I am writing a research study on the reliability and validity of a foot scanning app. I want to evaluate the ICC (intraclass correlation coefficient, absolute agreement) for a couple of datasets I have. In one set, I only measured each person once (about 45 people). In another, I measured 3 people 15 times. I have extracted 10 measurements from each person (5 different foot measurements of each foot). I also have some ICC calculation code to help me calculate these things. The code requires that I know what the "objects of measurement" are (rows) and the "judge or measurement" (columns). How should I arrange my data to calculate an ICC value for each foot measurement type?
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The ICC compares different measurements/judges, so are you really comparing different apps? However, it is unclear what your hypothesis is, so the question should be, what is your hypothesis and which statistical test are you using it to evaluate it? (We know now that you are approaching it via ICC, but is it really what you want?) – Irreducible Oct 09 '18 at 10:26
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@Irreducible Honestly I am still not 100% sure how the ICC works... but I know that it represents the effect of columns on the rows (or vice versa). I am basing my paper quite heavily on [this paper](https://www.tandfonline.com/doi/abs/10.1080/19424281003685694) which calculates an ICC value for every measurement taken by the instrument. – Krishna Basude Oct 14 '18 at 19:59