Timeline for Interpretation of 3-way interaction in a linear mixed model
Current License: CC BY-SA 4.0
6 events
| when toggle format | what | by | license | comment | |
|---|---|---|---|---|---|
| Aug 15, 2023 at 13:43 | comment | added | Peter Flom | I would look at effect size, its affect on other parameters, and its substantive meaning. The results of that would determine whether I added it. The Gelman paper is the one I named in my answer "The Difference between Statistically ... "etc. | |
| Aug 15, 2023 at 13:40 | comment | added | Sandi | Thanks. Would you evaluate the influence of diagnosis in another way? Would you not add it as a 3-way interaction but only diagnosis as a predictor? Which paper from Gelman exactly do you mean? | |
| Aug 11, 2023 at 11:40 | comment | added | Peter Flom | I would NOT interpret it. That is, I would not pay attention to the fact that it went from significant to not significant. See Gelman's paper. I would look at changes in effect size and in predictions. | |
| Aug 11, 2023 at 8:14 | comment | added | Sandi | However, in this model, the 2-way interaction (which represents the treatment effect) is no longer apparent (CI of coefficient includes 0, p-val not significant any longer). How would you interpret this? That there is not enough evidence to claim diagnosis moderates the treatment effect, but there is evidence of a treatment effect (resulting from the 1. model)? Many thanks | |
| Aug 11, 2023 at 8:14 | comment | added | Sandi | Many thanks for your reply. The interpretation of the 2-way interaction is clear to me. I am also aware of the importance of effect sizes. But what about the 3-way interaction? If I add the variable diagnosis (1=yes; 2=no ref group), the 3-way interaction itself is not significant (CI includes 0). This means that diagnosis has no impact on the treatment effect. | |
| Aug 10, 2023 at 11:16 | history | answered | Peter Flom | CC BY-SA 4.0 |