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We are running a learning experiment with a between-subjects design on alpine skiing: one group training according to an interleaved schedule whereas the other group train according to a blocked schedule. We have measured the skiers' performance on the same courses on the pre and post-test.

The population has a hierarchical structure; that is, we sample ski groups of (5-12) that belong to a ski academy. And we randomly allocate these skiers into the two groups: interleaved and blocked.

I thought running an ancova model where I predict the post-test would be appropriate for this situation

Post-test = b0 + b1×pretest + b2×group

However, I also think it would be interesting to use a linear mixed-effect model where we predict the post-test and include a random intercept and slope for each skier.

post-test ~ time * group + (1|skier)

Perhaps also for include ski academies as a fixed or random effect

post-test ~ time * group +  skiacademy + (1|skier)

I do not have much experience with these linear mixed models and I don't know if it would be a simple pre and post-test experiment? I appreciate your help.

(We are aiming to recruit a total of 100 alpine skiers and so far we have tested 40 skiers)

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  • $\begingroup$ Beginner courses or more advanced training? Children/adults? Relevant: stats.stackexchange.com/questions/3466/… $\endgroup$
    – kjetil b halvorsen ♦
    Commented Dec 8, 2020 at 14:53
  • $\begingroup$ The skiers are junior elite alpine skiers (16-25 years of age). Alpine skiers are ranked by their total time on a course. Thank you, I think I have read that post before actually but that is a long time ago, so thanks for pointing me to it :) Are you a Norwegian btw? $\endgroup$
    – Cmagelssen
    Commented Dec 8, 2020 at 15:23
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    $\begingroup$ Yes, I am norwegian! but at present more biking than skiing. To little snow here in Atacama! $\endgroup$
    – kjetil b halvorsen ♦
    Commented Dec 8, 2020 at 16:06

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