I've recently started analysing the data for a project using linear mixed models but am not sure how to deal with crossed and nested factors at the same time.
In my study, each participant reported two events (Event A: a negative personal experience they had & Event B: a negative news story they learnt from the media). In other words, each participant reported two negative events that had happened already, so there won't be cases wherein a participant shared an event that did not happen. Also, these events were independent of each other.
After 24 hours, we asked participants whether they discussed each event with someone else (Status: shared or not shared). A participant can have discussed Event A but not Event B (or vice versa), both events, or neither of the events.
EDIT 1: Then, we asked them to rate how much they thought their feelings about each event had changed during the 24 hours. In other words, each participant provided 2 ratings, one per event.
The dependent variable (DV) is perceived emotional change. I want to test:
- whether there's a main effect of Event on the DV
- whether there's a main effect of Status on the DV
- whether there's an interaction between Event and Status on the DV (e.g., let's say sharing leads to greater emotional change than not sharing, but this difference may be significant only in the case of Event A but not B)
I initially structured the model in this way in Python:
smf.mixedlm("affect_change ~ Event*Status", data, groups=data["participant"]).fit()
Or, in the R version: affect change ~ Event * Status + (1 | participant)
However, I realised that my study design is probably crossed and nested: Each level of Event (A, B) occurs with each level of Participant (that is, every participant reported both events), hence crossed. However, each level of Status (Shared, Not Shared) occurs with only either level of Event (that is, Event A was either shared or not shared, and same was Event B), hence nested.
Is my model structured correctly such that it reflects my study design and is able to test my predictions?
EDIT 2: I have another variable, happiness, that I measured before the 24-hour period and after it for each event. To be more specific, we asked participants to rate how happy they were about each event right after they reported it and asked again after they indicated whether they shared each event.
How should I account for the additional effect of Time? That is, besides the effects of Event and Status (and possibly their interaction), I also want to check for the effect of Time (and if it interacts with the other two predictors) on happiness.
Thank you for reading my question.