya, and Rockwood See Appendix B of the 2nd edition of the book for guidance. Note that in version 3. Some other things to try can be found here or here or here , though these may not work either. Version 3 allows you to create your own model, bypassing the model number system. But there is an important constraint built into model 2 that does not exist in model 3. This paper also discusses parallel and serial multiple mediator versions of this model not originally addressed by Judd et al. Probably the best way to find examples is by looking at papers that include a citation to Introduction to Mediation, Moderation, and Conditional Process Analysis. I've been told that it is wrong to control for a mediator when estimating the effect of X on Y. It appears that I have evidence of an indirect effect of X on Y through a proposed mediator, but there is no evidence of an association between X and Y. If you don't see a model that corresponds exactly to what you want to estimate, try creating your own. You have specified an M variable in a model that does not use it. In a mediation analysis, another common mistake I see users make is estimating the effect of X on M and the effect of M on Y controlling for X in separate regressions without acknowledging the existence of missing data. The syntax in that white paper will not work on version 3. All this information is in the output, but I recommend you avoid the use of these terms or interpreting your analysis based on the significance of the total and direct effects and whether the effect of X becomes nonsignificant after adding the mediator to the model. If you control for M, then you are estimating only the direct effect of X, meaning that your estimate of X's effect does not include the component of X's effect that operates through the mediator. The procedure has remained pretty consistent until the release of SPSS24, at which point the procedure for installation of a dialog file changed. In my opinion, this position confuses the roles of data analysis, research design, and theory in causal inference. For a discussion of why mean centering is a choice you can make rather than a requirement, the dangers of manually centering and standardizing, and some other myths about centering and standardization, see Introduction to Mediation, Moderation, and Conditional Process Analysis. I stopped circulating this paper in , and it is now outdated and not a sensible citation for PROCESS since it corresponds to version 2. You can read about the bootstrap with multiple imputation in mediation analysis here. As I discuss in my book on mediation analysis, in my opinion, mediated moderation is rarely very interesting or substantively interpretable. This topic is also discussed in Chapter 10 of the 2nd edition of Introduction to Mediation, Moderation, and Conditional Process Analysis.
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