I want to analyze data from an experiment where participant’s performed a task sitting next to each other. Each participant is assigned to only one dyad. Both participant's performance (dv, continuous) on the task was measured. My hypothesis concerns a cross-level interaction of an experimental factor (condition) that was manipulated within participants and a factor (role) manipulated between participants but within dyads (so participants in the dyads are also distinguishable by this factor).
The data structure looks as follows:
participant dyad condition role dv
1 1 2 1 1 284
2 1 2 -1 1 290
3 2 2 1 -1 262
4 2 2 -1 -1 266
5 3 3 1 -1 287
6 3 3 -1 -1 292
7 4 3 1 1 314
8 4 3 -1 1 300
From what I understood about repeated measures dyadic designs from e.g., West (2013) Repeated measures with dyads, my data structure has these characteristics:
- participants are nested in dyads
- condition is crossed with participants
- participants are nested in role and role is crossed with dyads
I thought to analyze this with a mixed model looking like this
model <- lmer(dv ~ condition * role + (1|dyad) + (1|dyad:participant), data)
I am unsure, however, whether this correctly models all the intra-unit dependencies that may occur in this data? If not, what would be better way to model this data?