Timeline for Why is the random intercept variance so much larger in R than in SPSS in my model and how do I interpret the results?
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| Sep 14, 2018 at 11:51 | comment | added | Sebastian Siuda | Okay, so in my case I have 171 persons (level 2) in 29 groups (level 3) who have to guess (5-6 per person = 839 observations, level 1) if targets will later on engage in a certain activity. I want to see if the target's actual behavior is a significant predictor for rater's guesses (if rater's were able to correctly guess the targets' later behavior). This is why I have the guesses as outcome so I can model raters' random intercept. In this case: I would want to interpret the coefficient condition on the persons, right? What is the odds ratio if people changed their guesses from yes to no? | |
| Sep 14, 2018 at 10:16 | comment | added | Dimitris Rizopoulos | @Sebastian yes, the interpretation will be conditional on the person. Most often you're interested in marginal interpretation. That is, what is the odds ratio between the group of persons with predictor value $x$ and the group of persons with predictor value $x + 1$. For example, what is the odds ratio between males and females (i.e., groups of people) not the odds ratio if you changed the sex of a specific person. For a summary of these points, check slide 332 of my course notes: drizopoulos.com/courses/EMC/CE08.pdf | |
| Sep 14, 2018 at 9:56 | comment | added | Sebastian Siuda | Moreover, I tried using your GLMMadaptive package and it gives similar results than the glmer package I've used so far. So I guess the odds ratio from the coefficients from these models would be the change in odds for the same person in the same group, right? And the marginal_coefs() function doing in GLMMadaptive is giving me the change in odds across persons and groups , right? | |
| Sep 14, 2018 at 9:53 | comment | added | Sebastian Siuda | Hi @Dimitris, thank you so much. You're helping me a lot! :) To make sure I unterstand correctly: The coefficients have to be interpreted by essentially saying "holding co-variables (which I don't have) and the random intercepts for persons and groups constant, the odds ratio for the coefficient is X and therefore the predictor increases the odds by...", right? So would the odds ratio give me the change in odds for the average person in the average group? I guess I'm still a bit unsure how to interpret it exactly. | |
| Sep 13, 2018 at 19:36 | comment | added | Dimitris Rizopoulos | @Sebastian yes, if you want to include random effects for both persons & groups, you can only do Laplace in R. With regard to the estimated coefficients, you have to be aware of the fact that in GLMMs because of the nonlinear link function used in the specification of the model, they have an interpretation conditional on the random effects. For more on this, check the discussion in this question: stats.stackexchange.com/questions/365907/… | |
| Sep 13, 2018 at 19:21 | comment | added | Sebastian Siuda | Thanks @Dimitris! This answer makes a lot of sense and I have found similar information elsewhere, too. However, in my case I can't use higher numerical integration with the nAGQ argument and will have to stay with the Laplace approximation because I have to estimate random intercepts for persons AND groups, right? Also, just to clarify I am not doing something wrong: I have read that using likelihood ratio tests to test for fixed effects is superior to just simply looking at the p-level for a predictor in the model. Is this correct? And can I still interpret the odds-ratio from the model? | |
| Sep 13, 2018 at 18:56 | history | answered | Dimitris Rizopoulos | CC BY-SA 4.0 |