Abstract
Study designs for the biomechanical evaluation of running footwear often encompass repeated measurements of subjects running in a laboratory setup. During post-processing, within-subject variance is usually discarded by pooling subjects’ data, although it can be used in a beneficial way to derive intervals for relevant effect magnitudes. These are essentially required for the a priori calculation of sample size as well as the interpretation of statistical test results. Based on the calculation of the random error component of repeated measurements, a sample dataset on rearfoot eversion measurements was used to demonstrate the concept of detecting practically relevant effect magnitudes. This paper provides calculation procedures as well as the interpretational concept, which can both be easily adapted to many other research scenarios.
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