I have a question about comparing importance scores from HB analysis of 2 separate samples (A, B) completing same tasks, but models estimated separately.
Take the example of mean scores for items X & Z, with the HB scores scaled to sum to 100 in both cases:
• For group A, the mean importance scores are X and Z
• For group B, the mean importance scores are X and Z
My understanding is that I can argue that
1. for A: X is 4 times more important than Z
2. for B: X is 2 times more important than Z
What about comparisons between groups A and B?
Can I infer that:
3. Z is more important for B than for A?
4. Z is twice as important for B than for A?
Although in both samples/models the scores are scaled to sum to 100, the scale factor could be different in the 2 samples which suggests I can’t make comparisons 3 or 4 - I can compare ratios between models, but not absolute values. Or is that overly cautious?
Can anyone advise on (in)appropriate inference?