Timeline for Mixed effects model: model fitting vs conceptual sense
Current License: CC BY-SA 3.0
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| Apr 13, 2017 at 12:44 | history | edited | CommunityBot |
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| May 12, 2015 at 20:11 | comment | added | Rooirokbokkie | The scary part is, I did do some a priori power analysis based on the effect sizes reported by similar studies and I aimed for 25-30, but ended up with 20 (I had to discard one participant due to high error rates). There are literally dozens of studies in the literature that have 12-20 participants in their sample that did something similar. By looking at the ANOVA results (and plots of my interactions) my hypothesis was spot on. But the MLM model is making me doubt the robustness of my findings as the results are borderline, whereas the ANOVA lights up like a Christmas tree. | |
| May 12, 2015 at 19:31 | history | edited | le_andrew | CC BY-SA 3.0 |
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| May 12, 2015 at 19:29 | comment | added | le_andrew | When a hypothesis is not supported by the data, and you modeled it correctly, you either have an incorrect hypothesis or an under-powered study. We're getting off topic here, but did you do any kind of power analysis prior to running the study? The guy who answered that question I linked to has a great mixed model power program on his website. Using some assumptions about the variance of the random effects, I figured you'd need an effect size d of about .3 (effect size f=.15) to get 80% power. Was what you found anywhere near that? | |
| May 12, 2015 at 19:12 | history | edited | le_andrew | CC BY-SA 3.0 |
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| May 12, 2015 at 18:00 | comment | added | Rooirokbokkie | Thank you very much. This cleared it up quite nicely. The link with Henrik's post also helped clarify a few things. But now my three way interaction seems to be diminished somewhat...which is bad for me | |
| May 12, 2015 at 17:12 | vote | accept | Rooirokbokkie | ||
| May 12, 2015 at 16:00 | history | answered | le_andrew | CC BY-SA 3.0 |