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:

    "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?


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