# How to correctly plan for Missing Data in Longitudinal / Repeated Measures Design?

Using an hierarchical questionnaire for a longitudinal study, I face strict constrains in how many items can be processed by each subject per week. The questionnaire contains 51 Items in total, but except from an initial baseline-assessment, where each participant processes the entire questionnaire, I'm restricted to only 6 Items a week per participant. Consider the structure of the questionnaire:

list(
"Main Component 1" = list(
"Subscale A" = c("Item", "Item", "Item"),
"Subscale B" = c("Item", "Item", "Item"),
"Subscale C" = c("Item", "Item", "Item"),
"Subscale D" = c("Item", "Item", "Item"),
"Subscale E" = c("Item", "Item", "Item"),
"Subscale F" = c("Item", "Item", "Item"),
"Subscale G" = c("Item", "Item", "Item"),
"Subscale H" = c("Item", "Item", "Item"),
"Subscale I" = c("Item", "Item", "Item"),
),
"Main Component 2" = list(
"Subscale J"  = c("Item", "Item", "Item"),
"Subscale K"  = c("Item", "Item", "Item"),
"Subscale L"  = c("Item", "Item", "Item"),
"Subscale M"  = c("Item", "Item", "Item")
),
"Main Component 3" = list(
"Subscale N" = c("Item", "Item", "Item"),
"Subscale O" = c("Item", "Item", "Item"),
"Subscale P" = c("Item", "Item", "Item"),
"Subscale Q" = c("Item", "Item", "Item"),
)
)


The goal of the study is NOT to evaluate individual performance, but to track changes for an entire group of n > 30 participants over time. Both subscale and domains show sufficient internal conistency (> .75). So, in summary, i have 30 people each week, generating 6 data points (=180) for X weeks, with one full asssessment for each person at day 1. Now, how do I figure out the optimal presentation-logic to assess all 17 subscales for the entire group each week?