Timeline for Modelling longitudinal data with crossed random effects
Current License: CC BY-SA 4.0
7 events
| when toggle format | what | by | license | comment | |
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| Oct 29, 2019 at 21:02 | history | bounty ended | CommunityBot | ||
| Oct 28, 2019 at 21:16 | vote | accept | Dave | ||
| Oct 27, 2019 at 14:53 | history | edited | Robert Long | CC BY-SA 4.0 |
added clarity about why random slopes do not make sense with these data
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| Oct 26, 2019 at 6:36 | comment | added | Robert Long |
@Dave, that's right, it is simply that in this (simulated) case it does not make sense to fit random slopes fro Type. With real data it very well might. As for nesting in Participant, I assume you are referring to session, which is a fixed effect, not random.
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| Oct 25, 2019 at 22:28 | comment | added | Dave | Hi Robert, thanks for the answer! Can you explain why having a subject slope for Type does not make sense? Doesn't it make sense to allow for the fact that the effect of Type might be different for each participant? Perhaps this was an issue with the simulated data, but imagine a study in which participants respond to either red or blue circles (making Type a colour factor), woudl it not make sense to allow for the effect of colour to vary by person? Could you also elaborate on why nesting session in participant doesn't make sense? | |
| Oct 25, 2019 at 10:51 | history | edited | Robert Long | CC BY-SA 4.0 |
clarification
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| Oct 25, 2019 at 10:04 | history | answered | Robert Long | CC BY-SA 4.0 |