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Oct 21, 2016 at 2:23 comment added Lisa Oh, okay. Yes, it does make sense! Thank you! Maybe I'm having a problem with interpretation in general - because all these other predictors that I have included in the model should have an effect, and I'm not sure why they don't.
Oct 19, 2016 at 22:58 comment added amoeba Sure. This question suggests that perhaps you do not quite understand what is going on here. Comparing m1 and m2 is equivalent to testing whether type is a significant predictor in m1. So running anova(m1, m2) and running lmerTest on m1 are just two different ways to test if type is significant. You can use either of these two ways, or both, if you like. You are testing the same thing two times. Does it make sense?
Oct 19, 2016 at 22:52 comment added Lisa My question is: Can I compare these mixed models despite the fact that the individual models do not show any significant effects?
Oct 19, 2016 at 22:51 history edited amoeba CC BY-SA 3.0
edited title
Oct 19, 2016 at 22:49 comment added amoeba That's right, this means that the difference is not statistically significant (whether it is "likely" that type has an effect or not, we cannot really say; it depends on how "likely" you thought it was before running your experiment and on many other things). So in your case the conclusions of lmerTest and of anova agree. I am not sure what exactly is then your question.
Oct 19, 2016 at 22:49 comment added Lisa I have run this code (my actual code has a few more predictor variables, though) and get a p-value of 0.9885, which I take to mean that these models are not significantly different and therefore it is unlikely that the predictor variable type has a significant effect
Oct 19, 2016 at 22:45 comment added amoeba Have you already run this code? Do you get a significant or a non-significant difference?
Oct 19, 2016 at 22:44 history edited amoeba CC BY-SA 3.0
edited tags; edited tags
Oct 19, 2016 at 22:37 history asked Lisa CC BY-SA 3.0