# Repeated measures dyadic data with lme4

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?