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We might envision a repeated-measures design with multiple sprinting trials for each condition (no fatigue assumed). Correlated measurements would be expected, so we need to employ a repeated-measures test. The expectation is that multiple trials helps us arrive at a value that is closer to 'reality'.
However, what if each sprinting trial involves multiple steps (peak forces following contact, for example)? The measurements corresponding with each step would also be correlated within each trial. Since there is an expected improvement in statistical power when we employ a repeated-measures design, how can we take advantage of multiple steps?
Another example might be calculating average values over of some measure the entire gait cycle from running trials collected for 1 minute. At 90 strides/180 steps per minute, that is a lot of measurements. It would seem that we would fail to benefit from statistical power considerations if we simply put one single average value from that 1 minute trial into the hypothesis test calculations.
Conceptually or otherwise, is there such a thing as a within-trial factor nested inside a within-subject factor?
However, what if each sprinting trial involves multiple steps (peak forces following contact, for example)? The measurements corresponding with each step would also be correlated within each trial. Since there is an expected improvement in statistical power when we employ a repeated-measures design, how can we take advantage of multiple steps?
Another example might be calculating average values over of some measure the entire gait cycle from running trials collected for 1 minute. At 90 strides/180 steps per minute, that is a lot of measurements. It would seem that we would fail to benefit from statistical power considerations if we simply put one single average value from that 1 minute trial into the hypothesis test calculations.
Conceptually or otherwise, is there such a thing as a within-trial factor nested inside a within-subject factor?
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